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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Defect Recognition for Eddy Current Testing of Spent Nuclear Fuel Canister using Convolutional Neural Network

This paper proposes an accurate and robust defect detection solution for 304L and 306L stainless steel (SS) weld. In the proposed solution, Eddy current testing (ECT) is employed to generate 2-dimensional (2D) data for samples under test with defects. The 2D data can be treated as images for deep learning-based defect detection. Since convolutional neural networks (CNNs) are powerful in processing images, CNN is employed in this study for defect detection. Experiments are conducted on a submerged arc welding (SAW) 304L SS weld sample with an artificial crack generated by waterjet cutting. The ECT data on this seeded fault sample is utilized to verify the proposed solution. For this purpose, the ECT measurement are separated as from Fault area and Normal area, which are used for CNN training. After training, the testing data is used for verification. Experimental results demonstrate the feasibility and effectiveness of the proposed solution.

Niu, Guangxing↗
DOE OSTI2020

Real-time Canister Welding Health Monitoring and Prediction System for Spent Fuel Dry Storage

In this report, the salt mist corrosion testing at controlled temperature was carried out successfully on 304L SS submerged arc welded coupons by Unifog Dispersion Tower at ORNL. Corrosion in-situ monitoring system from Intelligent Automation, Inc. (IAI) was setup in the corrosion test and running smoothly to record signals during the corrosion test. Parts and device selection, sensors attachment, and experimental procedures are discussed. There wasn’t any visible corrosion or crack propagation on the weld coupon after about a month of corrosion testing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Aerosol Deposition Inside a Spent Fuel Transportation and Storage Canister (Interim Report)

This report documents the progress in preparing the ANSYS/Fluent ® and MELCOR models to perform characterization studies of aerosols dispersal and deposition within a select spent fuel cask system. Steady state thermal-hydraulic cask response is modeled with both codes, at present, while the MELCOR source code is being modified to allow imposed thermal-hydraulic conditions with aerosol physics calculations. This will allow the MELCOR model to assume the thermal-hydraulic calculation from the ANSYS/Fluent ® , while only computing the aerosol physics. Detailed results are presented on the thermal-hydraulic analysis of the MAGNASTOR ® cask for the current ANSYS/Fluent ® model, with convergent conditions observed over two fidelities. While the MELCOR computation computes the steady state conditions, they differ sufficiently from that computed with ANSYS/Fluent ® . The MELCOR analysis include a set of UO 2 sources to investigate system response for interim reporting. The airborne concentrations and evolving distributions are presented. Model development is anticipated to continue as additional components impacting flow conditions or aerosol deposition within the MAGNASTOR ® cask and Westinghouse fuel assemblies are identified.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

FY21 Status Report: Probabilistic SCC Model for SNF Dry Storage Canisters

Stress corrosion cracking (SCC) is an important failure degradation mechanism for storage of spent nuclear fuel. Since 2014, Sandia National Laboratories has been developing a probabilistic methodology for predicting SCC. The model is intended to provide qualitative assessment of data needs, model sensitivities, and future model development. In fiscal year 2021, improvement of the SCC model focused on the salt deposition, maximum pit size, and crack growth rate models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗