A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship

Abstract

This paper presents a novel and promising approach to turbulence model formulation, rather than putting forward a particular new model. Evolutionary computation has brought symbolic regression of scalar fields into the domain of algorithms and this paper describes a novel expansion of Gene Expression Programming for the purpose of tensor modeling. By utilizing high-fidelity data and uncertainty measures, mathematical models for tensors are created. The philosophy behind the framework is to give freedom to the algorithm to produce a constraint-free model; its own functional form that was not previously imposed. Turbulence modeling is the target application, specifically the improvement of sep..