Effect of fluid motion on colony formation in Microcystis aeruginosa

moffatt’s method -回复Moffatt's method is a mathematical technique used to calculate the flow velocity field in a three-dimensional fluid system. This method was developed by Keith Moffatt, a renowned mathematician and fluid dynamicist. In this article, we will delve into Moffatt's method, explaining its fundamental principles and step-by-step process. By the end, you will have a comprehensive understanding of this powerful mathematical tool.Flow velocity fields play a crucial role in fluid dynamics as they describe the motion of fluid particles within a system. Understanding the flow velocity field provides valuable insights into the fluid's behavior, such as turbulence, vorticity, and potential for heat transfer. Moffatt's method is a unique approach that allows researchers to solve for the velocity field in complex fluid systems efficiently.To begin our exploration of Moffatt's method, we must first establish a basic understanding of vorticity. Vorticity is a vector field that describes the local spinning motion of fluid particles. It is calculated as the curl of the velocity field,ω = ∇× v,where ω represents vorticity, ∇ is the Nabla operator (a vector differential operator), and v denotes the velocity field. By analyzing the vorticity, we can gain insights into the flow patterns and areas of rotation within the fluid system.Now that we have established the importance of vorticity, we can move on to the core idea behind Moffatt's method. The central concept is to express the vorticity field in terms of elementary solutions, which are simple vortices or doublets. These elementary solutions provide a basis for constructing more complex velocity fields.Moffatt's method follows a step-by-step process to achieve this goal. We will go through each step in detail.Step 1: DecompositionThe first step is to decompose the vorticity field into elementary solutions. This is done by expressing the vorticity as a sum of elementary vortices and doublets. The fundamental idea is that any flow can be approximated by a combination of theseelementary solutions.Step 2: SuperpositionNext, we superpose the elementary vortices and doublets to obtain the overall formulation of the vorticity field. This step involves integrating the individual contributions of each elementary solution. The superposition principle allows us to combine multiple solutions to form a more complex velocity field.Step 3: Application of Boundary ConditionsIn this step, we apply the appropriate boundary conditions to the vorticity field. Boundary conditions include information about the flow at the edges or surfaces of the fluid system. These conditions help constrain the solution and make it physically meaningful.Step 4: Solution for Velocity FieldOnce the vorticity field is obtained, we can solve for the velocity field. This is done by taking the curl of the vorticity field, as described earlier. The resulting velocity field contains information about the flow patterns and velocities at different points within the fluid system.Step 5: Analysis and InterpretationFinally, we analyze and interpret the obtained velocity field. This step involves studying the flow patterns, areas of high or low velocities, and any other characteristics of interest. By understanding the velocity field, we can gain insights into the behavior and dynamics of the fluid system.Moffatt's method offers a powerful and efficient approach to calculate flow velocity fields in complex fluid systems. By decomposing the vorticity field into elementary solutions and superposing them, researchers can obtain accurate and detailed information about the fluid dynamics. This method has found applications in various fields, including meteorology, oceanography, and engineering.In conclusion, Moffatt's method provides a systematic approach to solve for flow velocity fields in three-dimensional fluid systems. By decomposing the vorticity field, superposing elementary solutions, applying boundary conditions, and solving for the velocity field, researchers can gain valuable insights into the fluid's behavior. This method has proven to be exceptionally useful innumerous scientific and engineering applications, contributing to our understanding of fluid dynamics.。

Fluid-Structure Interaction and Dynamics Fluid-structure interaction (FSI) is a captivating field of study that delves into the intricate interplay between fluids and structures. It examines how the forces exerted by fluids impact the behavior of structures and vice versa. This dynamic interaction governs a vast array of natural phenomena and engineering applications, ranging from the fluttering of leaves in the wind to the intricate workings of the human heart. The fundamental principle underlying FSI lies in the recognition that fluids and structures are not isolated entities but exist in a state of constant interplay. When a fluid flows over a structure, it exerts pressure and shear stresses on its surface. These forces can induce deformations, vibrations, and even complete structural failure if they exceed the structural integrity. Conversely, the movement and deformation of a structure can alter the flow patterns of the surrounding fluid, leading to changes in pressure distribution, turbulence, and vortex shedding. The complexity of FSI arises from the inherently coupled nature of the governing equations. The motion of the fluid is described by the Navier-Stokes equations, which account for the conservation of mass, momentum, and energy. The behavior of the structure, on the other hand, is governed by the laws of solid mechanics, encompassing concepts such as stress, strain, and material properties. Solving these equations simultaneously, while accounting for the dynamic interaction between the fluid and the structure, poses a formidable challenge. To tackle these challenges, researchers and engineers employ sophisticated numerical methods, such as computational fluid dynamics (CFD) and finite element analysis (FEA). CFD simulates the flow of fluids, providing insights into pressure distributions, velocity fields, and turbulence patterns. FEA, on the other hand, analyzes the structural response to external forces, revealing stress concentrations, deformations, and potential failure points. By coupling these techniques, FSI simulations provide a comprehensive picture of the interplay between fluids and structures. The applications of FSI are as diverse as the phenomena it governs. In aerospace engineering, understanding FSI iscrucial for designing aircraft wings that can withstand the aerodynamic loads imposed by high-speed flight. In civil engineering, FSI plays a vital role in assessing the stability of bridges and buildings subjected to wind and watercurrents. In biomedical engineering, FSI is employed to study the hemodynamics of blood flow through arteries and the mechanics of heart valves. The study of FSI continues to advance, driven by the ever-increasing computational power and the development of more sophisticated numerical algorithms. As our understanding of this intricate interplay deepens, we unlock new possibilities for designing more efficient, resilient, and innovative engineering systems that seamlessly integrate with the natural world.。

miR-498过表达对鼻咽癌细胞增殖、侵袭、迁移和上皮间质转化的影响及其机制袁洛花1，李文丽2，刘海兵2，范嘉佳2，彭中华2，邹剑11 四川大学华西医院耳鼻咽喉头颈外科，成都610041；2 四川省妇幼保健院耳鼻喉科摘要：目的　探讨微小RNA-498（miR-498）过表达对鼻咽癌细胞增殖、侵袭、迁移和上皮间质转化（EMT）的影响及其机制。

方法　体外传代培养人永生化鼻咽上皮细胞系NP69及人鼻咽癌细胞系CNE-2Z、CNE-1、HNE-1、SUNE-1，采用RT-qPCR法检测各细胞miR-498表达，选择miR-498表达最低的鼻咽癌细胞进行后续实验。

取传3代、对数生长期、生长状态良好的鼻咽癌细胞，随机分为miR-498 mimics组和NC mimics组，分别转染miR-498 mimics、NC mimics。

转染6 h更换新鲜培养基继续培养24 h，收集细胞。

采用CCK-8法检测细胞增殖活性，采用平板克隆形成实验检测集落形成能力，采用Transwell小室法检测细胞侵袭和迁移能力，采用Western blotting法检测EMT相关上皮型钙黏素（E-cadherin）、波形蛋白（Vimentin）、纤连蛋白（Fibronectin）以及高迁移率族蛋白A2（HMGA2）表达。

结果　CNE-1、HNE-1、CNE-2Z、SUNE-1细胞miR-498相对表达量均低于NP69细胞（P均<0.05）。

以CNE-2Z细胞miR-498相对表达量最低，故选择该细胞系进行后续实验。

经验证，miR-498 mimics组miR-498相对表达量高于NC mimics组（P<0.05）。

miR-498 mimics组培养0、24、48、72 h细胞增殖活性均低于NC mimics组（P均<0.05）；miR-498 mimics组集落形成能力以及细胞侵袭和迁移能力均低于NC mimics组（P均<0.05）；miR-498 mimics组Vimentin、Fibronectin、HMGA2蛋白相对表达量均低于NC mimics组，E-cadherin蛋白相对表达量高于NC mimics组（P均<0.05）。

The Effect of Fluid Flow on Eutectic GrowthJ.H. LEE, SHAN LIU, and R. TRIVEDIThe effect of fluid flow on eutectic microstructure is systematically examined in Al-Cu alloys of com-positions varying from 19.5 to 45.0 wt pct Cu. It is shown that significantly different fluid-flow effects are present in hypo- and hypereutectic alloys, since the modes of convection are different in these two cases. In hypoeutectic alloys, the rejected solute is copper, which is heavier than aluminum,and fluid flow gives rise to radial solute segregation in cylindrical samples. In hypereutectic alloys, a lighter aluminum is rejected that causes a double diffusive convection and gives rise to macrosegregation.These composition variations are shown to produce nonuniform microstructures that vary either radially (in hypoeutectic alloys) or axially (in hypereutectic alloys) and can give rise to a single phase–to-eutectic, lamellar-to–rod eutectic, or rod-to–lamellar eutectic transition in a given sample. Composi-tion measurements are carried out to characterize solute segregation due to fluid flow. The fluid-flow effect on eutectic spacing in eutectic or near-eutectic alloys is found to be very small, whereas it increases the eutectic spacing in hypoeutectic alloys for a given local composition and it can increase or decrease the spacing in hypereutectic alloys, depending on the microstructure and solidification fraction. Theoretical models, based on diffusive growth, are modified to predict the spatio-temporal variation in eutectic microstructure caused by fluid flow.I.INTRODUCTIONT HE theoretical model of eutectic growth under diffusivegrowth conditions is now well established for lamellar growth in three dimensions.[1]In contrast, most directional solidifica-tion experiments in bulk metallic systems are carried out under conditions where significant natural convection is present during the growth process, and the effect of fluid flow on eutectic microstructures has remained controversial. For nat-ural convection in the Bridgman system, Drevet et al .[2]and Curreri et al .[3]showed that the eutectic spacing of a sample unidirectionally solidified under microgravity is not the same as that on the ground. Drevet et al .[2]ascribed this effect to the presence of a long-range solute-boundary layer in off-eutectic alloys that is altered by the presence of fluid flow in the melt. They developed a theoretical model based on the boundary-layer concept. Although this model is applicable for laminar flow in hypereutectic Al-Cu alloys in which a lighter solute is rejected, the bulk composition in the boundary-layer model changes with solid fraction so that the spacing and microstructure will be a function of solid fraction, so that a time evolution of the eutectic pattern needs to be examined.In addition, the boundary-layer model is not applicable for large-diameter samples in which convective flows can be more complex, and it is not appropriate for hypoeutectic Al-Cu alloys in which the rejected solute is heavier and fluid flow causes significant radial variations in composition.[4]All the previous studies were carried out to characterize the effect of fluid flow on eutectic spacing only, and Curreri et al .[3]concluded that the eutectic spacing can increase,decrease, or remain constant in a low-gravity environment,depending on the alloy system. A more quantitative study is,thus, required that precisely establishes how different modes of fluid flow influence not only the spacing but also the microstructure. The aim of this article is to present the results of detailed systematic experimental studies on the effect of fluid flow on the eutectic microstructure in the Al-Cu system.Since the fluid flow gives rise to composition variations in the liquid in the radial and/or axial direction, we shall first char-acterize these composition variations and then establish how it influences the eutectic spacing, primary phase–to-eutectic transition, and lamellar-to-rod or rod-to-lamellar transition.Experiments are carried out by using two concentric cylin-drical ampoules [5,6,7]in which diffusive growth is present in the inner-capillary sample for a hypoeutectic alloy and con-vective growth is present in the larger concentric (or bulk)region. Thus, the effect of convection can be quantitatively established by comparing the results in the thin and in the bulk region. The radial and axial segregation patterns induced by convection were analyzed through composition measurements,and the effects of these composition variations on microstructure evolution were quantitatively characterized. The effect of fluid flow on eutectic spacing and on morphological transitions is analyzed quantitatively, and the necessary modifications in the models required to include the fluid-flow effect are discussed.II.EXPERIMENTAL PROCEDUREAl-Cu alloys of compositions in the range of 19.5 to 45.0wt pct Cu were examined, and this composition range is shown on the phase diagram in Figure 1. The eutectic composition is Al-33.2 wt pct Cu.[8]Experiments were carried out by using an upward Bridgman solidification technique described pre-viously.[5,6]To examine the effect of convection, a thin cylin-drical tube, Յ1.0 mm inner diameter (i.d.), was inserted inside the bulk cylindrical sample of 5.5 mm i.d., as shown in Figure 2. The growth in the capillary tube was found to be diffusive for hypoeutectic alloys, whereas convection effects were present in the concentric region. In general, the fluid-flow effect can be quite complex and can give rise to laminar,oscillating, or turbulent flow.[9,10,11]We shall only considerMETALLURGICAL AND MATERIALS TRANSACTIONS AU.S. GOVERNMENT WORKVOLUME 36A, NOVEMBER 2005—3111NOT PROTECTED BY U.S. COPYRIGHTJ.H. LEE, Associate Professor, is with the Department of Metallurgy and Materials Science, Changwon National University, Kyungnam, South Korea 741-773. SHAN LIU, Division of Materials and Engineering Physics, and R. TRIVEDI, Senior Scientist, Department of Materials Science and Engi-neering, are with Ames Laboratory, Iowa State University, Ames, IA 50011.Contact e-mail: trivedi@ Manuscript submitted April 6, 2005.3112—VOLUME 36A, NOVEMBER 2005METALLURGICAL AND MATERIALS TRANSACTIONS Alaminar flow, which can be readily analyzed and obtained experimentally by controlling the width of the concentric region, which was kept at about 2.0 mm.A seed crystal was used so that the orientations in the thin and the concentric region were the same. The orientation was usually maintained in the thin tube, but small variations in orientations were often observed in the concentric region.Only those grains in a bulk sample that had the same orien-tation as the thin sample were selected for spacing mea-surements. In this manner, the measurements in the thin and bulk samples were comparable and not influenced by the orientation effect. Microstructure observations and spacingmeasurements in the bulk region were made at three different locations, shown in Figure 2, and these locations will be referred as the inner, middle, and outer (edge) regions of the concentric region. They will be specified by the radial dis-tance (r ) from the axis of the concentric samples.The temperature gradient in the liquid at the growth interface was first characterized for different solidification rates and was found to vary from 9.0 to 11.5 K/mm for a fixed furnace temperature of T F ϭ900 °C. Solidification studies were con-ducted at relatively low solidification rates (Ͻ3.0␮m/s), where significant fluid-flow effects are expected and where the eutectic spacing is larger. Solidification microstructures were investigated with both optical metallography and scanning electron microscopy (SEM). The composition was analyzed using electron-probe microanalysis (EPMA). To improve the composition measurements, several standards with different compositions (Al-4.0, 20.0, 32.7, and 40.0 wt pct Cu) were used. The eutectic spacing and volume fractions of two phases were obtained by using an image analyzer. About 60 to 200individual lamellar or rod spacings were measured to determine the spacing distribution.III.EXPERIMENTAL RESULTSThe microstructure evolution in different alloy compositions was first examined under different growth rates, and the results are summarized in Table I. We shall present the results to show the effect of convection on (1) the microstructure and radial segregation, (2) the axial segregation, (3) the eutectic spacing,(4) the eutectic-interface temperature, and (5) the morphological transitionsA.Microstructural and Compositional Inhomogeneities The results on the effect of fluid flow on microstructure evolution will be presented for the eutectic alloy, the hypoeu-tectic alloys (19.5 to 30.0 wt pct Cu), and the hypereutec-tic alloys (40.0 and 45.0 wt pct Cu).1.Eutectic alloyThe microstructure in a thin sample is shown in Figure 3for an alloy of eutectic composition solidified at V ϭ1.25␮m/s. The SEM picture of two perpendicular sections was viewed at the edge of the two sections at 45 deg from each plane. The microstructure in the thin sample is a single crystal with some faults present in the transverse section.The orientations of the lamellae in all experiments were found to be within 1 to 5 deg from the growth direction,so that the angular correction for spacing measurement was negligible.For the measurement of individual spacings, a line scan was taken by using image-analysis software. The eutectic-spacing distributions in the thin sample and in the neighboring bulk were measured and are compared in Figure 4. The spacing dis-tributions in the thin sample and in the radially different loca-tions of the bulk sample were found to be very close, although a small shift toward a smaller spacing in the bulk edge is observed where some fluid flow is present (Figures 4(a) and (b)). The average spacing in the thin sample is 8.60 ␮m,whereas the average spacing in the middle and outer regions of the bulk sample are 8.53 and 8.39 ␮m, respectively. Thus,the fluid-flow effect on spacing in eutectic alloys is quite small.Fig. 1—A partial phase diagram of Al-Cu, showing the range of compo-sitions used in this study. The composition range from 19.5 to 45.0 wt pctCu encompasses both the hypo- and hypereutectic alloys.Fig. 2—A schematic drawing of the thin-tube immersion technique used in this study. Experimental observations of microstructures were made at the radial position of r ϭ0 in the thin sample and at different radial positions from the center in the bulk region, as shown by small filled circles. These three locations in the bulk will be referred to as locations 1 (inner edge),2 (middle edge), and 3 (outer edge).METALLURGICAL AND MATERIALS TRANSACTIONS AVOLUME 36A, NOVEMBER 2005—3113Table I.Directional Solidification Experiments from Hypo- to Hypereutectic Alloys (G ؍9.0 to 11.5 K/mm)Interface MicrostructureWt Pct Cu V , ␮m/s Exp.T F (°C)f s Bulk (5.5-mm i.d.)Thin (0.8-mm i.d.)19.50.5109000.45␣D␣D 0.4129000.51RE (I.E.) ϩME (O.E.)ME 0.4138750.46␣C (I.E.) ϩME (O.E.)RE 0.311*9500.48RE RE 20.00.5219000.60␣D ␣D 0.4229000.45␣D ME 21.00.5239000.65␣D ␣D 0.4249000.56␣DRE 22.00.7198500.54␣D (I.E.) ϩME (O.E.)ME 0.5178500.51␣C (I.E.) ϩME (O.E.)ME 0.4168500.69RE (I.E.) ϩME (O.E.)ME 24.00.9209000.62␣DME 0.7189000.60␣D (I.E.) ϩME (O.E.)ME 0.5159000.55RE (I.E.) ϩME (O.E.)ME 0.4289000.45RE (I.E.) ϩME (O.E.)ME 0.3259000.70RE (I.E.) ϩME (O.E.)ME 30.00.599000.51LE LE 32.02279000.65LE LE 1269000.65LE LE 0.5319000.62LE LE 33.25E1900—LE LE 2.5E2900—LE LE 1.25E3900—LE LE 40.02359000.64␪C ␪C 1349000.52␪C ␪C 0.532*9000.67LE LE 45.00.5369000.66LE ␪CAbbreviations: RE: rod eutectic, LE: lamellar eutectic, ME: rod and lamellar coexistence, ␣C and ␣D: cellular and dendritic growth of the ␣phase, ␪C:cellular growth of the ␪phase, I.E.: inner edge region, O.E.: outer edge region in the concentric region, and T F : furnace temperature.*A 0.6-mm-i.d. thin tube was used.Fig. 3—Three-dimensional view of eutectic growth in a 0.8-mm-i.d. thin sample.Since the effect of convection on the eutectic spacing in an alloy of eutectic composition is small, we now examine all the results on the average eutectic spacing in the bulk alloy of Al-Cu of eutectic composition, as presented in the literature.[12–16]Figure 5 shows the results over the velocity range of 0.6 to 1000 ␮m/s, and all the data with some scatter follow the relationship V ␭2ϭ95 ␮m 3/s. Through detailedexperimental studies in the Al-Cu system in thin samples with oriented single crystals, Walker et al .[1]have shown that the minimum observed spacing agrees with the theoretical value (␭m ) corresponding to the minimum undercooling pre-dicted by the Jackson and Hunt model.[17]The average spac-ing was shown to be given by 1.10 ␭m . Using the values of the system parameters (Table II), the theoretical value is obtained as V ␭ave 2ϭ91.0 ␮m 3/s, and this predicted value is shown in Figure 5. A good match is observed except at higher rates, where slightly larger spacings are observed which could be due to the orientation effects, since the orientation was not controlled in these experiments and the average spacings were measured over several eutectic subgrains.2.Hypoeutectic alloysWhen hypoeutectic alloys were directionally solidified,significant convection effects were observed in the bulk region. The effect of convection was examined under different velocity and composition conditions. We first consider the effect of velocity, and examine the result in an Al-22.0 wt pct Cu alloy solidified at V ϭ0.4 and 0.5 ␮m/s for the same solidification fraction. Both these velocity conditions fall within the theoretically calculated coupled zone, so that a uniform eutectic microstructure is predicted by the theory based on diffusive growth. Figures 6(a) and (b) show the microstructures (longitudinal sections) in a thin sample of 0.8 mm i.d. and in the bulk region for V ϭ0.4 and 0.5 ␮m/s,respectively. At V ϭ0.4 ␮m/s, both the thin sample and the3114—VOLUME 36A, NOVEMBER 2005METALLURGICAL AND MATERIALS TRANSACTIONS A(a )(b)Fig. 4—A comparison of eutectic spacing (Al-33.2 wt pct Cu, V ϭ1.25 ␮m/s): (a ) between the thin sample and the adjacent concentric region and (b )betweenthe middle and the edge of the concentric region.Fig. 5—Comparison of the present experimental results with the previous studies on the eutectic spacing in Al-Cu alloys of eutectic composition.Table II.Physical Properties of ␣/␪EutecticPropertyValue ReferenceDiffusion coefficient thin: 2.4 ϫ10Ϫ97(D L ), m 2/s bulk: 3.26 ϫ10Ϫ9213.2 ϫ10Ϫ922Eutectic temperature (T E ), K821.48Eutectic composition (C E ), wt pct Cu33.28␣-phase contact angle with liquid (␪␣), deg7023␪-phase contact angle with liquid (␪␪), deg5223␣-phase Gibbs–Thomson coefficient (⌫␣), K m 2.4 ϫ10Ϫ723␪-phase Gibbs–Thomson coefficient (⌫␣), K m 0.55 ϫ10Ϫ723Density of ␣phase at T E (d ␣), kg/m 32.74 ϫ10318Density of ␪phase at T E (d ␪), kg/m 34.0 ϫ10318␣-phase solubility at T E (C S ␣), wt pct Cu5.658␪-phase solubility at T E (C S ␪), wt pct Cu52.58Liqudius slope of ␣phase at T E (m L ␣), K/wt pct5.78Liquidus slope of ␪phase at T E (m L ␪), K/wt pct4.58Solute-distribution coefficient of ␣phase at T E (k ␣)0.188Solute-distribution coefficient of ␪phase at T E (k ␪)0.058Volume fraction of ␣phase at T E (f ␣)0.506Volume fraction of ␪phase at T E (f ␪)0.494bulk region show the coupled eutectic-growth interface. How-ever, magnified views of the transverse sections show that the microstructure in the thin sample is a uniform rod eutectic,except at the edge where some coalescence of rods onto a lamellar eutectic is observed (Figure 7(a). In the bulk region,even though a coupled growth is observed across the quenched interface, a significant radial segregation is present (Figure 6(c)) that influences the eutectic morphology. In the region closer to the thin sample (i.e ., the inner edge region),the morphology is rod eutectic (Figure 7(b)), whereas at the periphery (i.e ., the outer edge region), a mixed rod and lamel-lar microstructure is observed (Figure 7(c)). The fraction of lamellar eutectic is found to increase with distance toward the outer edge of the sample.When the velocity was increased to 0.5 ␮m/s, convection effects in the bulk region altered the microstructure quite significantly, as seen in Figure 6(b). The thin sample shows a flat coupled-growth interface, while the microstructure in the neighboring bulk shows cellular ␣growth in the region close to the thin sample and a coupled eutectic growth nearthe outer edge of the bulk. The cell length decreases radi-ally toward the periphery of the bulk sample until it becomes zero where coupled eutectic growth takes over.METALLURGICAL AND MATERIALS TRANSACTIONS AVOLUME 36A, NOVEMBER 2005—3115(a)(b )(d )(c)Fig. 6—The effect of growth velocity on microstructures in samples grown in (a ) Al-22.0 wt pct Cu (V ϭ0.4 ␮m/s) and (b ) Al-22.0 wt pct Cu (V ϭ0.5␮m/s).The composition variation in solid below the eutectic interface for (c ) Al-22.0 wt pct Cu (V ϭ0.4 ␮m/s) and (d ) Al-22.0 wt pct Cu (V ϭ0.5 ␮m/s), showing a significant segregation in the radial direction in the concentric region.In order to correlate the morphological variations in the radial direction with the composition variations caused by fluid flow, the composition in the solid just below the quenched eutectic interface was measured, and the results are shown in Figures 6(c) and (d) for the two velocities. The compositions in thin samples were found to be very close to the initial alloy compositions, indicating that steady-state growth under a diffusive condition was present at both veloc-ities. This conclusion is also supported by the uniformity of the eutectic structure in the radial direction, as shown in Figure 7(a). In contrast, the composition in the concentric region is found to increase radially outward. The composition near the thin sample is smaller than the alloy composition,whereas it is higher than the alloy composition near the outer edge of the concentric region. This variation in compositions is similar to the earlier experimental observations in single-phase growth of Al-4.0 wt pct Cu,[4,5,6]where the composition variation in the radial direction was shown to occur due to the fluid-flow effect. Figure 6(c) shows that the composition in the bulk near the thin sample is about 19.5 wt pct Cu,which is much lower than the alloy composition of 22.0 wt pct Cu, and a rod eutectic morphology is favored. The com-position at the periphery of the bulk is 23.3 wt pct, which is higher than the nominal alloy composition, and it favors the formation of a rod/lamellar eutectic coexistence. Similarly,in Figure 6(d), the composition in the bulk near the thin sample is reduced sufficiently such that the undercooling-composition point goes outside the coupled-growth region that causes the formation of a primary phase with an inter-cellular lamellar eutectic. In both cases, the nonuniform microstructures in the radial direction can be correlated with the composition variation caused by fluid flow, as will be analyzed later.The effect of alloy composition is now examined by studying more-concentrated alloys. In the Al-24.0 wt pct Cu alloy,eutectic microstructures were observed in both the thin and the bulk region for the solidification rates of 0.4 and 0.5 ␮m/s.The microstructure for 24.0 wt pct Cu at V ϭ0.5 ␮m/s is shown in Figure 8(a), which is different from the microstructure in the Al-22.0 wt pct Cu alloy solidified at V ϭ0.5 ␮m/s,although it is analogous to that in the 22.0 wt pct Cu alloy at a lower velocity of V ϭ0.4 ␮m/s (Figure 6(a)). The eutectic morphology in the thin sample of this Al-24.0 wt pct Cu alloy was mainly lamellar, although a very small fraction of rod eutectic was also observed. For the concentric region closer to the thin sample (i.e ., the inner-edge region), the eutectic was completely rodlike, whereas at the outer edge of the concentric region, lamellar and rod eutectics occupied about 80 and 20pct of the area, respectively. For the regions in between, the rodlike region shrank and the lamellar region expanded toward the edge of the bulk. When the velocity was increased to 0.7 ␮m/s,primary ␣-phase growth was observed in the bulk sample near the thin tube (Figure 8(b)). This microstructure is analogous to that observed in 22.0 wt pct Cu (Figure 6(b)), but it forms at a higher velocity.The radial-composition variations in the solid in the con-centric region for 24.0 wt pct Cu are shown in Figures 8(c)and (d). Similar to Figures 6(c) and (d), the fluid flow causes3116—VOLUME 36A, NOVEMBER 2005METALLURGICAL AND MATERIALS TRANSACTIONS A(a)(b )(c)Fig. 7—Magnified views of eutectic microstructures in the thin sample and in the bulk region in the Al-22.0 wt pct Cu alloy (V ϭ0.4 ␮m/s and G ϭ8.9 K/mm).(a ) A rod eutectic microstructure in the thin sample, which forms under diffusive growth conditions. (b ) The formation of a rod eutectic in the bulk sample near the inner edge of the bulk sample (r ϭ0.8 mm). (c ) A mixed rod and lamellar microstructure at the periphery of the bulk sample (r ϭ2.7 mm). The scale bar in the upper-left corner of (b) and (c) is 200-␮m long.the solute composition to continuously increase radially out-ward in the concentric region, which gives rise to different morphologies in the radial direction. The measured compo-sition in the thin sample is the same as in the initial alloy composition, indicating that steady-state diffusive growth has been reached. From Figures 6(d) and 8(d), we can also deter-mine the local composition at which the transition from the leading primary phase to the coupled eutectic-growth interface occurs in the bulk region. The local composition for the tran-sition in the Al-22.0 wt pct Cu alloy, at V ϭ0.5␮m/s, is about 19.0 wt pct Cu, while that in the Al-24.0wt pct alloy,at V ϭ0.7 ␮m/s, is about 22.0 wt pct Cu. These results will be used later on to determine the boundary of the coupled growth under convective conditions.The radial-composition variation in the concentric region should also result in the variation in local volume fraction of the two phases. For completely coupled growth in the hypoeutectic alloys, obtained in compositions from 21.0 to 30.0 wt pct Cu, an independent check was, thus, made bymeasuring the local volume fraction of the ␪phase. The results are summarized in Figure 9 and are compared with the theoretical calculations (the solid line) based on the com-positions of the solid phases from the phase diagram and the densities of the ␣and ␪phase.[18]The experimentally measured f ␪value as a function of local composition agrees well with the computed result. These results clearly show that the local morphology and local volume fractions are related to the local composition at the interface, rather than to the initial alloy composition.3.Hypereutectic alloyExperimental studies were carried out in two hypereu-tectic alloys: Al-40.0 wt pct Cu and Al-45.0 wt pct Cu (Table I),where the convection effects were found to be significantly different from those in hypoeutectic alloys due to the rejection of a lighter Al. The rejection of a lighter solute into the bulk liquid increases the composition of Al with fraction solidi-fied until it reaches the eutectic composition. The changeMETALLURGICAL AND MATERIALS TRANSACTIONS AVOLUME 36A, NOVEMBER 2005—3117(a)(b)(c )(d)Fig. 8—Magnified views of the eutectic microstructures in thin and bulk samples: (a ) Al-24.0 wt pct Cu (V ϭ0.5 ␮m/s and G ϭ8.9 K/mm), (b ) Al-24.0wt pct Cu (V ϭ0.7 ␮m/s). The composition variations in the bulk for (a) and (b) are shown in (c ) and (d).Fig. 9—Change in the ␪-phase volume percentage in the eutectic phase with the measured local composition. The solid line is calculated from the phase diagram using the densities of the ␣and ␪phases.in bulk composition gives rise to a variation in microstructure with fraction solidified (f s ). In this experiment, an even thin-ner sample, 0.6 mm i.d., was used, and fluid-flow effects were still found to be significant.Microstructure observations were carried out on several transverse sections taken at different f s values, and the mainfeatures are shown in Figure 10 for different f s values in an Al-40.0 wt pct Cu alloy solidified at V ϭ0.5 ␮m/s. Initially,leading primary ␪phase is observed in both the thin and the bulk samples for 0 Ͻf s Յ0.24. Figure 10(a) corresponds to a solidification fraction f s ϭ0.22, where faceted cells of primary ␪phase are present in the entire bulk sample, while the ␪phase is close to disappearing in the thin sample. The eutectic microstructure between the primary ␪phase is lamellar. With increasing f s value, the ␪phase disappears and coupled eutectic growth occurs in the thin sample, while cells of the primary phase are still present in the bulk region,as seen in Figure 10(b) for f s ϭ0.36. When f s Ͼ0.40, cou-pled eutectic growth was observed in both the thin and the bulk sample. The cell-to-eutectic transition was found to occur at about f s ϭ0.24 in the thin sample and at about f s ϭ0.40 in the concentric region. In addition, the intercellular eutectic is lamellar, which transforms to a rod eutectic when the cells disappear, as shown in Figure 10(c) for the thin sample at f s ϭ0.28. This rod eutectic subsequently trans-forms to a lamellar eutectic as f s is increased, as shown in Figure 10(d) for f s ϭ0.64. The dynamics of the lamellar-to-rod transition and that back to lamellar is discussed in separate articles.[19,20]The dependence of microstructure on solid fraction unam-biguously shows the presence of macrosegregation induced by convection. In the bulk region, ␪-phase cells grow uniformly over the entire cross section, i.e ., there was no observable3118—VOLUME 36A, NOVEMBER 2005METALLURGICAL AND MATERIALS TRANSACTIONS A(a)(b )(c )(d)Fig. 10—Microstructure evolution in the Al-40.0 wt pct Cu alloy solidified at V ϭ0.5 ␮m/s: (a ) f s ϭ0.22 and (b ) f s ϭ0.36. Eutectic microstructures in thin samples showing (c ) rod eutectic formation at f s ϭ0.28 and (d ) lamellar eutectic formation at f s ϭ0.64.Fig. 11—Longitudinal and radial segregations with solidification fractions at V ϭ0.5 ␮m/s in the hypereutectic alloy of Al-40.0 wt pct Cu.microstructure variation in the radial directions in the bulk region (Figures 10(a) and (b)). This is in strong contrast to the hypoeutectic alloys, in which radially different microstruc-tures developed (Figures 6(b) and 8(b)). A significant differ-ence in composition was also observed in the thin and the concentric regions, which were quantitatively measured and are shown in Figure 11. In concentric regions, compositions in the middle and the edge regions are the same, indicating that strong convection exists in the concentric region that causes uniform solute distribution in the radial direction. Also,the copper content decreases (or the aluminum content increases) with increasing fraction solidified, so that the far-field composition moves toward the eutectic composition. In contrast, the thin sample shows both axial and radial segre-gation when the solid fraction is less than 0.44. The copper composition near the edge of the sample is much lower than in the center region. For f s Ͼ0.44, no appreciable compositiondifference between the center and the edge is observed inMETALLURGICAL AND MATERIALS TRANSACTIONS AVOLUME 36A, NOVEMBER 2005—3119Fig. 12—Comparison of macrosegregation between an Al-22.0 wt pct Cu alloy and an Al-40.0 wt pct Cu alloy. For the hypereutectic alloy, the measurement was made in the center of a 0.6-mm-i.d. sample (Fig. 11,inverse triangle), where laminar convection was present in the melt. For the hypoeutectic alloy, the measurements were made in a 0.8-mm-i.d. thin sample where diffusive growth was present, and the growth in this samplereached a steady state when about 15 pct of the sample was solidified.Fig. 13—Solute profiles in the solid and the quenched liquid for the Al-24.0 wt pct Cu alloy directionally solidified at V ϭ0.5 ␮m/s and G ϭ10.0K/mm. The filled circle is for a 0.8-mm-i.d. thin sample, and the open circle is for the middle of the concentric region.the thin sample. As the composition approaches the eutectic composition with increasing solidification fraction, the radial composition becomes uniform in both the thin and the large samples.B.Axial-Composition ProfilesThe variations in composition of copper in solid with frac-tion solidified in hypo- and hypereutectic alloys are compared in Figure 12, in which the axial segregation is shown for thin samples of both hyper- and hypoeutectic alloys. In hyper-eutectic alloys, the composition in the center is used and it decreases with the fraction solidified, whereas in hypoeutec-tic alloys. The composition variation showed an initial sharp increase during the transient growth, which then became con-stant after the solid fraction of about 0.12. The observation of a constant composition of eutectic in the hypoeutectic alloy,which was equal to the initial alloy composition, also shows that steady-state growth was established in thin samples.To examine the effect of fluid flow on mass transport in hypoeutectic alloys, axial-composition profiles in the thin sam-ple and in the bulk were measured in both solid and quenched liquid around the growth interface. A typical result for Al-24.0wt pct Cu, directionally solidified at V ϭ0.5␮m/s, is shown in Figure 13. The composition profile in the bulk region was taken at the middle point, i.e ., r ϭ1.67 mm from the center.Two important observations are made. (1) The composition in the liquid at the interface is the same, but the composition in the solid is lower in the convective regime, indicating that the composition in the solid rises slowly in presence of con-vection. (2) Both the composition profiles decay exponentially in the liquid with different decay constants that can be related to the diffusion coefficient. The diffusion coefficient in the thin sample is obtained as 2.4 ϫ10Ϫ9m 2/s, whereas the effec-tive diffusion coefficient in the bulk region is 3.2 ϫ10Ϫ9m 2/s.The increase in the effective diffusion coefficient in the bulk region comes from the additional mass transfer due to fluid flow. Recently, Lee et al .[7]have obtained similar results, and they have shown that the effective diffusion coefficient is always larger in hypoeutectic Al-Cu alloys and increases as the diameter of the sample used in the experiment is increased.The enhanced diffusion coefficient (3.2 ϫ10Ϫ9m 2/s), obtained in the concentric region of about 2.0 mm in width, is the same as that obtained by Sharp and Hellawell [21]and Jordan and Hunt [22]in 2.0 mm i.d. tubes, and we shall use this diffusion-coefficient value to analyze the results in the bulk region.C.Eutectic SpacingWe shall first consider the spacing variation in hypoeutectic alloys. Eutectic spacings were measured on the transverse section for alloys of compositions in the range 21.5to 30.0wt pct Cu. Since the eutectic spacing in the bulk sample varies significantly along the radial direction where rod and/or lamel-lar microstructures are observed, it is necessary to specify the location, local composition, and local morphology where the spacing was measured.In order to establish the effect of convection, spacing dis-tributions in the thin and bulk regions were determined in the same experiment in which the growth rate and thermal gra-dient are identical. Furthermore, the spacing in the bulk sam-ple was measured at the location where the local composition was the same as in the thin sample. Typical results for two growth conditions with different eutectic morphologies are shown in Figures 14(a) and (b). Figure 14(a) shows the spac-ing distribution for the lamellar growth with the measured local composition of 24.0 wt pct Cu, and Figure 14(b) is for the rod-eutectic growth with a local composition of 22.0 wt pct Cu. The spacings in the thin sample and the bulk follow a normal distribution, but the average spacing in the bulk is larger than that in the thin sample by a factor of 1.17 for both the rod and lamellar morphologies. Thus, convection always increases the rod or lamellar spacing in hypoeutectic alloys for the same condition of composition, velocity, and thermal gradient.In the hypereutectic alloy, the composition changes in the axial direction so that the eutectic morphology and eutectic。

Computational Fluid Dynamics – or How to Make a Good Boat FastDavid VacantiThe term CFD is showing up more often these days in articles describing the design efforts used to make Volvo 60 round the world racers and America’s Cup yachts faster. Computational Fluid Dynamics or CFD actually covers a great many engineering specialties and is not the sole domain of boat and ship design. In this article we will review what types of CFD products exist and hopefully provide some understanding of when and how CFD products are best suited to a project. Computational Fluid Dynamics is the application of computers to the modeling of fluid characteristics when either the fluid is in motion or when an object disturbs a fluid. A few examples of a fluid in motion are water or chemical flow in pipes, heating and ventilation systems conducting cooling, heating or fresh air supplies to a building. Fluids in motion also include flame and fire effects in combustion or jet engines. Surprised by these fields of interest?What about examples of an object disturbing a fluid? Examples include stirring paddles submerged in a tank of water and effluent in a waste treatment plant, aircraft of all kinds, cars and trucks at highway or racing speeds and even monohull sailboats, ship, multihull sailboats to name but a few. Obviously, an open mind is important when considering what constitutes a fluid. Fluids can exist in gaseous and liquid states and science has recently found that even some solids can exhibit fluid like characteristics under right conditions. Scientists have found that some of the most spectacular and deadly landslides or rock falls behave as a fluid while the mass of stone and soil or sand is in motion, only to return to a most decidedly solid form when the motion subsides.The general field of fluid dynamics differs from the field of boat design in one critical way. Onlyboat design deals with a vehicle passing through the two fluids of air and water simultaneously.Our atmosphere is a compressible fluid, though not at yachting or even high-powered boat racing speeds. Air can change in density according to altitude, temperature and humidity. Water is an incompressible fluid that can vary in viscosity according to its salinity and temperature.For most of us, small effects such as variable salinity and temperature are not of concern, but can make the difference between winning and loosing a major international yacht race.How do CFD programs Work?CFD programs are based on the laws of physics, such as the law of conservation of momentum, and special “boundary conditions”. The law of conservation of momentum states that the total momentum of a system remains constant regardless of how the system may change. A boundary condition limits how and where a fluid can travel. A simple example is that motion of the fluid must remain tangent or parallel to the surface of an object passing through it. Another example is that pressure applied by the fluid against the object must be perpendicular to the surface at all points. These laws and conditions are critical to the development of a CFD program because they allow an aerodynamicist to write equations that describe the system that is being studied. Without the physical laws and boundary conditions there would be no way to write equations that describe fluid motion. The complex equations that result take into account the viscosity, mass and other characteristics of the fluid. The equations are written using integral and differential calculus and require specialized computer techniques to solve them. Typically the programmer writes an algorithm that makes a series of estimates using algorithms that iteratively solve the sets of equations by looking for “balance” in the system of equations. A final answer is obtained when the algorithm converges on a solution with an error that is sufficiently small for the desired accuracy. Once an algorithm has been developed to implement the laws of momentum and boundary conditions, it cannot be applied to the entire surface of the hull and appendages at once. The surface area of the hull, keel and rudder are broken into thousands of small patches (collectively called a mesh) and the algorithm applied to each patch. Each patch in turn influences the fluid flow on the patch area of its neighbors and therefore the solution must account for the conditions surrounding the patch currently being solved. As a result the program must solve and resolve the equations for all of the patches until the solution obeys the physical laws and boundary conditions. Sometimes the complexities of the laws of physics are too difficult to implement all at the same time. As a result the aerodynamicists choose to write programs that make certain limiting assumptions that permit the programming to become more practical and still result in reasonable results. A specific example arises in the case of what actually happens to fluids very near the surface of an object. The boundary layer as it is called experiences shear forces in the objects direction of travel that result in viscous drag. These shear forces are described in a special set of equations called the Navier Stokes relationships. The Navier Stokes equations are sufficiently complex themselves that attempting to include them within every aerodynamics or hydrodynamics program would make the solutions nearly impossible. As a result there are Navier Stokes based programs that specifically address viscous drag and Panel method programs that compute lift, wave drag and induced drag. A complete estimate of the drag encountered by a boat requires the data supplied by both programs.What do CFD programs Calculate?The most obvious calculation that would be of interest in boat design is the determination of drag forces. But drag comes in several forms that can include, wave, viscous, and induced drag. Therefore, a designer must evaluate the effects of his design in each of these drag areas. The second general area of calculation is lift. The term lift arises from its application to aircraft and becomes a bit confusing when applied to the field of boat design. Lift applies to the forces generated by a keel or centerboard to resist the side force of sails and the driving force of the sails themselves. It also applies to the turning forces of a rudder, and the supportive force acting on “foils” to elevate a hydrofoil sail or powerboat above the water surface.There is also a distinction between 2 and 3 dimensional fluid dynamics analysis. Specifically, there are programs that predict the performance of foils as if they existed on a wing of infinite length. Here the term “foil” is used to define the shape of a keel or rudder along the chord from the leading to the trailing edge. Foil shapes are best known by the alphanumerical names given to them such as NACA 63A012. So a 2D fluids program would compute the lift, drag, velocity distribution, turbulence onset and the generation of bubbles similar to cavitation for a 2D shape such as a wing or keel foil, and would not include any 3D information such as keel span or thickness distribution or the presence of a bulb. A 3D fluids program would compute wave and induced drag from a hull, keel and rudder, including the effects of a bulbed keel carrying winglets.CFD codes are critical for more than optimizing the performance of a top-notch America’s Cup class racing yacht. These codes can be of great value to determine loads placed on boat structures of all types and are invaluable when applied to unique marine structures such as oil platforms that are frequently subjected to the world’s worst storms.Lift and drag effects translate directly into loads that must be sustained by the boat or oil derrick if it is to remain intact in its intended operational conditions. For example, several years ago when the race was known as the Whitbread Round The World Race, many boats developed life threatening hull delamination when subjected to the continuous pounding of high speed downwind surfing and upwind beating. While delamination of a boat at sea is definitely related to structural design errors, those errors were caused by a lack of detailed information about the fluid forces experienced by the boats. Knowledge of these forces would have enabled designers to prevent the hull damage in the first place.Therefore, the potential application of CFD to your design project should depend on whether or not the design regime that your vessel will operate in has well understood engineering data available to prevent hull damage in addition to overall performance of the vessel. For example, the last few years have seen the development of high-speed hydrofoil sailboats for the consumer market. These top performance boats experience not only significant speeds and loads, but the potential for unstable characteristics could make it highly dangerous to ride in one. However, the judicious use of field-testing and computer analysis has produced a crop of very exciting hydrofoil sport boats that are a joy to fly in.Finally, several years ago a multihull sailboat arrived in port after participating in a trans-Atlantic race. When the centerboards were raised in the outer hulls of the trimaran, the skipper wasshocked to learn that the boards had been sheered off just below hull depth and he had not had their use for some indeterminate time during the later portion of the race. Clearly, the structural design of the boards had not taken into account the true forces of lift, drag or perhaps cavitation that would be experienced at sea.CFD programs do not calculate how fast a boat of any type will pass through the water or predict the time to complete a course around the buoys. Predicting speed on a racecourse is the domain of another class of programs called Velocity Prediction Programs or VPP. The VPP makes use of lift and drag numbers calculated in a CFD program to estimate the speed about will sail a course given the sail drive forces and the stability or righting moment of the hull. The VPP is a closed loop simulation continuously varies estimated speed and resulting lift, drag and righting forces until retarding and driving forces are balanced and a stable speed results. A CFD program on the other hand is an open loop simulation that simply states that given an angle of heel and speed for a specified hull and appendage configuration, here are the forces that will result for that instant in time. No consideration is given to how the vessel achieved that speed or sailing condition.In summary then, CFD programs not only calculate lift and drag forces of a hull with appendages, they can also be used to compute pressure loads due to waves and wave impact at speed. The forces of lift, drag and pressure can be translated into structural requirements and provide the means to optimize a hull working in concert with its appendages to produce lift in the most efficient manner possible while satisfying the needs for stability. Predictions of lift and drag at various speeds can be used to develop a mathematical model needed to accurately close the analysis loop of a velocity prediction program.When is a CFD Computer Program Required?CFD codes are not always required or justified however, when simpler means of estimating the forces involved are available. In the case of a typical sailboat design, the forces generated by the keel and rudder can be easily estimated if the keel lacks a bulb and if the keel and rudder shape are essentially straight leading and trailing edges. It is possible to make use of analytical methods that are easily implemented on personal computers. A simple example is the program I wrote called LOFT that makes use of analytical methods developed by NASA and the US Air Force for initial performance prediction of wing designs.However, while simple programs like LOFT can adequately address typical bulb-less keels and rudders they cannot analyze the performance of an America’s Cup racing keel with bulb and winglets. Only 3D CFD programs can address that complex task.Who can operate a CFD program?While CFD programs can be of tremendous value, getting accurate and meaningful results is not typically within the reach of amateur and many professional boat designers. A degreed Naval Architect or a fluids dynamicist is required to generate the key initial input to a CFD program called a mesh.The mesh is a mathematical description of the hull and appendages that are to be analyzed. It is not sufficient or even possible to use standard stations, waterlines and buttocks as inputs to a CFD program. The detailed shapes of the hull and appendages must be defined by a mesh of squarepatches that adjoin one another and whose dimensions are chosen according to the local curvature of the hull or appendage or by the occurrence of the intersection of the hull and a keel, rudder or lifting strut of a hydrofoil. The generation of a mesh is a science unto itself and can require iterations by the analysts running successive trials to be sure that the mesh is sufficiently dense in critical areas. Some meshing can be done automatically and then refined by hand.Typically the developers run complex 3D CFD programs or thosetrained in their use and as result are not really meant for use by therest of us. However, 2D fluids programs designed for the analysisand development of 2D air or hydrofoil shapes (recall the 63A010example) are sufficiently easy to use for a designer with basicmathematics skills and general knowledge of airfoil characteristics.Analytical programs such as Vacanti Yacht Design’s FOIL programcan aero / hydrofoil lift, drag, turbulence onset and bubbleformation characteristics for anyone with basic computer skills anda working knowledge of basic foil design.What CFD Programs Exist?Panel Method and Navier Stokes programs are two generalclasses of CFD programs that apply to the issues of boat design.The most commonly used and most available are Panel Method programs. Panel methods allow the prediction of wave drag, free surface effects and induced drag due to lift generated by a keel or rudder but they do not account for viscous drag. Programs using panel methods assume that there are only forces normal to the surface of the hull within the fluid. However, due to viscosity, the fluid is subject to forces in shear – more or less parallel to the hull surface that causes turbulence. Therefore the panel programs are referred to as “inviscid” analysis methods. As a result they compute wave and induced drag but not the effects of viscous drag. Viscous drag computations are computed by specialized codes known as Navier Stokes programs. These programs are difficult to use and apply and are best left to a professional skilled in their use. When a designer has a task that justifies the use of CFD programs, he should be using design tools that that can export true 3D surface shapes in the form of common Computer Aided Design (CAD) file formats. Designing in a typical CAD program such as AutoCAD using lines and polylines, even though in 3D are not sufficient for use with CFD programs. True surface definitions such as Non-Uniform Rational B-spline (NURB) surfaces are required. Most professional versions of the commonly known yacht design programs (AeroHydro, AutoShip, Maxsurf, New Wave, PROLINES) all provide this kind of file exchange.Licensing costs or consulting time is available from the companies or sources listed below.Company Name Program(s) Web AddressAerologic Cmarc,Postmarc/dwt.htmlAnalytical Methods Inc VSAERO,FSWAVEFluent FLUENT/solutions/marine/index.htm South BaySimulationsSPLASH /~brosenVacanti YachtDesignFOIL 97 Virginia Technical University Several Freesimple programs– Code Compilermay be required/aoe/faculty/marchman/softwareCFD Online Very extensivelinks to manysuppliers of CFDprograms ofevery possibletypeSpecialized consulting companies include:Bruce RosenSouth Bay Simulations44 Sumpwams AveBabylon, NY 11702 631 587 3770, brosen@Joe LaisoaFluid Motion Analysis Consulting, Inc.3062 Queensberry Dr.Huntingtown, MD 20639, 410 535 0307 X3351, laiosa@ConclusionCFD programs are best applied when there are either significant engineering unknown effects or load levels or where design optimization for a specific application in specific conditions are essential to the goal. For instance, there are many books of scantlings or building standards for typical sailboat or powerboat designs intended for inland cruising. But an attempt at the world record speed sailing at the “ditch” in France at speeds approaching 50 knots clearly calls for specialized analysis to prevent catastrophic failure that could risk lives or incur that last bit of drag that could prevent success in inching the speed record that much higher.Some CFD codes are only usable in the hands of a skilled practioner and others are designed and intended for use by those with reasonable technical skills and willingness to do a bit of reading or research to help them understand the results and limitations of their modeling efforts. CFD andanalytical programs are very important to the development of high performance vessels from the perspective of optimization for speed and safety. High speed sailing craft and those destined for offshore use can benefit the most from computer analysis methods. One final key point here is that we have only discussed vessels in displacement mode and have not referred to high performance planning powerboats. The prediction of planning vessel performance is an art unto itself and is the domain of yet another class of programs. I refer those of you who wish to know more about that subject area to research the Society of Naval Architects and Marine Engineers() web site.。

V ol.20 No.3 JOURNAL OF TROPICAL METEOROLOGY September 2014Article ID: 1006-8775(2014) 03-0242-09EFFECTS OF AEROSOLS ON AUTUMN PRECIPITATION OVERMID-EASTERN CHINACHEN Si-yu ( )1, HUANG Jian-ping ( )1, QIAN Yun ( )2, GE Jin-ming ( )1,SU Jing ( )1(1. Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, Lanzhou University, Lanzhou 730000 China; 2. Atmospheric Science and Global Change Division, Pacific Northwest NationalLaboratory, Richland, WA, USA) Abstract: Long-term observational data indicated a decreasing trend for the amount of autumn precipitation (i.e. 54.3 mm per decade) over Mid-Eastern China, especially after the 1980s (~ 5.6% per decade). To examine the cause of the decreasing trend, the mechanisms associated with the change of autumn precipitation were investigated from the perspective of water vapor transportation, atmospheric stability and cloud microphysics. Results show that the decrease of convective available potential energy (i.e. 12.81 J kg -1/ decade) and change of cloud microphysics, which were closely related to the increase of aerosol loading during the past twenty years, were the two primary factors responsible for the decrease of autumn precipitation. Our results showed that increased aerosol could enhance the atmospheric stability thus weaken the convection. Meanwhile, more aerosols also led to a significant decline of raindrop concentration and to a delay of raindrop formation because of smaller size of cloud droplets. Thus, increased aerosols produced by air pollution could be one of the major reasons for the decrease of autumn precipitation. Furthermore, we found that the aerosol effects on precipitation in autumn was more significant than in other seasons, partly due to relatively more stable synoptic systems in autumn. The impact of large-scale circulation dominant in autumn and the dynamic influence on precipitation was more important than the thermodynamic activity. Key words: aerosol; autumn precipitation; atmospheric stability; cloud microphysical properties CLC number: X513 Document code: AReceived 2013-05-16; Revised 2014-05-06; Accepted 2014-07-15Foundation item: National Basic Research Program of China (2012CB955301)Biography: CHEN Si-yu, Ph.D., Lecturer, primarily undertaking research on climate change and atmospheric remote-sensing.Corresponding author: HUANG Jian-ping, e-mail: hjp@1 INTRODUCTIONPrecipitation is a key physical process that links many aspects of climate, weather and the hydrological cycle, which have significant impacts on agricultural production, aviation, marine, transportation, water conservancy construction, floods and droughts, etc.[1]. Previous studies mainly focused on the precipitation change during summer at both global and regional scales such as China [2-5]. However, the change of autumn precipitation will directly affect agricultural production in the coming year. In addition, autumn precipitation anomaly could also have potential effects on winter precipitation in the current year and summer precipitation in the following year, resulting in floods, droughts and other disasters [6]. In order to comprehensively understand the climate change and its effects, more emphasis is needed on autumn precipitation. So far, very few studies have investigated the spatial-temporal variations of autumn precipitation. Xu and Lin [7] analyzed autumn precipitation change characteristics and related physical mechanisms over Western China. Li et al.[8] investigated the spatial and temporal variations of spring temperature anomalies over Western China and the correlation between the sea surface temperature anomalies (SSTA) of the North Pacific and the spring rainfall anomalies in Western China for the period of 1960 1994. In addition, Li et al.[9, 10] analyzed the autumn rainfall anomaly over Northwest China and investigated the circulation variations during the El Niño/La Niña periods. Their results showed good relationship between the SSTA over East Equatorial Pacific and the autumn rainfall anomaly over the Northwest China. In El Niño years, relatively smaller amount of autumn precipitation was generated over the Northwest China whereas more autumn precipitation was generated in La Niña years. Song and Zhang [11] pointed out that the variability and trendNo.3 CHEN Si-yu ( ), HUANG Jian-ping ( ) et al. 243243of autumn precipitation are more significant than that in other seasons during the 20th century over the Northwest China. Zhang et al.[12] found that the change of general atmospheric circulation and the influence of ENSO events under global warming could be the direct factors in effecting the autumn precipitation generation over Northwest China. Shi [13] found a good relationship between precipitation anomaly in autumn and rainfall in the flood season of the following year. However, the above studies were limited only to the Northwest China region, with a focus on the interactions between autumn precipitation and natural climate variability (e.g., ENSO) at large scale. In this study, we investigated the causes of the autumn precipitation anomalies from the perspective of physical mechanisms influencing the precipitation change over Mid-Eastern China.Generally speaking, there are three dominant conditions for the formation of precipitation: (1) Horizontal transportation: water vapor is transported to the precipitation zones from source regions; (2) Convergence and upward movement: the strong upward motion is associated with the surface convergence over the precipitation regions. Water vapor condenses into cloud droplets or ice crystals in the adiabatic expansion during the lifting process. (3) Microphysical conditions with growth of the cloud droplets: The saturated air tends to condense on condensation nuclei and then water droplets grow, become heavier, and fall to the ground as precipitation [1]. Aerosol particles, which have close relationship with the last two points of conditions, are the dominant factors for the formation of precipitation [14-16]. Based on the observational data and modeling approach, Qian et al.[17] found a decreasing trend of light rain events over North America, Europe and Asia from 1973–2009, especially over East Asia, where a remarkable shift from light to heavy rain events has been observed. Besides, Qian et al.[18] pointed out that aerosols are at least partly responsible for the decreasing frequency and amount of light rain in summer over Eastern China. Huang et al.[19, 20] found that dust could heat cloud droplets, increase the evaporation of cloud droplets and further reduce the cloud water path, which may play an important role in cloud development and contribute to the reduction of precipitation over arid and semi-arid areas. Zhao et al.[21] also found a positive feedback mechanism between increased aerosol loadings and reduced precipitation over Mid-Eastern China. Based on 194 observation stations, Gong et al.[22] investigated the frequency change of daily precipitation in summer during 1979–2002 and found an obvious weekly cycle effects.Was the change of autumn precipitation over Mid-Eastern China related to the high aerosol loadings in autumn when the weather system isrelatively stable? How do aerosols affect autumn precipitation in heavily polluted China? What kinds of precipitation will be affected mostly by aerosol? In this study, we try to address these questions from the perspective of precipitation formation mechanism over Mid-Eastern China which is featured by the heavy pollution, dense meteorological station and records, and frequent precipitation events records [23]. The paper is organized as follows: Data and methodology are described in sections 2. The part of effects of aerosols on autumn precipitation over Mid-Eastern China is presented in section 3. Conclusions and discussions are presented in section 4.2 DATA AND METHODOLOGYThe following types of data are used in this study: (1) Daily observational data were obtained from China Meteorological Data Sharing Service System (/home.do). We screened all daily precipitation records from 1959 to 2008 over 503 observation stations and included them in our following analysis. It also should be noted that the drizzle being not recorded by precipitation instruments was used and assigned the value of 0 mm and only the liquid precipitation was considered in our study.(2) Visibility data were widely used to study the relationship between aerosol and precipitation, due to its relatively long historic records [24-26]. The variability of atmospheric visibility, which is a good measure of environmental pollution, is consistent with that of aerosol optical depth (AOD)[27]. The unit of visibility dataset changed from level (bins) to kilometer after 1980 in China, resulting in systematic discrepancies in data processing. Thus, only the visibility data after 1980 were used in this study and data was further revised by applying the Rosenfeld method [28] to reduce the effects induced by relative humidity and precipitation.(3) We collected the sounding data from Chinese international exchange stations, including the mean monthly air pressure, altitude, temperature, dew temperature and wind speed from January 1951. The dew temperature and air temperature from 28 observational stations were used to calculate the specific humidity, of which only the results under 300 hPa were considered for deducing the perceptible water vapor.(4) Aerosol optical depth, cloud fraction and cloud particle effective radius from Moderate Resolution Imaging Spectroradiometer (MODIS)[29, 30] were used in this study. The AOD at 550 nm as a proxy of aerosol loading was sorted into 10 bins at a regular interval of 0.1. Cloud microphysical and optical parameters for cloud optical depth are sorted244 Journal of Tropical Meteorology Vol.20244into individual AOD bins, of which mean values and standard errors (i.e., / (n 1)1 / 2, where and n are standard deviation and the number of data points, respectively) are then calculated. 3 RESULTS3.1 Change trends of precipitationFigure 1 shows the spatial distributions of trends for seasonal precipitation amounts during the past 50 years over Mid-Eastern China, estimated using the least squares technique. Decreasing trends of spring precipitation were mainly centered in the regions around the Yangtze River and Southeast China, with the largest decreasing trends (i.e. from –2%/decade to –8%/decade) over the Loess Plateau region. Increasing trends of spring mean precipitation were mainly found over the Northeastern China, Beijing, Tianjin, North China Plain, Yunnan-Guizhou Plateau and Tibet Plateau. The decreasing trends for summer precipitation are larger (i.e. from –1%/decade to –5%/decade) than for spring over Northeast China, Beijing, Tianjin, Shandong Peninsula and Loess Plateau. Positive change trends of summer mean precipitation were found over Southern China, with the largest increasing trends (from 1%/decade to 10%/decade) over the Yangtze River Plain. The trends for winter mean precipitation were generally positive over China, except for Beijing, Tianjin and Inner Mongolia. The decreasing trends of precipitation in autumn were distributed more broadly and homogeneously than that in other seasons. Significant decreasing trends of autumn mean precipitation were found over the Yangtze River Plain, Sichuan Plateau, North China Plain and Yunnan-Guizhou Plateau. As a whole, lower amount of precipitation was generated over the Mid-Eastern China, especially over Northeast China, Central China and South China.Figure 1. Spatial distribution of the trend (% per decade) of precipitation in (a) spring (b) summer (c) autumn (d) winter precipitation amount from 1959 to 2008.Figure 2 shows the temporal variation of seasonal mean precipitation averaged for all observationalNo.3 CHEN Si-yu ( ), HUANG Jian-ping ( ) et al. 245245stations in the Mid-Eastern China. Increasing trends of precipitation were found for summer and winter whereas decreasing trends were found for spring and autumn. The trend of autumn mean precipitation was more significant over Yangtze-Delta region (i.e. –5.6%/decade) since 1980 and later on, contributed to the characteristic of autumn mean precipitation variations over Mid-Eastern China.Figure 2. Time series of precipitation anomaly (%) in spring, summer, autumn and winter from 1959 to 2008 averaged over Mid-Eastern China.3.2 Change trends of atmospheric water content Figure 3 shows the temporal variations of autumn atmospheric water content averaged over Mid-Eastern China during 1959–2002. The year of 1975 tends to be the turning point of atmospheric water content, indicating a transition from negative to positive trend. The lowest atmospheric water content during the past 50 years was found at the end of 1960s, while two peaks at the end of 1990s (i.e. 15%) and at the begging of the 21 century (25%) respectively. Most of the regions over Mid-Eastern China experienced a positive change trend of atmospheric water content, especially over Yangtze River, Southeast, Southwest and Northeast of China with an increase of 0.3–0.6 mm/decade generally. The least square method was applied to calculate the change trends of autumn mean precipitation during the past 50 years over Mid-Eastern China. Based on the above analysis, no evidences were found to support that the decreasing autumn precipitation trend over Mid-Eastern China was related to changes of the large-scale atmospheric water content. We believe other factors could play more important roles in influencing the autumn precipitation characteristics over Mid-Eastern China.246Journal of Tropical Meteorology Vol.20246Figure 3. Time series of autumn precipitable water anomaly (%) in autumn from 1959 to 2008 averaged over Mid-Eastern China. The dashed line represents the linear trend of precipitable water anomaly. The solid line represents the moving average of precipitable water.3.3 Effects of aerosols on precipitation3.3.1 S PATIAL VARIATION OF AOD AND VISIBILITYAerosols could directly and indirectly affect the radiation budgets of the earth system, with substantial effects on regional even global climate [31-36]. Thus, further analysis was done to demonstrate the role of aerosols on precipitation over Mid-Eastern China. Fig. 4 shows the spatial distribution of annual mean AOD derived from MODIS retrievals and visibilities from observational data in autumn. AOD values larger than 0.6 were found over the Sichuan Basin, Yellow River, the middle and lower reaches of Yangtze River. The spatial variations of annual mean AOD were also consistent with the findings of previous studies (Luo et al.[37], Wang et al.[38]). Visibility data also showed a consistent spatial pattern with that for AOD. It should be noted that the spatial distribution of visibility was of the opposite sign with that of AOD (i.e. larger AOD with smaller visibility), which gave more confidence to represent the characteristics of aerosol particles using visibility datasets.3.3.2 S VD ANALYSIS OF VISIBILITY AND PRECIPITATIONThe Singular Value Decomposition (SVD) analysis, a useful tool in demonstrating the spatial correlation of two atmospheric variables, has been widely used in various studies [39]. Autumn meanvisibility was treated as the left field whereas the autumn mean precipitation was treated as the right field. The coupling relationship between the anomalous distribution of autumn mean precipitation and the variation of visibility has been investigated using SVD analysis. It should be pointed out that only the simultaneous correlation was considered in this study due to the large spatial variation and short life period of aerosol particles.The variance contribution, cumulative variance and correlation coefficient for the first five SVD modes of singular vector were shown in Table 1. It was found that the first five pairs of singular vector contributed up to 75% of the total variance. Thus, they can be used to depict the coupling relationship of precipitation and visibility variations over Mid-Eastern China. The first mode of visibility and precipitation indicated a significant decrease of temporal coefficient (R =0.87, P <0.01). The heterogeneous correlation coefficient of the right field of the mode for visibility and precipitation was dominated by positive values, with negative correlation found over a few small regions. High positive correlation was found over Southeastern China and Northeastern China (17.5°–36°N, 105°–120°E), which was characterized by the significant coupling of the first mode. Thus, it can be inferred from the consistence of spatial variations between aerosol and precipitation that aerosol couldNo.3 CHEN Si-yu ( ), HUANG Jian-ping ( ) et al. 247247have large impacts on precipitation, with more aerosolloadings and lower precipitation.Figure 4. Spatial distribution of AOD retrievals from MODIS averaged for the autumns of 2002-2008 (a) and the corrected visibility for the autumns of 1959-2008 (b) over Mid-Eastern China.Table 1. The variance contribution, cumulative variance and correlation coefficient explained by the first five pairs (q =1, 2, 3, 4, 5) of singular vector in this study.Singular vector Variance contribution Cumulativevariance Correlation coefficient q =1 36.56% 36.56% 0.83 q =2 21.34% 58.19% 0.90 q =3 7.94% 66.13% 0.92 q =4 6.50% 72.63% 0.87 q =5 4.86%77.47%0.90By absorbing and scattering radiation, aerosols could alter regional atmospheric stability and vertical motions, and affect the large-scale circulation and hydrologic cycle with significant regional climate effects [40-43]. In addition, aerosol particles serve ascondensation nuclei for the formation of both cloud droplets and atmospheric ice particles, exerting large influence on the formation of precipitation [44-48]. With the rapid development of urbanization, pollutant emission has increased dramatically over Mid-Eastern China for the last few decades. Due to the production of more black carbon aerosols and sulfate aerosols, Mid-Eastern China became the unique experimental region for studying the impacts of aerosol on regional climate and hydrological cycle. Next, we try to focus on the effects of aerosol mechanisms on autumn precipitation from the perspective of atmospheric stability conditions and the cloud microphysical conditions.3.3.3 C HANGE TRENDS OF CONVECTIVE AVAILABLEPOTENTIAL ENERGY (CAPE), CONVECTIVE INHIBITION ENERGY AND REVISED VISIBILITYFigure 5 shows the temporal variations of CAPE, Convective Inhibition Energy (CIN) and revised visibility in autumn averaged over Mid-Eastern China during the past 50 years. High CAPE was accompanied by low CIN, suggesting an inverse correlation between CAPE and CIN. CIN increased by 28.67 J/kg per decade, while CAPE decreased by 12.81 J/Kg per decade. More specifically, air stability increased significantly after the 1980s, accompanied by the decrease of CIN by –2.1%/year and the increase of CAPE by 4.7%/year.A decreasing trend (i.e. –2.7%/year) was found for visibility variation, which was consistent with the trend of CAPE. Thus, it can be concluded that aerosols could suppress vertical motion of air and increase regional atmospheric stability, resulting in the decrease of autumn mean precipitation.3.3.4 R ELATIONSHIP BETWEEN AEROSOLS AND CLOUDEFFECTIVE PARTICLE RADIUSFigure 6 shows the comparison of autumn mean aerosol optical depth and cloud droplet effective radius under different cloud top temperature and liquid water path for 2002–2008. The solid line represents the relationship between aerosols and cloud effective particle radius when liquid water path exceeds 70 g m -2. A positive correlation between cloud droplet effective radius and liquid water path could be found when the magnitude of aerosol optical depth was lower than 0.2. Cloud droplet effective radius decreases quickly with the increase of aerosol optical depth, which is more significant with higher cloud top temperature. A decrease of cloud droplet effective radius (i.e. 5 m) was found with the increase of aerosol optical depth when cloud droplet effective radius exceeds 289 K. Thus, it can be inferred that more aerosols in autumn over Mid-Eastern China lead to smaller cloud droplet effective radius, which in turn decreases the conversion efficiency of cloud droplet into raindrop248 Journal of Tropical Meteorology Vol.20248and suppresses the formation of precipitation in autumn.Figure 5. The first SVD mode of the autumn visibility and autumn precipitation for the period 1980-2005. (a) The temporal coefficients of autumn visibility (solid line) and autumn precipitation (dash line); (b) The spatial pattern of heterogeneous correlation coefficient.From the perspective of precipitation formation mechanism, increased atmospheric stability and modified cloud microphysics could result in the decrease of autumn precipitation over Mid-Eastern China. Increased atmospheric stability induced by more aerosols may decrease the surface net radiation, inhibit the ascending motion of air and decrease the formation of precipitation. On the other hand, aerosols also change the characteristics of cloud microphysics by influencing the process of cloud condensation nucleus and lead to decreased precipitation. 4 CONCLUSIONS AND DISCUSSION In this study, the effects of aerosol on the autumn precipitation over Mid-Eastern China were investigated using ground observations, satellite retrievals and NCEP reanalysis dataset. Main conclusions are as follows.Precipitation decreased more significantly in autumn (i.e. 54.3 mm/decade) than in other seasons during the past 50 years over Mid-Eastern China. The decreasing trend of autumn precipitation was more significant after the 1980s, with an average decrease of 5.6% per year.From the perspective of precipitation formation mechanisms, water vapor, atmospheric stability and cloud microphysics could lead to the decrease of autumn precipitation. We found that the increased aerosols produced by enhanced industrialization and environmental pollution could result in the change of atmospheric stability and cloud microphysics, which were at least partly responsible for the decreased autumn precipitation observed over Mid-Eastern China for the past 20 years.The effects of aerosol on autumn precipitation were more outstanding than that on other seasons because the weather system in autumn was relatively stable compared to that in other seasons and the dynamical influence was greater than that of thermal dynamical activity. The wet removal of precipitation on aerosol was relatively small in autumn compared to that in summer, leading to the greater effects of aerosol on precipitation in autumn than in summer. Generally speaking, the high frequency of dust aerosol occurred in spring, which may be caused by relatively high frequency of convective weather outbreak and the dry loose soil. However, the impact of dynamical activity in spring also plays an important role in the spring precipitation change. Therefore it is hard to distinguish which factor is the most significant for precipitation change in spring. In winter, various types of precipitation could occur, including rain, snow and hail, leading to larger difficulty in detecting precipitation in winter than in other seasons. Furthermore, relatively more stable weather systems near the surface and the stable visibility could both lead to difficulty in studying the aerosol variations in winter. Thus, the precipitation change in winter was not considered in this study.In summary, the effects of aerosols on autumn precipitation change over Mid-Eastern China were demonstrated using observed datasets from the perspective of precipitation formation mechanisms. More efforts will be spent to study the physical interactions between precipitation change and aerosol variations.No.3 CHEN Si-yu ( ), HUANG Jian-ping ( ) et al. 249249Figure 6. Time series of convective available potential energy (dash line), convective inhibition energy (solid line) and revised visibility (red histogram) anomalies in autumn over Mid-Eastern China. The red line represents linear trend of revised visibility anomaly.Acknowledgement: We appreciate three anonymous reviewers for their valuable comments and suggestions. The contribution of PNNL in this research was supported by the Office of Science of the U.S. Department of Energy as part of the Regional & Global Climate Modeling (RGCM) Program through the bilateral agreement between U.S. Department of Energy and China Ministry of Science and Technology on regional climate research. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC06-76RLO 1830. And we also thank Dr. SHANG Ke-zheng, Dr. WANG Tian-he, SHAN Hai-xia and WANG Shan-shan from Lanzhou University for their help in this study.REFERENCES:[1] ZHU Qian-gen, LIN Jin-rui, SHOU Shao-wen, et al. The principle and method of Synoptic meteorology [M]. Beijing: China Meteorological Press, 2000: 458.[2] JIAN Mao-qiu, LUO Hui-bang, QIAO Yun-ting. Linkage between the interannual variation patterns of seasonal SST in Indian Ocean-Pacific and their relationship with the summer rainfall over China [J]. J. Trop. Meteor., 2006, 22(2): 131-137. [3] ZHANG Tian-Yu, SUN Zhao-bo, LI Zhong-Xian, et al. Relation between Spring Kuroshto SSTA and summer rainfall in China [J]. J. Trop. Meteor., 2007, 23(2): 189-195.[4] CHEN Shao-dong, WANG Qian-qian, QIAN Yong-pu. Preliminary discussions of basic climatic characteristics of precipitation during raining seasons in regions south of Changjiang River and its relationship with SST anomalies [J]. J. Trop. Meteor., 2003, 19(3): 260-268.[5] JIN Zu-hui, LUO Shao-hua. On the relationship between rainfall anomaly in middle and lower Yangtze valley during the Mei-Yu season and the anomaly of sea-surface temperature in South China Sea [J]. Acta Meteor. Sinica, 1986, 44(3): 360-372.[6] CHEN Yun, SHI Neng. Spatial and temporal distribution of autumn precipitation and temperature in China and climatic change [J]. J. Nanjing Instit. Meteor., 2003, 26(5): 662-630. [7] XU Gui-Yu, LIN Chun-Yu. Survey of the causes and features of autumn rain in Western China [J]. Sci. Meteor. Sinica, 1994, 12(4): 149-154.[8] LI Yao-hui, LI Dong-liang, ZHAO Qing-Yun. A study on spring rainfall anomaly in Northwest China and Pacific SSTA features in autumn and their correlations [J]. Plateau Meteor., 2000, 19(1): 100-110.[9] LI Yao-hui, LI Dong-liang, ZAHO Qing-yun. An analysis on characteristic of autumn rainfall anomaly in Northwest China [J]. Plateau Meteor., 2001, 20(2): 94-99.[10] LI Yao-hui, LI Dong-liang, ZHAO Qing-yun et al. Effect of ENSO on the autumn rainfall anomaly in northwest China [J]. Clim. Environ. Res., 2000, 25(2): 102-112.[11] SONG Lian-chun, ZHANG Cun-jie. Changing features of precipitation over Northwest China during the 20th century [J]. J. Glaciol. Geocry., 2003, 25(2): 143-147.[12] ZHANG Cun-jie, GAO Xue-jie, ZHAO Hong-yan. Impact of global warming on autumn precipitation in Northwest China [J]. J. Glaciol. Geocry., 2003, 25(2): 157-164.[13] SHI Neng. The temporal and spatial characteristics of monthly temperature and rainfall field during autumn and winter in China and their application in early summer precipitation forecasting [J]. Chin. J. Atmos. Sci., 1988, 12(3): 283-291.[14] HUANG J, MINNIS P, LIN B, et al. Possible influences of Asian dust aerosols on cloud properties and radiative forcing observed from MODIS and CERES [J]. Geophys. Res. Lett., 2006, 33, L06824, doi: 10.1029/2005GL024724.[15] CHEN Si-yu, HUANG Jian-ping, LIU Jin-jin, et al. Effects of dust aerosols on cloud in semi-arid regions as inferred from OMI and MODIS retrievals [J]. Adv. Ear. Sci., 2010, 25, doi: 1001-8166(2010) add- 0188-11.[16] HUANG J, MINNIS P, LINin B, et al. Advanced retrievals of multilayered cloud properties using multispectral。

Shading材质此区块下的属性用于将内置的材质效果应用到流体中。

Transparency 透明度设置流体的不透明度。

注意：0.5,0.5,0.5的透明度数值在渲染时会稍快于其他数值Glow Intensity 辉光强度控制辉光的明亮度（流体亮部的光晕）。

默认值为0，无辉光。

Glow Intensity （辉光强度）与Incandescence（炽热，或称自发光）属性不同:辉光在渲染后期加入，而自炽热只是使物体表面更明亮。

辉光强度增加光晕，炽热无。

Dropoff Shape 衰减形状创建软边界流体的外边界当Use Falloff Grid （使用衰减方格）被启用，流体将不会出现在容器中。

Edge Dropoff 边线衰减0为无衰减。

增大数值，将从中心向外形成圆滑的形状。

Color颜色定义流体颜色值的范围，可用于渲染效果。

例如，当Color Input（颜色输入）设为Density（密度）的方式并且颜色渐变范围从左往右是蓝至绿在Density（密度）值为0至1的2D容器中，密度的颜色范围将从蓝至绿渐变。

Selected Position 被选位置Selected Color 被选颜色Interpolation插值None 无Linear 线性Smooth 平滑Spline 样条曲线Color Input颜色输入定义用于颜色值贴图的属性。

Constant 恒定将整个容器的流体颜色进行渐变设置。

X, Y, Z, and Center Gradient X,Y,Z和中心渐变将整个容器的流体颜色进行统一的渐变设置。

其他选项将流体颜色与方格的属性值保持一致。

例如，设置ColorInput为Density，渐变起始颜色将用于数值为0的密度，渐变结束颜色则用于数值为1的密度。

Input Bias 输入偏移输入偏移调节着所选颜色输入的敏感度。

以下为负数，0以及正数时的InputBias对流体渐变色的影响。

Incandescence炽热（自发光）炽热控制着由自身照明的密度区发射光线颜色的数量。

ＬｉｃｏｃｈａｌｃｏｎｅＡｉｎｈｉｂｉｔｓｏｓｔｅｏｓａｒｃｏｍａｐｒｏｌｉｆｅｒａｔｉｏｎｂｙＡｋｔ／ＥＲＫｓｉｇｎａｌｉｎｇｐａｔｈｗａｙＦＡＮＸｉａｏ ｌｉ１，ＷＡＮＧＬｉ ｍｉｎｇ２（１．ＣｏｌｌｅｇｅｏｆＰｈａｒｍａｃｙ，ＧｕｉｌｉｎＭｅｄｉｃａｌＵｎｉｖｅｒｓｉｔｙ，ＧｕｉｌｉｎＧｕａｎｇｘｉ　５４１１００，Ｃｈｉｎａ；２．ＣｏｌｌｅｇｅｏｆＣｌｉｎｉｃａｌＭｅｄｉｃｉｎｅ，ＧｕｉｌｉｎＭｅｄｉｃａｌＵｎｉｖｅｒｓｉｔｙ，ＧｕｉｌｉｎＧｕａｎｇｘｉ　５４１００１，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：Ａｉｍ　Ｔｏｉｎｖｅｓｔｉｇａｔｅｔｈｅｅｆｆｅｃｔｏｆｌｉｃｏｃｈａｌ ｃｏｎｅＡ（ＬＣＡ）ｏｎｐｒｏｌｉｆｅｒａｔｉｏｎａｎｄａｐｏｐｔｏｓｉｓｏｆｏｓｔｅｏ ｓａｒｃｏｍａＨＯＳａｎｄＵ２ＯＳｃｅｌｌｓａｎｄｔｏｅｘｐｌｏｒｅｉｔｓｐｏｓｓｉ ｂｌｅｍｏｌｅｃｕｌａｒｍｅｃｈａｎｉｓｍ．Ｍｅｔｈｏｄｓ　ＴｈｅＨＯＳａｎｄＵ２ＯＳｃｅｌｌｓｗｅｒｅｃｕｌｔｕｒｅｄｉｎｖｉｔｒｏ．ＭＴＴａｓｓａｙｗａｓｕｓｅｄｔｏｄｅｔｅｃｔｔｈｅｐｒｏｌｉｆｅｒａｔｉｏｎｏｆｔｈｅｃｅｌｌｓａｆｔｅｒｂｅｉｎｇｔｒｅａｔｅｄｗｉｔｈｄｉｆｆｅｒｅｎｔｃｏｎｃｅｎｔｒａｔｉｏｎｓｏｆＬＣＡａｔｄｉｆｆｅｒｅｎｔｉｎｔｅｒ ｖｅｎｔｉｏｎｔｉｍｅ．ＴｈｅｎＨＯＳａｎｄＵ２ＯＳｃｅｌｌｓｗｅｒｅｔｒｅａｔｅｄｗｉｔｈ０ １％ＤＭＳＯ，ｏｒｄｉｆｆｅｒｅｎｔｃｏｎｃｅｎｔｒａｔｉｏｎｓｏｆＬＣＡ（５，１０，２０μｍｏｌ／Ｌ），ａｎｄｆｌｏｗｃｙｔｏｍｅｔｒｙｗａｓｕｓｅｄｔｏａｓｓｅｓｓｔｈｅｃｅｌｌａｐｏｐｔｏｓｉｓ．Ｔｈｅｅｘｐｒｅｓｓｉｏｎｏｆａｐｏｐｔｏｓｉｓ ｒｅｌａｔｅｄｐｒｏｔｅｉｎｃｌｅａｖｅｄＰＡＲＰ１，Ｂｃｌ ２，Ｂａｘ，ａｎｄＡｋｔ，ＥＲＫｗｅｒｅｄｅｔｅｃｔｅｄｂｙＷｅｓｔｅｒｎｂｌｏｔ．ＴｈｅａｎｔｉｔｕｍｏｒｅｆｆｅｃｔｏｆＬＣＡｗａｓｄｅｔｅｃｔｅｄｏｎＵ２ＯＳｘｅｎｏｇｒａｆｔｍｉｃｅｉｎｖｉｖｏ．Ｒｅｓｕｌｔｓ　ＬＣＡｃｏｕｌｄｉｎｈｉｂｉｔｔｈｅｐｒｏｌｉｆｅｒａｔｉｏｎｏｆＨＯＳａｎｄＵ２ＯＳｃｅｌｌｓｉｎａｔｉｍｅ ａｎｄｄｏｓｅ ｄｅｐｅｎｄｅｎｔｍａｎｎｅｒ．ＦｌｏｗｃｙｔｏｍｅｔｒｙｓｈｏｗｅｄｔｈａｔＬＣＡｔｒｅａｔｍｅｎｔｃｏｕｌｄｉｎｄｕｃｅｃｅｌｌａｐｏｐｔｏｓｉｓ．ＷｅｓｔｅｒｎｂｌｏｔｒｅｓｕｌｔｓｓｈｏｗｅｄｔｈａｔＬＣＡｃｏｕｌｄｉｎｈｉｂｉｔｔｈｅｐｈｏｓｐｈｏｒｙｌａｔｉｏｎｏｆＡｋｔａｎｄＥＲＫ，ｉｎｃｒｅａｓｅｔｈｅｅｘｐｒｅｓｓｉｏｎｏｆｃｌｅａｖｅｄＰＡＲＰ１ａｎｄＢａｘ，ａｎｄｄｅｃｒｅａｓｅｔｈｅｅｘｐｒｅｓｓｉｏｎｏｆＢｃｌ ２．Ｉｎｔｈｅｔｕｍｏｒ ｂｅａｒｉｎｇｍｏｕｓｅｍｏｄｅｌｓ，ＬＣＡｓｉｇｎｉｆｉｃａｎｔｌｙｄｅｃｒｅａｓｅｄｔｈｅｔｕｍｏｒｖｏｌｕｍｅ（Ｐ＜０ ０５）ａｎｄｗｅｉｇｈｔ（Ｐ＜０ ０１）．Ｃｏｎｃｌｕｓｉｏｎｓ　ＬＣＡｃｏｕｌｄｉｎｈｉｂｉｔｔｈｅｐｒｏｌｉｆｅｒａｔｉｏｎｏｆＨＯＳａｎｄＵ２ＯＳａｎｄｉｎｄｕｃｅａｐｏｐｔｏｓｉｓｐｏｓｓｉｂｌｙｂｙｉｎｈｉｂｉｔｉｎｇｔｈｅＡｋｔ／ＥＲＫｓｉｇｎａｌｉｎｇｐａｔｈｗａｙ．Ｋｅｙｗｏｒｄｓ：ｌｉｃｏｃｈａｌｃｏｎｅＡ（ＬＣＡ）；ｏｓｔｅｏｓａｒｃｏｍａ；ｃｅｌｌｐｒｏｌｉｆｅｒａｔｉｏｎ；ｃｅｌｌａｐｏｐｔｏｓｉｓ；Ａｋｔ／ＥＲＫｓｉｇｎａｌｉｎｇｐａｔｈｗａｙ；ｔｕｍｏｒｆｏｒｍａｔｉｏｎｉｎｎｕｄｅｍｉｃｅ网络出版时间：２０２３－０３－２０１６：２９：０２　网络出版地址：ｈｔｔｐｓ：／／ｋｎｓ．ｃｎｋｉ．ｎｅｔ／ｋｃｍｓ／ｄｅｔａｉｌ／３４．１０８６．Ｒ．２０２３０３１９．２３５２．０２２．ｈｔｍｌ知母皂苷ＡⅢ通过ＪＡＫ ２／ＳＴＡＴ３／ＰＤ Ｌ１信号通路增加Ａ５４９／ＤＤＰ对顺铂的敏感性李嘉旗１， 轶群２，许　玲１，姚嘉麟１（上海中医药大学附属岳阳中西医结合医院１．肿瘤一科；２．泌尿外科，上海　２００４３７）ｄｏｉ：１０．１２３６０／ＣＰＢ２０２２０４０１３文献标志码：Ａ文章编号：１００１－１９７８（２０２３）０４－０６５８－０７中国图书分类号：Ｒ２８４ １；Ｒ３２９ ２５；Ｒ３４５ ５７；Ｒ７３４ ２；Ｒ９７９ １摘要：目的　探索知母皂苷ＡⅢ（ｔｉｍｏｓａｐｏｎｉｎＡⅢ，ＴＡ３）增加非小细胞肺癌顺铂耐药株Ａ５４９／ＤＤＰ细胞顺铂敏感性的作用机制。

AE流体效果制作指南Adobe After Effects（简称AE）是一个专业的视频编辑和合成软件，常用于制作各种特效和动画效果。

其中，流体效果是在AE中常见且广泛应用的一种效果。

本文将介绍AE中制作流体效果的一些技巧和方法，帮助读者提升其视频编辑和特效合成的能力。

1. 创建合成和图层首先，在AE中创建一个新的合成（Composition），设置好合成的宽度、高度、帧率等参数。

然后，导入需要制作流体效果的素材，并将其添加到合成中作为图层（Layer）。

2. 使用插件AE本身提供了一些流体效果的插件，如CC Particle World和CC Mr. Mercury等。

这些插件可以帮助快速制作流体效果。

选择适合的插件，将其应用到图层上，并根据需求调整参数，如粒子的大小、速度、颜色等。

3. 使用遮罩使用遮罩是制作流体效果的常用方法。

在图层上创建一个遮罩（Mask），并使用AE中的绘图工具进行绘制。

可以通过改变遮罩的形状、位置和动画来制作流体效果。

可以使用多个遮罩叠加，以达到更复杂的效果。

4. 利用轨道和运动模糊在AE中，可以使用轨道（Tracker）来跟踪视频中的运动，并将流体效果与运动相结合。

使用Tracker工具对视频进行跟踪，然后将流体效果应用到跟踪点上，以实现更逼真的效果。

另外，使用AE中的运动模糊（Motion Blur）效果可以让流体效果在运动中更加自然和流畅。

5. 使用高级效果和脚本除了AE自带的插件外，还有一些第三方插件和脚本可以辅助制作流体效果。

比如，Trapcode Particular是一款十分强大且常用的粒子系统插件，可以制作各种复杂的流体效果。

另外，一些脚本如Flow等可以帮助自动生成流体效果的路径，简化制作流程。

6. 调整颜色和光照在制作流体效果时，可以通过调整颜色和光照来增强效果。

使用AE中的色彩校正工具，可以改变流体效果的颜色和饱和度，使其更符合视频的整体色调。

同时，添加适当的光照效果，可以让流体效果更加立体和真实。