Blackparameterisering

Blackparameterisering is a technique used in the field of computational fluid dynamics (CFD) to improve the accuracy of numerical simulations of turbulent flows. Traditional turbulence models, such as the k-epsilon or k-omega models, rely on empirical constants that may not be universally applicable across different flow conditions. Blackparameterisering aims to address this limitation by dynamically adjusting these constants based on the flow characteristics.

The method involves the use of machine learning algorithms, particularly neural networks, to learn the optimal values of these constants from high-fidelity data, such as direct numerical simulations (DNS) or large-eddy simulations (LES). By training the neural network on a diverse set of flow cases, the model can generalize and provide accurate predictions for new, unseen flow conditions.

Blackparameterisering has shown promise in improving the accuracy of CFD simulations, particularly in complex flows where traditional models may struggle. However, it also introduces additional complexity and computational cost, as the neural network needs to be trained and integrated into the CFD solver. Ongoing research is focused on optimizing the method to make it more efficient and robust for practical engineering applications.