As part of our Simulation Experts Webinar Series, we recently hosted a live session on How to Get Started with Conjugate Heat Transfer (CHT) Analysis of Compressible Flows. This webinar was ideal for engineers tackling high-speed flow and thermal management challenges, especially in applications like hydrogen storage and automotive components.
If you missed the session or want a quick refresher, here are the five key highlights from the event—including technical insights, real-world use cases, and a look at how our AI Assistant accelerates simulation workflows.
- Conjugate heat transfer meets compressible flow – without the headaches
- Simulating hydrogen tank filling: A transient CHT case
- Using AI to set up a complete CHT simulation – with just a few prompts
- Fast, scalable simulation – powered by the cloud
- Getting started: Access, accuracy & support
On-Demand Webinar
If the above highlights caught your interest, there are many more to see. Watch the on-demand Simulation Expert Series webinar from SimScale on how real-time simulation with AI is driving faster design cycles and superior products by clicking the link below.
1. Conjugate Heat Transfer Meets Compressible Flow—Without the Headaches
We kicked things off with a technical overview of our multi-purpose solver, purpose-built for simulating compressible flows and conjugate heat transfer in a single environment. Unlike traditional solvers that often struggle with convergence or require extensive tuning, this solver handles complex physics, including real gas behavior and transient conditions, with a high level of robustness and automation.
Thanks to its robust meshing and smart numerical defaults, users can simulate everything from subsonic to supersonic regimes (up to Mach 5) with ease, even across multiple solid and fluid domains.
Key Takeaway:
Running compressible CHT simulations doesn’t need to be difficult. With the right solver setup, it’s possible to simulate real-world heat transfer scenarios – quickly, reliably, and without extensive manual tuning.
2. Simulating Hydrogen Tank Filling: A Transient CHT Case
In our first live demo, we walked through a hydrogen fuel tank filling simulation, a transient application involving real-gas behavior and multi-material heat transfer. The setup covered every essential step:
- Uploading and simplifying the CAD geometry (including symmetry extraction)
- Selecting the Multi-Purpose analysis type
- Assigning materials (e.g., hydrogen as a real gas, insulation, aluminium liner)
- Defining transient pressure inlet conditions (e.g., ramping from 1 to 500 bar)
- Applying convective heat loss to the tank exterior
- Using adaptive meshing with local refinements
We also set up result control items such as probe points and area averages, and ran the simulation entirely in the browser on cloud hardware. The post-processing revealed clear insights into temperature distribution and jet behavior during fill-up – critical for validating safety and performance!
Key Takeaway:
This case showed how to model complex flow and thermodynamic phenomena with minimal effort. Perfect for those optimizing fill-time, insulation, or structural design in hydrogen applications.
3. Using AI to Set Up a Complete CHT Simulation—With Just a Few Prompts
Our second case study featured an automotive muffler and a different kind of demonstration: how to set up the entire simulation using our built-in AI Assistant.
By typing simple prompts like “run a CHT simulation” or “assign air as the fluid,” the AI automatically:
- Chose the appropriate solver configuration
- Assigned materials to solid and fluid regions
- Created inlet and outlet boundary conditions
- Suggested convective heat transfer coefficients and ambient settings
- Generated monitoring outputs like surface averages
This AI-guided workflow eliminated nearly all manual steps. For engineers who are new to simulation or simply want to accelerate setup, the assistant provides both recommendations and automation—along with contextual support links where needed.
Key Takeaway:
AI doesn’t just assist with troubleshooting – it can build entire simulation setups, letting you go from CAD to results in record time.
4. Fast, Scalable Simulation—Powered by the Cloud
Both case studies demonstrated how easy and fast it is to run advanced simulations directly in the browser. There’s no need for installations, licenses, or local compute power. Simulations ran on scalable cloud hardware, automatically selecting optimal cores (or letting users configure their own).
For example, the hydrogen tank simulation was set up and launched in about 10 minutes, while the muffler case, set up via the AI assistant, ran in under 30 minutes.
Key Takeaway:
SimScale’s cloud-native platform removes infrastructure barriers, allowing teams to run high-fidelity simulations with minimal hardware requirements and rapid turnaround.
5. Getting Started: Access, Accuracy & Support
We wrapped up the session with an open Q&A. Attendees asked about solver availability, performance, and support:
- The multi-purpose solver used in both demos is available to professional users—just reach out to our sales team to request access or a trial.
- Accuracy was a key point: internal validation and customer results show 2–5% deviation compared to physical tests, even for high-speed or thermally complex systems.
- The AI Assistant is available on request – contact us if you’d like it enabled on your account.
Key Takeaway:
If you’re looking to bring advanced thermal-fluid simulation into your workflow, we’re here to support you with the tools, accuracy, and guidance you need to get started confidently.
Final Thoughts
Conjugate heat transfer and compressible flow problems can be some of the more challenging simulations to set up and run. However, with the right tools, they don’t have to be.
Whether you’re designing hydrogen systems, automotive components, or any application where heat and flow interact under pressure or at high speed, the workflows demonstrated in this webinar show how quickly you can go from concept to insight.
With robust solvers, cloud-native speed, and AI-assisted setup, it’s never been easier to simulate with confidence—even for complex multiphysics scenarios. We’re excited to see what you’ll build with these capabilities.
