Coding Fluid Dynamics: Eulerian Simulations with NumPy

Simulating how liquids and gases move is a fundamental challenge in scientific computing, but complex mathematical equations can feel overwhelming to implement. This text-based course demystifies computational fluid dynamics by breaking down the math into clean, readable Python code. You will transition from understanding basic physics concepts to writing your own efficient fluid simulations. By leveraging the power of NumPy, you will learn how to replace slow nested loops with vectorized array operations, allowing you to run grid-based simulations directly on your machine. What you'll learn: Understand the foundational principles of fluid dynamics, including pressure, velocity, and the Navier-Stokes equations; Implement the Eulerian grid method to track fluid properties at fixed points in space; Apply vectorized NumPy operations to write fast, efficient simulation code without complex loops; Configure boundary conditions to simulate walls, obstacles, and inlets within your fluid domain; Solve the Poisson equation for pressure to ensure realistic, incompressible fluid flow; Structure your scientific Python code using modern clean-code practices and efficient memory management. The course begins with core terminology and the mathematical foundations of fluid flow before guiding you step-by-step through setting up a grid, implementing forces, and solving pressure equations. You will read detailed explanations and analyze complete code implementations that you can run and modify. This course is designed for beginners in computational science, physics enthusiasts, and Python developers looking to apply their programming skills to scientific modeling. No prior experience with fluid dynamics is required, though a basic familiarity with Python arrays is helpful. Start reading today to build your first computational fluid dynamics simulation from the ground up.