Two-Parameter Mixed for Large-Eddy Simulations
In an effort to improve prediction performance of large-eddy simulation in a transitional boundary layer flow, two-parameter mixed models (i.e., combination of scale-similarity model and sub-grid stress model) have been developed and assessed.
Wall-Modeled Large-Eddy Simulations
Interface-Sharpening Procedure for Incompressible Multi-Phase Flows
Machine Learning in Turbulent Flows
Time-varying turbulent flows are ubiquitous in engineering field and in the life sciences, such as biological propulsion, bio-inspired engineering, automobiles, ships, airplanes. It is observed that the control of unsteady turbulent flow can improve the efficiency, thrust, lift, maneuverability. Machine learning aims to categorize on such dominant patterns of unsteady turbulent flow. In particular, artificial neural networks are one of the types in machine learning. As the "neural" of word suggests, they are human brain-inspired systems. They are excellent tools for finding patterns which are far too complex, such as unsteady turbulent flow.
[Figure 1] Types of machine learning algorithm
[Figure 2] Schematic of the fully connected artificial neural network (ANN). Left, network structure; right, single neuron