Configurable Microreactor for Nuclear Training and Testing
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Solution Overview
Problem
Existing nuclear reactors are complex and costly, making simulations and training difficult, limiting the ability to validate computational tools and investigate novel fuel mixtures and designs, and are not well-suited for educational purposes.
Innovation Solution
A robust, highly instrumented, and flexible microreactor design with a reactor core, testing cavity, rotating control drums, beam ports, moveable particle filter rings, and advanced sensors, coupled with computing devices for high-fidelity measurements and analysis, enabling advanced reactor testing and education.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional nuclear reactors are used for training and experimentation, then operational experience can be gained, but the complexity and cost of the reactor systems make them unsuitable for educational purposes and validation of computational tools
Solution Approach 1:
The patent divides the traditional reactor system into a simplified educational version by separating essential training functions from complex operational systems. The microreactor retains core nuclear physics principles while removing unnecessary complexity, allowing educational institutions to conduct experiments and training without the burden of managing full-scale reactor systems.
Solution Approach 2:
The patent creates a scaled-down replica or model reactor that copies the essential features and operational principles of traditional reactors. This microreactor serves as a faithful representation that enables students to learn reactor operations, safety procedures, and nuclear physics without the risks and complexities of full-scale systems.
2Measurement precision
If computational simulations are used for reactor analysis, then costs are reduced, but the simulations cannot be validated accurately without real reactor data
Solution Approach 1:
The microreactor is equipped with comprehensive instrumentation and sensors that automatically collect experimental data for validating computational models. The system self-monitors various parameters such as neutron flux, temperature, and pressure, providing real-world data that directly validates simulation accuracy without requiring external verification facilities.
Solution Approach 2:
The patent implements feedback mechanisms where experimental data from the microreactor continuously validates and refines computational models. The comparison between simulated predictions and actual measurements creates a feedback loop that improves model accuracy, allowing iterative refinement of computational tools based on real experimental results.
3Adaptability or versatility
If full-scale reactors are modified for experimentation, then real reactor conditions are maintained, but the reactors are not well-suited for investigating novel fuel mixtures and designs
Solution Approach 1:
The microreactor employs dynamic, interchangeable fuel assemblies and test configurations that can be easily changed to test different fuel mixtures and designs. The system allows rapid reconfiguration of experimental parameters, enabling researchers to investigate novel fuel concepts without compromising the operational stability and safety of the reactor system.
Data Source
AI summary
A configurable microreactor for testing and education is described. The microreactor includes a reactor core comprising a plurality of fuel rods, a plurality of guide tubes, and a plurality of rotating control drums configured to control operation of the microreactor. Further, the microreactor includes a testing cavity disposed in an area within the reactor configured to store an item therein for experimentation; a plurality of beam ports; a moveable particle filter ring; a moveable spectrum shifter; and at least one sensor. A computing device is directed to receive measurements from the at least one sensor and perform a physics-based analysis of the microreactor using one or more machine learning (ML) routines.