Multi-Objective Optimization of Road Vehicle Passive Suspensions With Inerter

2016 ◽  
Author(s):  
Kesavan Ramakrishnan ◽  
Liunan Yang ◽  
Federico Maria Ballo ◽  
Massimiliano Gobbi ◽  
Giampiero Mastinu
Keyword(s):  
Ride Comfort ◽  
Damping Ratio ◽  
Spring Stiffness ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Suspension Spring ◽  
Multi Objective ◽  
Working Space ◽  
Design Variables ◽  
Equivalent Mass

The influence of inerter on the performance of passive suspension systems is studied by comparing six different suspension architectures using a simplified quarter-car model. The suspension architectures can have one or two springs, damper, and inerter. Ride comfort, road holding, and working space are considered as the objective functions, while the suspension spring stiffness, damping ratio, and inerter equivalent mass are taken as the design variables for the multi-objective optimization. The Pareto-optimal solutions are computed and compared in the objective functions domain. The results confirm that specific inerter architectures provide advantages when all the design variables are varied. The inerter benefits are more evident in all the considered architectures, when the suspension spring stiffness is kept constant.

Use of nonlinear asymmetrical shock absorbers in multi-objective optimization of the suspension system in a variety of road excitations

2016 ◽  
Vol 231 (2) ◽  
pp. 372-387 ◽  
Author(s):  
Abolfazl Seifi ◽  
Reza Hassannejad ◽  
Mohammad Ali Hamed
Keyword(s):  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Low Cost ◽  
Suspension System ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Vehicle Handling ◽  
Multi Objective ◽  
Shock Absorbers ◽  
The Impact

In this study, a new method to improve ride comfort, vehicle handling, and workspace was presented in multi-objective optimization using nonlinear asymmetrical dampers. The main aim of this research was to provide suitable passive suspension based on more efficiency and the low cost of the mentioned dampers. Using the model with five degrees of freedom, suspension system parameters were optimized under sinusoidal road excitation. The main functions of the suspension system were chosen as objective functions. In order to better illustrate the impact of each objective functions on the suspension parameters, at first two-objective and finally five-objective were considered in the optimization problem. The obtained results indicated that the optimized viscous coefficients for five-objective optimization lead to 3.58% increase in ride comfort, 0.74% in vehicle handling ability, and 2.20% in workspace changes for the average of forward and rear suspension.

Robustness Against Large Variations in Multi-Objective Optimization Problems

2013 ◽  
Author(s):  
Weijun Wang ◽  
Stéphane Caro ◽  
Fouad Bennis
Keyword(s):  
Optimization Problems ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Design Environment ◽  
Multi Objective ◽  
Uncertainty Sources ◽  
Design Variables ◽  
Robustness Index ◽  
Environment Parameters

In the presence of multiple optimal solutions in multi-modal optimization problems and in multi-objective optimization problems, the designer may be interested in the robustness of those solutions to make a decision. Here, the robustness is related to the sensitivity of the performance functions to uncertainties. The uncertainty sources include the uncertainties in the design variables, in the design environment parameters, in the model of objective functions and in the designer’s preference. There exist many robustness indices in the literature that deal with small variations in the design variables and design environment parameters, but few robustness indices consider large variations. In this paper, a new robustness index is introduced to deal with large variations in the design environment parameters. The proposed index is bounded between zero and one, and measures the probability of a solution to be optimal with respect to the values of the design environment parameters. The larger the robustness index, the more robust the solution with regard to large variations in the design environment parameters. Finally, two illustrative examples are given to highlight the contributions of this paper.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION OF REINFORCED CONCRETE FRAMES USING NSGA-II ALGORITHM

2016 ◽  
Vol 8 (4) ◽  
pp. 157-164 ◽  
Author(s):  
Mehdi Babaei ◽  
Masoud Mollayi
Keyword(s):  
Genetic Algorithm ◽  
Reinforced Concrete ◽  
Steel Structures ◽  
American Concrete Institute ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Design Variables ◽  
Pareto Fronts

In recent decades, the use of genetic algorithm (GA) for optimization of structures has been highly attractive in the study of concrete and steel structures aiming at weight optimization. However, it has been challenging for multi-objective optimization to determine the trade-off between objective functions and to obtain the Pareto-front for reinforced concrete (RC) and steel structures. Among different methods introduced for multi-objective optimization based on genetic algorithms, Non-Dominated Sorting Genetic Algorithm II (NSGA II) is one of the most popular algorithms. In this paper, multi-objective optimization of RC moment resisting frame structures considering two objective functions of cost and displacement are introduced and examined. Three design models are optimized using the NSGA-II algorithm. Evaluation of optimal solutions and the algorithm process are discussed in details. Sections of beams and columns are considered as design variables and the specifications of the American Concrete Institute (ACI) are employed as the design constraints. Pareto-fronts for the objective space have been obtained for RC frame models of four, eight and twelve floors. The results indicate smooth Pareto-fronts and prove the speed and accuracy of the method.

Multi-Objective Optimization of Pin-Fin Heatsinks

2009 ◽  
Author(s):  
Keisuke Horiuchi ◽  
Atsuo Nishihara ◽  
Kazuyuki Sugimura
Keyword(s):  
Pressure Drop ◽  
Thermal Resistance ◽  
Approximation Model ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Multi Objective ◽  
Pin Fin ◽  
Design Variables ◽  
Kriging Approximation

The multi-objective optimization of pin-fin heatsinks using a Kriging approximation model is presented based on systematic experimental results. Thermal resistance and pressure drop are the objective functions in this study. Pareto solutions to the objective functions are illustrated. We derived the design rules for the diameter, height, and pitches for the uniform staggered arrays of pin-fin heatsinks by correlating the objective functions with design variables. We also analyzed the contribution of all design variables to the thermal resistance as well as the pressure drop. We found that both the thermal resistance and the pressure drop are the most sensitive to the ratio of transverse pitch to pin-fin diameter.

Multi-Objective Optimization Design of the Helical Gear Transmission Based on Coalition Cooperative Game Theory

2010 ◽  
Vol 44-47 ◽  
pp. 1525-1532 ◽  
Author(s):  
Rui Meng ◽  
Neng Gang Xie ◽  
Xiao Jing Han
Keyword(s):  
Game Theory ◽  
Cooperative Game ◽  
Optimization Design ◽  
Cooperative Game Theory ◽  
Helical Gear ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Strategy Space ◽  
Multi Objective ◽  
Design Variables

Considering helical gear transmission's economic performance and drive reliability, construct multi-objective optimization model of the helical gear transmission with taking normal module, teeth number of small helical gear, helix angle and the gear width coefficient as design variables and taking the volume of small and large helical gear and opposite number of overlap ratio as objective functions. Propose multi-objective optimization design method based on coalition cooperative game theory where the two design goals are seen as two game players. By calculating the impact factor of design variables to objective functions and fuzzy clustering, the design variables are divided into strategy space of game players. Each game player takes its own revenue function as target and does single objective optimization in its own strategy space in order to get its own best strategy. The best strategies of all players form a combination of one round game and the optimal solution can be obtained through several game rounds. Example results show the effectiveness of game method.

Optimization of a Solid Oxide Fuel Cell and Gas Turbine Hybrid System

2015 ◽  
Author(s):  
Setthawut Kanarit ◽  
Wirinya Karunkeyoon ◽  
Ali Al-Alili ◽  
Valerie Eveloy ◽  
Peter Rodgers
Keyword(s):  
Hybrid System ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Design Variables ◽  
Parametric Studies

The low efficiency and high environmental impact of conventional power cycles are a major concern. The integration of solid oxide fuel cells (SOFCs) and gas turbines (GTs) has been proposed in the literature to increase fuel to power conversion. Parametric studies are usually conducted to identify suitable operating conditions for such integrated systems. However, parametric studies only consider the main effects of the design variables and do not provide information on the interactive effect of the design variables. In this study, a multi-objective optimization is performed to optimize the performance of a SOFC-GT hybrid system. The objective functions are system efficiency and total cost rate, including capital, operating, and environmental penalty costs. The design variables are selected based on sensitivity analysis to assess the effect of the variables on the objective functions. The performance of the SOFC-GT hybrid system is modeled in Aspen Plus, while MATLAB is used for multi-objective optimization. The multi-objective optimization solution is presented in terms of a Pareto frontier.

Enhanced Multi-Objective Optimization of a Microchannel Heat Sink Using Multiple Surrogates Modeling

2009 ◽  
Author(s):  
Afzal Husain ◽  
Kwang-Yong Kim
Keyword(s):  
Heat Sink ◽  
Design Space ◽  
Surrogate Models ◽  
Pareto Optimal ◽  
Microchannel Heat Sink ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Design Variables

A liquid flow microchannel heat sink has been studied and optimized with the help of three-dimensional numerical analysis and multiple surrogate methods. Two objective functions, thermal resistance and pumping power have been selected to assess the performance of the microchannel heat sink. The design variables related to the microchannel top and bottom widths, depth and fin width, which contribute to objective functions, have been identified and design space has been explored through some preliminary calculations. Design of experiments was performed and a three-level full factorial design was selected to exploit the design space. The numerical solutions obtained at these design points were utilized to construct surrogate models namely Response Surface Approximations and Kriging. A hybrid multi-objective evolutionary algorithm coupled with surrogate models and a gradient-based search algorithm is applied to find global Pareto-optimal solutions. Since, the surrogate models are highly problem-dependent, the accuracy of the two surrogate models has been discussed in view of their predictions at on- and off-Pareto-optimal front. The trade-off analysis was performed in view of the two competing objectives. The Pareto-optimal sensitivity (change in value along the Pareto-optimal front) of the design variables has been found out to economically compromise with the design variables contributing relatively less to the objective functions. The application of the multiple surrogate methods not only improves quality of multi-objective optimization but also gives the feedback of the fidelity of the model near the optimum region.

Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

2006 ◽  
Vol 34 (3) ◽  
pp. 170-194 ◽  
Author(s):  
M. Koishi ◽  
Z. Shida
Keyword(s):  
Inverse Problems ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Design Stage ◽  
Objective Functions ◽  
Pareto Solutions ◽  
Design Problems ◽  
Multi Objective ◽  
Design Engineers ◽  
Design Variables

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.

scholarly journals A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

2021 ◽  
Vol 11 (10) ◽  
pp. 4575
Author(s):  
Eduardo Fernández ◽  
Nelson Rangel-Valdez ◽  
Laura Cruz-Reyes ◽  
Claudia Gomez-Santillan
Keyword(s):  
Portfolio Optimization ◽  
Imperfect Information ◽  
Model Parameters ◽  
Final Decision ◽  
Project Portfolio ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Group Member

This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

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