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Optimum Gate Design of FreeForm Injection Mould using the Abductive Network基于Abductive网络的最佳任意注射模具浇口设计J. C. LinDepartment of Mechanical Design Engineering, National Hu-Wei Institute of Technology, Yunlin, TaiwanThis study uses the injection position and size of the gate as the major control parameters for a simulated injection-mould. Once the injection parameters (gate size and gate position) are given, the product performance (deformation) can be accuratelypredicted by the abductive network developed.本研究以注射位置和浇口的大小作为主要控制参数来模拟注射模具。
一旦给出了喷雾参数(浇口大小和浇口位置,可用成熟的Abductive网络准确的预测产品性能(变形)。
To avoid the numerous influencing factors, first the part-line of the parameter equation is created by an abductive network to limit therange of the gate. The optimal injection parameters can be searched for by a simulation annealing (SA) optimization algorithm, with a performance index, to obtain a perfectpart. 为了避免众多的影响因素,使用 abductive网络建立第一个线性参数方程来限制浇口的范围。
Design and Implementation of a Bionic Robotic Hand with Multimodal Perception Based on ModelPredictive Controlline 1:line 2:Abstract—This paper presents a modular bionic robotic hand system based on Model Predictive Control (MPC). The system's main controller is a six-degree-of-freedom STM32 servo control board, which employs the Newton-Euler method for a detailed analysis of the kinematic equations of the bionic robotic hand, facilitating the calculations of both forward and inverse kinematics. Additionally, MPC strategies are implemented to achieve precise control of the robotic hand and efficient execution of complex tasks.To enhance the environmental perception capabilities of the robotic hand, the system integrates various sensors, including a sound sensor, infrared sensor, ultrasonic distance sensor, OLED display module, digital tilt sensor, Bluetooth module, and PS2 wireless remote control module. These sensors enable the robotic hand to perceive and respond to environmental changes in real time, thereby improving operational flexibility and precision. Experimental results indicate that the bionic robotic hand system possesses flexible control capabilities, good synchronization performance, and broad application prospects.Keywords-Bionic robotic hand; Model Predictive Control (MPC); kinematic analysis; modular designI. INTRODUCTIONWith the rapid development of robotics technology, the importance of bionic systems in industrial and research fields has grown significantly. This study presents a bionic robotic hand, which mimics the structure of the human hand and integrates an STM32 microcontroller along with various sensors to achieve precise and flexible control. Traditional control methods for robotic hands often face issues such as slow response times, insufficient control accuracy, and poor adaptability to complex environments. To address these challenges, this paper employs the Newton-Euler method to establish a dynamic model and introduces Model Predictive Control (MPC) strategies, significantly enhancing the control precision and task execution efficiency of the robotic hand.The robotic hand is capable of simulating basic human arm movements and achieves precise control over each joint through a motion-sensing glove, enabling it to perform complex and delicate operations. The integration of sensors provides the robotic hand with biological-like "tactile," "auditory," and "visual" capabilities, significantly enhancing its interactivity and level of automation.In terms of applications, the bionic robotic hand not only excels in industrial automation but also extends its use to scientific exploration and daily life. For instance, it demonstrates high reliability and precision in extreme environments, such as simulating extraterrestrial terrain and studying the possibility of life.II.SYSTEM DESIGNThe structure of the bionic robotic hand consists primarily of fingers with multiple joint degrees of freedom, where each joint can be controlled independently. The STM32 servo acts as the main controller, receiving data from sensors positioned at appropriate locations on the robotic hand, and controlling its movements by adjusting the joint angles. To enhance the control of the robotic hand's motion, this paper employs the Newton-Euler method to establish a dynamic model, conducts kinematic analysis, and integrates Model Predictive Control (MPC) strategies to improve operational performance in complex environments.In terms of control methods, the system not only utilizes a motion-sensing glove for controlling the bionic robotic hand but also integrates a PS2 controller and a Bluetooth module, achieving a fusion of multiple control modalities.整整整整如图需要预留一个图片的位置III.HARDWARE SELECTION AND DESIGN Choosing a hardware module that meets the functional requirements of the system while effectively controlling costs and ensuring appropriate performance is a critical consideration prior to system design.The hardware components of the system mainly consist of the bionic robotic hand, a servo controller system, a sound module, an infrared module, an ultrasonic distance measurement module, and a Bluetooth module. The main sections are described below.A.Bionic Mechanical StructureThe robotic hand consists of a rotating base and five articulated fingers, forming a six-degree-of-freedom motion structure. The six degrees of freedom enable the system to meet complex motion requirements while maintaining high efficiency and response speed. The workflow primarily involves outputting different PWM signals from a microcontroller to ensure that the six degrees of freedom of the robotic hand can independently control the movements of each joint.B.Controller and Servo SystemThe control system requires a variety of serial interfaces. To achieve efficient control, a combination of the STM32 microcontroller and Arduino control board is utilized, leveraging the advantages of both. The STM32 microcontroller serves as the servo controller, while the Arduino control board provides extensive interfaces and sensor support, facilitating simplified programming and application processes. This integration ensures rapid and precise control of the robotic hand and promotes efficient development.C.Bluetooth ModuleThe HC-05 Bluetooth module supports full-duplex serial communication at distances of up to 10 meters and offers various operational modes. In the automatic connection mode, the module transmits data according to a preset program. Additionally, it can receive AT commands in command-response mode, allowing users to configure control parameters or issue control commands. The level control of external pins enables dynamic state transitions, making the module suitable for a variety of control scenarios.D.Ultrasonic Distance Measurement ModuleThe US-016 ultrasonic distance measurement module provides non-contact distance measurement capabilities of up to 3 meters and supports various operating modes. In continuous measurement mode, the module continuously emits ultrasonic waves and receives reflected signals to calculate the distance to an object in real-time. Additionally, the module can adjust the measurement range or sensitivity through configuration response mode, allowing users to set distance measurement parameters or modify the measurement frequency as needed. The output signal can dynamically reflect the measurement results via level control of external pins, making it suitable for a variety of distance sensing and automatic control applications.IV. DESIGN AND IMPLEMENTATION OF SYSTEMSOFTWAREA.Kinematic Analysis and MPC StrategiesThe control research of the robotic hand is primarily based on a mathematical model, and a reliable mathematical model is essential for studying the controllability of the system. The Denavit-Hartenberg (D-H) method is employed to model the kinematics of the bionic robotic hand, assigning a local coordinate system to each joint. The Z-axis is aligned with the joint's rotation axis, while the X-axis is defined as the shortest distance between adjacent Z-axes, thereby establishing the coordinate system for the robotic hand.By determining the Denavit-Hartenberg (D-H) parameters for each joint, including joint angles, link offsets, link lengths, and twist angles, the transformation matrix for each joint is derived, and the overall transformation matrix from the base to the fingertip is computed. This matrix encapsulates the positional and orientational information of the fingers in space, enabling precise forward and inverse kinematic analyses. The accuracy of the model is validated through simulations, confirming the correct positioning of the fingertip actuator. Additionally, Model Predictive Control (MPC) strategies are introduced to efficiently control the robotic hand and achieve trajectory tracking by predicting system states and optimizing control inputs.Taking the index finger as an example, the Denavit-Hartenberg (D-H) parameter table is established.The data table is shown in Table ITABLE I. DATA SHEETjoints, both the forward kinematic solution and the inverse kinematic solution are derived, resulting in the kinematic model of the ing the same approach, the kinematic models for all other fingers can be obtained.The movement space of the index finger tip is shownin Figure 1.Fig. 1.Fig. 1.The movement space at the end of the index finger Mathematical Model of the Bionic Robotic Hand Based on the Newton-Euler Method. According to the design, each joint of the bionic robotic hand has a specified degree of freedom.For each joint i, the angle is defined as θi, the angular velocity asθi, and the angular acceleration as θi.The dynamics equation for each joint can be expressed as:τi=I iθi+w i(I i w i)whereτi is the joint torque, I i is the joint inertia matrix, and w i and θi represent the joint angular velocity and acceleration, respectively.The control input is generated by the motor driver (servo), with the output being torque. Assuming the motor input for each joint is u i, the joint torque τi can be mapped through the motor's torque constant as:τi=kτ∙u iThe system dynamics equation can be described as:I iθi+b iθi+c iθi=τi−τext,iwhere b i is the damping coefficient, c i is the spring constant (accounting for joint elasticity), and τext,i represents external torques acting on the joint i, such as gravity and friction.The primary control objective is to ensure that the end-effector of the robotic hand (e.g., fingertip) can accurately track a predefined trajectory. Let the desired trajectory be denoted as y d(t)and the actual trajectory as y(t)The tracking error can be expressed as:e(t)=y d(t)−y(t)The goal of MPC is to minimize the cumulative tracking error, which is typically achieved through the following objective function:J=∑[e(k)T Q e e(k)]N−1k=0where Q e is the error weight matrix, N is the prediction horizon length.Mechanical constraints require that the joint angles and velocities must remain within the physically permissible range. Assuming the angle range of the i-th joint is[θi min,θi max]and the velocity range is [θi min,θi max]。
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Modulated Symbols for Advanced T-DMB System》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html96.《Pooling-Based Intra Prediction Mode Coding for Mobile Video Applications》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html97.《A Suboptimal Tone Reservation Algorithm Based on Cross-Entropy Method for PAPR Reduction in OFDM Systems》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html98.《A Measurement Method of the DTMB Modulator》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html99.《Interference Elimination for Chinese DTMB System With Transmit Diversity》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html100.《61st Annual IEEE Broadcast Symposium》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html101.《IBC2011 Experience the Future》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html102.《A Low-Complexity SLM Scheme Using Additive Mapping Sequences for PAPR Reduction of OFDM Signals》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html103.《Illumination-Sensitive Background Modeling Approach for Accurate Moving Object Detection》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html104.《Coordinating Allocation of Resources for Multiple Virtual IPTV Providers to Maximize Revenue》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html105.《Inter-Sequence Error Concealment Techniques for Multi-Broadcast TV Reception》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html106.《Performance Evaluation of the DVB-RCT Standard for Interactive Services》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html107.《An Efficient Predistorter Design for Compensating Nonlinear Memory High Power Amplifiers》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html108.《Accurate BER Analysis of OFDM Systems Over Static Frequency-Selective Multipath Fading Channels》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html109.《A Frame-Related Approach for Performance Improvement of MPE-FEC in DVB-H》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html110.《Balanced Multiple Description Coding for 3D DCT Video》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html111.《Performance Validation of the DVB-SH Standard for Satellite/Terrestrial Hybrid Mobile Broadcasting Networks》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html112.《An Improved Tone Reservation Scheme With Fast Convergence for PAPR Reduction in OFDM Systems》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html113.《Metaheuristic Procedure to Optimize Transmission Delays in DVB-T Single Frequency Networks》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html114.《Adaptive Resource Allocation for MIMO-OFDM Based Wireless Multicast Systems》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html115.《An Analytical Approach for Performance Evaluation of Hybrid (Broadcast/Mobile) Networks》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html116.《Cost-Aware Wireless Data Broadcasting》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html117.《Subspace-Based Semi-Blind Channel Estimation in Uplink OFDMA Systems》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html118.《Performance of the Consumer ATSC-DTV Receivers in the Presence of Single or Double Interference on Adjacent/Taboo Channels》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html119.《A Cooperative Cellular and Broadcast Conditional Access System for Pay-TV Systems》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html120.《A Narrow-Angle Directional Microphone With Suppressed Rear Sensitivity》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html121.《Peak-to-Average Power Ratio Reduction in OFDM Systems Using All-Pass Filters》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html122.《Development of Advanced Terrestrial DMB System》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html123.《HDTV Subjective Quality of H.264 vs. MPEG-2, With and Without Packet Loss》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html124.《Estimation of RF Electromagnetic Levels Around TV Broadcast Antennas Using Fuzzy Logic》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html125.《Statistical Multiplexing for Digital Audio Broadcasting Applications》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html126.《A Composite PN-Correlation Based Synchronizer for TDS-OFDM Receiver》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html127.《Application of Quantum-Inspired Evolutionary Algorithm to Reduce PAPRof an OFDM Signal Using Partial Transmit Sequences Technique》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html128.《Improved Decoding Algorithm of Bit-Interleaved Coded Modulation for LDPC Code》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html129.《Precoding for PAPR Reduction of OFDM Signals With Minimum Error Probability》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html130.《Network Design and Field Application of ATSC Distributed Translators》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html131.《On the Channel and Signal Cross Correlation of Downlink and Uplink Mobile UHF DTV Channels With Antenna Diversity》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html132.《Performance Evaluation of TV Over Broadband Wireless Access Networks》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html133.《IBC2010 Experience the State-of-the-Art》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html134.《Peak-to-Average Power Ratio Reduction of OFDM Signals With Nonlinear Companding Scheme》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html135.《Motion-Compensated Frame Rate Up-Conversion—Part I: Fast Multi-Frame Motion Estimation》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html136.《Comments on Equation (4) in “Single Frequency Networks in DTV”》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html137.《Motion-Compensated Frame Rate Up-Conversion—Part II: New Algorithms for Frame Interpolation》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html138.《A Novel Equalization Scheme for ZP-OFDM System Over Deep Fading Channels》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html139.《A Synchronization Design for UWB-Based Wireless Multimedia Systems》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html140.《Frequency Domain Decision Feedback Equalization for Uplink SC-FDMA》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html141.《A 2 2 MIMO DVB-T2 System: Design, New Channel Estimation Scheme and Measurements With Polarization Diversity》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html142.《Impact of the Receive Antenna Arrays on Spatio-Temporal Availability in Satellite-to-Indoor Broadcasting》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html143.《Reducing Channel Zapping Time in IPTV Based on User's Channel Selection Behaviors》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html144.《On the Methodology for Calculating SFN Gain in Digital Broadcast Systems》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html145.《Statistical Multiplexing of Upstream Transmissions in DOCSIS Cable Networks》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html146.《Bit-Rate Allocation for Broadcasting of Scalable Video Over Wireless Networks》原文链接:https:///academic-journal-foreign_broadcasting-ieee-transactions_thesis/020*********.html147.《Full-Reference Video Quality Metric for Fully Scalable and Mobile SVC Content》。
摘要摘要随着医疗数字化技术、人工智能和大数据技术的发展，医疗模式逐渐从以治疗为主转变为以预防为主。
将人工智能和大数据技术结合用于疾病风险预测是智能医疗领域的一个研究重点。
疾病风险预测是指发现疾病的潜在风险和趋势，对于疾病的预防、干预和管理具有重要作用。
在实际生活中，经常发现人们同时患有多种疾病的潜在风险和趋势，这种问题属于多疾病风险预测问题。
为了有效地处理多疾病风险预测问题，研究学者已经设计了许多较好的算法。
本文采用深度学习来处理多疾病风险预测问题，因为深度学习技术最近非常受欢迎。
在多疾病风险预测模型设计中，本文专注于深度学习算法设计和改进。
本文首先采用问题转化方法将多疾病风险预测问题转化为多标记学习问题。
因为问题转化方法能够使算法独立，只需进行多疾病标记转换工作，并采用Binary Relevance (BR)和Label Powerset (LP)这两种常见的问题转化方法对多疾病标记分别进行转化。
在问题转化方法基础之上，本文设计了一个新颖的卷积神经网络框架，命名为GroupNet，并分别与BR和LP方法进行结合。
GroupNet网络框架的核心组成部分是本文提出的组模块，组模块由组卷积和聚类卷积两部分组成,组模块具有缓解卷积冗余和聚类作用。
通过实验结果比较可知，GroupNet网络框架的性能优于几个经典的卷积神经网络框架。
其次针对BR方法没有考虑到标记之间的关联性这个局限性，本文提出了一种关联损失函数来缓解这个局限性。
本文将关联损失函数与焦点损失函数和交叉熵损失函数进行比较，实验结果表明关联损失函数的表现优于其他两种损失函数。
为了进一步提高多疾病风险预测模型的性能，本文将GroupNet和集成算法（如随机森林算法、LightGBM）进行有机地结合，得到集成模型，集成模型能够集成多种算法的优点。
实验结果表明，集成模型比单一的GroupNet网络和集成算法在准确率上至少提高1%。
最后为了验证本文提出的方法的性能，本文采用多种经典的机器学习算法与本文提出的方法进行对比。
六西格玛专业用语词汇表ANOVA（ANalysis Of Variance）：变异数分析。
一比较两个或以上的群体之间平均值的差异程度, 作为相关性辨别的方法。
Balanced Design ：设计在每组试验中有相同的实验单位。
BB（Black Belt）：黑带。
Black Belt Certification：黑带认证。
完成两个符合条件的项目后取得的认证。
Block：一群具有同构型的实验单位。
Blocking：一个试验在既定的顺序或条件下完成。
任何有妨碍的因子并不会影响真正的结果或重要性。
Capability：能力，达成目标的过程中能维持下去的能力。
Cause & Effect Diagrams ：因果关系图。
能表达出一个结果及可能的原因两者关系的图表。
Center Points：以所有因子的最高及最低点的中点值来执行的实验。
只能用在计量的数据。
CI（Confidence Interval）：信赖区间。
响应的数值能真实代表母体，使人信赖的百分比程度。
Confounded Effects ：不能被独立预测出的令人困惑的结果。
Confounding：一个或多个结果，无法明确的归因于某个因素或相互间的影响。
Control Chart ：控制图。
用来辨识一个控制下的操作过程的方法（在既定的统计范畴内）。
Cp（Process Capability）：衡量过程能力的指数 Cp = 公差(Tolerance) / 6s。
Cpk ： Performance Capability Index – Cpk = (USL – mean)或(mean - LSL)的最小值除以3s。
CRD （Completely Randomized Design）：完全随机设计。
在各种程度下，研究某个重要的因子，而实验以完全随机的顺序来执行，使不可控制的变因最小化。
CTQ Flow down ：以非常严谨的方法分配需求，并评估比关键性的产品及其部门的能力。
第37卷 第7期2003年7月 西 安 交 通 大 学 学 报JO U RNA L O F XI′AN JIAO TO NG UN IV ERSIT YVol.37 №7Jul.2003低温氦透平膨胀机的热力设计及性能分析侯 予,陈纯正,熊联友,刘立强,王 瑾(西安交通大学低温工程研究所,710049,西安)摘要:针对我国航天领域某重点项目的研制任务,设计了一台氦气体轴承低温透平膨胀机,并对其热力性能进行了分析和讨论.提出了一种考虑膨胀机整体热力性能及机械性能的透平膨胀机系统多目标优化方法.解决了透平膨胀机使用不同工质时相似准则的选取方法,进而在自行开发的较为完善的透平膨胀机一元流动性能预测程序的基础上,获得了模化时所应遵循的相似准则数.以人工神经网络为基础实现了透平膨胀机的性能转换问题.试验结果表明,所研制的氦气体轴承透平膨胀机的绝热效率大于71%;在出口温度为12.8K 时,膨胀机效率已达到75%;膨胀机的最大制冷量接近2kW.关键词:透平膨胀机;氦;性能分析中图分类号:TK04 文献标识码:A 文章编号:0253-987X(2003)07-0666-04Design and Analysis of Thermal Performance forCryogenic Helium Expansion TurbineHou Y u,Chen Chunzheng,X iong Lianyou,Liu Liqiang,Wang J in(Institute of Cryogenic Engineering,Xi′an Jiaotong University,Xi′an710049,China)A bstract:To accomplish the research task of helium turbo-expander used in an impo rtant item of space area,a helium turbo-expander using gas bearings has been designed.The therm al performance of this helium turbo-ex-pander is discussed,and a multiple objects optimization design for w hole performance of a radial-ax ial flow cry o-genic turbo-ex pander is developed.A method of selecting similarity criteria for different wo rking fluids in tur-boexpander is proposed.In theoretical research on predicting the therm al performance of expansion turbine,an effective performance prediction program based on a one-dimensional analy sis of expansion turbine is developed, and the similarity criteria used to simulate modeling tests are obtained.Furthermore,the artificial neural net-w ork is used to deal with the transfo rming problem of turbine performance.The efficiency of this turbine has been increased to75%at the exit temperature12.8K,and the sy stem cooling capacity2kW has been obtained. Keywords:turbo-expander;helium;performance analysis 载人航天工程是一个庞大的系统,是众多行业大力协作的结晶,也是体现一个国家科技水平及综合国力的标志.我国的载人航天工程在20世纪80年代立项论证,20世纪90年代我国的航天计划开始实施.作为航天计划的先期项目———KM6载人航天器空间环境试验设备(简称KM6设备),是发展我国载人航天工程必不可少的重大基础设施.卫星和飞船飞行在地球大气层以外的宇宙空间,那里的环境是深冷黑体(简称热沉)、真空环境和太阳辐射.空间环境模拟器是在地面上模拟空间环境,提供卫星、飞船发射前进行检验的地面设备,其中的深冷氦板借用热沉壁板的保护,以减少辐射能量的消耗,并采用20K深冷泵提供深冷抽气,这就需要采用极低温氦气来冷却低温泵冷板以获得最收稿日期:2002-10-17. 作者简介:侯 予(1973～),男,博士,副教授. 基金项目:国家自然科学基金资助项目(50206015);真空低温技术与物理国防科技重点实验室基金资助项目.佳效果.氦制冷系统就是用来为内装式低温泵提供充裕的低温冷量,以保证试验舱内达到高真空环境.在该制冷系统中,关键的产冷机械是氦气体轴承透平膨胀机,它被普遍认为是氦制冷系统的心脏,是氦制冷系统中技术含量高、研制难度大的核心部件,也是氦制冷系统研制过程中技术研究的重点.KM 6设备是世界三大载人航天器空间环境的试验设备之一(另外2台为美国及俄罗斯所有),其中的氦气体轴承透平膨胀机研制重担最终由西安交通大学制冷与低温工程系承担.1 设计要求氦制冷系统采用80K 液氮预冷逆布雷顿循环,设置2台透平膨胀机的并联使用.KM 6低温氦透平膨胀机的主要设计参数:进口压力为0.8MPa ,进口温度为22.5,出口压力为0.15MPa ,效率大于或等于65%,出口温度小于或等于16K .KM 6氦制冷系统设备如图1所示.2 氦气体轴承透平膨胀机的设计为了获得透平膨胀机的较高性能,必然借助于最优化设计.在低温氦透平膨胀机中,由于进口介质的温度低,膨胀比较大,尤其是在小流量时,工作轮直径较小,势必造成膨胀机转子的转速较高,而过高的转速将使膨胀机的工作可靠性下降[1].参考空气透平膨胀机和国内外现有氦透平膨胀机的参数,并根据KM 6氦透平膨胀机的具体特点和要求,选取基本热力参数和结构参数,对反动度、轮径比等参数进行优化.氦透平膨胀机除了其热力性能外,膨胀机机械性能尤其是轴向力的大小及轴承-转子系统的稳定性也是一个重要因素,它决定了膨胀机设计的可行性和运行可靠性.因此,针对氦透平膨胀机热力性能及机械性能进行整体性能多目标优化设计是十分必要的.在本设计中,以低温透平膨胀机一元流动热力性质数值计算及轴承系统有限元计算为基础,考虑热力性能及机械性能的约束条件,对透平膨胀机的工作轮、制动风机、轴向力及轴承-转子系统进行关于膨胀机的特性比(u 1)、反动度(ρ)、轮径比(μ)、叶高轮径比(l 1/D 1)等包括热力性能及机械性能的系统多目标优化,全面地考虑约束条件,从数据库中选取其他热力及结构参数,并加入专家的经验以提高搜索效率[2].主要设计结果:氦气膨胀气量为900Nm 3/h ,工作轮直径为35mm ,制动风机轮D =60mm ,ρ=0.463,u 1=0.624,工作转速为116×103r /min ,绝热效率大于0.65.3 性能预测由于透平膨胀机内部的工质流动是一种极为复杂的三维流动,加上尾迹流的影响、叶轮与喷嘴间流动相互干扰形成的不稳定流动的影响、粘性影响、边界层及二次流发展的影响、大焓降透平膨胀机中激波间断和阻塞工况的影响等,致使理论预测透平膨胀机的热力性能变得十分困难.目前,依靠试验了解透平膨胀机的热力性能仍为最重要也是最可靠的手段.但是,对于氦透平膨胀机而言,由于氦气的昂贵以及加工现场条件的限制,通常采用空气或其他相对便宜的替代工质进行试验,为此要设法解决不同工质(主要指具有不同绝热指数的工质)试验结果之图1 K M 6氦制冷系统设备图667 第7期 侯 予,等:低温氦透平膨胀机的热力设计及性能分析间的相互转化问题.为此,我们采用了2种方法:一是利用相似模化的方法,寻求试验时应遵循的决定性相似准则数;二是利用人工智能领域最新发展起来的人工神经网络技术来实现不同工质的性能转换[3].在透平膨胀机性能的理论预测研究中,课题组应用并发展了NASA 理论,确定了速度系数φ、ψ的变化规律及冲击损失系数β的计算方法,考虑到膨胀端与制动端的功率匹配问题,建立了空气、氦气和二氧化碳物性数据库,从而实现了程序可根据多种工质按实际物性进行计算.在透平膨胀机热力性能预测程序的基础上,推导出了使用同种工质时透平膨胀机所应遵循的决定性相似准则,总结出了透平膨胀机使用空气与氦气时所应遵循的决定性相似准则,提出了分段选用κ(绝热指数)与M u (对应于速度u 的马赫数)的不同组合作为决定性相似准则之一的思想及方法,并进行了数值模拟试验.由于M u 、p 0/p 3、κ与绝热效率(ηs )之间是一种高度的非线性映射关系,故在透平膨胀机的性能转换研究中应用人工神经网络(ANN )技术,以解决透平膨胀机使用不同工质时的性能转换问题.结果表明,以大量的试验数据做为训练样本,将使人工神经网络方法在透平膨胀机使用不同工质的性能转换中更加可靠、有效[4].氦透平膨胀机理论预测曲线如图2所示,图中u 1为出口工作轮的径向速度;C s 为等熵速度.4 试验分析为考察氦透平膨胀机的性能,暴露设计中存在的问题及预测其使用工况下的性能,在制造厂内将样机在制氧机上先后多次进行了常、低温(液氮级)空气试验,符合要求后运至使用现场.在用氦气多次开车的整个试验过程中,2台氦气体轴承透平膨胀机均一次开车成功,并表现出良好的性能,尤其是膨胀机轴承-转子系统的稳定性优异,在整个试验过程中未出现任何轴承失稳和转子卡死现象.氦透平膨胀机始终运转稳定,最高转速已超过了设计值,并经历了各种工况考验,创造了现场多次试车中氦透平膨胀机没有发生一次事故的新纪录.试验还表明,该机在制冷温度、制冷量等多项指标上,完全满足了飞船的试验要求,其优异的热力性能也发挥得很突出,如用空气作试验时,氦透平膨胀机的最高绝热效率已达68%.在环境模拟现场进行了氦低温试验后,直至1999年氦制冷系统参加飞船联调试验取得圆满成功,充分显示了氦透平膨胀机的技术指标均达到并明显优于原设计任务的要求.氦透平膨胀机空气常、低温的试验结果及氦气试车膨胀机的特性曲线如图3～图5所示.根据测试记录,透平膨胀机出口的最低温度小于13K ,比原设计要求的出口温度小于16K 或更低.根据测试数据的计算,该透平膨胀机在设计工况下的实测绝热效率大于71%,在出口温度为12.817K 时,膨胀机效率已达到75%,明显高于原设计的要求值(大于65%).膨胀机的最大制冷量接近2kW ,这使原设计的双机并开到现在的一开一备,即可满足系统对制冷量的要求.从试验结果来分析,膨胀机的实际运行指标大大超过设计工况,再结合空气及低温试验前后的数据及设备装拆记录,发现主要原因在于:①由于保障了加工精度,膨胀机通流部分的流动损失大大降低;②轴承-转子系统良好的稳定性和超速性能使膨胀机达到了最佳特性比,等熵效率显著提高;③整机材料用导热系数小的钛合金及不锈钢件,在结构上也采用了积极措施,从而减少系统的跑冷损失;④系统采用的新型低温密封结构杜绝了极冷氦气的内漏与外漏.图2 氦透平膨胀机理论预测曲线图3 在常低温下用空气试车时氦透平膨胀机的试验结果668西 安 交 通 大 学 学 报 第37卷 图4 用空气试车时氦透平膨胀机的特性曲线图5 用氦气试车时氦透平膨胀机的特性曲线5 结 论通过对KM 6设备低温氦气体轴承透平膨胀机的成功研制与试验分析,获得了低温氦气制冷系统及低温高速气体轴承透平膨胀机的设计准则、试验数据与运行经验,这具有重要的学术价值和工程指导意义.本文所设计的逆布雷顿循环氦制冷系统的关键设备氦气体轴承透平膨胀机已通过使用单位的验收并交付使用.运行表明,该机具有较好的热力性能和机械性能,获得了使用单位较好的评价,并在“神舟号”飞船热真空试验中发挥了很好的作用.该项目的技术指标已达到国际先进水平,2000年在北京由教育部组织的鉴定会上获得了很高的评价,它的成功研制被认为是我国氦制冷技术水平显著提高的重要标志.由西安交通大学研制的氦气体轴承透平膨胀机与氦制冷螺杆压缩机一起荣获了2001年度中国高校科学技术进步二等奖,所配套的KM 6载人航天器空间环境试验设备荣获2001年度国家科学技术进步二等奖.致谢 感谢中国航天科技集团公司第五研究院511所、苏州制氧机有限公司、苏州三川换热器厂、西安交通大学瑞森集团公司等诸多单位在KM 6氦气体轴承透平膨胀机研制过程中提供的支持和技术协作.参考文献:[1] 西安交通大学制冷教研室.国外氢氦透平膨胀机评述[R ].西安交通大学科技参考资料,75-010.西安:西安交通大学,1973.11～14.[2] 侯予,王瑾,熊联友,等.径-轴流低温透平膨胀机整体性能多目标优化设计[J ].低温工程,2001,122(4):13～17.[3] 刘立强,熊联友,侯予.透平膨胀机热力性能的理论预测[J ].低温工程,1998,104(4):5～10.[4] 刘立强.气体轴承氦透平膨胀机的研究[D ].西安:西安交通大学能源与动力工程学院,1997.(编辑 王焕雪)《科技英语论文实用写作指南》简介《科技英语论文实用写作指南》是为提高我国研究生的科技论文英语写作能力而编写的研究生教材.本书从实用的角度出发,以论述与实例相结合的方式,介绍了科技英语论文各章节的写作要点、基本结构、常用句型、时态及语态的用法和标点符号的使用规则,以及常用词、常用短语的正确用法,指出了撰写论文时常出现的错误,并在附录中列出了投稿信函、致谢、学术演讲和图表设计及应用的注意事项等.本书适用于博士生、硕士生、高校教师和研究院所的科研人员,还可以作为对参加国际学术会议参加人员进行培训的教材.本书由西安交通大学俞炳丰教授编著,西安交通大学出版社出版.出版发行科电话:029-*******,2667874.669 第7期 侯 予,等:低温氦透平膨胀机的热力设计及性能分析。
Box—BehnkenDesign效应面法在制剂处方优化中的应用Box-Behnken Design（BBD）效应面法是一种实验条件寻优的方法，采用多元二次回归方程拟合各因素与响应值之间的函数关系，找出预测的响应最优值以及相应的实验条件，是种多因素非线性实验优化方法，可以评估因素的非线性影响，在处方优化中广泛使用。
该设计方法试验次数少，应用方便，优选的条件预测性好。
目前，微球、自乳化释药体系、脂质体等制剂的处方优化越来越多选择BBD的优化法。
[Abstract] Box-Behnken Design （BBD）response surface method is a method of optimal experimental conditions. It finds out the optimal prediction of response values and the corresponding experimental conditions between the multivariate regression equation fitting each factor and response values. It is a kind of multi-factor non-linear optimization experiment method，can be used for assessed the non-linear effects of factors，and it is widely used in formulation optimization. This design has the advantages of fewer experiments，convenience and better prediction. At present，BBD can be used more and more frequently to optimize the formulation of microspheres，self-emulsifying drug release system，liposome and so on.[Key words] Box-Behnken Design；Response surface methodology；Formulation optimization响应面优化法，即响应曲面法（response surface methodology，RSM）是通过一定的实验设计考察自变量，即影响因素对效应的作用并对其进行优化的方法，为一种新的集数学与统计学于一体，利用计算机技术进行数据处理的优化方法。
Abstract—Component-Based software engineering provides an opportunity for better quality and increased productivity in software development by using reusable software components [10]. One of the most critical aspects of the quality of a software system is its performance. The systematic application of software performance engineering techniques throughout the development process can helpto identify design alternatives that preserve desirable qualities such as extensibility and reusability while meeting performance objectives [1]. In the present scenario, software engineering methodologies strongly focus on the functionality of the system, while applying a “fix- it-later” approach to software performance aspects [3]. As a result, lengthy fine-tunings, expensive extra hard ware, or even redesigns are necessary for the system to meet the performance requirements. In this paper, we propose design based, implementation independent, performance prediction approach to reduce the overhead associated in the later phases while developing a performance guaranteed software product with the help of Unified Modeling Language (UML).Keywords—Software Reuse, Component-based development, Unified Modeling Language, Software performance, Software components, Performance engineering, Software engineering.I.I NTRODUCTIONHE design and construction of future software systems will require the integration of software analysis and design methods with Software Performance Engineering (SPE) in reuse based software development. This integration allows software designers to explore design alternatives and select a design that provides the best overall combination of understandability, reusability, modifiability and performance so that software systems can meet performance goals [1]. Central to improve the practice of performance implementation is the understanding that good design and management of resources will avoid the component communication bottleneck. Effective planning enables the organization to identify what type of practices is required for their products and plan ahead of time [2].K. S. Jasmine is with RVCE, Visveswaraya Technological University, Sr.Lecturer, Dept of MCA, Bangalore, India (phone: +919342969571; e-mail: jasminesadeep@yahoo.co.in).R. Vasantha is with RVCE, visveswaraya Technological University, Prof, Dept of ISE, Bangalore, India (e-mail: vasanthaprak@)..ponent-Based Development and ReuseThe reuse approach to software development has been used for many years. However, the recent emergence of new technologies has significantly increased the possibilities of building systems and applications from reusable components. Large scale component reuse leads to savings in development resources, enabling these resources to be applied to areas such as quality improvement. Experience has shown that component-based development requires a systematic approach to and focus on the component aspects of software development [19]. The building of systems from components and the building of components for different systems require established methodologies and processes not only in relation to the development/maintenance aspects, but also to the entire component and system lifecycle. There are a number of software engineering disciplines and processes, which require specific methodologies for application in component-based development.Current thinking is that the progress of software development in the near future will depend very much on the successful establishment of CBSE and this is recognized by both industry and academia.B.Software Performance Engineering (SPE)SPE is a method for constructing systems to meet performance objectives [12]. The process begins early in development and uses quantitative techniques to identify satisfactory designs and to eliminate those that are likely to have unacceptable performance before developers invest significant time in their implementation. SPE continues through the detailed-design, implementation and testing phases to predict and manage the performance of the evolving software and to monitor and report actual performance against specifications and predictions.In particular, performance properties are essential in the context of component based software production for two basic reasons [13]:1. Among multiple component implementations providing the same functional behavior, the clients will choose those components that best fit their performance requirements.2. If components have performance specifications, then the performance of the system can be compositionally derived based on its components, while the componentDesign Based Performance Prediction ofComponent Based Software ProductsK. S. Jasmine, and R. VasanthaTimplementations need not be re-analyzed in each new context where they are used.Our research work aims at developing a design based, implementation independent performance guaranteed software product by combining the most recent advances in the fields of: (i) Component based software engineering (CBSE) (ii) Software Performance Engineering (SPE) and (iii) UML modeling of CB systems design. Our basic idea is to adapt the SPE approach to CB development in the design phase to achieve success in both the components and CB applications that guarantee specific performance requirements.C. Present State in Software Reuse World & SPEIn the research community, there are notable approaches to software performance engineering. Recent interest in software architectures has underscored the importance of architecture in achieving software quality objectives, including performance. While decisions made at every phase of the development process are important, architectural decisions have the greatest impact on quality attributes such as modifiability, reusability, reliability, and performance [11].The methodology for performance engineering demands extra effort and capabilities. Much recent researches are aimed at automating the performance modeling process [3][7][8]. But there is a need to specify performance parameters in these models. It requires skilled people. Our research aimed at facilitating this modeling process in the design level with the help of most widely used software-modeling language, namely unified modeling language (UML). Consequently UML diagrams, especially sequence diagrams, collaboration diagrams, activity diagrams and deployment diagrams play an important role in this process.II.I MPLEMENTATIONA.Performance Prediction MethodologyThe SPE process begins with the system's use cases [6]. Use cases describe the major functionalities of the system. Here we focus on the scenarios that describe the use cases. The scenario shows the objects that participate and the messages that flow between them. Performance scenarios are the subset of the use case scenarios that are executed frequently, or those that are critical to the perceived performance of the system. We use Unified Modeling Language (UML) sequence diagrams (SD) to represent performance scenarios. The SD objects represent the components involved, and the SD messages represent the requests of execution of a component service or correspond to information/data exchanged between the components.We can show synchronous and asynchronous messages in the UML using different types of arrowheads. In Fig. 1 the communication between CompB and CompC is a synchronous communication and between CompC and CompD is an asynchronous communication. Also CompD has a self-delegation loop. All these examples use standard UML notations. Additional extensions to the sequence diagram notation are in [14].Fig. 1 Sequence DiagramA UML activity diagram shows the operational workflow of a system i.e., it will tell us which activities are executing sequentially and concurrently.Fig. 2 Simple Activity DiagramIn Fig. 2, activity 1 to activity 4 is sequential in nature. Then a condition check is taking place, if the condition is true (corresponding to the self-delegation loop in sequence diagram, control will go back to action 4 itself. If the condition is false, the control will go to activity 5.Fig. 3 Activity Diagram depicts concurrent activitiesIn Fig. 3 action 1 and action 2 are concurrent activities.A UML deployment diagram (DD) shows the allocation of the software components of the system to the processing nodes and the interconnection between the processing nodes (processes, workstations, I/O devices). The same diagrams can be re-used for similar applications, by only updating the associated parameters. The SD and DD diagrams have to be annotated with the proper performance values and parameters (PAs). For example, system and component execution times, response times, resource utilization (CPU utilization, disk,memory, network) I/O rates and average service time, network utilization, message size etc. A sample DD is shown in Fig. 4.Fig. 4 Deployment DiagramUML collaboration diagram describes how the software components interact. An illustration is given in Fig. 5. The transformation from a sequence diagram into a collaboration diagram is a bi-directional function. The difference between sequence diagrams and collaboration diagrams is that collaboration diagrams emphasize more on the structure than the sequence of interactions. Within sequence diagrams the order of interactions is established by vertical positioning whereas in collaboration diagrams the sequence is given by numbering the interactions. By observing the number of arrows leading to a particular component, the utilization of that component can be predicted. So the requests sent to that component by other components have to wait, therefore response time will be more for them, resulting in performance degradation.Fig. 5 Collaboration diagramConsidering the DD nodes, the PA attributes concern the resource scheduling policy (i.e. the strategy by which the resource handles the different jobs), the resource utilization and the resource throughput that represents the amount of work provided per unit of time by a resource belonging to a certain node.In Fig. 5, there is a two-way communication taking place between CompB and CompC.Also the CompD has to respond to CompC and it also has a self-loop. So from the diagram, CompC and CompD are the most utilized component nodes compared to other component nodes. So the performance attributes of these components have to be monitored seriously.B.Stages in Design Phase of SPE based Software developmentIn the design phase of the SPE based Software development, the following steps can be adopted.Fig. 6 Steps involved in performance PredictionIII.C ONCLUSIONThis paper presents a starting point towards an engineering approach to encompass performance prediction in component-based systems on the basis of design specification. We have defined an original approach that relies on, the most recent advances in the fields of: (i) Component based software engineering (CBSE) (ii) Software Performance Engineering (SPE) and (iii) UML modeling of CB systems. A stepwise approach to adapt the SPE approach to CB development in the design phase to achieve success in both the components and CB applications that guarantee specific performance requirements is given. Future work can propose an automated environment for implementation of the steps mentioned and its application to case studies coming from the industrial world.A CKNOWLEDGMENTThis study has been possible thanks to the support and collaboration of several industry people they are directly or indirectly contributed towards the successful completion of this research.R EFERENCES[1]Mangano, “An Approach to Performance Evaluation of SoftwareArchitectures”, Workshop on Software and Performance, Santa Fe, NM,ACM, 1998, pp. 178-190.[2] B. W. Boehm, "Verifying and Validating Software Requirements andDesign Specifications," IEEE Software, vol. 1, no. 1, pp. 75-88, 1984. [3]Tom Verdict, Bart Dhoedt, Frank Gielen, and Piet Demeester, Seniormember, IEEE, “ automatic Inclusion Of Middleware Performance Attributes into Architectural UML Software Models”, IEEE Trans. On Soft.Engg. VOL 31,no.8, August 2005.[4] D.E. Harms and B.W. Weide, “Copying and Swapping: Influences onthe Design of Reusable Software Components”, IEEE Trans. Soft. Eng.17, 5 (1991), 424-43s.[5]Yan liu, Member, IEEE, Alan Fekete, Member IEEE Computer Society,and Ian Gorton, Member, IEEE, “Design-Level Performance predictionof Component-Based Applications”, IEEE Trans. On Soft.Engg. VOL31,no.11, November 2005.[6]G. Booch, J. Rumbaugh, and I. Jacobson, The Unified ModelingLanguage, User Guide, Reading, MA, Addison Wesley, 1999.[7]V.Cortelessa, A, D’Ambrogio, and G.Iazolla, “Automatic Derivation ofsoftware performance models from case Documents”, performance evaluation, vol.45, no.s 2-3,pp.81-105, July 2001.[8]P.G. Gu and D.C. Petriu, “XSLT transformation from UML Model toLQN performance models”, proc. Third Int’l workshop Software performance (WOSP ‘2002), pp.227-234.July 2002.[9]R. Kazman, et al., “Scenario-Based Analysis of Software Architecture”,IEEE Software, vol. 13, no. 6, 1996, pp.47-55.[10]X.Wu and M. Woodside, “Performance Modeling from Softwarecomponents”, proceedings of the Fourth International Workshop onSoftware and Performance, pp.290-301, 2004.[11]Balsamo and Mangano 1998] S. Balsamo, P. Inverardi, and C. Mangano,“An Approach to Performance Evaluation of Software Architectures”,Workshop on Software and Performance, Santa Fe, NM, ACM, 1998,pp. 178-190.[12] C. U. Smith, Performance Engineering of Software Systems, Reading,MA, Addison-Wesley, 1990.[13]Sitaraman M., et al., “Performance specification of softwarecomponents”, Proc. of SSR '01, p. 310. ACM/SIGSOFT, May 2001. [14]Connie U. Smith and Lloyd G. Williams, Responsive, Scalable Systems:Practical Performance Engineering for Object-Oriented Software, 2001. [15]Cortellessa, V., Mirandola, R. PRIMA-UML: a Performance ValidationIncremental Methodology on Early UML Diagrams, Science of Computer Programming, 44 (2002), 101-129, July 2002, Elsevier Science.[16]Lavenberg S.S. Computer Performance Modeling Handbook, AcademicPress, New York, 1983.[17]UML Profile for Schedulability, Performance, and Time Specification:ttp:///docs/ptc/02-03-02.pdf.[18]UML Documentation version 1.4 Web site. Online/uml/resources/documentation/.[19]Crnkovic, I. and Larsson, M. “A Case Study: Demands on Component-based Development”, Proceedings 22nd International Conference onSoftware Engineering, ACM Press, 2000.Jasmine K. S. born in the ErnakulamDistrict of Kerala state on October 14th inthe year 1971. She received BSc degree inMathematics from Mahatma GandhiUniversity, Kerala in 1991, MSc degree incomputer science from Kerala University,Kerala in 1994 and M.Phil degree incomputer science from BharathidasanUniversity, Tamilnadu in 2005. She is currently doing her PhD in computer science in Mother Teresa University, Kodaikanal, Tamilnadu.She is a Senior lecturer in the Department of MCA, R.V.College of Engineering, Bangalore. Since from 1995, she is working as a lecturer in the field of computer science. During 98-99,She held a visiting faculty position at Visveswarapura College of science, Bangalore. She has authored 11 research papers in the national and international level conferences. Her research interests include Software reuse, Software performance, Software testing, data mining and experimental software engineering.Ms. Jasmine is the member of Indian society for technical education (ISTE), Computer society of India (CSI) and International Society for computer applications (ISCA).R. Vasantha received BSc degree, majored inPhysiscs, Chemistry and Mathematics fromUniversity of Mysore, India in 1976, MSc degreein Mathematics from Manasa Gangotri, Universityof Mysore, India in 1978 and PhD from IndianInstitute of Science, Bangalore, India in 1985.She is a professor in the Department ofInformation science and Engineering, R.V.Collegeof Engineering, Bangalore. She got more than 20 years of research experience. During Oct.1991-Oct.1994, she worked as a Scientist in National Aeronautical Laboratory, Bangalore, India, on Turbulence modeling of aerofoil.Sept1988-Sept.1991: Worked as Research Associate in the Dept. of Mechanical Engineering, University of New South Wales & University of Sydney, Sydney, Australia on turbulence modeling. Oct.1987-Sept.1988: Worked as a Senior Research Associate in the School of Mathematics, University of East Anglia, Norwich, England, on the initiation of detonation waves, Oct.1986-Oct.1987: Worked as a Senior Visiting Fellow in the School of Mathematics, University of East Anglia, Norwich, England. Aug.1985-Aug.1986: Worked as Research Associate in Dept. of Aerospace Engineering, IISc, Bangalore, India. She also has many years of teaching experience. During 1978-1980, she worked as a Lecturer, 1994-2002: Worked as Associate Professor of Mathematics, 2002-2006: Worked as Professor & HOD of Mathematics in an Engineering College in India. Her research interests are in the field of computational fluid mechanics. She is the author and coauthor of several publications appearing in international journals, books, and conference proceedings in the fields of Applied Mathematics and Computational fluid mechanics.Dr. Vasantha is a Gold medal contender for PhD dissertation. And also she Won Scholarship from B.Sc. till the end of Ph.D.。
