用于深度学习应用程序的参数化U曲流的数据集.
Jens Decke1, Olaf Wünsch2, Bernhard Sick1
1Intelligent Embedded Systems, University of Kassel, Wilhelmshöher Allee 73, Kassel D-34121, Germany.
Data in brief
|August 30, 2023
概括
这个数据集提供了10,000个流体流动模拟U曲形状,帮助设计优化研究. 它支持各种深度学习方法,并提供各种数据表示以进行高级分析.
科学领域:
- 计算流体动力学 (CFD) 是一种计算流体动力学.
- 设计优化设计优化
- 机器学习 机器学习
背景情况:
- 在许多工程应用中,U曲线几何体中的流体流动和热传递至关重要.
- 开发高效的设计优化方法需要大量的模拟数据.
- 现有的数据集可能缺乏先进机器学习技术所需的多样化表示.
研究的目的:
- 引入1万个流体流动和热传输模拟的U曲形数据集.
- 为评估设计优化算法和深度学习方法提供一个基准.
- 通过提供多个数据表示,包括基于网格的数据来促进研究.
主要方法:
- 利用计算流体动力学 (CFD) 来生成10,000个模拟.
- 通过28个设计参数来描述每个模拟.
- 开发了三种不同的数据表示:参数-目标组合,2D图像分辨率和数值网格单元值.
主要成果:
- 现在可以获得10,000个U-bend流体流量和热传递模拟的数据集.
- 数据集包括适用于各种机器学习范式 (监督,半监督,无监督) 的各种数据类型.
- 源代码和数据生成容器被发布,确保可重复性和可访问性.
结论:
- 这一数据集是推动流体动力学设计优化和机器学习的宝贵资源.
- 包含基于网格的数据表示,为将深度学习应用于模拟数据开辟了新的途径.
- 数据生成工具的开源性质促进了透明度和该领域的进一步研究.
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