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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

De-Energized Line Testing Interface (DELTI) Report

To guarantee the safety of power system operations, it is crucial to understand a de-energized power line’s status before restoring power. It is hypothesized that the Grid Resonance Probe (GRP) and De-Energized Line Testing Interface (DELTI) can be utilized to identify a faulted distribution line. Therefore, the project team is targeting development of a methodology to identify faulted distribution lines by using the DELTI system. In this R&D task, two field tests on Plum Island have been conducted, including DELTI tests with multiple configurations and fault scenarios. This report provides the analysis results of distribution line fault identification from the two field tests. In the remainder of this report, Section 1 is the introduction of the DELTI system, Section 2 provides the analyses of the two field tests in June and October of 2021, respectively, Section 3 discusses the existing line fault detection devices on market and how they compare to DELTI and Section 4 includes the lessons learned and recommendations for future work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Behavior of Single-Line-Ground Faults in Inverter-Based Resource Dominated Grids Explained

It has been observed by protection engineers that it is difficult for a protective relay to identify the faulted phase during a single-line-ground (SLG) fault in a power system with a high ingression of inverter-based resources (IBR) using currents (phase or sequence). Further studies using electromagnetic transient (EMT) simulation show that the initial operating conditions of the IBRs influence the response of phase currents during an SLG fault. In this letter, we conduct a quantitative analysis using sequence components. We find that the pre-fault condition determines the relative position of the current contributed by the grid versus that from the IBR, and further dictates which phase has the largest magnitude during an SLG condition. Finally, this finding is further verified by the EMT simulation results.

electromagnetic transient simulation↗
DOE OSTI2020

Characterizing the uppermost 100 m structure of the San Jacinto fault zone southeast of Anza, California, through joint analysis of geological, topographic, seismic and resistivity data

We present results from complementary geological, topographic, seismic and electrical resistivity surveys at the Sagebrush Flat (SGB) site along the Clark fault (CF) strand of the San Jacinto fault zone trifurcation area southeast of Anza, California. Joint interpretation of these data sets, each with unique spatiotemporal sensitivities, allow us to better characterize the shallow (<100 m) fault zone at this structurally complex site. Geological mapping at the surface shows the CF has three main subparallel strands within a <100 m zone with varying degrees of rock damage. These strands intersect units of banded gneiss and tonalite, and various sedimentary units. Near the surface, the weathered but more intact tonalite and gneiss to the southwest have relatively high VP. The low-lying flat sedimentary basins around the two southwestern-most CF strands and elevated damaged gneiss to the northeast have lowest VP <500 m s–1. The high relief of the northeast gneiss unit may in part be explained by its extensive damage and inferred increased relative rock uplift. Resistivity imaging shows the unconsolidated dry basin sediments (maximum >1300 Ohm.m) contrasted against the compacted fine-grained (potentially wet) materials within the CF core and the Bautista Formation (minimum <40 Ohm.m), which is slightly elevated above the flat basins. The inverse relationship between VP (increases) and resistivity (decreases) in the uppermost ~15 m can be characterized as log–log linear with slopes of –2.6 to –4. At depths >30 m, the velocity heterogeneity near the surface merges into larger-scale structures that are generally slower on the northeast side of the CF core compared to the southwest side (as much as ~40 per cent reduction in average VP). A previous study revealed a 20–37 per cent variability in peak ground velocities across the SGB site from local earthquakes. The upper end of that range is associated with the near-surface unconsolidated sedimentary basins and northeast damaged gneiss unit. Preliminary analysis of time-dependent topography mostly shows effects of changing vegetation and anthropogenic activity.

Geochemistry & Geophysics↗
DOE OSTI2022

Simulation and Postmortem Analysis of Angeles Forest Disturbance Event

Multiple unexpected solar photovoltaic (PV) plant responses in the California region following contingencies in recent years warrant postmortem analysis leveraging digital fault recorder (DFR) data for a better understanding of such events and preventing similar events in the future. Most utilities in US, in general, possess transient stability (TS) phasor-domain models of the power grid. Since traditional phasor-based transient stability planning models cannot perform such analysis, this paper presents an approach to extract a region of such models and convert it to Electromagnetic transient (EMT) models. The approach is based on the determination of the minimum impedance-weighted spanning tree between the fault location and affected PV plants in a Western Electricity Coordinating Council (WECC) planning model followed by conversion of this region into an EMT model. The accuracy of the proposed approach is validated against DFR records obtained from the 2018 Angeles Forest disturbance event.

Samanta, Sayan↗
DOE OSTI2025

Evaluation of HVAC & refrigeration system fault behaviors and impacts: A systematic review

Achieving the goals of green buildings critically depends on the fault-free operation of heating, ventilation and air conditioning and refrigeration (HVAC&R) systems. However, faults frequently occur in these systems, causing a range of negative consequences, including increased energy consumption, diminished operational performance, compromised indoor environmental quality, higher operational costs, and shortened system lifespan. The evaluation of fault behaviors and impacts plays a critical role in revealing fault characteristics and consequently supports many research areas, including the design of the high-performance equipment, development of fault detection and diagnostics (FDD) and robust control approaches, as well as the enhancement of maintenance decision-making activities. This paper systematically reviews 112 research publications that reported the analysis and evaluation of fault behaviors and impacts in HVAC&R systems over the past thirty years. Here, we designed a review approach to address five crucial research questions, namely: 1) the objectives of analysis and evaluation of fault behaviors and impacts, 2) data sources, 3) equipment/system types and fault types, 4) evaluation methods including evaluation measures and associated metrics, and 5) challenges and future directions in the research on evaluating of fault behaviors and impacts. In-depth discussions on these questions help bridge the gap between the evaluation of fault behaviors and impacts and their practical applications, such as the development of high-performance systems, fault models, FDD methods, and maintenance decision-making tools within the HVAC&R FDD domain.

Chen, Yimin [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2025

Constant-Overhead Fault-Tolerant Bell-Pair Distillation Using High-Rate Codes

We present a fault-tolerant Bell-pair distillation scheme achieving constant overhead through high-rate quantum low-density parity-check (qLDPC) codes. Our approach maintains a constant distillation rate equal to the code rate while requiring no additional overhead beyond the physical qubits of the code. Full circuit-level analysis demonstrates fault-tolerance for input Bell-pair infidelities below a threshold ∼10%, readily achievable with near-term capabilities. Unlike previous proposals, our scheme keeps the output Bell pairs encoded in qLDPC codes at each node, eliminating unencoding overhead and enabling direct use in distributed quantum applications through recent advances in qLDPC computation. These results establish qLDPC-based distillation as a practical route toward resource-efficient quantum networks and distributed quantum computing.

quantum communication, protocols & technology↗
DOE OSTI2025

Optimizing Hydronic Heating for Comfort and Performance in Multifamily Housing

Inefficient control settings in multifamily boilers often lead to substantial energy and cost penalties. To address this, a Fault Detection and Diagnostic (FDD) tool was developed to automate data analysis and identify operational faults such as suboptimal outdoor temperature sensor placement, misconfigured outdoor air reset (OAR) curves, excess boiler cycling, and domestic hot water (DHW) setpoint errors. By comparing pre- and post-implementation periods and applying engineering models, the tool quantifies energy savings and reduces manual analysis time by over 90%. Testing on over 100 monitored sites and a targeted subset of 12 buildings showed an average 11% energy savings from remote optimization; further validation across 19 OAR curve changes confirmed the tool’s accuracy, predicting actual savings within ±5% for most cases. Simple payback can be under three years for many multifamily buildings, though rising hardware, labor, and fuel costs create uncertainties, and decarbonization goals increasingly shift focus to electrification. The FDD tool remains invaluable for optimizing existing boilers, enhancing future electrification measures, and adapting to new technologies by refining building load estimates. In doing so, it supports both near-term efficiency and long-term transitions to low-carbon alternatives, ensuring buildings achieve substantial cost and energy benefits throughout their system lifecycles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

De-risking fault leakage risk and containment integrity for subsurface storage applications

The subsurface is pivotal in the energy transition, for the sequestration of CO 2 and energy storage. It is crucial to understand to what extent geological faults may form leakage pathways that threaten the containment integrity of these projects. Fault flow behavior has been studied in the context of hydrocarbon development, supported by observations from wells drilled through faults, but such observations are rare in geoenergy projects. Focusing on mechanical behavior as early indicator of potential leakage risks, a probabilistic Coulomb Failure Stress workflow is developed and demonstrated using data from the Decatur CO 2 sequestration project to rank faults based on their containment risk. The analysis emphasizes the importance of fault throw relative to reservoir thickness and pore pressure change in assessing reactivation risks. Integrating this mechanical assessment with geological and dynamic fault analyses contributes to derisking fault containment for geoenergy applications, providing valuable insights for the successful development of subsurface storage projects.

58 GEOSCIENCES↗
DOE OSTI2022

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

Abstract In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

Donovan, Richard P.↗
DOE OSTI2024

Monitoring of Photovoltaic (PV) Performance and Degradation: Integrated Renewable Energy Systems (IRES) - PV Monitoring Task

The Integrated Renewable Energy System (IRES) testbed demonstration at the Pacific Northwest National Laboratory (PNNL) Sequim campus includes development of a suite of capabilities for tracking the performance of photovoltaic (PV) renewable energy components located on a floating platform (floating PV) and on a shoreline building rooftop. The marine environment represents potentially harsh and corrosive conditions for PV modules. Compared to terrestrial PV, potential concerns for offshore PV arising from high humidity and occasional contact with saltwater, marine wildlife, and aquaculture. These environmental factors can reduce electricity generation efficiency and increase the risk of electrical faults, polymer insulation or jacketing degradation and hydrolysis of the PV cell encapsulant materials. Offshore PV modules may also be exposed to lower temperatures and buoyant and vibrating motions with a floating platform. It is not clear how the long-term performance of floating PV will be affected by these factors. To understand expected energy generation through solar cells on a floating platform, monitoring of the performance of PV modules in the marine environment is needed. This report outlines a plan for long-term testing of IRES PV components utilizing resources of the PNNL Material Aging and Detection (MAaD) Science team and the Marine and Coastal Research Laboratory (MCRL). Equipment applicable for onsite testing and real-time monitoring of the PV performance associated with environmental conditions including temperature, solar irradiance, shading and soiling is included. In case of performance loss, equipment for fault detection, failure analysis, material testing, and further troubleshooting are also available. Through establishment of this capabilities, the enabling IRES project sets the stage for future research to advance off-shore and near-shore energy options for businesses and communities.

14 SOLAR ENERGY↗
DOE OSTI2025

Analysis of a Disturbance Event with Inverter-Based Resources Using EMT Simulations

Increasing penetration of inverter-based resources (IBRs) necessitates newer methods of planning and analysis of disturbances. The existing phasor-domain transient stability (TS) analysis may not capture the dynamics of IBRs during fault events. Here, in this paper, electromagnetic transient (EMT) simulations using high-fidelity detailed model of power grid and one of the affected photovoltaic (PV) plants during the Angeles Forest disturbance in 2018 are performed. In these simulations, the processes to develop EMT models of power grid from traditional phasor-domain TS data and PV plant from collected data are described. Thereafter, using these simulations, the response of the PV plant during the fault event in 2018 is replicated and a sensitivity analysis is performed. The sensitivity analysis consists of making changes to the components within the PV plant and in the power grid to evaluate the impact they have on the response observed by the PV plant during the fault event. This analysis provides an understanding of the components that impact the operation of a PV plant during fault events and provide guidance to system planners on the studies that need to be performed to maintain a reliable power grid as new IBR plants are integrated.

42 ENGINEERING↗
DOE OSTI2026

Model-Based Investigation of Multi-Fault Interactions and Performance Degradation in Residential Heat Pump Systems

Faults in heat pump systems can significantly degrade performance, reduce efficiency, and accelerate component wear, leading to higher operating costs and maintenance demands. While numerous studies have investigated the impact of individual faults, the interactions between multiple concurrent faults remain insufficiently understood, despite their common occurrence in real-world operation. This study conducts a comprehensive simulation analysis of multiple simultaneous faults in a vapor compression heat pump using a validated heat pump design model (HPDM) tool. Detailed component-level modeling methods are implemented to examine performance sensitivity under combinations of refrigerant flow and heat exchanger faults. The results reveal complex fault interactions that can mask or amplify system deviations, challenging conventional diagnostic approaches. Findings from this work provide meaningful insights for the development of more robust fault detection and diagnosis algorithms, supporting improved reliability and energy efficiency in next-generation heat pump technologies.

Hu, Yifeng [ORNL] (ORCID:0000000242875185)↗
DOE OSTI2022

ARETE: Accurate Error Assessment via Machine Learning-Guided Dynamic-Timing Analysis

Nanometer circuits are increasingly prone to timing errors, escalating the need for fault injection frameworks to accurately evaluate their impact on applications. Here in this paper, we propose ARETE, a novel cross-layer, fault-injection framework that combines dynamic-binary instrumentation with machine learning-guided dynamic-timing analysis. ARETE enables accurate fault-injection into any application by estimating the location of the injecting errors via dynamic-timing analysis. To accelerate fault-injection, we develop a novel, data-aware, machine learning-based mechanism that dynamically pre-selects the error-prone instructions and limits the application of the costly dynamic-timing analysis only to them. To evaluate ARETE's accuracy, our fully automated toolflow is configured to support fault-injection based on detailed post-layout gate-level simulations as well as via existing workload-agnostic error models. Our results for various workloads, including an autonomous-driving library, show that the location and time of injected errors performed by ARETE, is 89.9% consistent with fault-injection based on full gate-level simulation. On average, ARETE executes 84.6x faster than gate-level simulation and at a cost of 3.4% loss in the program output quality estimation. When compared to the existing statistical fault-injection tools that are based on workload-agnostic error models, ARETE improves the accuracy of fault-injection rate and output quality estimation by 143.9% and 40.4% on average, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types

To improve the viability of nuclear power plants, there is a need to reduce their operational costs. Operational costs account for a significant portion of a plant’s yearly budget, due to their scheduled-based maintenance approach. In order to reduce these costs, proactive methods are required that estimate and forecast the state of a machine in real time to optimize maintenance schedules. In this research, we use Bayesian networks to develop a framework that can forecast the remaining useful life of a centrifugal pump. To do so, we integrate survival analysis with Bayesian statistics to forecast the health of the pump conditional to its current state. We complete our research by successfully using the Bayesian network on a case study. This solution provides an informed probabilistic viewpoint of the pumping system for the purpose of predictive maintenance.

42 ENGINEERING↗
DOE OSTI2023

En echelon faults reactivated by wastewater disposal near Musreau Lake, Alberta

We use machine-learning and cross-correlation techniques to enhance earthquake detectability by two magnitude units for the earthquake sequence near Musreau Lake, Alberta, which is induced by wastewater disposal. This deep catalogue reveals a series of en echelon ~N–S oriented strike-slip faults that are favourably oriented for reactivation. These faults require only ~0.6 MPa overpressure for triggering to occur. Earthquake activity occurs in bursts, or episodes; episodes restricted to the largest fault tend to have earthquakes starting near the southern end (distant from injectors) and progressing northwards (towards the injectors). While most events are concentrated along these ~N–S oriented faults, we also delineate smaller faults. Together, these findings suggest pore pressure as the triggering mechanism, where a time-dependent increase in pore pressure likely caused these faults to progressively reawaken. Analysis of the ‘next record-breaking event’, a statistical model that forecasts the sequencing of earthquake magnitudes, suggests that the next largest event would be M L ~4.3. The seismically illuminated length of the largest fault indicates potential magnitudes as large as M w 5.3.

58 GEOSCIENCES↗