Surrogate materials are in the form of solid particles that include surrogate isotopes, namely, short-lived isotopes selected and formed to serve as surrogates for the radioactive materials of a nuclear fallout without including isotopes that are, or that decay to, biologically or environmentally deleterious and persistent isotopes. The surrogate material may be formed using high-purity reactant material and irradiation and separation techniques that enable tailoring of the isotopes and ratios thereof included in the surrogate material, and the surrogate material may be dispersed, e.g., in a training environment, in solid form.

A system for simulating maintenance of a reactor core protection system that has at least two or more channels, includes: a simulation signal generation unit for generating a simulation state signal including a normal state or an abnormal state, a communication unit connected to each of the channels of the reactor core protection system to transmit the state signal to the channel, and a control unit for receiving a result signal output from the channel in response to the input simulation state signal and confirming whether the reactor core protection system normally determines a reactor core state by analyzing the result signal.

A dynamic characteristic analysis method of DET and RELAP5 coupling based on a universal instrumental variable method includes steps of: constructing a DET simulation model of a discrete dynamic event tree and modifying TRIP cards of an input file by adding universal instrumental TRIP variables according to state transition types of DET simulation objects, the universal instrumental TRIP variable being variable type or logical type; setting a simulation time of the RELAP5, controlling a simulation step, and analyzing an output result file of each simulation step of the RELAP5; backtracking the RELAP5 according to state transition types of DET simulation objects. The dynamic characteristic analysis method has advantages of simplifying TRIP setting process and method of DET state transition objects in an input file of the RELAP5 required for the coupling of DET and RELAP5, reducing a modeling complexity and improving a modeling efficiency.

Various examples are related to prediction of neutronics parameters using deep learning. In one embodiment, a method includes generating a training data set based upon one or more principled approaches that provide a gradient of values; generating a neural network using structured or unstructured sampling of a hyperparameter space augmented by probabilistic machine learning; training the generated neural network based on the training data set to produce one or more neutronics parameters; and generating at least one neutronics parameter utilizing the trained neural network.

This invention relates to the monitoring and diagnosing of nuclear power plants for its thermal performance using the NCV Method. Its applicability comprises any nuclear reactor such as used for research, gas-cooled and liquid metal cooled systems, fast neutron systems, and the like; all producing a useful output. Its greatest applicability lies with conventional Pressurized Water Reactor (PWR) and Boiling Water Reactor (BWR) nuclear power plants generating an electric power. Its teachings of treating fission as an inertial process, a phenomena which is self-contained following incident neutron capture, allows the determination of an absolute neutron flux. This process is best treated by Second Law principles producing a total fission exergy. This invention also applies to the design of a fusion thermal system regards the determination of its Second Law viability and absolute plasma flux.

Methods implemented by at least one electronic processor for generating pointwise fast neutron spectra may include receiving composition data; receiving source data or calculating the source data; receiving nuclear data; and calculating the pointwise fast neutron spectrum based on numerical integration using the composition, source, and nuclear data. Systems for generating pointwise fast neutron spectra may include a bus; at least one electronic processor connected to the bus; an input device connected to the bus; and a communication link connected to the bus. The at least one electronic processor may be configured to receive composition data from the input device via the bus, to receive source data from the input device via the bus or to calculate the source data, to receive nuclear data from the communication link via the bus, and to calculate the pointwise fast neutron spectrum based on numerical integration using the composition, source, and nuclear data.

Provided is an apparatus for quality rating-based three-dimensional modeling of nuclear power plant equipment. The apparatus performs: receiving, from a design information database, design information of atomic power plant equipment and information on multiple components constituting the atomic power plant equipment; receiving, from a quality rating database, information on the quality rating of each of the multiple components; performing, according to the design information of the nuclear power plant equipment, three-dimensional modeling of at least some of the multiple components constituting the atomic power plant equipment; and displaying the at least some three-dimensionally modeled components among the multiple components, wherein the apparatus may further display information on the quality rating corresponding to each of the multiple components. Therefore, the present invention makes it possible to understand the quality rating-based construction status of a nuclear power plant, and can contribute to an improvement in safety through a quality rating-based alarm.

A method to determine a global core reactivity bias and the corresponding estimated critical conditions of a nuclear reactor core prior to achieving reactor criticality. The method first requires collection and evaluation of the inverse count rate ratio (ICRR) data; specifically, fitting measured ICRR vs. predicted ICRR data. The global core reactivity bias is then determined as the amount of uniform reactivity adjustment to the prediction that produces an ideal comparison between the measurement and the prediction.

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 of determining a core design parameter of a nuclear reactor, includes: calibrating an isolated voltage output from a NIS cabinet associated with the nuclear reactor using a calibrated signal source as an input to the NIS cabinet; recording values of the calibrated signal source used in the calibrating and corresponding values of the output voltage from the calibrating in an as-left cabinet calibration data table; using a computing device connected to the isolated voltage output from the NIS cabinet, converting the voltage output signal to a converted detector signal using at least some of the values in the as-left cabinet calibration data table in an improved signal conversion equation; and using the computing device, employing the converted detector signal to determine the core design parameter.