Chen receives NRC grant for molten salt spill experimental database project
March 7, 2023
Nuclear engineering professor’s research on advanced reactors featured in the Innovation Platform
March 1, 2023
Nuclear engineering faculty member part of STEAM Day outreach
February 21, 2023
Study: El-Genk ranked among the worlds top 2% most cited
February 17, 2023
Perfetti receives funding for project to improve accuracy of nuclear data evaluations
July 11, 2019 - By Kim Delker
Christopher Perfetti, assistant professor in The University of New Mexico Department of Nuclear Engineering, has received funding for a project to help researchers achieve more accurate nuclear measurement data.
The project, “Using Benchmark Experiments to Improve Differential Nuclear Data Evaluations,” was funded under the Nuclear Energy Research and Development Program (NEUP) of the U.S. Department of Energy. The total amount of the award is $400,000.
According to Perfetti, advanced modeling and simulation tools rely on high-fidelity nuclear data evaluations to accurately model interactions between neutrons and matter. The accuracy of these tools is limited by a variety of approximations and assumptions, thereby introducing bias in the results of these computational simulations.
The project that Perfetti is involved with will use the results of larger, “integral” benchmark experiment to improve the accuracy of fine-energy-resolution differential nuclear data measurements and evaluations. The team will first develop capabilities to assess the sensitivity of the benchmark results to the evaluated/measured nuclear data parameters, will then apply Bayesian-based data assimilation tools to directly calibrate the evaluated data parameters, thereby improving the accuracy of nuclear data evaluations and modeling and simulation tools.
The project will enable the team to:
- Understand the impact of uncertainty in evaluated nuclear data parameters on the predictions from high-fidelity modeling and simulation tools
- Identify the underlying sources of discrepancies between criticality experiment measurements and predicted results from modeling and simulation tools
- Use information about computational biases inferred from nuclear criticality experiments to generate nuclear data evaluations that have been calibrated to maximize the predictive capability of modeling and simulation tools.
The expected outcome is to produce a new way for nuclear data scientists to improve the accuracy of their data at all stages of the nuclear data collection and evaluation process.
Perfetti is leading the research and is joined by collaborators from Oak Ridge National Laboratory.