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Accelerating simulations of the flow and heat transfer for military vehicles
An article about iMSEL's research with Army/GVSC and CFD Research Corporation was published and highlighted in CEC's news on June 12, 2023 for developing reduced order modeling techniques to accelerate thermal and flow simulation of military vehicles for thermal signature generation.
The project sponsored by Army/GVSC aims to develop a coupled thermal flow solver based on a hybrid technique that combines reduced order models and full order models for rapid thermal signature generation. Read More
Reliable Perception for Unmanned Maritime Systems
iMSEL in collaboration with electric engineering, computer science and engineering at UofSC, and Integer Technologies was awarded a project of $4.75 M by the Office of Naval Research (ONR) to develop reliable perception for unmanned maritime systems.
This is the largest contract iMSEL has received to develop computer vision and AI-enabled sensor systems. Read More
Self-learning ground robotics for controlling combat mechanical systems and solving problems
An article about iMSEL's research with Army was published and highlighted on CEC's news on November 22, 2021 for developing "Self-learning ground robotics for controlling combat mechanical systems and solving problems".
The project sponsored by Army/ARL aims to develop an integrated plug-and-play (PnP) tool based on neural network models and Model Predictive Control (MPC) for real-time monitoring and control of mechanical systems. Read More
An Intelligent Framework of Adaptive Parametric Reduced Order Model Database for Aerostructural Control
An article about iMSEL's research with NASA was published and highlighted on CEC's news on July 7, 2020 for developing "Parametric Reduced Order Model (ROM) for Aerostructural Control".
The project sponsored by NASA/LaRC aims to Develop an “intelligent” framework to automatically build adaptive parametric reduced order model (PROM) database for aeroelastic (AE) / aeroservoelastic (ASE) analysis and aerostructural control.
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2021 Breakthrough Star Award
Dr. Wang was selected as one of the 12 recipients of the prestigious "2021 Breakthrough Star" award across all UofSC campuses for his outstanding accomplishments in "Reduced Order Modeling" research and technology development that aims to significantly reduce time and resource usage for computer modeling.
"Simulation and computing is a mainstay in engineering design, a mathematical modeling process that allows engineers to predict the behavior of a machine or system in real-world conditions. But if the datasets are huge and complex, modeling can take days or even weeks to sort out. That’s why Yi Wang is using a method called reduced-order models to speed things up."Read More
