Collaborative Research: MATH-DT: Digital Twin for Laboratory Science (DTLS) - Application to Liquid Sodium Experiments in Geosciences

About This Grant

The Earth’s magnetic field is critical for sustaining our planet’s habitability, deflecting harmful solar radiation. However, the processes generating this field deep within the Earth’s core remain mysterious, hindering our ability to predict its future behavior – a concern given recent observations of relatively rapid changes in the field. This project addresses this fundamental challenge by developing a “digital twin” system for a large-scale laboratory experiment that mimics conditions inside the Earth. The digital twin will allow researchers to operate and optimize complex experiments remotely, explore scenarios inaccessible through physical experimentation alone, and ultimately improve our understanding of the forces shaping the geodynamo – the engine driving Earth’s magnetic field. By creating extensible tools for laboratory science, this research advances computational mathematics, supports training for a new generation of scientists and engineers, and has potential benefits for diverse fields including medical device design and external aerodynamics. This translational science collaborative project between University of Maryland (UM) and University of Illinois creates a digital twin consisting of the 3-meter liquid sodium geodynamo experiment at UM, coupled with advanced numerical modeling schemes based on high-order spectral element methods (Nek5000/RS) and data assimilation techniques including Ensemble Kalman Filters. The research team will develop Reduced Order Models ROM incorporating Deep Learning Neural Networks to enhance predictive capabilities and enable flow control strategies. By synchronizing the model with experimental observations, researchers aim to achieve a 1:1 correspondence between geometry changes and simulation results, ultimately allowing for bidirectional interaction between the physical experiment and its digital counterpart. This work will leverage high-performance computing resources to advance computational mathematics and provide a framework extensible to other laboratory science domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.