A Python toolkit to calculate and visualize turbulence anisotropy and turbulent viscosity from Reynolds stress tensor data.

Progressive data-augmented k-omega SST model for OpenFOAM

Simulation, Forecasting and Filtering of Stochastic Triad Models of Turbulence

A package of codes that imports, displays, sorts, and calculates (PDF, JPDF, Histogram, correlation coefficient, Autocorrelation) time signals from OpenFOAM.

A python toolset to augment RANS models with LES/DNS data, using Random or Mondrian forests.

Integration of Ansys Fluent hydrogen-air mixer in Python and optimization with Genetic Algorithm

Assignments from Turbulence Modelling, DTU Autumn 2022

Online Material for Vidal et al. (2024). Cholinergic-related pupil activity reflects level of emotionality during motor performance. EJN, 59(9):2193–2207.

A highly configurable Reservoir Computing (RC) Python package, supporting both Quantum and Classical implementations. FYP Masters project by Raihaan Usman!

Machine Learning-Driven Clear Air Turbulence Prediction for Aviation Safety

This repository contains implementation of the Lam-Bremhorst k-epsilon turbulence mode in FEniCS.

Homebrew Godunov-type computational fluid dynamics solver with some additional physics.

Quasilinear model for evolution of Weibel turbulence, a simplified version of which is described in the article: A.A. Kuznetsov and others "Quasilinear Simulation of the Development of Weibel Turbulence in Anisotropic Collisionless Plasma"

Quasilinear model for coupled evolution of Langmuir and Weibel turbulence in beam-plasma systems from the article: A.A. Kuznetsov, Vl.V. Kocharovsky "Quasilinear interaction between Langmuir and Weibel turbulence in a beam-plasma system"

The source code for Finite volume Explicit STructured 3Dimensional (FEST-3D) solver.

Ninetails is a personal project which aims to develop a higher order gyrofluid model using high-level python only.

A fully developed turbulent channel flow is numerically solved using FVM.

Sophisticated approach to predicting turbulence dynamics by leveraging the power of Physics-Informed Neural Networks (PINNs). This project is built on the PyTorch Lightning framework, facilitating streamlined model training, evaluation, and management.