A Vehicle Design Optimization Problem Associated with Rolling Resistance Reduction

1984 ◽  
Author(s):  
X. P. Lu ◽  
L. Segel
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Rolling Resistance ◽  
Vehicle Design ◽  
Resistance Reduction

Adaptive Complex Method for Efficient Design Optimization

2007 ◽  
Author(s):  
Marcus Pettersson ◽  
Johan O¨lvander
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Global Optimum ◽  
Direct Search ◽  
Complex Method ◽  
Efficient Design ◽  
Direct Search Methods ◽  
Simulation Based ◽  
Set Of Points ◽  
Quasi Newton

Box’s Complex method for direct search has shown promise when applied to simulation based optimization. In direct search methods, like Box’s Complex method, the search starts with a set of points, where each point is a solution to the optimization problem. In the Complex method the number of points must be at least one plus the number of variables. However, in order to avoid premature termination and increase the likelihood of finding the global optimum more points are often used at the expense of the required number of evaluations. The idea in this paper is to gradually remove points during the optimization in order to achieve an adaptive Complex method for more efficient design optimization. The proposed method shows encouraging results when compared to the Complex method with fix number of points and a quasi-Newton method.

scholarly journals EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Yi Ren ◽  
Alparslan Emrah Bayrak ◽  
Panos Y. Papalambros
Keyword(s):  
Optimization Problem ◽  
Solution Space ◽  
Efficient Global Optimization ◽  
Vehicle Design ◽  
Control Problems ◽  
Short Term ◽  
Algorithm Efficiency ◽  
Np Complete ◽  
And Control

We compare the performance of human players against that of the efficient global optimization (EGO) algorithm for an NP-complete powertrain design and control problem. Specifically, we cast this optimization problem as an online competition and received 2391 game plays by 124 anonymous players during the first month from launch. We found that while only a small portion of human players can outperform the algorithm in the long term, players tend to formulate good heuristics early on that can be used to constrain the solution space. Such constraining of the search enhances algorithm efficiency, even for different game settings. These findings indicate that human-assisted computational searches are promising in solving comprehensible yet computationally hard optimal design and control problems, when human players can outperform the algorithm in a short term.

scholarly journals Structural reliability under uncertainty in moments: distributionally-robust reliability-based design optimization

2021 ◽  
Author(s):  
Yoshihiro Kanno
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Structural Reliability ◽  
Reliability Based Design Optimization ◽  
Input Distribution ◽  
Reliability Based Design ◽  
Optimal Value ◽  
Reliability Constraint ◽  
Distributionally Robust ◽  
Robust Reliability

AbstractThis study considers structural optimization under a reliability constraint, in which the input distribution is only partially known. Specifically, when it is only known that the expected value vector and the variance-covariance matrix of the input distribution belong to a given convex set, it is required that the failure probability of a structure should be no greater than a specified target value for any realization of the input distribution. We demonstrate that this distributionally-robust reliability constraint can be reduced equivalently to deterministic constraints. By using this reduction, we can handle a reliability-based design optimization problem under the distributionally-robust reliability constraint within the framework of deterministic optimization; in particular, nonlinear semidefinite programming. Two numerical examples are solved to demonstrate the relation between the optimal value and either the target reliability or the uncertainty magnitude.

Multidisciplinary Design Optimization: A New Way to Bring Softer Bows

2008 ◽  
Author(s):  
Zhiqiang Hu ◽  
Weicheng Cui ◽  
Jianmin Yang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Force Density ◽  
Optimization Analysis ◽  
Resistance Reduction ◽  
Collision Force ◽  
Bulbous Bow

It is well known that sharp bulbous bow has a good performance on ship resistance reduction, but it is also threatens the struck ships and the environment greatly. For their own economy profit, ship owners would like the bulbous bow to be designed sharp and rigid. However, from the viewpoint of environmental protection, the bulbous bow should be designed blunt and soft. Multidisciplinary Design Optimization (MDO) is a prosperous design concept and technique, to reconcile this problem effectively. The basic concept and theories of MDO are introduced in this paper. An optimization analysis is accomplished on the bulbous bow design for a container ship, using Collaborative Optimization Method. The characters of the bulbous bow on resistance reduction, collision force density and structural strength requirement are all considered at the same time. A compatible bulbous bow can be obtained by this way.

Sequential Radial Basis Function-Based Optimization Method Using Virtual Sample Generation

2020 ◽  
Vol 142 (11) ◽  
Author(s):  
Yifan Tang ◽  
Teng Long ◽  
Renhe Shi ◽  
Yufei Wu ◽  
G. Gary Wang
Keyword(s):  
Radial Basis Function ◽  
Design Optimization ◽  
Basis Function ◽  
Optimization Problem ◽  
Optimization Method ◽  
Support Vector ◽  
Virtual Sample ◽  
Real Samples ◽  
Virtual Samples ◽  
Radial Basis

Abstract To further reduce the computational expense of metamodel-based design optimization (MBDO), a novel sequential radial basis function (RBF)-based optimization method using virtual sample generation (SRBF-VSG) is proposed. Different from the conventional MBDO methods with pure expensive samples, SRBF-VSG employs the virtual sample generation mechanism to improve the optimization efficiency. In the proposed method, a least squares support vector machine (LS-SVM) classifier is trained based on expensive real samples considering the objective and constraint violation. The classifier is used to determine virtual points without evaluating any expensive simulations. The virtual samples are then generated by combining these virtual points and their Kriging responses. Expensive real samples and cheap virtual samples are used to refine the objective RBF metamodel for efficient space exploration. Several numerical benchmarks are tested to demonstrate the optimization capability of SRBF-VSG. The comparison results indicate that SRBF-VSG generally outperforms the competitive MBDO methods in terms of global convergence, efficiency, and robustness, which illustrates the effectiveness of virtual sample generation. Finally, SRBF-VSG is applied to an airfoil aerodynamic optimization problem and a small Earth observation satellite multidisciplinary design optimization problem to demonstrate its practicality for solving real-world optimization problems.

Evolutionary Algorithm Embedded With Bump-Hunting for Constrained Design Optimization

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Kamrul Hasan Rahi ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Computational Cost ◽  
Search Space ◽  
Bump Hunting ◽  
Simulation Based ◽  
Engineering Problems ◽  
Turbine Design

Abstract Real-world design optimization problems commonly entail constraints that must be satisfied for the design to be viable. Mathematically, the constraints divide the search space into feasible (where all constraints are satisfied) and infeasible (where at least one constraint is violated) regions. The presence of multiple constraints, constricted and/or disconnected feasible regions, non-linearity and multi-modality of the underlying functions could significantly slow down the convergence of evolutionary algorithms (EA). Since each design evaluation incurs some time/computational cost, it is of significant interest to improve the rate of convergence to obtain competitive solutions with relatively fewer design evaluations. In this study, we propose to accomplish this using two mechanisms: (a) more intensified search by identifying promising regions through “bump-hunting,” and (b) use of infeasibility-driven ranking to exploit the fact that optimal solutions are likely to be located on constraint boundaries. Numerical experiments are conducted on a range of mathematical benchmarks and empirically formulated engineering problems, as well as a simulation-based wind turbine design optimization problem. The proposed approach shows up to 53.48% improvement in median objective values and up to 69.23% reduction in cost of identifying a feasible solution compared with a baseline EA.

Hierarchical Arrangement of Characteristics in Product Design Optimization Problems for Deeper Insight Into Optimized Results

2004 ◽  
Author(s):  
Masataka Yoshimura ◽  
Masahiko Taniguchi ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Optimum Design ◽  
Design Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Method ◽  
Hierarchical Level ◽  
Hierarchical Arrangement ◽  
Insight Into

This paper proposes a design optimization method for machine products that is based on the decomposition of performance characteristics, or alternatively, extraction of simpler characteristics, to accommodate the specific features or difficulties of a particular design problem. The optimization problem is expressed using hierarchical constructions of the decomposed and extracted characteristics and the optimizations are sequentially repeated, starting with groups of characteristics having conflicting characteristics at the lowest hierarchical level and proceeding to higher levels. The proposed method not only effectively enables achieving optimum design solutions, but also facilitates deeper insight into the design optimization results, and aids obtaining ideas for breakthroughs in the optimum solutions. An applied example is given to demonstrate the effectiveness of the proposed method.

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