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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Multiscale fatigue crack initiation in hierarchical additively manufactured alloys

Bioinspired hierarchical microstructures offer a route toward engineered fatigue resistance in additively manufactured alloys. However, it remains unclear how discrete structural constituents independently govern damage accumulation, particularly during the critical fatigue initiation regime where short cracks strongly interact with local microstructure. Here, we investigate multiscale fatigue initiation in a dual-phase, nanolamellar AlCoCrFeNi 2.1 high-entropy alloy. By comparing microscale specimens that isolate the nanolamellar structure against macroscale specimens containing the full melt-pool architecture, we identify size-dependent fatigue initiation mechanisms. We find that failure is dictated by nanolamellar interfaces at the microscale, whereas mesoscale melt pool boundaries serve to initiate fatigue at the macroscale. This mechanistic shift is accompanied by a transition from macroscale quasi-brittle failure to microscale plasticity-driven crack extension. Our results provide a physical framework for understanding how structural hierarchy governs the transition from discrete microstructural deformation to continuum fatigue fracture behavior, informing the design of damage-tolerant, additively manufactured alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Preliminary Modeling of Chloride Deposition on Spent Nuclear Fuel Canisters in Dry Storage Relevant to Stress Corrosion Cracking

This study presents a method that can be used to gain information relevant to determining the corrosion risk for spent nuclear fuel (SNF) canisters during extended dry storage. Currently, it is known that stainless steel canisters are susceptible to chloride-induced stress corrosion cracking (CISCC). However, the rate of CISCC degradation and the likelihood that it could lead to a through-wall crack is unknown. This study uses well-developed computational fluid dynamics and particle-tracking tools and applies them to SNF storage to determine the rate of deposition on canisters. The deposition rate is determined for a vertical canister system and a horizontal canister system, at various decay heat rates with a uniform particle size distribution, ranging from 0.25 to 25 µm, used as an input. In all cases, most of the dust entering the overpack passed through without depositing. Most of what was retained in the overpack was deposited on overpack surfaces (e.g., inlet and outlet vents); only a small fraction was deposited on the canister itself. These results are provided for generalized canister systems with a generalized input; as such, this paper is intended to demonstrate the technique. This study is a part of an ongoing effort funded by the U.S. Department of Energy, Nuclear Energy Office of Spent Fuel Waste Science and Technology, which is tasked with doing research relevant to developing a sound technical basis for ensuring the safe extended storage and subsequent transport of SNF. This work is being presented to demonstrate a potentially useful technique for SNF canister vendors, utilities, regulators, and stakeholders to utilize and further develop for their own designs and site-specific studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Effect of thermal aging and irradiation on microstructure and crack growth response of Alloy 690

Nickel-base Alloy 690 and the associated weld Alloys 52 and 152 are typically used for nozzle penetrations in replacement heads for pressurized water reactor (PWR) vessels, because of their excellent overall resistance to general corrosion and environmental degradation, primarily stress corrosion cracking (SCC). However, many of the existing PWRs reactors are expected to operate for 40-80 years. Likewise, advanced water-cooled small modular reactors (SMRs) will use Ni-Cr alloys and are expected to receive initial operating licenses for 60 years. Hence, the thermal stability of Ni-Cr alloys is a potential concern for the long-term performance of both existing and advanced nuclear plants, and possibly spent fuel storage containers. The objective of this research is to understand the microstructural changes occurring in high-Cr, Ni-based Alloy 690 during long time exposure to the reactor operating temperatures, and the effect of these changes on the service performance. One area of particular concern is the potential for long range ordering (LRO), i.e. formation of the intermetallic Ni 2 Cr phase under prolonged exposure to reactor temperatures and/or irradiation, which can increase strength, decrease ductility, and cause dimensional changes or lead to in-service embrittlement of components made with these alloys. Hence, this research focused on the microstructural evolution and the SCC response of Alloy 690 under accelerated thermal aging and irradiation conditions. The materials studied involved Alloy 690 from two sources: (i) Alloy 690 plate from an Alloy 152 dissimilar metal weld (DMW) joining the Alloy 690 plate to Alloy 533 LAS, aged at three different temperatures (370°C, 400°C and 450°C) for up to 75,000h (equivalent to 60 years of service), and (ii) specimens neutron-irradiated in the BOR-60 reactor up to 40 dpa. For aged Alloy 690 specimens, hardness was found to increase with aging time, however, the microstructural characterization by means of synchrotron X-ray did not find evidence of LRO. The microstructural characterization of neutron-irradiated specimens by TEM found no evidence of LRO either. Testing in a primary water environment of Alloy 690 specimens aged to a 60-year service equivalent revealed a fatigue and corrosion fatigue crack growth responses similar to those measured on the un-aged alloy. The SCC CGR response was also low. Overall, the two Alloy 690 heats investigated in this work, aged up to 60-year service equivalents or exposed to neutron irradiation up to 40 dpa, did not exhibit a deterioration in microstructure or performance.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

FY21 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

FY22 Progress Report: SRNL Analysis of ICCWR LCM and WAMS Data for Corrosion and Cracking

Algorithms for machine learning and data analysis for the 3013 Surveillance Program are being developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). To detect the presence of corrosion and cracking, data is collected from large binary files generated by a Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS). Software is being developed to use the physical attributes in the data files (e.g., height, color, and grayscale values; all as functions of a location in a plane projection) to detect the presence of surface corrosion and cracking. A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data for training ML algorithms, flag significant features, execute Machine Learning (ML) algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Surface defects can be called out by setting user-specified thresholds, feature based analysis or machine learning algorithms. Enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the very large volume of data required to train ML algorithms.

3013 Corrosion↗
DOE OSTI2023

Ribbons Affect Movement of Cracked Solar Cells [Poster]

Cracking of crystalline silicon photovoltaic cells remains a challenging topic in accurately assessing the long-term reliability and performance of affected modules. Cells can be damaged in every stage throughout the lifetime of a photovoltaic module, ranging from manufacturing, transportation, and installation to operation. Initially, the metallization can be able to bridge the gap of fractured cells and keep individual cell fragments electrically connected. However, photovoltaic modules and cells experience thermo-mechanical stresses during operation from temperature changes and pressure cycles of wind and snow loads. This causes the cell fragments to move, which, in turn, can lead to the wear out of the metallization and, consequently, to power loss or a safety hazard. The rate at which this degradation mechanism proceeds is currently unknown. Hence, in this work, we quantify the cell fragment movement of polycrystalline and monocrystalline mini-modules. By using digital image correlation, we were able to extract the normal crack opening and tangential sliding distances of adjacent cell fragments during heating of the mini-modules. Those distances are essential to develop wear-out models for the metallization and determine the rate of the degradation mechanism. We found that the interconnect technology has a significant impact on the direction and quantity of the cell fragment movements.

14 SOLAR ENERGY↗
DOE OSTI2023

Engineering-scale Modeling of High-Temperature Creep and Creep Crack Growth in Alloy 316H

This document demonstrates completion of the goals described in the technical narrative of the Department of Energy’s Industry Funding Opportunity Announcement (iFOA) project entitled “Modeling and Simulation Development Pathways to Accelerating KP-FHR Licensing,” which relates to the development and demonstration of capabilities for conducting engineering-scale simulations of Alloy 316H components under high-temperature conditions. This work encompassed two major aspects: integrating and testing constitutive models for the creep response of 316H at high temperatures, and developing tools for modeling creep crack growth (CCG) in 316H. Two classes of constitutive models were used in this effort: phenomenological models based on behavior observed at the engineering scale, and reduced-order models (ROMs) that represent the nonlinear response of mesoscale models that capture the sensitivity to material microstructure and processing. Likewise, the approaches employed for CCG modeling considered both simplified engineering approaches and detailed simulations of creep and damage ahead of the crack tip. These developments, which were performed by utilizing the Grizzly code as well as the open-source libraries it depends on, strengthen Grizzly’s ability to support licensing and safety analyses of high-temperature reactor components.

316H↗
DOE OSTI2024

FY24 Progress Report: Disposition of Cracks & Features Observed in the Inner Can Closure Weld Region (ICCWR) of the 3013 Package

The long-term integrity of the 3013 containers is of interest for the safe storage of Pu materials. Although the 3013 standard embodies multiple barrier concept, the integrity of the inner container is considered to be crucial since it protects the Safety Class outer container from its contents. The container is designed to withstand high pressures that could result from the complete radiolysis of the maximum water content permissible by the 3013 standard. Shelf-life studies and destructive examination have not found high gas pressures but have found that corrosive gases are generated. Pitting and stress corrosion cracking (SCC) are considered to be critical corrosion modes for the performance of the inner container and have been observed during destructive examinations. Considerable research has been conducted on the possibility of aqueous electrolytes condensing on the inner container that can promote SCC. These studies indicate the uncertainties surrounding the formation of corrosive environments and the corrosion behavior of container materials. In this initial report, a Bayesian network (BN) model is described that can consider the uncertainties and the causal connections between various factors influencing the corrosion modes of the inner container. The BN model is preliminary and provides an initial framework to identify the necessary information. The report also provides initial experimental results on the electrochemical behavior of stainless steels in anticipated condensed environments from gas phase migration of acidic gases. The experimental results are consistent with the corrosion model. Recommendation for further work on the BN model include assembling an expert group to provide input to the BN structure and quantification of the conditional probability matrix, experimental studies to characterize the microstructure of the container, electrochemical studies to identify critical potentials for localized corrosion and SCC, and crack growth rate studies

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Surrogate Model Integration with MOOSE XFEM for Creep Crack Growth

Ferritic-martensitic steels are key structural materials for advanced reactors but experience time-dependent deformation and damage under prolonged high temperature and irradiation, leading to creep-driven crack initiation and growth. High-fidelity models—crystal plasticity with irradiation mechanisms, phase-field for microstructural evolution, and continuum-damage viscoplasticity—capture the underlying physics but are too computationally intensive for broad design-space exploration and uncertainty quantification. This milestone advances a scalable alternative by integrating a microstructure-sensitive surrogate creep model into the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework and extending it to fracture via the extended finite element method (XFEM). The surrogate model, developed with collaborators at Sandia and Los Alamos National Laboratories, maps relevant microstructural descriptors to the viscoplastic response of HT9. We embed this surrogate within a coupled deformation-damage workflow in MOOSE/XFEM to simulate creep-driven crack initiation and propagation. Implementation enhancements include updates to the material interface, a plastic correction phase involving microstructure evolution, and fracture criteria to ensure numerical robustness and compatibility with the surrogate structure. Demonstrations on canonical creep benchmarks spanning uniaxial and multiaxial states show that the surrogate reproduces key trends of high-fidelity models while substantially reducing computational cost. The resulting capability bridges physics fidelity and performance, providing a practical path to a predictive, microstructure-aware assessment of creep and fracture in reactor materials.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

You Only Look Once v5 and Multi-Template Matching for Small-Crack Defect Detection on Metal Surfaces

This paper compares the performance of Deep Learning (DL) and multi-template matching (MTM) models for detecting small defects. DL models extract distinguishing features of objects but require a large dataset of images. In contrast, alternative computer vision techniques like MTM need a relatively small dataset. The lack of large datasets for small metal-surface defects has inhibited the adoption of automation in small-defect detection in remanufacturing settings. This motivated this preliminary study to compare template-based approaches, like MTM, with feature-based approaches, such as DL models, for small-defect detection on an initial laboratory and remanufacturing industry dataset. This study used You Only Look Once v5 (YOLOv5) as the DL model and compared its performance against the MTM model for small-crack detection. The findings of our preliminary investigation are as follows: (i) YOLOv5 demonstrated higher performance than MTM in detecting small cracks; (ii) an extra-large variant of YOLOv5 outperformed a small-size variant; (iii) the size and object variety of the data are crucial in achieving robust pre-trained weights for use in transfer learning; and (iv) enhanced image resolution contributes to precise object detection.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Multi-Scale Characterization of Porosity and Cracks in Silicon Carbide Cladding after Transient Reactor Test Facility Irradiation

Silicon carbide (SiC) ceramic matrix composite (CMC) cladding is currently being pursued as one of the leading candidates for accident-tolerant fuel (ATF) cladding for light water reactor applications. The morphology of fabrication defects, including the size and shape of voids, is one of the key challenges that impacts cladding performance and guarantees reactor safety. Therefore, quantification of defects’ size, location, distribution, and leak paths is critical to determining SiC CMC in-core performance. This research aims to provide quantitative insight into the defect’s distribution under multi-scale characterization at different length scales before and after different Transient Reactor Test Facility (TREAT) irradiation tests. A non-destructive multi-scale evaluation of irradiated SiC will help to assess critical microstructural defects from production and/or experimental testing to better understand and predict overall cladding performance. X-ray computed tomography (XCT), a non-destructive, data-rich characterization technique, is combined with lower length scale electronic microscopic characterization, which provides microscale morphology and structural characterization. This paper discusses a fully automatic workflow to detect and analyze SiC-SiC defects using image processing techniques on 3D X-ray images. Following the XCT data analysis, advanced characterizations from focused ion beam (FIB) and transmission electron microscopy (TEM) were conducted to verify the findings from the XCT data, especially quantitative results from local nano-scale TEM 3D tomography data, which were utilized to complement the 3D XCT results. In this work, three SiC samples (two irradiated and one unirradiated) provided by General Atomics are investigated. The irradiated samples were irradiated in a way that was expected to induce cracking, and indeed, the automated workflow developed in this work was able to successfully identify and characterize the defects formation in the irradiated samples while detecting no observed cracking in the unirradiated sample. These results demonstrate the value of automated XCT tools to better understand the damage and damage propagation in SiC-SiC structures for nuclear applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

42 ENGINEERING↗
DOE OSTI2022

GCR: Alloy 617 Crack Growth Testing Status

Environment can have an appreciable effect on crack growth. This presentation provides updates on environmental crack growth testing of Alloy 617 at INL.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A617 Notch Effect and Crack Growth Testing Status

This is an update on the Alloy 617 notched specimen creep testing and crack growth rate testing performed in FY23. Long-term notch testing continues, with some new results on double V-notch specimens. Creep-fatigue crack growth rate testing has begun and some preliminary results are shown.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unlocking Larger Scales and Aspect Ratios in 3D Printed Glass: Coupling Active Mixing and UV Curing for Advanced Printability and Crack Resistance

Recent developments in additive manufacturing (AM) of glass via silica-filled inks have facilitated fabrication of previously unattainable geometries and compositions. However, the maximum processable size of 15 mm limits the use of these prints in applications such as optics. A key limitation lies in the trade-off between material printability and green strength: increasing silica content in the feedstock improves crack resistance and reduces shrinkage but results in dramatic changes in viscoelastic properties that hinder flowability. Here, this paper presents a novel approach that offers expanded versatility in processable size, feedstock formulation, and printing. Described here is a direct ink writing (DIW) system coupled with an active high-shear micromixer and UV light source, capable of simultaneously printing multiple inks with a wide range of rheological properties. Choice of silica sourc, solvent, UV-curable binder, and dispersant is used to tune the ink rheology and improve printability and mechanical properties. Imparting high shear with the micromixer while UV-curing the extrudate allows for increased ink viscosities and reduced nozzle diameters, enabling printing finer feature sizes. With these advances, thin-walled high-aspect ratio structures and a crack-free glass disk measuring 44 mm in diameter are demonstrated, an increase of 3× in the greatest dimension compared to current state-of-the-art.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Dislocation channel broadening–A new mechanism to improve irradiation-assisted stress corrosion cracking resistance of additively manufactured 316 L stainless steel

Additively manufactured (AM) 316 L stainless steel (SS) after hot isostatic pressing (HIP) was found to exhibit superior resistance to irradiation-assisted stress corrosion cracking (IASCC) in high-temperature water, as compared to wrought 316 L SS. The well-accepted IASCC factors of radiation-induced segregation (RIS) and radiation hardening are not accurate descriptions of IASCC susceptibility in this case. A decreased strain localization along grain boundaries, caused by dislocation channel broadening, was confirmed to suppress crack initiation. A unique distribution of irradiation-induced defects in HIP AM 316 L SS eased dislocation cross-slip compared to those in the wrought counterpart, thus increasing the channel width near the grain boundaries. Here for the first time, this study highlights the importance of dislocation channel broadening as a potential mechanism to further improve the IASCC resistance of 316 L SS and provides direct experimental evidence based on commercial-grade materials.

316L stainless steel↗
DOE OSTI2023

Non-planar growth of high Al-mole-fraction AlGaN on patterned GaN platforms for mitigating strain-induced cracks beyond the critical layer thickness

Non-planar growth of Al x Ga 1-x N epitaxial layers with an average alloy composition up to Al-mole-fraction of x ~ 0.21 was performed on patterned c-plane GaN on (0001) sapphire substrates with stripe-shaped mesa structures. This approach successfully realized the growth of crack-free AlGaN layers on the top of mesas with layer thicknesses greater than the expected critical layer thickness by relaxing the in-plane stress on the top of the mesa. The effectiveness of the relaxation strongly depends on the width and depth of the stripe mesa. In conclusion, the relaxation of in-plane stress and resultant suppression of cracks in AlGaN layer are qualitatively discussed.

36 MATERIALS SCIENCE↗