A Feasibility Robust Optimization Method Using Sensitivity Region Concept

2004 ◽  
Vol 127 (5) ◽  
pp. 858-865 ◽  
Author(s):  
S. Gunawan ◽  
S. Azarm
Keyword(s):  
Probability Distribution ◽  
Robust Optimization ◽  
Control Valve ◽  
Optimization Method ◽  
Parameter Variation ◽  
Design Parameters ◽  
Optimized Design ◽  
Large Parameter ◽  
Parameter Variations ◽  
Feasible Domain

We present a robust optimization method that ensures feasibility of an optimized design when there are uncontrollable variations in design parameters. This method is developed based on the notion of a sensitivity region, which is a measure of how far a feasible design is from the boundary of a feasible domain in the parameter variation space. In this method, as the design moves further inside the feasible domain, and thus becoming more feasibly robust, the sensitivity region becomes larger. Our method is not sampling based so it does not require a presumed probability distribution as input and is reasonably efficient in terms of function evaluations. In addition, our method does not use gradient approximation and thus is applicable to problems that have nondifferentiable constraint functions and large parameter variations. As a demonstration, we applied our method to an engineering example, the design of a control valve actuator linkage. In this example, we show that our method finds an optimum design which is feasibly robust.

An Efficient Feasibility Robust Optimization Method Using a Sensitivity Region Concept

2004 ◽  
Author(s):  
S. Gunawan ◽  
S. Azarm
Keyword(s):  
Probability Distribution ◽  
Robust Optimization ◽  
Control Valve ◽  
Optimization Method ◽  
Parameter Variation ◽  
Design Parameters ◽  
Optimized Design ◽  
Large Parameter ◽  
Parameter Variations ◽  
Feasible Domain

We present a new robust optimization method that ensures feasibility of an optimized design when there are uncontrollable variations in design parameters. This method is developed based on the notion of a sensitivity region, which is a measure of how far a feasible design is from the boundary of a feasible domain in the parameter variation space. As the design moves further inside the feasible domain, and thus becoming more feasibly robust, the sensitivity region becomes larger. Our method is not sampling-based so it does not require a presumed probability distribution as input and is efficient in terms of function evaluations. In addition, our method does not use gradient approximation and thus is applicable to problems having non-differentiable constraint functions and large parameter variations. As a demonstration, we applied our method to an engineering example, the design of a control valve actuator linkage. In this example, we show that the method is efficient and the optimum design obtained is robust.

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.

Knowledge Discovery and Management from Numerical Simulation and its Application to Robust Optimization of Extrusion-Forging Processing

2007 ◽  
Vol 340-341 ◽  
pp. 659-664 ◽  
Author(s):  
Hu Jie ◽  
Ji Long Yin
Keyword(s):  
Numerical Simulation ◽  
Robust Optimization ◽  
Knowledge Discovery ◽  
Domain Knowledge ◽  
Process Development ◽  
Optimization Method ◽  
Design Parameters ◽  
Forming Process ◽  
Useful Knowledge ◽  
Plastic Forming

Numerical simulation technology has been used widely in plastic forming area. However, the simulation of increasingly complex forming process leads to the generation of vast quantities of data, which implies much useful knowledge. Consequently domain knowledge is very significant to product design and process development in metal plastic forming area. The paper presented a new robust optimization method based on knowledge discovery from numerical simulation. Firstly, the knowledge discovery model from numerical simulation is established. In this model, interval-based rule presentation is adopted to describe the uncertainty of design parameters quantitatively to enhance the design robustness. Secondly, the optimization process based on knowledge discovery and management is presented, and genetic arithmetic is used to obtain the robust optimization parameter. Finally, the application to robust optimization of extrusion-forging processing is analyzed to show the scheme to be effective. The proposed method can overcome the pathologies in simulation optimization and improve the efficiency & robustness in design optimization.

scholarly journals Weight-Vibration Pareto Optimization of a Triple Mass Flywheel for Heavy-Duty Truck Powertrains

Machines ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 50
Author(s):  
Viktor Berbyuk
Keyword(s):  
Total Mass ◽  
Optimization Method ◽  
Pareto Optimization ◽  
Design Parameters ◽  
Vibration Absorber ◽  
Optimized Design ◽  
Heavy Duty ◽  
Mean Values ◽  
Input Shaft ◽  
Heavy Duty Truck

Enhanced efficiency of heavy-duty truck powertrains with constraints imposed on noise, vibration, and harshness requires novel solutions for torsion vibrations attenuation. In the paper, the weight-vibration Pareto optimization problem for a novel vibration absorber, a triple mass flywheel, for application in heavy-duty truck powertrains is considered. Global sensitivity analysis and Pareto optimization method are used to design a novel vibration absorber. The optimization method attempts to minimize oscillations of the torque at the transmission input shaft as well as to minimize total mass inertia of the absorber. It is shown that there exists a Pareto front between the measure of the attenuation of oscillations of the torque and the total mass inertia of a triple mass flywheel. The optimized design parameters for the absorber are obtained that provide the best attenuation of oscillations of the torque at the transmission input shaft for different mean values of the engine driving torque. The analysis shows real evidence of the feasibility of the application of this concept of vibration absorbers in heavy-duty truck powertrains. It is also shown that optimized design parameters of a triple mass flywheel put this concept in a superior position in comparison with a dual mass flywheel.

A Variability-Aware Robust Design Methodology for Integrated Circuits by Geometric Programming

2019 ◽  
Vol 28 (05) ◽  
pp. 1950073 ◽  
Author(s):  
Yi Zhang ◽  
Junlong Zhou ◽  
Li Chen ◽  
Jin Sun
Keyword(s):  
Integrated Circuits ◽  
Robust Optimization ◽  
Circuit Design ◽  
Geometric Programming ◽  
Performance Modeling ◽  
Programming Model ◽  
Process Variations ◽  
Optimization Method ◽  
Design Parameters ◽  
Strong Nonlinearity

Process variations have continuously posed significant challenges to the performance and yield of integrated circuits (ICs). The performance modeling and robust optimization method considering process variations has become an important research task in today’s IC design. Aiming at solving the problems of strong nonlinearity and high-dimensional problems in circuit design, this paper proposes a general robust optimization method for ICs by geometric programming. This method first employs regularization sparse models to model a specific performance metric as a posynomial function in terms of design parameters, in order to reduce parameter space dimensionality and to accurately capture the nonlinear relationship between performance perturbations and process variations. Based on the posynomial performance models, this method further uses an uncertainty set to represent the uncertainties of process variations, and formulates the problem of robust optimization under process variations as a general geometric programming model that can be efficiently solved. Experimental results demonstrate that, the proposed method not only enhances the accuracy and efficiency of circuit performance modeling, but also improves the performance yield significantly compared with traditional circuit design methods.

The Improvement of Newton Method and the Validation of Optimization Idea of Blind Walking Repeatedly

2011 ◽  
Vol 250-253 ◽  
pp. 4061-4064
Author(s):  
Chun Ling Zhang
Keyword(s):  
Saddle Point ◽  
Newton Method ◽  
Optimization Problem ◽  
Initial Point ◽  
Optimization Method ◽  
Maximum Point ◽  
Optimal Result ◽  
Feasible Domain ◽  
Parallel Arithmetic ◽  
Newton Direction

The existence of maximum point, oddity point and saddle point often leads to computation failure. The optimization idea is based on the reality that the optimum towards the local minimum related the initial point. After getting several optimal results with different initial point, the best result is taken as the final optimal result. The arithmetic improvement of multi-dimension Newton method is improved. The improvement is important for the optimization method with grads convergence rule or searching direction constructed by grads. A computational example with a saddle point, maximum point and oddity point is studied by multi-dimension Newton method, damped Newton method and Newton direction method. The importance of the idea of blind walking repeatedly is testified. Owing to the parallel arithmetic of modernistic optimization method, it does not need to study optimization problem with seriate feasible domain by modernistic optimization method.

Parameter study and optimization design of a hat oblique tunnel portal

2021 ◽  
pp. 095440972110140
Author(s):  
Zijian Guo ◽  
Tanghong Liu ◽  
Wenhui Li ◽  
Yutao Xia
Keyword(s):  
High Speed ◽  
Optimization Design ◽  
Pareto Front ◽  
Optimization Problem ◽  
Optimization Method ◽  
Design Parameters ◽  
Parameter Study ◽  
Buffer Structure ◽  
Tunnel Entrance ◽  
The Ideal

The present work focuses on the aerodynamic problems resulting from a high-speed train (HST) passing through a tunnel. Numerical simulations were employed to obtain the numerical results, and they were verified by a moving-model test. Two responses, [Formula: see text] (coefficient of the peak-to-peak pressure of a single fluctuation) and[Formula: see text] (pressure value of micro-pressure wave), were studied with regard to the three building parameters of the portal-hat buffer structure of the tunnel entrance and exit. The MOPSO (multi-objective particle swarm optimization) method was employed to solve the optimization problem in order to find the minimum [Formula: see text] and[Formula: see text]. Results showed that the effects of the three design parameters on [Formula: see text] were not monotonous, and the influences of[Formula: see text] (the oblique angle of the portal) and [Formula: see text] (the height of the hat structure) were more significant than that of[Formula: see text] (the angle between the vertical line of the portal and the hat). Monotonically decreasing responses were found in [Formula: see text] for [Formula: see text] and[Formula: see text]. The Pareto front of [Formula: see text] and[Formula: see text]was obtained. The ideal single-objective optimums for each response located at the ends of the Pareto front had values of 1.0560 for [Formula: see text] and 101.8 Pa for[Formula: see text].

Optimization of a Shock Absorber Design Using Model-Based Approach

2012 ◽  
Vol 452-453 ◽  
pp. 1351-1355 ◽  
Author(s):  
Grzegorz Wszołek ◽  
Piotr Czop ◽  
Dawid Jakubowski ◽  
Damian Slawik
Keyword(s):  
Automotive Industry ◽  
Shock Absorber ◽  
Flow Characteristic ◽  
Optimization Method ◽  
Design Parameters ◽  
Coupled Models ◽  
Model Based ◽  
Noise Evaluation ◽  
Optimal Values ◽  
Application Scope

The aim of this paper is to demonstrate a possibility to optimize a shock absorber design to minimize level of vibrations with the use of model-based approach. The paper introduces a proposal of an optimization method that allows to choose the optimal values of the design parameters using a shock absorber model to minimize the level of vibrations. A model-based approach is considered to obtain the optimal pressure-flow characteristic by simulations conducted with the use of coupled models, including the damper and the servo-hydraulic tester model. The presence of the tester model is required due to high non-linear coupling of the tested object (damper) and the tester itself to be used for noise evaluation. This kind of evaluation is used in the automotive industry to investigate dampers, as an alternative to vehicle-level tests. The paper provides numerical experimental case studies to show application scope of the proposed method

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