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
In this paper,a parametric level-set-based topology optimization framework is proposed to concurrently optimize the structural topology at the macroscale and the effective infill properties at the micro/meso scale.The...In this paper,a parametric level-set-based topology optimization framework is proposed to concurrently optimize the structural topology at the macroscale and the effective infill properties at the micro/meso scale.The concurrent optimization is achieved by a computational framework combining a new parametric level set approach with mathematical programming.Within the proposed framework,both the structural boundary evolution and the effective infill property optimization can be driven by mathematical programming,which is more advantageous compared with the conventional partial differential equatiodriven level set approach.Moreover,the proposed approach will be more efficient in handling nonlinear problems with multiple constraints.Instead of using radial basis functions(RBF),in this paper,we propose to construct a new type of cardinal basis functions(CBF)for the level set function parameterization.The proposed CBF parameterization ensures an explicit impose of the lower and upper bounds of the design variables.This overcomes the intrinsic disadvantage of the conventional RBF-based parametric level set method,where the lower and upper bounds of the design variables oftentimes have to be set by trial and error;A variational distance regularization method is utilized in this research to regularize the level set function to be a desired distanceregularized shape.With the distance information embedded in the level set model,the wrapping boundary layer and the interior infill region can be naturally defined.The isotropic infill achieved via the mesoscale topology optimization is conformally fit into the wrapping boundary layer using the shape-preserving conformal mapping method,which leads to a hierarchical physical structure with optimized overall topology and effective infill properties.The proposed method is expected to provide a timely solution to the increasing demand for multiscale and multifunctional structure design.展开更多
Scheme evaluation and selection is an optimum selecting and sequencing problem with multi-objective and multi-level. It can’t follow single objective function or rule. Meanwhile,these objectives are coupled with each...Scheme evaluation and selection is an optimum selecting and sequencing problem with multi-objective and multi-level. It can’t follow single objective function or rule. Meanwhile,these objectives are coupled with each other and the attribution information is fuzzy also. It is necessary to find an effective evaluation method which can consider all conditions and restrictions. In this paper,AHP and rough set theory are applied to fuzzy optimization to determine important weight of each attribution. The rough set fuzzy optimum selection is used to eliminate the useless information. Autonomous underwater vehicle (AUV) is large-scale systems with many coupled design variables and objective functions. Their scheme evaluation and selection are very important,which relate to multiple factors,such as reliability; security,service time; the lifecycle,etc. Results of application in torpedo design indicate that this method is feasible.展开更多
In this paper,a new fuzzy approach is applied to optimal design of the anti-skid control for electric vehicles.The anti-skid control is used to maintain the wheel speed when there are uncertainties.The control is able...In this paper,a new fuzzy approach is applied to optimal design of the anti-skid control for electric vehicles.The anti-skid control is used to maintain the wheel speed when there are uncertainties.The control is able to provide an appropriate torque for wheels when the vehicle is about to skid.The friction coefficient and the moments of inertia of wheels and motor are considered as uncertain parameters.These nonlinear,bounded and time-varying uncertainties are described by fuzzy set theory.The control is deterministic and is not based on IF-THEN fuzzy rules.Then,the optimal design for this fuzzy system and control cost is proposed by fuzzy information.In this way,the uniform boundedness and uniform ultimate boundedness are guaranteed and the average fuzzy performance is minimized.Numerical simulations show that the control can prevent vehicle skidding with the minimum control cost under uncertainties.展开更多
Structural bionic design lacks mature and scientific theories, and the excellent structural characteristics of natural organisms sometimes cannot be transferred into engineering structures effectively. Aiming at overc...Structural bionic design lacks mature and scientific theories, and the excellent structural characteristics of natural organisms sometimes cannot be transferred into engineering structures effectively. Aiming at overcoming the existing problems, this paper summarizes three related theories: similarity theory, fuzzy evaluation theory and optimization theory. Based on the related theories, a method of structural bionic design is introduced, which includes four steps: selecting the most useful structural characteristic of natural organism; analyzing the structural characteristic finally chosen for engineering problem; completing the structural bionic design for engineering structure; and verifying the structural bionic design. Similarity theory and fuzzy evaluation theory are employed to achieve Step 1. In Step 2 and Step 3, optimization theory is employed to analyze the parameters of structures. Together with the thoughts of simplification and grouping, optimization theory can reveal the relationship between organism structure and engineering structure, providing a way to structural bionic design. A general evaluation criterion is proposed in Step 4, which is feasible to evaluate the performance of different structures. Finally, based on the method, a structural bionic design of thin-walled cylindrical shell is introduced.展开更多
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the National Science Foundation of the United States(Grant Nos.CMMI1462270 and CMMI1762287)Ford University Research Program(URP),and the start-up fund from the State University of New York at Stony Brook.
文摘In this paper,a parametric level-set-based topology optimization framework is proposed to concurrently optimize the structural topology at the macroscale and the effective infill properties at the micro/meso scale.The concurrent optimization is achieved by a computational framework combining a new parametric level set approach with mathematical programming.Within the proposed framework,both the structural boundary evolution and the effective infill property optimization can be driven by mathematical programming,which is more advantageous compared with the conventional partial differential equatiodriven level set approach.Moreover,the proposed approach will be more efficient in handling nonlinear problems with multiple constraints.Instead of using radial basis functions(RBF),in this paper,we propose to construct a new type of cardinal basis functions(CBF)for the level set function parameterization.The proposed CBF parameterization ensures an explicit impose of the lower and upper bounds of the design variables.This overcomes the intrinsic disadvantage of the conventional RBF-based parametric level set method,where the lower and upper bounds of the design variables oftentimes have to be set by trial and error;A variational distance regularization method is utilized in this research to regularize the level set function to be a desired distanceregularized shape.With the distance information embedded in the level set model,the wrapping boundary layer and the interior infill region can be naturally defined.The isotropic infill achieved via the mesoscale topology optimization is conformally fit into the wrapping boundary layer using the shape-preserving conformal mapping method,which leads to a hierarchical physical structure with optimized overall topology and effective infill properties.The proposed method is expected to provide a timely solution to the increasing demand for multiscale and multifunctional structure design.
文摘Scheme evaluation and selection is an optimum selecting and sequencing problem with multi-objective and multi-level. It can’t follow single objective function or rule. Meanwhile,these objectives are coupled with each other and the attribution information is fuzzy also. It is necessary to find an effective evaluation method which can consider all conditions and restrictions. In this paper,AHP and rough set theory are applied to fuzzy optimization to determine important weight of each attribution. The rough set fuzzy optimum selection is used to eliminate the useless information. Autonomous underwater vehicle (AUV) is large-scale systems with many coupled design variables and objective functions. Their scheme evaluation and selection are very important,which relate to multiple factors,such as reliability; security,service time; the lifecycle,etc. Results of application in torpedo design indicate that this method is feasible.
基金Supported by China Scholarship Council(Grant No.201806690019)Fundamental Research Funds for Chinese Central Universities(Grant No.300102258306)Anhui Provincial Natural Science Foundation of China(Grant No.1908085QE194).
文摘In this paper,a new fuzzy approach is applied to optimal design of the anti-skid control for electric vehicles.The anti-skid control is used to maintain the wheel speed when there are uncertainties.The control is able to provide an appropriate torque for wheels when the vehicle is about to skid.The friction coefficient and the moments of inertia of wheels and motor are considered as uncertain parameters.These nonlinear,bounded and time-varying uncertainties are described by fuzzy set theory.The control is deterministic and is not based on IF-THEN fuzzy rules.Then,the optimal design for this fuzzy system and control cost is proposed by fuzzy information.In this way,the uniform boundedness and uniform ultimate boundedness are guaranteed and the average fuzzy performance is minimized.Numerical simulations show that the control can prevent vehicle skidding with the minimum control cost under uncertainties.
基金Supported by National Natural Science Foundation of China (No. 50975012)Research Fund for the Doctoral Program of Higher Education of China (No. 20091102110022)
文摘Structural bionic design lacks mature and scientific theories, and the excellent structural characteristics of natural organisms sometimes cannot be transferred into engineering structures effectively. Aiming at overcoming the existing problems, this paper summarizes three related theories: similarity theory, fuzzy evaluation theory and optimization theory. Based on the related theories, a method of structural bionic design is introduced, which includes four steps: selecting the most useful structural characteristic of natural organism; analyzing the structural characteristic finally chosen for engineering problem; completing the structural bionic design for engineering structure; and verifying the structural bionic design. Similarity theory and fuzzy evaluation theory are employed to achieve Step 1. In Step 2 and Step 3, optimization theory is employed to analyze the parameters of structures. Together with the thoughts of simplification and grouping, optimization theory can reveal the relationship between organism structure and engineering structure, providing a way to structural bionic design. A general evaluation criterion is proposed in Step 4, which is feasible to evaluate the performance of different structures. Finally, based on the method, a structural bionic design of thin-walled cylindrical shell is introduced.
