In this manuscript,we propose an analytical equivalent linear viscoelastic constitutive model for fiber-reinforced composites,bypassing general computational homogenization.The method is based on the reduced-order hom...In this manuscript,we propose an analytical equivalent linear viscoelastic constitutive model for fiber-reinforced composites,bypassing general computational homogenization.The method is based on the reduced-order homogenization(ROH)approach.The ROH method typically involves solving multiple finite element problems under periodic conditions to evaluate elastic strain and eigenstrain influence functions in an‘off-line’stage,which offers substantial cost savings compared to direct computational homogenization methods.Due to the unique structure of the fibrous unit cell,“off-line”stage calculation can be eliminated by influence functions obtained analytically.Introducing the standard solid model to the ROH method enables the creation of a comprehensive analytical homogeneous viscoelastic constitutive model.This method treats fibrous composite materials as homogeneous,anisotropic viscoelastic materials,significantly reducing computational time due to its analytical nature.This approach also enables precise determination of a homogenized anisotropic relaxation modulus and accurate capture of various viscoelastic responses under different loading conditions.Three sets of numerical examples,including unit cell tests,three-point beam bending tests,and torsion tests,are given to demonstrate the predictive performance of the homogenized viscoelastic model.Furthermore,the model is validated against experimental measurements,confirming its accuracy and reliability.展开更多
Aerothermoelasticity is one of the key technologies for hypersonic vehicles. Accurate and efficient computation of the aerothermodynamics is one of the primary challenges for hypersonic aerothermoelastic analysis. Aim...Aerothermoelasticity is one of the key technologies for hypersonic vehicles. Accurate and efficient computation of the aerothermodynamics is one of the primary challenges for hypersonic aerothermoelastic analysis. Aimed at solving the shortcomings of engineering calculation, compu- tation fluid dynamics （CFD） and experimental investigation, a reduced order modeling （ROM） framework for aerothermodynamics based on CFD predictions using an enhanced algorithm of fast maximin Latin hypercube design is developed. Both proper orthogonal decomposition （POD） and surrogate are considered and compared to construct ROMs. Two surrogate approaches named Kriging and optimized radial basis function （ORBF） are utilized to construct ROMs. Furthermore, an enhanced algorithm of fast maximin Latin hypercube design is proposed, which proves to be helpful to improve the precisions of ROMs. Test results for the three-dimensional aerothermody- namic over a hypersonic surface indicate that： the ROMs precision based on Kriging is better than that by ORBF, ROMs based on Kriging are marginally more accurate than ROMs based on POD- Kriging. In a word, the ROM framework for hypersonic aerothermodynamics has good precision and efficiency.展开更多
This paper describes a method proposed for modeling large deflection of aircraft in nonlinear aeroelastic analysis by developing reduced order model（ROM）.The method is applied for solving the static aeroelastic and ...This paper describes a method proposed for modeling large deflection of aircraft in nonlinear aeroelastic analysis by developing reduced order model（ROM）.The method is applied for solving the static aeroelastic and static aeroelastic trim problems of flexible aircraft containing geometric nonlinearities;meanwhile,the non-planar effects of aerodynamics and follower force effect have been considered.ROMs are computational inexpensive mathematical representations compared to traditional nonlinear finite element method（FEM） especially in aeroelastic solutions.The approach for structure modeling presented here is on the basis of combined modal/finite element（MFE） method that characterizes the stiffness nonlinearities and we apply that structure modeling method as ROM to aeroelastic analysis.Moreover,the non-planar aerodynamic force is computed by the non-planar vortex lattice method（VLM）.Structure and aerodynamics can be coupled with the surface spline method.The results show that both of the static aeroelastic analysis and trim analysis of aircraft based on structure ROM can achieve a good agreement compared to analysis based on the FEM and experimental result.展开更多
Based on Recursive Radial Basis Function(RRBF)neural network,the Reduced Order Model(ROM)of compressor cascade was established to meet the urgent demand of highly efficient prediction of unsteady aerodynamics performa...Based on Recursive Radial Basis Function(RRBF)neural network,the Reduced Order Model(ROM)of compressor cascade was established to meet the urgent demand of highly efficient prediction of unsteady aerodynamics performance of turbomachinery.One novel ROM called ASA-RRBF model based on Adaptive Simulated Annealing(ASA)algorithm was developed to enhance the generalization ability of the unsteady ROM.The ROM was verified by predicting the unsteady aerodynamics performance of a highly-loaded compressor cascade.The results show that the RRBF model has higher accuracy in identification of the dimensionless total pressure and dimensionless static pressure of compressor cascade under nonlinear and unsteady conditions,and the model behaves higher stability and computational efficiency.However,for the strong nonlinear characteristics of aerodynamic parameters,the RRBF model presents lower accuracy.Additionally,the RRBF model predicts with a large error in the identification of aerodynamic parameters under linear and unsteady conditions.For ASA-RRBF,by introducing a small-amplitude and highfrequency sinusoidal signal as validation sample,the width of the basis function of the RRBF model is optimized to improve the generalization ability of the ROM under linear unsteady conditions.Besides,this model improves the predicting accuracy of dimensionless static pressure which has strong nonlinear characteristics.The ASA-RRBF model has higher prediction accuracy than RRBF model without significantly increasing the total time consumption.This novel model can predict the linear hysteresis of dimensionless static pressure happened in the harmonic condition,but it cannot accurately predict the beat frequency of dimensionless total pressure.展开更多
Active stability augmentation system is an attractive and promising technology to suppress flutter and limit cycle oscillation (LCO). In order to design a good active control law, the control plant model with low orde...Active stability augmentation system is an attractive and promising technology to suppress flutter and limit cycle oscillation (LCO). In order to design a good active control law, the control plant model with low order and high accuracy must be provided, which is one of the most important key points. The traditional model is based on low fidelity aerodynamics model such as panel method, which is unsuitable for transonic flight regime. The physics-based high fidelity tools, reduced order model (ROM) and CFD/CSD coupled aeroservoelastic solver are used to design the active control law. The Volterra/ROM is applied to constructing the low order state space model for the nonlinear unsteady aerodynamics and static output feedback method is used to active control law design. The detail of the new method is demonstrated by the Goland+ wing/store system. The simulation results show that the effectiveness of the designed active augmentation system, which can suppress the flutter and LCO successfully.展开更多
The objective of this study is to identify system parameters from the recorded response of base isolated buildings,such as USC hospital building,during the 1994 Northridge earthquake.Full state measurements are not av...The objective of this study is to identify system parameters from the recorded response of base isolated buildings,such as USC hospital building,during the 1994 Northridge earthquake.Full state measurements are not available for identification.Additionally,the response is nonlinear due to the yielding of the lead-rubber bearings.Two new approaches are presented in this paper to solve the aforementioned problems.First,a reduced order observer is used to estimate the unmeasured states.Second,a least squares technique with time segments is developed to identify the piece-wise linear system properties.The observer is used to estimate the initial conditions needed for the time segmented identification.A series of equivalent linear system parameters are identified in different time segments.It is shown that the change in system parameters,such as frequencies and damping ratios,due to nonlinear behavior of the lead-rubber bearings,are reliably estimated using the presented technique.It is shown that the response was reduced due to yielding of the lead-rubber bearings and period lengthening.展开更多
A Reduced Order Model(ROM)based analysis method for turbomachinery cascade coupled mode flutter is presented in this paper.The unsteady aerodynamic model is established by a system identification technique combined wi...A Reduced Order Model(ROM)based analysis method for turbomachinery cascade coupled mode flutter is presented in this paper.The unsteady aerodynamic model is established by a system identification technique combined with a set of Aerodynamic Influence Coefficients(AIC).Subsequently,the aerodynamic model is encoded into the state space and then coupled with the structural dynamic equations,resulting in a ROM of the cascade aeroelasticity.The cascade flutter can be determined by solving the eigenvalues of the ROM.Bending-torsional coupled mode flutter analysis for the Standard Configuration Eleven(SC11)cascade is used to validate the proposed method.展开更多
This paper presents a design of continuous-time sliding mode control for the higher order systems via reduced order model. It is shown that a continuous-time sliding mode control designed for the reduced order model g...This paper presents a design of continuous-time sliding mode control for the higher order systems via reduced order model. It is shown that a continuous-time sliding mode control designed for the reduced order model gives similar performance for thc higher order system. The method is illustrated by numerical examples. The paper also introduces a technique for design of a sliding surface such that the system satisfies a cost-optimality condition when on the sliding surface.展开更多
This paper considers the problem of simulating the humidity distributions of a grain storage system. The distributions are described by partial differential equations(PDE). It is quite difficult to obtain the humidity...This paper considers the problem of simulating the humidity distributions of a grain storage system. The distributions are described by partial differential equations(PDE). It is quite difficult to obtain the humidity profiles from the PDE model. Hence, a discretization method is applied to obtain an equivalent ordinary differential equation model. However, after applying the discretization technique, the cost of solving the system increases as the size increases to a few thousands. It may be noted that after discretization,the degree of freedom of the system remain the same while the order increases. The large dynamic model is reduced using a proper orthogonal decomposition based technique and an equivalent model but of much reduced size is obtained. A controller based on optimal control theory is designed to obtain an input such that the output humidity reaches a desired profile and also its stability is analyzed.Numerical results are presented to show the validity of the reduced model and possible further extensions are identified.展开更多
Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational...Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational domains,etc.Typical reduced order modeling techniques accelerate the solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the ofline stage.These methods often need a predefined mesh as well as a series of precomputed solution snapshots,and may struggle to balance between the efficiency and accuracy due to the limitation of the linear ansatz.Utilizing the nonlinear representation of neural networks(NNs),we propose the Meta-Auto-Decoder(MAD)to construct a nonlinear trial manifold,whose best possible performance is measured theoretically by the decoder width.Based on the meta-learning concept,the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training stage.Fast adaptation to new(possibly heterogeneous)PDE parameters is enabled by searching on this trial manifold,and optionally fine-tuning the trial manifold at the same time.Extensive numerical experiments show that the MAD method exhibits a faster convergence speed without losing the accuracy than other deep learning-based methods.展开更多
To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-posi...To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-position(POD).First,POD is employed to reduce the di-mensionality of the wind field data,extracting spatiotempo-rally correlated modal coefficients and modes.These reduced-order variables can effectively capture the essential features of unsteady wake behaviors.Next,MFRFNN is utilized to predict the time series of modal coefficients.Fi-nally,by combining the predicted modal coefficients with their corresponding modes,a flow field is reconstructed,al-lowing accurate prediction of unsteady wake dynamics.The predicted wake data exhibit high consistency with large eddy simulation results in both the near-and far-wake re-gions and outperform existing data-driven methods.This ap-proach offers significant potential for optimizing wind farm design and provides a new solution for the precise prediction of wind turbine wake behavior.展开更多
In the process of signal transmission,an inevitable phenomenon is that the signal may change abruptly at certain discrete moments,negatively contributing to the performance of the system.In this paper,we focus on the ...In the process of signal transmission,an inevitable phenomenon is that the signal may change abruptly at certain discrete moments,negatively contributing to the performance of the system.In this paper,we focus on the distributed state estimation problem for continuous-time linear time-invariant(LTI)systems with intermittent abrupt jumps in communication information.First,the phenomenon of intermittent sudden jumps during information transmission is characterized as impulsive communication disturbances.Under impulsive communication disturbances,a distributed reduced order observer(DROO)is designed with each local observer grouped with two modules:a reduced-order observer and a consensus-based observer.Then,sufficient conditions for the existence of the DROO are presented,by using the graph theory and impulsive analysis.The relationship between the feedback gain and the unstable system matrix,the network topology,the impulse intensity,and the average impulsive interval is obtained.Finally,the validity of the proposed DROO under impulsive communication disturbances is substantiated by numerical simulations.展开更多
Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow confi...Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.展开更多
A novel statistical second-order reduced multiscale(SSRM)approach is established for nonlinear composite materials with random distribution of grains.For these composites considered in this work,the complex microstruc...A novel statistical second-order reduced multiscale(SSRM)approach is established for nonlinear composite materials with random distribution of grains.For these composites considered in this work,the complex microstructure of grains,including their shape,orientation,size,spatial distribution,volume fraction and so on,results in changing of the macroscopic mechanical properties.The first-and second-order unit cell functions based on two-scale asymptotic expressions are constructed at first.Then,the expected homogenized parameters are defined,and the nonlinear homogenization equation on global structure is established,successively.Further,an effective reduced model format for analyzing second-order nonlinear unit cell problem with less computation cost is introduced in detail.Finally,some numerical examples for the materials with varying distribution models are evaluated and compared with the data by theoretical models and experimental results.These examples illustrate that the proposed SSRM approaches are effective for predicting the macroscopic properties of the random composite materials and supply a potential application in actual engineering computation.展开更多
Flow around a cavity is characterized by a self-sustained mechanism in which the shear layer impinges on the downstream edge of the cavity resulting in a feedback mechanism.Direct Numerical Simulations of the flow at ...Flow around a cavity is characterized by a self-sustained mechanism in which the shear layer impinges on the downstream edge of the cavity resulting in a feedback mechanism.Direct Numerical Simulations of the flow at low Reynolds number has been carried out to get pressure and velocity fluctuations,for the case of un-actuated and multi frequency actuation.A Reduced Order Model for the isentropic compressible equations based on the method of Proper Orthogonal Decomposition has been constructed.The model has been extended to include the effect of control.The Reduced Order dynamical system shows a divergence in time integration.A method of calibration based on the minimization of a linear functional of error,to the sensitivity of the modes,is proposed.The calibrated low order model is used to design a feedback control of cavity flows based on an observer design.For the experimental implementation of the controller,a state estimate based on the observed pressure measurements is obtained through a linear stochastic estimation.Finally the obtained control is introduced into the Direct Numerical Simulation to obtain a decrease in spectra of the cavity acoustic mode.展开更多
Model Order Reduction is an approximation of the main system so that the simplified system retains important features of the main system.Deep learning technology is a recent breakthrough in artificial neural networks ...Model Order Reduction is an approximation of the main system so that the simplified system retains important features of the main system.Deep learning technology is a recent breakthrough in artificial neural networks that can find more hidden information from the data.In this paper,a non-intrusive reduced order model(NIROM)based on combining deep neural networks(DNNs)and POD abilities,namely FAE-CAE-LSTM is presented.This method combines the obtained features based on Fully connected autoencoders(FAE),Convolutional autoencoders(CAE),and POD and then,a deep Long short-term memory network is trained by obtained features to predict the pressure and velocity fields at future time instances.We investigate the performance of the proposed methodology by solving two well-known canonical cases:a strong shear flow exhibiting the Kelvin–Helmholtz instability,and flow past a cylinder.The performance of the proposed FAE-CAE-LSTM method in future state prediction of the flow is compared with other NIROM methods such as CAELSTM,autoencoder-LSTM,autoencoder-DMD and POD-RNN based models.Results show that the FAE-CAE-LSTM method is considerably capable of predicting fluid flow evolution and obtains the best results in the prediction of the pressure and velocity fields in future time instances.展开更多
Based on linear matrix inequalities (LMI), the design method of reduced order controllers of mixed sensitivity problem is studied for flight control systems. It is shown that there exists a controller with order not ...Based on linear matrix inequalities (LMI), the design method of reduced order controllers of mixed sensitivity problem is studied for flight control systems. It is shown that there exists a controller with order not greater than the difference between the generalized plant order and the number of independent control variables, if the mixed sensitivity problem is solvable for strict regular flight control plants. The proof is constructive, and an approach to design such a controller can be obtained in terms of a pair of feasible solution to the well known 3 LMI. Finally, an example of mixed sensitivity problem for a flight control system is given to demonstrate practice of the approach.展开更多
In this paper a singularly perturbed linear second order hyperbolic problem with zeroth order reduced equation is discussed. Firstly, an energy inequality of the solution and an estimate of the remainder term of the a...In this paper a singularly perturbed linear second order hyperbolic problem with zeroth order reduced equation is discussed. Firstly, an energy inequality of the solution and an estimate of the remainder term of the asymptotic solution are given. Then an exponentially fitted difference scheme is developed in an equidistant mesh. Finally, uniform convergence in small parameter is proved in the sense of discrete energy norm.展开更多
The enumeration of elements of c.e. sets in the theory of computability and computational complexity has already been investigated. However, the order of this enumeration has received less attention. The enumeration o...The enumeration of elements of c.e. sets in the theory of computability and computational complexity has already been investigated. However, the order of this enumeration has received less attention. The enumeration orders of elements of c.e. sets by means of Turing machines on natural numbers are investigated. In this paper, we consider the enumeration orders of elements of c.e. sets on rational numbers. We present enumeration order reducibility and enumeration order equivalence on rational numbers and propose some lemmas and theorems on these concepts. Also, we show that the theories here hold for Rc and we could repeat the same theories in this domain, in a same way.展开更多
The improved line sampling （LS） technique, an effective numerical simulation method, is employed to analyze the probabilistic characteristics and reliability sensitivity of flutter with random structural parameter i...The improved line sampling （LS） technique, an effective numerical simulation method, is employed to analyze the probabilistic characteristics and reliability sensitivity of flutter with random structural parameter in transonic flow. The improved LS technique is a novel methodology for reliability and sensitivity analysis of high dimensionality and low probability problem with implicit limit state function, and it does not require any approximating surrogate of the implicit limit state equation. The improved LS is used to estimate the flutter reliability and the sensitivity of a two-dimensional wing, in which some structural properties, such as frequency, parameters of gravity center and mass ratio, are considered as random variables. Computational fluid dynamics （CFD） based unsteady aerodynamic reduced order model （ROM） method is used to construct the aerodynamic state equations. Coupling structural state equations with aerodynamic state equations, the safety margin of flutter is founded by using the critical velocity of flutter. The results show that the improved LS technique can effectively decrease the computational cost in the random uncertainty analysis of flutter. The reliability sensitivity, defined by the partial derivative of the failure probability with respect to the distribution parameter of random variable, can help to identify the important parameters and guide the structural optimization design.展开更多
基金support by the National Key R&D Program of China(Grant No.2023YFA1008901)the National Natural Science Foundation of China(Grant Nos.11988102,12172009)is gratefully acknowledged.
文摘In this manuscript,we propose an analytical equivalent linear viscoelastic constitutive model for fiber-reinforced composites,bypassing general computational homogenization.The method is based on the reduced-order homogenization(ROH)approach.The ROH method typically involves solving multiple finite element problems under periodic conditions to evaluate elastic strain and eigenstrain influence functions in an‘off-line’stage,which offers substantial cost savings compared to direct computational homogenization methods.Due to the unique structure of the fibrous unit cell,“off-line”stage calculation can be eliminated by influence functions obtained analytically.Introducing the standard solid model to the ROH method enables the creation of a comprehensive analytical homogeneous viscoelastic constitutive model.This method treats fibrous composite materials as homogeneous,anisotropic viscoelastic materials,significantly reducing computational time due to its analytical nature.This approach also enables precise determination of a homogenized anisotropic relaxation modulus and accurate capture of various viscoelastic responses under different loading conditions.Three sets of numerical examples,including unit cell tests,three-point beam bending tests,and torsion tests,are given to demonstrate the predictive performance of the homogenized viscoelastic model.Furthermore,the model is validated against experimental measurements,confirming its accuracy and reliability.
基金supported by the National Natural Science Foundation of China (Nos. 11372036, 50875024)Excellent Young Scholars Research Fund of Beijing Institute of Technology of China (No. 2010Y0102)
文摘Aerothermoelasticity is one of the key technologies for hypersonic vehicles. Accurate and efficient computation of the aerothermodynamics is one of the primary challenges for hypersonic aerothermoelastic analysis. Aimed at solving the shortcomings of engineering calculation, compu- tation fluid dynamics （CFD） and experimental investigation, a reduced order modeling （ROM） framework for aerothermodynamics based on CFD predictions using an enhanced algorithm of fast maximin Latin hypercube design is developed. Both proper orthogonal decomposition （POD） and surrogate are considered and compared to construct ROMs. Two surrogate approaches named Kriging and optimized radial basis function （ORBF） are utilized to construct ROMs. Furthermore, an enhanced algorithm of fast maximin Latin hypercube design is proposed, which proves to be helpful to improve the precisions of ROMs. Test results for the three-dimensional aerothermody- namic over a hypersonic surface indicate that： the ROMs precision based on Kriging is better than that by ORBF, ROMs based on Kriging are marginally more accurate than ROMs based on POD- Kriging. In a word, the ROM framework for hypersonic aerothermodynamics has good precision and efficiency.
文摘This paper describes a method proposed for modeling large deflection of aircraft in nonlinear aeroelastic analysis by developing reduced order model（ROM）.The method is applied for solving the static aeroelastic and static aeroelastic trim problems of flexible aircraft containing geometric nonlinearities;meanwhile,the non-planar effects of aerodynamics and follower force effect have been considered.ROMs are computational inexpensive mathematical representations compared to traditional nonlinear finite element method（FEM） especially in aeroelastic solutions.The approach for structure modeling presented here is on the basis of combined modal/finite element（MFE） method that characterizes the stiffness nonlinearities and we apply that structure modeling method as ROM to aeroelastic analysis.Moreover,the non-planar aerodynamic force is computed by the non-planar vortex lattice method（VLM）.Structure and aerodynamics can be coupled with the surface spline method.The results show that both of the static aeroelastic analysis and trim analysis of aircraft based on structure ROM can achieve a good agreement compared to analysis based on the FEM and experimental result.
基金co-National Science and Technology Major Project(No.2017-II-0009-0023)Innovation Guidance Support Project for Taicang Top Research Institutes(No.TC2019DYDS09)。
文摘Based on Recursive Radial Basis Function(RRBF)neural network,the Reduced Order Model(ROM)of compressor cascade was established to meet the urgent demand of highly efficient prediction of unsteady aerodynamics performance of turbomachinery.One novel ROM called ASA-RRBF model based on Adaptive Simulated Annealing(ASA)algorithm was developed to enhance the generalization ability of the unsteady ROM.The ROM was verified by predicting the unsteady aerodynamics performance of a highly-loaded compressor cascade.The results show that the RRBF model has higher accuracy in identification of the dimensionless total pressure and dimensionless static pressure of compressor cascade under nonlinear and unsteady conditions,and the model behaves higher stability and computational efficiency.However,for the strong nonlinear characteristics of aerodynamic parameters,the RRBF model presents lower accuracy.Additionally,the RRBF model predicts with a large error in the identification of aerodynamic parameters under linear and unsteady conditions.For ASA-RRBF,by introducing a small-amplitude and highfrequency sinusoidal signal as validation sample,the width of the basis function of the RRBF model is optimized to improve the generalization ability of the ROM under linear unsteady conditions.Besides,this model improves the predicting accuracy of dimensionless static pressure which has strong nonlinear characteristics.The ASA-RRBF model has higher prediction accuracy than RRBF model without significantly increasing the total time consumption.This novel model can predict the linear hysteresis of dimensionless static pressure happened in the harmonic condition,but it cannot accurately predict the beat frequency of dimensionless total pressure.
基金National Natural Science Foundation of China (10902082)New Faculty Research Foundation of XJTUthe Fundamental Research Funds for the Central Universities (xjj20100126)
文摘Active stability augmentation system is an attractive and promising technology to suppress flutter and limit cycle oscillation (LCO). In order to design a good active control law, the control plant model with low order and high accuracy must be provided, which is one of the most important key points. The traditional model is based on low fidelity aerodynamics model such as panel method, which is unsuitable for transonic flight regime. The physics-based high fidelity tools, reduced order model (ROM) and CFD/CSD coupled aeroservoelastic solver are used to design the active control law. The Volterra/ROM is applied to constructing the low order state space model for the nonlinear unsteady aerodynamics and static output feedback method is used to active control law design. The detail of the new method is demonstrated by the Goland+ wing/store system. The simulation results show that the effectiveness of the designed active augmentation system, which can suppress the flutter and LCO successfully.
文摘The objective of this study is to identify system parameters from the recorded response of base isolated buildings,such as USC hospital building,during the 1994 Northridge earthquake.Full state measurements are not available for identification.Additionally,the response is nonlinear due to the yielding of the lead-rubber bearings.Two new approaches are presented in this paper to solve the aforementioned problems.First,a reduced order observer is used to estimate the unmeasured states.Second,a least squares technique with time segments is developed to identify the piece-wise linear system properties.The observer is used to estimate the initial conditions needed for the time segmented identification.A series of equivalent linear system parameters are identified in different time segments.It is shown that the change in system parameters,such as frequencies and damping ratios,due to nonlinear behavior of the lead-rubber bearings,are reliably estimated using the presented technique.It is shown that the response was reduced due to yielding of the lead-rubber bearings and period lengthening.
基金supported by the National Science and Technology Major Project, China (No. 2017-II-0009-0023)the Aeronautical Science Foundation of China(No. 2020Z039053004)the Fundamental Research Funds for the Central Universities, China (No. 3102019OQD701)
文摘A Reduced Order Model(ROM)based analysis method for turbomachinery cascade coupled mode flutter is presented in this paper.The unsteady aerodynamic model is established by a system identification technique combined with a set of Aerodynamic Influence Coefficients(AIC).Subsequently,the aerodynamic model is encoded into the state space and then coupled with the structural dynamic equations,resulting in a ROM of the cascade aeroelasticity.The cascade flutter can be determined by solving the eigenvalues of the ROM.Bending-torsional coupled mode flutter analysis for the Standard Configuration Eleven(SC11)cascade is used to validate the proposed method.
文摘This paper presents a design of continuous-time sliding mode control for the higher order systems via reduced order model. It is shown that a continuous-time sliding mode control designed for the reduced order model gives similar performance for thc higher order system. The method is illustrated by numerical examples. The paper also introduces a technique for design of a sliding surface such that the system satisfies a cost-optimality condition when on the sliding surface.
文摘This paper considers the problem of simulating the humidity distributions of a grain storage system. The distributions are described by partial differential equations(PDE). It is quite difficult to obtain the humidity profiles from the PDE model. Hence, a discretization method is applied to obtain an equivalent ordinary differential equation model. However, after applying the discretization technique, the cost of solving the system increases as the size increases to a few thousands. It may be noted that after discretization,the degree of freedom of the system remain the same while the order increases. The large dynamic model is reduced using a proper orthogonal decomposition based technique and an equivalent model but of much reduced size is obtained. A controller based on optimal control theory is designed to obtain an input such that the output humidity reaches a desired profile and also its stability is analyzed.Numerical results are presented to show the validity of the reduced model and possible further extensions are identified.
基金supported by the National Key R&D Program of China under Grant No.2021ZD0110400.
文摘Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational domains,etc.Typical reduced order modeling techniques accelerate the solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the ofline stage.These methods often need a predefined mesh as well as a series of precomputed solution snapshots,and may struggle to balance between the efficiency and accuracy due to the limitation of the linear ansatz.Utilizing the nonlinear representation of neural networks(NNs),we propose the Meta-Auto-Decoder(MAD)to construct a nonlinear trial manifold,whose best possible performance is measured theoretically by the decoder width.Based on the meta-learning concept,the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training stage.Fast adaptation to new(possibly heterogeneous)PDE parameters is enabled by searching on this trial manifold,and optionally fine-tuning the trial manifold at the same time.Extensive numerical experiments show that the MAD method exhibits a faster convergence speed without losing the accuracy than other deep learning-based methods.
基金The National Natural Science Foundation of China (No. 51908107)。
文摘To enhance the prediction accuracy of unsteady wakes behind wind turbines,a novel reduced-order model is proposed by integrating a multifunctional recurrent fuzzy neural network(MFRFNN)and proper orthogonal decom-position(POD).First,POD is employed to reduce the di-mensionality of the wind field data,extracting spatiotempo-rally correlated modal coefficients and modes.These reduced-order variables can effectively capture the essential features of unsteady wake behaviors.Next,MFRFNN is utilized to predict the time series of modal coefficients.Fi-nally,by combining the predicted modal coefficients with their corresponding modes,a flow field is reconstructed,al-lowing accurate prediction of unsteady wake dynamics.The predicted wake data exhibit high consistency with large eddy simulation results in both the near-and far-wake re-gions and outperform existing data-driven methods.This ap-proach offers significant potential for optimizing wind farm design and provides a new solution for the precise prediction of wind turbine wake behavior.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.62425602 and 62273159
文摘In the process of signal transmission,an inevitable phenomenon is that the signal may change abruptly at certain discrete moments,negatively contributing to the performance of the system.In this paper,we focus on the distributed state estimation problem for continuous-time linear time-invariant(LTI)systems with intermittent abrupt jumps in communication information.First,the phenomenon of intermittent sudden jumps during information transmission is characterized as impulsive communication disturbances.Under impulsive communication disturbances,a distributed reduced order observer(DROO)is designed with each local observer grouped with two modules:a reduced-order observer and a consensus-based observer.Then,sufficient conditions for the existence of the DROO are presented,by using the graph theory and impulsive analysis.The relationship between the feedback gain and the unstable system matrix,the network topology,the impulse intensity,and the average impulsive interval is obtained.Finally,the validity of the proposed DROO under impulsive communication disturbances is substantiated by numerical simulations.
基金Supported by National Natural Science Foundation of China(Grant No.11372036)
文摘Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.
基金This study was funded by the National Natural Science Foundation of China(Grant 11701123)Fundamental Research Funds for the Central Universities(Grant HIT.NSRIF.2020017).
文摘A novel statistical second-order reduced multiscale(SSRM)approach is established for nonlinear composite materials with random distribution of grains.For these composites considered in this work,the complex microstructure of grains,including their shape,orientation,size,spatial distribution,volume fraction and so on,results in changing of the macroscopic mechanical properties.The first-and second-order unit cell functions based on two-scale asymptotic expressions are constructed at first.Then,the expected homogenized parameters are defined,and the nonlinear homogenization equation on global structure is established,successively.Further,an effective reduced model format for analyzing second-order nonlinear unit cell problem with less computation cost is introduced in detail.Finally,some numerical examples for the materials with varying distribution models are evaluated and compared with the data by theoretical models and experimental results.These examples illustrate that the proposed SSRM approaches are effective for predicting the macroscopic properties of the random composite materials and supply a potential application in actual engineering computation.
基金supported by a Marie Curie Early Stage Research Training Fellowship of the European Community’s Sixth Framework Programme under contract number MEST CT 2005020301.
文摘Flow around a cavity is characterized by a self-sustained mechanism in which the shear layer impinges on the downstream edge of the cavity resulting in a feedback mechanism.Direct Numerical Simulations of the flow at low Reynolds number has been carried out to get pressure and velocity fluctuations,for the case of un-actuated and multi frequency actuation.A Reduced Order Model for the isentropic compressible equations based on the method of Proper Orthogonal Decomposition has been constructed.The model has been extended to include the effect of control.The Reduced Order dynamical system shows a divergence in time integration.A method of calibration based on the minimization of a linear functional of error,to the sensitivity of the modes,is proposed.The calibrated low order model is used to design a feedback control of cavity flows based on an observer design.For the experimental implementation of the controller,a state estimate based on the observed pressure measurements is obtained through a linear stochastic estimation.Finally the obtained control is introduced into the Direct Numerical Simulation to obtain a decrease in spectra of the cavity acoustic mode.
文摘Model Order Reduction is an approximation of the main system so that the simplified system retains important features of the main system.Deep learning technology is a recent breakthrough in artificial neural networks that can find more hidden information from the data.In this paper,a non-intrusive reduced order model(NIROM)based on combining deep neural networks(DNNs)and POD abilities,namely FAE-CAE-LSTM is presented.This method combines the obtained features based on Fully connected autoencoders(FAE),Convolutional autoencoders(CAE),and POD and then,a deep Long short-term memory network is trained by obtained features to predict the pressure and velocity fields at future time instances.We investigate the performance of the proposed methodology by solving two well-known canonical cases:a strong shear flow exhibiting the Kelvin–Helmholtz instability,and flow past a cylinder.The performance of the proposed FAE-CAE-LSTM method in future state prediction of the flow is compared with other NIROM methods such as CAELSTM,autoencoder-LSTM,autoencoder-DMD and POD-RNN based models.Results show that the FAE-CAE-LSTM method is considerably capable of predicting fluid flow evolution and obtains the best results in the prediction of the pressure and velocity fields in future time instances.
基金Aeronautical Science Foundation of China! ( 97E5 10 18) Shanghai Provincial Young Science Foundation of China !( 199910 18)
文摘Based on linear matrix inequalities (LMI), the design method of reduced order controllers of mixed sensitivity problem is studied for flight control systems. It is shown that there exists a controller with order not greater than the difference between the generalized plant order and the number of independent control variables, if the mixed sensitivity problem is solvable for strict regular flight control plants. The proof is constructive, and an approach to design such a controller can be obtained in terms of a pair of feasible solution to the well known 3 LMI. Finally, an example of mixed sensitivity problem for a flight control system is given to demonstrate practice of the approach.
文摘In this paper a singularly perturbed linear second order hyperbolic problem with zeroth order reduced equation is discussed. Firstly, an energy inequality of the solution and an estimate of the remainder term of the asymptotic solution are given. Then an exponentially fitted difference scheme is developed in an equidistant mesh. Finally, uniform convergence in small parameter is proved in the sense of discrete energy norm.
文摘The enumeration of elements of c.e. sets in the theory of computability and computational complexity has already been investigated. However, the order of this enumeration has received less attention. The enumeration orders of elements of c.e. sets by means of Turing machines on natural numbers are investigated. In this paper, we consider the enumeration orders of elements of c.e. sets on rational numbers. We present enumeration order reducibility and enumeration order equivalence on rational numbers and propose some lemmas and theorems on these concepts. Also, we show that the theories here hold for Rc and we could repeat the same theories in this domain, in a same way.
基金Foundation items： National Natural Science Foundation of China （NSFC 10572117, 10802063, 50875213） National High-tech Research and Development Program （2007AA04Z401）+2 种基金 Aeronautical Science Foundation of China （2007ZA53012） New Century Program For Excellent Talents of Ministry of Education of China （NCET-05-0868） Ph.D. Program Foundation of Northwestern Polytechnical University （CX200801）.
文摘The improved line sampling （LS） technique, an effective numerical simulation method, is employed to analyze the probabilistic characteristics and reliability sensitivity of flutter with random structural parameter in transonic flow. The improved LS technique is a novel methodology for reliability and sensitivity analysis of high dimensionality and low probability problem with implicit limit state function, and it does not require any approximating surrogate of the implicit limit state equation. The improved LS is used to estimate the flutter reliability and the sensitivity of a two-dimensional wing, in which some structural properties, such as frequency, parameters of gravity center and mass ratio, are considered as random variables. Computational fluid dynamics （CFD） based unsteady aerodynamic reduced order model （ROM） method is used to construct the aerodynamic state equations. Coupling structural state equations with aerodynamic state equations, the safety margin of flutter is founded by using the critical velocity of flutter. The results show that the improved LS technique can effectively decrease the computational cost in the random uncertainty analysis of flutter. The reliability sensitivity, defined by the partial derivative of the failure probability with respect to the distribution parameter of random variable, can help to identify the important parameters and guide the structural optimization design.
