With the continuous upgrading of traditional manufacturing industries and the rapid rise of emerging technology fields,the performance requirements for the permanent magnet synchronous motors(PMSMs)have become higher ...With the continuous upgrading of traditional manufacturing industries and the rapid rise of emerging technology fields,the performance requirements for the permanent magnet synchronous motors(PMSMs)have become higher and higher.The importance of fast and accurate electromagnetic thermal coupling analysis of such motors becomes more and more prominent.In view of this,the surfacemounted PMSM(SPMSM)equipped with unequally thick magnetic poles is taken as the main object and its electromagnetic thermal coupling analytical model(ETc AM)is investigated.First,the electromagnetic analytical model(EAM)is studied based on the modified subdomain method.It realizes the fast calculation of key electromagnetic characteristics.Subsequently,the 3D thermal analytical model(TAM)is developed by combining the EAM,the lumped parameter thermal network method(LPTNM),and the partial differential equation of heat flux.It realizes the fast calculation of key thermal characteristics in 3D space.Further,the information transfer channel between EAM and TAM is built with reference to the intrinsic connection between electromagnetic field and temperature field.Thereby,the novel ETcAM is proposed to realize the fast and accurate prediction of electromagnetic and temperature fields.Besides,ETcAM has a lot to commend it.One is that it well accounts for the complex structure,saturation,and heat exchange behavior.Second,it saves a lot of computer resources.It offers boundless possibilities for initial design,scheme evaluation,and optimization of motors.Finally,the validity,accuracy,and practicality of this study are verified by simulation and experiment.展开更多
The complex flow characteristics of transverse jet in high-speed crossflow involve several separation regions and multiple shock waves,which make it difficult to capture and precisely predict the flow field state in r...The complex flow characteristics of transverse jet in high-speed crossflow involve several separation regions and multiple shock waves,which make it difficult to capture and precisely predict the flow field state in real time merely by relying on traditional approaches.With the rapid advancement of deep learning technology,its powerful data processing capability offers a fast method for the prediction of the transverse jet flow field.Consequently,a prediction model based on deep learning is established,with the aim of obtaining the flow characteristics of a transverse jet under different freestream and jet conditions.This study segments the complex grid into several individual grids and trains them independently.The trained model can successfully establish the nonlinear mapping relationship between the transverse jet flow field and the input parameters.The prediction accuracy of the established model for the wall pressure under different conditions exceeds 99%,and the established model is also capable of reproducing structures such as shock waves and recirculation zones in the overall flow field,thereby achieving highly precise and efficient prediction of the jet structure and flow information.The results suggest that in contrast to the traditional numerical simulation,this deep learning model demonstrates greater efficiency in predicting the transverse jet flow field.展开更多
In order to predict acoustic radiation from a structure in waveguide, a method based on wave superposition is proposed, in which the free-space Green＇s function is used to match the strength of equivalent sources. In...In order to predict acoustic radiation from a structure in waveguide, a method based on wave superposition is proposed, in which the free-space Green＇s function is used to match the strength of equivalent sources. In addition, in order to neglect the effect of sound reflection from boundaries, necessary treatment is conducted, which makes the method more efficient. Moreover, this method is combined with the sound propagation algorithms to predict the sound radiated from a cylindrical shell in waveguide. Numerical simulations show the effect of how reflections can be neglected if the distance between the structure and the boundary exceeds the maximum linear dimension of the structure. It also shows that the reflection from the bottom of the waveguide can be approximated by plane wave conditionally. The proposed method is more robust and efficient in computation, which can be used to predict the acoustic radiation in waveguide.展开更多
An improved Reduced-Order Model(ROM)is proposed based on a flow-solution preprocessing operation and a fast sampling strategy to efficiently and accurately predict ionized hypersonic flows.This ROM is generated in low...An improved Reduced-Order Model(ROM)is proposed based on a flow-solution preprocessing operation and a fast sampling strategy to efficiently and accurately predict ionized hypersonic flows.This ROM is generated in low-dimensional space by performing the Proper Orthogonal Decomposition(POD)on snapshots and is coupled with the Radial Basis Function(RBF)to achieve fast prediction speed.However,due to the disparate scales in the ionized flow field,the conventional ROM usually generates spurious negative errors.Here,this issue is addressed by performing flow-solution preprocessing in logarithmic space to improve the conventional ROM.Then,extra orthogonal polynomials are introduced in the RBF interpolation to achieve additional improvement of the prediction accuracy.In addition,to construct high-efficiency snapshots,a trajectory-constrained adaptive sampling strategy based on convex hull optimization is developed.To evaluate the performance of the proposed fast prediction method,two hypersonic vehicles with classic configurations,i.e.a wave-rider and a reentry capsule,are used to validate the proposed method.Both two cases show that the proposed fast prediction method has high accuracy near the vehicle surface and the free-stream region where the flow field is smooth.Compared with the conventional ROM prediction,the prediction results are significantly improved by the proposed method around the discontinuities,e.g.the shock wave and the ionized layer.As a result,the proposed fast prediction method reduces the error of the conventional ROM by at least 45%,with a speedup of approximately 2.0×105compared to the Computational Fluid Dynamic(CFD)simulations.These test cases demonstrate that the method developed here is efficient and accurate for predicting ionized hypersonic flows.展开更多
The traditional oriented FAST and rotated BRIEF（ORB） algorithm has problems of instability and repetition of keypoints and it does not possess scale invariance. In order to deal with these drawbacks, a modified ORB...The traditional oriented FAST and rotated BRIEF（ORB） algorithm has problems of instability and repetition of keypoints and it does not possess scale invariance. In order to deal with these drawbacks, a modified ORB（MORB） algorithm is proposed. In order to improve the precision of matching and tracking, this paper puts forward an MOK algorithm that fuses MORB and Kanade-Lucas-Tomasi（KLT）. By using Kalman, the object＇s state in the next frame is predicted in order to reduce the size of search window and improve the real-time performance of object tracking. The experimental results show that the MOK algorithm can accurately track objects with deformation or with background clutters, exhibiting higher robustness and accuracy on diverse datasets. Also, the MOK algorithm has a good real-time performance with the average frame rate reaching 90.8 fps.展开更多
A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The pre...A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The predicted data are used to draw washability curves and to provide a rapid evaluation of the effect from heavy medium induced separation.Thirty-one production shifts worth of fast float/sink data and the corresponding quick ash data are used to verify the model.The results show a small error with an arithmetic average of 0.53 and an absolute average error of 1.50.This indicates that this model has high precision.The theoretical yield from the washability curves is 76.47% for the monthly comprehensive data and 81.31% using the model data.This is for a desired cleaned coal ash of 9%.The relative error between these two is 6.33%,which is small and indicates that the predicted data can be used to rapidly evaluate the separation effect of gravity separation equipment.展开更多
Energy materials play an important role in renewable and green energy technologies.The exploration of new materials,including nanomaterials,is important for breaking through the current bottlenecks of energy density a...Energy materials play an important role in renewable and green energy technologies.The exploration of new materials,including nanomaterials,is important for breaking through the current bottlenecks of energy density and charging rates.However,traditional theoretical computational methods face the dilemma of long research cycles.Machine learning methods have in recent years shown considerable potential for accelerating research efforts.However,most approaches are limited to specific properties of particular devices.In this paper,we propose a forward prediction and screening framework for functional materials,which includes database selection,attributes,descriptors,machine learning models,and prediction and screening.Based on the Materials Project database,auto-encoding methods are employed to generate Coulomb matrices as the input to train the convolutional neural networks,which finally screen 12 lithium-ion,6 zinc-ion,and 8 aluminum-ion battery cathode materials satisfying the criteria from 4,300 materials.The results show that the proposed framework can predict material performance well toward rapid initial screening.The proposed framework can provide a specific and complete working process reference for energy materials design work,contributing to the theoretical foundation for the design of core industrial software for materials engineering.展开更多
Flow field computation plays a critical role in both scientific research and engineering applications.For decades,computational fluid dynamics(CFD)has served as the cornerstone of flow field analysis;however,high-reso...Flow field computation plays a critical role in both scientific research and engineering applications.For decades,computational fluid dynamics(CFD)has served as the cornerstone of flow field analysis;however,high-resolution simulations are often hindered by considerable computational costs and lengthy processing times.In recent years,deep learning(DL),with its exceptional capability to handle high-dimensional nonlinear problems,has achieved remarkable progress in the field of fluid mechanics.This paper provides a comprehensive review of recent advances in applying DL methods to accelerate flow field computation,with particular emphasis on complex indoor environments characterized by multi-physics coupling.We begin by outlining the fundamental frameworks of deep learning and,on this basis,summarize four representative neural network architectures for flow prediction:end-to-end mapping networks,reduced-order mapping networks,physics-informed neural networks(PINNs),and operator neural networks(ONNs).We then systematically review the specific applications of these DL algorithms in indoor flow field prediction.In addition,we discuss key challenges faced by current research,including the lack of large,high-quality databases,limited interpretability and generalization capability of existing models,and the difficulty of accurately representing real indoor environments.Finally,we propose several promising research directions,such as exploring advanced algorithms,enhancing self-supervised learning techniques,and developing geometry-aware network models and multi-task hybrid frameworks.Advancing these frontiers is expected not only to significantly improve the efficiency and accuracy of flow field computations but also to provide a solid theoretical foundation and technical support for the optimization of intelligent indoor environments.展开更多
In recent years,artificial intelligence(AI)technologies have been widely applied in many different fields including in the design,maintenance,and control of aero-engines.The air-cooled turbine vane is one of the most ...In recent years,artificial intelligence(AI)technologies have been widely applied in many different fields including in the design,maintenance,and control of aero-engines.The air-cooled turbine vane is one of the most complex components in aero-engine design.Therefore,it is interesting to adopt the existing AI technologies in the design of the cooling passages.Given that the application of AI relies on a large amount of data,the primary task of this paper is to realize massive simulation automation in order to generate data for machine learning.It includes the parameterized three-dimensional(3-D)geometrical modeling,automatic meshing and computational fluid dynamics(CFD)batch automatic simulation of different film cooling structures through customized developments of UG,ICEM and Fluent.It is demonstrated that the trained artificial neural network(ANN)can predict the cooling effectiveness on the external surface of the turbine vane.The results also show that the design of the ANN architecture and the hyper-parameters have an impact on the prediction precision of the trained model.Using this established method,a multi-output model is constructed based on which a simple tool can be developed.It is able to make an instantaneous prediction of the temperature distribution along the vane surface once the arrangements of the film holes are adjusted.展开更多
Mechanical and natural ventilations are effective measures to remove indoor airborne contaminants,thereby creating improved indoor air quality(IAQ).Among various simulation techniques,Markov chain model is a relativel...Mechanical and natural ventilations are effective measures to remove indoor airborne contaminants,thereby creating improved indoor air quality(IAQ).Among various simulation techniques,Markov chain model is a relatively new and efficient method in predicting indoor airborne pollutants.The existing Markov chain model(for indoor airborne pollutants)is basically assumed as first-order,which however is difficult to deal with airborne particles with non-negligible inertial.In this study,a novel weight-factor-based high-order(second-order and third-order)Markov chain model is developed to simulate particle dispersion and deposition indoors under fixed and dynamic ventilation modes.Flow fields under various ventilation modes are solved by computational fluid dynamics(CFD)tools in advance,and then the basic first-order Markov chain model is implemented and validated by both simulation results and experimental data from literature.Furthermore,different groups of weight factors are tested to estimate appropriate weight factors for both second-order and third-order Markov chain models.Finally,the calculation process is properly designed and controlled,so that the proposed high-order(second-order)Markov chain model can be used to perform particle-phase simulation under consecutively changed ventilation modes.Results indicate that the proposed second-order model does well in predicting particle dispersion and deposition under fixed ventilation mode as well as consecutively changed ventilation modes.Compared with traditional first-order Markov chain model,the proposed high-order model performs with more reasonable accuracy but without significant computing cost increment.The most suitable weight factors of the simulation case in this study are found to be(λ_(1)=0.7,λ_(2)=0.3,λ_(3)=0)for second-order Markov chain model,and(λ_(1)=0.8,λ_(2)=0.1,λ_(3)=0.1)for third-order Markov chain model in terms of reducing errors in particle deposition and escape prediction.With the improvements of the efficiency of state transfer matrix construction and flow field data acquisition/processing,the proposed high-order Markov chain model is expected to provide an alternative choice for fast prediction of indoor airborne particulate(as well as gaseous)pollutants under transient flows.展开更多
基金supported by the Project of National Natural Science Foundation of China under Grant 52077122。
文摘With the continuous upgrading of traditional manufacturing industries and the rapid rise of emerging technology fields,the performance requirements for the permanent magnet synchronous motors(PMSMs)have become higher and higher.The importance of fast and accurate electromagnetic thermal coupling analysis of such motors becomes more and more prominent.In view of this,the surfacemounted PMSM(SPMSM)equipped with unequally thick magnetic poles is taken as the main object and its electromagnetic thermal coupling analytical model(ETc AM)is investigated.First,the electromagnetic analytical model(EAM)is studied based on the modified subdomain method.It realizes the fast calculation of key electromagnetic characteristics.Subsequently,the 3D thermal analytical model(TAM)is developed by combining the EAM,the lumped parameter thermal network method(LPTNM),and the partial differential equation of heat flux.It realizes the fast calculation of key thermal characteristics in 3D space.Further,the information transfer channel between EAM and TAM is built with reference to the intrinsic connection between electromagnetic field and temperature field.Thereby,the novel ETcAM is proposed to realize the fast and accurate prediction of electromagnetic and temperature fields.Besides,ETcAM has a lot to commend it.One is that it well accounts for the complex structure,saturation,and heat exchange behavior.Second,it saves a lot of computer resources.It offers boundless possibilities for initial design,scheme evaluation,and optimization of motors.Finally,the validity,accuracy,and practicality of this study are verified by simulation and experiment.
基金co-supported by the National Natural Science Foundation of China(No.12202488)the National Postdoctoral Researcher Program(Grants No.GZB20230985)+1 种基金the Natural Science Program of National University of Defense Technology(No.ZK22-30)the Independent Innovation Science Fund of National University of Defense Technology(No.24-ZZCX-BC-05)。
文摘The complex flow characteristics of transverse jet in high-speed crossflow involve several separation regions and multiple shock waves,which make it difficult to capture and precisely predict the flow field state in real time merely by relying on traditional approaches.With the rapid advancement of deep learning technology,its powerful data processing capability offers a fast method for the prediction of the transverse jet flow field.Consequently,a prediction model based on deep learning is established,with the aim of obtaining the flow characteristics of a transverse jet under different freestream and jet conditions.This study segments the complex grid into several individual grids and trains them independently.The trained model can successfully establish the nonlinear mapping relationship between the transverse jet flow field and the input parameters.The prediction accuracy of the established model for the wall pressure under different conditions exceeds 99%,and the established model is also capable of reproducing structures such as shock waves and recirculation zones in the overall flow field,thereby achieving highly precise and efficient prediction of the jet structure and flow information.The results suggest that in contrast to the traditional numerical simulation,this deep learning model demonstrates greater efficiency in predicting the transverse jet flow field.
基金Foundation item： Supported by the National Natural Science Foundation of China under Grant No. 11274080, and the Young Scientists Fund of the National Natural Science Foundation of China under Grant No. 11404313.
文摘In order to predict acoustic radiation from a structure in waveguide, a method based on wave superposition is proposed, in which the free-space Green＇s function is used to match the strength of equivalent sources. In addition, in order to neglect the effect of sound reflection from boundaries, necessary treatment is conducted, which makes the method more efficient. Moreover, this method is combined with the sound propagation algorithms to predict the sound radiated from a cylindrical shell in waveguide. Numerical simulations show the effect of how reflections can be neglected if the distance between the structure and the boundary exceeds the maximum linear dimension of the structure. It also shows that the reflection from the bottom of the waveguide can be approximated by plane wave conditionally. The proposed method is more robust and efficient in computation, which can be used to predict the acoustic radiation in waveguide.
基金supported by the National Natural Science Foundation of China(Nos.11902271 and 91952203)the Fundamental Research Funds for the Central Universities of China(No.G2019KY05102)111 project on“Aircraft Complex Flows and the Control”of China(No.B17037)。
文摘An improved Reduced-Order Model(ROM)is proposed based on a flow-solution preprocessing operation and a fast sampling strategy to efficiently and accurately predict ionized hypersonic flows.This ROM is generated in low-dimensional space by performing the Proper Orthogonal Decomposition(POD)on snapshots and is coupled with the Radial Basis Function(RBF)to achieve fast prediction speed.However,due to the disparate scales in the ionized flow field,the conventional ROM usually generates spurious negative errors.Here,this issue is addressed by performing flow-solution preprocessing in logarithmic space to improve the conventional ROM.Then,extra orthogonal polynomials are introduced in the RBF interpolation to achieve additional improvement of the prediction accuracy.In addition,to construct high-efficiency snapshots,a trajectory-constrained adaptive sampling strategy based on convex hull optimization is developed.To evaluate the performance of the proposed fast prediction method,two hypersonic vehicles with classic configurations,i.e.a wave-rider and a reentry capsule,are used to validate the proposed method.Both two cases show that the proposed fast prediction method has high accuracy near the vehicle surface and the free-stream region where the flow field is smooth.Compared with the conventional ROM prediction,the prediction results are significantly improved by the proposed method around the discontinuities,e.g.the shock wave and the ionized layer.As a result,the proposed fast prediction method reduces the error of the conventional ROM by at least 45%,with a speedup of approximately 2.0×105compared to the Computational Fluid Dynamic(CFD)simulations.These test cases demonstrate that the method developed here is efficient and accurate for predicting ionized hypersonic flows.
基金supported by the National Natural Science Foundation of China(61471194)the Fundamental Research Funds for the Central Universities+2 种基金the Science and Technology on Avionics Integration Laboratory and Aeronautical Science Foundation of China(20155552050)the CASC(China Aerospace Science and Technology Corporation) Aerospace Science and Technology Innovation Foundation Projectthe Nanjing University of Aeronautics And Astronautics Graduate School Innovation Base(Laboratory)Open Foundation Program(kfjj20151505)
文摘The traditional oriented FAST and rotated BRIEF（ORB） algorithm has problems of instability and repetition of keypoints and it does not possess scale invariance. In order to deal with these drawbacks, a modified ORB（MORB） algorithm is proposed. In order to improve the precision of matching and tracking, this paper puts forward an MOK algorithm that fuses MORB and Kanade-Lucas-Tomasi（KLT）. By using Kalman, the object＇s state in the next frame is predicted in order to reduce the size of search window and improve the real-time performance of object tracking. The experimental results show that the MOK algorithm can accurately track objects with deformation or with background clutters, exhibiting higher robustness and accuracy on diverse datasets. Also, the MOK algorithm has a good real-time performance with the average frame rate reaching 90.8 fps.
基金National Natural Science Foundation of China (No. 51174202)Doctoral Fund of Ministry of Education of China (No. 20100095110013)
文摘A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The predicted data are used to draw washability curves and to provide a rapid evaluation of the effect from heavy medium induced separation.Thirty-one production shifts worth of fast float/sink data and the corresponding quick ash data are used to verify the model.The results show a small error with an arithmetic average of 0.53 and an absolute average error of 1.50.This indicates that this model has high precision.The theoretical yield from the washability curves is 76.47% for the monthly comprehensive data and 81.31% using the model data.This is for a desired cleaned coal ash of 9%.The relative error between these two is 6.33%,which is small and indicates that the predicted data can be used to rapidly evaluate the separation effect of gravity separation equipment.
基金financially supported by the Defense Industrial Technology Development Program(JCKY2021-601B019).
文摘Energy materials play an important role in renewable and green energy technologies.The exploration of new materials,including nanomaterials,is important for breaking through the current bottlenecks of energy density and charging rates.However,traditional theoretical computational methods face the dilemma of long research cycles.Machine learning methods have in recent years shown considerable potential for accelerating research efforts.However,most approaches are limited to specific properties of particular devices.In this paper,we propose a forward prediction and screening framework for functional materials,which includes database selection,attributes,descriptors,machine learning models,and prediction and screening.Based on the Materials Project database,auto-encoding methods are employed to generate Coulomb matrices as the input to train the convolutional neural networks,which finally screen 12 lithium-ion,6 zinc-ion,and 8 aluminum-ion battery cathode materials satisfying the criteria from 4,300 materials.The results show that the proposed framework can predict material performance well toward rapid initial screening.The proposed framework can provide a specific and complete working process reference for energy materials design work,contributing to the theoretical foundation for the design of core industrial software for materials engineering.
基金supported by the National Natural Science Foundation of China(grant number 52178072).
文摘Flow field computation plays a critical role in both scientific research and engineering applications.For decades,computational fluid dynamics(CFD)has served as the cornerstone of flow field analysis;however,high-resolution simulations are often hindered by considerable computational costs and lengthy processing times.In recent years,deep learning(DL),with its exceptional capability to handle high-dimensional nonlinear problems,has achieved remarkable progress in the field of fluid mechanics.This paper provides a comprehensive review of recent advances in applying DL methods to accelerate flow field computation,with particular emphasis on complex indoor environments characterized by multi-physics coupling.We begin by outlining the fundamental frameworks of deep learning and,on this basis,summarize four representative neural network architectures for flow prediction:end-to-end mapping networks,reduced-order mapping networks,physics-informed neural networks(PINNs),and operator neural networks(ONNs).We then systematically review the specific applications of these DL algorithms in indoor flow field prediction.In addition,we discuss key challenges faced by current research,including the lack of large,high-quality databases,limited interpretability and generalization capability of existing models,and the difficulty of accurately representing real indoor environments.Finally,we propose several promising research directions,such as exploring advanced algorithms,enhancing self-supervised learning techniques,and developing geometry-aware network models and multi-task hybrid frameworks.Advancing these frontiers is expected not only to significantly improve the efficiency and accuracy of flow field computations but also to provide a solid theoretical foundation and technical support for the optimization of intelligent indoor environments.
基金the Program for National Natural Science Foundation of China(51876005).
文摘In recent years,artificial intelligence(AI)technologies have been widely applied in many different fields including in the design,maintenance,and control of aero-engines.The air-cooled turbine vane is one of the most complex components in aero-engine design.Therefore,it is interesting to adopt the existing AI technologies in the design of the cooling passages.Given that the application of AI relies on a large amount of data,the primary task of this paper is to realize massive simulation automation in order to generate data for machine learning.It includes the parameterized three-dimensional(3-D)geometrical modeling,automatic meshing and computational fluid dynamics(CFD)batch automatic simulation of different film cooling structures through customized developments of UG,ICEM and Fluent.It is demonstrated that the trained artificial neural network(ANN)can predict the cooling effectiveness on the external surface of the turbine vane.The results also show that the design of the ANN architecture and the hyper-parameters have an impact on the prediction precision of the trained model.Using this established method,a multi-output model is constructed based on which a simple tool can be developed.It is able to make an instantaneous prediction of the temperature distribution along the vane surface once the arrangements of the film holes are adjusted.
基金The investigation was supported by the National Science&Technology Supporting Program(No.2015BAJ03B00)the Natural Science Foundation of Hunan Province(Youth Program)(No.2021JJ40591)+1 种基金the Doctoral Scientific Research Foundation of Changsha University of Science and Technology(No.097/000301518)the Scientific Research Project of Hunan Provincial Department of Education(No.20C0033).
文摘Mechanical and natural ventilations are effective measures to remove indoor airborne contaminants,thereby creating improved indoor air quality(IAQ).Among various simulation techniques,Markov chain model is a relatively new and efficient method in predicting indoor airborne pollutants.The existing Markov chain model(for indoor airborne pollutants)is basically assumed as first-order,which however is difficult to deal with airborne particles with non-negligible inertial.In this study,a novel weight-factor-based high-order(second-order and third-order)Markov chain model is developed to simulate particle dispersion and deposition indoors under fixed and dynamic ventilation modes.Flow fields under various ventilation modes are solved by computational fluid dynamics(CFD)tools in advance,and then the basic first-order Markov chain model is implemented and validated by both simulation results and experimental data from literature.Furthermore,different groups of weight factors are tested to estimate appropriate weight factors for both second-order and third-order Markov chain models.Finally,the calculation process is properly designed and controlled,so that the proposed high-order(second-order)Markov chain model can be used to perform particle-phase simulation under consecutively changed ventilation modes.Results indicate that the proposed second-order model does well in predicting particle dispersion and deposition under fixed ventilation mode as well as consecutively changed ventilation modes.Compared with traditional first-order Markov chain model,the proposed high-order model performs with more reasonable accuracy but without significant computing cost increment.The most suitable weight factors of the simulation case in this study are found to be(λ_(1)=0.7,λ_(2)=0.3,λ_(3)=0)for second-order Markov chain model,and(λ_(1)=0.8,λ_(2)=0.1,λ_(3)=0.1)for third-order Markov chain model in terms of reducing errors in particle deposition and escape prediction.With the improvements of the efficiency of state transfer matrix construction and flow field data acquisition/processing,the proposed high-order Markov chain model is expected to provide an alternative choice for fast prediction of indoor airborne particulate(as well as gaseous)pollutants under transient flows.
