Modelling and Simulation of Effusion Cooling—A Review of Recent Progress

Abstract

Effusion cooling is often regarded as one of the critical techniques to protect solid surfaces from exposure to extremely hot environments, such as inside a combustion chamber where temperature can well exceed the metal melting point. Designing such efficient cooling features relies on thorough understanding of the underlying flow physics for the given engineering scenarios, where physical testing may not be feasible or even possible. Inevitably, under these circumstances, modelling and numerical simulation become the primary predictive tools. This review aims to give a broad coverage of the numerical methods for effusion cooling, ranging from the empirical models (often based on first principles and conservation laws) for solving the Reynolds-Averaged Navier–Stokes (RANS) equations to higher-fidelity methods such as Large-Eddy Simulation (LES) and hybrid RANS-LES, including Detached-Eddy Simulation (DES). We also highlight the latest progress in machine learning-aided and data-driven RANS approaches, which have gained a lot of momentum recently. They, in turn, take advantage of the higher-fidelity eddy-resolving datasets performed by, for example, LES or DES. The main examples of this review are focused on the applications primarily related to internal flows of gas turbine engines.

Keywords

• effusion cooling
• cooling effectiveness
• Reynolds-averaged Navier–Stokes equations
• large-eddy simulation
• hybrid RANS-LES
• machine learning