The global energy landscape is undergoing a nuclear renaissance, driven by rising energy demands, economic growth, and the computational needs of artificial intelligence (AI) and machine learning (ML). With around 65 reactors under construction across 15 countries, and more planned, including in nations new to nuclear energy, the industry is advancing rapidly. While light water reactor (LWR) designs will still make the majority of nuclear power plants in operation, traditional nuclear vendors as well as a growing number of startups are actively developing advanced concepts like heat pipe, gas-cooled, liquid metal-cooled, and molten salt reactors. These “new” advanced designs, grounded in mid-20th-century thermohydraulic (TH) research, face modern demands for safety, economic viability, and regulatory compliance. A key challenge is the reliance on outdated TH experimental data, which, limited by past techniques’ resolution, lacks the spatial and temporal detail to capture next-generation reactors’ complex flow physics, hindering validation of more advanced simulation tools such as CFD. In this paper, we present examples of high-resolution thermohydraulic experiments designed to support the development of advanced nuclear reactors and the validation of the computer models required for licensing. These experiments address key challenges in model validation. Using cutting-edge diagnostics and instrumentation, they deliver accurate, high-fidelity data that capture critical flow phenomena under relevant conditions. The resulting datasets are used to validate CFD and multi-physics simulation codes, reduce modeling uncertainties, and support improved physical understanding. By strengthening the predictive capability of simulation tools, these experiments can contribute to refined reactor designs, optimized performance, and more accurate assessment of safety limits required for licensing, ultimately enabling the safe and effective deployment of next-generation nuclear technologies.
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Victor Petrov
University of Michigan
Annalisa Manera
University of Michigan
Nuclear Engineering and Technology
University of Michigan
ETH Zurich
Paul Scherrer Institute
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Petrov et al. (Sun,) studied this question.
synapsesocial.com/papers/69cd79e15652765b073a6b27 — DOI: https://doi.org/10.1016/j.net.2026.104311
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