BWXT Advanced Technologies LLC

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.

A method is provided for using a hierarchical polynomial model for physics simulation. The method includes obtaining coupled equations for a physics simulation. The coupled equations have variables and boundary conditions that constrain the variables. The method also includes generating meshes corresponding to the coupled equations. The method iteratively solves for the boundary conditions that depend on the variables within the meshes to convergence to improve numerical stability of the physics simulation, including: (i) solving for each variable in a first mesh, while holding the other meshes to a weak convergence, to obtain a first solution; (ii) applying the first solution to resolve a second mesh, to obtain a second solution; and (iii) generating a hierarchical polynomial based on the second solution. The hierarchical polynomial is a functional form of a pre-determined physics equation. The method also (iv) computes new boundary conditions for resolving the meshes, using the hierarchical polynomial.

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.

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.

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.