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

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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.