Over the past half century,a variety of computational fluid dynamics(CFD)methods and the direct simulation Monte Carlo(DSMC)method have been widely and successfully applied to the simulation of gas flows for the conti...Over the past half century,a variety of computational fluid dynamics(CFD)methods and the direct simulation Monte Carlo(DSMC)method have been widely and successfully applied to the simulation of gas flows for the continuum and rarefied regime,respectively.However,they both encounter difficulties when dealing with multiscale gas flows in modern engineering problems,where the whole system is on the macroscopic scale but the nonequilibrium effects play an important role.In this paper,we review two particle-based strategies developed for the simulation of multiscale nonequilibrium gas flows,i.e.,DSMC-CFD hybrid methods and multiscale particle methods.The principles,advantages,disadvantages,and applications for each method are described.The latest progress in the modelling of multiscale gas flows including the unified multiscale particle method proposed by the authors is presented.展开更多
A hybrid VOF and PIC multi-material interface treatment method for Eulerian method is presented in this study in order to solve the problem that the Eulerian method is not robust enough to treat the dynamic fracture o...A hybrid VOF and PIC multi-material interface treatment method for Eulerian method is presented in this study in order to solve the problem that the Eulerian method is not robust enough to treat the dynamic fracture of material. This treatment method is used in the important computational region such as the material interface,large deformation region and fracture region where more particles are added for calculation,while the continuous transport method is used in the other regions. Through this method,a series of penetration problems such as blunt projectile penetration into the steel target and steel projectile penetration into concert target are numerically simulated. The comparison of the numerical result and experimental result shows that the hybrid algorithm can effectively track the deformation in the process of penetration and also insure precision and efficiency.展开更多
Greater complexity is involved in the transient pressure analysis of horizontal oil wells in contrast to vertical wells,as the horizontal wells are considered entirely horizontal and parallel with the top and undernea...Greater complexity is involved in the transient pressure analysis of horizontal oil wells in contrast to vertical wells,as the horizontal wells are considered entirely horizontal and parallel with the top and underneath boundaries of the oil reserve.Therefore,there is an essential need to estimate productivity of horizontal wells accurately to examine the effectiveness of a horizontal well in terms of technical and economic prospects.In this work,novel and rigorous methods based on two different types of intelligent approaches including the artificial neural network(ANN)linked to the particle swarm optimization(PSO)tool are developed to precisely forecast the productivity of horizontal wells under pseudo-steady-state conditions.It was found that there is very good match between the modeling output and the real data taken from the literature,so that a very low average absolute error percentage is attained(e.g.,<0.82%).The developed techniques can be also incorporated in the numerical reservoir simulation packages for the purpose of accuracy improvement as well as better parametric sensitivity analysis.展开更多
Immobilization of the atom transfer radical polymerization (ATRP) macroinitiators at the silica nanoparticle surfaces was achieved through surface modification with excess toluene-2,4-diisocynate (TDI), after which th...Immobilization of the atom transfer radical polymerization (ATRP) macroinitiators at the silica nanoparticle surfaces was achieved through surface modification with excess toluene-2,4-diisocynate (TDI), after which the residual isocyanate groups were converted into ATRP macroinitiators. Structurally well-defined polystyrene chains were grown from the nanoparticle surfaces to yield individual particles composed of a silica core and a well-defined, densely grafted outer polystyrene by ATRP, which was initiated by the as-synthesized silica-based macroinitiator. FTIR, NMR and gel permeation chro-matography (GPC) were used to characterize the polystyrene/silica hybrid particles.展开更多
Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete en...Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.展开更多
Location layout of aircraft assembly is an important factor affecting product quality.Most of the existing re-searches use the combination of finite element analysis and intelligent algorithm to optimize the location ...Location layout of aircraft assembly is an important factor affecting product quality.Most of the existing re-searches use the combination of finite element analysis and intelligent algorithm to optimize the location layout,which are limited by numerical simulation accuracy and the selection and improvement of intelligent algorithms.At present,the analysis and decision-making technology based on field data is gradually applied in aircraft manufacturing.Based on the perception data of intelligent assembly unit of aircraft parts,a regression model of multi-input and multioutput support vector machine with Gauss kernel function as radial basis function is established,and the hyperparameters of the model are optimized by hybrid particle swarm optimization genetic algorithm(PSO-GA).GA-MSVR,PSO-MSVR and PSOGA-MSVR model are constructed respectively,and their results show that PSOGA-MSVR model has the best performance.Finally,the design of the aircraft wing location layout is taken as an example to verify the effectiveness of the method.展开更多
Model updating issues with high-dimensional and strong-nonlinear optimization processes are still unsolved by most optimization methods.In this study,a hybrid methodology that combines the Gaussian-white-noise-mutatio...Model updating issues with high-dimensional and strong-nonlinear optimization processes are still unsolved by most optimization methods.In this study,a hybrid methodology that combines the Gaussian-white-noise-mutation particle swarm optimization(GMPSO),back-propagation neural network(BPNN)and Latin hypercube sampling(LHS)technique is proposed.In this approach,as a meta-heuristic algorithm with the least modification to the standard PSO,GMPSO simultaneously offers convenient programming and good performance in optimization.The BPNN with LHS establishes the meta-models for FEM to accelerate efficiency during the updating process.A case study of the model updating of an actual bridge with no distribution but bounded parameters was carried out using this methodology with two different objective functions.One considers only the frequencies of the main girder and the other considers both the frequencies and vertical displacements of typical points.The updating results show that the methodology is a sound approach to solve an actual complex bridge structure and offers good agreement in the frequencies and mode shapes of the updated model and test data.Based on the shape comparison of the main girder at the finished state with different objective functions,it is emphasized that both the dynamic and static responses should be taken into consideration during the model updating process.展开更多
Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactio...Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.展开更多
Pneumatic artificial muscles(PAM)have been recently considered as a prominent challenge regarding pneumatic actuators specifically for rehabilitation and medical applications.Since accomplishing accurate control of th...Pneumatic artificial muscles(PAM)have been recently considered as a prominent challenge regarding pneumatic actuators specifically for rehabilitation and medical applications.Since accomplishing accurate control of the PAM is comparatively complicated due to time-varying behavior,elasticity and ambiguous characteristics,a high performance and efficient control approach should be adopted.Besides of the mentioned challenges,limited course length is another predicament with the PAM control.In this regard,this paper proposes a new hybrid dynamic neural network(DNN)and proportional integral derivative(PID)controller for the position of the PAM.In order to enhance the proficiency of the controller,the problem under study is designed in the form of an optimization trend.Considering the potential of particle swarm optimization,it has been applied to optimally tune the PID-DNN parameters.To verify the performance of the proposed controller,it has been implemented on a real-time system and compared to a conventional sliding mode controller.Simulation and experimental results show the effectiveness of the proposed controller in tracking the reference signals in the entire course of the PAM.展开更多
基金National Numerical Windtunnel Project(Grant 2018-ZT3A05)National Natural Science Foundation of China(Grant No.11772034)Engineering and Physical Sciences Research Council(EPSRC,Grant No.EP/N016602/1).
文摘Over the past half century,a variety of computational fluid dynamics(CFD)methods and the direct simulation Monte Carlo(DSMC)method have been widely and successfully applied to the simulation of gas flows for the continuum and rarefied regime,respectively.However,they both encounter difficulties when dealing with multiscale gas flows in modern engineering problems,where the whole system is on the macroscopic scale but the nonequilibrium effects play an important role.In this paper,we review two particle-based strategies developed for the simulation of multiscale nonequilibrium gas flows,i.e.,DSMC-CFD hybrid methods and multiscale particle methods.The principles,advantages,disadvantages,and applications for each method are described.The latest progress in the modelling of multiscale gas flows including the unified multiscale particle method proposed by the authors is presented.
基金supported by the National Natural Science Foundation of China (Grant Nos. 10625208 and 10872034)the National Basic Research Program of China (Grant No. 2010CB8327006)the Foundation of State Key Laboratory of Explosion Science and Technology (Grant No. ZDKT08-02)
文摘A hybrid VOF and PIC multi-material interface treatment method for Eulerian method is presented in this study in order to solve the problem that the Eulerian method is not robust enough to treat the dynamic fracture of material. This treatment method is used in the important computational region such as the material interface,large deformation region and fracture region where more particles are added for calculation,while the continuous transport method is used in the other regions. Through this method,a series of penetration problems such as blunt projectile penetration into the steel target and steel projectile penetration into concert target are numerically simulated. The comparison of the numerical result and experimental result shows that the hybrid algorithm can effectively track the deformation in the process of penetration and also insure precision and efficiency.
文摘Greater complexity is involved in the transient pressure analysis of horizontal oil wells in contrast to vertical wells,as the horizontal wells are considered entirely horizontal and parallel with the top and underneath boundaries of the oil reserve.Therefore,there is an essential need to estimate productivity of horizontal wells accurately to examine the effectiveness of a horizontal well in terms of technical and economic prospects.In this work,novel and rigorous methods based on two different types of intelligent approaches including the artificial neural network(ANN)linked to the particle swarm optimization(PSO)tool are developed to precisely forecast the productivity of horizontal wells under pseudo-steady-state conditions.It was found that there is very good match between the modeling output and the real data taken from the literature,so that a very low average absolute error percentage is attained(e.g.,<0.82%).The developed techniques can be also incorporated in the numerical reservoir simulation packages for the purpose of accuracy improvement as well as better parametric sensitivity analysis.
基金Supported by Hunan Provincial Natural Science Foundation of China (Grant No. 06JJ20036)
文摘Immobilization of the atom transfer radical polymerization (ATRP) macroinitiators at the silica nanoparticle surfaces was achieved through surface modification with excess toluene-2,4-diisocynate (TDI), after which the residual isocyanate groups were converted into ATRP macroinitiators. Structurally well-defined polystyrene chains were grown from the nanoparticle surfaces to yield individual particles composed of a silica core and a well-defined, densely grafted outer polystyrene by ATRP, which was initiated by the as-synthesized silica-based macroinitiator. FTIR, NMR and gel permeation chro-matography (GPC) were used to characterize the polystyrene/silica hybrid particles.
基金Project supported by the National High-Tech R&D Program(863)of China(No.2014AA041501)
文摘Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.
基金supported by the Equipment Pre-research Project of China (No. 41423010202)
文摘Location layout of aircraft assembly is an important factor affecting product quality.Most of the existing re-searches use the combination of finite element analysis and intelligent algorithm to optimize the location layout,which are limited by numerical simulation accuracy and the selection and improvement of intelligent algorithms.At present,the analysis and decision-making technology based on field data is gradually applied in aircraft manufacturing.Based on the perception data of intelligent assembly unit of aircraft parts,a regression model of multi-input and multioutput support vector machine with Gauss kernel function as radial basis function is established,and the hyperparameters of the model are optimized by hybrid particle swarm optimization genetic algorithm(PSO-GA).GA-MSVR,PSO-MSVR and PSOGA-MSVR model are constructed respectively,and their results show that PSOGA-MSVR model has the best performance.Finally,the design of the aircraft wing location layout is taken as an example to verify the effectiveness of the method.
基金National Natural Science Foundation of China under Grant No.51438002the research fund of Jiangsu Province Key Laboratory of Structure Engineering,China under Grant No.ZD1803+3 种基金Natural Science Foundation of Suzhou University of Science and Technology under Grant No.XKQ2018008Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No.19KJB560021Science and Technology Project of Jiangsu Construction System under Grant No.2020ZD07Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘Model updating issues with high-dimensional and strong-nonlinear optimization processes are still unsolved by most optimization methods.In this study,a hybrid methodology that combines the Gaussian-white-noise-mutation particle swarm optimization(GMPSO),back-propagation neural network(BPNN)and Latin hypercube sampling(LHS)technique is proposed.In this approach,as a meta-heuristic algorithm with the least modification to the standard PSO,GMPSO simultaneously offers convenient programming and good performance in optimization.The BPNN with LHS establishes the meta-models for FEM to accelerate efficiency during the updating process.A case study of the model updating of an actual bridge with no distribution but bounded parameters was carried out using this methodology with two different objective functions.One considers only the frequencies of the main girder and the other considers both the frequencies and vertical displacements of typical points.The updating results show that the methodology is a sound approach to solve an actual complex bridge structure and offers good agreement in the frequencies and mode shapes of the updated model and test data.Based on the shape comparison of the main girder at the finished state with different objective functions,it is emphasized that both the dynamic and static responses should be taken into consideration during the model updating process.
基金This work was supported by the“Zhujiang Talent Program”High Talent Project of Guangdong Province(Grant No.2017GC010614)the National Natural Science Foundation of China(Grant No.22078372).
文摘Modeling and optimization is crucial to smart chemical process operations.However,a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations,chemical reactions and separations.This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity.Thus,this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties.An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method.Firstly,a data set was generated based on process mechanistic simulation validated by industrial data,which provides sufficient and reasonable samples for model training and testing.Secondly,four well-known machine learning methods,namely,K-nearest neighbors,decision tree,support vector machine,and artificial neural network,were compared and used to obtain the prediction models of the processes operation.All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features.Finally,optimal process operations were obtained by using the particle swarm optimization approach.
文摘Pneumatic artificial muscles(PAM)have been recently considered as a prominent challenge regarding pneumatic actuators specifically for rehabilitation and medical applications.Since accomplishing accurate control of the PAM is comparatively complicated due to time-varying behavior,elasticity and ambiguous characteristics,a high performance and efficient control approach should be adopted.Besides of the mentioned challenges,limited course length is another predicament with the PAM control.In this regard,this paper proposes a new hybrid dynamic neural network(DNN)and proportional integral derivative(PID)controller for the position of the PAM.In order to enhance the proficiency of the controller,the problem under study is designed in the form of an optimization trend.Considering the potential of particle swarm optimization,it has been applied to optimally tune the PID-DNN parameters.To verify the performance of the proposed controller,it has been implemented on a real-time system and compared to a conventional sliding mode controller.Simulation and experimental results show the effectiveness of the proposed controller in tracking the reference signals in the entire course of the PAM.
