关于使用深度学习和计算流体动力学来估计螺旋的统一动量源组件
Raúl Martínez-Cuenca1, Jaume Luis-Gómez1, Sergio Iserte2
1Department of Mechanical Engineering and Construction, Universitat Jaume I, 12071 Castelló de la Plana, Comunitat Valenciana, Spain.
iScience
|November 29, 2023
概括
本研究介绍了一种深度学习方法,以精确模拟工业设备的流体动力学. 这种新的方法降低了计算成本,并改善了螺旋和风力轮机的模拟.
科学领域:
- 计算流体动力学 (CFD) 是一种计算流体动力学.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 在CFD中模拟螺旋和风力轮机等工业设备需要大量的计算资源,并且通常依赖于简化假设.
- 现有的方法可能需要详细的设备信息,限制其适用性和准确性.
研究的目的:
- 开发一种新的基于深度学习 (DL) 的方法,用于在雷诺兹-平均纳维埃-斯托克斯 (RANS) 方程中解决统一的动量源项.
- 为了降低计算成本并提高CFD中模拟工业设备的准确性.
主要方法:
- 一个DL系统被训练使用数百个CFD模拟特征统一的动量源.
- 经过训练的DL模型使用设备附近的有限流速测量计算了螺旋的动量源.
主要成果:
- 对于均动量源,DL方法实现了总相对误差低于1%的总相对误差.
- 在模拟真实螺旋时观察到一个中度的错误,表明了良好的概括性.
结论:
- 这种基于DL的方法为模拟CFD中的工业设备提供了更准确,更高效的计算替代方案.
- 该方法对工程和设计中更广泛的应用具有前景,其中精确的流体动力学模拟是至关重要的.
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