How Monolith supports aerospace and defence engineering professionals.
Our no-code AI software is trusted by the world’s top aerospace and defence engineering teams, including Rolls Royce and BAE Systems, to develop better quality products in half the time.
Featured in Forbes magazine and named one of the UK’s top 100 startups, Monolith founder Dr. Richard Ahlfeld received his PhD in Aerospace Engineering from Imperial College and was named in MIT Technology Review’s Top 10 Innovators under 35.
“Our products create massive amounts of data throughout their entire lifecycle, bringing this data together requires a new set of skills, no blueprint exists yet because the ways of working have changed significantly.”
- Dave Holmes, Operations & Technology Director, BAE Systems
Challenges facing aerospace and defense engineers
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Knowledge retention is becoming harder, as engineers rarely remain at a company for the entire product lifecycle. As such, data-driven processes are the key to product knowledge and continuity.
The aerospace industry is committed to carbon neutrality, but product testing within the aerospace industry is extremely harmful to that goal. Data-driven modeling enables the reduction of physical testing and makes it easier to achieve carbon neutrality.
Robust active learning for next test recommendations
Integrating Monolith in your verification and validation process can enhance operational efficiency and streamline testing procedures, reducing reliance on excessive physical tests.
AI has a significant impact on validation testing in engineering product development. You can reduce testing by up to 70% based on battery test research from Stanford, MIT, and Toyota Research Institute.
Battery testing with AI: Build a more efficient test plan you can trust
In the first part of the EV webinar series, we reviewed the latest research on using AI models to significantly reduce the testing needed for EV batteries. In this follow-up webinar, we’ll show how to implement these concepts using Monolith software.
By simulating an aircraft using computational methods such as Computational Fluid Dynamics (CFD), engineers can mimic the physical domain and run virtual wind tunnel experiments for a fraction of the price. This allows aerospace engineers to understand the performance of designs in advance, thus narrowing down the right design choices for the prototype stage.
Build 3D AI models that directly predict rolling road wind tunnel performance of a rim from the CAD design – faster and more accurate than CFD!
By importing data from the sensors, test rig, and airflow, the customer can accurately predict the readings from sensors that have failed, the aerodynamic performance of the product, and the impact of air contamination on the test.
Predict turbulent aircraft behaviour with Monolith. Perform dynamics tests in 72–80% less time using Bayesian Deep Learning in a clever new way.
As gusts occur at many different wind speeds, flight altitudes, and for differently loaded aircraft, engineers need to manually check 1000s of scenarios – a tremendously complicated task.
With a deep neural network predicting changes in real-time as part of an interactive dashboard, this actually becomes quite fun to do.
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