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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Sister Rod Destructive Examinations (FY22) Appendix F2: Evaluation of Fuel Rod Fatigue During Spent Fuel Transportation

This report documents work performed under the Spent Fuel and Waste Disposition’s Spent Fuel and Waste Science and Technology program for the US Department of Energy (DOE) Office of Nuclear Energy (NE). This work was performed to fulfill Level 2 Milestone M2SF-23OR010201024, “FY22 Report on ORNL Sibling Rod Testing Results,” within work package SF-23OR01020102 and is an update to the work reported in M2SF-22OR010201047, M2SF-21OR010201032, M2SF-19ORO010201026, and M2SF- 19OR010201028. As a part of DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin (LT) Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rods’ condition before dry storage and are focused on understanding overall SNF rod strength and durability. Composite fuel and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition. This appendix documents an evaluation of the fatigue data to enhance the technical basis for post-storage transportation, handling, and subsequent disposition and to identify future testing needs for Phase 2 of the project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

New ASME Section III, Division 5 Creep-Fatigue Design Rules Based on EPP and SMT Approaches

The integrated elastic-perfectly plastic (EPP) and simplified model test (SMT) creep-fatigue (CF) design methodology, referred to as the EPP-SMT method, is being developed as an alternative for CF evaluation in the design of pressure boundary components for high-temperature reactors. This report reviews the conceptual basis of the EPP-SMT methodology, summarizes the SMT experimental development efforts and results, and provides the technical basis for finalizing the EPP-SMT CF design curves for Alloy 617, based on a combined experimental and numerical approach conducted in FY 2024. This report presents the effect of hold time on the CF design curves for Alloy 617 at elevated temperatures. It includes proposed EPP-SMT CF design curves and tabulated values for continuous cycling, along with the effects of maximum hold time, for the use of this EPP-SMT CF evaluation method.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Offshore Wind Turbine Blade (85 to120meters ultra-long blade) static and fatigue test

Offshore blades are not just longer, they are wider, heavier and more flexible. These longer blades are a key component of larger wind turbines and for turbines to be more efficient in low wind speeds. These longer blades help reduce the LCOE further and make offshore wind even more competitive. As described earlier, it is essential for the blades to be fully tested to verify and validate the blade designs and manufacturing processes to ensure a reliable product is installed in the field. Through this project WTTC developed and procured new testing equipment required to test longer blades with larger deflections and higher loads. R&D’s BCDA system commissioned was the largest fatigue test exciter system in the world and set the bar for other blade testing facilities worldwide.

17 WIND ENERGY↗
DOE OSTI2021

Deep learning approaches to semantic segmentation of fatigue cracking within cyclically loaded nickel superalloy

Improvements to synchrotron-based micro-computed tomography scanning capabilities have gifted researchers the ability to characterize 4D material thermomechanical responses more thoroughly than ever before. These advancements, however, have brought about new challenges in analyzing the resulting deluge of data. We report on a nickel-based superalloy specimen imaged 26 times in-situ during cyclic loading at Argonne National Laboratory Advanced Photon Source beamline 1ID, in order to monitor crack growth within the microstructure. Therefore, several deep learning approaches which utilize convolutional neural networks are implemented to segment crack features from reconstructed tomography scans. U-Net architecture implementations are found to be especially effective, achieving IoU = 0.995 +/- 0.004 and Matthews correlation coefficient scores of Φ = 0.826 +/- 0.085. These advancements broaden possibilities for scientists seeking to automate segmentation analyses of similar large datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Four-dimensional microstructurally small fatigue crack growth in cyclically loaded nickel superalloy specimen

Here, we nondestructively image a low-solvus high refractory (LSHR) nickel base specimen microstructure during cyclic loading to characterize crack propagation during the microstructurally small crack (MSC) growth regime. Micro-computed tomography (uCT) is used to characterize the material density and identify crack interfaces, while high-energy diffraction microscopy (HEDM) provides snapshots of the microstructural orientation field changes. From these measurements, data are computationally reconstructed and coregistered to describe the specimen’s configuration before load, before crack nucleation, and throughout the crack propagation/loading process. An MSC forms at a FIB-cut notch, which was previously placed to concentrate stress at the center of the specimen gauge, and propagates along a facet parallel to the local {1 1 1} crystallographic plane. Analysis of available crystallographic slip directions and measured point-to-point disorientation axes indicate probable single and coplanar slip activity along FCC octahedral planes within this region. Similar analyses of other crack propagation facets reveal generally slower growth along directions favoring zig-zag cross and codirectional cross slip, which are often disfavored at room temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A digital twin based on OpenFAST linearizations for real-time load and fatigue estimation of land-based turbines

Monitoring a wind turbine requires intensive instrumentation, which would be too cost-prohibitive to deploy to an entire wind plant. This work presents a technique that uses readily available measurements to estimate signals that would otherwise require additional instrumentation. This study presents a digital twin concept with a focus on estimating wind speed, thrust, torque, tower-top position, and loads in the tower using supervisory control and data acquisition (SCADA) measurements. The model combines a linear state-space model obtained using OpenFAST linearizations, a wind speed estimator, and a Kalman filter algorithm that integrates measurements with the state model to perform state estimations. The measurements are: top acceleration, generator torque, pitch, and rotational speed. The article extends previous work that derived the linear state-space model using a different method. The new implementation, based on OpenFAST linearization capability, allows for a systematic extension of the method to more states, inputs, outputs, and to the offshore environment. Results from the two methods are compared, and the validation is made with additional measurements using the GE 1.5-MW turbine located at the National Renewable Energy Laboratory test site. Real-time damage equivalent loads of the tower bottom moment are estimated with an average accuracy of approximately 10%. Overall, the results from this proof of concept are encouraging, and further application of the model will be considered.

17 WIND ENERGY↗