Exploring Heart Flow Dynamics: Integrating CFD Simulations With Neural Networks

This study explores heart flow dynamics through Computational Fluid Dynamics (CFD) simulations conducted with StarCCM+ software. The research includes a thorough cross-validation using Ansys Fluent, showcasing the reliability of our approach. Key flow features are analyzed to deepen our understanding of the complex fluid dynamics within the cardiovascular system, providing valuable insights into physiological implications. Additionally, efforts are underway to integrate the simulations with AdaLED, a Neural Network-based code from the Computational Science and Engineering (CSE) lab at Harvard. This novel combination of CFD simulations and AI techniques holds promise for advancing our comprehension and predictive capabilities regarding heart flow dynamics.

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Speaker: Eneko Lazpita Suinaga is a PhD student in Aerospatial Engineering in the School of Aeronautics and Space (ETSIAE) at the Technical University of Madrid (UPM). He is currently a UPM fellow and visiting student at Harvard University, conducting research on the integration of heart flow CFD simulations with AdaLED, a Neural Network-based code developed by the Computational Science and Engineering (CSE) lab at Harvard

Sponsors: RCCHU; UPM; Harvard University