Anil Deane
University of Maryland, MD·Earth & Environmental Science
Earth's magnetic field shields the planet from harmful solar radiation. It is generated by the geodynamo, the churning of liquid metal deep in Earth's core, and that process is still poorly understood.
- Fields
Earth & Environmental Science
Mathematics & Statistics
Engineering
- Topics
digital twin
geodynamo
Earth's magnetic field
- Grants
- 1 known grant, through Aug 2028
What they work on
- Build a digital twin linking the University of Maryland's 3-meter liquid sodium geodynamo experiment to high-order spectral element simulations (Nek5000/RS) of the flow inside it.
- Synchronize simulations with experimental measurements using data assimilation methods such as Ensemble Kalman Filters, aiming for a one-to-one match between experimental geometry changes and simulation results.
- Develop reduced-order models that incorporate deep learning neural networks to predict the experiment's flow quickly and to design flow control strategies.
- Enable two-way interaction between the physical experiment and its digital counterpart, so researchers can operate and optimize the experiment remotely and explore scenarios it cannot reach.
More about the research
The field has recently been observed changing relatively quickly, which makes better predictions important. One way to study the geodynamo is with a 3-meter laboratory experiment at the University of Maryland that spins liquid sodium to mimic conditions inside the Earth.
Why it matters
A better grasp of how Earth's core generates its magnetic field could help scientists understand, and eventually forecast, changes in the field that protects life from solar radiation. The digital twin tools being built for this experiment could also be adapted to other laboratory sciences, with possible benefits for fields such as medical device design and external aerodynamics.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Adrian Sandu
Virginia Polytechnic Institute and State University, VA·Computer Science
Develop high-fidelity multiphysics models of jet engine combustion and flow together with scalable reduced-order models, both with quantified uncertainties, as the computational core of a predictive digital twin.
Xinfeng Gao
University of Virginia, VA·Engineering
Develop rigorous data assimilation methods that combine measurements with computational fluid dynamics (CFD) simulations, to uncover why simulated fluid flows disagree with physical experiments.
Alexander Robel
Georgia Institute of Technology, GA·Earth & Environmental Science·Early-career
Build an open-source software package that applies transient ensemble Kalman filtering, a method that repeatedly corrects many model runs against observations, to a wide range of ice sheet models.
The school
University of Maryland: 49 labs on PI Pages, $1.5B a year on research, 602 research doctorates a year.
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