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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Achieving Improved Reliability with Failure Analysis

Reliability is the ability of a product to properly function, within specified performance limits, for a specified period of time, under the life cycle application conditions. Failure analysis is a vital tool in the effort to ensure reliability of electronic products and systems throughout their product lifecycle. Today, organizations involved in activities within the electronics supply chain are facing new challenges, not just from complex assembly styles, harsher lifecycle environments, and sophisticated supply chains, but also from customers who are demanding a quicker turn-around. Unfortunately, root cause failure analysis is often performed incompletely, leading to a poor understanding of failure mechanisms and causes and, customer dissatisfaction due to recurring failures. The PDC (Professional Development Course) starts with an introduction to reliability concepts, physics of failure and an overview of failure mechanisms that affect PCBs (Printed Circuit Boards), PCBAs (Printed Circuit Board Assembly) and components. The PDC then dives into root cause hypothesizing techniques (Pareto, FMEA (Failure Modes and Effects Analysis), fishbone (Cause-And-Effect Diagram), FTA (Fault Tree Analysis)), non-destructive and destructive analysis and, materials characterization will be discussed. Numerous failure analysis case studies will be used to illustrate the techniques and analysis principles to arrive at the root cause(s) of field failures on printed circuit boards, active components, and assemblies. What Attendees will Learn: Topics include: Overview of Reliability Concepts Failure mechanisms of electronic products Root cause analysis Failure analysis techniques -Non-destructive techniques (optical, CSAM (Confocal Scanning Electron Microscopy) etc.) -Destructive analysis (DPA (Destructive Physical Analysis), Decap (Decapsulation), FIB (Focused Ion Beam) etc.) -Materials characterization (XRF (X-Ray Fluorescence) , EDS (Error Detection Sequential), TMA/DSC (Thermal Mechanical Analysis/Differential Scanning Calorimetry) etc.)

PCB quality↗
DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Geomechanical characterization of Rock Valley carbonates

In Rock Valley, Nevada, the USA, past earthquake sequences and shallow faulting in Paleozoic carbonates remain poorly understood. Carbonates are typically more ductile rocks that do not experience large stress drops or fracture coalescence, which contradicts previous observations in the region. As part of the Source Physics Experiment, an effort has been made to experimentally characterize the petrophysical and geomechanical properties of carbonates from Rock Valley. Well core and outcrop samples were used to determine the difference between shallow-buried Tertiary limestones and deeply buried Paleozoic limestones and dolostones. The carbonates possessed porosities between 3 and 9%, with V P and V S in the Tertiary carbonates around 2700 and 1800 m/s, respectively, and 6000 and 3100 m/s, respectively, in the Paleozoic carbonates. Microstructural characterization revealed that the Paleozoic carbonates contained significantly more pre-existing damage and alteration than the Tertiary carbonates, though the deformation varies from localized to diffuse. Unconfined Brazilian tests and triaxial tests with confining pressures between 0 and 50 MPa showed that, although samples all experienced failure, the dolostones typically failed at greater stresses and possessed greater E/ν ratios than the limestones. Furthermore, the Hoek–Brown failure criterion was used to construct failure envelopes with the compressive and tensile failure tests. Velocity, density, and porosity measurements were used to construct a hypothetical velocity-depth profile and compare the results with the theoretical petrophysical measurements. Using brittleness analysis, the likely failure conditions were determined to be low-porosity dolomitic rock for fault nucleation in the Paleozoic basement.

Brittleness↗
DOE OSTI2024

Reusable Verification Components for High-Energy Physics readout ASICs

Verification is a critical aspect of designing front-end (FE) readout ASICs for High-Energy Physics (HEP) experiments. These ASICs share several similar functional features, resulting in similar verification objectives, which can be addressed using comparable verification strategies. This contribution presents a set of re-usable verification components for addressing common verification tasks, such as clock generation, reset handling, configuration, as well as hit and fault injections. The components were developed as part of the CHIPS initiative and they have been successfully used in the verification of multiple HEP ASICs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Disconnection-Mediated Transition in Segregation Structures at Twin Boundaries

Twin boundaries play an important role in the thermodynamics, stability, and mechanical properties of nanocrystalline metals. Understanding their structure and chemistry at the atomic scale is key to guide strategies for fabricating nanocrystalline materials with improved properties. In this work, we report an unusual segregation phenomenon at gold-doped platinum twin boundaries, which is arbitrated by the presence of disconnections, a type of interfacial line defect. By using atomistic simulations, we show that disconnections containing a stacking fault can induce an unexpected transition in the interfacial-segregation structure at the atomic scale, from a bilayer, alternating-segregation structure to a trilayer, segregation-only structure. This behavior is found for faulted disconnections of varying step heights and dislocation characters. Supported by a structural analysis and the classical Langmuir–McLean segregation model, we reveal that this phenomenon is driven by a structurally induced drop of the local pressure across the faulted disconnection accompanied by an increase in the segregation volume.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Digital twin framework for PIP-II linac: AI-driven multi-scale modeling from ion source to 800 MeV

The PIP-II superconducting linac at Fermilab is designed to deliver multi-megawatt proton beams for neutrino physics and other high-intensity applications. To expedite commissioning and enhance operational reliability, we have developed an EPICS-based data flow framework that seamlessly integrates digital twins (DT) with physical twins (PT). These digital twins comprise high-fidelity beam dynamics models or data-driven surrogate models connected to their physical counterparts through real-time diagnostics and advanced machine-learning algorithms.Central to this framework is Linac_Gen, an accelerated simulation tool that incorporates convolutional neural networks, random forests, and genetic algorithms to provide up to a tenfold speedup in optimizing the accelerator geometry model. An EPICS translator layer ensures interoperability by efficiently mapping lattice parameters across diverse simulation platforms.Our EPICS-based framework supports multiple operational modes—monitoring, passive learning, closed-loop control, and online learning—covering the entire machine lifecycle. By leveraging HPC resources and multi-objective optimization techniques, the digital twin enables adaptive trajectory correction, real-time fault detection, and predictive modeling of beam stability. This comprehensive approach paves the way for robust, high-intensity operation and data-driven accelerator R&D at Fermilab.

Pathak, Abhishek [Fermilab]↗
DOE OSTI2022

Sequences of seismic and aseismic slip on bimaterial faults show dominant rupture asymmetry and potential for elevated seismic hazard

Here we perform numerical simulations of sequences of earthquake and aseismic slip on planar rate and state faults separating dissimilar material within the 2-D plane strain approximation. We resolve all stages of the earthquake cycle from aseismic slip to fast ruptures while incorporating full inertia effects during seismic event propagation. We show that bimaterial coupling results in favorable nucleation site and subsequent asymmetric rupture propagation. We demonstrate that increasing the material contrast enhances this asymmetry leading to higher slip rates and normal stress drops in the preferred rupture propagation direction. The normal stress drop, induced by the bimaterial effect, leads to strong dynamic weakening of the fault and may destabilize the creeping region on a heterogeneous rate and state fault, resulting in extended rupture propagation. Such rupture penetration into creeping patches may lead to more frequent opening of earthquake gates, causing increased seismic hazard. Furthermore, bimaterial coupling may lead to irregular seismicity pattern in terms of event length, peak slip rates, and hypocenter location, depending on the properties of the creeping patches bordering the seismogenically active part of the fault. Our results highlight robust characteristics of bimaterial interfaces that persist over long sequence of events and suggest the need for further exploration of the role of material contrast in earthquake physics and models of seismic hazard.

58 GEOSCIENCES↗
DOE OSTI2024

Enabling Efficient Surveillance, Control, and Automation of Geothermal Operations with Advanced Predictive Analytics

Automation and control of geothermal energy production and operations require reliable and efficient predictive tools. While physics-based simulation offers a comprehensive tool for predicting energy production performance in geothermal systems, predicting the behavior of geothermal reservoirs involves complex multi-physics processes with coupling effects, highly uncertain input parameters and subsurface descriptions. Moreover, building, running, and integrating simulation models into standard model calibration and optimization workflows entail significant technical and computational efforts. An emerging alternative to physics-based simulation is data-driven predictive analytics models that have gained popularity in energy industry. In this report, we develop novel predictive models for integration into real-time fault diagnosis and model predictive control algorithms to improve the efficiency of energy production operations in geothermal reservoirs. The report includes two major research Thrust Areas, that is, the surface power plant and the subsurface reservoir.

15 GEOTHERMAL ENERGY↗
NASA NTRS1988

Modeling surface deformations at complex strike-slip plate boundaries

Two new versions of the physical base-traction model of Li and Rice (1987) have been developed to study the loading processes and surface deformation near plate boundaries with geometric complexities, including a shallow creeping fault segment and two subparallel faults. Predictions of surface velocity and surface slip rate for the region between the San Andreas fault and the Pacific coast at the latitude of the Parkfield-Cholame segment using model parameters based on geologic, geodetic, and seismic considerations were found to be in good agreement with contemporary geodetic field data (not used in constrainning model parameters). Also, predicted surface velocity for the Salton Sea-Coachella Valley area were fit to recently derived contemporary geodetic data and very large baseline interferometry data, althoug here the model parameters are not well constrained.

Li, Victor C.↗
DOE OSTI2023

The ARENA Test Bed – A Versatile Resource for I&C Development and Validation

The Accelerated and Real-time Experimental Nodal Assessment (ARENA) Test Bed at the Pacific Northwest National Laboratory (PNNL) is a versatile resource for development and validation of instrumentation and control (I&C) technologies. This capability was created to facilitate in-situ testing of nuclear electrical cables in various simulated operational environments. Using cable trays, a control box, and selected test components, low voltage cables can be staged to experience local adverse environments such as elevated temperature and water immersion. Cable condition can be continuously monitored over time to track the effect of local stresses using nondestructive assessment tools. A heads-up display (ARENA TV) plots key data in real-time for users. The ARENA Test Bed has recently been used to evaluate the potential for spread spectrum time domain reflectometry (SSTDR) to monitor thermal aging of a portion of live cable powering a three-phase motor. The arrangement provided the opportunity to directly compare the performance of the novel online SSTDR method with offline results from the more standard frequency domain reflectometry (FDR) method. The ability of SSTDR and FDR to identify the presence of water in immersed shielded and unshielded cables and to detect ground faults was also assessed. A digital twin is being developed to track and predict FDR signals from a thermally aging conceptual cable region to compare with measured signals from the ARENA physical counterpart. The test bed concept addresses an important need in nuclear I&C monitoring tool development. New tools and techniques can be developed in the test bed and validated versus known methods and physical measurements. Digital twins and machine learning engines can be populated with measured data in a controlled environment that would not be readily available in the actual nuclear power plant. Proposed monitoring strategies can be confirmed for effectiveness through objective evaluation. It is anticipated that the PNNL ARENA Test Bed will be a valuable resource in advancing nuclear plant instrumentation.

nuclear electrical cables, ARENA Test Bed, conditi↗
DOE OSTI2023

Benchmarking quantum logic operations relative to thresholds for fault tolerance

Contemporary methods for benchmarking noisy quantum processors typically measure average error rates or process infidelities. However, thresholds for fault-tolerant quantum error correction are given in terms of worst-case error rates—defined via the diamond norm—which can differ from average error rates by orders of magnitude. One method for resolving this discrepancy is to randomize the physical implementation of quantum gates, using techniques like randomized compiling (RC). In this work, we use gate set tomography to perform precision characterization of a set of two-qubit logic gates to study RC on a superconducting quantum processor. We find that, under RC, gate errors are accurately described by a stochastic Pauli noise model without coherent errors, and that spatially correlated coherent errors and non-Markovian errors are strongly suppressed. We further show that the average and worst-case error rates are equal for randomly compiled gates, and measure a maximum worst-case error of 0.0197(3) for our gate set. Our results show that randomized benchmarks are a viable route to both verifying that a quantum processor’s error rates are below a fault-tolerance threshold, and to bounding the failure rates of near-term algorithms, if—and only if—gates are implemented via randomization methods which tailor noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Effective pair potential for random fcc CoCrFeMnNi alloys

The single-phase equiatomic CoCrFeMnNi alloy is a random solid solution of five elements on the face-centered cubic lattice, whose pure constituents crystallize in very different structures and exhibit diverse magnetic properties. Due to the randomness of the alloy, 80% of nearest neighbor bonds are between unlike elements and thus the details of bonding in pure structures are less important. The elastic moduli of this alloy give rise to small Cauchy pressure C 12 - C 44 , which suggests that the dominant part of bonding may be described by a simple pair potential. We test this hypothesis by developing a long-range Lennard-Jones potential in which the equilibrium crystal structures of pure constituents are taken as reference. The standard mixing rules for regular solutions are then adopted to obtain parameters for bonds between unlike elements in the quinary system. The transferability of this potential to quaternary CoCrFeNi, ternary CoCrNi, and binary FeNi alloys is investigated and the predictions compared with experiments and density functional theory calculations. By sampling over a large number of random configurations, we investigate the effect of compositional randomness on misfit volumes, energies of point defects and stacking faults, and the dislocation friction stresses experienced by moving edge and screw dislocations.

Materials Science↗
DOE OSTI2023

Dynamics of episodic supershear in the 2023 M7.8 Kahramanmaraş/Pazarcik earthquake, revealed by near-field records and computational modeling

Abstract The 2023 M7.8 Kahramanmaraş/Pazarcik earthquake was larger and more destructive than what had been expected. Here we analyzed nearfield seismic records and developed a dynamic rupture model that reconciles different currently conflicting inversion results and reveals spatially non-uniform propagation speeds in this earthquake, with predominantly supershear speeds observed along the Narli fault and at the southwest (SW) end of the East Anatolian Fault (EAF). The model highlights the critical role of geometric complexity and heterogeneous frictional conditions in facilitating continued propagation and influencing rupture speed. We also constrained the conditions that allowed for the rupture to jump from the Narli fault to EAF and to generate the delayed backpropagating rupture towards the SW. Our findings have important implications for understanding earthquake hazards and guiding future response efforts and demonstrate the value of physics based dynamic modeling fused with near-field data in enhancing our understanding of earthquake mechanisms and improving risk assessment.

Environmental Sciences & Ecology↗
DOE OSTI2025

Reinforcement Learning for Anomaly Detection in Nuclear Power Plant Operation and Maintenance

In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.

Reinforcement learning↗
NASA NTRS2002

Transients in Pacific/North American Plate Boundary Deformation: Synthesis and Modeling of GPS and Borehole Strain Observations

This is the Final Technical Report on research conducted between 1 June 1997 and 14 September 2001 entitled "Transients in Pacific/North American plate boundary deformation: Synthesis and modeling of GPS and borehole strain observations." As the project title implies, our effort involved a geodetic study of strain transients, i.e., temporal variations in deformation rates, that occur within plate boundary zones and their relationship to earthquakes and plate motions. Important transients occur during and following large earthquakes, and there are also strain transients not apparently associated with earthquakes. A particularly intriguing class of transients, for which there is a modest but growing list of examples, are preseismic anomalies. Such earthquake precursors, if further documented and understood, would have obvious importance for earthquake hazard mitigation. Because the timescales for these diverse transients range over at least 6 orders of magnitude (minutes to years), no single geodetic technique is optimum. We therefore undertook a systematic synthesis of Global Positioning Satellite (GPS) and borehole strainmeter data in three areas in California where there are adequate numbers of both types of instruments (or their equivalent): the San Francisco Bay region (within the Bay Area Regional Deformation network), southern California (within the Southern California Integrated GPS Network), and Parkfield (where a two-color laser system provides a proxy for continuous GPS measurements). An integral component of our study was the elucidation of the physical mechanisms by which such transients occur and propagate. We therefore initiated the development of multiple forward models, using two independent approaches. In the first, we explored the response to specified earthquake slip in viscoelastic models that incorporated failure criteria and the geometry of major faults in California. In the second approach, we examined the dynamical response of a complex rheological medium to the application of a far-field stress imposed by plate motions. The forward models were used both to gain insight into the range of strain transients to be expected under different assumed mechanical conditions and to develop representations for strain fields that allow GPS, borehole, and other strain data to be combined in a self-consistent, yet well-determined, manner. The models also provided a basis for hypothesis testing, by which data from a strain transient well characterized by GPS and borehole observations were utilized to distinguish among competing candidates for the causative physical mechanism and the governing physical characteristics. During the three years of this project, continued to a fourth year through a no-cost extension of the grant, we published 14 papers and presented or co-authored 37 papers at national scientific meetings.

Solomon, Sean C.↗
DOE OSTI2025

Stability-preserving Lossy Compression for Large-scale Partial Differential Equations

Checkpoint/Restart (C/R) strategies are vital for fault tolerance in PDE-based scientific simulations, yet traditional checkpointing incurs significant I/O overhead. Lossy compression offers a scalable solution by reducing checkpoint data size, but conventional methods often lack control over physical invariants (e.g., energy), leading to instability such as oscillations or divergence in Partial Differential Equations (PDE) systems. This paper introduces a stability-preserving compression approach tailored for PDE simulations by explicitly controlling kinetic and potential energy perturbations to ensure stable restarts. Extensive experiments conducted across diverse PDE configurations demonstrate that our method maintains numerical stability with minimal error magnification—even across multiple checkpoint-restart cycles—outperforming state-of-the-art lossy compressors. Parallel evaluations on the Frontier supercomputer show up to 8.4× improvement in checkpoint write performance and 6.3× in read performance, while maintaining relative L2 errors ∼ 2e-6 throughout continued simulation. These results provide practical guidance for balancing compression accuracy, stability, and computational efficiency in large-scale PDE applications.

Gong, Qian [ORNL] (ORCID:0000000235704142)↗
NASA NTRS2013

A Cryogenic Fluid System Simulation in Support of Integrated Systems Health Management

Simulations serve as important tools throughout the design and operation of engineering systems. In the context of sys-tems health management, simulations serve many uses. For one, the underlying physical models can be used by model-based health management tools to develop diagnostic and prognostic models. These simulations should incorporate both nominal and faulty behavior with the ability to inject various faults into the system. Such simulations can there-fore be used for operator training, for both nominal and faulty situations, as well as for developing and prototyping health management algorithms. In this paper, we describe a methodology for building such simulations. We discuss the design decisions and tools used to build a simulation of a cryogenic fluid test bed, and how it serves as a core technology for systems health management development and maturation.

cryogenics↗