Data underlying the PhD thesis: Machine learning for complex fluid mechanics and heat transfer

Dataset and code related to the PhD thesis: "Machine learning for complex fluid mechanics and heat transfer", Rafael Diez Sanhueza, 2024.<br>The objective of the research was to study machine learning for fluid mechanics, considering rough surfaces and variable property flows (separately). The baseline data is obtained from post-processed DNS cases, or field inversion (non-linear optimization) in the study for variable-property flows. The full RANS solver, field inversion optimizer, and neural network system of Chapter 3 (variable property flows) is included. The 2-D maps with the local skin friction factors and Nusselt numbers of rough surfaces are generated by the wall force/heat_flux interpolation software attached, starting from 3-D fields with time-averaged DNS data. The DNS solver to simulate turbulent flows past rough surfaces in Chapter 5 is included, along with the full implementation of the immersed-boundary method. All data corresponds to text files, without binaries. Files resembling the JSON format are mainly used. The tecplot files for the rough surfaces can be readily opened in Paraview for 3-D visualization, or read as CSV files (the header has a simple format).