The aerospace industry is on the cusp of a revolution with AI’s potential to transform spacecraft design. AI can enable broader design space exploration through near real-time performance calculations and generate high-performing variants using mission requirements as inputs. However, engineering teams are hesitant to adopt AI due to concerns about its reliability and viability.
The real challenge lies not with the AI algorithms themselves, but with the quality and structure of the data they rely on. Aerospace manufacturers possess vast amounts of CAD files, simulation outputs, and test results, but this data is often unstructured, irrelevant, or of poor quality. For AI to be effective, it needs high-quality, usable, and simulation-ready data.
Spacecraft design presents unique challenges due to its highly integrated systems. Changes in one area can have ripple effects across the entire design, making iteration slow and laborious. Multiphysics simulations take days to converge and require significant computational power. Moreover, workflows often break down due to issues like CAD problems or solver crashes, resulting in fragmented data.
Despite these challenges, forward-thinking engineering teams are making breakthroughs in applying machine learning (ML) in areas with repeatable simulations and high return on speed. For instance, a global aerospace company optimized the internal geometry of a heat exchanger using AI by parameterizing the geometry, automating the design-of-experiments process, and running high-fidelity simulations. This resulted in a clean, structured dataset that trained a surrogate model to predict performance metrics.
To determine if your workflow is ML-ready, ask yourself three questions:
- Does your problem have a strong physics foundation?
- Is simulation speed a bottleneck?
- Do you have the right data or a way to create it?
If you’ve answered yes, here’s a blueprint for getting started:
- Define a clear prediction goal based on physics-based outcomes.
- Generate quality data at scale using robust modeling approaches.
- Train a stable, accurate model using an appropriate ML framework.
- Integrate the model into your workflow, ensuring it’s accessible and usable by engineers.
- Build for traceability and governance, enabling inspectable design decisions.
The goal is to empower engineers, not automate design decisions. With the right AI tools, engineers can explore more, iterate faster, and make better-informed decisions. By treating simulation data as capital and investing in robust pipelines, clear targets, and scalable datasets, organizations can unlock AI’s true potential in spacecraft design.
