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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Field and Laboratory Study to Characterize Fault Prevalence in Residential Comfort Systems

This report describes a project whose goal was to determine the prevalence of residential comfort system faults. The focus is on air conditioners and heat pumps, and on key faults that can occur during installation and have significant impacts on performance: (1) incorrect refrigerant charge level (undercharge or overcharge); (2) indoor coil airflow rate; (3) liquid line restrictions; (4) non-condensable gas in the refrigerant; and (5) duct leakage. In addition to quantifying fault prevalence, we collected metadata that could be studied to determine whether there are correlations that suggest drivers of fault prevalence, such as regional variations in practice, economic factors, climatic impacts, and others.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings

Data-driven building fault detection and diagnostics (FDD) is heavily dependent on sensors. However, common sensors from Building Automation Systems are not optimized to maximize accuracy in FDD. Installing additional sensors that provide more detailed building system information is key to maximizing the performance of FDD solutions. Here in this paper, we present a sensor cost analysis workflow to quantify the economic implications of installing new sensors for FDD using the concept of sensor threshold marginal cost (STMC). STMC does not represent actual sensor cost. Rather, it represents a target cost based on the economic benefit that would be realized through improved FDD performance and one or more specified economic criteria. We calculate STMCs for multiple possible fault types and use fault prevalence information to aggregate STMCs into a single dollar value to determine the cost-effectiveness of a potential sensor investment. We conducted a case study using Oak Ridge National Laboratory's Flexible Research Platform (FRP) test facility as a reference. The case study demonstrates the feasibility of the analysis and highlights the key cost considerations in sensor selection for FDD. The results also indicate that identifying and installing the few key sensor(s) is critical to cost-effectively improve FDD performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Development of a Unified Taxonomy for HVAC System Faults

Detecting and diagnosing HVAC faults is critical for maintaining building operation performance, reducing energy waste, and ensuring indoor comfort. An increasing deployment of commercial fault detection and diagnostics (FDD) software tools in commercial buildings in the past decade has significantly increased buildings’ operational reliability and reduced energy consumption. A massive amount of data has been generated by the FDD software tools. However, efficiently utilizing FDD data for ‘big data’ analytics, algorithm improvement, and other data-driven applications is challenging because the format and naming conventions of those data are very customized, unstructured, and hard to interpret. This paper presents the development of a unified taxonomy for HVAC faults. A taxonomy is an orderly classification of HVAC faults according to their characteristics and causal relations. The taxonomy includes fault categorization, physical hierarchy, fault library, relation model, and naming/tagging scheme. The taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model to reveal the root causes of faults in HVAC systems. A structured and standardized vocabulary library is developed to increase data representability and interpretability. The developed fault taxonomy can be used for HVAC system ‘big data’ analytics such as HVAC system fault prevalence analysis or the development of an HVAC FDD software standard. A common type of HVAC equipment-packaged rooftop unit (RTU) is used as an example to demonstrate the application of the developed fault taxonomy. Two RTU FDD software tools are used to show that after mapping FDD data according to the taxonomy, the meta-analysis of the multiple FDD reports is possible and efficient.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements

While automated fault detection and diagnosis (AFDD) in residential heating, ventilation, and air-conditioning (HVAC) using smart thermostat data is gaining increasing attention in recent times, it still requires in-depth investigation for market adoption, especially with real-life data. Furthermore, this paper proposes an Internet of Things (IoT) - based approach that adds a smart sensor to the smart thermostat data to carry out AFDD. The approach uses a model which predicts enthalpy change across the evaporator and compares the prediction to the measured enthalpy change. Deviations which exceed analytically determined thresholds then signal faults in the HVAC system. The faults detected are either installation related or degradation related. Experimental tests were carried out in four homes located in Norman, Oklahoma. From the tests, installation issues like indoor/outdoor mismatch were detected in two homes, while a 30% low charge and low indoor airflow rate were detected in one home. The results show that the proposed AFDD algorithm was able to successfully detect two prevalent faults, namely low indoor airflow and low refrigerant charge. Unlike most of the smart thermostat-based approaches, the proposed IoT-based approach can detect and diagnose both faults but only require one additional sensor which is provided by smart thermostat manufacturers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Atomic-Scale Behavior of Radiation-Resistant ZnO under High-Energy Electron Bombardment

Understanding the atomic structure and defect characteristics of ZnO thin films is crucial for optimizing their electronic properties and performance in advanced applications. Here, we investigate the atomic structure and defect characteristics of atomic layer deposition (ALD)-grown ZnO thin films by using aberration-corrected scanning transmission electron microscopy (STEM). Atomic-resolution imaging identifies prevalent stacking faults, dipole disorder, and various grain boundary types, which are believed to influence the electronic properties of ZnO. Additionally, real-time electron beam exposure experiments demonstrate structural transformations, including crystal growth and surface rearrangements. These findings provide insights into the growth mechanisms of ALD ZnO under high-energy electron irradiation conditions, an important finding for the use of polycrystalline ZnO wide bandgap semiconductors in space-like conditions. In conclusion, our results underscore the capability of STEM in directly visualizing and quantifying atomic-scale defects and beam-induced transformations in radiation-resistant ZnO.

Defects↗
DOE OSTI2024

Development and Validation of Home Comfort System for Total Performance Deficiency/Fault Detection and Optimal Comfort Control

In this project, we developed and tested a learning-based home thermal model that facilitates the operation of a model predictive control (MPC)-based optimization agent and an automated fault detection and diagnosis (AFDD) agent. The home thermal model was constructed using a two-node resistor-capacitor model. Moreover, two accompanying parameter identification methods were introduced, least-squares and optimization. Based on the home thermal model, the MPC-based optimization agent was developed to optimize residential HVAC operation. Using two FDD methods, the AFDD agent was constructed to detect and diagnose two prevalent residential AC faults, airflow reduction and refrigerant undercharge. The home thermal model, along with the MPC-based optimization agent and AFDD agent, were tested at the Norman Test House, Miami Test House, Pacific Northwest National Laboratory (PNNL) Test House A, and PNNL Test House B. Finally, they were also field tested in nine demonstration homes with real occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale (V.2.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires coordination between various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in future HPC systems, they are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. Therefore, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. Additionally, few implementations of current resilience solutions are portable to newer architectures and software environments that will be deployed on future systems. We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers' understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types. Version 2.0 expands the resilience design pattern classification and catalog to include self-stabilization patterns and reliability, availability and performance models for each structural pattern.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Barriers to Broader Utilization of Fault Detection Technologies for Improving Residential HVAC Equipment Efficiency

Faults in residential heating, ventilating, and air conditioning (HVAC) equipment may occur due to poor installation practices or develop over time, and these faults can negatively impact system efficiency, thermal comfort, and equipment lifespan. Automated fault detection and diagnostic (AFDD) technologies identify energy wasting HVAC faults, such as low indoor airflow and improper refrigerant charge, and guide technicians in improving system efficiency. For residential HVAC, AFDD consists of a range of fault detecting and diagnostic capabilities, sensor configurations, and target applications. AFDD technology can either be permanently installed by the original equipment manufacturer (OEM) using embedded sensors or as an add-on product either during or after installation. Additionally, several advanced installation tools and refrigerant gauge sets include AFDD features for temporary use during equipment installation and tune-ups. Some technologies can detect a fault but have limited diagnostic capabilities. For example, a single-point measurement from the home's thermostat or energy monitor can provide certain fault detection capability by analyzing the equipment runtime or energy consumption. These technologies, though limited at determining the cause of a given fault, may have significant energy savings potential due to their low cost and prevalence in the residential HVAC market. Despite the potential benefits, fault detection technologies face many technical and market barriers preventing broad adoption. Beyond the cost barriers due to the added sensor requirements and technology development, fault detection technologies face many implementation and adoption barriers such as installer training, customer awareness, standardized communication protocols, and methods of test for evaluating accuracy. The purpose of this whitepaper is to characterize market and technical barriers impeding broader utilization of fault detection technology for residential HVAC energy efficiency applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Modeling Air Handling Units to Create a Diverse Fault Dataset for FDD Innovation: Lessons Learned and Recommendations

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault datasets for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling unit and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for the air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, two detailed AHU models, which included the single duct AHU and dual duct AHU developed in the Modelica language and HVACSIM+ were employed to carry out annual simulations of numerous common sensor faults, mechanical faults, and control sequence faults. The fault inclusive data were then validated by comparing fault effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. We report some lessons learnt during the efforts of validating the high volumes of the FDD data sets. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗
DOE OSTI2022

Development of a Annual Air Handling Unit Fault Dataset for FDD Tools: Lessons Learned and Considerations for FDD Developers

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault data for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling units and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for a single duct air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, a detailed AHU model was employed to carry out annual simulations of numerous common sensor and mechanical faults, which were then validated by comparing their effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗
DOE OSTI2025

DER Inverter Control Fault Ride Through Model in Accordance with IEEE 1547-2018 Std

Distributed Energy Resources (DER) with smart inverters are becoming more prevalent as the need for renewable energy and grid stability increases. An important challenge arises when considering that inverterbased generation methods contribute less current during faults, rendering traditional overcurrent protection unsatisfactory. DERs have fault ride-through requirements when operating in high or low voltage, outlined by IEEE Std. 1547-2018. Faults cause the voltage to reach abnormal steady state magnitudes, depending on the fault resistance and fault type. There are several high voltage and low voltage ride-through zones defined by IEEE Std. 1547-2018. Each zone’s ride through duration decreases as the applicable voltage measurement, i.e., the phase RMS voltage, deviates from its nominal value. This presentation demonstrates the implementation of IEEE Std. 1547-2018 high and low voltage ridethrough grid support functions using a preexisting RSCAD model, discussing the challenges presented during this process. The implemented controls monitor the filtered phase voltages to have a more accurate reading of the applicable voltages. The controls sense the duration that the applicable voltage remains in a specific zone. The breaker trips and ceases energization to the grid when the duration is exceeded. The standard allows the operator to adjust the ride-through times and voltage zones from the default settings. These ranges are implemented into the runtime, which acts as the operator’s SCADA. The results show the accuracy of the voltage measurements, which remain within the IEEE Std. 1547-2018 for all cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2022

Evaluating Methods for Measuring Grid Frequency in Low-Inertia Power Systems: Preprint

Accurate measurement of grid frequency is a critical component of reliable grid control. Traditionally, methods such as phase locked loops (PLLs) and discrete Fourier transforms (DFTs) have been used in inverters and phasor measurement units (PMUs) to measure frequency. However, as the percentage of inverter-based resources (IBRs) such as solar and wind has increased, these conventional frequency measurement methods are proving unable to guarantee reliable control in some cases. One challenge is measuring frequency during transient events, where there is a disruption in the steady state sinusoidal voltage. During these events, the underlying frequency of the grid may barely change, but measurement methods report a large spike in frequency due to the disrupted waveform. New methods must balance between suppressing spikes in frequency during faults, and providing fast, accurate, measurements in all other grid operation conditions, especially during events with high rate-of-change-of frequency (ROCOF), which are more prevalent in high-IBR power systems. This paper first surveys frequency measurement methods that have been proposed to reduce measurement errors during transient events. Then, both conventional and more novel frequency measurement methods are tested against an IEEE standard and industry recommendations, and their performance is evaluated for events simulated in PSCAD. Results quantify the trade-offs in performance during different grid conditions and lead to suggestions for the most appropriate frequency and ROCOF measurement methods for low inertia grids.

frequency↗
DOE OSTI2025

Facet-Dependent Cold Welding of Au Nanorods Revealed by Liquid Cell Transmission Electron Microscopy

Cold welding of metals at the nanoscale has been demonstrated to play a significant role in bottom-up manufacturing and self-healing processes of nanostructures and nanodevices. However, the welding mechanism at the nanoscale is not well understood. In this study, a comprehensive demonstration of the cold welding process of gold nanorods with different modes is presented through in situ liquid cell transmission electron microscopy. The experimental results and molecular dynamics simulations reveal that the nanorods are welded through the facet-dependent atomic surface diffusion and rearrangement along {100} facets. The density functional theory calculations indicate that the preferred coalescence of two {100} surfaces is thermodynamically favorable. Unlike the prevalent “oriented attachment” in the nanoparticle coalescence, the misalignment of nanorod orientations and local stresses can induce grain boundaries and stacking faults in the welded interface.

42 ENGINEERING↗
DOE OSTI2022

Air Handling Unit Shutdowns During Scheduled Unoccupied Hours: US Commercial Building Stock Prevalence and Energy Impact

Commercial buildings account for 18% of U.S. energy consumption, with 44% used for heating, ventilation, and air conditioning (HVAC). American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE) 90.1 requires HVAC systems to shutdown fans and outdoor air ventilation during unoccupied times, only allowing fans to cycle on, without outdoor air, to maintain thermostat setpoints. However, it is minimally understood how often existing building operations align with energy code requirements and the energy implications of not doing so. This study used building automation system data from 843 buildings containing 5706 air handling units (AHUs) to determine three unoccupied AHU shutdown control schemes ranging in efficiency and then estimated their prevalence in the U.S. commercial building stock, segmented by building type. ComStock was then used to analyze the energy savings potential of implementing the most energy efficient unoccupied shutdown control scheme in non-participating buildings across the U.S commercial building stock. Results show that only 23% of AHUs align completely with the ASHRAE 90.1 requirement. ComStock modeling results show 4% annual stock energy savings by switching all non-participating buildings to the most efficient scheme, with 19% annual energy savings demonstrated for the median building switching from the least efficient scheme to the most efficient. Findings also show 114.5 TBtu electricity and 75.8 TBtu natural gas fuel savings when converting to the most efficient scheme. Furthermore, these findings help stakeholders understand the high prevalence of buildings not aligning with the ASHRAE-90.1 requirements for unoccupied AHU shutdowns and the energy savings potential of utilizing the most efficient unoccupied AHU shutdown scheme.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Mitigating commutation failures in HVDC systems with SFCL deployment

Commutation failures represent a prevalent issue encountered in line-commutated-converter high voltage direct current (LCC-HVDC) systems. As the widespread deployment of HVDC systems continues, the risk associated with commutation failures increases, posing a growing threat to power grids due to their potential to trigger severe consequences, including cascading failures and widespread blackouts. This research paper aims to address the significant issue of commutation failure within direct current (DC) systems through advocating for the use of resistive-type Superconducting Fault Current Limiters (R-SFCLs). To substantiate the efficacy of this proposed strategy, an array of simulations are executed using the PSCAD/EMTDC software. This comprehensive study investigates the performance characteristics of R-SFCLs configured with varying resistance values, scrutinizing their response under diverse fault resistance scenarios and distinct fault initiation times within the LCC-HVDC system. The outcomes of these simulations are that SFCLs confer significant advantages for mitigating commutation failures, surpassing traditional mitigation methods in terms of effectiveness. Consequently, SFCLs emerge as an optimal solution to prevent commutation failures in the HVDC systems.

Commutation failure↗
DOE OSTI2026

Limited Dynamic Earthquake Triggering in Nevada

Dynamic triggering occurs when seismic waves from distant large earthquakes temporarily alter stress conditions along faults, potentially triggering new earthquakes hundreds to thousands of kilometers away from the source. Previous studies have linked triggered seismicity to anthropogenic activities such as geothermal, oil, and gas production. Although these activities are present in Nevada, little work has been conducted to explore dynamically triggered seismicity in Nevada. Here, we analyze a newly published, high-resolution earthquake catalog for Nevada to identify local seismicity dynamically triggered by teleseismic events (Mw≥7) from 2008 to 2023. We identify 94 dynamically triggered earthquakes concentrated in four distinct regions, which qualitatively show a modest positive correlation with geothermal well locations. Triggered seismicity in Nevada is predominantly delayed, with some instantaneously triggered by Rayleigh waves. The prevalence of delayed triggering indicates that pore fluid interactions may play a critical role in controlling dynamic triggering susceptibility in Nevada. Our results demonstrate that dynamic triggering can provide valuable insight to help identify critically stressed regions.

58 GEOSCIENCES↗