Stochastic Optimization of Nonlinear Energy Sinks Using Resonance-Based Clustering

2016 ◽  
Author(s):  
Ethan Boroson ◽  
Samy Missoum
Keyword(s):  
Stochastic Optimization ◽  
Adaptive Sampling ◽  
Dimensional Space ◽  
Optimization Method ◽  
Expected Value ◽  
Design Parameters ◽  
Support Vector ◽  
Design Variables ◽  
Energy Dissipated ◽  
Nonlinear Energy

Nonlinear energy sinks (NESs) are promising devices for achieving passive vibration mitigation. Unlike traditional tuned mass dampers (TMDs), NESs, characterized by nonlinear stiffness properties, are not tuned to specific frequencies and absorb energy over a wider range of frequencies. NES efficiency is achieved through time-limited resonances, leading to the capture and dissipation of energy. However, the efficiency with which a NES dissipates energy is highly dependent on design parameters and loading conditions. In fact, it has been shown that a NES can exhibit a near-discontinuous efficiency. Thus, NES optimal design must account for uncertainty. The premise of the stochastic optimization method proposed is the segregation of efficiency regions separated by discontinuities in potentially high dimensional space. Clustering, support vector machine classification, and dedicated adaptive sampling constitute the basic techniques for maximizing the expected value of NES efficiency. Previous works depended solely on the ratio of energy dissipated by the NES for clustering. This work also includes information about the type of m:p resonances present. Three examples of optimization for the maximization of the expected value of efficiency for NESs subjected to transient loading are presented. The optimization accounts for both design variables with uncertainty and aleatory variables to characterize loading.

The Effect of Different Optimization Methods on Robustness for Robust Optimization Design

2014 ◽  
Vol 721 ◽  
pp. 464-467
Author(s):  
Tao Fu ◽  
Qin Zhong Gong ◽  
Da Zhen Wang
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Robust Design ◽  
Optimization Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Hybrid Particle Swarm Optimization ◽  
Design Variables ◽  
Robust Optimization Design

In view of robustness of objective function and constraints in robust design, the method of maximum variation analysis is adopted to improve the robust design. In this method, firstly, we analyses the effect of uncertain factors in design variables and design parameters on the objective function and constraints, then calculate maximum variations of objective function and constraints. A two-level optimum mathematical model is constructed by adding the maximum variations to the original constraints. Different solving methods are used to solve the model to study the influence to robustness. As a demonstration, we apply our robust optimization method to an engineering example, the design of a machine tool spindle. The results show that, compared with other methods, this method of HPSO(hybrid particle swarm optimization) algorithm is superior on solving efficiency and solving results, and the constraint robustness and the objective robustness completely satisfy the requirement, revealing that excellent solving method can improve robustness.

scholarly journals Thermal optimization research of oil-immersed transformer winding based on the support machine response surface

2021 ◽  
pp. 264-264
Author(s):  
Fating Yuan ◽  
Wentao Yang ◽  
Bo Tang ◽  
Yue Wang ◽  
Fa Jiang ◽  
...  
Keyword(s):  
Response Surface ◽  
Low Voltage ◽  
Hot Spot ◽  
Optimization Method ◽  
Design Parameters ◽  
Support Vector ◽  
Structure Parameters ◽  
Transformer Winding ◽  
Polynomial Response Surface ◽  
Spot Temperature

In this paper, the CFD (computational fluid dynamics) model is established for the low voltage winding region of an oil-immersed transformer according to the design parameters, and the detailed temperature distribution within the region is obtained by numerical simulation. On this basis, the RSM (response surface methodology) is adopted to optimize the structure parameters with the purpose of minimizing the hot spot temperature. After a sequence of designed experiments, the second-order polynomial response surface and the SVM (support vector machine) response surface are established respectively. The analysis of their errors shows that the SVM response surface can be better used to fit the approximation. Finally, the PSO (particle swarm optimization) algorithm is employed to get the optimal structure parameters of the winding based on the SVM response surface. The results show that the optimization method can significantly reduce the hot spot temperature of the winding, which provides a guiding direction for the optimal design of the winding structure of transformers.

scholarly journals Improved Immune Genetic Algorithm to Optimize the Design of Wing Structure of Competition Aircraft

2021 ◽  
Vol 2137 (1) ◽  
pp. 012075
Author(s):  
Xi Feng ◽  
Yafeng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Aircraft Design ◽  
Flight Test ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Immune Genetic Algorithm ◽  
Maximum Deformation ◽  
Design Variables ◽  
Wing Structure

Abstract An improved immune genetic algorithm is used to design and optimize the wing structure parameters of a competition aircraft. According to the requirements of aircraft design, multi-objective optimization index is established. On this basis, the basic steps of using immune algorithm to optimize the main design parameters of aircraft wing structure are proposed, and the optimization of the wing parameters of a competition aircraft is used as an example for simulation calculation. The design variables in the optimization are the size of the wing components, and the optimization goal is to minimize the weight of the wing and the maximum deformation of the wing structure. Research shows that compared with traditional optimization methods; the improved immune genetic algorithm is a very effective optimization method. At the same time, a prototype is made to check the validity and feasibility of the design. Flight test results show that the optimization method is very effective. Although the method is proposed for competition aircraft, it is also applicable to other types of aircraft.

scholarly journals Optimization Under Uncertainty of Nonlinear Energy Sinks

2014 ◽  
Author(s):  
Ethan Boroson ◽  
Samy Missoum ◽  
Pierre-Olivier Mattei ◽  
Christophe Vergez
Keyword(s):  
Support Vector Machine ◽  
Small Perturbation ◽  
Support Vector Machine Classifier ◽  
Initial Conditions ◽  
Optimization Under Uncertainty ◽  
Design Parameters ◽  
Support Vector ◽  
Novel Technique ◽  
Main System ◽  
Nonlinear Energy

Nonlinear Energy Sinks (NES) are used to passively reduce the amplitude of vibrations. This reduction is made possible by introducing a nonlinearly stiffening behavior in the NES, which might lead to an irreversible transfer of energy between the main system (e.g., a building) and the NES. However, this irreversible transfer, and therefore the efficiency of the NES, is strongly dependent on the design parameters of the NES. In fact, the efficiency of the NES might be so sensitive to changes in design parameters and other factors (e.g., initial conditions) that it is discontinuous, switching from efficiency to inefficiency for a small perturbation of parameters. For this reason, this work introduces a novel technique for the optimization under uncertainty of NES. The approach is based on a support vector machine classifier, which is insensitive to discontinuities and allows one to efficiently propagate uncertainties. This enables one to efficiently solve an optimization under uncertainty problem. The various techniques presented in this paper are applied to an analytical NES example.

Study of Parameters Optimization on Seismic Isolated Railway Bridges

2011 ◽  
Vol 50-51 ◽  
pp. 135-139
Author(s):  
Tie Yi Zhong ◽  
Chao Yi Xia ◽  
Feng Li Yang
Keyword(s):  
Optimal Design ◽  
Optimization Design ◽  
Seismic Isolation ◽  
Optimization Method ◽  
Relative Displacement ◽  
Parameters Optimization ◽  
Design Parameters ◽  
Isolation System ◽  
Railway Bridges ◽  
Design Variables

Based on optimization theories, considering soil-structure interaction and running safety, the optimal design model of the seismic isolation system with lead-rubber bearings (LRB) for a simply supported railway beam bridge is established by using the first order optimization method in ANSYS, which the parameters of the isolation bearing are taken as design variables and the maximum moments at the bottom of bridge piers are taken as objective functions. The optimal calculations are carried out under the excitation of three practical earthquake waves respectively. The research results show that the ratio of the stiffness after yielding to the stiffness before yielding has important effect on the structural seismic responses. Through the optimal analysis of isolated bridge system, the optimal design parameters of isolation bearing can be determined properly, and the seismic forces can be reduced maximally as meeting with the limits of relative displacement between pier top and beam, which provides efficient paths and beneficial references for dynamic optimization design of seismic isolated bridges.

scholarly journals Discrete Optimization on Unsteady Pressure Fluctuation of a Centrifugal Pump Using ANN and Modified GA

2021 ◽  
Author(s):  
Wenjie Wang ◽  
Qifan Deng ◽  
Ji Pei ◽  
Jinwei Chen ◽  
Xingcheng Gan
Keyword(s):  
Genetic Algorithm ◽  
Centrifugal Pump ◽  
Pressure Fluctuation ◽  
Optimization Method ◽  
Design Parameters ◽  
Optimization Strategy ◽  
Unsteady Pressure ◽  
Design Variables ◽  
Rotor Stator Interaction ◽  
Unsteady Simulation

Abstract Pressure fluctuation due to the rotor-stator interaction in turbomachinery is unavoidable, inducing strong vibration and even shortening the lifecycle. The investigation on optimization method of an industrial centrifugal pump was carried out to reduce the pressure fluctuation intensity. Considering the time-consuming transient calculation of unsteady pressure, a novel optimization strategy was proposed by discretizing design variables and genetic algorithm. Four highly related design parameters were chosen, and 40 transient sample cases were generated and simulated using an automatic simulation program. Furthermore, a modified discrete genetic algorithm (MDGA) was proposed to reduce the optimization cost by unsteady simulation. For the benchmark test, the proposed MDGA showed a great advantage over the original genetic algorithm in terms of searching speed and could deal with the discrete variables effectively. After optimization, an improvement in terms of the performance and stability of the inline pump was achieved.

scholarly journals A Stochastic Optimization Method for Energy Storage Sizing Based on an Expected Value Model

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 702 ◽  
Author(s):  
Delong Zhang ◽  
Jianlin Li ◽  
Xueqin Liu ◽  
Jianbo Guo ◽  
Shaohua Xu
Keyword(s):  
Energy Storage ◽  
Stochastic Optimization ◽  
Storage System ◽  
Optimization Method ◽  
Gaussian Mixture ◽  
Expected Value ◽  
Total System ◽  
Deterministic Method ◽  
Expected Value Model ◽  
Value Model

Energy storage technologies have been rapidly evolving in recent years. Energy storage plays different roles in various scenarios. For electricity consumers, they are concerned with how to use the energy storage system (ESS) to reduce their costs of electricity or increase their profits. In this paper, a stochastic optimization method for energy storage sizing based on an expected value model for consumers with Photovoltaic Generation (PV) is proposed. Firstly, the Gaussian mixture model clustering method is used to cluster the historical load and PV data and calculate the probability of each cluster. Secondly, the optimal model of total system profit is established. Finally, according to the expected value model, the optimal ESS power and capacity are determined. Two case studies are used to demonstrate the calculation of optimal ESS capacity. The results obtained by the method proposed in this paper are compared with the results produced by the deterministic method. Through the analysis and comparison, the validity and superiority of the method proposed in this paper are verified. The profits obtained by the method proposed in this paper are 0.87% to 127.16% more than the deterministic method.

scholarly journals Multi-Objective Optimization Design of Permanent Magnet Torque Motor

2021 ◽  
Vol 12 (3) ◽  
pp. 131
Author(s):  
Jiawei Chai ◽  
Tianyi Zhao ◽  
Xianguo Gui
Keyword(s):  
Permanent Magnet ◽  
Optimization Method ◽  
Torque Ripple ◽  
Numerical Control ◽  
Design Parameters ◽  
Support Vector ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Output Torque ◽  
Low Torque

Permanent magnet torque motor (PMTM) is widely used in aerospace, computer numerical control (CNC) machine tools, and industrial robots with many advantages such as high torque density, strong overload capacity, and low torque ripple. With the upgrading of industrial manufacturing, the requirements for the performance of torque motors have become more stringent. At present, how to achieve high output torque and low torque ripple has become a research hotspot of torque motors. In the optimization process, it is necessary to increase the output torque while the torque ripple can be reduced, and it is difficult to get a good result with the single-objective optimization. In this paper, a multi-objective optimization method based on the combination of design parameter stratification and support vector machine (SVM) is proposed. By analyzing the causes of torque ripple, the output torque, efficiency, cogging torque, and total harmonic distortion (THD) of back electromotive force (EMF) are selected as the optimization objectives. In order to solve the coupling problem between the motor parameters, the calculation formula of Pearson correlation coefficient is used to analyze the relationship between the design parameters and the optimization objectives, and the design parameters are layered ac-cording to the sensitivity. In order to shorten the optimization cycle of the motor, SVM is used as a fitting method of the mathematical model. The performance between initial and optimal motors is compared, and it can be found that the optimized motor has a higher torque and lower torque ripple. The simulation results verify the effectiveness of the proposed optimization method.

Simulation Based Optimal Tolerancing for Multibody Systems

2007 ◽  
Author(s):  
Henry Arenbeck ◽  
Samy Missoum ◽  
Anirban Basudhar ◽  
Parviz E. Nikravesh
Keyword(s):  
Multibody Systems ◽  
Multibody System ◽  
Failure Detection ◽  
Optimization Method ◽  
Computational Effort ◽  
Efficient Estimation ◽  
Design Parameters ◽  
Support Vector ◽  
Manufacturing Cost ◽  
Analysis Scheme

This paper introduces a new methodology for probabilistic optimal design of multibody systems. Specifically, the effects of dimensional uncertainties on the behavior of a system are considered. The proposed reliability-based optimization method addresses difficulties such as high computational effort and non-smoothness of the system’s responses, for example, as a result of contact events. The approach is based on decomposition of the design space into regions, corresponding to either acceptable or non-acceptable system performance. The boundaries of these regions are defined using Support Vector Machines (SVMs), which are explicit in terms of the design parameters. A SVM can be trained based on a limited number of samples, obtained from a design of experiments, and allows a very efficient estimation of probability of failure, even when Monte Carlo Simulation (MCS) is used. A modularly structured tolerance analysis scheme for automatic estimation of system production cost and probability of system failure is presented. In this scheme, detection of failure is based on multibody system simulation, yielding high computational demand. A SVM-based replication of the failure detection process is derived, which ultimately allows for automatic optimization of tolerance assignments. A simple multibody system, whose performance usually shows high tolerance sensitivity, is chosen as an exemplary system for illustration of the proposed approach. The system is optimally designed for minimum manufacturing cost while satisfying a target performance level with a given probability.

A global sensitivity analysis-assisted sequential optimization tool for plant-fin heat sink design

2019 ◽  
Vol 37 (2) ◽  
pp. 591-614
Author(s):  
Enying Li ◽  
Zheng Zhou ◽  
Hu Wang ◽  
Kang Cai
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Optimization Method ◽  
Sequential Optimization ◽  
Support Vector ◽  
Sensitivity Index ◽  
Content Type ◽  
Global Sensitivity ◽  
Design Variables ◽  
Heat Sink Design

Purpose This study aims to suggest and develops a global sensitivity analysis-assisted multi-level sequential optimization method for the heat transfer problem. Design/methodology/approach Compared with other surrogate-assisted optimization methods, the distinctive characteristic of the suggested method is to decompose the original problem into several layers according to the global sensitivity index. The optimization starts with the several most important design variables by the support vector regression-based efficient global optimization method. Then, when the optimization process progresses, the filtered design variables should be involved in optimization one by one or the setting value. Therefore, in each layer, the design space should be reduced according to the previous optimization result. To improve the accuracy of the global sensitivity index, a novel global sensitivity analysis method based on the variance-based method incorporating a random sampling high-dimensional model representation is introduced. Findings The advantage of this method lies in its capability to solve complicated problems with a limited number of sample points. Moreover, to enhance the reliability of optimum, the support vector regression-based global efficient optimization is used to optimize in each layer. Practical implications The developed optimization tool is built by MATLAB and can be integrated by commercial software, such as ABAQUS and COMSOL. Lastly, this tool is integrated with COMSOL and applied to the plant-fin heat sink design. Compared with the initial temperature, the temperature after design is over 49°. Moreover, the relationships among all design variables are also disclosed clearly. Originality/value The D-MORPH-HDMR is integrated to obtain the coupling relativities among the design variables efficiently. The suggested method can be decomposed into multiplier layers according to the GSI. The SVR-EGO is used to optimize the sub-problem because of its robustness of modeling.

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