In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimizatio...In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.展开更多
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
In contemporary geotechnical projects,various approaches are employed for forecasting the settlement of shallow foundations(S_(m)).However,achieving precise modeling of foundation behavior using certain techniques(suc...In contemporary geotechnical projects,various approaches are employed for forecasting the settlement of shallow foundations(S_(m)).However,achieving precise modeling of foundation behavior using certain techniques(such as analytical,numerical,and regression)is challenging and sometimes unattainable.This is primarily due to the inherent nonlinearity of the model,the intricate nature of geotechnical materials,the complex interaction between soil and foundation,and the inherent uncertainty in soil parameters.Therefore,thesemethods often introduce assumptions and simplifications,resulting in relationships that deviate from the actual problem’s reality.In addition,many of these methods demand significant investments of time and resources but neglect to account for the uncertainty inherent in soil/rock parameters.This study explores the application of innovative intelligent techniques to predict S_(m) to address these shortcomings.Specifically,two optimization algorithms,namely teaching-learning-based optimization(TLBO)and harmony search(HS),are harnessed for this purpose.The modeling process involves utilizing input parameters,such as thewidth of the footing(B),the pressure exerted on the footing(q),the count of SPT(Standard Penetration Test)blows(N),the ratio of footing embedment(Df/B),and the footing’s geometry(L/B),during the training phase with a dataset comprising 151 data points.Then,the models’accuracy is assessed during the testing phase using statistical metrics,including the coefficient of determination(R^(2)),mean square error(MSE),and rootmean square error(RMSE),based on a dataset of 38 data points.The findings of this investigation underscore the substantial efficacy of intelligent optimization algorithms as valuable tools for geotechnical engineers when estimating S_(m).In addition,a sensitivity analysis of the input parameters in S_(m) estimation is conducted using@RISK software,revealing that among the various input parameters,the N exerts the most pronounced influence on S_(m).展开更多
Accurately predicting the powder factor during blasting is essential for sustainable production planning in low-grade mines.This research presents a method for predicting powder factor based on the heterogeneity of ro...Accurately predicting the powder factor during blasting is essential for sustainable production planning in low-grade mines.This research presents a method for predicting powder factor based on the heterogeneity of rock mass rating(RMR).Considering a low-grade metal mine as an example,this study exploited geostatistical methods to obtain independent RMR for each block unit.A three-dimensional spatial distribution model for the powder factor was developed on the basis of the relationships between the RMR and the powder factor.Subsequently,models for blasting cost and mining value were built and employed to optimize the open-pit limit.The multi-variable model based on the RMR performed well in predicting the powder factor,achieving a correlation coefficient of 0.88(root mean square error of 4.3)and considerably outperforming the uniaxial compressive strength model.After model optimization,the mean size and standard deviation of the fragments in the blast pile decreased by 8.5%and 35.1%,respectively,whereas the boulder yield and its standard deviation decreased by 33.3%and 58.8%,respectively.Additionally,optimizing the open-pit limit using this method reduced the amount of rock,increased the amount of ore,and lowered blasting costs,thereby enhancing the economic efficiency of the mine.This study provides valuable insights for blasting design and mining decisions,demonstrating the advantages and potential applications of powder factor prediction based on the heterogeneity of rock mass quality.展开更多
This paper presents an allowable-tolerance-based group search optimization(AT-GSO),which combines the robust GSO(R-GSO)and the external quality design planning of the Taguchi method.AT-GSO algorithm is used to optimiz...This paper presents an allowable-tolerance-based group search optimization(AT-GSO),which combines the robust GSO(R-GSO)and the external quality design planning of the Taguchi method.AT-GSO algorithm is used to optimize the heat transfer area of the heat exchanger system.The R-GSO algorithm integrates the GSO algorithm with the Taguchi method,utilizing the Taguchi method to determine the optimal producer in each iteration of the GSO algorithm to strengthen the robustness of the search process and the ability to find the global optima.In conventional parameter design optimization,it is typically assumed that the designed parameters can be applied accurately and consistently throughout usage.However,for systems that are sensitive to changes in design parameters,even minor inaccuracies can substantially reduce overall system performance.Therefore,the permissible variations of the design parameters are considered in the tolerance-optimized design to ensure the robustness of the performance.The optimized design of the heat exchanger system assumes that the system’s operating temperature parameters are specific.However,fixing the systemoperating temperature parameters at a constant value is difficult.This paper assumes that the system operating temperature parameters have an uncertainty error when optimizing the heat transfer area of the heat exchanger system.Experimental results show that the AT-GSO algorithm optimizes the heat exchanger system and finds the optimal operating temperature in the absence of tolerance and under three tolerance conditions.展开更多
A new approach for flexoelectricmaterial shape optimization is proposed in this study.In this work,a proxymodel based on artificial neural network(ANN)is used to solve the parameter optimization and shape optimization...A new approach for flexoelectricmaterial shape optimization is proposed in this study.In this work,a proxymodel based on artificial neural network(ANN)is used to solve the parameter optimization and shape optimization problems.To improve the fitting ability of the neural network,we use the idea of pre-training to determine the structure of the neural network and combine different optimizers for training.The isogeometric analysis-finite element method(IGA-FEM)is used to discretize the flexural theoretical formulas and obtain samples,which helps ANN to build a proxy model from the model shape to the target value.The effectiveness of the proposed method is verified through two numerical examples of parameter optimization and one numerical example of shape optimization.展开更多
Directional roof cutting(DRC)is one of the key techniques in non-pillar coal mining with self-formed entries(NCMSE)mining method.Due to the inability to accurately measure the expansion coefficient of the goaf rock ma...Directional roof cutting(DRC)is one of the key techniques in non-pillar coal mining with self-formed entries(NCMSE)mining method.Due to the inability to accurately measure the expansion coefficient of the goaf rock mass,the implementation of this technology often encounters design challenges,leading to suboptimal results and increased costs.This paper establishes a structural analysis model of the goaf working face roof,revealing the failure mechanism of DRC,and clarifies the positive role of DRC in improving the stress of the roadway surrounding rock and reducing the subsidence of the roof through numerical simulation experiments.On this basis,the paper further analyses the roadway pressure and roof settlement under different DRC design heights,and ultimately proposes an optimized design method for the DRC height.The results indicate that the implementation of DRC can significantly optimize the stress environment of the working face roadway surrounding rock.At the same time,during the application of DRC,three scenarios may arise:insufficient,reasonable,and excessive DRC height.Insufficient height will significantly reduce the effectiveness of the technology,while excessive height has little impact on the implementation effect but will greatly increase construction costs and difficulty.Engineering verification shows that the optimized DRC design method proposed in this paper reduces the peak stress of the protective coal pillar in the roadway by 27.2%and the central subsidence of the roof by 41.8%,demonstrating excellent application results.This method provides technical support for the further promotion of NCMSE mining method.展开更多
With the continuous advancement in topology optimization and additive manufacturing(AM)technology,the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying micr...With the continuous advancement in topology optimization and additive manufacturing(AM)technology,the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly.However,a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures,potentially resulting in diminished efficiency or macroscopic failure.A Hybrid Level Set Method(HLSM)is proposed,specifically designed to enhance connectivity among non-uniform microstructures,contributing to the design of functionally graded cellular structures.The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces.Initially,an interpolation algorithm is presented to construct transition microstructures seamlessly connected on both sides.Subsequently,the algorithm enables the morphing of non-uniform unit cells to seamlessly adapt to interconnected adjacent microstructures.The method,seamlessly integrated into a multi-scale topology optimization framework using the level set method,exhibits its efficacy through numerical examples,showcasing its prowess in optimizing 2D and 3D functionally graded materials(FGM)and multi-scale topology optimization.In essence,the pressing issue of interface connections in complex structure design is not only addressed but also a robust methodology is introduced,substantiated by numerical evidence,advancing optimization capabilities in the realm of functionally graded materials and cellular structures.展开更多
The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the effi...The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the efficiency of RBDO algorithm,which hinders their application to high-dimensional engineering problems.To address these issues,this paper proposes an efficient decoupled RBDO method combining high dimensional model representation(HDMR)and the weight-point estimation method(WPEM).First,we decouple the RBDO model using HDMR and WPEM.Second,Lagrange interpolation is used to approximate a univariate function.Finally,based on the results of the first two steps,the original nested loop reliability optimization model is completely transformed into a deterministic design optimization model that can be solved by a series of mature constrained optimization methods without any additional calculations.Two numerical examples of a planar 10-bar structure and an aviation hydraulic piping system with 28 design variables are analyzed to illustrate the performance and practicability of the proposed method.展开更多
To address the scheduling problem involving energy storage systems and uncertain energy,we propose a method based on multi-stage robust optimization.This approach aims to regulate the energy storage system by using a ...To address the scheduling problem involving energy storage systems and uncertain energy,we propose a method based on multi-stage robust optimization.This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method,which helps overcome the limitations of traditional methods in terms of time scale.The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day.To achieve this,a mathematical model is constructed to represent uncertain energy sources such as photovoltaic and wind power.The generalized Benders Decomposition method is then employed to solve the multi-stage objective optimization problem.By decomposing the problem into a series of sub-objectives,the system scale is effectively reduced,and the algorithm’s convergence ability is improved.Compared with other algorithms,the multi-stage robust optimization model has better economy and convergence ability and can be used to guide the power dispatching of uncertain energy and energy storage systems.展开更多
To explore the optimal evaluation mechanism of open-cast mining procedure,this paper takes the actual operation status of Huolinhe No.1 Open-cast Mine as the research basis,and makes a deep analysis of the four repres...To explore the optimal evaluation mechanism of open-cast mining procedure,this paper takes the actual operation status of Huolinhe No.1 Open-cast Mine as the research basis,and makes a deep analysis of the four representative mining procedures proposed by this mine.A detailed and comprehensive evaluation system is constructed using rank-sum ratio(RSR)method.The system covers 17 key indicators and aims to evaluate the advantages and disadvantages of each scheme in an all-round and multi-angle manner.Through the calculation and analysis by RSR method,the comprehensive evaluation of the four types of mining procedure schemes is carried out,and finally the secondary river improvement project is determined as the optimal mining implementation scheme,and the joint mining scheme of the south and north areas is the alternative strategy.The research results of this paper are objective,clear and definite,can not only reveal the effectiveness and feasibility of RSR method in solving the problem of open-cast mining procedure optimization,but also provide a strong technical support and decision-making basis for the future production development of Huolinhe No.1 Open-cast Mine.Thus,this study is expected to further promote the scientific and refined process of mining operations.展开更多
The exploration of urban underground spaces is of great significance to urban planning,geological disaster prevention,resource exploration and environmental monitoring.However,due to the existing of severe interferenc...The exploration of urban underground spaces is of great significance to urban planning,geological disaster prevention,resource exploration and environmental monitoring.However,due to the existing of severe interferences,conventional seismic methods cannot adapt to the complex urban environment well.Since adopting the single-node data acquisition method and taking the seismic ambient noise as the signal,the microtremor horizontal-to-vertical spectral ratio(HVSR)method can effectively avoid the strong interference problems caused by the complex urban environment,which could obtain information such as S-wave velocity and thickness of underground formations by fitting the microtremor HVSR curve.Nevertheless,HVSR curve inversion is a multi-parameter curve fitting process.And conventional inversion methods can easily converge to the local minimum,which will directly affect the reliability of the inversion results.Thus,the authors propose a HVSR inversion method based on the multimodal forest optimization algorithm,which uses the efficient clustering technique and locates the global optimum quickly.Tests on synthetic data show that the inversion results of the proposed method are consistent with the forward model.Both the adaption and stability to the abnormal layer velocity model are demonstrated.The results of the real field data are also verified by the drilling information.展开更多
In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order derivative test for optimality o...In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order derivative test for optimality of the Lagrangian function with respect to the primary variables of the problem, decomposes the solution process into two independent ones, in which the primary variables are solved for independently, and then the secondary variables, which are the Lagrange multipliers, are solved for, afterward. This is an innovation that leads to solving independently two simpler systems of equations involving the primary variables only, on one hand, and the secondary ones on the other. Solutions obtained for small sized problems (as preliminary test of the method) demonstrate that the new method is generally effective in producing the required solutions.展开更多
This study aims to optimize the inbound traffic flow on on-ramps by considering low time costs,good speed stability,and high driving safety for mixed traffic flow.The optimal inlet gap is identified in advance,and tra...This study aims to optimize the inbound traffic flow on on-ramps by considering low time costs,good speed stability,and high driving safety for mixed traffic flow.The optimal inlet gap is identified in advance,and trajectory guidance for vehicles entering the gap is determined under safety constraints.Based on the initial state and sequence of vehicles entering the merging area,individual vehicle trajectories are optimized sequentially.An optimization model and method for ramp entry trajectories in mixed traffic flow are developed,incorporating on-ramp vehicle entry sequencing and ordinary vehicle trajectory prediction.Key performance indicators,including driving safety,total travel time,parking wait probability,and trajectory smoothness,are compared and analyzed to evaluate the proposed approach.展开更多
Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization ...Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization （MDO） techniques for the design of complex engineering system. An advanced first order second moment method-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and the reliability analysis methods. It is seen through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.展开更多
An effective optimization method for the shape/sizing design of composite wing structures is presented with satisfying weight-cutting results. After decoupling, a kind of two-layer cycled optimization strategy suitabl...An effective optimization method for the shape/sizing design of composite wing structures is presented with satisfying weight-cutting results. After decoupling, a kind of two-layer cycled optimization strategy suitable for these integrated shape/sizing optimization is obtained. The uniform design method is used to provide sample points, and approximation models for shape design variables. And the results of sizing optimization are construct- ed with the quadratic response surface method （QRSM）. The complex method based on QRSM is used to opti- mize the shape design variables and the criteria method is adopted to optimize the sizing design variables. Compared with the conventional method, the proposed algorithm is more effective and feasible for solving complex composite optimization problems and has good efficiency in weight cutting.展开更多
Local and global optimization methods are widely used in geophysical inversion but each has its own advantages and disadvantages. The combination of the two methods will make it possible to overcome their weaknesses. ...Local and global optimization methods are widely used in geophysical inversion but each has its own advantages and disadvantages. The combination of the two methods will make it possible to overcome their weaknesses. Based on the simulated annealing genetic algorithm （SAGA） and the simplex algorithm, an efficient and robust 2-D nonlinear method for seismic travel-time inversion is presented in this paper. First we do a global search over a large range by SAGA and then do a rapid local search using the simplex method. A multi-scale tomography method is adopted in order to reduce non-uniqueness. The velocity field is divided into different spatial scales and velocities at the grid nodes are taken as unknown parameters. The model is parameterized by a bi-cubic spline function. The finite-difference method is used to solve the forward problem while the hybrid method combining multi-scale SAGA and simplex algorithms is applied to the inverse problem. The algorithm has been applied to a numerical test and a travel-time perturbation test using an anomalous low-velocity body. For a practical example, it is used in the study of upper crustal velocity structure of the A＇nyemaqen suture zone at the north-east edge of the Qinghai-Tibet Plateau. The model test and practical application both prove that the method is effective and robust.展开更多
The principle of direct method used in optimal control problem is introduced. Details of applying this method to flight trajectory generation are presented including calculation of velocity and controls histories. And...The principle of direct method used in optimal control problem is introduced. Details of applying this method to flight trajectory generation are presented including calculation of velocity and controls histories. And capabilities of flight and propulsion systems are considered also. Combined with digital terrain map technique, the direct method is applied to the three dimensional trajectory optimization for low altitude penetration, and simplex algorithm is used to solve the parameters in optimization. For the small number of parameters, the trajectory can be optimized in real time on board.展开更多
A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. Th...A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. The Kriging approximate model with updating is introduced to reduce the computational cost of MDF caused by the complex structure. The computational efficiency is remarkably improved as the lack of iterative process during reliability analysis. Special attention is paid to a turbine blade design optimization by adopting the proposed method. Results show that the method is much more efficient than the commonly used double-loop based RBMDO method. It is feasible and efficient to apply the method to the engineering design.展开更多
Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary a...Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.展开更多
基金sponsored by R&D Program of Beijing Municipal Education Commission(KM202410009013).
文摘In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.
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.
文摘In contemporary geotechnical projects,various approaches are employed for forecasting the settlement of shallow foundations(S_(m)).However,achieving precise modeling of foundation behavior using certain techniques(such as analytical,numerical,and regression)is challenging and sometimes unattainable.This is primarily due to the inherent nonlinearity of the model,the intricate nature of geotechnical materials,the complex interaction between soil and foundation,and the inherent uncertainty in soil parameters.Therefore,thesemethods often introduce assumptions and simplifications,resulting in relationships that deviate from the actual problem’s reality.In addition,many of these methods demand significant investments of time and resources but neglect to account for the uncertainty inherent in soil/rock parameters.This study explores the application of innovative intelligent techniques to predict S_(m) to address these shortcomings.Specifically,two optimization algorithms,namely teaching-learning-based optimization(TLBO)and harmony search(HS),are harnessed for this purpose.The modeling process involves utilizing input parameters,such as thewidth of the footing(B),the pressure exerted on the footing(q),the count of SPT(Standard Penetration Test)blows(N),the ratio of footing embedment(Df/B),and the footing’s geometry(L/B),during the training phase with a dataset comprising 151 data points.Then,the models’accuracy is assessed during the testing phase using statistical metrics,including the coefficient of determination(R^(2)),mean square error(MSE),and rootmean square error(RMSE),based on a dataset of 38 data points.The findings of this investigation underscore the substantial efficacy of intelligent optimization algorithms as valuable tools for geotechnical engineers when estimating S_(m).In addition,a sensitivity analysis of the input parameters in S_(m) estimation is conducted using@RISK software,revealing that among the various input parameters,the N exerts the most pronounced influence on S_(m).
基金supported by the National Key Research and Development Program of China(No.2022YFC2903902)the National Natural Science Foundation of China(Nos.52204080and 52174070)the Fundamental Research Funds for the Central Universities of China(No.2023GFYD17)。
文摘Accurately predicting the powder factor during blasting is essential for sustainable production planning in low-grade mines.This research presents a method for predicting powder factor based on the heterogeneity of rock mass rating(RMR).Considering a low-grade metal mine as an example,this study exploited geostatistical methods to obtain independent RMR for each block unit.A three-dimensional spatial distribution model for the powder factor was developed on the basis of the relationships between the RMR and the powder factor.Subsequently,models for blasting cost and mining value were built and employed to optimize the open-pit limit.The multi-variable model based on the RMR performed well in predicting the powder factor,achieving a correlation coefficient of 0.88(root mean square error of 4.3)and considerably outperforming the uniaxial compressive strength model.After model optimization,the mean size and standard deviation of the fragments in the blast pile decreased by 8.5%and 35.1%,respectively,whereas the boulder yield and its standard deviation decreased by 33.3%and 58.8%,respectively.Additionally,optimizing the open-pit limit using this method reduced the amount of rock,increased the amount of ore,and lowered blasting costs,thereby enhancing the economic efficiency of the mine.This study provides valuable insights for blasting design and mining decisions,demonstrating the advantages and potential applications of powder factor prediction based on the heterogeneity of rock mass quality.
基金funded by the National Science and Technology Council,Taiwan,under Grant Number MOST110-2221-E035-092-MY3.
文摘This paper presents an allowable-tolerance-based group search optimization(AT-GSO),which combines the robust GSO(R-GSO)and the external quality design planning of the Taguchi method.AT-GSO algorithm is used to optimize the heat transfer area of the heat exchanger system.The R-GSO algorithm integrates the GSO algorithm with the Taguchi method,utilizing the Taguchi method to determine the optimal producer in each iteration of the GSO algorithm to strengthen the robustness of the search process and the ability to find the global optima.In conventional parameter design optimization,it is typically assumed that the designed parameters can be applied accurately and consistently throughout usage.However,for systems that are sensitive to changes in design parameters,even minor inaccuracies can substantially reduce overall system performance.Therefore,the permissible variations of the design parameters are considered in the tolerance-optimized design to ensure the robustness of the performance.The optimized design of the heat exchanger system assumes that the system’s operating temperature parameters are specific.However,fixing the systemoperating temperature parameters at a constant value is difficult.This paper assumes that the system operating temperature parameters have an uncertainty error when optimizing the heat transfer area of the heat exchanger system.Experimental results show that the AT-GSO algorithm optimizes the heat exchanger system and finds the optimal operating temperature in the absence of tolerance and under three tolerance conditions.
基金supported by a Major Research Project in Higher Education Institutions in Henan Province,with Project Number 23A560015.
文摘A new approach for flexoelectricmaterial shape optimization is proposed in this study.In this work,a proxymodel based on artificial neural network(ANN)is used to solve the parameter optimization and shape optimization problems.To improve the fitting ability of the neural network,we use the idea of pre-training to determine the structure of the neural network and combine different optimizers for training.The isogeometric analysis-finite element method(IGA-FEM)is used to discretize the flexural theoretical formulas and obtain samples,which helps ANN to build a proxy model from the model shape to the target value.The effectiveness of the proposed method is verified through two numerical examples of parameter optimization and one numerical example of shape optimization.
基金funded by the National Natural Science Foundation of China(52074298)Beijing Municipal Natural Science Foundation(8232056)+1 种基金Guizhou Province science and technology plan project([2020]3008)Liulin Energy and Environment Academician Workstation(2022XDHZ12).
文摘Directional roof cutting(DRC)is one of the key techniques in non-pillar coal mining with self-formed entries(NCMSE)mining method.Due to the inability to accurately measure the expansion coefficient of the goaf rock mass,the implementation of this technology often encounters design challenges,leading to suboptimal results and increased costs.This paper establishes a structural analysis model of the goaf working face roof,revealing the failure mechanism of DRC,and clarifies the positive role of DRC in improving the stress of the roadway surrounding rock and reducing the subsidence of the roof through numerical simulation experiments.On this basis,the paper further analyses the roadway pressure and roof settlement under different DRC design heights,and ultimately proposes an optimized design method for the DRC height.The results indicate that the implementation of DRC can significantly optimize the stress environment of the working face roadway surrounding rock.At the same time,during the application of DRC,three scenarios may arise:insufficient,reasonable,and excessive DRC height.Insufficient height will significantly reduce the effectiveness of the technology,while excessive height has little impact on the implementation effect but will greatly increase construction costs and difficulty.Engineering verification shows that the optimized DRC design method proposed in this paper reduces the peak stress of the protective coal pillar in the roadway by 27.2%and the central subsidence of the roof by 41.8%,demonstrating excellent application results.This method provides technical support for the further promotion of NCMSE mining method.
基金the National Key Research and Development Program of China(Grant Number 2021YFB1714600)the National Natural Science Foundation of China(Grant Number 52075195)the Fundamental Research Funds for the Central Universities,China through Program No.2172019kfyXJJS078.
文摘With the continuous advancement in topology optimization and additive manufacturing(AM)technology,the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly.However,a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures,potentially resulting in diminished efficiency or macroscopic failure.A Hybrid Level Set Method(HLSM)is proposed,specifically designed to enhance connectivity among non-uniform microstructures,contributing to the design of functionally graded cellular structures.The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces.Initially,an interpolation algorithm is presented to construct transition microstructures seamlessly connected on both sides.Subsequently,the algorithm enables the morphing of non-uniform unit cells to seamlessly adapt to interconnected adjacent microstructures.The method,seamlessly integrated into a multi-scale topology optimization framework using the level set method,exhibits its efficacy through numerical examples,showcasing its prowess in optimizing 2D and 3D functionally graded materials(FGM)and multi-scale topology optimization.In essence,the pressing issue of interface connections in complex structure design is not only addressed but also a robust methodology is introduced,substantiated by numerical evidence,advancing optimization capabilities in the realm of functionally graded materials and cellular structures.
基金supported by the Innovation Fund Project of the Gansu Education Department(Grant No.2021B-099).
文摘The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the efficiency of RBDO algorithm,which hinders their application to high-dimensional engineering problems.To address these issues,this paper proposes an efficient decoupled RBDO method combining high dimensional model representation(HDMR)and the weight-point estimation method(WPEM).First,we decouple the RBDO model using HDMR and WPEM.Second,Lagrange interpolation is used to approximate a univariate function.Finally,based on the results of the first two steps,the original nested loop reliability optimization model is completely transformed into a deterministic design optimization model that can be solved by a series of mature constrained optimization methods without any additional calculations.Two numerical examples of a planar 10-bar structure and an aviation hydraulic piping system with 28 design variables are analyzed to illustrate the performance and practicability of the proposed method.
文摘To address the scheduling problem involving energy storage systems and uncertain energy,we propose a method based on multi-stage robust optimization.This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method,which helps overcome the limitations of traditional methods in terms of time scale.The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day.To achieve this,a mathematical model is constructed to represent uncertain energy sources such as photovoltaic and wind power.The generalized Benders Decomposition method is then employed to solve the multi-stage objective optimization problem.By decomposing the problem into a series of sub-objectives,the system scale is effectively reduced,and the algorithm’s convergence ability is improved.Compared with other algorithms,the multi-stage robust optimization model has better economy and convergence ability and can be used to guide the power dispatching of uncertain energy and energy storage systems.
文摘To explore the optimal evaluation mechanism of open-cast mining procedure,this paper takes the actual operation status of Huolinhe No.1 Open-cast Mine as the research basis,and makes a deep analysis of the four representative mining procedures proposed by this mine.A detailed and comprehensive evaluation system is constructed using rank-sum ratio(RSR)method.The system covers 17 key indicators and aims to evaluate the advantages and disadvantages of each scheme in an all-round and multi-angle manner.Through the calculation and analysis by RSR method,the comprehensive evaluation of the four types of mining procedure schemes is carried out,and finally the secondary river improvement project is determined as the optimal mining implementation scheme,and the joint mining scheme of the south and north areas is the alternative strategy.The research results of this paper are objective,clear and definite,can not only reveal the effectiveness and feasibility of RSR method in solving the problem of open-cast mining procedure optimization,but also provide a strong technical support and decision-making basis for the future production development of Huolinhe No.1 Open-cast Mine.Thus,this study is expected to further promote the scientific and refined process of mining operations.
基金Supported by projects of National Natural Science Foundation of China(No.42074150)National Key Research and Development Program of China(No.2023YFC3707901)Futian District Integrated Ground Collapse Monitoring and Early Warning System Construction Project(No.FTCG2023000209).
文摘The exploration of urban underground spaces is of great significance to urban planning,geological disaster prevention,resource exploration and environmental monitoring.However,due to the existing of severe interferences,conventional seismic methods cannot adapt to the complex urban environment well.Since adopting the single-node data acquisition method and taking the seismic ambient noise as the signal,the microtremor horizontal-to-vertical spectral ratio(HVSR)method can effectively avoid the strong interference problems caused by the complex urban environment,which could obtain information such as S-wave velocity and thickness of underground formations by fitting the microtremor HVSR curve.Nevertheless,HVSR curve inversion is a multi-parameter curve fitting process.And conventional inversion methods can easily converge to the local minimum,which will directly affect the reliability of the inversion results.Thus,the authors propose a HVSR inversion method based on the multimodal forest optimization algorithm,which uses the efficient clustering technique and locates the global optimum quickly.Tests on synthetic data show that the inversion results of the proposed method are consistent with the forward model.Both the adaption and stability to the abnormal layer velocity model are demonstrated.The results of the real field data are also verified by the drilling information.
文摘In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order derivative test for optimality of the Lagrangian function with respect to the primary variables of the problem, decomposes the solution process into two independent ones, in which the primary variables are solved for independently, and then the secondary variables, which are the Lagrange multipliers, are solved for, afterward. This is an innovation that leads to solving independently two simpler systems of equations involving the primary variables only, on one hand, and the secondary ones on the other. Solutions obtained for small sized problems (as preliminary test of the method) demonstrate that the new method is generally effective in producing the required solutions.
文摘This study aims to optimize the inbound traffic flow on on-ramps by considering low time costs,good speed stability,and high driving safety for mixed traffic flow.The optimal inlet gap is identified in advance,and trajectory guidance for vehicles entering the gap is determined under safety constraints.Based on the initial state and sequence of vehicles entering the merging area,individual vehicle trajectories are optimized sequentially.An optimization model and method for ramp entry trajectories in mixed traffic flow are developed,incorporating on-ramp vehicle entry sequencing and ordinary vehicle trajectory prediction.Key performance indicators,including driving safety,total travel time,parking wait probability,and trajectory smoothness,are compared and analyzed to evaluate the proposed approach.
基金National Natural Science Foundation of China (10377015)
文摘Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization （MDO） techniques for the design of complex engineering system. An advanced first order second moment method-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and the reliability analysis methods. It is seen through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.
文摘An effective optimization method for the shape/sizing design of composite wing structures is presented with satisfying weight-cutting results. After decoupling, a kind of two-layer cycled optimization strategy suitable for these integrated shape/sizing optimization is obtained. The uniform design method is used to provide sample points, and approximation models for shape design variables. And the results of sizing optimization are construct- ed with the quadratic response surface method （QRSM）. The complex method based on QRSM is used to opti- mize the shape design variables and the criteria method is adopted to optimize the sizing design variables. Compared with the conventional method, the proposed algorithm is more effective and feasible for solving complex composite optimization problems and has good efficiency in weight cutting.
基金supported by the National Natural Science Foundation of China (Grant Nos.40334040 and 40974033)the Promoting Foundation for Advanced Persons of Talent of NCWU
文摘Local and global optimization methods are widely used in geophysical inversion but each has its own advantages and disadvantages. The combination of the two methods will make it possible to overcome their weaknesses. Based on the simulated annealing genetic algorithm （SAGA） and the simplex algorithm, an efficient and robust 2-D nonlinear method for seismic travel-time inversion is presented in this paper. First we do a global search over a large range by SAGA and then do a rapid local search using the simplex method. A multi-scale tomography method is adopted in order to reduce non-uniqueness. The velocity field is divided into different spatial scales and velocities at the grid nodes are taken as unknown parameters. The model is parameterized by a bi-cubic spline function. The finite-difference method is used to solve the forward problem while the hybrid method combining multi-scale SAGA and simplex algorithms is applied to the inverse problem. The algorithm has been applied to a numerical test and a travel-time perturbation test using an anomalous low-velocity body. For a practical example, it is used in the study of upper crustal velocity structure of the A＇nyemaqen suture zone at the north-east edge of the Qinghai-Tibet Plateau. The model test and practical application both prove that the method is effective and robust.
文摘The principle of direct method used in optimal control problem is introduced. Details of applying this method to flight trajectory generation are presented including calculation of velocity and controls histories. And capabilities of flight and propulsion systems are considered also. Combined with digital terrain map technique, the direct method is applied to the three dimensional trajectory optimization for low altitude penetration, and simplex algorithm is used to solve the parameters in optimization. For the small number of parameters, the trajectory can be optimized in real time on board.
基金Supported by the National High Technology Research and Development Program of China("863" Program) (2009AA04Z418, 2007AA04Z404)the National "111" Project(B07050)~~
文摘A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. The Kriging approximate model with updating is introduced to reduce the computational cost of MDF caused by the complex structure. The computational efficiency is remarkably improved as the lack of iterative process during reliability analysis. Special attention is paid to a turbine blade design optimization by adopting the proposed method. Results show that the method is much more efficient than the commonly used double-loop based RBMDO method. It is feasible and efficient to apply the method to the engineering design.
文摘Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.
