Emilio Baglietto

Predictive Multiphase Modeling for Boiling and Critical Heat Flux

Development of high-fidelity multiphase CFD frameworks that can predict boiling heat transfer and critical heat flux (CHF) from comprehensive representation of the underlying physical mechanisms. By resolving microscale interactions such as bubble growth and interaction, sliding, microlayer evaporation, and dry area evolution the MIT-boiling (MITB) framework captures the complex interplay between surface conditions and flow dynamics providing greatly extended generality and fidelity. The resulting models offer mechanistic representation for wall heat partitioning and hydrodynamic forces which enables consistent predictions across varied coolant types, pressures, and geometries, addressing long-standing industry challenges. The ability to shift from empirical correlations to physically grounded simulation marks a transformative advance in thermal engineering, extending to nuclear fuel performance and life extension assessments

Structure Resolution Based Turbulence Modeling and Uncertainty Quantification

This research area centers on advancing turbulence modeling to support the design and licensing of advanced nuclear reactors. Traditional Reynolds-Averaged Navier-Stokes (RANS) models suffer from limitations in capturing flow anisotropy and coherent unsteadiness, critical for advanced nuclear systems. To overcome these gaps, the STRUCT (Structure-Based Turbulence) model was developed, offering mesh-independent scale resolution guided by local flow features. STRUCT achieves Large Eddy Simulation (LES)-like accuracy at a fraction of the computational cost, enabling practical, high-fidelity simulations of complex reactor geometries. Coupled with uncertainty quantification techniques pioneered by the group, this framework supports robust performance predictions and error estimation, laying the groundwork for regulatory adoption of CFD in next-generation reactor licensing.

High Fidelity Digital Twins for Nuclear Systems

Addresses the grand challenge of integrating simulation-based modeling with real-time system diagnostics through digital twins. Because experimental validation in nuclear environments is often infeasible, trust in high-fidelity simulations must be underpinned by rigorous uncertainty quantification (UQ). The group has pioneered stochastic methods to quantify model form uncertainty in turbulence and heat transfer modeling, including non-stationary random fields for eddy viscosity. These UQ frameworks are being embedded into digital twin architectures, exemplified by a DOE-ARPA-E-supported collaboration with GE-Hitachi to build a digital twin of the BWRX300 feedwater line. This approach enables real-time forecasting of reactor component performance under uncertainty, setting a new paradigm for operational monitoring, life extension, and predictive maintenance in the nuclear sector.

High-fidelity Multiphase CFD for Cross-Sector Applications

Expanding beyond nuclear applications, this research area exploits physics-driven multiphase CFD modeling to solve thermal-fluid challenges in other industries. The group’s boiling and interfacial transport models have been applied to solar power systems (e.g., direct steam generation), the chemical process industry, and cryogenic systems for space exploration. The generalized closure relations for interfacial forces, turbulence modulation, and heat transfer mechanisms offer robust predictiveness across a wide range of flow regimes is supporting for instance the development of models for cryogenic propellant chill-down and transfer under varying gravity conditions.