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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Using DR52c/Ni{sup 2+} mimotope tetramers to detect Ni{sup 2+} reactive CD4{sup +} T cells in patients with joint replacement failure

T cell mediated hypersensitivity to nickel (Ni{sup 2+}) is one of the most common causes of allergic contact dermatitis. Ni{sup 2+} sensitization may also contribute to the failure of Ni{sup 2+} containing joint implants, and revision to non-Ni{sup 2+} containing hardware can be costly and debilitating. Previously, we identified Ni{sup 2+} mimotope peptides, which are reactive to a CD4{sup +} T cell clone, ANi2.3 (Vα1, Vβ17), isolated from a Ni{sup 2+} hypersensitive patient with contact dermatitis. This T cell is restricted to the major histocompatibility complex class II (MHCII) molecule, Human Leukocyte Antigen (HLA)-DR52c (DRA, DRB3*0301). However, it is not known if Ni{sup 2+} induced T cell responses in sensitized joint replacement failure patients are similar to subjects with Ni{sup 2+} induced contact dermatitis. Here, we generated DR52c/Ni{sup 2+} mimotope tetramers, and used them to test if the same Ni{sup 2+} T cell activation mechanism could be generalized to Ni{sup 2+} sensitized patients with associated joint implant failure. We confirmed the specificity of these tetramers by staining of ANi2.3 T cell transfectomas. The DR52c/Ni{sup 2+} mimotope tetramer detected Ni{sup 2+} reactive CD4{sup +} T cells in the peripheral blood mononuclear cells (PBMC) of patients identified as Ni{sup 2+} sensitized by patch testing and a positive Ni{sup 2+} LPT. When HLA-typed by a DR52 specific antibody, three out of four patients were DR52 positive. In one patient, Ni{sup 2+} stimulation induced the expansion of Vβ17 positive CD4{sup +} T cells from 0.8% to 13.3%. We found that the percentage of DR52 positivity and Vβ17 usage in Ni{sup 2+} sensitized joint failure patients are similar to Ni sensitized skin allergy patients. Ni{sup 2+} independent mimotope tetramers may be a useful tool to identify the Ni{sup 2+} reactive CD4{sup +} T cells. - Highlight: • Nickel hypersensitive patients with implant failure are dominantly DR52 positive. • These patients may have similar TCR beta chain usage to that of contact dermatitis. • The nickel mimotope tetramers are a tool to identify the nickel reactive CD4{sup +} T cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Oxygen migration during resistance switching and failure of hafnium oxide memristors

While the recent establishment of the role of thermophoresis/diffusion-driven oxygen migration during resistance switching in metal oxide memristors provided critical insights required for memristor modeling, extended investigations of the role of oxygen migration during ageing and failure remain to be detailed. Such detailing will enable failure-tolerant design, which can lead to enhanced performance of memristor-based next-generation storage-class memory. Furthermore, we directly observed lateral oxygen migration using in-situ synchrotron x-ray absorption spectromicroscopy of HfO x memristors during initial resistance switching, wear over millions of switching cycles, and eventual failure, through which we determined potential physical causes of failure. Using this information, we reengineered devices to mitigate three failure mechanisms and demonstrated an improvement in endurance of about three orders of magnitude.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Shock-induced twinning/detwinning and spall failure in Cu–Ta nanolaminates at atomic scales

Here, this study provides new insights into the role of interfaces on the deformation and failure mechanisms in shock-loaded Cu–Ta–Cu trilayer system. The thickness of the Ta layer, piston velocities, and shock pulse durations were varied to explore the impact of impedance mismatch and loading conditions on spallation behavior and twin formation. It was found that the interfaces play a crucial role in the dynamic response of these multilayered systems since secondary reflection waves generated at the interfaces significantly affected the peak stress and pressure profiles, influencing void nucleation and failure modes. In the trilayer systems, failure predominantly occurred at interfaces and within the Ta layer, with void nucleation sites and twinning behavior being markedly different compared to single-crystal Cu and Ta. Increasing the Ta layer thickness modified the wave interactions, leading to different failure locations. Higher piston velocities were associated with increased spall strength by enhancing wave interactions and void formation, particularly at the interfaces and within the Ta layer, under specific configurations. Additionally, shorter shock pulse durations facilitated earlier initiation of the release fan, reducing twin formation and altering the failure dynamics by accelerating twin annihilation and pressure release.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Techno-Economic Impact of Partial String Failure in Multi-string Energy Storage Systems

Electric utilities and large industrial/commercial customers, which integrate multi-MW multi-string energy storage systems (ESS), are typically at a significant risk of string failure. This results in the loss of system performance and capacity. In order to evaluate the accurate costs and benefits associated with a particular battery system, it is essential to incorporate the costs associated with battery string failure and, as a result, reduced capacity. This report analyzes how the economic losses can be estimated based on market rules and ESS availability. A framework for calculation of economic losses is developed and implemented with an illustrative string failure case for Independent System Operator (ISO-NE) forward capacity market. Further scenarios and case studies are analyzed by varying the probability of string failure as well as the capacity bid into the market. These scenarios are used to assess the variability of economic losses and the impact of string failure on the final realized benefits from the battery's market participation.

Fotedar, Vanshika↗
DOE OSTI2018

Shrink or Substitute: Handling Process Failures in HPC Systems Using In-Situ Recovery

Efficient utilization of today's high-performance computing (HPC) systems with complex software and hardware components requires that the HPC applications are designed to tolerate process failures at runtime. With low mean-time-to-failure (MTTF) of current and future HPC systems, long running simulations on these systems requires capabilities for gracefully handling process failures by the applications themselves. In this paper, we explore the use of fault tolerance extensions to Message Passing Interface (MPI) called user-level failure mitigation (ULFM) for handling process failures without the need to discard the progress made by the application. We explore two alternative recovery strategies, which use ULFM along with application-driven in-memory checkpointing. In the first case, the application is recovered with only the surviving processes, and in the second case, spares are used to replace the failed processes, such that the original configuration of the application is restored. Our experimental results demonstrate that graceful degradation is a viable alternative for recovery in environments where spares may not be available.

Ashraf, Rizwan↗
DOE OSTI2024

From Failure to Insight: Analyzing Disk Breakdowns in Large-Scale HPC Environments

Disk failure data provides valuable insights for preventing failures, enhancing storage robustness, guiding system design and deployment, and ensuring reliable operations at data centers. This paper introduces two disk failure datasets collected from large-scale HPC production environments over the past five years, comprising over 5,000 failure records from more than 40,000 disks. We analyzed these datasets across multiple dimensions, including temporal, spatial, and relational trends, and performed a comprehensive reliability assessment. Our analysis yielded numerous observations and insights that influence various operational aspects of HPC storage systems. We believe this study offers a holistic understanding of disk failure trends likely to interest the HPC storage community.

George, Anjus↗
DOE OSTI2025

FIRE: A Failure-Adaptive RL Framework for Edge Computing Migrations

In edge computing, users' service profiles are migrated between edge servers due to user mobility. Reinforcement Learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like AR/VR and real- time obstacle detection. These rare failures, being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. We introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose FIRE-ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove FIRE-ImRE's boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (FIRE-ImDQL) and actor critic (FIRE-ImACRE) versions of our algorithm to enhance scalability. Here, we extend our framework to accommodate users with varying risk tolerances of rare failure events. Through trace-driven experiments, we show that FIRE reduces edge computing costs compared to vanilla RL and the greedy baseline in the event of failures.

Edge computing↗
DOE OSTI2016

SU-G-TeP4-05: An Evaluation of a Low Dose Rate (LDR) Prostate Brachytherapy Procedure Using a Failure Modes and Effects Analysis (FMEA)

Purpose: The aim of the study is the application of a Failure Modes and Effects Analysis (FMEA) to access the risks for patients undergoing a Low Dose Rate (LDR) Prostate Brachytherapy Treatment. Methods: FMEA was applied to identify all the sub processes involved in the stages of identifying patient, source handling, treatment preparation, treatment delivery, and post treatment. These processes characterize the radiation treatment associated with LDR Prostate Brachytherapy. The potential failure modes together with their causes and effects were identified and ranked in order of their importance. Three indexes were assigned for each failure mode: the occurrence rating (O), the severity rating (S), and the detection rating (D). A ten-point scale was used to score each category, ten being the number indicating most severe, most frequent, and least detectable failure mode, respectively. The risk probability number (RPN) was calculated as a product of the three attributes: RPN = O X S x D. The analysis was carried out by a working group (WG) at UPMC. Results: The total of 56 failure modes were identified including 32 modes before the treatment, 13 modes during the treatment, and 11 modes after the treatment. In addition to the protocols already adopted in the clinical practice, the prioritized risk management will be implanted to the high risk procedures on the basis of RPN score. Conclusion: The effectiveness of the FMEA method was established. The FMEA methodology provides a structured and detailed assessment method for the risk analysis of the LDR Prostate Brachytherapy Procedure and can be applied to other radiation treatment modes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

An Investigation of Plume Response to Soil Vapor Extraction and Hypothetical Drum Failure

Core Ideas Soil vapor extraction (SVE) wells are an effective interim remediation technique. Flow and transport modeling assists in evaluation of vapor plume behavior. Plume simulations assuming drum failure and SVE provide a framework for remediation planning. A small borehole subset could be monitored for detection of VOC releases due to drum failure. Soil vapor extraction (SVE) has been used at sites across the Department of Energy complex, including sites where legacy subsurface wastes represent a potential source of groundwater contamination. At Los Alamos National Laboratory (LANL), leakage from waste drums buried at an inactive chemical waste site has created a subsurface vapor plume of volatile organic compounds (VOCs). Soil vapor extraction operation in 2015 and rebound testing through 2017 were successful in reducing the plume's mass and mitigating VOC migration toward the water table. However, the possibility that waste drums could fail and release VOCs could pose a challenge in the future. To explore the impacts of drum failure, as well as the capabilities of SVE remediation, we simulated hypothetical contaminant release scenarios and subsequent SVE remediation. Three‐dimensional subsurface VOC behavior, including advection, diffusion, and plume interactions with topography, were simulated using the porous flow simulator Finite Element Heat and Mass Transfer. Simulations of future site conditions have allowed identification of “sentry” boreholes that can be monitored for early detection in case of drum failure. Sentry boreholes can also be used to set concentration thresholds above which SVE should be initiated. For the LANL site, simulations show that SVE can be started 3 yr following drum failure and remain a viable remediation tool. More broadly, the principles outlined in this work can be used to support remediation planning at other subsurface waste sites. Predictive models of future releases can be analyzed to set concentration threshold values, guide selection of sentry boreholes, and increase operational efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mesh Failure Prediction Using Deep Learning Techniques

The goal of this joint effort between SRI and LLNL has been to investigate the use of state-of-the-art deep learning architectures to effectively explore the correlations between a set of mesh zone attributes and manifestations of failure in a given mesh simulation and draw useful conclusions towards a practical failure detection system. Each zone attribute is a scalar feature that is recorded separately for each zone during simulation, resulting in a multivariate time series signal for each zone. Currently, these features are used to manually decide where to apply manual relaxations so that simulations run without any failures. Our long-term vision is to build a machine learning system that is capable of reliably detecting upcoming failures and is able to apply finer-grained targeted relaxations automatically to relevant zones to avoid an expected failure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Thermal-Mechanical Elastic-Plastic and Ductile Failure Model Calibrations for 304L Stainless Steel Alloy

Numerical simulations of metallic structures undergoing rapid loading into the plastic range require material models that accurately represent the response. In general, the material response can be seen as having four interrelated parts: the baseline response under slow loading, the effect of strain rate, the conversion of plastic work into heat and the effect of temperature. In essence, the material behaves in a thermal-mechanical manner if the loading is fast enough so when heat is generated by plastic deformation it raises the temperature and therefore influences the mechanical response. In these cases, appropriate models that can capture the aspects listed above are necessary. The material of interest here is 304L stainless steel, and the objective of this work is to calibrate thermal-mechanical models: one for the constitutive behavior and another for failure. The work was accomplished by first designing and conducting a material test program to provide data for the calibration of the models. The test program included uniaxial tension tests conducted at room temperature, 150 and 300 C and at strain rates between 10 –4 and 10 3 1/s. It also included notched tension and shear-dominated compression hat tests specifically designed to calibrate the failure model. All test specimens were extracted from a single piece of plate to maintain consistency. The constitutive model adopted was a modular $J_2$ plasticity model with isotropic hardening that included rate and temperature dependence. A criterion for failure initiation based on a critical value of equivalent plastic strain fitted the failure data appropriately and was adopted. Possible ranges of the values of the parameters of the models were determined partially on historical data from calibrations of the same alloy from other lots and are given here. The calibration of the parameters of the models were based on finite element simulations of the various material tests using relatively ne meshes and hexahedral elements. When using the model in structural finite element calculations, however, element formulations and sizes different from those in the calibration are likely to be used. A brief investigation demonstrated that the failure initiation predictions can be particularly sensitive to the element selection and provided an initial guide to compensate for the effect of element size in a specific example.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Feedstock-Conversion Interface Consortium: Failure Mode and Effects Analysis Summary Report (FY2022)

This report provides an overview of the development of failure modes and effects analysis (FMEA) and its implementation as a systematic criticality and risk assessment tool supporting a quality by design (QbD) approach for FCIC research. This report also provides a high-level overview of the results for the FMEA evaluation of two feedstock preprocessing system configurations: (1) generation of pine residue materials for high-temperature pyrolysis conversion and (2) generation of corn stover materials for low-temperature conversion using deacetylation and disc mechanical refining pretreatment for fermentation to hydrocarbons. For the results presented in this report, our FMEA interviews included two approaches. The first approach was to perform FMEA interviews for the entire system of unit operations giving a wholistic system level view. The second approach consisted of detailed interviews for each individual unit operation within the system allowing for a “deep dive” into the specific failures for the individual components within the configuration. These two approaches provide different resolutions of information. The FMEA results of this report were focused on failures associated with meeting critical quality attributes (CQAs) identified for the target conversion processes for each processed feedstock type. The information gathered through the FMEA interviews include estimations of risk scores for meeting each given CQA specification, identification of the impacts for not meeting a CQA specification, capturing causes associated with material attributes and process parameters for each failure, identification of current detection methods, and speculation of potential mitigation strategies for decreasing a failure’s risk score. The complete results of all FMEA interviews are provided in the Appendices of this report.

09 BIOMASS FUELS↗
DOE OSTI2023

Predicting Failure Using Deep Learning SAND Report

Accurate prediction of ductile failure is critical to Sandia’s NW mission, but the models are computationally heavy. The costs of including high-fidelity physics and mechanics that are germane to the failure mechanisms are often too burdensome for analysts either because of the person-hours it requires to input them or because of the additional computational time, or both. In an effort to deliver analysts a tool for representing these phenomena with minimal impact to their existing workflow, our project sought to develop modern data-driven methods that would add microstructural information to business-as-usual calculations and expedite failure predictions. The goal is a tool that receives as input a structural model with stress and strain fields, as well as a machine-learned model, and output predictions of structural response in time, including failure. As such, our project spent substantial time performing high-fidelity, three-dimensional experiments to elucidate materials mechanisms of void nucleation and evolution. We developed crystal-plasticity finite-element models from the experimental observations to enrich the findings with fields not readily measured. We developed engineering length-scale simulations of replicated test specimens to understand how the engineering fields evolve in the presence of fine-scale defects. Finally, we developed deep learning convolutional neural networks, and graph-based neural networks to encode the findings of the experiments and simulations and make forward predictions in time for structural performance. This project demonstrated the power of data-driven methods for model development, which have the potential to vastly increase both the accuracy and speed of failure predictions. These benefits and the methods necessary to develop them are highlighted in this report. However, many challenges remain to implementing these in real applications, and these are discussed along with potential methods for overcoming them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Probabilistic Predictions for Fastener Failure in the Sandia Mechanics Challenge Using the Discrete-Direct Uncertainty Quantification Approach

This paper documents the blind and post-blind analysis predictions for the 2023 Sandia Mechanics Challenge (SMC), which involved predicting the behavior of a threaded fastener joint structure subjected to shock loading. Utilizing repeat sets of fastener calibration data from various experimental configurations including tension, double shear, and joint tension, we developed a library of calibrated models which were propagated through the application model using the Discrete-Direct (DD) uncertainty quantification (UQ) approach. Although the initial blind predictions did not incorporate spare-sample processing to quantify fastener failure probabilities, the analyses yielded reasonable conclusions aligned with experimental results. In the post-blind analysis phase, we focused on enhancing the fidelity of the aluminum constitutive model and innovating the DD approach to obtain probabilistic predictions for fastener failure, particularly when quantities of interest (QoIs) approach their bounds. The improved aluminum model captures the behavior of the cantilever under shock loading more accurately, predicting both partial and complete cracks, although it tends to underpredict failure propagation. The enhanced DD approach facilitates probabilistic predictions that reflect the interdependent failure mechanisms of the fasteners and the cantilever, revealing that while certain fasteners are more likely to fail, the failure does not necessarily follow a progressive pattern. Overall, the post-blind analyses significantly improved the predictive capabilities of the model, providing valuable insights into the SMC application and establishing a robust foundation for informed engineering decisions. The methodology demonstrates a cost-effective and extensible approach suitable for a wide range of applications, highlighting the importance of uncertainty quantification to provide context for engineering decision making.

42 ENGINEERING↗
NASA NTRS1972

Space tug propulsion system failure mode, effects and criticality analysis

For purposes of the study, the propulsion system was considered as consisting of the following: (1) main engine system, (2) auxiliary propulsion system, (3) pneumatic system, (4) hydrogen feed, fill, drain and vent system, (5) oxygen feed, fill, drain and vent system, and (6) helium reentry purge system. Each component was critically examined to identify possible failure modes and the subsequent effect on mission success. Each space tug mission consists of three phases: launch to separation from shuttle, separation to redocking, and redocking to landing. The analysis considered the results of failure of a component during each phase of the mission. After the failure modes of each component were tabulated, those components whose failure would result in possible or certain loss of mission or inability to return the Tug to ground were identified as critical components and a criticality number determined for each. The criticality number of a component denotes the number of mission failures in one million missions due to the loss of that component. A total of 68 components were identified as critical with criticality numbers ranging from 1 to 2990.

Boyd, J. W.↗
NASA NTRS1972

Tensile failure criteria for fiber composite materials

The analysis provides insight into the failure mechanics of these materials and defines criteria which serve as tools for preliminary design material selection and for material reliability assessment. The model incorporates both dispersed and propagation type failures and includes the influence of material heterogeneity. The important effects of localized matrix damage and post-failure matrix shear stress transfer are included in the treatment. The model is used to evaluate the influence of key parameters on the failure of several commonly used fiber-matrix systems. Analyses of three possible failure modes were developed. These modes are the fiber break propagation mode, the cumulative group fracture mode, and the weakest link mode. Application of the new model to composite material systems has indicated several results which require attention in the development of reliable structural composites. Prominent among these are the size effect and the influence of fiber strength variability.

Rosen, B. W.↗
NASA NTRS1972

Failure modes, effects and criticality analyses.

Failure mode, effects and criticality analyses were developed by NASA as a means of assuring that hardware built for space applications has the desired reliability characteristics. The failure mode and effects analysis is a qualitative reliability technique for systematically analyzing each possible failure mode within a hardware system, and identifying the resulting effect on that system, the mission and personnel. The criticality analysis is a quantitative procedure which ranks the critical failure modes according to their probability of occurrence. This paper describes the failure modes, effects analysis and the criticality analysis. It employs a simple hardware system, not related to the aerospace field, to illustrate the method. It encourages application of this type of analysis to industrial development programs outside the aerospace and defense complex.

Jordan, W. E.↗
NASA NTRS1973

Failure analysis of aluminum alloy components

Analysis of six service failures in aluminum alloy components which failed in aerospace applications is reported. Identification of fracture surface features from fatigue and overload modes was straightforward, though the specimens were not always in a clean, smear-free condition most suitable for failure analysis. The presence of corrosion products and of chemically attacked or mechanically rubbed areas here hindered precise determination of the cause of crack initiation, which was then indirectly inferred from the scanning electron fractography results. In five failures the crack propagation was by fatigue, though in each case the fatigue crack initiated from a different cause. Some of these causes could be eliminated in future components by better process control. In one failure, the cause was determined to be impact during a crash; the features of impact fracture were distinguished from overload fractures by direct comparisons of the received specimens with laboratory-generated failures.

Johari, O.↗