Project description
Improving turbulence models of low Prandtl number flows for advanced modular reactors
Advanced modular reactors are considered a promising new technology for the energy transition. They have the potential to provide reliable, low-carbon electricity generation. Such nuclear reactors often use low Prandtl number coolants such as liquid metals, whose thermal-hydraulic flow behaviour is marked by complex interactions among buoyancy, turbulence and heat transfer that are not well understood. Supported by the Marie Skłodowska-Curie Actions programme, the NS-LPNR project aims to develop high-fidelity direct numerical simulation data for mixed convection flows with low Prandtl numbers by analysing critical phenomena such as laminar-turbulent transition and the interplay between buoyancy and turbulence under conditions representative of advanced nuclear reactors. These insights into turbulent flow and heat transfer mechanisms will provide the basis for developing improved turbulence models.
Objective
The decarbonization of the energy sector is crucial for achieving global climate goals. Nuclear energy, with its capacity for reliable, low-emission electricity generation, is essential for the transition to a sustainable, low-carbon energy future. Advanced Modular Reactors, which often employ low Prandtl number coolants like liquid metals, hold significant promise for improving safety, efficiency, and cost-effectiveness. However, the thermal-hydraulic behavior of low Prandtl number flows in these reactors, characterized by complex interactions between buoyancy, turbulence, and heat transfer, remains poorly understood. This knowledge gap poses a challenge for the modeling and safe operation of next-generation nuclear reactors.
The proposed research project aims to address this gap by generating high-fidelity Direct Numerical Simulation (DNS) data for mixed convection flows with low Prandtl numbers. This study will focus on critical phenomena such as laminar-turbulent transition, and the intricate interplay between buoyancy and turbulence under conditions representative of advanced nuclear reactors. By leveraging advanced computational techniques, this project will provide 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.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2024-PF-01
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SW7 2AZ London
United Kingdom
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