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A simulation-based inverse design of preset aircraft cabin environment

机译：预设机舱环境的基于仿真的逆设计

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摘要

The inverse design of preset aircraft cabin environment (ACE) is presented. Five design variables (inlet velocity, angle, temperature, position and outlet position) and three design objectives (PMV, DR and Air Age) are involved in the current inverse design. The Artificial Neural Network (ANN) and genetic algorithm (GA) are combined to design ACE based on the Computational Fluid Dynamics (CFD) analysis. To eliminate the uncertainty and risk of accumulative errors in the design process, both ANN and CFD are used to obtain the design objectives of new individuals generated by GA. To enhance the prediction accuracy of ANN, three single-output ANNs for each design objective are adopted instead of one mutiple-output ANN. The results obtained by GA alone and the proposed method are compared. Instead of applying GA, 57% of computational costs are reduced when the proposed method is used. Comparing the design results of different scales of CFD databases, it is found that the CFD database with 110 samples has the less computational cost, while that with 70 samples has better solutions.
机译：提出了预设飞机机舱环境（ACE）的逆向设计。当前的逆向设计涉及五个设计变量（入口速度，角度，温度，位置和出口位置）和三个设计目标（PMV，DR和空气年龄）。基于计算流体动力学（CFD）分析，将人工神经网络（ANN）和遗传算法（GA）相结合来设计ACE。为了消除设计过程中的不确定性和累积误差的风险，ANN和CFD均用于获得由GA生成的新个体的设计目标。为了提高人工神经网络的预测精度，针对每个设计目标采用三个单输出人工神经网络，而不是一个多输出人工神经网络。比较了仅通过遗传算法和提出的方法获得的结果。代替应用遗传算法，使用建议的方法可以减少57％的计算成本。比较不同规模的CFD数据库的设计结果，发现110个样本的CFD数据库具有较低的计算成本，而70个样本的CFD数据库具有更好的解决方案。

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