barbagroup/CFDPython
CFDPython
CFDPython is an educational resource for computational fluid dynamics and numerical analysis. It provides a structured curriculum to learn the physics of fluid flow by implementing numerical solutions to Navier-Stokes and partial differential equations.
The project is organized as a series of incremental coding exercises delivered via Jupyter notebooks. Users build mathematical models for linear convection, diffusion, and Poisson equations across one and two dimensions to understand concepts such as convergence, stability, and numerical diffusion.
The implementation utilizes NumPy for vectorization and Matplotlib for visual analysis, including the generation of heatmaps and contour plots. Technical coverage includes finite-difference discretization and the monitoring of stability via the Courant-Friedrichs-Lewy condition.
Features
- Interactive Notebook Learning Resources - Delivers a structured CFD curriculum via interactive Jupyter notebooks with incremental coding exercises.
- Computational Fluid Dynamics Curricula - Provides a comprehensive educational resource for learning computational fluid dynamics through coding.
- Fluid Dynamics Analysis - Teaches the analysis of liquid behavior by implementing numerical fluid dynamics models.
- Fluid Dynamics Simulation - Provides a framework for simulating fluid dynamics by solving Navier-Stokes equations.
- Convection-Diffusion Solvers - Implements numerical solutions for linear convection, diffusion, and Poisson equations.
- Finite Difference Calculations - Implements finite-difference discretization to approximate partial differential equations on a discrete grid.
- Poisson Equation Solvers - Provides numerical solvers for the Poisson equation to determine pressure fields in fluid flow.
- CFL Condition Monitoring - Implements stability monitoring via the Courant-Friedrichs-Lewy condition to maintain simulation accuracy.
- Convergence & Diffusion Analysis - Analyzes numerical stability and convergence through the measurement of numerical diffusion.
- Matplotlib - Uses Matplotlib to generate heatmaps and contour plots for visualizing fluid velocity and pressure.
- Differential Equation Solvers - Uses differential equation solvers to study numerical analysis concepts like convergence and stability.
- Scientific Computing - Employs scientific computing techniques in Python to solve physics and fluid flow problems.
- NumPy-Based Linear Algebra Implementations - Utilizes NumPy vectorization to perform bulk mathematical operations on multi-dimensional arrays.
- Numerical Analysis Learning - Provides exercises to understand convergence, stability, and numerical diffusion in PDE solvers.
- Scientific Computing Curricula - Implements a structured educational series for learning scientific computing and numerical analysis.
