Building-block-flow computational model for large-eddy simulation of external aerodynamic applications

Researchers in the Computational Turbulence Group integrate computational fluid dynamics simulations with artificial neural networks to enhance the accuracy of predicting airflow around aircraft and other aerospace-related vehicles.

Authors: Gonzalo Arranz, Yuenong Ling, Sam Costa, Konrad Goc, Adrian Lozano-Duran
Citation: Communications Engineering, 2024

Abstract:
Computational fluid dynamics is an essential tool for accelerating the discovery and adoption of transformative designs across multiple engineering disciplines. Despite its many successes, no single approach consistently achieves high accuracy for all flow phenomena of interest, primarily due to limitations in the modeling assumptions. Here, we introduce a closure model for wall-modeled large-eddy simulation to address this challenge. The model, referred to as the Building-block Flow Model (BFM), rests on the premise that a finite collection of simple flows encapsulates the essential missing physics necessary to predict more complex scenarios. The BFM is designed to: (1) predict multiple flow regimes, (2) unify the closure model at solid boundaries and the rest of the flow, (3) ensure consistency with numerical schemes and gridding strategies by accounting for numerical errors, (4) be directly applicable to arbitrary complex geometries, and (5) be scalable to model additional flow physics in the future. The BFM is utilized to predict key quantities in five cases, including an aircraft in landing configuration, demonstrating similar or superior capabilities compared to previous state-of-the-art models. The design of BFM opens up new opportunities for developing closure models that can accurately represent various flow physics across different scenarios.