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Multi-objective Optimization of a Multifunctional Structure through a MOGA and SOM based Methodology

机译:通过MOGA和SOM的方法多功能结构进行多功能结构

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A Multi-Objective Optimization (MOO) problem concerning the thermal control problem of Multifunctional Structures (MFSs) is here addressed. In particular the use of Multi-Objective algorithms from an optimization tool and Self-Organizing Maps (SOM) is proposed for the identification of the optimal topological distribution of the heating components for a multifunctional test panel, the Advanced Bread Board (ABB). MFSs are components that conduct many functions within a single piece of hardware, shading the clearly defined boundaries that identify traditional subsystems. Generally speaking, MFSs have already proved to be a disrupting technology, especially in aeronautics and space application fields. The case study exploited in this paper refers to a demonstrator breadboard called ABB. ABB belongs to a particular subset of an extensive family of MFS, that is, of thermo-structural panels with distributed electronics and a health monitoring network. The scope of this work is to speculate upon the ABB concept, by investigating further developments that could improve the design of this class of multifunctional structures. This paper is focused on the application of Multi-Objective Optimization strategies to the selection of thermal hardware layout and control logic for spaceborne multifunctional structures, in order to minimize energy consumption and, to a lesser extent, to ensure a good temperature distribution. In particular the methodology we propose encompasses the combined use of Multi-Objective algorithms and SOM to address the optimization problem.
机译:有关多功能结构的热控制问题(MFSS)的多目标优化(MOO)问题在此处得到解决。特别是从一个优化工具和自组织映射(SOM)的使用的多目标的算法提出了一种用于加热元件的最佳拓扑分布的鉴定的多功能测试板,高级面包板(ABB)。 MFSS是在单件硬件中进行许多功能的组件,阴影明确定义的识别传统子系统的边界。一般来说,MFSS已经被证明是一个扰乱技术,特别是在航空和空间应用领域。本文利用的案例研究是指名为ABB的示威资源板。 ABB属于广泛的MF系列的特定子集,即,具有分布式电子和健康监控网络的热结构面板。这项工作的范围是推测ABB概念,通过调查可以改善这类多功能结构的设计的进一步发展。本文的重点是对多目标优化策略来的热硬件布局和控制逻辑星载多官能结构的选择中的应用,以最小化能量消耗,并且在较小程度上,以确保良好的温度分布。特别是我们提出的方法包括组合使用多目标算法和SOM来解决优化问题。

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