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Direct Numerical Simulation of Low Prandtl Number Flows for Future Nuclear Reactors

CORDIS · observation · Publication date unknown

ide detailed insights into the turbulence dynamics and heat transfer mechanisms, offering a robust foundation for developing and validating improved turbulence models. The methodology integrates DNS, machine learning (ML) algorithms, and advanced vortex dynamics techniques to capture and analyze the complex fluid behaviors in low Prandtl number flows. The interdisciplinary approach combines fluid mechanics, nuclear thermal hydraulics, and ML. Hosted by Imperial College London, under the supervision of Professor Sylvain Laizet, this fellowship will significantly enhance my expertise in high-fidelity simulations and turbulence modeling. It will position me to contribute to the development of safer and more efficient nuclear reactors, while advancing my career in nuclear thermal-hydraulic research. High-fidelity simulations, low Prandtl number, turbulence modeling, CFD, machine learning, nuclear thermal hydraulics , advanced nuclear reactors

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recordType

award

status

SIGNED

region

EU

value

276187.92

unit

EUR