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Optimization of a trapezoidal cavity absorber for the Linear Fresnel Reflector

机译:线性菲涅耳反射镜梯形腔吸收器的优化

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摘要

To increase the efficiency of Concentrated Solar Power (CSP) plants, the use of optimization methods is a current topic of research.This paper focuses on applying an integrated optimization technology to a solar thermal application, more specifically for the optimizationof a trapezoidal cavity absorber of an LFR (Linear Fresnel Reflector), also called a Linear Fresnel Collector (LFC), CSP plant. LFRtechnology has been developed since the 1960s, and while large improvements in efficiencies have been made, there is still room forimprovement. Once such area is in the receiver design where the optimal cavity shape, coatings, insulation thickness, absorber pipe selection,layout and spacing always need to be determined for a specific application. This paper uses a commercial tool to find an optimaldesign for a set of operating conditions. The objective functions that are used to judge the performance of a 2-D cavity are the combinedheat loss through convection, conduction and radiation, as well as a wind resistance area. In this paper the effect of absorbed irradiationis introduced in the form of an outer surface of pipe temperature. Seven geometrical parameters are used as design variables. Based on asample set requiring 79 CFD simulations, a global utopia point is found that minimizes both objectives. The most sensitive parameterswere found to be the top insulation thickness and the cavity depth. Based on the results, the Multi-Objective Genetic Algorithm (MOGA)as contained in ANSYS DesignXplorer is shown to be effective in finding candidate optimal designs as well as the utopia point.
机译:为了提高集中式太阳能发电厂的效率,优化方法的使用是当前的研究课题。本文着重于将集成优化技术应用于太阳能热应用,更具体地讲,是用于优化梯形空腔吸收器。 LFR(线性菲涅尔反射镜),也称为线性菲涅尔收集器(LFC),CSP工厂。 LFR技术自1960年代以来就得到了发展,尽管效率已经有了很大的提高,但仍有改进的空间。一旦这样的区域进入接收器设计,就必须针对特定应用确定最佳的腔体形状,涂层,隔热层厚度,吸收器管道的选择,布局和间距。本文使用商业工具为一组运行条件寻找最佳设计。用来判断二维腔体性能的目标函数是通过对流,传导和辐射产生的综合热损失,以及风阻面积。在本文中,吸收辐射的影响以管道温度的外表面形式引入。七个几何参数用作设计变量。基于需要79个CFD模拟的样本集,发现了一个将两个目标都最小化的全局乌托邦点。发现最敏感的参数是顶部绝缘厚度和空腔深度。根据结果​​,ANSYS DesignXplorer中包含的多目标遗传算法(MOGA)被证明可有效地找到候选最佳设计以及乌托邦点。

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