Building-Block-Flow Model for Large-Eddy Simulation

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Computational fluid dynamics stands as an essential tool for the design and optimization of aerodynamic/hydrodynamic vehicles. It is estimated that the impact of reducing transportation drag by 5% would be equivalent to that of doubling the US wind energy production. However, computational predictions of fluid flows around realistic vehicles poses a unique challenge due to the ubiquity of complex flow physics, including adverse pressure-gradient effects, flow separation, and laminar-to-turbulent transition. While some computational models predict one or two scenarios, no model performs accurately across all flow phenomena. This project will seek to devise a unified closure model for computational fluid dynamics capable of accounting for a rich collection of flow physics. The goals of this project are to couple fundamental physics and machine-learning modeling for a new computational fluids model. The project also leverages existing programs to promote diversity and inclusion in engineering, including participation in annual summer research programs and undergraduate research opportunities to engage women and underrepresented minorities.The core assumption of the closure model proposed is that a finite set of simple canonical flows contains the essential physics to predict more complex scenarios. The approach is implemented using artificial neural networks with large-eddy simulation and brings together five unique advances: (1) the model is directly applicable to arbitrary complex geometries, (2) it is constructed to predict different flow regimes (zero/favorable/adverse mean-pressure-gradient wall turbulence, separation, statistically unsteady turbulence with mean-flow three-dimensionality, and laminar flow), (3) the model can be scaled-up to capture additional flow physics if needed (e.g., shock waves), (4) the model guarantees consistency with the numerical discretization and the gridding strategy by compensating for numerical errors, and (5) the output of the model is accompanied by a confidence score in the prediction used for uncertainty quantification and grid refinement. The cases of study range from canonical flat plate turbulence to complex flows such as realistic aircraft configurations. The foundations established in this work will enable new venues to model multiple flow regimes in computational fluid dynamics.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.