Thanks to the power of Deep Learning and the Neural Concept approach, Product designers can feed a software with arbitrary CAD shapes and get 3D Engineering predictions (surrogates of CAE: CFD, stress & thermal etc.) as quasi-real-time output, even on an everyday laptop. The data-driven approach yields the type of output that engineers need without the burden of the mesher and solver.
Let us start with a specific application case (you can extend the usage of Deep Learning to any CAE: CFD, FEA, Electromagnetics...).
I will focus on thermal analysis, namely an electronics cooling case for aerospace, namely the onboard electronics (chips on a cold plate) on a satellite (see above figures).
As Engineering prediction outputs, in general from you can get:
In the above figure, you can see a visual comparison between High-Fidelity physics-driven simulation (left) and Deep Learning data-driven prediction. High-Fidelity physics-driven simulation takes 20 minutes Deep Learning data-driven prediction takes 20 milliseconds!
The final users can rely on plug-and-play usage and, within an App or some CAD-embedded solution, users can create shapes and get their desired outputs on the go.
Behind this friendly usage, there is an underlying data-driven approach that requires, as the word says, a database to feed a Deep Learning neural network. The underlying "data science project" is part of training and testing the Deep Learning neural network. and can be carried out by consultants, or data science experts within your company.
Courtesy ESA BIC Zurich, we can expose the Deep Learning elements for this specific project here and in a downloadable white paper.
The picture shows the main results from the Deep Learning project:
The importance of the Deep Learning approach goes far beyond "predicting in fractions of a millisecond". The implications for product design can be:
On the last point 3., I do not agree on separating high fidelity CAE and designers' CAE, leading to double standards within organizations.
In the past, reasons for this "divorce" were:
In 2021, it would be much better to exploit available technology and to have a data-driven solution for CAD designers based on high-fidelity data. Therefore, we propose plug-and-play a trained surrogate model into CAD or a company Intranet. The design engineers will not need to do any training since they already know what to do - design their products. The prediction part is one button push time's away.
Two keywords explain how this tool is technically serious:
Transfer learning is another ingredient that facilitates an even larger return on investment from Deep Learning: the initial neural network time/effort is reduced by orders of magnitude when the case is just giving new High Fidelity simulations.
Imagine your focus on aerodynamics moves from luxury cars to new projects on SUVs and do some preliminary high fidelity CFD. The aerospace of SUVs (CFD and maybe even experimental data) will complete the Deep Learning model's initial training based on the aerodynamics of luxury cars. The effort for SUVs will be a fraction of the initial attempt for luxury cars aerodynamics.
In conclusion, there are several open roads. Industry leaders have tested and adopted the Deep Learning solution, and any of the above three implications should be a call to action for other organizations.