A nuclear reactor protection system includes a plurality of functionally independent modules, each of the modules configured to receive a plurality of inputs from a nuclear reactor safety system, and logically determine a safety action based at least in part on the plurality of inputs; and one or more nuclear reactor safety actuators communicably coupled to the plurality of functionally independent modules to receive the safety action determination based at least in part on the plurality of inputs.

The present disclosure provides a method of multi-objective and multi-dimensional online joint monitoring for a nuclear turbine. The method includes: obtaining first temperature monitoring data of the nuclear turbine by performing online thermal monitoring on a rotor, a valve cage and a cylinder of the nuclear turbine under quick starting-up; obtaining second temperature monitoring data of tightness of a flange association plane of the cylinder of the nuclear turbine by performing online thermal monitoring on the tightness of the flange association plane; obtaining operation monitoring data of a shafting vibration of a rotor and bearing system of the nuclear turbine by performing online safety monitoring on the shafting vibration of the rotor and bearing system; and optimizing operation and maintenance control of the nuclear turbine according to at least one type of monitoring data among the first temperature monitoring data, the second temperature monitoring data and the operation monitoring data.

Systems and methods are described herein for real-time data processing and for emergency planning. Scenario test data may be collected in real-time based on monitoring local or regional data to ascertain any anomaly phenomenon that may indicate an imminent danger or of concern. A computer-implemented method may include filtering a plurality of different test scenarios to identify a sub-set of test scenarios from the plurality of different test scenarios that may have similar behavior characteristics. A sub-set of test scenarios is provided to a trained neural network to identify one or more sub-set of test scenarios. The one or more identified sub-set of test scenarios may correspond to one or more anomaly test scenarios from the sub-set of test scenarios that is most likely to lead to an undesirable outcome. The neural network may be one of: a conventional neural network and a modular neural network.

A method for manipulating fractal forming information, also referred to as ct states, in a dimensional form of increasing and decreasing fractal compression roughly generated by the denominator of pi (fpix), n+1, and the formula 2f(x)^(2^x) including transitional steps between those stepwise increases and decreases by altering the compression of decompression targeting fractal states of the composite dimensional features (next lower dimensional features) or the resulting dimensional features (next higher dimensional features). Steps include identifying the ct states which are to be manipulated, select a compression or decompression ct state component to change the selected ct states, adding the compression or decompression components to yield the new ct states.

A vision-based state estimation framework to estimate the state of an underwater remotely operated vehicle (ROV) used for inspection of an underwater structure, for example, a nuclear reactor pressure vessel. The framework employs an external overhead, pan-tilt-zoom (PTZ) camera as the primary sensing modality and incorporates prior knowledge of the geometry of the structure.

Systems and methods are described herein for real-time data processing and for emergency planning. Scenario test data may be collected in real-time based on monitoring local or regional data to ascertain any anomaly phenomenon that may indicate an imminent danger or of concern. A computer-implemented method may include filtering a plurality of different test scenarios to identify a sub-set of test scenarios from the plurality of different test scenarios that may have similar behavior characteristics. A sub-set of test scenarios is provided to a trained neural network to identify one or more sub-set of test scenarios. The one or more identified sub-set of test scenarios may correspond to one or more anomaly test scenarios from the sub-set of test scenarios that is most likely to lead to an undesirable outcome. The neural network may be one of: a conventional neural network and a modular neural network.

A method of determining variable trip setpoints at the time of performing a safety analysis on a plant protection system includes: selecting a fixed analysis setpoint including a first analysis setpoint at which safety functions are initiated according to process variables of a power plant, and a first reaching time representing a time required to reach the first analysis setpoint; deriving a variable analysis setpoint satisfying conditions of the first fixed analysis setpoint; and determining a variable trip setpoint by reflecting uncertainty of an instrumentation and control system in relation to the variable analysis setpoint.

The invention relates to statistical processing of multi-dimensional samples according to a step-wise sequence of iterative tolerance limit definitions using rank statistics. The processing is performed in the context of defining tolerance limits for a population that are compared to multiple process limits or acceptance criteria, with the requirement that a specified fraction of the population be confirmed to fall within the stated acceptance criteria. The symmetry (or asymmetry) may be allocated and controlled by selecting the frequency of occurrence of a specific figure of merit, and its order or position, in the sequential embedding processing sequence.

The present invention provides a method for integrated online monitoring and a system for integrated online monitoring from a remote location for a nuclear power plant. The method includes a first step of collecting machine monitoring data of a plurality of units in the nuclear power plant selectively from the local data acquisition device via the monitoring data relay device, and a second step of collecting machine diagnostic data of a plurality of units in the nuclear power plant selectively from the local data acquisition device via the diagnostic data relay device.

A system configured to monitor the structural health of reactor vessel internals of a nuclear reactor is disclosed herein. The system includes a memory configured to store historical information associated with past performance of the nuclear reactor, and an anomaly detection subsystem including a control circuit configured to receive a signal from a sensor. The anomaly detection subsystem is configured to determine, via the control circuit, a characteristic of a vibrational response of the reactor vessel internals based, at least in part, on the signal; access, via the control circuit, the historical information stored in the memory; compare, via the control circuit, the determined characteristic to the historical information stored in the memory; and determine, via the control circuit, a condition of the reactor vessel internals based, at least in part, on the comparison of the determined characteristic and the historical information.