Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing...Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review.展开更多
The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount ...The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.展开更多
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linea...A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.展开更多
Probabilistic method requires a lot of sample information to describe the probability distributions of uncertain variables and has difficulty in dealing with the optimization problem with uncertain parameters which co...Probabilistic method requires a lot of sample information to describe the probability distributions of uncertain variables and has difficulty in dealing with the optimization problem with uncertain parameters which contains unsufficient information.To solve this problem,a robust optimization operation method based on information gap decision theory(IGDT) is presented considering the non-probabilistic uncertainties of parameters.By the proposed method the maximum resistance to the disturbance of uncertain parameters is achieved and the optimization strategies with uncertain parameters are presented.Finally,numerical simulation is performed on the modified IEEE-14 bus system.Numerical results show the effectiveness of the proposed approach.展开更多
As the core of spatial planning in China,delineation of the production-living-ecological space(PLES)refers to dividing the overall land use into three functional spaces.Spatial units are optimally configured as the mo...As the core of spatial planning in China,delineation of the production-living-ecological space(PLES)refers to dividing the overall land use into three functional spaces.Spatial units are optimally configured as the most suitable functional type,while beset by various uncertainties.Weight uncertainties,being affected by subjective preferences,are highly arbitrary and seriously affect PLES.Taking Xuzhou as the study area,this paper studies the perturbation mechanism and response measure of weight uncertainties on PLES.First,weight samples are obtained through quasi-random sampling to serve as sources of uncertainties for input into the optimized delineation of PLES.Next,the Monte Carlo simulation is applied to simulate the spatial probability distribution of PLES.The global sensitivity analysis method is then adopted to identify the main sources that cause uncertainties in the delineation of PLES.Subsequently,the flexible space(FS)of PLES at a certain level of significance is formulated by comparing the distribution probabilities of spatial units for different functional spaces,acting as a countermeasure for the perturbation.The results show that weight uncertainties bring disturbances to the PLES by affecting the multi-criteria evaluation(MCE)of PLES delineation.The PLES is affected by the weight uncertainties of the factors alone or through interactions with other weights.FS is the spatial response measure of PLES when uncertainties occurred at a certain level of significance.The study introduces the perspective of uncertainty for PLES,which contributes toward improving the scientificity and reliability of PLES.展开更多
This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on...This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.展开更多
The research addresses the prevalence of gassy soil, containing methane (CH4), within the soil particles of southeast coastal areas of China, such as the Quaternary deposit in the Hangzhou Bay area. This soil exhibits...The research addresses the prevalence of gassy soil, containing methane (CH4), within the soil particles of southeast coastal areas of China, such as the Quaternary deposit in the Hangzhou Bay area. This soil exhibits spatial variability in the distribution of gas pressure, posing a potential threat of engineering disasters, including fire outbreaks and blasting, during the construction of underground projects. Consequently, it is crucial to assess the risk state of gas pressure, involving accurate identification and reduction of associated uncertainty, through site investigation. This is indispensable prior to the commencement of underground projects. However, during the site investigation stage, the random field parameters that quantify the spatial variability distribution of gas pressure (e.g., mean value, standard deviations, and scale of fluctuation) are unknown, introducing corresponding statistical uncertainty. Therefore, the most significant consideration for planning site investigation from an engineering perspective involves determining the risk state of gas pressure while considering the statistical uncertainty of these random field parameters. This consideration heavily relies on the engineering experience gained from current site investigation practices. To address this challenge, the study introduces a probabilistic site investigation optimization method designed for planning the site investigation scheme for gassy soils, including determining the number and locations of boreholes. The method is based on the expected state-identification probability, representing the probability of identifying the risk state of gas pressure, and takes into account the statistical uncertainty of random field parameters. The proposed method aims to determine an optimal investigation scheme before conducting the site investigation, leveraging prior knowledge. This optimal scheme is identified using Subset Simulation Optimization (SSO) in the space of candidate site investigations, maximizing the value of the expected state-identification probability at the minimal value point. Finally, the paper illustrates the proposed approach through a case study.展开更多
It is essential to consider the effects of incomplete measurement,inaccurate information and inadequate cognition on structural topology optimization.For the multi-material structural topology optimization with non-pr...It is essential to consider the effects of incomplete measurement,inaccurate information and inadequate cognition on structural topology optimization.For the multi-material structural topology optimization with non-probability uncertainty,the multi-material interpolation model is represented by the ordered rational approximation of mat erial properties(ordered RAMP).Combined with structural compliance minimization,the multi-material topology optimization with reliability constraints is established.The corresponding non-probability uncertainties are described by the evidence theory,and the uniformity processing method is introduced to convert the evidence variables into random variables.The first-order reliability method is employed to search the most probable point under the reliability index constraint,and then the random variables are equivalent to the deterministic variables according to the geometric meaning of the reliability index and sensitivity information.Therefore,the non-probabilistic reliability-based multi-material topology optimization is transformed into the conventional deterministic optimization format,followed by the ordered RAMP method to solve the optimization problem.Finally,through numerical examples of 2D and 3D structures,the feasibility and effectiveness of the proposed method are verified to consider the geometrical dimensions and external loading uncertainties.展开更多
A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing ...A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems.A hybrid carbon trading mechanism that combines shortterm and long-term carbon trading is constructed,and a fuzzy set based onWasserstein measurement is proposed to address the uncertainty of wind power access.Moreover,a robust scheduling optimization method for wind–fire storage systems is formed.Results of the multi scenario comparative analysis of practical cases show that the proposed method can deal with the uncertainty of large-scale wind power access and can effectively reduce operating costs and carbon emissions.展开更多
Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. ...Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization (PSO) algorithm is developed for nonlinear optimization problems with both contin- uous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.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
Abstract In this paper, we propose an uncertainty analysis and design optimization method and its applications on a hybrid rocket motor （HRM） powered vehicle. The multidisciplinary design model of the rocket system ...Abstract In this paper, we propose an uncertainty analysis and design optimization method and its applications on a hybrid rocket motor （HRM） powered vehicle. The multidisciplinary design model of the rocket system is established and the design uncertainties are quantified. The sensitivity anal- ysis of the uncertainties shows that the uncertainty generated from the error of fuel regression rate model has the most significant effect on the system performances. Then the differences between deterministic design optimization （DDO） and uncertainty-based design optimization （UDO） are discussed. Two newly formed uncertainty analysis methods, including the Kriging-based Monte Carlo simulation （KMCS） and Kriging-based Taylor series approximation （KTSA）, are carried out using a global approximation Kriging modeling method. Based on the system design model and the results of design uncertainty analysis, the design optimization of an HRM powered vehicle for suborbital flight is implemented using three design optimization methods： DDO, KMCS and KTSA. The comparisons indicate that the two UDO methods can enhance the design reliability and robustness. The researches and methods proposed in this paper can provide a better way for the general design of HRM powered vehicles.展开更多
Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters.This paper presents a hybrid uncertainty-based design o...Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters.This paper presents a hybrid uncertainty-based design optimization（UDO） method developed from probability theory and interval theory.Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory,while the others are defined as interval variables with interval theory.Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncertainty propagation from the design parameters to system responses.Three design optimization strategies,including deterministic design optimization（DDO）,probabilistic UDO and hybrid UDO,are applied to the conceptual design of a hybrid rocket motor（HRM） used as the ascent propulsion system in Apollo lunar module.By comparison,the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.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
To improve the vibration isolation performance of suspensions,various new structural forms of suspensions have been proposed.However,there is uncertainty in these new structure suspensions,so the deterministic researc...To improve the vibration isolation performance of suspensions,various new structural forms of suspensions have been proposed.However,there is uncertainty in these new structure suspensions,so the deterministic research cannot refect the performance of the suspension under actual operating conditions.In this paper,a quasi-zero stifness isolator is used in automotive suspensions to form a new suspension−quasi-zero stifness air suspension(QZSAS).Due to the strong nonlinearity and structural complexity of quasi-zero stifness suspensions,changes in structural parameters may cause dramatic changes in suspension performance,so it is of practical importance to study the efect of structural parameter uncertainty on the suspension performance.In order to solve this problem,three suspension structural parameters d_(0),L_(0) and Pc_(0) are selected as random variables,and the polynomial chaos expansion(PCE)theory is used to solve the suspension performance parameters.The sensitivity of the performance parameters to diferent structural parameters was discussed and analyzed in the frequency domain.Furthermore,a multi-objective optimization of the structural parameters d_(0),L_(0) and Pc_(0) of QZSAS was performed with the mean and variance of the root-mean-square(RMS)acceleration values as the optimization objectives.The optimization results show that there is an improvement of about 8%−1_(0)%in the mean value and about 4_(0)%−55%in the standard deviation of acceleration(RMS)values.This paper verifes the feasibility of the PCE method for solving the uncertainty problem of complex nonlinear systems,which provide a reference for the future structural design and optimization of such suspension systems.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 simpli...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
In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and en...In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.展开更多
Production optimization has gained increasing attention from the smart oilfield community because it can increase economic benefits and oil recovery substantially.While existing methods could produce high-optimality r...Production optimization has gained increasing attention from the smart oilfield community because it can increase economic benefits and oil recovery substantially.While existing methods could produce high-optimality results,they cannot be applied to real-time optimization for large-scale reservoirs due to high computational demands.In addition,most methods generally assume that the reservoir model is deterministic and ignore the uncertainty of the subsurface environment,making the obtained scheme unreliable for practical deployment.In this work,an efficient and robust method,namely evolutionaryassisted reinforcement learning(EARL),is proposed to achieve real-time production optimization under uncertainty.Specifically,the production optimization problem is modeled as a Markov decision process in which a reinforcement learning agent interacts with the reservoir simulator to train a control policy that maximizes the specified goals.To deal with the problems of brittle convergence properties and lack of efficient exploration strategies of reinforcement learning approaches,a population-based evolutionary algorithm is introduced to assist the training of agents,which provides diverse exploration experiences and promotes stability and robustness due to its inherent redundancy.Compared with prior methods that only optimize a solution for a particular scenario,the proposed approach trains a policy that can adapt to uncertain environments and make real-time decisions to cope with unknown changes.The trained policy,represented by a deep convolutional neural network,can adaptively adjust the well controls based on different reservoir states.Simulation results on two reservoir models show that the proposed approach not only outperforms the RL and EA methods in terms of optimization efficiency but also has strong robustness and real-time decision capacity.展开更多
It is an inherent uncertainty problem that the application of laminar flow technology to the wing of large passenger aircraft is affected by flight conditions.In order to seek a more robust natural laminar flow contro...It is an inherent uncertainty problem that the application of laminar flow technology to the wing of large passenger aircraft is affected by flight conditions.In order to seek a more robust natural laminar flow control effect,it is necessary to develop an effective optimization design method.Meanwhile,attention must be given to the impact of crossflow(CF)instability brought on by the sweep angle.This paper constructs a robust optimization design framework based on discrete adjoint methods and non-intrusive polynomial chaos.Transition prediction is implemented by coupled Reynolds-Averaged Navier-Stokes(RANS)and simplified e^(N)method,which can consider both Tollmien-Schlichting(TS)wave and crossflow vortex instability.We have performed gradient enhancement processing on the general Polynomial Chaos Expansion(PCE),which is advantageous to reduce the computational cost of single uncertainty propagation.This processing takes advantage of the gradient information obtained by solving the coupled adjoint equations considering transition.The statistical moment gradient solution used for the robust optimization design also uses the derivatives of coupled adjoint equations.The framework is applied to the robust design of a 25°swept wing with infinite span in transonic flow.The uncertainty quantification and sensitivity analysis on the baseline wing shows that the uncertainty quantification method in this paper has high accuracy,and qualitatively reveals the factors that dominate in different flow field regions.By the robust optimization design,the mean and standard deviation of the drag coefficient can be reduced by 29%and 45%,respectively,and compared with the deterministic optimization design results,there is less possibility of forming shock waves under flight condition uncertainties.Robust optimization results illustrate the trade-off between the transition delay and the wave drag reduction.展开更多
文摘Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review.
基金supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52375073)。
文摘The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
基金Project supported by the National Natural Science Foundation of China (Nos.12072007,12072006,12132001,and 52192632)the Ningbo Natural Science Foundation of Zhejiang Province of China (No.202003N4018)the Defense Industrial Technology Development Program of China (Nos.JCKY2019205A006,JCKY2019203A003,and JCKY2021204A002)。
文摘A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.
基金National Natural Science Foundation of China(No.61533010)Science and Technology Commission of Shanghai Municipality,China(No.14ZR1415300)
文摘Probabilistic method requires a lot of sample information to describe the probability distributions of uncertain variables and has difficulty in dealing with the optimization problem with uncertain parameters which contains unsufficient information.To solve this problem,a robust optimization operation method based on information gap decision theory(IGDT) is presented considering the non-probabilistic uncertainties of parameters.By the proposed method the maximum resistance to the disturbance of uncertain parameters is achieved and the optimization strategies with uncertain parameters are presented.Finally,numerical simulation is performed on the modified IEEE-14 bus system.Numerical results show the effectiveness of the proposed approach.
基金Under the auspices of National Natural Science Foundation of China(No.42171248,42371273)。
文摘As the core of spatial planning in China,delineation of the production-living-ecological space(PLES)refers to dividing the overall land use into three functional spaces.Spatial units are optimally configured as the most suitable functional type,while beset by various uncertainties.Weight uncertainties,being affected by subjective preferences,are highly arbitrary and seriously affect PLES.Taking Xuzhou as the study area,this paper studies the perturbation mechanism and response measure of weight uncertainties on PLES.First,weight samples are obtained through quasi-random sampling to serve as sources of uncertainties for input into the optimized delineation of PLES.Next,the Monte Carlo simulation is applied to simulate the spatial probability distribution of PLES.The global sensitivity analysis method is then adopted to identify the main sources that cause uncertainties in the delineation of PLES.Subsequently,the flexible space(FS)of PLES at a certain level of significance is formulated by comparing the distribution probabilities of spatial units for different functional spaces,acting as a countermeasure for the perturbation.The results show that weight uncertainties bring disturbances to the PLES by affecting the multi-criteria evaluation(MCE)of PLES delineation.The PLES is affected by the weight uncertainties of the factors alone or through interactions with other weights.FS is the spatial response measure of PLES when uncertainties occurred at a certain level of significance.The study introduces the perspective of uncertainty for PLES,which contributes toward improving the scientificity and reliability of PLES.
基金supported by the Israeli Ministry of Infrastructure,Energy and Water Resources.
文摘This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts,which are integral to an optimal energy storage control system.By expanding on an existing algorithm,this study resolves issues discovered during implementation and addresses previously overlooked concerns,resulting in significant enhancements in both performance and reliability.The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model,which is utilized for illustrative simulations to highlight its potential efficacy on a real site.Furthermore,a comprehensive comparison with the original formulation was conducted to cover all possible scenarios.This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system.Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management,significant financial losses and penalties,and potential contract violations.The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements.These advancements yield a reliable and efficient real-time correction algorithm for optimal site management,designed as an independent white box that can be integrated with any day-ahead optimization control system.
文摘The research addresses the prevalence of gassy soil, containing methane (CH4), within the soil particles of southeast coastal areas of China, such as the Quaternary deposit in the Hangzhou Bay area. This soil exhibits spatial variability in the distribution of gas pressure, posing a potential threat of engineering disasters, including fire outbreaks and blasting, during the construction of underground projects. Consequently, it is crucial to assess the risk state of gas pressure, involving accurate identification and reduction of associated uncertainty, through site investigation. This is indispensable prior to the commencement of underground projects. However, during the site investigation stage, the random field parameters that quantify the spatial variability distribution of gas pressure (e.g., mean value, standard deviations, and scale of fluctuation) are unknown, introducing corresponding statistical uncertainty. Therefore, the most significant consideration for planning site investigation from an engineering perspective involves determining the risk state of gas pressure while considering the statistical uncertainty of these random field parameters. This consideration heavily relies on the engineering experience gained from current site investigation practices. To address this challenge, the study introduces a probabilistic site investigation optimization method designed for planning the site investigation scheme for gassy soils, including determining the number and locations of boreholes. The method is based on the expected state-identification probability, representing the probability of identifying the risk state of gas pressure, and takes into account the statistical uncertainty of random field parameters. The proposed method aims to determine an optimal investigation scheme before conducting the site investigation, leveraging prior knowledge. This optimal scheme is identified using Subset Simulation Optimization (SSO) in the space of candidate site investigations, maximizing the value of the expected state-identification probability at the minimal value point. Finally, the paper illustrates the proposed approach through a case study.
基金supported by the Natural Science Foundation of China(Grant No.51705268)Shandong Provincial Natural Science Foundation,China(Grant No.ZR2016EEB20)China Postdoctoral Science Foundation Funded Project(Grant No.2017M612191)。
文摘It is essential to consider the effects of incomplete measurement,inaccurate information and inadequate cognition on structural topology optimization.For the multi-material structural topology optimization with non-probability uncertainty,the multi-material interpolation model is represented by the ordered rational approximation of mat erial properties(ordered RAMP).Combined with structural compliance minimization,the multi-material topology optimization with reliability constraints is established.The corresponding non-probability uncertainties are described by the evidence theory,and the uniformity processing method is introduced to convert the evidence variables into random variables.The first-order reliability method is employed to search the most probable point under the reliability index constraint,and then the random variables are equivalent to the deterministic variables according to the geometric meaning of the reliability index and sensitivity information.Therefore,the non-probabilistic reliability-based multi-material topology optimization is transformed into the conventional deterministic optimization format,followed by the ordered RAMP method to solve the optimization problem.Finally,through numerical examples of 2D and 3D structures,the feasibility and effectiveness of the proposed method are verified to consider the geometrical dimensions and external loading uncertainties.
基金supported by Liaoning Provincial Doctoral Research Initiation Fund Project(2022-BS-225)Liaoning Provincial Department of Education Scientific Research Project(LJKZ1091).
文摘A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems.A hybrid carbon trading mechanism that combines shortterm and long-term carbon trading is constructed,and a fuzzy set based onWasserstein measurement is proposed to address the uncertainty of wind power access.Moreover,a robust scheduling optimization method for wind–fire storage systems is formed.Results of the multi scenario comparative analysis of practical cases show that the proposed method can deal with the uncertainty of large-scale wind power access and can effectively reduce operating costs and carbon emissions.
基金Supported by the National 863 Project (No. 2003AA412010) and the National 973 Program of China (No. 2002CB312201)
文摘Blending is an important unit operation in process industry. Blending scheduling is nonlinear optimiza- tion problem with constraints. It is difficult to obtain optimum solution by other general optimization methods. Particle swarm optimization (PSO) algorithm is developed for nonlinear optimization problems with both contin- uous and discrete variables. In order to obtain a global optimum solution quickly, PSO algorithm is applied to solve the problem of blending scheduling under uncertainty. The calculation results based on an example of gasoline blending agree satisfactory with the ideal values, which illustrates that the PSO algorithm is valid and effective in solving the blending scheduling problem.
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 by the National Natural Science Foundation of China(No.51305014)China Postdoctoral Science Foundation(No.2013M540842)
文摘Abstract In this paper, we propose an uncertainty analysis and design optimization method and its applications on a hybrid rocket motor （HRM） powered vehicle. The multidisciplinary design model of the rocket system is established and the design uncertainties are quantified. The sensitivity anal- ysis of the uncertainties shows that the uncertainty generated from the error of fuel regression rate model has the most significant effect on the system performances. Then the differences between deterministic design optimization （DDO） and uncertainty-based design optimization （UDO） are discussed. Two newly formed uncertainty analysis methods, including the Kriging-based Monte Carlo simulation （KMCS） and Kriging-based Taylor series approximation （KTSA）, are carried out using a global approximation Kriging modeling method. Based on the system design model and the results of design uncertainty analysis, the design optimization of an HRM powered vehicle for suborbital flight is implemented using three design optimization methods： DDO, KMCS and KTSA. The comparisons indicate that the two UDO methods can enhance the design reliability and robustness. The researches and methods proposed in this paper can provide a better way for the general design of HRM powered vehicles.
基金supported by the National Natural Science Foundation of China(No.51305014)
文摘Design reliability and robustness are getting increasingly important for the general design of aerospace systems with many inherently uncertain design parameters.This paper presents a hybrid uncertainty-based design optimization（UDO） method developed from probability theory and interval theory.Most of the uncertain design parameters which have sufficient information or experimental data are classified as random variables using probability theory,while the others are defined as interval variables with interval theory.Then a hybrid uncertainty analysis method based on Monte Carlo simulation and Taylor series interval analysis is developed to obtain the uncertainty propagation from the design parameters to system responses.Three design optimization strategies,including deterministic design optimization（DDO）,probabilistic UDO and hybrid UDO,are applied to the conceptual design of a hybrid rocket motor（HRM） used as the ascent propulsion system in Apollo lunar module.By comparison,the hybrid UDO is a feasible method and can be effectively applied to the general design of aerospace systems.
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.51875256)Open Platform Fund of Hunan Institute of Technology of China(Grant No.KFA20009)Hong Kong,Macao and Taiwan Science and Technology Cooperation Project in Jiangsu Province of China(Grant No.BZ2020050)。
文摘To improve the vibration isolation performance of suspensions,various new structural forms of suspensions have been proposed.However,there is uncertainty in these new structure suspensions,so the deterministic research cannot refect the performance of the suspension under actual operating conditions.In this paper,a quasi-zero stifness isolator is used in automotive suspensions to form a new suspension−quasi-zero stifness air suspension(QZSAS).Due to the strong nonlinearity and structural complexity of quasi-zero stifness suspensions,changes in structural parameters may cause dramatic changes in suspension performance,so it is of practical importance to study the efect of structural parameter uncertainty on the suspension performance.In order to solve this problem,three suspension structural parameters d_(0),L_(0) and Pc_(0) are selected as random variables,and the polynomial chaos expansion(PCE)theory is used to solve the suspension performance parameters.The sensitivity of the performance parameters to diferent structural parameters was discussed and analyzed in the frequency domain.Furthermore,a multi-objective optimization of the structural parameters d_(0),L_(0) and Pc_(0) of QZSAS was performed with the mean and variance of the root-mean-square(RMS)acceleration values as the optimization objectives.The optimization results show that there is an improvement of about 8%−1_(0)%in the mean value and about 4_(0)%−55%in the standard deviation of acceleration(RMS)values.This paper verifes the feasibility of the PCE method for solving the uncertainty problem of complex nonlinear systems,which provide a reference for the future structural design and optimization of such suspension systems.
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Supported by Dalian University of Technology, the US National Science Foundation （No.CTS-0407494） and the Texas Advanced Technology program （No.003581-0044-2003）
文摘In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.
基金This work is supported by the National Natural Science Foundation of China under Grant 52274057,52074340 and 51874335the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008the Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002,111 Project under Grant B08028.
文摘Production optimization has gained increasing attention from the smart oilfield community because it can increase economic benefits and oil recovery substantially.While existing methods could produce high-optimality results,they cannot be applied to real-time optimization for large-scale reservoirs due to high computational demands.In addition,most methods generally assume that the reservoir model is deterministic and ignore the uncertainty of the subsurface environment,making the obtained scheme unreliable for practical deployment.In this work,an efficient and robust method,namely evolutionaryassisted reinforcement learning(EARL),is proposed to achieve real-time production optimization under uncertainty.Specifically,the production optimization problem is modeled as a Markov decision process in which a reinforcement learning agent interacts with the reservoir simulator to train a control policy that maximizes the specified goals.To deal with the problems of brittle convergence properties and lack of efficient exploration strategies of reinforcement learning approaches,a population-based evolutionary algorithm is introduced to assist the training of agents,which provides diverse exploration experiences and promotes stability and robustness due to its inherent redundancy.Compared with prior methods that only optimize a solution for a particular scenario,the proposed approach trains a policy that can adapt to uncertain environments and make real-time decisions to cope with unknown changes.The trained policy,represented by a deep convolutional neural network,can adaptively adjust the well controls based on different reservoir states.Simulation results on two reservoir models show that the proposed approach not only outperforms the RL and EA methods in terms of optimization efficiency but also has strong robustness and real-time decision capacity.
文摘It is an inherent uncertainty problem that the application of laminar flow technology to the wing of large passenger aircraft is affected by flight conditions.In order to seek a more robust natural laminar flow control effect,it is necessary to develop an effective optimization design method.Meanwhile,attention must be given to the impact of crossflow(CF)instability brought on by the sweep angle.This paper constructs a robust optimization design framework based on discrete adjoint methods and non-intrusive polynomial chaos.Transition prediction is implemented by coupled Reynolds-Averaged Navier-Stokes(RANS)and simplified e^(N)method,which can consider both Tollmien-Schlichting(TS)wave and crossflow vortex instability.We have performed gradient enhancement processing on the general Polynomial Chaos Expansion(PCE),which is advantageous to reduce the computational cost of single uncertainty propagation.This processing takes advantage of the gradient information obtained by solving the coupled adjoint equations considering transition.The statistical moment gradient solution used for the robust optimization design also uses the derivatives of coupled adjoint equations.The framework is applied to the robust design of a 25°swept wing with infinite span in transonic flow.The uncertainty quantification and sensitivity analysis on the baseline wing shows that the uncertainty quantification method in this paper has high accuracy,and qualitatively reveals the factors that dominate in different flow field regions.By the robust optimization design,the mean and standard deviation of the drag coefficient can be reduced by 29%and 45%,respectively,and compared with the deterministic optimization design results,there is less possibility of forming shock waves under flight condition uncertainties.Robust optimization results illustrate the trade-off between the transition delay and the wave drag reduction.
