Bwxt Advanced Technologies LLC

Bwxt Nuclear Energy, Inc.

Ryan Trigg Swanson

James Brian Inman

Jonathan K Witter

Roger Dale Ridgeway

Joshua J Bergman

Eric A Barringer

Jeremy Leslie Gustafson

Matt Ales

Russell R Jensen

Danny Galicki

Matt Krecicki

Ross Evan Pivovar

Carbide-based fuel assembly includes outer structural member of ceramic matrix composite material (e.g., SiC—SiC composite), insulation layer of porous refractory ceramic material (e.g., zirconium carbide with open-cell foam structure or fibrous zirconium carbide), and interior structural member of refractory ceramic-graphite composite material (e.g., zirconium carbide-graphite or niobium carbide-graphite). Spacer structures between various layers provide a defined and controlled spacing relationship. A fuel element bundle positioned between support meshes includes a plurality of distributively arranged fuel elements or a solid, unitary fuel element with coolant channels, each having a fuel composition including high assay, low enriched uranium (HALEU). Fuel assemblies are distributively arranged in a moderator block and the upper end of the outer structural member is attached to a metallic inlet tube for hydrogen propellant and the lower end of the outer structural member is interfaced with a support plate, forming a nuclear thermal propulsion reactor.

Carbide-based fuel assembly for thermal propulsion applications

Carbide-based fuel assembly includes outer structural member of ceramic matrix composite material, the interior surface of which is lined in higher temperature regions with an insulation layer of porous refractory ceramic material. Continuous insulation layer extends the length of the fuel assembly or separate insulation layer sections have a thickness increasing step-wise along the length of the fuel assembly from upper (inlet) section towards bottom (outlet) section. A fuel element positioned inward of the insulation layer and between support meshes has a fuel composition including HALEU and the form of a plurality of individual elongated fuel bodies or one or more fuel monolith bodies containing coolant flow channels. Fuel assemblies are distributively arranged in a moderator block, with upper end of the outer structural member attached to an inlet for propellant and lower end of the outer structural member operatively interfaced with a nozzle forming a nuclear thermal propulsion reactor.

Carbide-based fuel assembly includes outer structural member of ceramic matrix composite material (e.g., SiC&#x2014;SiC composite), insulation layer of porous refractory ceramic material (e.g., zirconium carbide with open-cell foam structure or fibrous zirconium carbide), and interior structural member of refractory ceramic-graphite composite material (e.g., zirconium carbide-graphite or niobium carbide-graphite). Spacer structures between various layers provide a defined and controlled spacing relationship. A fuel element bundle positioned between support meshes includes a plurality of distributively arranged fuel elements or a solid, unitary fuel element with coolant channels, each having a fuel composition including high assay, low enriched uranium (HALEU). Fuel assemblies are distributively arranged in a moderator block and the upper end of the outer structural member is attached to a metallic inlet tube for hydrogen propellant and the lower end of the outer structural member is interfaced with a support plate, forming a nuclear thermal propulsion reactor.

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.

Rapid digital nuclear reactor design using machine learning

Growing community of inventors

Lynchburg, VA, United States of America

Ryan Trigg Swanson