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CFD Fundamentals: Theory & Applications

CFD Fundamentals: Theory & Applications banner
Preview this course
Self-paced Advanced

CFD Fundamentals: Theory & Applications

4(1579)
21 enrolled
5136 views
₹ 7000
1227 min
Anytime
English
Team EveryEng
Team EveryEngMechanical Engineering
AerospaceAutomotive
  • 7-day money-back guarantee
  • Lifetime access
  • Certificate of completion
Volume pricing for groups of 5+

Why enroll

People enroll in this course to gain a deep understanding of Computational Fluid Dynamics (CFD) and its practical applications in engineering and research. Whether they are beginners looking to build a strong foundation or professionals aiming to refine their simulation skills. With a focus on real-world applications, advanced modeling, and optimization, learners can enhance their problem-solving abilities and boost their careers in industries like aerospace, automotive, and energy.

Your instructor

Team EveryEng

Team EveryEng

Engineer

Mechanical Engineering

22+ yrs experience·India

150Courses
43Articles
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Is this course for you?

You should take this if

  • You work in Aerospace or Automotive
  • You're a Mechanical Engineering professional
  • You have 3+ years of hands-on experience in this field
  • You prefer self-paced learning you can revisit

You should skip if

  • You're new to this field with no prior experience
  • You need a different specialisation outside Mechanical Engineering
  • You need live interaction with an instructor

Course details

This course provides a rigorous introduction to Computational Fluid Dynamics (CFD), focusing on the fundamental theory and numerical methods behind modern CFD tools.

You will begin with the basics of fluid mechanics and move toward the derivation of key governing equations, including the continuity, momentum (Navier–Stokes), and energy equations.

The course emphasizes numerical methods such as the Finite Difference and Finite Volume Methods, with Python-based coding exercises for solving steady and unsteady diffusion and convection-diffusion problems.

Additional topics include the classification of fluid flow PDEs, time discretization schemes, error sources in CFD, mesh quality metrics, and the derivation and modeling of turbulence using RANS equations and common turbulence models (Spalart-Allmaras, k–ε, k–ω).

This course is ideal for students, researchers, and professionals aiming to build a strong theoretical and computational foundation for CFD applications in aerospace, automotive, energy, and related engineering fields.

Course suitable for

Key topics covered

* Introduction to CFD

* Basic concepts of fluid mechanics: Fluid Properties, Types of fluid flow, Fluid Motion Description (Eulerian & Lagrangian), Fluid acceleration

* Overall CFD workflow: Geometry Preparation, Mesh Generation, Initial Condition & Boundary Condition, Solver Setup & Solving, Post Processing & Results Visualization

* Derivation of Continuity, Momentum & Energy equation

* Derivation of Navier-Stokes equation

* Characteristics of Fluid flow PDEs: Elliptic, Parabolic & Hyperbolic

* Basics of Discretization

* Finite Difference Method - Forward, Backward and Central difference schemes, Order of accuracy of Discretization schemes, Time Marching Schemes

* Solution of 2D Steady Diffusion Problem using FDM with Python coding

* Solution of 2D Unsteady Diffusion Problem using FDM with Python coding

* Finite Volume Method: Discretization of Diffusion Equation & Convection-Diffusion Equation

* Finite Volume Method: Upwind Scheme for Convection Dominated Flow

* Python code for the solution of 1D Steady Diffusion Problem using FVM

* Python code for the solution of Steady Convection-Diffusion Problem using FVM

* Solution of Incompressible Fluid Flow Equation: SIMPLE Algorithm

* Errors in CFD, Overall Accuracy of CFD Solution & Grid Generation Techniques

* Mesh quality matrices in CFD

* Introduction to Turbulent Flows

* Overview of turbulence models: RANS, LES, and DNS

* Resolution challenge in Turbulence

* Reynolds Averaged Navier-Stokes Equation Derivation

* Turbulence Modeling: Spalart-Almaras Model, k- ε Model & k- ω Model

* Best practices in CFD

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

7 modules23 lectures20 hr 27 min

Opportunities that await you!

Career opportunities

✎ Where this fits — what comes before, what comes next
CFD Fundamentals: Theor… Introduction to CFD & i… Finite Difference Metho… Optimization Theory and…

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Questions and Answers

A: 7×10^6 is the trap. Below 10^7, a fully turbulent flat-plate estimate still lands in the few-milli range. Run the numbers: Re^(-1/5) at 7×10^6 is about 0.043, times 0.059 gives roughly 0.0025–0.004 depending on constants. Anything down in 10^-4 or 10^-5 means viscosity has effectively vanished, which violates the boundary condition physics no matter how pretty the contour plot looks.

A: 3% is the tell. Modern finite-volume solvers enforce conservation tightly if the domain is watertight. Residuals converging just mean the algebra stopped moving. A pinhole or non-manifold face lets mass escape without blowing up continuity norms. Model choice and compressibility errors shift pressure and velocity fields, but they don't silently dump a few percent of flow unless the control volume is broken.

A: Ambient seawater is the boundary. Carbon steel doesn't passivate; it corrodes uniformly and roughness grows in the tens to hundreds of microns, which feeds straight into wall functions. Oxidation needs heat. Cavitation may exist locally but doesn't explain global roughness. SCC is real, but it's a cracking failure mode, not the main driver of hydraulic roughness.

A: 3% is inside the grey zone where facilities matter more than numerics. If the wake profile lines up, momentum deficit is consistent; the drag delta often comes from tunnel corrections, not physics. Mesh and model tweaks after correlation data are backwards and hard to justify in a safety review.