At Inductiva, we are committed to supporting academic teams that are tackling real-world engineering challenges through simulation. One such collaboration is with MotoStudent FEUP, a student-led team from the Faculty of Engineering of the University of Porto (FEUP), building their first electric racing motorcycle for the MotoStudent Electric competition.
To push the limits of their prototype’s performance, the team needed to deeply understand how aerodynamics would affect the motorcycle’s speed, stability, and structure. This required high-fidelity CFD simulations, which present significant computational demands — especially when working with large, turbulent domains.
Here is their experience in their own words:
At Moto Student FEUP, precision and performance are paramount. Developing our first electric racing motorcycle posed numerous technical challenges — chief among them, optimizing the prototype’s aerodynamic performance.
The effect of the surrounding air on the motorcycle affects things like the top speed of the motorcycle, which in turn affects the gear ratio of the motor; the center of pressure, which has a big role in the overall dynamics of the motorcycle; and the fairings themselves, which need to be able to support the forces exerted on them without deforming excessively or breaking.
Therefore, it was imperative that we run CFD simulations to evaluate key parameters, such as the drag coefficient, Cd. This is not as simple a task as it may seem, however, since the flow around the motorcycle is highly turbulent and complex and it extends far behind the motorcycle itself. Therefore, the computational domain required to simulate this flow is quite large. This means that a great number of cells is required to capture the key features of the flow. Studies suggest that accurately capturing this complexity may demand 18 to 22 million cells. This is a far cry from the number of cells that a personal computer with a typical 4 core processor and 16GB RAM can handle.
That’s why we rely on Inductiva’s cutting-edge High-Performance Computing platform. Without access to such powerful computing, these simulations could not even be run, due to memory constraints, let alone in a time effective manner. Inductiva’s platform, with its intuitive Python-based interface, enabled us to streamline our simulation workflow. We could easily set up batch simulations to test different mesh sizes and wind conditions, significantly reducing manual effort and saving valuable development time. Another key feature for us is the possibility for various members to access the team’s remote storage in Inductiva’s platform and analyse data directly in the dashboard. This was critical to share results within the team, enabling different team members to analyse and process results from various simulations, without the need for additional storage space in paid drives and, once more, saving time, in upload and download times. This partnership accelerated our development process, allowing us to test designs and optimize performance. With Inductiva’s support, we were able to:
- Run reliable, detailed simulations of the flow around the motorcycle that would otherwise be out of reach for our team.
- Accelerate aerodynamics studies, by enabling us to run batch simulations for different mesh sizes and wind speeds, vital for ensuring the robustness of our results.
- Enhance in-team cooperation, by making it easier for us to share results within the aerodynamics team and cut down on storage costs and upload/download time.
Beyond the platform itself, Inductiva’s dedicated support helped us maximize the value of their resources, making them an indispensable partner in our journey to build our first electric racing motorcycle.
If you’re leading a university or a company project working on simulation-heavy research, we invite you to explore Inductiva’s Platform and discover how it can accelerate your development and deepen your analysis, just like it did for the MotoStudent FEUP team.
MotoStudent FEUP
Student group from various engineering fields united by a drive to design and construct a electric motorcycle prototype that can compete in MotoStudent International Competition.
Inductiva.AI
Cloud platform for scientific computing. Scalable CFD, Molecular Dynamics, Renewable Energy simulations and more.
https://inductiva.ai/
