Rapid digital nuclear reactor design using machine learning
US11574094B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 8, 2020 |
| Grant date | Feb 7, 2023 |
| Priority date | — |
| Expiry date | May 4, 2041 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02E30/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method designs nuclear reactors using design variables and metric variables. A user specifies ranges for the design variables and threshold values for the metric variables and selects design parameter samples. For each sample, the method runs three processes, which compute metric variables for thermal-hydraulics, neutronics, and stress. The method applies a cost function to compute an aggregate residual of the metric variables compared to the threshold values. The method deploys optimization methods, either training a machine learning model using the samples and computed aggregate residuals, or using genetic algorithms, simulated annealing, or differential evolution. When using Bayesian optimization, the method shrinks the range for each design variable according to correlation between the respective design variable and estimated residuals using the machine learning model. These steps are repeated until a sample having a smallest residual is unchanged for multiple iterations. The final model assesses relative importance of each design variable.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.