MOGA-II for an Automotive Cooling Duct Optimization on Distributed Resources

Poles, Silvia; Geremia, Paolo; Campos, Fco. Alberto; Weston, Stephen; Islam, Moni

doi:10.1007/978-3-540-70928-2_48

Cited by 16 publications

(11 citation statements)

References 3 publications

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“…In order to identify the set of good solutions, it is necessary to use the proper algorithms that, starting from tentative solutions, allow the evolution towards the optimum. According to the authors' experience, MOGA results to be well-suited for solving this kind of problems and its goodness it certified by several previous works [4][5][6]. Govindan et al [7] reports an overfitting problem in the neural network (NN) module in modeFRONTIER proposing a very interesting approach [8,9].…”

Section: Structural Geometrical Optimizationsupporting

confidence: 68%

Manufacturing Process Simulation for Product Design Chain Optimization

Gramegna

Corte

Poles

2011

Materials and Manufacturing Processes

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Engineering simulation is, without any doubt, the key strategy to win in a globally competitive environment. The integrated Computer Aided Engineering (CAE) approach considers all life-cycle steps from the design stage to the in-service use. In this work, engineering simulation technologies provide detailed virtual production processes able to predict the final component quality in terms of defects and virtual structural behavior. In turn, the structural behavior provides information about the performance of the component. Furthermore, through the combined use of process and structural simulations, optimization techniques allow to define the component design by automatic design change and to verify the change's effectiveness in terms of structural strength. The most innovative aspect is the possibility to carry out a structural simulation using, as initial condition, the local mechanical properties and the prestress status due to residual stresses at the end of the manufacturing process. In this article, the redesign of the roller support (manufactured in ductile iron GJS400) is presented: conflicting objectives such as minimization of components weight and minimization of deformation. The use of the optimization software modeFRONTIER has allowed exploring different geometric configurations and, thanks to its multiobjective genetic algorithm (MOGA), finding the trade-off curve of conflicting objectives. The final results evidence some areas with high residual peak stresses, which have a decreasing effect on the fatigue life, and underline the importance of considering an extended optimization analysis that includes both the casting simulation and load-case analyses. IntroductionIn the latest years, the industry has studied and implemented innovative Computer Aided Engineering (CAE) design method to improve the performance and the weight of components. The innovative CAE procedure consists in integrating the whole aspects that characterize the component life, from the manufacturing to the withdrawal, in order to address an always increasing quality request, a complete knowledge of the component potentials, cost reduction, and time to market savings. In particular, the study of the mechanical properties and of the residual stress mapping, due to the manufacturing process, helps to improve the performance and the reliability of the component.CAE offers a large number of efficient computational codes to simulate each phase of product development from the manufacturing process to thermomechanic fatigue life. modeFRONTIER allows to optimize each phase of product development and at the same time to integrate the different design tools, offering to the designers the necessary design chain results to predict the component in service life. This new approach is a winning "weapon" for leveraging a company's knowledge base and to improve competitiveness of a new product.As an example of multidisciplinary optimization approach, an interesting case study regarding a roller

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Section: Structural Geometrical Optimizationsupporting

confidence: 68%

Manufacturing Process Simulation for Product Design Chain Optimization

Gramegna

Corte

Poles

2011

Materials and Manufacturing Processes

Self Cite

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“…Poles et al [12] use bubble charts for representing their multi-objective optimization results, but in a different way: bubble position reflects the two quality values, bubble color and size represent standard deviations of the two quality values. Sasaki et al [14] use a visualization similar to bubble charts to display solutions of the multi-objective optimization problem in three and four-dimensional spaces.…”

Section: Related Workmentioning

confidence: 99%

Visualization and exploration of optimal variants in product line engineering

Murashkin

Antkiewicz

Rayside

et al. 2013

Proceedings of the 17th International Software Product Line Conference

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The decision-making process in Product Line Engineering (PLE) is often concerned with variant qualities such as cost, battery life, or security. Pareto-optimal variants, with respect to a set of objectives such as minimizing a variant's cost while maximizing battery life and security, are variants in which no single quality can be improved without sacrificing other qualities. We propose a novel method and a tool for visualization and exploration of a multi-dimensional space of optimal variants (i.e., a Pareto front). The visualization method is an integrated, interactive, and synchronized set of complementary views onto a Pareto front specifically designed to support PLE scenarios, including: understanding differences among variants and their positioning with respect to quality dimensions; solving trade-offs; selecting the most desirable variants; and understanding the impact of changes during product line evolution on a variant's qualities. We present an initial experimental evaluation showing that the visualization method is a good basis for supporting these PLE scenarios.

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“…Each element in the Pareto-optimal set constitutes a non-inferior solution to the multi-objective optimization, and they are distributed along the Pareto-optimal front [24,25]. In order to locate the Pareto front, a Multi Objective Genetic Algorithm (MOGA) in modeFRONTIER V4.3 produced by ESTECO is used [26]. This algorithm repeatedly modifies a population of design variable vectors i x  according to the design requirement.…”

Section: Optimal Proceduresmentioning

confidence: 99%

“…The new individual is then created moving in a randomly weighted direction that lies within the ones characterized by the given individual and the other two. The DNA string mutation ratio gives the percentage of the individual DNA that has to be changed by the mutation operator [26].…”

Section: Optimal Proceduresmentioning

confidence: 99%

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Optimal Thermal Design of a Stacked Mini-Channel Heat Sink Cooled by a Low Flow Rate Coolant

Pang

Wang

et al. 2013

Entropy

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Application requirements for avionics are often very strict. For example, the heat sinks of avionics need very good temperature uniformity, but the flow rate of coolant is very restricted. In addition, the use of micro-channels is not recommended due to the potential clogging issue. Considering these design requirements, we will discuss a multiple-objective optimal design method to obtain a good stacked mini-channel structure for avionics applications. In our thermal design, the design variables are the mini-channel geometry parameters. Temperature uniformity, entropy generation, max temperature of heat sink and pump work are chosen as the objective functions. A Multi Objective Genetic Algorithm (MOGA) and Fluent solver are used together to minimize multiple objective functions subject to constraints, and locate the Pareto front. By analyzing the multiple objective optimal results, we can draw the conclusion that the objective functions of T max and s g have same effect on the optimization, and the multiple optimal results are a set and not a single value. If mostly focusing on the temperature uniformity, we can recommend some optimal structures to design a stacked mini-channel heat sink.

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MOGA-II for an Automotive Cooling Duct Optimization on Distributed Resources

Cited by 16 publications

References 3 publications

Manufacturing Process Simulation for Product Design Chain Optimization

Manufacturing Process Simulation for Product Design Chain Optimization

Visualization and exploration of optimal variants in product line engineering

Optimal Thermal Design of a Stacked Mini-Channel Heat Sink Cooled by a Low Flow Rate Coolant

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