Deep learning to develop zero-equation based turbulence model for CFD simulations of the built environment

• Published in: Building simulation Vol. 17; no. 3; pp. 399 - 414
• Main Authors: Calzolari, Giovanni, Liu, Wei
• Format: Journal Article
• Language: English
• Published: Beijing Tsinghua University Press 01.03.2024; Springer Nature B.V
• Subjects: Air quality; Algorithms; Atmospheric Protection/Air Quality Control/Air Pollution; Building Construction and Design; Built environment; Computational fluid dynamics; computational fluid dynamics (CFD); Computer simulation; Coupling; Deep learning; Engineering; Engineering Thermodynamics; Fluid flow; Heat and Mass Transfer; Indoor environments; Machine learning; Mapping; Model accuracy; Monitoring/Environmental Analysis; Multilayer perceptrons; Multilayers; Neural networks; OpenFOAM; Research Article; Simulation; Thermal comfort; turbulence model; Turbulence models; Turbulent flow; computational fluid dynamics (CFD); OpenFOAM; turbulence model; neural networks
• ISSN: 1996-3599; 1996-8744; 1996-8744
• DOI: 10.1007/s12273-023-1083-4

This study aims to improve the accuracy and speed of predictions for thermal comfort and air quality in built environments by creating a coupled framework between computational fluid dynamics (CFD) simulations and deep learning models. The coupling approach is showcased by the development of a data-driven turbulence model. The new turbulence model is built using a deep learning neural network, whose mapping structure is based on a zero-equation turbulence model for built environment simulations, and is coupled with the CFD software OpenFOAM to create a hybrid framework. The neural network is a standard shallow multi-layer perceptron. The number of hidden layers and nodes per layer was optimized using Bayesan optimization algorithm. The framework is trained on an indoor environment case study, as well as tested on an indoor office simulation and an outdoor building array simulation. Results show that the deep learning based turbulence model is more robust and faster than traditional two-equation Reynolds average Navier-Stokes (RANS) turbulence models, while maintaining a similar level of accuracy. The model also outperforms the standard algebraic zero-equation model due to its superior ability to generalize to various flow scenarios. Despite some challenges, namely the mapping constraint, the limited training dataset size and the source of generation of training data, the hybrid framework demonstrates the viability of the coupling technique and serves as a starting point for future development of more reliable and advanced models.