This study presents a robustness optimization method for rapid prototyping(RP)of functional artifacts based on visualized computing digital twins(VCDT).A generalized multiobjective robustness optimization model for RP...This study presents a robustness optimization method for rapid prototyping(RP)of functional artifacts based on visualized computing digital twins(VCDT).A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built,where thermal,structural,and multidisciplinary knowledge could be integrated for visualization.To implement visualized computing,the membership function of fuzzy decision-making was optimized using a genetic algorithm.Transient thermodynamic,structural statics,and flow field analyses were conducted,especially for glass fiber composite materials,which have the characteristics of high strength,corrosion resistance,temperature resistance,dimensional stability,and electrical insulation.An electrothermal experiment was performed by measuring the temperature and changes in temperature during RP.Infrared thermographs were obtained using thermal field measurements to determine the temperature distribution.A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the VCDT.Moreover,manufacturability was verified based on a thermal-solid coupled finite element analysis.The physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties.展开更多
We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and geneti...We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and genetic algorithm(GA) is numerically simulated. Then, using a high speed digital micromirror device, we carry out light focusing experiments with the modified PSO algorithm and GA. The experimental results show that the modified PSO algorithm has greater robustness and faster convergence speed than GA. This modified PSO algorithm has great application prospects in optical focusing and imaging inside in vivo biological tissue, which possesses a complicated background.展开更多
For the purpose of improving the mechanical performance indices of uncertain structures with interval parameters and ensure their robustness when fluctuating under interval parameters, a constrained interval robust op...For the purpose of improving the mechanical performance indices of uncertain structures with interval parameters and ensure their robustness when fluctuating under interval parameters, a constrained interval robust optimization model is constructed with both the center and halfwidth of the most important mechanical performance index described as objective functions and the other requirements on the mechanical performance indices described as constraint functions. To locate the optimal solution of objective and feasibility robustness, a new concept of interval violation vector and its calculation formulae corresponding to different constraint functions are proposed. The math?ematical formulae for calculating the feasibility and objective robustness indices and the robustness?based preferential guidelines are proposed for directly ranking various design vectors, which is realized by an algorithm integrating Kriging and nested genetic algorithm. The validity of the proposed method and its superiority to present interval optimization approaches are demonstrated by a numerical example. The robust optimization of the upper beam in a high?speed press with interval material properties demonstrated the applicability and effectiveness of the proposed method in engineering.展开更多
Trajectory planning under uncertain dynamics is critical for safety-critical systems like Unmanned Aerial Vehicles(UAVs),where uncertainties in aerodynamic force and control surface failure can lead to mission failure...Trajectory planning under uncertain dynamics is critical for safety-critical systems like Unmanned Aerial Vehicles(UAVs),where uncertainties in aerodynamic force and control surface failure can lead to mission failure.This paper proposes a Multi-stage Robust Optimization(MRO)framework to address nonlinear trajectory planning with bounded but unknown parameters.By integrating first-order sensitivity analysis and sequential optimization,the proposed method ensures robustness against worst-case parameter deviations while maintaining high terminal accuracy.Unlike existing approaches,this paper explicitly quantifies uncertainty propagation through sensitivity bounds and divides long-term planning into sub-stages to reduce cumulative errors.Simulations on a UAV model with uncertainties in aerodynamic coefficients,wind fields and coefficients of control inputs demonstrate that MRO achieves high terminal state accuracy and strong robustness.展开更多
Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainabili...Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.展开更多
Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehi...Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes.However,due to demand fluctuations from changing product orders and unforeseen railway scheduling delays,manually adjusted allocation and loading may lead to excessive loading and unloading distances and times,ultimately increasing transportation costs for enterprises.To address these issues,this paper proposes a data-driven two-stage robust optimization(TSRO)framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism(GSTAC-AM),which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading.Specifically,GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers.Then,a robust counterpart model is formulated to ensure computational tractability.In addition,a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes.Finally,the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.展开更多
In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants ...In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants in energy trading.Firstly,the energy trading process is analyzed between each subject based on the establishment of the operation framework of multi-agent participation in energy trading.Secondly,the optimal operation model of each energy trading agent is established to develop a bi-level game model including each energy participant.Finally,a combination algorithm of improved robust optimization over time(ROOT)and CPLEX is proposed to solve the established game model.The experimental results indicate that under different fitness thresholds,the robust optimization results of the proposed algorithm are increased by 56.91%and 68.54%,respectively.The established bi-level game model effectively balances the benefits of different energy trading entities.The proposed algorithm proposed can increase the income of each participant in the game by an average of 8.59%.展开更多
Trapped ion hardware has made significant progress recently and is now one of the leading platforms for quantum computing.To construct two-qubit gates in trapped ions,experimentalmanipulation approaches for ion chains...Trapped ion hardware has made significant progress recently and is now one of the leading platforms for quantum computing.To construct two-qubit gates in trapped ions,experimentalmanipulation approaches for ion chains are becoming increasingly prevalent.Given the restricted control technology,how implementing high-fidelity quantum gate operations is crucial.Many works in current pulse design optimization focus on ion–phonon and effective ion–ion couplings while ignoring the first-order derivative terms expansion impacts of these two terms brought on by experiment defects.This paper proposes a novel robust quantum control optimization method in trapped ions.By introducing the first-order derivative terms caused by the error into the optimization cost function,we generate an extremely robust Mølmer–Sørensen gate with infidelity below 10^(−3) under a drift noise range of±10 kHz,the relative robustness achieves a tolerance of±5%,compared to the 200-kHz frequency spacing between phonon modes,and for time noise drift,the tolerance reached to 2%.Our work reveals the vital role of the first-order derivative terms of coupling in trapped ion pulse control optimization,especially the first-order derivative terms of ion–ion coupling.It provides a robust optimization scheme for realizing more efficient entangled states in trapped ion platforms.展开更多
A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncer...A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.展开更多
Blade fouling has been proved to be a great threat to compressor performance in operating stage.The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplif...Blade fouling has been proved to be a great threat to compressor performance in operating stage.The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness.Moreover,little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling.In this paper,a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling.A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades.To lower computational cost in robust design optimization,the support vector regression(SVR)metamodel is combined with the Monte Carlo simulation(MCS)method to conduct the uncertainty analysis of fouled impeller performance.The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip.The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties.After design optimization,the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition.This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling,providing a practical guidance to the design of advanced centrifugal compressors.展开更多
Combining the design of experiments(DOE)and three-dimensional finite element(3D-FE)method,a sequential multiobjectiveoptimization of larger diameter thin-walled(LDTW)Al-alloy tube bending under uncertainties was propo...Combining the design of experiments(DOE)and three-dimensional finite element(3D-FE)method,a sequential multiobjectiveoptimization of larger diameter thin-walled(LDTW)Al-alloy tube bending under uncertainties was proposed andimplemented based on the deterministic design results.Via the fractional factorial design,the significant noise factors are obtained,viz,variations of tube properties,fluctuations of tube geometries and friction.Using the virtual Taguchi’s DOE of inner and outerarrays,considering three major defects,the robust optimization of LDTW Al-alloy tube bending is achieved and validated.For thebending tools,the robust design of mandrel diameter was conducted under the fluctuations of tube properties,friction and tubegeometry.For the processing parameters,considering the variations of friction,material properties and manufacture deviation ofmandrel,the robust design of mandrel extension length and boosting ratio is realized.展开更多
Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestio...Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches.展开更多
An aeroelastic two-level optimization methodology for preliminary design of wing struc- tures is presented, in which the parameters for structural layout and sizes are taken as design vari- ables in the first-level op...An aeroelastic two-level optimization methodology for preliminary design of wing struc- tures is presented, in which the parameters for structural layout and sizes are taken as design vari- ables in the first-level optimization, and robust constraints in conjunction with conventional aeroelastic constraints are considered in the second-level optimization. A low-order panel method is used for aerodynamic analysis in the first-level optimization, and a high-order panel method is employed in the second-level optimization. It is concluded that the design of the abovementioned structural parameters of a wing can be improved using the present method with high efficiency. An improvement is seen in aeroelastic performance of the wing obtained with the present method when compared to the initial wing. Since these optimized structures are obtained after consideration of aerodynamic and structural uncertainties, they are well suited to encounter these uncertainties when they occur in reality.展开更多
Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter varia...Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data-driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data.展开更多
Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensiv...Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.展开更多
To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitme...To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.展开更多
The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-d...The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-dimensional curve or robust control design is used to find an accurate robust solution. However, there may exist complex interaction between parameters and practical engineering system. With the increase of the number of parameters, it is getting hard to determine high-dimensional curves and robust control methods, thus it＇s difficult to get the robust design solutions. In this paper, a method of global sensitivity analysis based on divided variables in groups is proposed. By making relevant variables in one group and keeping each other independent among sets of variables, global sensitivity analysis is conducted in grouped variables and the importance of parameters is evaluated by calculating the contribution value of each parameter to the total variance of system response. By ranking the importance of input parameters, relatively important parameters are chosen to conduct robust design analysis of the system. By applying this method to the robust optimization design of a real complex nonlinear system-a vehicle occupant restraint system with multi-parameter, good solution is gained and the response variance of the objective function is reduced to 0.01, which indicates that the robustness of the occupant restraint system is improved in a great degree and the method is effective and valuable for the robust design of complex nonlinear system. This research proposes a new method which can be used to obtain solutions for complex nonlinear system robust design.展开更多
The design optimization taking into account the impact of uncertainties favors improving the robustness of the design.A Surrogate-Assisted Gradient-Based(SAGB)method for the robust aerodynamic design optimization of t...The design optimization taking into account the impact of uncertainties favors improving the robustness of the design.A Surrogate-Assisted Gradient-Based(SAGB)method for the robust aerodynamic design optimization of turbomachinery blades considering large-scale uncertainty is introduced,verified and validated in the study.The gradient-based method is employed due to its high optimization efficiency and any one surrogate model with sufficient response accuracy can be employed to quantify the nonlinear performance changes.The gradients of objective performance function to the design parameters are calculated first for all the training samples,from which the gradients of cost function can be fast determined.To reveal the high efficiency and high accuracy of SAGB on gradient calculation,the number of flow computations needed is evaluated and compared with three other methods.Through the aerodynamic design optimization of a transonic turbine cascade minimizing total pressure loss at the outlet,the SAGB-based gradients of the base and optimized blades are compared with those obtained by the Monte Carlo-assisted finite difference method.Moreover,the results of both the robust and deterministic aerodynamic design optimizations are presented and compared to demonstrate the practicability of SAGB on improving the aerodynamic robustness of turbomachinery blades.展开更多
In engineering practice, influencing factors including material properties,external load,dimension error and so on,are uncertain to structural fatigue life,and those uncertain factors make the structure fatigue life h...In engineering practice, influencing factors including material properties,external load,dimension error and so on,are uncertain to structural fatigue life,and those uncertain factors make the structure fatigue life have a wide dispersion. Aiming at this problem,the parametric model is built in this paper,and it is used to obtain the structural first principal stress in the module of probabilistic design system( PDS). Parameters of P-S-N are added to the parametric model,and then,the fatigue life of chosen points has been calculated automatically under the reliability of 0. 99. The fatigue life response surface model is obtained by simulation of the sampling points using Monte Carlo method. The six sigma robust optimization mathematical model of fatigue life is established with the combination of six sigma robust optimization and fatigue life response surface model. Take a railway passenger car battery hanging device as an example, and the mathematical model of optimization is established. The minimum mean and mean squared of structural fatigue life are obtained under the requirements of design fatigue life. The results show that the material has been saved by the new method,and the robustness of the fatigue life has been improved.展开更多
According to the actual requirements,profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear,roll diameter has som...According to the actual requirements,profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear,roll diameter has some uncertainty during the rolling process,ignoring which will cause poor robustness of rolling schedule.In order to solve this problem,a robust multi-objective optimization model of rolling schedule for tandem cold rolling was established.A differential evolution algorithm based on the evolutionary direction was proposed.The algorithm calculated the horizontal angle of the vector,which was used to choose mutation vector.The chosen vector contained converging direction and it changed the random mutation operation in differential evolution algorithm.Efficiency of the proposed algorithm was verified by two benchmarks.Meanwhile,in order to ensure that delivery thicknesses have descending order like actual rolling schedule during evolution,a modified Latin Hypercube Sampling process was proposed.Finally,the proposed algorithm was applied to the model above.Results showed that profile was improved and rolling energy consumption was reduced compared with the actual rolling schedule.Meanwhile,robustness of solutions was ensured.展开更多
基金the National Natural Science Foundation of China,Nos.51935009 and 51821093National key research and development project of China,No.2022YFB3303303+2 种基金Zhejiang University president special fund financed by Zhejiang province,No.2021XZZX008Zhejiang provincial key research and development project of China,Nos.2023C01060,LZY22E060002 and LZ22E050008The Ng Teng Fong Charitable Foundation in the form of ZJU-SUTD IDEA Grant,No.188170-11102.
文摘This study presents a robustness optimization method for rapid prototyping(RP)of functional artifacts based on visualized computing digital twins(VCDT).A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built,where thermal,structural,and multidisciplinary knowledge could be integrated for visualization.To implement visualized computing,the membership function of fuzzy decision-making was optimized using a genetic algorithm.Transient thermodynamic,structural statics,and flow field analyses were conducted,especially for glass fiber composite materials,which have the characteristics of high strength,corrosion resistance,temperature resistance,dimensional stability,and electrical insulation.An electrothermal experiment was performed by measuring the temperature and changes in temperature during RP.Infrared thermographs were obtained using thermal field measurements to determine the temperature distribution.A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the VCDT.Moreover,manufacturability was verified based on a thermal-solid coupled finite element analysis.The physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties.
基金Supported by the National Key Research and Development Program of China under Grant No 2017YFB1104500the Natural Science Foundation of Beijing under Grant No 7182091,the National Natural Science Foundation of China under Grant No 21627813the Fundamental Research Funds for the Central Universities under Grant No PYBZ1801
文摘We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and genetic algorithm(GA) is numerically simulated. Then, using a high speed digital micromirror device, we carry out light focusing experiments with the modified PSO algorithm and GA. The experimental results show that the modified PSO algorithm has greater robustness and faster convergence speed than GA. This modified PSO algorithm has great application prospects in optical focusing and imaging inside in vivo biological tissue, which possesses a complicated background.
基金Supported by National Natural Science Foundation of China(Grant Nos.51775491,51475417,U1608256,51405433)
文摘For the purpose of improving the mechanical performance indices of uncertain structures with interval parameters and ensure their robustness when fluctuating under interval parameters, a constrained interval robust optimization model is constructed with both the center and halfwidth of the most important mechanical performance index described as objective functions and the other requirements on the mechanical performance indices described as constraint functions. To locate the optimal solution of objective and feasibility robustness, a new concept of interval violation vector and its calculation formulae corresponding to different constraint functions are proposed. The math?ematical formulae for calculating the feasibility and objective robustness indices and the robustness?based preferential guidelines are proposed for directly ranking various design vectors, which is realized by an algorithm integrating Kriging and nested genetic algorithm. The validity of the proposed method and its superiority to present interval optimization approaches are demonstrated by a numerical example. The robust optimization of the upper beam in a high?speed press with interval material properties demonstrated the applicability and effectiveness of the proposed method in engineering.
基金supported by the National Natural Science Foundation of China(No.92471204)Youth Innovation Promotion Association,CAS,China。
文摘Trajectory planning under uncertain dynamics is critical for safety-critical systems like Unmanned Aerial Vehicles(UAVs),where uncertainties in aerodynamic force and control surface failure can lead to mission failure.This paper proposes a Multi-stage Robust Optimization(MRO)framework to address nonlinear trajectory planning with bounded but unknown parameters.By integrating first-order sensitivity analysis and sequential optimization,the proposed method ensures robustness against worst-case parameter deviations while maintaining high terminal accuracy.Unlike existing approaches,this paper explicitly quantifies uncertainty propagation through sensitivity bounds and divides long-term planning into sub-stages to reduce cumulative errors.Simulations on a UAV model with uncertainties in aerodynamic coefficients,wind fields and coefficients of control inputs demonstrate that MRO achieves high terminal state accuracy and strong robustness.
基金supported by National Key Research and Development Program(2024YFE0115600).
文摘Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.
基金supported in part by the National Natural Science Foundation of China(NSFC)(92267205)the Natural Science Foundation of Hunan Province(2025JJ10007,2025JJ60423)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(ICT2024 B66).
文摘Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes.However,due to demand fluctuations from changing product orders and unforeseen railway scheduling delays,manually adjusted allocation and loading may lead to excessive loading and unloading distances and times,ultimately increasing transportation costs for enterprises.To address these issues,this paper proposes a data-driven two-stage robust optimization(TSRO)framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism(GSTAC-AM),which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading.Specifically,GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers.Then,a robust counterpart model is formulated to ensure computational tractability.In addition,a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes.Finally,the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.
基金supported by the National Nature Science Foundation of China(Nos.62063019)Natural Science Foundation of Gansu Province(22JR5RA241,2023CXZX-465).
文摘In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants in energy trading.Firstly,the energy trading process is analyzed between each subject based on the establishment of the operation framework of multi-agent participation in energy trading.Secondly,the optimal operation model of each energy trading agent is established to develop a bi-level game model including each energy participant.Finally,a combination algorithm of improved robust optimization over time(ROOT)and CPLEX is proposed to solve the established game model.The experimental results indicate that under different fitness thresholds,the robust optimization results of the proposed algorithm are increased by 56.91%and 68.54%,respectively.The established bi-level game model effectively balances the benefits of different energy trading entities.The proposed algorithm proposed can increase the income of each participant in the game by an average of 8.59%.
文摘Trapped ion hardware has made significant progress recently and is now one of the leading platforms for quantum computing.To construct two-qubit gates in trapped ions,experimentalmanipulation approaches for ion chains are becoming increasingly prevalent.Given the restricted control technology,how implementing high-fidelity quantum gate operations is crucial.Many works in current pulse design optimization focus on ion–phonon and effective ion–ion couplings while ignoring the first-order derivative terms expansion impacts of these two terms brought on by experiment defects.This paper proposes a novel robust quantum control optimization method in trapped ions.By introducing the first-order derivative terms caused by the error into the optimization cost function,we generate an extremely robust Mølmer–Sørensen gate with infidelity below 10^(−3) under a drift noise range of±10 kHz,the relative robustness achieves a tolerance of±5%,compared to the 200-kHz frequency spacing between phonon modes,and for time noise drift,the tolerance reached to 2%.Our work reveals the vital role of the first-order derivative terms of coupling in trapped ion pulse control optimization,especially the first-order derivative terms of ion–ion coupling.It provides a robust optimization scheme for realizing more efficient entangled states in trapped ion platforms.
基金Supported by the National Natural Science Foundation of China(No.U24B20156)the National Defense Basic Scientific Research Program of China(No.JCKY2021204B051)the National Laboratory of Space Intelligent Control of China(Nos.HTKJ2023KL502005 and HTKJ2024KL502007)。
文摘A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.
基金Supported by National Natural Science Foundation of China(Grant No.51406148)National Science Technology Support Program of China(Grant No.2012BAA08B06)Postdoctoral Scientific Foundation of China(Grant No.2014M552444)
文摘Blade fouling has been proved to be a great threat to compressor performance in operating stage.The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness.Moreover,little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling.In this paper,a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling.A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades.To lower computational cost in robust design optimization,the support vector regression(SVR)metamodel is combined with the Monte Carlo simulation(MCS)method to conduct the uncertainty analysis of fouled impeller performance.The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip.The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties.After design optimization,the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition.This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling,providing a practical guidance to the design of advanced centrifugal compressors.
基金Project(51275415) supported by the National Natural Science Foundation of ChinaProject(51522509) supported by the National Science Fund for Excellent Young Scholars,China
文摘Combining the design of experiments(DOE)and three-dimensional finite element(3D-FE)method,a sequential multiobjectiveoptimization of larger diameter thin-walled(LDTW)Al-alloy tube bending under uncertainties was proposed andimplemented based on the deterministic design results.Via the fractional factorial design,the significant noise factors are obtained,viz,variations of tube properties,fluctuations of tube geometries and friction.Using the virtual Taguchi’s DOE of inner and outerarrays,considering three major defects,the robust optimization of LDTW Al-alloy tube bending is achieved and validated.For thebending tools,the robust design of mandrel diameter was conducted under the fluctuations of tube properties,friction and tubegeometry.For the processing parameters,considering the variations of friction,material properties and manufacture deviation ofmandrel,the robust design of mandrel extension length and boosting ratio is realized.
基金supported the National Natural Science Foundation of China (71621001, 71825004, and 72001019)the Fundamental Research Funds for Central Universities (2020JBM031 and 2021YJS203)the Research Foundation of State Key Laboratory of Rail Traffic Control and Safety (RCS2020ZT001)
文摘Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches.
基金supported by the National Natural Science Foundation of China (No. 11172025 and No. 91116005)
文摘An aeroelastic two-level optimization methodology for preliminary design of wing struc- tures is presented, in which the parameters for structural layout and sizes are taken as design vari- ables in the first-level optimization, and robust constraints in conjunction with conventional aeroelastic constraints are considered in the second-level optimization. A low-order panel method is used for aerodynamic analysis in the first-level optimization, and a high-order panel method is employed in the second-level optimization. It is concluded that the design of the abovementioned structural parameters of a wing can be improved using the present method with high efficiency. An improvement is seen in aeroelastic performance of the wing obtained with the present method when compared to the initial wing. Since these optimized structures are obtained after consideration of aerodynamic and structural uncertainties, they are well suited to encounter these uncertainties when they occur in reality.
文摘Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data-driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data.
基金supported by National Natural Science Foundation of China (Grant No.60572007)National Basic Research Program of China(973 Program,Grant No.613580202)
文摘Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.
基金supported by the Special Research Project on Power Planning of the Guangdong Power Grid Co.,Ltd.
文摘To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.
基金Supported by National Natural Science Foundation of China(Grant No.51275164)
文摘The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-dimensional curve or robust control design is used to find an accurate robust solution. However, there may exist complex interaction between parameters and practical engineering system. With the increase of the number of parameters, it is getting hard to determine high-dimensional curves and robust control methods, thus it＇s difficult to get the robust design solutions. In this paper, a method of global sensitivity analysis based on divided variables in groups is proposed. By making relevant variables in one group and keeping each other independent among sets of variables, global sensitivity analysis is conducted in grouped variables and the importance of parameters is evaluated by calculating the contribution value of each parameter to the total variance of system response. By ranking the importance of input parameters, relatively important parameters are chosen to conduct robust design analysis of the system. By applying this method to the robust optimization design of a real complex nonlinear system-a vehicle occupant restraint system with multi-parameter, good solution is gained and the response variance of the objective function is reduced to 0.01, which indicates that the robustness of the occupant restraint system is improved in a great degree and the method is effective and valuable for the robust design of complex nonlinear system. This research proposes a new method which can be used to obtain solutions for complex nonlinear system robust design.
基金National Natural Science Foundation of China(Nos.51676003,51976183)National Science and Technology Major Project of China(No.J2019II-0012-0032)。
文摘The design optimization taking into account the impact of uncertainties favors improving the robustness of the design.A Surrogate-Assisted Gradient-Based(SAGB)method for the robust aerodynamic design optimization of turbomachinery blades considering large-scale uncertainty is introduced,verified and validated in the study.The gradient-based method is employed due to its high optimization efficiency and any one surrogate model with sufficient response accuracy can be employed to quantify the nonlinear performance changes.The gradients of objective performance function to the design parameters are calculated first for all the training samples,from which the gradients of cost function can be fast determined.To reveal the high efficiency and high accuracy of SAGB on gradient calculation,the number of flow computations needed is evaluated and compared with three other methods.Through the aerodynamic design optimization of a transonic turbine cascade minimizing total pressure loss at the outlet,the SAGB-based gradients of the base and optimized blades are compared with those obtained by the Monte Carlo-assisted finite difference method.Moreover,the results of both the robust and deterministic aerodynamic design optimizations are presented and compared to demonstrate the practicability of SAGB on improving the aerodynamic robustness of turbomachinery blades.
基金National Natural Science Foundation of Liaoning Province,China(No.2014028020)Liaoning Province Education Administration Project,China(No.L2013182)+1 种基金Railw ay Corporation Project,China(No.2014J004-N)Dalian Science and Technology Project,China(No.2015A11GX026)
文摘In engineering practice, influencing factors including material properties,external load,dimension error and so on,are uncertain to structural fatigue life,and those uncertain factors make the structure fatigue life have a wide dispersion. Aiming at this problem,the parametric model is built in this paper,and it is used to obtain the structural first principal stress in the module of probabilistic design system( PDS). Parameters of P-S-N are added to the parametric model,and then,the fatigue life of chosen points has been calculated automatically under the reliability of 0. 99. The fatigue life response surface model is obtained by simulation of the sampling points using Monte Carlo method. The six sigma robust optimization mathematical model of fatigue life is established with the combination of six sigma robust optimization and fatigue life response surface model. Take a railway passenger car battery hanging device as an example, and the mathematical model of optimization is established. The minimum mean and mean squared of structural fatigue life are obtained under the requirements of design fatigue life. The results show that the material has been saved by the new method,and the robustness of the fatigue life has been improved.
基金funded by the Science and Technology Research Project of Education Department of Liaoning(L2015387)Natural Science Foundation of Liaoning(201602542)the National Natural Science Foundation of China(51407119)
文摘According to the actual requirements,profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear,roll diameter has some uncertainty during the rolling process,ignoring which will cause poor robustness of rolling schedule.In order to solve this problem,a robust multi-objective optimization model of rolling schedule for tandem cold rolling was established.A differential evolution algorithm based on the evolutionary direction was proposed.The algorithm calculated the horizontal angle of the vector,which was used to choose mutation vector.The chosen vector contained converging direction and it changed the random mutation operation in differential evolution algorithm.Efficiency of the proposed algorithm was verified by two benchmarks.Meanwhile,in order to ensure that delivery thicknesses have descending order like actual rolling schedule during evolution,a modified Latin Hypercube Sampling process was proposed.Finally,the proposed algorithm was applied to the model above.Results showed that profile was improved and rolling energy consumption was reduced compared with the actual rolling schedule.Meanwhile,robustness of solutions was ensured.
