Digital Twins: HPC Evolving Engineering | HPC Evolving Engineering

Digital Twins: HPC Evolving Engineering | HPC Evolving Engineering

Where We’re Coming From

Imagine the ground outside of da Vinci’s window. I picture a heap of scrap wood and canvas as he experimented with the idea of flying machines. His ink drawings and iterative physical models set the baseline for engineering. Early CAD systems simply automated the sketching process, adding modeling, dimensioning and manufacturing plans along the way—eventually yielding the capability to develop 3-D solid models with a high fidelity representation of the system. Designers, analysts, systems engineers, manufacturers and sustainers can now work from the same high fidelity model. Now, with the application of HPC, we are gaining true digital twins: realistic instantiations of actual systems. We’ve moved from trial-and-error to a systematic science-based optimization process to explore trade spaces, component performance and interaction, manufacturing processes and life cycle operation. Think of the guy in a garage vs. a systematic scientific exploration of the trade space.

Dominating with Digital

The methods of the past had their flaws. The design and the as-built object were distinct and often exhibited differences in performance due to simplifications, manufacturing variances and limited computational capabilities. Ideas, concepts and models were only “representative” of the actual system. The benefit of a full fidelity digital model, coupled with a computational performance model, is that the digital system will perform in a manner more accurately reflecting the actual physical system. This vastly reduces the unexpected events in tests and enables the exploration of a much greater design space. It also enables the modeling of the system life cycle to support performance-based maintenance. The remarkable developments in computational and computer sciences have brought simulation theory and technology into a new era where digital models can realistically model the key physics driving system performance. Digital twin technology will allow for better designs as it can consistently, reliably and easily adapt to changing factors.

Scientific Discovery

The implications don’t end with engineering—as you might recall from grade school scientific theory attempts to explain some aspect of our world, based on observation. We then test and confirm to the best of our abilities. The concept is built on two pillars: theory and experimentation. Computational science offers a third pillar! As discussed in detail in a 2005 report from the President’s Information Technology Advisory Committee, “Together with theory and experimentation, computational science now constitutes the ‘third pillar’ of scientific inquiry, enabling researchers to build and test models of complex phenomena.”

Digital Twin HPC

What’s Next?

The possibilities offered by an age of digital twins also come with a warning: know what you’re doing! Fidelity refers to your trust level in your digital models. Engility works tirelessly to join scientific domain expertise with HPC expertise so that engineers know why a model is saying X instead of Y.

Trust in the models becomes even more important as HPC and Simulation-Based Engineering and Science move further into the mainstream. Manufacturers and users want higher fidelity design and decision support tools. Additionally, the complexity of systems is requiring systems-based research and development, systems-based design, systems-based engineering and physics-based technology assessment (trade space studies).

A host of critical technologies are on the horizon that cannot be understood, developed or utilized without simulation methods (bespoke manufacturing, self-driving cars, hypersonic transportation, space travel). Engility and its team of computational scientists are on the front-lines of those methods, partnering with our Government customers to build and use the third pillar.

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Posted by Hugh Thornburg

I am the DoD HPCMP PETTT program Technical Lead and has 28 years of experience in applied CFD, HPC, mesh generation and Multi-Physics tool development and analysis. I have extensive ties to peers in the CFD and related HPC communities. I have substantial experience developing training, consulting, defining software, and hardware requirements through interaction with customer base and other key workers. I provide leadership for acquisition of state of the practice tools and methods. I lead teams supporting the use of such tools and methods through application management, research, consulting and collaborative work with both users and developers. I have an M.S. and Ph.D. from the University of Cincinnati and a B.S. From Rose Hulman.