About this Research Topic
High-fidelity simulations are essential for the design, analysis, and optimization of advanced nuclear reactors. By utilizing high-fidelity models, analysts can effectively simulate the complex interactions between different physics such as neutronics, thermal-hydraulics, and structural mechanics. This leads to improved predictions of reactor behavior with greater accuracy and detail under different conditions.
However, the verification and validation (V&V) of high-fidelity advanced nuclear reactor simulations is challenging due to the scarcity of experimental data. These simulations rely on detailed physics models, but without sufficient benchmarks, it becomes difficult to ensure their accuracy. Additionally, the complexity and computational intensity of high-fidelity models make repeated validation impractical. Moreover, uncertainties in material properties, geometry, and boundary conditions further complicate the validation process. Addressing these challenges is crucial for ensuring reliable predictions of reactor behavior under various conditions.
This Research Topic aims to explore the methods and applications related to the V&V of high-fidelity and high-resolution models for advanced nuclear reactors.
Areas within the scope of this Topic include, but are not limited to:
• Innovative V&V techniques specifically designed for high-fidelity methods
• Review of comprehensive V&V campaigns for high-fidelity modeling and simulation
• Code-to-code verification and benchmarking against accurate solutions to test convergence and numerical accuracy
• Experimental validation of high-fidelity models for advanced reactors, using differential and integral experiments or reference results from complete operating systems
• Development of multistep benchmarks for V&V of high-fidelity reactor models
• Uncertainty quantification and sensitivity analysis for high-fidelity nuclear reactor simulations
• Application of machine learning and artificial intelligence to enhance V&V in high-fidelity simulations
• Advanced experimental and data acquisition techniques to support V&V of high-fidelity models
• Case studies highlighting practical applications and insights in V&V for high-fidelity reactor models
• Examination of regulatory requirements and establishment of industry standards for V&V in nuclear reactor simulations
• Interdisciplinary approaches to improve V&V across scientific and engineering fields
• Exploration of emerging technologies like quantum computing for future V&V advancements in high-fidelity simulations.
However, the verification and validation (V&V) of high-fidelity advanced nuclear reactor simulations is challenging due to the scarcity of experimental data. These simulations rely on detailed physics models, but without sufficient benchmarks, it becomes difficult to ensure their accuracy. Additionally, the complexity and computational intensity of high-fidelity models make repeated validation impractical. Moreover, uncertainties in material properties, geometry, and boundary conditions further complicate the validation process. Addressing these challenges is crucial for ensuring reliable predictions of reactor behavior under various conditions.
This Research Topic aims to explore the methods and applications related to the V&V of high-fidelity and high-resolution models for advanced nuclear reactors.
Areas within the scope of this Topic include, but are not limited to:
• Innovative V&V techniques specifically designed for high-fidelity methods
• Review of comprehensive V&V campaigns for high-fidelity modeling and simulation
• Code-to-code verification and benchmarking against accurate solutions to test convergence and numerical accuracy
• Experimental validation of high-fidelity models for advanced reactors, using differential and integral experiments or reference results from complete operating systems
• Development of multistep benchmarks for V&V of high-fidelity reactor models
• Uncertainty quantification and sensitivity analysis for high-fidelity nuclear reactor simulations
• Application of machine learning and artificial intelligence to enhance V&V in high-fidelity simulations
• Advanced experimental and data acquisition techniques to support V&V of high-fidelity models
• Case studies highlighting practical applications and insights in V&V for high-fidelity reactor models
• Examination of regulatory requirements and establishment of industry standards for V&V in nuclear reactor simulations
• Interdisciplinary approaches to improve V&V across scientific and engineering fields
• Exploration of emerging technologies like quantum computing for future V&V advancements in high-fidelity simulations.
Keywords: High-Fidelity, Multiphysics, V&V, Advanced Nuclear Reactors, Verification, Validation, High-resolution models, High-Performance Computing
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