Share with your friends
Fee: $99 (₹ 8100 incl. Tax)
Only limited seats to make the workshop lively and interactive.
If you face any difficulty in payment, alternately you can enroll using take this course option above.
This comprehensive workshop is designed to provide participants with a deep understanding of compressible fluid dynamics and Computational Fluid Dynamics (CFD) techniques for simulating compressible flows. The workshop will delve into the intricacies of flows in different Mach regimes (subsonic, supersonic, hypersonic) and equip participants with the necessary knowledge and hands-on experience to solve complex compressible flow problems using state-of-the-art tools and techniques.
- Introduction to compressible flows, basic fluid mechanics, and thermodynamics.
- Governing equations, non-dimensionalization, and characterization of PDEs.
- DNS, LES, and RANS, turbulence modelling.
- Numerical schemes, meshing: finite difference vs. finite volume, unstructured vs. structured meshes.
- Hands-on sessions: CFD codes with Python/MATLAB, simulations with OpenFOAM/Fluent.
- Solver development for 1D compressible Euler/Navier-Stokes equations.
- Boundary conditions for subsonic and supersonic flows.
- Hands-on sessions: solving canonical and industrial compressible flow problems.
- Post-processing and analysis.
Who Should Attend
This workshop is ideal for engineering professionals, researchers, and students interested in compressible fluid dynamics and CFD. Whether you are a beginner or an experienced practitioner, the workshop offers something for everyone. Basic knowledge of fluid mechanics and CFD fundamentals will be beneficial.
The workshop will be conducted by experts from academia and industry with vast experience in compressible flow simulations and CFD techniques. They will guide participants through the theoretical aspects and provide hands-on assistance during interactive sessions.
- Prof. Rajesh Ranjan (Main Instructor), Department of Aerospace Engineering, IIT Kanpur.
- Dr. Sandeep Mouvanal, MS, PhD, IIT Madras
- Mr. Nishant Soni, MS (Engg.), JNCASR Bengaluru
Registration for the workshop is now open. Limited seats are available, so we encourage interested participants to secure their spots early.
The workshop will be held online on Aug 19-20, Aug 26-27, Sep 2-3. Participants are encouraged to use their laptops, and workstations for the hands-on sessions.
- Date: (Weekends) Aug 19-20, Aug 26-27, Sep 2-3. 2023
- Time: 2.30 pm to 5.30 pm IST
- Total access to recordings of live sessions: 1 year
- Computer requirement: Minimum 4 GB RAM and i3 processor
- 1. ANSYS Fluent. You can download and install the free student version of ANSYS Fluent on your laptop provided by ANSYS for educational purposes.
- 2. OpenFOAM. Install OpenFOAM on your Laptop. Our support Team will help you to install OpenFOAM if you are new to it. Please get this done much before the workshop as this can take more time.
- 3. Python/MATLAB. We will give you access to our Python course. You can follow the instructions to download and install Python on your PC.
- Mode of class: Zoom video call (Once you make the payment, login details will be shared
- Get introduced to CFD
- Learn and master compressible flow theory and practical application
- Get introduced to complexities in modeling compressible flow
- CFD Engineers already working in the industry but want to learn and master a new meshing tool
- Do I get a certificate?
Yes, based on your attendance and completion of tutorials, you will be given the certificate.
- Do I need a powerful workstation/computer to learn this course?
No, a normal laptop with 4 or 8GB RAM and a decent processor (i3) is good enough for this course.
- What if I don’t understand some portion or need to clarify some doubts?
We will support you through emails and zoom meetings/discussion sessions to clear all doubts and questions
- Should I know the programming or any other CFD software to learn this course?
No, you don’t need.
- Is there any prerequisite?
The basic knowledge in fluid mechanics is required to learn CFD