CFD Study Modules

This page presents a structured sequence of study modules in computational fluid dynamics intended for graduate students, early-stage researchers, and engineers seeking a stronger theoretical and methodological foundation. The emphasis is on physical understanding, mathematical formulation, numerical reasoning, and research-oriented interpretation rather than software-centered training.

Foundations
Beginner to Intermediate Theory Practice

1. Foundations of Flow Mathematics

This module develops the mathematical framework required for advanced study in fluid mechanics and CFD, including vector and tensor notation, differential operators, ordinary and partial differential equations, and scaling analysis.

Teaching 32 hrs Practice 18 hrs

The purpose of this module is to establish the mathematical language used throughout continuum mechanics and numerical fluid analysis. Particular attention is given to how gradient, divergence, curl, conservation statements, and nondimensional groups arise in fluid-flow problems. The module is intended for learners who want to read CFD formulations with clarity and to understand the mathematical structure behind subsequent derivations.

Learning outcomes
  • Interpret the principal mathematical operators used in continuum fluid mechanics
  • Classify governing differential equations according to their mathematical character
  • Use scaling arguments to identify dominant balances in fluid-flow problems
  • Develop the mathematical preparation required for governing equations and discretization methods
Equations
Intermediate Theory Practice

2. Governing Equations of Fluid Motion

This module studies the derivation and interpretation of the conservation equations governing mass, momentum, and energy transport in fluid systems.

Teaching 40 hrs Practice 20 hrs

The module examines how physical conservation laws are translated into differential form and how those equations should be interpreted for incompressible, compressible, viscous, and inviscid flows. It is particularly useful for students who wish to understand where the Navier-Stokes equations come from, what assumptions are embedded in common simplifications, and how transport processes should be understood before any numerical approximation is introduced.

Learning outcomes
  • Derive the principal governing equations used in fluid dynamics and CFD
  • Distinguish elliptic, parabolic, and hyperbolic behavior in transport problems
  • Relate pressure, viscosity, inertia, diffusion, and compressibility to flow behavior
  • Interpret CFD formulations and research papers with stronger physical insight
Numerical
Intermediate Theory Coding Practice

3. Numerical Methods for CFD

This module introduces the numerical approximation of fluid-flow equations through discretization, stability analysis, convergence assessment, and algorithmic formulation.

Teaching 36 hrs Practice 24 hrs

The module focuses on the transition from continuous governing equations to computable algebraic systems. Finite-difference, finite-volume, and related approaches are discussed from the standpoint of accuracy, consistency, stability, and conservation. The aim is not only to learn numerical procedures, but also to understand why some methods are robust for specific classes of problems and why others fail.

Learning outcomes
  • Compare the strengths and limitations of major discretization frameworks used in CFD
  • Select numerical strategies appropriate to different classes of flow problems
  • Interpret truncation error, CFL restrictions, stability limits, and convergence criteria correctly
  • Build a sound conceptual basis for understanding solver structure and numerical analysis
Benchmarks
Intermediate Case Study Practice Theory

4. Canonical Flow Configurations

This module studies standard benchmark configurations that are central to physical intuition, code verification, and methodological comparison in CFD.

Teaching 28 hrs Practice 22 hrs

Canonical configurations such as lid-driven cavity flow, channel flow, boundary-layer development, flow over bluff bodies, and shock-tube problems remain essential because they connect analytical understanding, experimental evidence, and numerical behavior. This module uses such cases to teach how one evaluates physical fidelity, numerical sensitivity, and the usefulness of a given benchmark for validation or method development.

Learning outcomes
  • Recognize the role of canonical problems in CFD education, verification, and validation
  • Connect numerical behavior to known physical flow structures and regimes
  • Use benchmark configurations to assess method performance and interpret results critically
  • Develop a coherent overview of classical internal, external, separated, and compressible flows
Turbulence
Intermediate to Advanced Theory Case Study Research

5. Turbulence and Compressible Flow Physics

This module examines turbulence, wall-bounded flow structure, and compressibility effects that become important in realistic aerodynamic and high-speed applications.

Teaching 34 hrs Practice 20 hrs

The module is intended as an advanced bridge between introductory CFD and research-level flow analysis. It introduces the physical interpretation of turbulent transport, the structure of wall-bounded flows, relevant statistical quantities, and the modifications introduced by compressibility, shock interactions, and high-Mach-number effects. It is especially suitable for graduate students preparing for advanced aerodynamics or turbulence-related research.

Learning outcomes
  • Understand the main modelling and simulation frameworks used for turbulent flows
  • Interpret wall scaling, turbulent statistics, and the physical meaning of averaged quantities
  • Relate compressibility effects and high-speed flow phenomena to governing mechanisms
  • Build readiness for more advanced study in turbulence, aerodynamics, and compressible CFD
Multiphase
Advanced Theory Research Case Study

6. Multiphase and Particle-Laden Flow Systems

This module extends CFD concepts to dispersed, particle-laden, and coupled multiphase systems encountered in environmental, energy, and industrial applications.

Teaching 30 hrs Practice 24 hrs

The central goal of this module is to study how dispersed phases interact with a carrier fluid and how those interactions are represented in computational models. Topics include particle response time, drag and lift formulations, one-way and two-way coupling, Eulerian and Lagrangian viewpoints, and the interpretation of particle-laden turbulence. The material is intended for students moving toward advanced multiphase-flow or environmental-flow research.

Learning outcomes
  • Understand the principal modelling strategies used for dispersed multiphase flow systems
  • Evaluate common force closures including drag and related particle-force formulations
  • Distinguish clearly between coupling regimes and modelling assumptions in multiphase simulations
  • Develop conceptual readiness for research on particle-laden and multiphase turbulent flows
Computing
Intermediate to Advanced Coding HPC Research

7. Research Computing for CFD

This module addresses the computational practices that support research-grade CFD, including code structure, verification, data handling, reproducibility, and high-performance workflow organization.

Teaching 28 hrs Practice 20 hrs

Modern CFD research requires more than numerical formulation alone; it also requires disciplined computational practice. This module therefore focuses on the implementation side of simulation work: basic solver organization, verification and validation logic, reproducible case setup, post-processing strategy, and the computational habits required for large and reliable research workflows. It is particularly relevant to students entering graduate research groups or beginning independent simulation work.

Learning outcomes
  • Organize CFD workflows in a systematic and research-oriented manner
  • Understand the role of programming and code structure in solver development
  • Interpret verification, validation, and reproducibility with greater methodological rigor
  • Build readiness for high-performance computing environments and larger-scale simulation studies