Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches

Abstract

Engineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer technology continues to improve, machine learning has proven to be a useful solution to some of these problems. The purpose of this paper is to further promote the development of turbulence modeling using data-driven machine learning; it begins by reviewing the development of turbulence modeling techniques, as well as the development of turbulence modeling for machine learning applications using a time-tracking approach. Afterwards, it examines the application of different algorithms to turbulent flows. In addition, this paper discusses some methods for the assimilation of data. As a result of the review, analysis, and discussion presented in this paper, some limitations in the development process are identified, and related developments are suggested. There are some limitations identified and recommendations made in this paper, as well as development goals, which are useful for the development of this field to some extent. In some respects, this paper may serve as a guide for development.

Physics-informed neural network for turbulent flow reconstruction in composite porous-fluid systems

“…The proliferation of DL network architectures, including fully connected NNs [20], CNNs [21], and generative adversarial networks [22,23], has accelerated the application of DL in turbulence modeling [24,25]. Within the realm of DL, finding a solution of PDEs describing the physical phenomena has gained prominence, with two distinct approaches taking shape: purely data-driven and physics-informed [26,27]. Data-driven methods usually rely heavily on extensive and well-refined training datasets for accurate predictions, thereby requiring significant computational resources in the modeling of complex flow physics [25].…”

“…Within the domain of fluid dynamics, PINN has been applied to solve fluid flow problems [35][36][37][38]. While the effectiveness of PINNs in solving the Navier-Stokes equations for laminar flows has been demonstrated in several studies [27,[39][40][41], the utilization of PINNs to address turbulent flows with complex flow physics (e.g. composite porous-fluid systems) has garnered relatively scant attention in the literature.…”