Numericalsimulationontheinfluenceofurbandevelopmentonthelocalatmosphericenvironment

doi：10．3969/j．issn．1671－5446．2020．02．002基于MCS和改进遗传算法的进气消声器优化分析*朱传峰，毕嵘，韦静思，袁懋荣，李波，朱亚亚（广汽集团广汽研究院，广东广州511434）摘要：综合考虑发动机进气消声器声学性能和阻力特性，采用蒙特卡洛模拟（MCS），分别对基于传递矩阵和神经网络建立的进气消声器传递损失和压力损失数值模型进行参数贡献度分析，结合改进遗传算法（GA）对进气消声器进行单目标和多目标优化。

研究结果表明：MCS方法有效辨识出参数L2，L4，L6，D2，D3，D4对传递损失和压力损失贡献都较大，简化了优化分析模型。

基于神经网络建立的消声器压力损失数值模型精度较高，消声器压力损失大小的限制对进气消声器的优化结果影响较大。

在满足压力损失的情况下，单目标优化能使进气消声器的传递损失在单个共振带中心频率处传递损失达到最大值，而多目标优化得到的进气消声器比原始进气消声器控制进气噪声最多降低5．31dB，在整个工况范围，进气噪声基本都有所降低，性能优于单目标优化的结果。

关键词：蒙特卡洛模拟；神经网络；遗传算法；传递损失；压力损失中图分类号：TB535．2文献标志码：A文章编号：1671－5446（2020）02－0006－06Optimization Analysis of Acoustic and Resistance Characteristics of Intake Muffler Basedon Monte Carlo SimulationZHU Chuanfeng，BI Rong，WEI Jingsi，YUAN Maorong，LI Bo，ZHU Yaya（GAC Automotive Engineering Institute，Guangzhou511434，China）Abstract：Considering the acoustic and resistance characteristics of intake muffler，the transfer matrix and neural network were used to construct the numerical calculation model of intake muffler transmission loss and pressure loss．The contribution of intake muffler pa-rameters were analyzed based on Monte Carlo Simulation（MCS），combined with improved genetic algorithm（GA），the single objective and multiple objective optimization model were established respectively．The result shows that MCS can effective identification of pa-rameters L2，L4，L6，D2，D3，D4have great contribution to transmission loss and pressure loss，and simplify the optimization model．Theprecision of the intake muffler pressure loss model based on neural network is accurate and the limitation of pressure loss of intake muf-fler has great influence on the optimization．Under the condition of considering pressure loss，the transmission loss of intake muffler cor-responding to the center frequency of single resonant band through single objective optimization can reach maximum，however，the multi-objective optimization is better than that of the original intake muffler to control the intake noise maximum reduction is5．31dB，and the performance of the intake muffler is better than that of single target optimization．Key words：Monte Carlo Simulation；neural network；genetic algorithm；transmission loss；pressure loss引言发动机进气消声器的优劣将直接影响车辆的性能，在保证进气充足的情况下，如何高效率的设计出声学及阻力特性都满足性能要求的进气消声器是工程师面临的一个技术难题，而传递损失和压力损失是用来评价进气消声器声学性能和阻力特性的重要指标［1-3］。

f¨u r Mathematikin den NaturwissenschaftenLeipzigRandom perturbations of spiking activity in apair of coupled neuronsbyBoris Gutkin,J¨u rgen Jost,and Henry TuckwellPreprint no.:492007Random perturbations of spiking activity in apair of coupled neuronsBoris Gutkin∗,J¨u rgen Jost and Henry C.Tuckwell†May14,2007AbstractWe examine the effects of stochastic input currents on thefiring be-haviour of two coupled Type1or Type2neurons.In Hodgkin-Huxleymodel neurons with standard parameters,which are Type2,in the bistableregime,synaptic transmission can initiate oscillatory joint spiking,butwhite noise can terminate it.In Type1cells(models),typified by aquadratic integrate andfire model,synaptic coupling can cause oscilla-tory behaviour in excitatory cells,but Gaussian white noise can againterminate it.We locally determine an approximate basin of attraction,A,of the periodic orbit and explain thefiring behaviour in terms of theeffects of noise on the probability of escape of trajectories from A.1IntroductionHodgkin(1948)found that various squid axon preparations responded in quali-tatively different ways to applied currents.Some preparations gave a frequency offiring which rose smoothly from zero as the current increased whereas oth-ers manifested the sudden appearance of a train of spikes at a particular input current.Cells that responded in thefirst manner were called Class1(which we refer to as Type1)whereas cells with a discontinuous frequency-current curve were called Class2(Type2).Mathematical explanations for the two types are found in the bifurcation which accompanies the transition from rest state to a periodicfiring mode.For Type1behaviour,a resting potential vanishes via a saddle-node bifurcation whereas for Type2behaviour the instability of the rest point is due to an Andronov-Hopf bifurcation,see Rinzel and Ermentrout (1989).Stochastic effects in thefiring behaviour of neurons have been widely reported, discussed and analyzed since their discovery in the1940’s.One of thefirst reports for the central nervous system was by Frank and Fuortes(1955)for catX1X3X2X4X1X2TIMEFigure1:On the left are shown the solutions of(1)-(4)for two coupled QIF model neurons with the standard parameters.X1and X2are the potential variables of neurons1and2and X3and X4are the inputs to neurons1and2, respectively.On the right is shown the periodic orbit in the(x1,x2)-plane.The square marked P was explored in detail in reference to the extent of the basin of attraction of the periodic orbit.spinal neurons.Although there have been many single neuron studies,the effect of noise on systems of coupled neurons have not been extensively investigated. Some preliminary studies are those of Gutkin,Hely and Jost(2004)and Casado and Baltan´a s(2003).2The quadratic integrate andfire modelA relatively simple neural model which exhibits Type1firing behaviour is the quadratic integrate andfire(QIF)model.We couple two model neurons in the following manner(Gutkin,Hely and Jost,2004).Let{X1(t),X2(t),t≥0}be the depolarizations of neurons1and2,where t is the time index.Then the model equations are,for subthreshold states of two identical neurons,dX1=[(X1−x R)2+β+g s X3]dt+σdW1(1)dX2=[(X2−x R)2+β+g s X4]dt+σdW2(2)dX3=−X3τ+F(X1)(4)2where X3is the synaptic input to neuron1from neuron2and X4is the synaptic input to neuron2from neuron1.The quantity x R is a resting value.g s is the coupling strength.βis the mean background input.W1and W2are independent standard Wiener processes which enter with strengthσ.This term may model variations in nonspecific inputs to the circuit as well as possibly intrinsic membrane and channel noise.By construction,we take this term to be much weaker than the mutual coupling between the cells in our circuit.The function F is given byF(x)=1+tanh(α(x−θ))whereθcharacterizes the threshold effect of synaptic activation.Since when a QIF neuron is excited and it receives no inhibition,its potential reaches an infinite value in afinite time,for numerical simulations a cutoffvalue x max is introduced so that the above model equations for the potential apply only if X1 or X2are below x max.To complete a“spike”in any neuron,taken as occurring when its potential reaches x max,its potential is instantaneously reset to some value x reset which may be taken as−x max.At the bifurcation point g s=g∗s, two heteroclinic orbits between unstable rest points turn into a periodic orbit of antiphase oscillations.3Results and theoryIn the numerical work,the following constants are employed throughout.x R= 0,x max=20,θ=10,α=1,β=−1,g s=100andτ=0.25.The initial values of the neural potentials are X1(0)=1.1,X2(0)=0and the initial values of the synaptic variables are X3(0)=X4(0)=0.When there is no noise,σ=0,the results of Figure1are obtained.The spike trains of the two coupled neurons and their synaptic inputs are shown on the left.Thefiring settles down to be quite regular and the periodic orbit,S,is shown on the right.The patch marked P is the location of the region explored in detail below.The effects of a small amount of noise are shown in Figure2.The neural excitation variables are shown on the left and the corresponding trajectories in the(x1,x2)-plane are shown on the right.In the top portion an example of the trajectory forσ=0.1is shown.Here three spikes arise in neuron1and two in neuron2,but the time between spikes increases and eventually the orbit collapses away from the periodic orbit.In the example(lower part)forσ=0.2 there are no spikes in either neuron.In10trials,the average numbers of spikes obtained for the pair of neurons were(2.5,2.2)forσ=0.1,(1.4,1.1)forσ=0.2 and(1.3,0.9)forσ=0.3;these may be compared with(5,5)for zero noise. 3.1Exit-time and orbit stabilityIf a basin of attraction for a periodic orbit can be found,then the probabil-ity that the process with noise escapes from the region of attraction gives the probability,in the present context,that spiking will cease.Since the system3TIMEX1X21 X2Figure2:On the left are shown examples of the neuronal potentials for neurons 1and2(QIF model)for two values of the noise,σ=0.1andσ=0.2.On the right are shown the trajectories corresponding to the results on the left,showing how noise pushes or keeps the trajectories out of the basin of attraction of the periodic orbit.(1)-(4)is Markovian,we may apply standardfirst-exit time theory(Tuckwell, 1989).Letting A be a set in R4and letting x=(x1,x2,x3,x4)∈A be a values of X1,X2,X3,X4)at some given time,the probability p(x1,x2,x3,x4)that the process ever escapes from A is given byL p≡σ2∂x21+σ2∂x22(5)+[(x1−x R)2+β+g s x3]∂p∂x2+ F(x2)−x3∂x3+ F(x1)−x4∂x4=0,x∈Awith boundary condition that p=1on the boundary of A(since the process is continuous).If one also adds an arbitrarily small amount of noise for X3and X4(or considers those solutions of(5)that arise from the limit of vanishing noise for X3,X4),the solution of the linear elliptic partial differential equation (5)is unique and≡1,that is,the process will eventually excape from A with probability1.Hence,the expected time f(x)of exit of the process from A satisfies L f=−1,x∈A with boundary condition f=0on the boundary of A.In fact,for small noise,the logarithm of the expected exit time from A,that4is,the time at whichfiring stops,behaves like the inverse of the square of the noise amplitude(Freidlin and Wentzell,1998).These linear partial differential equations can be solved numerically,for example by Monte-Carlo techniques.The basin of attraction A must be found in order to identify the domain of(5).We have done this approximately for the square P in Figure1.The effects of perturbations of the periodic orbit S within P on the spiking activity were found by solving(1)-(4)with various initial conditions in the absence of noise.The values of x1were from−0.43to1.57in steps of0.2and the values of x2were from-4to2also in steps of0.2.For this particular region, as expected from geometrical considerations,the system responded sensitively to to variations in x1but not x2.For example,to the left of S there tended to be no spiking activity whereas just to the right there was a full complement of spikes and further to the right(but still inside P)one spike.4Coupled Hodgkin-Huxley neuronsAs an example of a Type2neuron,we use the standard Hodgkin-Huxley(HH) model augmented with synaptic input variables as in the model for coupled QIF neurons given by equations(3)and(4),but with different parameter values. It has been long known that additive noise has a facilitative effect on single HH neurons(Yu and Lewis,1989).Coupled pairs of HH neurons have been employed with a different approach using conductance noise in order to analyze synchronization properties(e.g.Casado and Balt´a nas,2003).For the present approach,with X1and X2as the depolarizations of the two cells,we putdX1=1g K n4(V K−X1)+it was found that transient synchronization can terminate sustained activity. For Type2neurons,we have investigated coupled Hodgkin-Huxley neurons and found that in the bistable regime,noise can again terminate sustained spiking activity initiated by synaptic connections.We have investigated a minimal cir-cuit model of sustained neural activity.Such sustained activity in the prefrontal cortex has been proposed as a neural correlate of working memory(Fuster and Alexander,1973).ReferencesCasado,J.M.,Balt´a nas,J.P.(2003).Phase switching in a system of two noisy Hodgkin-Huxley neurons coupled by a diffusive interaction.Phys.Rev.E68,061917,Frank,K.,Fuortes,M.G.(1955).Potentials recorded from the spinal cord with microelectrodes,J.Physiol.130,625-654.Freidlin,M.I.,Wentzell,A.D.(1998),Random Perturbations of Dynamical Sys-tems,2nd ed.,Springer,New York Fuster,J.M.and Alexander,G.E.(1971),Neuron activity related to short-term memory.Science652-654 Gutkin,B.,Ermentrout,G.B.(1998).Dynamics of membrane excitability de-termine interval variability:a link between spike generation mechanismsand cortical spike train statistics.Neural Comp.10,1047-1065. Gutkin,B.S.et al.(2001)Turning on and offwith p.Neurosc.11:2,121-134Gutkin,B.,Hely,T.,Jost,J.(2004).Noise delays onset of sustainedfiring in a minimal model of persistent activity.Neurocomputing58-60,753-760. Hodgkin,A.L.(1948).The local changes associated with repetitive action in a non-medullated axon.J.Physiol.107,165-181.Rinzel,J.,Ermentrout,G.B.(1989).Analysis of neural excitability and oscilla-tions;in:Koch C.&Segev I.,eds.MIT Press.Tateno,T.,Harsch,A.,Robinson,H.P.C.(2004).Thresholdfiring frequency-current relationships of neurons in rat somatosensory cortex:Type1and Type2dynamics.J.Neurophysiol.92,2283-2294.Tuckwell,H.C.(1989).Stochastic Processes in the Neurosciences.SIAM,Philadel-phia.Yu,X.,Lewis,E.R.(1989).Studies with spike initiators:linearization by noise allows continuous signal modulation in neural networks.IEEE Trans.Biomed.Eng.36,36-43.6。

包钢长材厂蓄热式加热炉数值模拟！刘中兴1冯猛1伍永福1张鹏1!2戈春刚2(1.内蒙古科技大学内蒙古自治区白云鄂博矿多金属资源综合利用重点实验室，2.包钢长材厂)摘要以包钢长材厂蓄热式加热炉为研究对象，利用A n sy s软件采用湍流模型、P- 1辐射模型等，模拟了采用交错燃烧组织方式加热炉内各物理场分布情况。

发现该种燃烧方式下，炉内流动涡流运动明显加强，有利于燃烧的混合和组织。

但出口附近的回流造成燃烧短路，高温烟气不易达到炉膛中心，易造成炉内温度不均匀，氧气浓度相对较高不利于钢坯生产，因此该平顶、平底炉型采用交错换向燃烧有待于进一步实践验证。

关键词蓄热式加热炉数值模拟物理场高温空气燃烧技术Numerical simulation on the long materitil factoryof Baogangd regenerative furnaceLiu Zhongxing1Feng Meng1Wu Yongfu1Zhang Peng1，2Ge Chungang2(1. Inner Mongolia University of Science and Technology，2.Long Material Factory of Baogang)Abstract The regenerative heating furnace of tlie long material factory was taken as the research ob­je c t，used the m odel of '- $ turbulence model and P -1radiation model using Ansys software，andsimulated the distribution of the physical field in the furnace with staggered combust is found that the flow vortex motion in the furnace is obviously enhanced under And it is also conducive to the mixing and organization of the combustion. But the reflow caused bycombustion short circuit near the exit. High temperature f lue gas is not easy to reach the center of thefurnace and cause t he furnace t uneven temperature distribution. Oxygen concentration is relativelyhigh，which is unfavorable to billet production. Therefore，the flat roof and flat hearth type adoptedstaggered reversing combustion need to be further verified.Keywords regenerative furnace numerical simulation physical field high temperature air com­bustion加热炉是工业加热的关键设备，广泛应用于 国民经济的各行各业。

Turbulence,Heat and Mass Transfer7c 2012Begell House,Inc.Numerical simulation of pulverized coal MILD combustion considering advanced heterogeneous combustion modelM.Vascellari1,2,S.Schulze1,D.Safronov1,P.Nikrytyuk1,C.Hasse11ZIK Virtuhcon,Dep.of Energy Process Enginnering and Chemical Engineering,University of Technology Freiberg,Fuchsmhlenweg9,09599Freiberg,Germany2Michele.Vascellari@vtc.tu-freiberg.deAbstract—A new advanced subgrid scale(SGS)model for coal particle combustion and gasification was devel-oped.The new model considers a detailed representation of the diffusion and convection phenomena in the direct proximity of the coal particle,which are generally neglected by standard models available in literature.This paper shows the coupling of the new model with the commercial CFD code Ansys-Fluent and its validation consider-ing a full-scale furnace.In particular the IFRF pulverized coal MILD combustion experiments are considered for validating the results of the new model,showing a better agreement with experiments with respect to a standard model.1.IntroductionNew“clean coal technologies”for reducing pollutants from coal power plants require new advanced design tools,able to accurately predict the performances and the emission of such systems.CFD simulations represent a very important tool for designing advanced coal conversion system.However,coal combustion and gasification require several mathematical submodels to represent the several chemical physical phenomena involved.Subgrid scale models are gener-ally developed and validated considering small scale experimental test,focusing the attention on only one ually,it is diffult to extrapolate the results of small scale laboratory tests to large scale system because of the complex nature of turbulent,reacting and multiphase flows in such systems.Eaton et al.[1]presented an overview of the main submodels required for modelling solid fuels systems and their application to comprehensive CFD models.This work presents the coupling of a new subgrid scale(SGS)model for coal char com-bustion with a CFD code and its validation considering a semi-industrial scale pulverized coal MILD test-case[2].The new models was previously developed and validated considering sin-gle coal particle direct numerical simulations(DNS)[3].The new model showed excellent agreement with single particle DNS,predicting enhanced char conversion rates with respect to standard Baum and Street[4]model.2.Numerical ModelsDuring coal combustion several chemical-physical phenomena take place.They require spe-cific mathematical models implemented in a comprehensive CFD code[1].The main models considered concern the following phenomena:turbulence,multiphaseflow and interphase in-teractions,homogeneous and heterogeneous chemical reactions,radiation,etc..Simulations of MILD coal combustion were performed considering the commercial CFD code Ansys-Fluent,version13.0.The Reynolds Average Navier Stokes(RANS)equations are2Turbulence,Heat and Mass Transfer7Table1:Experimental conditions of IFRF furnace[2]Massflowrate,kg/hTemp.,K Composition(%vol)PrimaryAir130313.15O221%,N279%Secondary air 6751623.15CO28.1%,O219.7%,N257.2%,H2O15.1%solved on an unstructured hybrid mesh using afinite volume discretization approach.The three-dimensional version of the pressure-based solver is considered.The SIMPLE[5]algorithm is used for velocity-pressure coupling.Convectivefluxes in all transport equations are discretized with a second-order accurate upwind scheme and the pressure gradient with a second-order accurate scheme.The realizable k− turbulence model[6]is considered for RANS equations closure.The P-1radiation model[7]is considered for radiation heat transfer.The coal discrete phase is modelled considering a Eulerian-Lagrangian approach.The main gas phase is solved considering transport equations for continuous phase in the Eulerian frame of reference,while the secondary discrete solid coal phase is solved considering a Lagrangian frame.The trajectories of the particles are evaluated by integrating the force balance on them with respect to time.The continuous phaseflow pattern is impacted by the discrete phase(and vice versa)and the calculation of the main phase is alternated with the discrete phase until a converged coupled solution is achieved.As the trajectory of a particle is computed,the heat, mass and momentum gained or lost by the particle are evaluated,and these interactions are taken into account in the Eulerian equations of the primary phase by means of source terms. The dispersion of particles due to turbulence is taken into account by considering the stochastic tracking model,including the effect of instantaneous turbulent velocityfluctuations on particle trajectories.The interaction between turbulentflow and chemical reaction plays a fundamental rule in MILD combustion modeling,whether considering solid or liquid and gaseous fuels.Indeed,fluid dynamic behaviour of MILD combustion strongly differs from conventional combustion, because gradients of temperature and chemical species concentrations are generally lower[8]. In this way,a well-definedflame front can no longer be observed.In particular,it was demon-strated[9]that better prediction of temperature and chemical speciesfield were obtained consid-ering advanced turbulence-chemistry interaction model,such as EDC[10]with detailed kinetic mechanisms.The DRM mechanism[11]with103reactions among22chemical species is chosen here.Coal combustion is modelled according to the following sequence of phenomena:drying, pyrolysis,volatile combustion and char burnout.Moisture drying is governed by the difference of water concentrations between the parti-cle surface and the bulk phase.The water concentration on the particle surface is evaluated by assuming that the partial pressure of vapor at the interface is equal to the saturated vapor pressure at the particle temperature.The mass transfer coefficient used for evaluating moisture evaporation is calculated by means of correlation of Ranz and Marshall[12].Pyrolysis can be regarded as a two-stage process[13].During primary pyrolysis,coal par-ticles decompose and release volatile matter(devolatilization),composed by TAR,light hy-drocarbons and gas.During secondary pyrolysis,TAR decomposes and produces soot,lightM.Vascellari et al.3Table2:Proximate and ultimate analysis of Guasare coal[2]Proximate analysis Ultimate analysis(%daf)V olatile matter37.1C78.41Fixed carbon56.7H 5.22Moisture 2.9O10.90Ash 3.3N 1.49LHV31.74MJ/kgTable3:V olatile yield predicted by CPD modelVolatile yield,%dafChar61.69TAR26.91H2O 5.51CO2 1.31CH4 2.26CO0.77N2 1.55hydrocarbons and gas.The devolatilization rate is modelled based on an empirical single ki-netic rate law[14].dY=A v exp(−E v/RT p)·(Y0−Y)(1)dtwhere T p is the particle temperature and Y and Y0are the instantaneous and the overall volatile yield on a dry ash-free(daf)basis,respectively.The model parameters A v and E v are the pre-exponential factor and the activation energy,which need to be adjusted for the given coal and the operating conditions.The CPD model[15]is used to determine the rate constants for single rate model.It requires chemical structure data from13C Nuclear Magnetic Resonance (13C NMR)spectroscopy on the specific coal.Since these detailed analysis data are usually not available,Genetti et al.[16]developed a non-linear correlation based on existing13C NMR data for30coals to determine the required(coal-structure-dependent)input data for the CPD model using the available proximate and ultimate analysis.This correlation is applied here. The volatile matter composition and the overall yield at high temperature were also estimated by means of the CPD model.V olatile matter is composed by light gases and hydrocarbons (CO,CO2,H2O,CH4,etc.)and heavy hydrocarbons(tar).Tar is approximated as an equivalent molecule C n H m,reacting with O2in the gas phase and producing CO and H2[13].2.1.Char Combustion ModelOnce volatile matter is completely released during primary pyrolysis,the char remaining in the coal particles reacts with the surrounding gas phase.The following four heterogeneous reactions were considered:4Turbulence,Heat and Mass Transfer7Figure1:Geometry of the IFRF furnaceC(s)+O2−→CO2(2)2C(s)+O2−→2CO(3)C(s)+CO2−→2CO(4)C(s)+H2O−→CO+H2O(5)Boudouard(Eq.(4))and gasification(Eq.(5))reactions play an important role in MILD combustion[9,17]and they can not be neglected as usually is done for conventional coal com-bustion with atmospheric air.Char burnout is governed by the diffusion of the oxidant species from the bulk phase to the particle surface and by the heterogeneous reactions on the particle surface.Reaction rates are calculated considering global kinetic rates from[18,19].The diffusion of each chemical species from the bulk phase(∞)to the particle surface(s)is given by:β(c∞,i−c s,i)+4j=1νj,iˆR j=0(6)WhereˆR j is the rate of the reaction j,νj,i is the stoichiometric coefficient of species i in reaction j andβis the mass transport coefficient,calculated from the Ranz and Marshall[12] correlation,assuming unitary Lewis number.Generally,standard models[4]neglect the in-fluence of the convection assuming stagnantflow around the particle.The diffusion of each species(Eq.(6))is equal to its production due to the heterogeneous reactions.The mass bal-ance of Eq.(6)account for the interactions between different surface reactions.In fact,CO2, produced on the particle surface from the char oxidization reaction(Eq.(2)),can react directly according to the Boudouard reaction(Eq.(4))increasing the overall char consumption.Gener-ally,standard models,such as[4]model,neglect any interaction between the different surface reactions.Further information about the model can befind in the paper of Schulze et al.[3].User Defined Function(UDF)capability of Ansys-Fluent were used for coupling the SGS model,coded in C language,with the CFD solver,replacing the standard models for char com-bustion.5(a)(b)Figure 2:Comparison of temperature considering Baum and Street [4](BS)and SGS models respectively:(a)axial section contour plot;(b)radial profiles at 0.15,0.44,0.735,and 1.32m from the burner and comparison with experimental results [2].(a)(b)Figure 3:Comparison of CO dry volume fraction considering Baum and Street [4](BS)and SGS models respectively:(a)axial section contour plot;(b)radial profiles at 0.15,0.44,0.735,and 1.32m from the burner and comparison with experimental results [2].3.Validation of the SGS Char Combustion ModelValidation of SGS model was performed considering the experimental pulverized coal MILD test-case at the International Flame Research Fundation (IFRF)[2].MILD or flameless com-bustion is a new technology developed for reducing pollutant emissions [8].Reactants are in-troduced at temperature generally higher than ignition temperature and the mixture is strongly diluted in order to reduce the temperature increase during reactions.The IFRF furnace is char-acterized by a square section of 2m ×2m and by a length of 6.25m,as shown in Fig.1.Primary air enters from the two lateral inlets,transporting pulverized coal particles.Secondary air is preheated by means of combustion with natural gas up to levels of 1350◦C before entering the furnace from the central inlet.Vitiated air is enriched with pure O 2in order to maintain the same concentration as atmospheric air.The furnace is fired with 66kg h −1,130kg h −1and 675kg h −1respectively of coal,primary and secondary air,corresponding to a stoichiometric ratio of 1.2,as reported in Tab.1.The wall of the furnace is considered at the constant temper-Transfer7Figure4:Char consumption rate(kg/s m2)for65µm particles at0.44,0.735,1.32and2.05m from the burner.Results of Baum and Street(BS)model are reported on the left(triangles)and results of SGS on the right(circles)for each sectionsature of about1000◦C.The furnace isfired with Guasare coal,which proximate and ultimate analyisis are reported in Tab.2.Coal isfinely pulverized to give a particle size distribution with80%less than90µm[2].Particle size distribution is covered considering six classes[20]. V olatile yields are calculated by means of the CPD model,as reported in Tab.3.The single rate devolatilization model(Eq.(1))is calibrated by means of the CPD model,obtaining a pre-exponential factor of26353.9s−1and an activation energy of45.424kJ/mol.Considering recirculation of exhaust gas,the furnace is characterized by high concentrations of CO2and H2O and consequently a large fraction of char is converted through the gasification (Eq.(4))and Boudoard(Eq.(5))reactions[17],representing an optimal test-case for validating the new char combustion model.The performances of the SGS model are therefore compared to the standard Baum and Street (BS)model andfinally validated against the experiments[2].Reactions Eq.(3)-(5)are consid-ered for Baum and Street model considering the same kinetic rates[18,19]used for the SGS model.Figure2(a)shows the comparison between the Baum and Street[4]and SGS models consid-ering the temperaturefield.As expected,temperature gradients are very small and no clear front offlame can be observed.Similar temperature profiles were predicted considering both models. The comparison with experiments is reported in Fig.2(b),considering four radial traverses at 0.15,0.44,0.735and1.32m from the burner.The SGS model predicts a lower temperature level in the inner jet zone,because of the increased conversion of char due to the endothermic reactions.Indeed,in this region,O2is almost completely consumed(see[9])and therefore only the endothermic gasification(Eq.(5))and Boudouard reactions(Eq.(4))take place,absorbing heat from the gas phase.Figure3(a)shows the comparison of dry CO molar fraction on the axial section between the Baum and Street[4]and SGS models.Lower levels of CO are predicted by SGS model withrespect to Baum and Street model.Indeed,considering SGS model,the char reacting with O2 produces either CO either CO2,reducing the overall production of CO from the discrete phase. Dry CO molar fraction from numerical simulations is compared to experiments[2]considering four radial traverses at0.15,0.44,0.735and1.32m from the burner,as shown in Fig.3(b).SGS models shows a better agreement with respect to experiments.Figure4shows the char consumption rate at four cross sections for Baum and Street and SGS models considering65µm particles.As already observed for single particle simulations [3],SGS model predicts an enhanced char consumption rate with respect to Baum and Street model,nevertheless the same kinetic rates are used.In fact,the SGS model takes in account the influence of the heat and mass transport from the bulk phase to the particle surface and the interaction between the heterogeneous reaction in the particle boundary layer,enhancing the overall char consumption rate.4.ConclusionsIn this paper a new SGS model for char combustion,previously developed and validated for a single particle combustion by Schulze et al.[3],has been coupled to the commercial CFD code Ansys-Fluent and validated considering a pulverized coal MILD combustion test-case. The results have been compared to the standard Baum and Street model,used as default char combustion model by Ansys-Fluent.The comparison shows an improved prediction of the chemical species concentrations for the new SGS model with respect to the standard model. References[1]Eaton,A.et al.“Components,formulations,solutions,evaluation,and application ofcomprehensive combustion models”.In:Prog Energ Combust25.4(1999),pp.387–436.[2]Orsino,S.et al.Excess Enthalpy Combustion of Coal(Results of High Temperature AirCombustion Trials).Tech.rep.IFRF Doc.No.F46/y/3.International Flame Research Foundation,2000.[3]Schulze,S.et al.“Sub-model for a spherical char particle moving in a hot air/steamatmosphere”.In:Flow Turbul Combust(2012).(submitted).[4]Baum,M.et al.“Predicting the Combustion Behaviour of Coal Particles”.In:CombustSci Technol3.5(1971),pp.231–243.[5]Patankar,S.et al.“A calculation procedure for heat,mass and momentum transfer inthree-dimensional parabolicflows”.In:International Journal of Heat and Mass Transfer15.10(1972),pp.1787–1806.[6]Shih,T.et al.“A new k-epsilon eddy viscosity model for high reynolds number turbulentflows”.In:Computers and Fluids24.3(1995),pp.227–238.[7]Cheng,P.“Two-dimensional radiating gasflow by a moment method”.In:AIAA Journal2.9(1964),pp.1662–1664.[8]Cavaliere,A.et al.“Mild Combustion”.In:Prog Energ Combust30.4(2004),pp.329–366.[9]Vascellari,M.et al.“Influence of turbulence and chemical interaction on CFD pulverizedcoal MILD combustion modeling”.In:Fuel(2012).doi:10.1016/j.fuel.2011.07.042.[10]Gran I.,R.et al.“A numerical study of a bluff-body stabilized diffusionflame.Part1.Influence of turbulence modeling and boundary conditions”.In:Combust Sci Technol 119.1-6(1996),pp.171–190.[11]Kazakov,A.et al.Reduced Reaction Sets based on GRI-Mech1.2.http://me.berk/drm/.1994.[12]Ranz,M.et al.“Evaporation from drops:Part I”.In:Chem Eng Prog48(1952),pp.141–146.[13]F¨o rtsch,D.et al.“A kinetic model for the prediction of NO emissions from staged com-bustion of pulverized coal”.In:Proceedings of the27th Symposium(Intl.)on Combus-tion,The Combustion Institute,Pittsburgh27.2(1998),pp.3037–3044.[14]Badzioch,S.et al.“Kinetics of Thermal Decomposition of Pulverized Coal Particles”.In:Ind.Eng.Chem.Proc.Des.Dev.9.4(1970),pp.521–530.[15]Grant D.,M.et al.“Chemical model of coal devolatilization using percolation latticestatistics”.In:Energy&Fuels3.2(1989),pp.175–186.[16]Genetti,D.et al.“Development and Application of a Correlation of13C NMR Chem-ical Structural Analyses of Coal Based on Elemental Composition and V olatile Matter Content”.In:Energy&Fuels13.1(1999),pp.60–68.[17]Stadler,H.et al.“On the influence of the char gasification reactions on NO formation inflameless coal combustion”.In:Combustion and Flame156.9(2009),pp.1755–1763.[18]Libby P.,A.et al.“Burning carbon particles in the presence of water vapor”.In:Com-bustion and Flame41.0(1981),pp.123–147.[19]Caram H.,S.et al.“Diffusion and Reaction in a Stagnant Boundary Layer about a CarbonParticle”.In:Industrial&Engineering Chemistry Fundamentals16.2(1977),pp.171–181.[20]Kim,J.et al.“Numerical modelling of MILD combustion for coal”.In:Progress in Com-putational Fluid Dynamics(2007).。

TiB 2/Al 复合材料喷丸后微区残余应力的有限元模拟卞凯,姜传海,栾卫志(上海交通大学材料科学与工程学院高温材料及高温测试教育部重点实验室,上海200240)摘 要:采用ANSYS/LS DYNA 有限元分析软件建立了颗粒增强T iB 2/Al 复合材料的喷丸模型,并对喷丸后残余应力分布进行了预测;然后对复合材料进行了喷丸试验,对残余应力进行了检测;将试验结果与模拟结果进行了对比。

结果表明:该复合材料喷丸后残余应力分布的试验结果与模拟结果基本相符;喷丸后最表层部分增强体呈拉应力状态,在材料残余压应力场内,由于增强体和基体材料力学性能的差异,增强体的残余应力值普遍大于基体中的。

关键词:复合材料;有限元模拟;喷丸;残余应力中图分类号:T B333 文献标志码:A 文章编号:1000 3738(2011)01 0086 03Finite Element Simulation of Micro region Residual Stress of ShotPeened TiB 2 / Al CompositeBIAN Kai,JIANG C huan hai,LUAN Wei zhi(K ey L abor ator y for H igh T emperatur e M ateria ls and T ests of M inist ry of Educatio n,Schoo l o f M aterials Science andEng ineering ,Shang hai Jiaot ong U niv ersity ,Shang hai 200240,China)Abstract:A model o f sho t peening of the part icle reinfo rced T iB 2/A l composites w as built w ith AN SY S/L S DYN A finite element simulat ion so ftwa re,then the residua l stress distr ibut ion after shot peening was predict ed.T he shot peening tests wer e perfor med on the co mpo sites,and the r esidual stresses w ere measured,then t he test results wer e co mpar ed with the simulatio n r esult s.It is show n that the ex perimetal results of residual st ress distr ibut ion of the composites after shot peening w ere in ag reement w ith the simulated one.T he r einfo rcement at the outmest layer after shot peening sho wed tensile stress state.In the residual pressure stress field,r esidual str ess in the r einfor cement was generally g reater than that in the substr ate,which was resulted fr om the differ ence of the mechanical pro per ties of the tw o mater ials.Key words:composite;finite element simulation;shot peening ;r esidual str ess0 引 言喷丸是一种广泛应用的材料表面强化手段,可显著提高材料的疲劳强度、表面强度、抗应力腐蚀性能等[1]。

第48卷第4期2019年4月热力发电THERMAL POWER GENERATIONVol.48 No.4Apr. 2019 ^因气循环倍丰对煤扮富氧撚烧影响数值模拟王鹏、郭军军2,吴海波、柳朝晖2,余学海、廖海燕1(1.神华国华（北京）电力研究院有限公司，北京100025;2.华中科技大学能源与动力工程学院，湖北武汉430074)[摘要]为了研究煤粉富氧燃烧方式下烟气循环倍率对燃烧和传热特性的影响，本文以某500k W 燃烧测试炉为研究对象，采用数值模拟方法对空气燃烧以及不同循环倍率下的富氧燃烧进行了研究；采用化学渗透脱挥发分（C P D)模型模拟煤粉的脱挥发分过程，挥发分成分考虑为多种轻质气体，挥发分的燃烧采用详细化学反应机理，介质辐射特性模型均针对富氧燃烧进行了修正。

研究结果表明：虽然富氧燃烧下二次风与一次风的动量比较空气燃烧下降了 50°%以上，但采用相同的旋流燃烧器仍可实现与空气燃烧相似的炉内流场特性；煤粉燃烧温度和着火位置均受循环倍率的影响，富氧燃烧下循环倍率为72°%时，炉内平均温度分布以及着火位置与空气燃烧下较为接近，随着循环倍率增加，辐射传热量降低。

[关键词]煤粉燃烧；富氧燃烧；烟气循环；燃烧特性；循环倍率；辐射传热；数值模拟[中图分类号]T K16[文献标识码]A[D O I编号]10.19666/j.r l f d.201809181[引用本文格式]王鹏，郭军军，吴海波，等.烟气循环倍率对煤粉富氧燃烧影响数值模拟[J].热力发电，2019, 48(4): 90-95. WANG Peng, GUO Junjun, WU Haibo, et al. Numerical simulation on influence of flue gas recirculation ratio on oxy-coal combustion[J]. Thermal Power Generation, 2019, 48(4): 90-95.Numerical simulation on influence of f lue gas recirculation ratio on oxy-coal combustion W A N G P e n g1, G U O J u n ju n2, W U H a ib o1, L IU Z h a o h u i2, Y U X u e h a i1, L IA O H a iy a n1(1. S h en h u a G u oh u a E lectric Pow er R esearch In stitu te C o., L td., B eijing 100025, C h in a;2. School of E n ergy an d P ow er E n gin eerin g, H u azh on g U niversity of Science a n d T ech n ology, W u h a n 430074, C h in a)A b s t r a c t:T o s t u d y t h e e f f e c t s o f f l u e g a s r e c i r c u l a t i o n r a t i o o n c o m b u s t i o n a n d h e a t t r a n s f e r c h a r a c t e r i s t i c s i no x y-c o a l c o m b u s t i o n,n u m e r i c a l i n v e s t i g a t i o n o n a i r c o m b u s t i o n a n d o x y-f u e l c o m b u s t i o n o f p u l v e r i z e d c o a l w a sc a r r i ed o u t i n a500k W c o m b u s t i o n te s tf a c i l i t y.M o r e o v e r,t h e c h e m i c a l p e r m e a t i o n d e v o l a t i l i z a t i o n(C P D)m o d e l w a s u s e d t o d e s c r i b e t h e d e v o l a t i l i z a t i o n b e h a v i o r o f p u l v e r i z e d c o a l.T h e v o l a t i l e c o m p o n e n t s a r e c o n s i d e r e d a s a v a r i e t y o f l igh t g a s e s.T h e d e t ai l e d r e a c t i o n m e c h a n i s m w a s a p p l i e d f o r v o l a t i l e s c o m b u s t i o n,a n d t h e r a d i a t i v ep r o p e r t y m o d e l s w e r e m o d i f i e d f o r o x y-f u e l c o m b u s t i o n.T h e r e s u l t s s h o w t h a t,a l t h o u g h t h e m o m e n t u m r a t i o o f t h e s e c o n d a r y a i r t o t h e p r i m a r y a i r i s r e d u c e d b y m o r e t h a n50%i n o x y-f u e l c o m b u s t i o n,e m p l o y i n g t h e s a m es w i r l b u r n e r c a n s t i l l o b t a i n t h e i n-f u r n a c e f l o w c h a r a c t e r i s t i c s w h i c h i s s i m i l a r t o t h e a i r c o m b u s t i o n.B o t h t h ec o m b u s t i o n t e m p e r a t u r e a nd t he i g n i t i o n p o s i t i o n of t h e p u l v e r i z e d c o a l a r e a f f e c t e d b y t h e c i r c u l a t i o n r a t e.I no x y-f u e l c o m b u s t i o n w i t h r e c i r c u l a t i o n r a t i o o f 72%,t h e d i s t r i b u t i o n o f t h e a v e r a g e t e m p e r a t u r e i n t h e f u r n a c e a n dt h e i g n i t i o n p o s i t i o n a r e s i m i l a r t o t h o s e i n a i r c o m b u s t i o n.A s t h e i n c r e a s e o f r e c i r c u l a t i o n r a t i o,t h e r a d i a t i v e h e a t t r a n s f e r d e c r e a s e s.K e y w o r d s:p u l v e r i z e d c o a l c o m b u s t i o n,o x y-f u e l c o m b u s t i o n,f l u e g a s r e c i r c u l a t i o n,c o m b u s t i o n c h a r a c t e r i s t i c s,r e c i r c u l a t i o n r a t i o,r a d i a t i v e h e a t t r a n s f e r,n u m e r i c a l s i m u l a t i o n中国是一次能源消耗大国，2007年中国超过美的碳减排压力。

第 54 卷第 3 期2023 年 3 月中南大学学报(自然科学版)Journal of Central South University (Science and Technology)V ol.54 No.3Mar. 2023基于数值仿真的复杂岩体TBM 掘进性能评估模型赵高峰1，姜宝元1，芮福鑫1，马洪素2，李洁勇3，赵晓豹4，龚秋明5(1. 天津大学 建筑工程学院 水利工程仿真与安全国家重点实验室，天津，300354；2. 核工业北京地质研究院 中国原子能机构高放废物地质处置创新中心，北京，100029；3. 中国铁建股份有限公司华东区域总部，浙江 杭州，310000；4. 南京大学 地球科学与工程学院，江苏 南京，210023；5. 北京工业大学 城市防灾与减灾教育部重点实验室，北京，100124)摘要：为了评估全断面隧道掘进机(TBM)在复杂岩体环境中的掘进性能，本文提出了基于数值仿真的全断面TBM 掘进预测模型。

首先，采用4D-LSM 和DDA 耦合模型数值重现工程尺度完整岩体和节理岩体的TBM 掘进测试过程，分析全断面TBM 掘进过程中刀盘的力学响应和岩体的破坏特征；其次，研究节理间距、节理方向、岩体单轴抗压强度以及脆性指数对可钻性指数的影响；最后，引入单神经元对数值仿真预测模型进行修正，并与岩体特征模型进行对比分析，验证基于数值仿真的全断面TBM 掘进性能预测模型的适用性。

研究结果表明：TBM 在低强度、高脆性以及节理发育的岩体中掘进效率更高，当节理面与TBM 掘进方向之间的夹角为60°~75°时，最有利于TBM 的运行。

基于数值仿真的TBM 掘进性能预测模型提供了一种经济、灵活的可用于评估复杂环境中TBM 施工性能的方法。

关键词：全断面隧道掘进机；全尺寸TBM 破岩模拟；掘进性能预测；耦合数值模型；离散弹簧模型；非连续变形分析方法中图分类号：U455 文献标志码：A 开放科学(资源服务)标识码(OSID)文章编号：1672-7207（2023）03-0984-14TBM tunneling performance evaluation model in complex rockmasses based on numerical simulationZHAO Gaofeng 1, JIANG Baoyuan 1, RUI Fuxin 1, MA Hongsu 2, LI Jieyong 3,ZHAO Xiaobao 4, GONG Qiuming 5(1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, School of Civil Engineering, TianjinUniversity, Tianjin 300354, China;收稿日期： 2022 −09 −20； 修回日期： 2022 −11 −18基金项目(Foundation item)：国家自然科学基金资助项目(51979187) (Project(51979187) supported by the National Natural ScienceDOI: 10.11817/j.issn.1672-7207.2023.03.016引用格式： 赵高峰, 姜宝元, 芮福鑫, 等. 基于数值仿真的复杂岩体TBM 掘进性能评估模型[J]. 中南大学学报(自然科学版), 2023, 54(3): 984−997.Citation: ZHAO Gaofeng, JIANG Baoyuan, RUI Fuxin, et al. TBM tunneling performance evaluation model in complex rock masses based on numerical simulation[J]. Journal of Central South University(Science and Technology), 2023, 54(3): 984−997.第 3 期赵高峰，等：基于数值仿真的复杂岩体TBM掘进性能评估模型2. CAEA Innovation Center on Geological Disposal of High-level Radioactive Waste, Beijing Research Institute ofUranium Geology, Beijing 100029, China;3. East China Regional Headquarters of China Railway Construction Corporation Limited,Hangzhou 310000, China;4. School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China;5. Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing University ofTechnology, Beijing 100124, China)Abstract:In order to evaluate the tunneling performance of full-face tunnel boring machine(TBM) in complex rock environments, a full-face TBM tunneling prediction model based on numerical simulation was proposed.Firstly, the 4D-LSM and DDA coupled model was used to numerically reproduce the TBM tunneling tests of engineering-scale intact rock mass and jointed rock mass. The mechanical response of the cutterhead and the failure characteristics of the rock mass during the tunneling process of the full-face TBM were analyzed. Secondly, the effects of joint spacing, joint orientation, uniaxial compressive strength and brittleness index on the boreability index were investigated. Finally, a single neuron was introduced to revise the numerical simulation prediction model. The comparative analysis was conducted with rock mass characteristic model, which verified the applicability of the full-face TBM tunneling prediction model based on numerical simulation. The results show that the tunneling efficiency of TBM is higher in rock mass with low strength, high brittleness and developed joints. The angle between joints and the tunneling direction of TBM is 60° to 75°, which is most conducive to the operation of TBM. TBM tunneling performance prediction model based on numerical simulation provides an economical and flexible method for evaluating TBM construction performance in complex environments.Key words: full-face tunnel boring machine; full-scale TBM rock breaking simulation; tunneling performance prediction; coupled numerical model; distinct lattice spring model(DLSM); discontinuous deformation analysis method(DDA)岩石高效破碎一直是岩石工程领域的热点，涉及矿产资源开发、地下空间建设等。