Optimization of expensive cost functions subject to complex multidimensional constraints
US12001766B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Jan 6, 2021 |
| Grant date | Jun 4, 2024 |
| Priority date | — |
| Expiry date | Dec 20, 2042 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02E30/30
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method is used to design nuclear reactors using design variables and metric variables. A user specifies ranges for the design variables and target values for the metric variables. A set of design parameter samples are selected. For each sample, the method runs three processes, which compute metric variables to thermal-hydraulics, neutronics, and stress. The method applies a cost function to each sample to compute an aggregate residual of the metric variables compared to the target values. The method trains a machine learning model using the samples and the computed aggregate residuals. 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 method then uses the final machine learning model to assess relative importance of each design variable.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.