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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Flaw Tolerance of Octet Truss Lattices with Random Flaws

We investigate the behavior of octet truss lattices in which some proportion of the bars are randomly removed from a perfect lattice. Using a lattice specimen of 9 × 9 × 9 octet truss unit cells as the nominal design, we simulate the response of prefect and flawed lattices with a finite element method implementation of rod theory. For each of two simple load cases, we perform a Monte Carlo analysis where we consider many instantiations of flawed lattices where between 1% and 20% of the bars are missing, and compare the axial loads in the remaining bars to those in the perfect lattice. We also compare the overall stiffness of the flawed lattices to the perfect lattice. Our results show progressive deterioration of lattice strength and stiffness with increasing proportion of missing bars, as expected, with the location and magnitude of the highest-loaded (most failure-prone) bars depending more on the load case and bar orientation than on the location and connectivity of missing bars. As such, the results reported herein may not be directly transferable to different lattice geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Weight function procedure for reduced order fracture analysis of arbitrary flaws in cylindrical pressure vessels

Fracture mechanics calculations using the finite element method can be computationally expensive, which makes them challenging to use in engineering evaluations of flaws in pressure vessels. The weight function (WF) technique is a reduced order fracture modeling method that is widely used to greatly reduce these computational costs. Its computational efficiency is essential for use in probabilistic fracture mechanics evaluations of embrittled nuclear reactor pressure vessels (RPVs), due to the large number of sampled flaws that must be evaluated. Although the WF technique is general, it is typically used only for axis-aligned flaws. Recent discoveries of off-axis flaws in operating nuclear reactors have necessitated detailed simulations of such flaws and the interactions between neighboring flaws. This paper presents a generalized WF approach applicable for analyzing arbitrary flaw geometries in thick-walled cylindrical vessels, including surface-breaking and subsurface flaws, which can either axis-aligned or off-axis, and can account for interactions with other flaws. Herein, this approach is demonstrated on representative simulations of multiple flaw geometries in a RPV subjected to transient loading conditions. In all cases, the WF technique gives good comparison with benchmark results from direct simulation with greatly reduced computational effort.

42 ENGINEERING↗
DOE OSTI2026

Flaw Tolerance Assessment for DOE Standard SNF Dry Storage Canisters - 26550

The U.S. DOE has designed four spent nuclear fuel (SNF) dry storage canisters for storing DOE standardized SNFs. The DOE standard canisters are cylindrical shells with a diameter of 24 inches (610 m) or 18 inches (457 m), a wall thickness of 0.5 inches (12.7 m) or 0.375 inches (9.53 m), and a length of 15 feet (4.57 m) or 10 feet (3.05 m). These DOE canister geometries are completely different from commercial canisters. The latter may experience chloride-induced stress cracking corrosion (CI-SCC) because they are stored near coastal regions. The former may not experience CI-SCC but face different challenges because they are stored in the SNF storage facilities. Because of large residual stresses, mechanical flaws may occur in the DOE canisters during long-distance transportation or lifting handling. To date, only limited structural integrity analyses were carried out through drop tests on the DOE canisters, but a more general flaw tolerance assessment has not been performed. Therefore, the failure assessment diagram (FAD)-based fracture mechanics method, as codified by the latest API 579-1/ASME FFS-1-2021 Edition, is adopted in this work to assess surface flaw tolerance for DOE canisters under operation loading and welding residual stresses (WRS), where the new code-recommended WRS distributions are used. To more adequately consider the transverse distribution of WRS, an equivalent WRS distribution is proposed to account for the WRS reduction with distance from the weld centerline. Moreover, the closed-form solutions of stress intensity factor K, which serves as the crack driving force during subcritical crack growth, are developed from the tabular data of the K factors provided in API 579-1/ASME FFS-1 and used to determine more accurate flaw sizes at flaw instability. Subsequently, the Level 2 assessment procedures with 12 assessment steps, as codified and detailed in API 579-1 and ASME FFS-1, are followed to assess the flaw tolerance for the surface flaws in the DOE standard canisters with consideration of normal or accident operation loads combined with WRS. The assessment results show that the four designs of DOE standard canisters can tolerate all surface flaws that meet the code permitted maximum sizes of a flaw length of 8 inches (i.e., 200 mm) and a flaw depth of 80% wall thickness. This demonstrates that all designs of DOE standard canisters are robust and reliable.

DOE standard canister↗
DOE OSTI2024

Intermediate Principal Stress Effects on the 3D Cracking Behavior of Flawed Rocks Under True Triaxial Compression

Crack initiation, growth, and coalescence in flawed rocks have been extensively studied for 2D (planar, penetrating) flaws under uniaxial/biaxial compression. However, little is known as to the mechanisms and processes of cracking from 3D flaws under true triaxial compression, where the intermediate principal stress (σ 2 ) is distinguished from the major and minor principal stresses. In this work, we systematically investigate the effects of σ 2 on the 3D cracking behavior of rock specimens with preexisting flaws, through the use of mechanistic simulations of mixed-mode fracture in rocks. We explore how two characteristics of σ 2 , namely, (i) its orientation with respect to the flaw and (ii) its magnitude, affect two aspects of the cracking behavior, namely, (i) the cracking pattern and (ii) the peak stress. Results show that the orientation of σ 2 exerts more control over the cracking pattern than the flaw inclination angle. The peak stress becomes highest when σ 1 is parallel to the flaw, whereas it becomes lowest when σ 2 is parallel to the flaw. Also, the effects of σ 2 magnitude are more significant when σ 2 becomes more oblique to the flaw plane. On the basis of our observations, we propose mechanisms underlying the cracking behavior of 3D flawed rocks under true triaxial compression.

58 GEOSCIENCES↗
DOE OSTI2024

Ability of x‐ray computed tomography to resolve critical flaw size in laser‐based, paste stereolithography ceramic printing of alumina

Abstract Complex alumina parts were printed using vat photopolymerization (VPP), which is a stereolithography‐based additive manufacturing (AM) technique used to shape ceramic preforms, or green parts. The critical flaw size was determined using classical fracture mechanics techniques. The strength and fracture toughness were measured and compared to flaws detected in x‐ray computed tomography (XCT or CT) distributions as well as the fracture surfaces. The strength was lower compared traditionally made alumina, and that is due to layering effects, slurry defects, and printing defects. The critical flaw size from fracture mechanics was 206 µm. XCT has high enough resolution to detect the critical flaw size and much smaller features, where the average flaw size observed in CT scans was around 80–100 µm. The fracture surfaces indicate that flaws causing failure are larger than that of the critical flaw size (∼300 µm), but fracture surfaces do not show definitive features compared to traditionally made ceramics. Since XCT can observe flaws smaller than the critical flaw size, this method can be used as a screening technique.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Crack nucleation at forging flaws studied by non-local peridynamics simulations

We present a computational study and framework that allows us to study and understand the crack nucleation process from forging flaws. Forging flaws may be present in large steel rotor components commonly used for rotating power generation equipment including gas turbines, electrical generators, and steam turbines. The service life of these components is often limited by crack nucleation and subsequent growth from such forging flaws, which frequently exhibit themselves as non-metallic oxide inclusions. The fatigue crack growth process can be described by established engineering fracture mechanics methods. However, the initial crack nucleation process from a forging flaw is challenging for traditional engineering methods to quantify as it depends on the details of the flaw, including flaw morphology. We adopt the peridynamics method to describe and study this crack nucleation process. For a specific industrial gas turbine rotor steel, we present how we integrate and fit commonly known base material property data such as elastic properties, yield strength, and S-N curves, as well as fatigue crack growth data into a peridynamic model. The obtained model is then utilized in a series of high-performance two-dimensional peridynamic simulations to study the crack nucleation process from forging flaws for ambient and elevated temperatures in a rectangular simulation cell specimen. The simulations reveal an initial local nucleation at multiple small oxide inclusions followed by micro-crack propagation, arrest, coalescence, and eventual emergence of a dominant micro-crack that governs the crack nucleation process. The dependence on temperature and density of oxide inclusions of both the details of the microscopic processes and cycles to crack nucleation is also observed. The results are compared with fatigue experiments performed with specimens containing forging flaws of the same rotor steel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Predicting strength distributions of MEMS structures using flaw size and spatial density

The populations of flaws in individual layers of microelectromechanical systems (MEMS) structures are determined and verified using a combination of specialized specimen geometry, recent probabilistic analysis, and topographic mapping. Strength distributions of notched and tensile bar specimens are analyzed assuming a single flaw population set by fabrication and common to both specimen geometries. Both the average spatial density of flaws and the flaw size distribution are determined and used to generate quantitative visualizations of specimens. Scanning probe-based topographic measurements are used to verify the flaw spacings determined from strength tests and support the idea that grain boundary grooves on sidewalls control MEMS failure. The findings here suggest that strength controlling features in MEMS devices increase in separation, i.e., become less spatially dense, and decrease in size, i.e., become less potent flaws, as processing proceeds up through the layer stack. The method demonstrated for flaw population determination is directly applicable to strength prediction for MEMS reliability and design.

Instruments & Instrumentation↗
DOE OSTI2018

APPLICATION OF THE FAVOR-OCI FRACTURE MECHANICS COMPUTER PROGRAM TO ASME CODE SECTION XI, IWB-3610 FLAW ACCEPTANCE CRITERIA EVALUATIONS

This paper presents an overview of added features in a new version of the FAVOR (Fracture Analysis of Vessels Oak Ridge) computer code called FAVOR-OCI. The original FAVOR code was developed at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) under the sponsorship of the US Nuclear Regulatory Commission (NRC). FAVOR is applied by US and international nuclear power industries to perform deterministic and probabilistic fracture mechanics analyses of commercial nuclear reactor pressure vessels (RPVs). Appli-cations of FAVOR are focused on insuring that the structural integrity of aging, and increasingly embrittled, RPVs is main-tained throughout their licensed service life. Based on the final ORNL release of FAVOR, v16.1, FAVOR-OCI extends existing deterministic features of FAVOR while preserving all prev-iously-existing probabilistic capabilities of FAVOR.The objective of this paper is to describe new deterministic features in FAVOR-OCI that can be applied to analytical evaluations of planar flaws. These evaluations are consistent with the acceptance criteria of ASME Code, Section XI, Article IWB-3610, including Subarticles IWB-3611 (acceptance based on flaw size) and IWB-3612 (acceptance based on applied stress intensity factor). The linear elastic fracture mechanics (LEFM) capabilities of FAVOR-OCI also incorporate the analytical procedures presented in the Nonmandatory Appendix A, Analysis of Flaws, Article A-3000, Method of KI Determination, for both surface and subsurface (embedded) flaws.The paper describes a computational methodology for determining critical values of fracture-related parameters that satisfy ASME Code Section XI acceptance criteria for flaws exposed to multiple thermal-hydraulic transients. These com-pute-intensive analyses can be carried out with a single execution of FAVOR-OCI. The new methodology determines critical values by solving for either the point of tangency or point of intersection between applied KI versus time histories and a user-selected fracture toughness material property (e.g., ASME KIc, FAVOR Weibull KIc, or Master Curve Weibull KJc) for surface or subsurface flaws. Situations where warm prestress conditions apply can also be addressed. The paper highlights a need for this new capability via applications to a recent independent review of safety cases for RPVs in two Belgian nuclear power plants (NPPs). That review required ASME Section XI assessments of several thousand embedded, quasi-laminar flaws in the wall of each RPV. Analysis results provided by the new capability contributed to the technical bases compiled from several sources by the Belgian nuclear regulatory agency (FANC) and eventually used by FANC to justify the restart of these NPPs.

Dickson, Terry L.↗
DOE OSTI2022

Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

The goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.

42 ENGINEERING↗
DOE OSTI2020

Multimodal Deep Learning for Flaw Detection in Software Programs

We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models. We present results on detecting software flaws using the Juliet Test Suite and Linux Kernel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Flaw characterization and correlation with cracking strength in Engineered Cementitious Composites (ECC)

Engineered Cementitious Composite (ECC) is a class of fiber reinforced composites showing strain hardening behavior. The variation of cracking strength among various sections of a tensile specimen is a key factor governing the cracking behaviors of ECC. This study employs X-ray computed tomography method to investigate the correlation between cracking strength and flaw distribution in ECC, and identifies the dimensions of pre-existing flaws to be the main influencing parameters. Specifically, the largest cross-sectional area of a flaw perpendicular to stress can be well correlated with cracking strength. To validate the observation, the classic model on first crack strength is modified in this study by adopting a refined fiber bridging relation and employing iterated crack profile. The simulated cracking strength vs flaw size relation is in good agreement with test results. This study provides an improved understanding of the multiple cracking mechanism in ECC, which is useful for material design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Dual X-ray computed tomography-aided classification of melt pool boundaries and flaws in crept additively manufactured parts

In metal additive manufacturing (AM), understanding the process-structure-performance relationships requires a combination of multi-scale characterization techniques that allows for the measurement of the melt pool shape and boundary and classifying various defects and flaws in the AM parts. Such approaches can be destructive, only 2D in nature, or have a small field of view and can be complex to co-register and analyze. Here, in this work, we present a non-destructive 3D inspection technique that employs dual-energy X-ray computed tomography (XCT) along with a model-based iterative reconstruction (MBIR) and a new segmentation algorithm. The proposed approach and algorithm are not only capable of classifying and quantifying flaws such as pores, cracks, and inclusions, but they also allow for the extraction of microstructural features such as melt pool boundaries (MPB) and melt pool regions (MPR), that can help understand process-structure-performance relationships for alloys under study. As an exemplar application, we employed the method for characterization of an additively manufactured aluminum alloy crept under tensile stress at 300 °C for 1064 h. Our results demonstrate high quality segmentation and classification of various flaws and MPB and MPR, for the first time, using 3D X-ray CT inspection. The delineated MPB and MPR in the crept samples reveal the preferential growth paths of cracks that formed during creep deformation. The technique was used for successfully quantifying the characteristics (number of defects, their density, volume fraction, etc.) of the manufacturing-induced pores and creep-induced cracks, which is necessary to better understand the creep failure mechanisms of the material.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Using 2.5D super-resolution to improve flaw detection in metal additive manufacturing parts

Industrial X-ray computed tomography (XCT) enables non-destructive inspection of additively manufactured (AM) parts, but high-resolution scanning requires long acquisition times and significant computational resources, limiting throughput in production environments. Super-resolution techniques can recover high-resolution information from low-resolution scans, but existing methods face a trade-off between 2D approaches that ignore inter-slice information and 3D methods that are computationally prohibitive for practical deployment. To address this trade-off, we propose a 2.5D deep learning-based super-resolution approach that uses seven neighbouring low-resolution slices to super-resolve the centre slice. This work evaluates the method on real XCT scans of steel AM parts, comparing reconstruction quality and flaw detection performance of 2D, 2.5D, and 3D ESRGAN-based super-resolution methods. Results demonstrate that 2.5D super-resolution significantly improves detection of small, process-induced flaws (e.g. porosity) compared to 2D methods, while avoiding the prohibitive computational burden of full 3D approaches. These findings provide initial evidence of 2.5D super-resolution as a practical, deployable solution for improving flaw detection in high-throughput industrial XCT inspection.

X-ray CT↗
DOE OSTI2025

FLAW STABILITY ANALYSIS OF SURFACE CRACKS IN DOE STANDARD CANISTERS UNDER OPERATION LOADS AND WELDING RESIDUAL STRESSES

There are over 3000 commercial spent nuclear fuel (SNF) storage canisters that are made of stainless steel and planned for multi-purpose functions of SNF handling: interim storage, transportation, and ultimate disposal at a future SNF disposition location. To date, many multi-purpose canisters (MPC) are located in coastal regions for long-term storage. The canisters are fabricated by welding, and post-welding heat treatment is not required for relieving welding residual stresses (WRS). As a result, these canisters may be susceptible to chloride induced stress corrosion cracking (CISCC) due to the chloride-bearing marine salts. The flaw stability analysis of MPC canisters was performed at SRNL (i.e., PVP2016-4935 and PVP2016-63887) for through-wall flaws and for surface flaws respectively, where WRS was considered, and the failure assessment diagram (FAD)-based fracture mechanics method codified by API 579-1/ASME FFS-1-2007 Edition was adopted.

DOE standard canister↗
DOE OSTI2020

Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models

Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems. In this work, we demonstrate that even better performance can be achieved using neural architecture search (NAS) combined with multimodal learning models. We adapt a NAS framework aimed at investigating image classification to the problem of software flaw detection and demonstrate improved results on the Juliet Test Suite, a popular benchmarking data set for measuring performance of machine learning models in this problem domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Flaw-insensitive fracture of a micrometer-sized brittle metallic glass

Brittle materials, such as oxide glasses, are usually very sensitive to flaws, giving rise to a macroscopic fracture strength that is much lower than that predicted by theory. The same applies to metallic glasses (MGs), with the important difference that these glasses can exhibit certain plastic strain prior to catastrophic failure. Here we consider the strongest metallic alloy known, a ternary Co 55 Ta10B 35 MG. In this work, we show that this macroscopically brittle glass is flaw-insensitive at the micrometer scale. This discovery emerges when testing pre-cracked specimens with self-similar geometries, where the fracture stress does not decrease with increasing pre-crack size. The fracture toughness of this ultra-strong glassy alloy is further shown to increase with increasing sample size. Both these findings deviate from our classical understanding of fracture mechanics, and are attributed to a transition from toughness-controlled to strength-controlled fracture below a critical sample size.

36 MATERIALS SCIENCE↗