Advancing High-Fidelity CFD Methods Toward Executable Digital Twins Development (Baglietto, MIT)

This talk will present exciting advancements in all three fundamental areas, starting from the proposal of a self-adapting STRUCTure based turbulence closure (STRUCT), which triggers controlled resolution of turbulence inside selected flow regions. The resolution is controlled by a single-point parameter representing the turbulent timescale separation, which quantitatively identifies topological flow structures of interest. I will conclude making the point that the growing maturity of the CFD methods, coupled to a renewed understanding of the turbulent heat flux contribution and an innovative approach for uncertainty quantification represent the base for the advancement of a new generation of high-performance energy systems.

Bio: Professor Baglietto is a PhD Graduate of the Tokyo Institute of Technology, and currently the Norm C. Rasmussen Professor and Associate Head of the Nuclear Science and Engineering Department at the Massachusetts Institute of Technology (MIT). His research goals include improving the effectiveness and expanding the reach of 3-D, first-principles computational tools for nuclear reactor design. Professor Baglietto research efforts have been instrumental in moving the nuclear industry, both within the US and overseas, from lumped-parameter and correlation-based engineering analysis, to high fidelity simulations in the form of computational fluid dynamics (CFD), with direct impact on the safety and economics of nuclear power stations.