Interval Optimization Design Based on Surrogate Models

2020 ◽  
pp. 243-258
Author(s):  
Xu Han ◽  
Jie Liu
Keyword(s):  
Optimization Design ◽  
Surrogate Models ◽  
Interval Optimization

scholarly journals Crashworthiness Design and Multiobjective Optimization for Hexagon Honeycomb Structure with Functionally Graded Thickness

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Ruixian Qin ◽  
Junxian Zhou ◽  
Bingzhi Chen
Keyword(s):  
Energy Absorption ◽  
Optimization Design ◽  
Surrogate Models ◽  
Honeycomb Structure ◽  
Functionally Graded ◽  
Thickness Variation ◽  
Static Compression ◽  
Crashworthiness Optimization ◽  
Energy Absorbing ◽  
Graded Thickness

Higher energy absorption efficiency and better crashworthiness performance are always the key objectives for different energy absorbing structures applied in numerous industries including aerospace, rail equipment transportation, and automotive. In this study, a functionally graded thickness (FGT) design method is introduced in the design of a hexagon honeycomb structure to improve energy absorbing efficiency on the basis of a traditional honeycomb with uniform thickness (UT). The validation of a numerical analysis model for a UT honeycomb under axial loading is implemented by a nonlinear finite element code LS-DYNA (V971). Furthermore, the multiobjective crashworthiness optimization of an FGT honeycomb subjected to axial quasi-static compression is conducted to maximize specific energy absorption (SEA) and minimize peak crashing force (PCF). In addition, three surrogate models, including radial basis function (RBF), response surface method (RSM), and kriging (KRG), are compared in the accuracy of predicting SEA and PCF and capacity for optimization design of FGT honeycomb structure; the Nondominated Sorting Genetic Algorithm (NSGA-II) is applied to obtain the Pareto optimal solutions for the maximum thickness, minimum thickness, and thickness variation gradient exponent of a honeycomb wall. The optimal points obtained by different surrogate models subjected to an SEA value of 18.5 kJ/kg, 20 kJ/kg, 22 kJ/kg, and 24 kJ/kg are validated, and corresponding optimal parameters are compared; RBF and RSM are more suitable in crashworthiness optimization design of the FGT honeycomb structure. It is indicated that the FGT honeycomb with optimal geometrical parameters presents remarkable enhancement and energy absorbing potential compared to the traditional honeycomb structure.

scholarly journals Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

2018 ◽  
Vol 29 (15) ◽  
pp. 3097-3107 ◽  
Author(s):  
Liheng Luo ◽  
Dianzi Liu ◽  
Meiling Zhu ◽  
Yijie Liu ◽  
Jianqiao Ye
Keyword(s):  
Optimal Design ◽  
Surrogate Model ◽  
Optimization Design ◽  
Local Level ◽  
Surrogate Modeling ◽  
Surrogate Models ◽  
Human Motion ◽  
Latin Hypercube ◽  
Multi Level ◽  
Modeling Strategy

Conventional engineering design optimization requires a large amount of expensive experimental tests from prototypes or computer simulations, which may result in an inefficient and unaffordable design process. In order to overcome these disadvantages, a surrogate model may be used to replace the prototype tests. To construct a surrogate model of sufficient accuracy from limited number of tests/simulations, a multi-level surrogate modeling strategy is introduced in this article. First, a chosen number of points determined by optimal Latin Hypercube Design of Experiments are used to generate global-level surrogate models with genetic programming and the fitness landscape can be explored by genetic algorithms for near-optimal solutions. Local-level surrogate models are constructed then from the extended-optimal Latin Hypercube samples in the vicinity of global optimum on the basis of a much smaller number of chosen points. As a result, an improved optimal design is achieved. The efficiency of this strategy is demonstrated by the parametric optimization design of a piezoelectric flex transducer energy harvester. The optimal design is verified by finite element simulations and the results show that the proposed multi-level surrogate modeling strategy has the advantages of faster convergence and more efficiency in comparison with the conventional single-single level surrogate modeling technique.

Adaptive Kriging surrogate model for the optimization design of a dense non-aqueous phase liquid-contaminated groundwater remediation process

2014 ◽  
Vol 15 (2) ◽  
pp. 263-270 ◽  
Author(s):  
Haibo Chu ◽  
Wenxi Lu
Keyword(s):  
Simulation Model ◽  
Aqueous Phase ◽  
Surrogate Model ◽  
Optimization Design ◽  
Integrated Approach ◽  
Surrogate Models ◽  
Kriging Surrogate Model ◽  
The Common ◽  
Adaptive Kriging ◽  
Remediation Process

The optimization model needs to call the simulation model to calculate the response under different conditions for many times, and this is computationally expensive and time-consuming. To solve this problem, surrogate models can be used to yield insight into the functional relationship between the design variables and the responses, instead of simulation models in the optimization. In this paper, an integrated optimization method based on adaptive Kriging surrogate models was proposed and applied to the cost optimization of a surfactant enhanced aquifer remediation process for dense non-aqueous phase liquids (DNAPLs). First, the initial samples were created by Latin hypercube sampling, and then the responses corresponding to the initial samples were computed by a simulation model. The initial Kriging model was derived through these samples. Secondly, the adaptive Kriging surrogate model was proposed based on updating initial Kriging with new samples via infill sampling criteria. The results showed that it had improved the accuracy of the surrogate model, and the added samples had provided more information about the simulation model than the common samples. Even with the same number of samples, the adaptive Kriging surrogate model performed better than the common Kriging surrogate model, which was built only once. What's more, the integrated approach not only greatly reduced the computational burden, but also determined the actual optimal DNAPLs remediation strategy.

Optimal design of a high homogeneous and small-size shim coil for atomic spin gyroscope based on 0-1 linear programming

2020 ◽  
Vol 64 (1-4) ◽  
pp. 165-172
Author(s):  
Dongge Deng ◽  
Mingzhi Zhu ◽  
Qiang Shu ◽  
Baoxu Wang ◽  
Fei Yang
Keyword(s):  
Linear Programming ◽  
Power Consumption ◽  
Low Power ◽  
Optimization Design ◽  
Structural Parameters ◽  
Axial Position ◽  
Low Power Consumption ◽  
Optimal Method ◽  
Atomic Spin ◽  
Shim Coil

It is necessary to develop a high homogeneous, low power consumption, high frequency and small-size shim coil for high precision and low-cost atomic spin gyroscope (ASG). To provide the shim coil, a multi-objective optimization design method is proposed. All structural parameters including the wire diameter are optimized. In addition to the homogeneity, the size of optimized coil, especially the axial position and winding number, is restricted to develop the small-size shim coil with low power consumption. The 0-1 linear programming is adopted in the optimal model to conveniently describe winding distributions. The branch and bound algorithm is used to solve this model. Theoretical optimization results show that the homogeneity of the optimized shim coil is several orders of magnitudes better than the same-size solenoid. A simulation experiment is also conducted. Experimental results show that optimization results are verified, and power consumption of the optimized coil is about half of the solenoid when providing the same uniform magnetic field. This indicates that the proposed optimal method is feasible to develop shim coil for ASG.

GEOMETRY OPTIMIZATION OF AIR-ASSISTED SWIRL NOZZLE BASED ON SURROGATE MODELS AND COMPUTATIONAL FLUID DYNAMICS

2019 ◽  
Vol 29 (7) ◽  
pp. 605-628
Author(s):  
Zongli Yi ◽  
Li Hou ◽  
Qi Zhang ◽  
Yousheng Wang ◽  
Yunxia You
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Geometry Optimization ◽  
Surrogate Models
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