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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Fail-Slow at Scale: Evidence of Hardware Performance Faults in Large Production Systems

Fail-slow hardware is an under-studied failure mode. We present a study of 114 reports of fail-slow hardware incidents, collected from large-scale cluster deployments in 14 institutions. We show that all hardware types such as disk, SSD, CPU, memory, and network components can exhibit performance faults. We made several important observations such as faults convert from one form to another, the cascading root causes and impacts can be long, and fail-slow faults can have varying symptoms. From this study, we make suggestions to vendors, operators, and systems designers.

fail-slow↗
DOE OSTI2016

A multiscale hydro-geochemical-mechanical approach to analyze faulted CO 2 reservoirs: Original Research Article: A multiscale hydro-geochemical-mechanical approach to analyze faulted CO 2 reservoirs

This paper applies a multiscale hydro-geochemical-mechanical approach to analyze faulted CO 2 reservoirs using the STOMP-CO 2 -R code that is coupled to the ABAQUS® finite element package. STOMP-CO 2 -R models the reactive transport of CO 2 causing mineral volume fraction changes that are captured by an Eshelby-Mori-Tanka model implemented in ABAQUS®. A three-dimensional (3D) STOMP-CO 2 -R model for a reservoir containing an inclined fault was built to analyze a formation containing a reaction network with 5 minerals: albite, anorthite, calcite, kaolinite and quartz. A 3D finite element mesh that exactly maps the STOMP-CO 2 -R grid is developed for coupled hydro-geochemical-mechanical analyses. The model contains alternating sandstone and shale layers. The impact of reactive transport of CO 2 on the geomechanical properties of reservoir rocks and seals are studied in terms of mineral composition changes that affect their geomechanical responses. Simulations assuming extensional and compressional stress regimes with and without coupled geochemistry are performed to study the stress regime effect on the risk of hydraulic fracture. The tendency for the fault to slip is examined in terms of stress regime, geomechanical and geochemical-mechanical effects as well as fault inclination. The results show that mineralogical changes due to long-term injection of CO 2 reduce the permeability and elastic modulus of the reservoir, leading to increased risk of hydraulic fracture in the injection location and at the caprock seal immediately above the injection zone. Fault slip is not predicted to occur. However, fault inclination and stress regime have an important impact on the slip tendency factor.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Development and evaluation of virtual refrigerant mass flow sensors for fault detection and diagnostics

Refrigerant mass flow rate is an important measurement for monitoring equipment performance and enabling fault detection and diagnostics. However, a traditional mass flow meter is expensive to purchase and install. A virtual refrigerant mass flow sensor (VRMF) uses a mathematical model to estimate flow rate using low-cost measurements and can potentially be implemented at low cost. This study evaluates three VRMFs for estimating refrigerant mass flow rate. The first model uses a compressor map that relates refrigerant flow rate to measurements of inlet and outlet pressure, and inlet temperature measurements. The second model uses an energy-balance method on the compressor that uses a compressor map for power consumption, which is relatively independent of compressor faults that influence mass flow rate. The third model is developed using an empirical correlation for an electronic expansion valve (EEV) based on an orifice equation. The three VRMFs are shown to work well in estimating refrigerant mass flow rate for various systems under fault-free conditions with less than 5% RMS error. Each of the three mass flow rate estimates can be utilized to diagnose and track the following faults: 1) loss of compressor performance, 2) fouled condenser or evaporator filter, 3) faulty expansion device, respectively. For example, a compressor refrigerant flow map model only provides an accurate estimation when the compressor operates normally. When a compressor is not delivering the expected flow due to a leaky suction or discharge valve or other internal fault, the energy-balance or EEV model can provide accurate flow estimates. In this paper, the flow differences provide an indication of loss of compressor performance and can be used for fault detection and diagnostics.

Kim, Woohyun↗
DOE OSTI2026

Fault Slip and Fluid Flow: Seismic Source Analysis to Assess Role of Multiple Slip Patches in Fault Permeability

The relationship between fault reactivation, microearthquakes (MEQs), and permeability evolution during fluid injection plays a critical role in energy harvesting and waste disposal. Recent studies have demonstrated the possibility of predicting fault permeability using cumulative seismic moments of MEQs quantitatively. To understand the underlying physical processes, we conduct fault reactivation experiments using Utah FORGE granitoid and analyze acoustic emission (AE) signals generated during stepwise increases in fluid injection pressure. Frequency analysis of thousands of calibrated AE signals reveals that fault reactivation produces multiple AE source patches with millimeter-scale radii—smaller than the sample fault radius. The cumulative area of the reactivated patches covers the fault multiple times over (∼10x–50x area) for each pressure step. These findings provide mechanistic insight that measured permeability enhancement is not driven by a single large slip event, but by the sequential and interacting activation of multiple slip patches that create a continuous flow pathway.

Nurshal, M. E. M. [Pennsylvania State University, ↗
DOE OSTI2021

Seismic fault weakening via CO 2 pressurization enhanced by mechanical deformation of dolomite fault gouges

Carbon dioxide emissions from dolomite decarbonation play an essential role in the weakening of carbonate faults by lowering the effective normal stress, which is thermally activated at temperatures above 600–700 °C. However, the mechanochemical effect of low-crystalline ultrafine fault gouge on the decarbonation and slip behavior of dolomite-bearing faults remains unclear. In this study, we obtained a series of artificial dolomite fault gouges with systematically varying particle sizes and dolomite crystallinities using a high-energy ball mill. The laboratory-scale pulverization of dolomite yielded MgO at temperatures below 50 °C, indicating that mechanical decarbonation without significant heating occurred due to the collapse of the crystalline structure, as revealed by X-ray diffraction and solid-state nuclear magnetic resonance results. Furthermore, the onset temperature of thermal decarbonation decreased to ~400 °C. Numerical modeling reproduced this two-stage decarbonation, where the pore pressure increased due to low-temperature thermal decarbonation, leading to slip weakening on the fault plane even at 400–500 °C; i.e., 200–300 °C lower than previously reported temperatures. Thus, the presence of small amounts of low-crystalline dolomite in a fault plane may lead to a severely reduced shear strength due to thermal decomposition at ~400 °C with a small slip weakening distance.

58 GEOSCIENCES↗
DOE OSTI2024

Residential HVAC Fault Data Collection Plan – Refrigerant Undercharge and Overcharge Faults

Heating, ventilation, and air-conditioning (HVAC) systems can develop faults due to poor installation practices or gradual wear and tear, leading to decreased HVAC system’s efficiency, compromised thermal comfort, and shortened equipment lifespan (EERE, 2018). Automated fault detection and diagnosis (AFDD) technologies offer a solution by identifying energy-wasting HVAC faults, such as inadequate indoor airflow and incorrect refrigerant charge, and guiding technicians to enhance system efficiency. In the realm of residential HVAC, AFDD can be implemented through various fault detection and diagnosis capabilities, sensor configurations, and target applications. These technologies typically fall into three categories: smart diagnostic tools, original equipment manufacturer (OEM)-embedded tools, and add-on tools. Smart diagnostic tools employ temporarily installed sensors to directly measure HVAC system characteristics, while OEM-embedded tools utilize factory-installed sensors to identify faults or assess system performance. However, both these types of AFDD technologies are often only accessible for high-end HVAC equipment or require additional sensor installation by qualified technicians, resulting in high investment costs and limited applicability for low-income residential buildings. On the other hand, add-on tools rely solely on data from smart thermostats and meters to detect faults by continuously analyzing equipment runtime or energy usage. As smart thermostat and meter costs decrease and their prevalence increases, these tools can be readily deployed in low-income residential buildings. However, they possess limited capabilities as they rely solely on basic trend analysis. Enhancing such tools with advanced machine learning algorithms can significantly improve their effectiveness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Fault Diagnosis of Hybrid Computing Systems Using Chaotic-Map Method

Computing systems are becoming increasingly complex with nodes consisting of a combination of multi-core central processing units (CPUs), many integrated core (MIC) and graphics processing unit (GPU) accelerators. These computing units and their interconnections are subject to different classes of hardware and software faults, which should be detected to support mitigation measures. We present the chaotic-map method that uses the exponential divergence and wide Fourier properties of the trajectories, combined with memory allocations and assignments to diagnose component-level faults in these hybrid computing systems. We propose lightweight codes that utilize highly parallel chaotic-map computations tailored to isolate faults in arithmetic units, memory elements and interconnects. The diagnosis module on a node utilizes pthreads to place chaotic-map threads on CPU and MIC cores, and CUDA C and OpenCL kernels on GPU blocks. We present experimental diagnosis results on five multi-core CPUs; one MIC; and, seven GPUs with typical diagnosis run-times under a minute.

Rao, Nageswara↗
NASA NTRS1987

Fault-free performance validation of fault-tolerant multiprocessors

A validation methodology for testing the performance of fault-tolerant computer systems was developed and applied to the Fault-Tolerant Multiprocessor (FTMP) at NASA-Langley's AIRLAB facility. This methodology was claimed to be general enough to apply to any ultrareliable computer system. The goal of this research was to extend the validation methodology and to demonstrate the robustness of the validation methodology by its more extensive application to NASA's Fault-Tolerant Multiprocessor System (FTMP) and to the Software Implemented Fault-Tolerance (SIFT) Computer System. Furthermore, the performance of these two multiprocessors was compared by conducting similar experiments. An analysis of the results shows high level language instruction execution times for both SIFT and FTMP were consistent and predictable, with SIFT having greater throughput. At the operating system level, FTMP consumes 60% of the throughput for its real-time dispatcher and 5% on fault-handling tasks. In contrast, SIFT consumes 16% of its throughput for the dispatcher, but consumes 66% in fault-handling software overhead.

Czeck, Edward W.↗
NASA NTRS1994

FTAPE: A fault injection tool to measure fault tolerance

The paper introduces FTAPE (Fault Tolerance And Performance Evaluator), a tool that can be used to compare fault-tolerant computers. The tool combines system-wide fault injection with a controllable workload. A workload generator is used to create high stress conditions for the machine. Faults are injected based on this workload activity in order to ensure a high level of fault propagation. The errors/fault ratio and performance degradation are presented as measures of fault tolerance.

Tsai, Timothy K.↗
NASA NTRS1995

Measuring fault tolerance with the FTAPE fault injection tool

This paper describes FTAPE (Fault Tolerance And Performance Evaluator), a tool that can be used to compare fault-tolerant computers. The major parts of the tool include a system-wide fault-injector, a workload generator, and a workload activity measurement tool. The workload creates high stress conditions on the machine. Using stress-based injection, the fault injector is able to utilize knowledge of the workload activity to ensure a high level of fault propagation. The errors/fault ratio, performance degradation, and number of system crashes are presented as measures of fault tolerance.

Tsai, Timothy K.↗
NASA NTRS1995

FTAPE: A fault injection tool to measure fault tolerance

The paper introduces FTAPE (Fault Tolerance And Performance Evaluator), a tool that can be used to compare fault-tolerant computers. The tool combines system-wide fault injection with a controllable workload. A workload generator is used to create high stress conditions for the machine. Faults are injected based on this workload activity in order to ensure a high level of fault propagation. The errors/fault ratio and performance degradation are presented as measures of fault tolerance.

Tsai, Timothy K.↗
DOE OSTI2016

Privacy-Assured Aggregation Protocol for Smart Metering: A Proactive Fault-Tolerant Approach [Proactive Fault-Tolerant Aggregation Protocol for Privacy-Assured Smart Metering]

Smart meters are integral to demand response in emerging smart grids, by reporting the electricity consumption of users to serve application needs. But reporting real-time usage information for individual households raises privacy concerns. Existing techniques to guarantee differential privacy (DP) of smart meter users either are not fault tolerant or achieve (possibly partial) fault tolerance at high communication overheads. In this paper, we propose a fault-tolerant protocol for smart metering that can handle general communication failures while ensuring DP with significantly improved efficiency and lower errors compared with the state of the art. Our protocol handles fail-stop faults proactively by using a novel design of future ciphertexts, and distributes trust among the smart meters by sharing secret keys among them. We prove the DP properties of our protocol and analyze its advantages in fault tolerance, accuracy, and communication efficiency relative to competing techniques. We illustrate our analysis by simulations driven by real-world traces of electricity consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Advanced Sensor Deployment for Distribution System State Estimation and Fault Identification

Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs) at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring, Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases, distribution system state estimation (DSSE) and fault identification are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1 feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.

distribution system↗
DOE OSTI2023

A labeled dataset for building HVAC systems operating in faulted and fault-free states

Abstract Open data is fueling innovation across many fields. In the domain of building science, datasets that can be used to inform the development of operational applications - for example new control algorithms and performance analysis methods - are extremely difficult to come by. This article summarizes the development and content of the largest known public dataset of building system operations in faulted and fault free states. It covers the most common HVAC systems and configurations in commercial buildings, across a range of climates, fault types, and fault severities. The time series points that are contained in the dataset include measurements that are commonly encountered in existing buildings as well as some that are less typical. Simulation tools, experimental test facilities, and in-situ field operation were used to generate the data. To inform more data-hungry algorithms, most of the simulated data cover a year of operation for each fault-severity combination. The data set is a significant expansion of that first published by the lead authors in 2020.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗