4 papers
Assimilating rough features: A data-driven framework to infer rough wall properties from sparse experimental data
Martina Formichetti, Uttam Cadambi Padmanaban, Ping He +2
Surface roughness influences turbulent boundary layers (TBLs) primarily through the roughness function and the equivalent sand-grain roughness height \(k_s\). Direct determi…
Mixed data-source transfer learning for a turbulence model augmented physics-informed neural network
Christian Toma, Bharathram Ganapathisubramani, Sean Symon
Physics-informed neural networks (PINNs) have recently emerged as a promising alternative for extracting unknown quantities from experimental data. Despite this potential, much of…
Three-dimensional variational data assimilation of separated flows using time-averaged experimental data
Uttam Cadambi Padmanaban, Bharathram Ganapathisubramani, Sean Symon
We present a novel framework for assimilating planar PIV experimental data using a variational approach to enhance the predictions of the Spalart-Allmaras RANS turbulence model. Ou…
Effects of fetch length on turbulent boundary layer recovery past a step-change in surface roughness
Martina Formichetti, Dea D. Wangsawijaya, Sean Symon +1
Recent studies focusing on the response of turbulent boundary layers (TBL) to a step-change in roughness have provided insight into the scaling and characterisation of TBLs and the…