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
This paper is devoted to application of the Reduced-Order Model(ROM)based on Volterra series to prediction of lift and drag forces due to airfoil periodic translation in transonic flow region.When there is large ampli...This paper is devoted to application of the Reduced-Order Model(ROM)based on Volterra series to prediction of lift and drag forces due to airfoil periodic translation in transonic flow region.When there is large amplitude oscillation of the relative Mach number,as appeared in helicopter rotor movement in forward flight,the conventional Volterra ROM is found to be unsatisfactory.To cover such applications,a matched Volterra ROM,inspired from previous multistep nonlinear indicial response method based on Duhamel integration,is thus considered,in which the step motions are defined inside a number of equal intervals with both positive and negative step motions to match the airfoil forward and backward movement,and the kernel functions are constructed independently at each interval.It shows that,at least for the translation movement considered,this matched Volterra ROM greatly improves the accuracy of prediction.Moreover,the matched Volterra ROM,with the total number of step motions and thus the computational cost close to those of the conventional Volterra ROM method,has the additional advantage that the same set of kernels can match various translation motions with different starting conditions so the kernels can be predesigned without knowing the specific motion of airfoil.展开更多
A modified reduced-order method for RC networks which takes a division-and-conquest strategy is presented.The whole network is partitioned into a set of sub-networks at first,then each of them is reduced by Krylov sub...A modified reduced-order method for RC networks which takes a division-and-conquest strategy is presented.The whole network is partitioned into a set of sub-networks at first,then each of them is reduced by Krylov subspace techniques,and finally all the reduced sub-networks are incorporated together.With some accuracy,this method can reduce the number of both nodes and components of the circuit comparing to the traditional methods which usually only offer a reduced net with less nodes.This can markedly accelerate the sparse-matrix-based simulators whose performance is dominated by the entity of the matrix or the number of components of the circuits.展开更多
Recently, flutter active control using linear parameter varying（LPV） framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroela...Recently, flutter active control using linear parameter varying（LPV） framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by synthesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant（LTI） models by using system identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency（p-LSCF） algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification,the dominant physical modes are determined by clear stabilization diagrams and clustering technique. In the second step, with prior knowledge of physical poles, the improved frequencydomain maximum likelihood（ML） estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.展开更多
With the rapid development of the world economy,IGBT has been widely used in motor drive and electric energy conversion.In order to timely detect the fatigue damage of IGBT,it is necessary to monitor the junction temp...With the rapid development of the world economy,IGBT has been widely used in motor drive and electric energy conversion.In order to timely detect the fatigue damage of IGBT,it is necessary to monitor the junction temperature of IGBT.In order to realize the fast calculation of IGBT junction temperature,a finite element method of IGBT temperature field reduction is proposed in this paper.Firstly,the finite element calculation process of IGBT temperature field is introduced and the linear equations of finite element calculation of temperature field are derived.Temperature field data of different working conditions are obtained by finite element simulation to form the sample space.Then the covariance matrix of the sample space is constructed,whose proper orthogonal decomposition and modal extraction are carried out.Reasonable basis vector space is selected to complete the low dimensional expression of temperature vector inside and outside the sample space.Finally,the reduced-order model of temperature field finite element is obtained and solved.The results of the reduced order model are compared with those of the finite element method,and the performance of the reduced-order model is evaluated from two aspects of accuracy and rapidity.展开更多
A Non-Intrusive Reduced-Order Model(NIROM)based on Proper Orthogonal Decomposition(POD)has been proposed for predicting the flow fields of transonic airfoils with geometry parameters.To provide a better reduced-order ...A Non-Intrusive Reduced-Order Model(NIROM)based on Proper Orthogonal Decomposition(POD)has been proposed for predicting the flow fields of transonic airfoils with geometry parameters.To provide a better reduced-order subspace to approximate the real flow field,a domain decomposition method has been used to separate the hard-to-predict regions from the full field and POD has been adopted in the regions individually.An Artificial Neural Network(ANN)has replaced the Radial Basis Function(RBF)to interpolate the coefficients of the POD modes,aiming at improving the approximation accuracy of the NIROM for non-samples.When predicting the flow fields of transonic airfoils,the proposed NIROM has demonstrated a high performance.展开更多
Fastening failures have frequently been found on China high-speed railway curved tracks in recent years.Thus the influence of fastening failures on high-speed train-track interaction in curved track needs to be analyz...Fastening failures have frequently been found on China high-speed railway curved tracks in recent years.Thus the influence of fastening failures on high-speed train-track interaction in curved track needs to be analyzed.A train-curved slab track interaction model is built,in which the real shape of the curved rail is considered and modeled with reduced beam model(RBM)and curved beam theory,and the slabs are modeled with four-nodes Kirchhoff-Love plate elements.The present model is validated at first with different traditional models.Then the influence of fastening failure in curved slab track on train-track interaction dynamics is studied.A different number of failed fastenings is assumed to occur at the curved track,and different types of fastening failure including the fatigue fracture of the clip structure and failure of the rail pad are considered.Based on the calculation results,the fatigue fracture of the clip structure has little influence on train-track interaction dynamics.But when rail pad failure happens and its equivalent vertical stiffness and damping are less than one-tenth of its original,the fastening failure seriously affects the high-speed train operation safety,and it must be prevented.展开更多
Brain hypothermia treatment （BHT） is an active therapy for severe brain injury. It makes the temperature of the brain track a given temperature input curve so as to reduce the risk of tissue damage. BHT requires a b...Brain hypothermia treatment （BHT） is an active therapy for severe brain injury. It makes the temperature of the brain track a given temperature input curve so as to reduce the risk of tissue damage. BHT requires a brain-temperature control system because of environmental disturbances and changes in the human body. The thermal models of the human body devised so far are usually of a very high order and are not suitable for controlling brain temperature. This paper presents a method of finding a reducedorder thermal model of the human body for use in BHT. It combines minimal realization and balanced realization. Unlike other methods, this method yields a reduced-order model that is based on system theory and that takes the frequency characteristics of human thermal sensation into account. It features high precision in the frequency band for BHT and is suitable for the control of brain temperature.展开更多
In this paper, a non-cooperative distributed MPC algorithm based on reduced order model is proposed to stabilize large-scale systems. The large-scale system consists of a group of interconnected subsystems. Each subsy...In this paper, a non-cooperative distributed MPC algorithm based on reduced order model is proposed to stabilize large-scale systems. The large-scale system consists of a group of interconnected subsystems. Each subsystem can be partitioned into two parts： measurable part, whose states can be directly measured by sensors, and the unmeasurable part. In the online computation phase, only the measurable dynamics of the corresponding subsystem and neighbour-to-neighbour communication are necessary for the local controller design. Satisfaction of the state constraints and the practical stability are guaranteed while the complexity of the optimization problem is reduced. Numerical examples are given to show the effectiveness of this algorithm.展开更多
Reduced-order models offer a cost-effective and accurate approach to analyzing high-dimensional combustion problems.These surrogate models are built in a data-driven manner by combining computational fluid dynamics si...Reduced-order models offer a cost-effective and accurate approach to analyzing high-dimensional combustion problems.These surrogate models are built in a data-driven manner by combining computational fluid dynamics simulations with Proper Orthogonal Decomposition(POD)for dimensionality reduction and Gaussian Process Regression(GPR)for nonlinear regression.However,these models can yield physically inconsistent results,such as negative mass fractions.As a linear decomposition method,POD complicates the enforcement of constraints in the reduced space,while GPR lacks inherent provisions to ensure physical consistency.To address these challenges,this study proposes a novel constrained reduced-order model framework that enforces physical consistency in predictions.Dimensionality reduction is achieved by downsampling the dataset through low-cost Singular Value Decomposition(lcSVD)using optimal sensor placement,ensuring that the retained data points preserve physical information in the reduced space.We integrate finite-support parametric distribution functions,such as truncated Gaussian and beta distribution scaled to the interval[a,b],into the GPR framework.These bounded likelihood functions explicitly model the observational noise in the bounded space and use variational inference to approximate analytically intractable posterior distributions,producing GP estimations that satisfy physical constraints by construction.We validate the proposed methods using a synthetic dataset and a benchmark case of one-dimensional laminar NH3/H2 flames.The results show that the thermo-chemical state predictions comply with physical constraints while maintaining the high accuracy of unconstrained reduced-order models.展开更多
With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roast...With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roaster is a key piece of large-scale equipment and plays a critical role in the manufacturing industry;its internal temperature field directly determines the quality of zinc calcine and other related products.However,due to its vast spatial dimensions,the limited observation methods,and the complex multiphase,multifield coupled reaction atmosphere inside it,accurately and timely perceiving its temperature field remains a significant challenge.To address these challenges,a spatial-temporal reduced-order model(STROM)is proposed,which can realize fast and accurate temperature field perception based on sparse observation data.Specifically,to address the difficulty in matching the initial physical field with the sparse observation data,an initial field construction based on data assimilation(IFCDA)method is proposed to ensure that the initial conditions of the model can be matched with the actual operation state,which provides a basis for constructing a high-precision computational fluid dynamics(CFD)model.Then,to address the high simulation cost of high-precision CFD models under full working conditions,a high uniformity(HU)-orthogonal test design(OTD)method with the centered L2 deviation is innovatively proposed to ensure high information coverage of the temperature field dataset under typical working conditions in terms of multiple factors and levels of the component,feed,and blast parameters.Finally,to address the difficulty in real-time and accurate temperature field prediction,considering the spatial correlation between the observed temperature and the temperature field,as well as the dynamic correlation of the observed temperature in the time dimension,a spatial-temporal predictive model(STPM)is established,which realizes rapid prediction of the temperature field through sparse observa-tion data.To verify the accuracy and validity of the proposed method,CFD model validation and reduced-order model prediction experiments are designed,and the results show that the proposed method can realize high-precision and fast prediction of the roaster temperature field under different working conditions through sparse observation data.Compared with the CFD model,the prediction root-mean-square error(RMSE)of STROM is less than 0.038,and the computational efficiency is improved by 3.4184×10^(4)times.In particular,STROM also has a good prediction ability for unmodeled conditions,with a prediction RMSE of less than 0.1089.展开更多
This paper presents an estimation of transient stability regions for large-scale power systems.In Part I,a Koopman operator based model reduction(KOMR)method is proposed to derive a low-order dynamical model with reas...This paper presents an estimation of transient stability regions for large-scale power systems.In Part I,a Koopman operator based model reduction(KOMR)method is proposed to derive a low-order dynamical model with reasonable accuracy for transient stability analysis of large-scale power systems.Unlike traditional reduction methods based on linearized models,the proposed method does not require linearization,but captures dominant modes of the original nonlinear dynamics by employing a Koopman operator defined in an infinite-dimensional observable space.Combined with the Galerkin projection,the obtained dominant Koopman eigenvalues and modes produce a reduced-order nonlinear model.To approximate the Koopman operator with sufficient accuracy,we introduce a Polynomial-based Multi-trajectory Kernel Dynamic Mode Decomposition(PMK-DMD)algorithm,which outperforms traditional DMD in various scenarios.In the end,the proposed method is applied to the IEEE 10-machine-39-bus power system and IEEE 16-machine-68-bus power system,which demonstrates that our method is significantly superior to the modal analysis method in both qualitative and quantitative aspects.展开更多
The efficient dynamic modeling and vibration transfer analysis of a fluid-delivering branch pipeline(FDBP)are essential for analyzing vibration coupling effects and implementing vibration reduction optimization.Theref...The efficient dynamic modeling and vibration transfer analysis of a fluid-delivering branch pipeline(FDBP)are essential for analyzing vibration coupling effects and implementing vibration reduction optimization.Therefore,this study proposes a reduced-order dynamic modeling method suitable for FDBPs and then analyzes the vibration transfer characteristics.For the modeling method,the finite element method and absorbing transfer matrix method(ATMM)are integrated,considering the fluid–structure coupling effect and fluid disturbances.The dual-domain dynamic substructure method is developed to perform the reduced-order modeling of FDBP,and ATMM is adopted to reduce the matrix order when solving fluid disturbances.Furthermore,the modeling method is validated by experiments on an H-shaped branch pipeline.Finally,transient and steady-state vibration transfer analyses of FDBP are performed,and the effects of branch locations on natural characteristics and vibration transfer behavior are analyzed.Results show that transient vibration transfer represents the transfer and conversion of the kinematic,strain,and damping energies,while steady-state vibration transfer characteristics are related to the vibration mode.In addition,multiple-order mode exchanges are triggered when branch locations vary in frequency-shift regions,and the mode-exchange regions are also the transformation ones for vibration transfer patterns.展开更多
The multiscale computational method with asymptotic analysis and reduced-order homogenization(ROH)gives a practical numerical solution for engineering problems,especially composite materials.Under the ROH framework,a ...The multiscale computational method with asymptotic analysis and reduced-order homogenization(ROH)gives a practical numerical solution for engineering problems,especially composite materials.Under the ROH framework,a partition-based unitcell structure at the mesoscale is utilized to give a mechanical state at the macro-scale quadrature point with pre-evaluated influence functions.In the past,the“1-phase,1-partition”rule was usually adopted in numerical analysis,where one constituent phase at the mesoscale formed one partition.The numerical cost then is significantly reduced by introducing an assumption that the mechanical responses are the same all the time at the same constituent,while it also introduces numerical inaccuracy.This study proposes a new partitioning method for fibrous unitcells under a reduced-order homogenization methodology.In this method,the fiber phase remains 1 partition,but the matrix phase is divided into 2 partitions,which refers to the“12”partitioning scheme.Analytical elastic influence+functions are derived by introducing the elastic strain energy equivalence(Hill-Mandel condition).This research also obtains the analytical eigenstrain influence functions by alleviating the so-called“inclusion-locking”phenomenon.In addition,a numerical approach to minimize the error of strain energy density is introduced to determine the partitioning of the matrix phase.Several numerical examples are presented to compare the differences among direct numerical simulation(DNS),“11”,and“12”partitioning schemes.The numerical simulations show improved++numerical accuracy by the“12”partitioning scheme.展开更多
During the startup of the hydraulic turbine generators,the hybrid magnetic bearing support system exhibits displacement fluctuations,and the nonlinearity and strong coupling characteristics of the magnetic bearings li...During the startup of the hydraulic turbine generators,the hybrid magnetic bearing support system exhibits displacement fluctuations,and the nonlinearity and strong coupling characteristics of the magnetic bearings limit the accuracy of rotor modeling,making traditional control methods difficult to adapt to parameter variations.To suppress startup disturbances and achieve a control strategy with low computational complexity and high precision,this paper proposes a five-degree-of-freedom hybrid magnetic bearing control strategy based on an improved cascaded reduced-order linear active disturbance rejection controller(CRLADRC).The front-stage reduced-order linear extended state observer(FRLESO)reduces the system’s computational complexity,enabling the system to maintain stability during motor startup disturbances.The second-stage reduced-order linear extended state observer(SRLESO)further enhances the system’s disturbance estimation accuracy while maintaining low computational complexity.Furthermore,the disturbance rejection and noise suppression capabilities are analyzed in the frequency domain and the stability of the proposed control method is proven using Lyapunov theory.Experimental results indicate that the proposed strategy effectively reduces displacement fluctuations in the hybrid magnetic bearing support system during motor startup,significantly enhancing the system’s robustness.展开更多
An equivalent source-load MTDC system including DC voltage control units,power control units and interconnected DC lines is considered in this paper,which can be regarded as a generic structure of low-voltage DC micro...An equivalent source-load MTDC system including DC voltage control units,power control units and interconnected DC lines is considered in this paper,which can be regarded as a generic structure of low-voltage DC microgrids,mediumvoltage DC distribution systems or HVDC transmission systems with a common DC bus.A reduced-order model is proposed with a circuit structure of a resistor,inductor and capacitor in parallel for dynamic stability analysis of the system in DC voltage control timescale.The relationship between control parameters and physical parameters of the equivalent circuit can be found,which provides an intuitive insight into the physical meaning of control parameters.Employing this model,a second-order characteristic equation is further derived to investigate system dynamic stability mechanisms in an analytical approach.As a result,the system oscillation frequency and damping are characterized in a straight forward manner,and the role of electrical and control parameters and different system-level control strategies in system dynamic stability in DC voltage control timescale is defined.The effectiveness of the proposed reduced-order model and the correctness of the theoretical analysis are verified by simulation based on PSCAD/EMTDC and an experiment based on a hardware low-voltage MTDC system platform.展开更多
As a high-dimensional complex nonlinear dynamic system,the analysis of the essence of flow has always been a difficult problem,especially in the flow including phase change.In recent years,it has become a feasible met...As a high-dimensional complex nonlinear dynamic system,the analysis of the essence of flow has always been a difficult problem,especially in the flow including phase change.In recent years,it has become a feasible method to reduce the dimension of flow structure by reduced-order modeling(ROM)methods.In this paper,through the cavitation numerical simulation of NACA0015 hydrofoil,two ROM methods are used to reduce and restore three different cavitation respectively-proper orthogonal decomposition(POD)and dynamic mode decomposition(DMD).The applicability of two methods in cavitation is discussed and reasons are analyzed.The results show that for stable cavitation,POD,DMD methods can accurately restore the flow field of a few modes with high energy.For unstable cavitation,only POD method can restore real flow field well.This situation is mainly due to the fact that POD,DMD method are applicable to different energy ratios,and different main mode selection criterion of DMD will lead to different main mode.ROM can greatly simplify the complexity of flow.Selecting a reasonable ROM can improve the accuracy of a small amount of database,and provide a basis for intelligent prediction of flow analysis.展开更多
Hazardous chemical gases will spread rapidly after leakage.For emergency response to that,people need to obtain the information of wind and pollutant in time.However,it takes a lot of time to calculate the flow in lar...Hazardous chemical gases will spread rapidly after leakage.For emergency response to that,people need to obtain the information of wind and pollutant in time.However,it takes a lot of time to calculate the flow in large-scale urban areas by numerical simulation.Therefore,reduced-order model(ROM)is developed to improve the efficiency.In this paper,we propose a model based on proper orthogonal decomposition(POD)and radial basis function(RBF)interpolation.We validate the model by calculating the wind field and the pollutant propagation process in a 144 square kilometer area of Beijing.The results show that ROM can reduce CPU times by more than 99%at the cost of only 0.1%information loss,comparing with the traditional approach of computational fluid dynamics(CFD).展开更多
Accurate real-time simulations of nuclear reactor circuit systems are particularly important for system safety analysis and design.To effectively improve computational efficiency without reducing accuracy,this study e...Accurate real-time simulations of nuclear reactor circuit systems are particularly important for system safety analysis and design.To effectively improve computational efficiency without reducing accuracy,this study establishes a thermal-hydraulics reduced-order model(ROM)for nuclear reactor circuit systems.The full-order circuit system calculation model is first established and verified and then used to calculate the thermal-hydraulic properties of the circuit system under different states as snapshots.The proper orthogonal decomposition method is used to extract the basis functions from snapshots,and the ROM is constructed using the least-squares method,effectively reducing the difficulty in constructing the ROM.A comparison between the full-order simulation and ROM prediction results of the AP1000 circuit system shows that the proposed ROM can improve computational efficiency by 1500 times while achieving a maximum relative error of 0.223%.This research develops a new direction and perspective for the digital twin modeling of nuclear reactor system circuits.展开更多
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.
文摘This paper is devoted to application of the Reduced-Order Model(ROM)based on Volterra series to prediction of lift and drag forces due to airfoil periodic translation in transonic flow region.When there is large amplitude oscillation of the relative Mach number,as appeared in helicopter rotor movement in forward flight,the conventional Volterra ROM is found to be unsatisfactory.To cover such applications,a matched Volterra ROM,inspired from previous multistep nonlinear indicial response method based on Duhamel integration,is thus considered,in which the step motions are defined inside a number of equal intervals with both positive and negative step motions to match the airfoil forward and backward movement,and the kernel functions are constructed independently at each interval.It shows that,at least for the translation movement considered,this matched Volterra ROM greatly improves the accuracy of prediction.Moreover,the matched Volterra ROM,with the total number of step motions and thus the computational cost close to those of the conventional Volterra ROM method,has the additional advantage that the same set of kernels can match various translation motions with different starting conditions so the kernels can be predesigned without knowing the specific motion of airfoil.
文摘A modified reduced-order method for RC networks which takes a division-and-conquest strategy is presented.The whole network is partitioned into a set of sub-networks at first,then each of them is reduced by Krylov subspace techniques,and finally all the reduced sub-networks are incorporated together.With some accuracy,this method can reduce the number of both nodes and components of the circuit comparing to the traditional methods which usually only offer a reduced net with less nodes.This can markedly accelerate the sparse-matrix-based simulators whose performance is dominated by the entity of the matrix or the number of components of the circuits.
基金co-supported by the National Natural Science Foundation of China (Nos. 61134004 and 61573289)Aeronautical Science Foundation of China (No. 20140753010)the Fundamental Research Funds for the Central Universities (No. 3102015BJ004)
文摘Recently, flutter active control using linear parameter varying（LPV） framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by synthesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant（LTI） models by using system identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency（p-LSCF） algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification,the dominant physical modes are determined by clear stabilization diagrams and clustering technique. In the second step, with prior knowledge of physical poles, the improved frequencydomain maximum likelihood（ML） estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.
基金supported in part by Heilongjiang Provincial Natural Science Foundation of China under Project TD2021E004in part by Ningbo Science and Technology Bureau under S&T Innovation 2025 Major Special Programme with project code 2019B10071。
文摘With the rapid development of the world economy,IGBT has been widely used in motor drive and electric energy conversion.In order to timely detect the fatigue damage of IGBT,it is necessary to monitor the junction temperature of IGBT.In order to realize the fast calculation of IGBT junction temperature,a finite element method of IGBT temperature field reduction is proposed in this paper.Firstly,the finite element calculation process of IGBT temperature field is introduced and the linear equations of finite element calculation of temperature field are derived.Temperature field data of different working conditions are obtained by finite element simulation to form the sample space.Then the covariance matrix of the sample space is constructed,whose proper orthogonal decomposition and modal extraction are carried out.Reasonable basis vector space is selected to complete the low dimensional expression of temperature vector inside and outside the sample space.Finally,the reduced-order model of temperature field finite element is obtained and solved.The results of the reduced order model are compared with those of the finite element method,and the performance of the reduced-order model is evaluated from two aspects of accuracy and rapidity.
基金supported by the National Natural Science Foundation of China(No.11802245).
文摘A Non-Intrusive Reduced-Order Model(NIROM)based on Proper Orthogonal Decomposition(POD)has been proposed for predicting the flow fields of transonic airfoils with geometry parameters.To provide a better reduced-order subspace to approximate the real flow field,a domain decomposition method has been used to separate the hard-to-predict regions from the full field and POD has been adopted in the regions individually.An Artificial Neural Network(ANN)has replaced the Radial Basis Function(RBF)to interpolate the coefficients of the POD modes,aiming at improving the approximation accuracy of the NIROM for non-samples.When predicting the flow fields of transonic airfoils,the proposed NIROM has demonstrated a high performance.
基金This work was supported by the National Natural Science Foundation of China(Grant No.12072293)the Project of State Key Laboratory of Traction Power for Southwest Jiaotong University(Grant No.2021TPL-T10)+2 种基金China Scholarship Council(Grant No.202007000115)the Key Scientific Research Fund Project of Sichuan Education Department(Grant No.18ZA0454)the Key Research Program of Xihua University(Grant No.Z1020212).
文摘Fastening failures have frequently been found on China high-speed railway curved tracks in recent years.Thus the influence of fastening failures on high-speed train-track interaction in curved track needs to be analyzed.A train-curved slab track interaction model is built,in which the real shape of the curved rail is considered and modeled with reduced beam model(RBM)and curved beam theory,and the slabs are modeled with four-nodes Kirchhoff-Love plate elements.The present model is validated at first with different traditional models.Then the influence of fastening failure in curved slab track on train-track interaction dynamics is studied.A different number of failed fastenings is assumed to occur at the curved track,and different types of fastening failure including the fatigue fracture of the clip structure and failure of the rail pad are considered.Based on the calculation results,the fatigue fracture of the clip structure has little influence on train-track interaction dynamics.But when rail pad failure happens and its equivalent vertical stiffness and damping are less than one-tenth of its original,the fastening failure seriously affects the high-speed train operation safety,and it must be prevented.
基金supported by the JSPS KAKENHI(No.26350673)National Natural Science Foundation of China(Nos.61473313 and 61210011)Hubei Provincial Natural Science Foundation of China(No.2015CFA010)
文摘Brain hypothermia treatment （BHT） is an active therapy for severe brain injury. It makes the temperature of the brain track a given temperature input curve so as to reduce the risk of tissue damage. BHT requires a brain-temperature control system because of environmental disturbances and changes in the human body. The thermal models of the human body devised so far are usually of a very high order and are not suitable for controlling brain temperature. This paper presents a method of finding a reducedorder thermal model of the human body for use in BHT. It combines minimal realization and balanced realization. Unlike other methods, this method yields a reduced-order model that is based on system theory and that takes the frequency characteristics of human thermal sensation into account. It features high precision in the frequency band for BHT and is suitable for the control of brain temperature.
基金This work was supported by the Republic of Singapore＇s National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore （BEARS） for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics （SinBerBEST） Program. BEARS has been established by the UniversiW of California, Berkeley as a center for intellectual excellence in research and education in Singapore. This work was also supported by the National Natural Science Foundation of China （Nos. 61573220, 61304045）.
文摘In this paper, a non-cooperative distributed MPC algorithm based on reduced order model is proposed to stabilize large-scale systems. The large-scale system consists of a group of interconnected subsystems. Each subsystem can be partitioned into two parts： measurable part, whose states can be directly measured by sensors, and the unmeasurable part. In the online computation phase, only the measurable dynamics of the corresponding subsystem and neighbour-to-neighbour communication are necessary for the local controller design. Satisfaction of the state constraints and the practical stability are guaranteed while the complexity of the optimization problem is reduced. Numerical examples are given to show the effectiveness of this algorithm.
基金support of the HECO2-Axe-1 project,funded by the Walloon Region under the REPowerEU initiative,Bel-giumfinancial support from the ERC proof of concept grant INVENT(grant agreement No:101123406).
文摘Reduced-order models offer a cost-effective and accurate approach to analyzing high-dimensional combustion problems.These surrogate models are built in a data-driven manner by combining computational fluid dynamics simulations with Proper Orthogonal Decomposition(POD)for dimensionality reduction and Gaussian Process Regression(GPR)for nonlinear regression.However,these models can yield physically inconsistent results,such as negative mass fractions.As a linear decomposition method,POD complicates the enforcement of constraints in the reduced space,while GPR lacks inherent provisions to ensure physical consistency.To address these challenges,this study proposes a novel constrained reduced-order model framework that enforces physical consistency in predictions.Dimensionality reduction is achieved by downsampling the dataset through low-cost Singular Value Decomposition(lcSVD)using optimal sensor placement,ensuring that the retained data points preserve physical information in the reduced space.We integrate finite-support parametric distribution functions,such as truncated Gaussian and beta distribution scaled to the interval[a,b],into the GPR framework.These bounded likelihood functions explicitly model the observational noise in the bounded space and use variational inference to approximate analytically intractable posterior distributions,producing GP estimations that satisfy physical constraints by construction.We validate the proposed methods using a synthetic dataset and a benchmark case of one-dimensional laminar NH3/H2 flames.The results show that the thermo-chemical state predictions comply with physical constraints while maintaining the high accuracy of unconstrained reduced-order models.
基金supported in part by the National Key Research and Development Program of China(2022YFB3304900)in part by the National Natural Science Foundation of China(62394340 and 62073340)in part by the Science and Technology Innovation Program of Hunan Province(2022JJ10083).
文摘With the intelligent transformation of process manufacturing,accurate and comprehensive perception information is fundamental for application of artificial intelligence methods.In zinc smelting,the fluidized bed roaster is a key piece of large-scale equipment and plays a critical role in the manufacturing industry;its internal temperature field directly determines the quality of zinc calcine and other related products.However,due to its vast spatial dimensions,the limited observation methods,and the complex multiphase,multifield coupled reaction atmosphere inside it,accurately and timely perceiving its temperature field remains a significant challenge.To address these challenges,a spatial-temporal reduced-order model(STROM)is proposed,which can realize fast and accurate temperature field perception based on sparse observation data.Specifically,to address the difficulty in matching the initial physical field with the sparse observation data,an initial field construction based on data assimilation(IFCDA)method is proposed to ensure that the initial conditions of the model can be matched with the actual operation state,which provides a basis for constructing a high-precision computational fluid dynamics(CFD)model.Then,to address the high simulation cost of high-precision CFD models under full working conditions,a high uniformity(HU)-orthogonal test design(OTD)method with the centered L2 deviation is innovatively proposed to ensure high information coverage of the temperature field dataset under typical working conditions in terms of multiple factors and levels of the component,feed,and blast parameters.Finally,to address the difficulty in real-time and accurate temperature field prediction,considering the spatial correlation between the observed temperature and the temperature field,as well as the dynamic correlation of the observed temperature in the time dimension,a spatial-temporal predictive model(STPM)is established,which realizes rapid prediction of the temperature field through sparse observa-tion data.To verify the accuracy and validity of the proposed method,CFD model validation and reduced-order model prediction experiments are designed,and the results show that the proposed method can realize high-precision and fast prediction of the roaster temperature field under different working conditions through sparse observation data.Compared with the CFD model,the prediction root-mean-square error(RMSE)of STROM is less than 0.038,and the computational efficiency is improved by 3.4184×10^(4)times.In particular,STROM also has a good prediction ability for unmodeled conditions,with a prediction RMSE of less than 0.1089.
文摘This paper presents an estimation of transient stability regions for large-scale power systems.In Part I,a Koopman operator based model reduction(KOMR)method is proposed to derive a low-order dynamical model with reasonable accuracy for transient stability analysis of large-scale power systems.Unlike traditional reduction methods based on linearized models,the proposed method does not require linearization,but captures dominant modes of the original nonlinear dynamics by employing a Koopman operator defined in an infinite-dimensional observable space.Combined with the Galerkin projection,the obtained dominant Koopman eigenvalues and modes produce a reduced-order nonlinear model.To approximate the Koopman operator with sufficient accuracy,we introduce a Polynomial-based Multi-trajectory Kernel Dynamic Mode Decomposition(PMK-DMD)algorithm,which outperforms traditional DMD in various scenarios.In the end,the proposed method is applied to the IEEE 10-machine-39-bus power system and IEEE 16-machine-68-bus power system,which demonstrates that our method is significantly superior to the modal analysis method in both qualitative and quantitative aspects.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.N2403006)the National Science and Technology Major Project,China(Grant No.J2019-I-0008-0008).
文摘The efficient dynamic modeling and vibration transfer analysis of a fluid-delivering branch pipeline(FDBP)are essential for analyzing vibration coupling effects and implementing vibration reduction optimization.Therefore,this study proposes a reduced-order dynamic modeling method suitable for FDBPs and then analyzes the vibration transfer characteristics.For the modeling method,the finite element method and absorbing transfer matrix method(ATMM)are integrated,considering the fluid–structure coupling effect and fluid disturbances.The dual-domain dynamic substructure method is developed to perform the reduced-order modeling of FDBP,and ATMM is adopted to reduce the matrix order when solving fluid disturbances.Furthermore,the modeling method is validated by experiments on an H-shaped branch pipeline.Finally,transient and steady-state vibration transfer analyses of FDBP are performed,and the effects of branch locations on natural characteristics and vibration transfer behavior are analyzed.Results show that transient vibration transfer represents the transfer and conversion of the kinematic,strain,and damping energies,while steady-state vibration transfer characteristics are related to the vibration mode.In addition,multiple-order mode exchanges are triggered when branch locations vary in frequency-shift regions,and the mode-exchange regions are also the transformation ones for vibration transfer patterns.
基金funded by the National Key R&D Program of China(Grant No.2023YFA1008901)the National Natural Science Foundation of China(Grant Nos.11988102,12172009)“The Fundamental Research Funds for the Central Universities,Peking University”.
文摘The multiscale computational method with asymptotic analysis and reduced-order homogenization(ROH)gives a practical numerical solution for engineering problems,especially composite materials.Under the ROH framework,a partition-based unitcell structure at the mesoscale is utilized to give a mechanical state at the macro-scale quadrature point with pre-evaluated influence functions.In the past,the“1-phase,1-partition”rule was usually adopted in numerical analysis,where one constituent phase at the mesoscale formed one partition.The numerical cost then is significantly reduced by introducing an assumption that the mechanical responses are the same all the time at the same constituent,while it also introduces numerical inaccuracy.This study proposes a new partitioning method for fibrous unitcells under a reduced-order homogenization methodology.In this method,the fiber phase remains 1 partition,but the matrix phase is divided into 2 partitions,which refers to the“12”partitioning scheme.Analytical elastic influence+functions are derived by introducing the elastic strain energy equivalence(Hill-Mandel condition).This research also obtains the analytical eigenstrain influence functions by alleviating the so-called“inclusion-locking”phenomenon.In addition,a numerical approach to minimize the error of strain energy density is introduced to determine the partitioning of the matrix phase.Several numerical examples are presented to compare the differences among direct numerical simulation(DNS),“11”,and“12”partitioning schemes.The numerical simulations show improved++numerical accuracy by the“12”partitioning scheme.
基金supported by the National Natural Science Foundation of China under Grant 52302458the CAS Project for Young Scientists in Basic Research,Grant No.YSBR-045.
文摘During the startup of the hydraulic turbine generators,the hybrid magnetic bearing support system exhibits displacement fluctuations,and the nonlinearity and strong coupling characteristics of the magnetic bearings limit the accuracy of rotor modeling,making traditional control methods difficult to adapt to parameter variations.To suppress startup disturbances and achieve a control strategy with low computational complexity and high precision,this paper proposes a five-degree-of-freedom hybrid magnetic bearing control strategy based on an improved cascaded reduced-order linear active disturbance rejection controller(CRLADRC).The front-stage reduced-order linear extended state observer(FRLESO)reduces the system’s computational complexity,enabling the system to maintain stability during motor startup disturbances.The second-stage reduced-order linear extended state observer(SRLESO)further enhances the system’s disturbance estimation accuracy while maintaining low computational complexity.Furthermore,the disturbance rejection and noise suppression capabilities are analyzed in the frequency domain and the stability of the proposed control method is proven using Lyapunov theory.Experimental results indicate that the proposed strategy effectively reduces displacement fluctuations in the hybrid magnetic bearing support system during motor startup,significantly enhancing the system’s robustness.
基金This work was supported in part by the National Natural Science Foundation of China under Grant No.51977142.
文摘An equivalent source-load MTDC system including DC voltage control units,power control units and interconnected DC lines is considered in this paper,which can be regarded as a generic structure of low-voltage DC microgrids,mediumvoltage DC distribution systems or HVDC transmission systems with a common DC bus.A reduced-order model is proposed with a circuit structure of a resistor,inductor and capacitor in parallel for dynamic stability analysis of the system in DC voltage control timescale.The relationship between control parameters and physical parameters of the equivalent circuit can be found,which provides an intuitive insight into the physical meaning of control parameters.Employing this model,a second-order characteristic equation is further derived to investigate system dynamic stability mechanisms in an analytical approach.As a result,the system oscillation frequency and damping are characterized in a straight forward manner,and the role of electrical and control parameters and different system-level control strategies in system dynamic stability in DC voltage control timescale is defined.The effectiveness of the proposed reduced-order model and the correctness of the theoretical analysis are verified by simulation based on PSCAD/EMTDC and an experiment based on a hardware low-voltage MTDC system platform.
基金Project supported by the National Natural Science Foundation of China(Grant No.52079142,51909131)supported by the Chinese Universities Scientific Fund(Grant No.2021TC107).
文摘As a high-dimensional complex nonlinear dynamic system,the analysis of the essence of flow has always been a difficult problem,especially in the flow including phase change.In recent years,it has become a feasible method to reduce the dimension of flow structure by reduced-order modeling(ROM)methods.In this paper,through the cavitation numerical simulation of NACA0015 hydrofoil,two ROM methods are used to reduce and restore three different cavitation respectively-proper orthogonal decomposition(POD)and dynamic mode decomposition(DMD).The applicability of two methods in cavitation is discussed and reasons are analyzed.The results show that for stable cavitation,POD,DMD methods can accurately restore the flow field of a few modes with high energy.For unstable cavitation,only POD method can restore real flow field well.This situation is mainly due to the fact that POD,DMD method are applicable to different energy ratios,and different main mode selection criterion of DMD will lead to different main mode.ROM can greatly simplify the complexity of flow.Selecting a reasonable ROM can improve the accuracy of a small amount of database,and provide a basis for intelligent prediction of flow analysis.
文摘Hazardous chemical gases will spread rapidly after leakage.For emergency response to that,people need to obtain the information of wind and pollutant in time.However,it takes a lot of time to calculate the flow in large-scale urban areas by numerical simulation.Therefore,reduced-order model(ROM)is developed to improve the efficiency.In this paper,we propose a model based on proper orthogonal decomposition(POD)and radial basis function(RBF)interpolation.We validate the model by calculating the wind field and the pollutant propagation process in a 144 square kilometer area of Beijing.The results show that ROM can reduce CPU times by more than 99%at the cost of only 0.1%information loss,comparing with the traditional approach of computational fluid dynamics(CFD).
基金supported by the National Natural Science Foundation of China(No.12205389)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515011735)Science and Technology on Reactor System Design Technology Laboratory(No.KFKT-05-FWHT-WU-2023014).
文摘Accurate real-time simulations of nuclear reactor circuit systems are particularly important for system safety analysis and design.To effectively improve computational efficiency without reducing accuracy,this study establishes a thermal-hydraulics reduced-order model(ROM)for nuclear reactor circuit systems.The full-order circuit system calculation model is first established and verified and then used to calculate the thermal-hydraulic properties of the circuit system under different states as snapshots.The proper orthogonal decomposition method is used to extract the basis functions from snapshots,and the ROM is constructed using the least-squares method,effectively reducing the difficulty in constructing the ROM.A comparison between the full-order simulation and ROM prediction results of the AP1000 circuit system shows that the proposed ROM can improve computational efficiency by 1500 times while achieving a maximum relative error of 0.223%.This research develops a new direction and perspective for the digital twin modeling of nuclear reactor system circuits.
