Classification of Machine Learning Frameworks for Data-Driven Thermal Fluid Models

TL;DR

This paper introduces five machine learning frameworks for data-driven thermal fluid simulation, compares their performance across applications, and demonstrates their use in heat diffusion, turbulence, and two-phase flow modeling.

Contribution

It defines and compares five novel ML frameworks for thermal fluid modeling, highlighting Type III's potential with big data and high computational needs.

Findings

Type II ML performs well with limited data.

Type III ML effectively utilizes large field data.

Deep learning-based slip closure shows bounded error.

Abstract

Thermal fluid processes are inherently multi-physics and multi-scale, involving mass-momentum-energy transport phenomena. Thermal fluid simulation (TFS) is based on solving conservative equations, for which - except for "first-principle" direct numerical simulation - closure relations (CRs) are required to provide microscopic interactions. In practice, TFS is realized through reduced-order modeling, and its CRs can be informed by observations and data from relevant and adequately evaluated experiments and high-fidelity simulations. This paper focuses on data-driven TFS models, specifically on the development using machine learning (ML). Five ML frameworks, dubbed Type I to V, are introduced. The frameworks vary in their performance for different applications depending on the level of knowledge of governing physics, the source, type, amount and quality of available data for training. Notably, outlined for the first time in this paper, Type III models present stringent requirements on modeling, substantial computing resources for training, and high potential in extracting value from "big data" in thermal fluid research. The paper demonstrates the ML frameworks by three examples. First, we utilize the heat diffusion equation with nonlinear conductivity formulated by convolutional neural networks to illustrate the applications of Type I, II, and III ML. The results indicate a preference for Type II ML under deficient data support. Type III ML can effectively utilize field data, potentially generating more robust predictions than Type I and II ML. Second, we illustrate how to employ Type I ML and Type II ML frameworks for data-driven turbulence modeling using reference works. Third, we demonstrate Type I ML by building a deep learning (DL)-based slip closure for two-phase flow modeling. The results showe that the DL-based closure exhibits a bounded error in prediction domain.