CDS&E: Collaborative Research: Deep learning enhanced parallel computations of fluid flow around moving boundaries on binarized octrees

CDS

• 批准号: 1953222
• 负责人: Amir Barati Farimani
• 金额: $ 24.5万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953222&HistoricalAwards=false
• 关键词: CDS& Collaborative; Research; Deep; learning

Computer simulations of heat and fluid flow find applications in many aspects of science and engineering. Notable examples are aerodynamic design of aircrafts and automobiles, and weather forecasting. These simulations are often computationally expensive, and they are performed on supercomputers. Special methods are used to implement the equations of heat and fluid flow as a simulation software. The end goal is to create an accurate computer code that can make optimal use of available computing power. However, this end goal is becoming challenging on modern extreme-scale supercomputers that deploy a large of number of computing processors to work in parallel. Existing algorithms face performance bottlenecks and do not realize the full potential of a modern supercomputer. The project team will develop new algorithms to overcome this performance bottleneck. The successful completion of this award is expected to result in an open-source heat and fluid flow simulation software. The project team will develop educational tutorials to pique the interest of high-school students in new capabilities of computer simulation and machine learning techniques in science and engineering. The technical objective is to enhance parallel performance of simulations of incompressible fluid flow around moving boundaries. A recently developed binarized octree generation technique will be further developed as an open-source parallel adaptive mesh refinement software infrastructure to solve the fluid flow equations on Cartesian domains with deep levels of mesh adaptations. Machine learning techniques and deep neural nets will be adopted in ways to ease potential bottlenecks that are expected to degrade scalability of parallel computations when large number of processors are deployed in simulations. The project team will develop multiple deep learning algorithms such as convolutional neural networks and generative adversarial networks to learn the fluid flow around complex geometries and apply the learning for rapid and accurate field estimation at arbitrary points. To successfully incorporate the effect of boundary conditions at the interface, conditional generative adversarial networks will be trained on different coarse and fine grids to learn the communication pattern among the blocks. This award by the Division of Chemical, Bioengineering, Environmental and Transport Systems within the NSF Directorate of Engineering is jointly supported by the NSF Office of Advanced Cyberinfrastructure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

热和流体流动的计算机模拟在科学和工程的许多方面都有应用。值得注意的例子是飞机和汽车的空气动力学设计以及天气预报。这些模拟通常是计算昂贵的，并且它们是在超级计算机上执行的。采用特殊的方法将热量和流体流动方程实现为仿真软件。最终目标是创建一个精确的计算机代码，可以最佳地利用可用的计算能力。然而，这一最终目标在部署大量计算处理器并行工作的现代极端规模超级计算机上变得具有挑战性。现有的算法面临性能瓶颈，并且没有实现现代超级计算机的全部潜力。项目团队将开发新的算法来克服这一性能瓶颈。该奖项的成功完成预计将导致开源热和流体流动模拟软件。该项目团队将开发教育教程，以激发高中生对科学和工程领域计算机模拟和机器学习技术新功能的兴趣。技术目标是提高不可压缩流体绕动边界流动模拟的并行性能。最近开发的二进制八叉树生成技术将进一步发展为一个开源的并行自适应网格细化软件基础设施，以解决笛卡尔域上的流体流动方程与深层次的网格适应。机器学习技术和深度神经网络将被采用，以缓解潜在的瓶颈，当在模拟中部署大量处理器时，这些瓶颈预计会降低并行计算的可扩展性。该项目团队将开发多种深度学习算法，如卷积神经网络和生成对抗网络，以学习复杂几何形状周围的流体流动，并将学习应用于任意点的快速准确的场估计。为了成功地将边界条件的影响纳入界面，将在不同的粗网格和细网格上训练条件生成对抗网络，以学习块之间的通信模式。该奖项由NSF工程理事会化学、生物工程、环境和运输系统部门颁发，并得到NSF高级网络基础设施办公室的共同支持。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Adaptive grey wolf optimizer

• DOI: 10.1007/s00521-021-06885-9
• 发表时间: 2022-01-11
• 期刊: NEURAL COMPUTING & APPLICATIONS
• 影响因子: 6
• 作者: Meidani, Kazem;Hemmasian, AmirPouya;Farimani, Amir Barati
• 通讯作者: Farimani, Amir Barati

Mesh deep Q network: A deep reinforcement learning framework for improving meshes in computational fluid dynamics

网格深度 Q 网络：用于改进计算流体动力学中的网格的深度强化学习框架

• DOI: 10.1063/5.0138039
• 发表时间: 2023
• 期刊: AIP Advances
• 影响因子: 1.6
• 作者: Lorsung, Cooper;Barati Farimani, Amir
• 通讯作者: Barati Farimani, Amir

Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations

• DOI: 10.1063/5.0062546
• 发表时间: 2021-10-01
• 期刊: PHYSICS OF FLUIDS
• 影响因子: 4.6
• 作者: Pant, Pranshu;Doshi, Ruchit;Barati Farimani, Amir
• 通讯作者: Barati Farimani, Amir

Reduced-order modeling of fluid flows with transformers

• DOI: 10.1063/5.0151515
• 发表时间: 2023-05
• 期刊: Physics of Fluids
• 影响因子: 4.6
• 作者: AmirPouya Hemmasian;Amir Barati Farimani

MAB-OS: Multi-Armed Bandits Metaheuristic Optimizer Selection

• DOI: 10.1016/j.asoc.2022.109452
• 发表时间: 2022-08
• 期刊: Appl. Soft Comput.
• 作者: Kazem Meidani;S. Mirjalili;A. Farimani