Engineering simulation is used across industries to accelerate product development. Simulations are used to check the safety of aircraft, cars, and buildings…
Overview
The article discusses how Luminary Cloud leverages NVIDIA GPUs to enhance engineering simulations, making them faster and more efficient. It highlights the challenges in computational fluid dynamics (CFD) and presents a case study with Joby Aviation, showcasing significant improvements in simulation turnaround time and productivity.
What You'll Learn
1
How to utilize NVIDIA GPUs for accelerated engineering simulations
2
Why cloud-based HPC resources enhance simulation efficiency
3
When to implement multi-GPU and multi-node simulations for complex tasks
Prerequisites & Requirements
- Understanding of computational fluid dynamics (CFD) concepts
- Familiarity with cloud computing platforms(optional)
Key Questions Answered
What challenges hinder the adoption of computational fluid dynamics (CFD)?
Challenges include long simulation turnaround times, with 36.2% of projects taking weeks, and 50% of non-users citing high license costs as a barrier. Additionally, inefficient model preparation can take months, impacting the adoption of CFD software.
How does Luminary Cloud improve simulation speed and efficiency?
Luminary Cloud utilizes NVIDIA GPUs to achieve speed improvements of over 100x compared to traditional methods. It allows for parallel simulations, drastically reducing wait times and enabling engineers to focus on design goals.
What is the significance of the Joby Aviation case study?
Joby Aviation demonstrates the effectiveness of Luminary Cloud, achieving 10x greater engineer productivity. The platform enables rapid assessment of aircraft designs, significantly reducing the time required for simulations compared to legacy tools.
Key Statistics & Figures
Speed improvement of simulations
Over 100x
Compared to traditional approaches, Luminary Cloud's platform enables significantly faster simulations.
Parallel simulations run
20 simulations
In a demo, Luminary Cloud completed 20 simulations in parallel, each with 40M control volumes in less than two minutes.
Reynolds number averaged Navier-Stokes calculation time
About 7 minutes
Using 40 NVIDIA A100 GPUs, this calculation would have taken 2–3K cores between 6–8 hours with traditional methods.
Technologies & Tools
Hardware
Nvidia Gpus
Used for accelerating engineering simulations and improving computational efficiency.
Software
Luminary Cloud
A cloud-based platform for conducting multi-physics engineering simulations.
Key Actionable Insights
1Leverage NVIDIA GPUs for your engineering simulations to drastically reduce computation times.Using GPU acceleration can enhance the efficiency of simulations, allowing for faster iterations and improved design outcomes, especially in industries like aerospace and automotive.
2Consider adopting cloud-based HPC resources to optimize your simulation workflows.Cloud resources provide flexibility and scalability, ensuring that you can access powerful computing capabilities as needed, which is crucial during peak design phases.
3Utilize Luminary Cloud’s AI-based tools to streamline mesh generation and simulation setup.By automating tedious tasks, engineers can focus more on analysis and optimization, significantly improving productivity and reducing time to market.
Common Pitfalls
1
Failing to optimize GPU allocation can lead to inefficient resource usage during simulations.
Without proper allocation, simulations may not run as efficiently as possible, leading to longer wait times and increased costs.
Related Concepts
Computational Fluid Dynamics (cfd)
High-performance Computing (hpc)
Cloud Computing
Multi-physics Simulations