July 15, 2024

Temperature Field Reconstruction of Surfaces Heated Through Radiative Heat Transfer Using Convolutional Neural Networks

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Authors

LMLuiz Carlos MachadoPennsylvania State University VLVictor Coppo LeiteIdaho National Laboratory EMElia MerzariPennsylvania State University

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Implication

Simulation study demonstrates accurate temperature field reconstruction across heated reactor surfaces, indicating strong utility for real-time thermal monitoring.

Key Points

Convolutional neural networks accurately reconstruct full temperature field distributions over convex reactor surfaces from limited spatial sensor measurements.
An R-square metric of 0.9991 was achieved when predicting surface temperature distributions, matching simulated heat conduction data with high fidelity.
Simulation using the MOOSE framework modeled radiative heat transfer on vessel walls, supporting real-time monitoring of reactor pressure vessels with minimal sensors.

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Machado et al. (2024) studied this question.

synapsesocial.com/papers/68e6055db6db64358759904a https://doi.org/10.1115/ht2024-130465

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Implication

Simulation study demonstrates accurate temperature field reconstruction across heated reactor surfaces, indicating strong utility for real-time thermal monitoring.

Temperature Field Reconstruction of Surfaces Heated Through Radiative Heat Transfer Using Convolutional Neural Networks

Discussion

Member takes

Implication

Key Points

Cite This Study

Temperature Field Reconstruction of Surfaces Heated Through Radiative Heat Transfer Using Convolutional Neural Networks

Discussion

Member takes

Implication

Key Points

Cite This Study

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