Intelligent Diagnostics.ai reads and analyzes your neutron flux monitor performance in real-time so you can avoid unnecessary maintenance costs and generation lossesย maximize the life of your flux monitors prevent TS violations by managing offline monitors more intelligently.
Model predicts the k (= ๐ผ/๐ผ0) during cycle planning If k falls below the threshold (0.11 or 0.16) during the future cycle
Tool highlights monitor strings that need to be replaced to maintain TS compliance in a future fuel cycle.
For monitors that are left in the core, tool defines when a monitor needs to be taken offline to avoid entering the EOL region.
Model is trained with comprehensive set of cycle data (Exposure, Rod Pattern History, etc..)
Neutron flux is a measure of the intensity of neutron activity in a given area, defined as the number of neutrons passing through a unit area per unit time, typically expressed in neutrons per square centimeter per second. It is a critical parameter in nuclear reactor operation, influencing the rate of fission reactions and power generation.
Neutron flux varies spatially and temporally within a reactor core, requiring precise monitoring and control to ensure optimal performance, fuel utilization, and adherence to safety margins.
Flux detectors contain a coating with both 235U and 234U.
Fission of 235U causes the primary ionization of the argon gas in the detector. This creates the current the detector is measuring.
234U replenishes 235U lost by the fission process.
After 234U is depleted, the electrical current induced by a flux starts declining.
The manufacturer recommends taking a detector out of service when the flux-induced current is ๐ผ<๐๐ผ0, where ๐ผ0 is the initial current for the same flux after complete 234U depletion.
๐ is a constant which depends on detector type (e.g. 0.11 or 0.16).
Intelligent Diagnostics.ai is a robust, state-of-the-art SaaS application for the nuclear power industry that provides unparalleled accuracy for neurton flux forecasting in both reload core design and cycle management engineering applications.
Additional features include the abilities to:
Savings from maximizing the effective life span of flux detectors.
Avoiding TS violations by keeping the maximum number of offline detectors in the core below the TS limit.
The above diagram depicts the Intelligent Diagnostics interface and how the user interacts with it optimizing core efficiency.
A number of techniques have been employed to enhance the datasets, including data augmentation for maintaining representative distributions, interpolation of training targets, and transfer learning to take maximum advantage of information from multiple sites. These techniques have made it possible to extend the development of highly accurate models to reactors possessing less data than would otherwise be required.
Intelligent Diagnostics.ai is accessed via a web browser and is available for all standard computing platforms with a high-speed Internet connection, running most modern 32- and 64-bit operating systems and mobile operating systems: Linux, Windows, macOS, Android, iOS, and UNIX architectures are all acceptable environments for Intelligent Diagnostics.ai.
Energize reload design with the BWnuclear.ai software suite. These AI-based predictive algorithms integrate seamlessly, whether it be for reload core design or cycle management applications.
Energize reload design with theย BWnuclear.ai software suite. Our AI-based proprietary predictive algorithms integrate seamlessly, whether it be for reload core design or cycle management applications.
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