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
Blackparameterisering has shown promise in improving the accuracy of CFD simulations, particularly in complex flows where