Abstract: Methods for evaluating robustness of solutions to constraint problems include: (a) determining one or more inputs for the constraint problem; (b) determining bias, uncertainty, or bias and uncertainty of the one or more inputs; (c) randomly perturbating at least one of the one or more inputs based on the bias, uncertainty, or bias and uncertainty of the one or more inputs to determine one or more modified inputs; (d) generating a solution to the constraint problem based on the one or more modified inputs; (e) storing the solution; (f) repeating (c) through (e) until multiple solutions are stored; and/or (g) determining the robustness of the stored multiple solutions by comparison of the stored multiple solutions to each other. The one or more modified inputs include the randomly perturbated input or inputs and/or the non-perturbated input or inputs.

Abstract: A system and method is provided for a continual updating of the optimization of multiple operational control-variables during the operation of a nuclear reactor over a plurality of fuel cycles. A networked computer system includes one or more hosts programmed to execute an optimization process to identify and make changes in quantitative values of operational control-variables that result in improved efficiency and operational flexibility. Optimization and updating of operational control-variables may proceed selectively under manual control for inputting specific optimization constraints and reactor state-point information or may proceed autonomously through a repetitive performing of the optimization process based upon a predetermined user-defined strategy stored on the network.

Abstract: Methods for evaluating robustness of solutions to constraint problems include: (a) determining one or more inputs for the constraint problem; (b) determining bias, uncertainty, or bias and uncertainty of the one or more inputs; (c) randomly perturbating at least one of the one or more inputs based on the bias, uncertainty, or bias and uncertainty of the one or more inputs to determine one or more modified inputs; (d) generating a solution to the constraint problem based on the one or more modified inputs; (e) storing the solution; (f) repeating (c) through (e) until multiple solutions are stored; and/or (g) determining the robustness of the stored multiple solutions by comparison of the stored multiple solutions to each other. The one or more modified inputs include the randomly perturbated input or inputs and/or the non-perturbated input or inputs.

Abstract: In the method, a set of limits applicable to a core may be defined, and a test core loading pattern design, to be used for loading the core, may be determined based on the limits. Reactor operation on at least a subset of the core may be simulated to produce a plurality of simulated results. The simulated results may be compared against the limits, and data from the comparison may indicate whether any of the limits were violated by the core during the simulation. A designer or engineer may use the data to modify the test core loading pattern, creating one or more derivative core loading pattern design(s) for simulation and eventual perfection as an acceptable core loading pattern design for the core.

Abstract: In the method, a set of limits applicable to a core may be defined, and a test core loading pattern design, to be used for loading the core, may be determined based on the limits. Reactor operation on at least a subset of the core may be simulated to produce a plurality of simulated results. The simulated results may be compared against the limits, and data from the comparison may indicate whether any of the limits were violated by the core during the simulation. A designer or engineer may use the data to modify the test core loading pattern, creating one or more derivative core loading pattern design(s) for simulation and eventual perfection as an acceptable core loading pattern design for the core.

Abstract: In the method, a set of limits applicable to a core may be defined, and a test fresh fuel loading pattern design, to be used for loading the core, may be determined based on the limits. Reactor operation on at least a subset of the core may be simulated to produce a plurality of simulated results. The simulated results may be compared against the limits, and data from the comparison may indicate whether any of the limits were violated by the core during the simulation. A designer or engineer may use the data to modify the test fresh fuel loading pattern, creating one or more derivative fresh fuel loading pattern design(s) for simulation and eventual perfection as an acceptable fresh fuel loading pattern design for the core.

Abstract: A system and method is provided for a continual updating of the optimization of multiple operational control-variables during the operation of a nuclear reactor over a plurality of fuel cycles. A networked computer system includes one or more hosts programmed to execute an optimization process to identify and make changes in quantitative values of operational control-variables that result in improved efficiency and operational flexibility. Optimization and updating of operational control-variables may proceed selectively under manual control for inputting specific optimization constraints and reactor state-point information or may proceed autonomously through a repetitive performing of the optimization process based upon a predetermined user-defined strategy stored on the network.

Abstract: A system and method is provided for a continual updating of the optimization of multiple operational control-variables during the operation of a nuclear reactor over a plurality of fuel cycles. A networked computer system includes one or more hosts programmed to execute an optimization process to identify and make changes in quantitative values of operational control-variables that result in improved efficiency and operational flexibility. Optimization and updating of operational control-variables may proceed selectively under manual control for inputting specific optimization constraints and reactor state-point information or may proceed autonomously through a repetitive performing of the optimization process based upon a predetermined user-defined strategy stored on the network.