基于深度学习的浮空器模型性能预测:升阻系数比案例研究

Faisal Fahmi, Rizqon Fajar, Sigit Tri Atmaja, Erwandi Erwandi, D. Rahuna
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引用次数: 0

摘要

由于浮空器设计的复杂性和有限的测试设施,开发工程设计需要大量资源和时间,尤其是浮空器设计的浮筒。基于智能的计算设计(IBCD)技术整合了计算设计技术和机器学习(ML)算法,提供了一种通过预测来减少所需测试的解决方案。本文提出了一种基于深度学习(DL)的 IBCD 方法,用于浮筒升阻系数比(CL/CD)建模,其中 DL 是最强大的 ML 之一。所提出的方法包括两个阶段:超参数优化和 DL 模型训练与评估。第一阶段采用遗传算法(GA)来有效探索复杂的超参数组合。使用 DL 模型对浮子的 CL/CD 预测进行评估,结果令人满意,R 方为 0.9329,最小均方误差(MSE)为 0,001536。这些结果表明,DL 模型能够准确预测浮筒的性能,并有助于进一步优化设计。因此,所提出的方法可为浮筒性能预测提供一种省时、经济的解决方案,有助于优化浮筒设计并增强其功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio
Developing an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.
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