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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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NASA NTRS2006

Sensor Data Qualification for Autonomous Operation of Space Systems

NASA's new Exploration initiative for both robotic and manned missions will require higher levels of reliability, autonomy and reconfiguration capability to make the missions safe, successful and affordable. Future systems will require diagnostic reasoning to assess the health of the system in order to maintain the system s functionality. The diagnostic reasoning and assessment will involve data qualification, fault detection, fault isolation and remediation control. A team of researchers at the NASA Glenn Research Center is currently working on a Sensor Data Qualification (SDQ) system that will support these critical evaluation processes, for both automated and human-in-the-loop applications. Data qualification is required as a first step so that critical safety and operational decisions are based on good data. The SDQ system would monitor a network of related sensors to determine the health of individual sensors within that network. Various diagnostic systems such as the Caution and Warning System would then use the sensor health information with confidence. The proposed SDQ technology will be demonstrated on a variety of subsystems that are relevant to NASA s Exploration systems, which currently include an electrical power system and a cryogenic fluid management system. The focus of this paper is the development and demonstration of a SDQ application for a prototype power distribution unit that is representative of a Crew Exploration Vehicle electrical power system; this provides a unique and relevant environment in which to demonstrate the feasibility of the SDQ technology.

Maul, William A.↗

NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

The automation of pre-launch diagnostics for launch vehicles offers three potential benefits: improving safety, reducing cost, and reducing launch delays. The Ares I-X Ground Diagnostic Prototype demonstrated anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage Thrust Vector Control and for the associated ground hydraulics while the vehicle was in the Vehicle Assembly Building at Kennedy Space Center (KSC) and while it was on the launch pad. The prototype combines three existing tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool from Qualtech Systems Inc. for fault isolation and diagnostics. The second tool, SHINE (Spacecraft Health Inference Engine), is a rule-based expert system that was developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification, and used the outputs of SHINE as inputs to TEAMS. The third tool, IMS (Inductive Monitoring System), is an anomaly detection tool that was developed at NASA Ames Research Center. The three tools were integrated and deployed to KSC, where they were interfaced with live data. This paper describes how the prototype performed during the period of time before the launch, including accuracy and computer resource usage. The paper concludes with some of the lessons that we learned from the experience of developing and deploying the prototype.

Schwabacher, Mark A.↗

DOE OSTI2020

Can We Fix It Automatically? Development of Fault Auto-Correction Algorithms for HVAC and Lighting Systems

A fault detection and diagnostics (FDD) tool is a type of energy management and information system designed to continuously identify the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and application of automated analytics. Building owners and operators at the leading edge of technology adoption are using FDD tools to enable average whole-building portfolio savings of 8 percent. Although FDD tools can inform building operators of operational faults, currently a manual action is always required to correct faults and generate the associated energy savings. A subset of faults, however, such as biased sensors and manual override, can be addressed automatically, removing the need for operations and maintenance staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability, as well as the technical and economic performance, of FDD technologies. In this paper, we present the development of 10 innovative fault auto-correction algorithms for HVAC and lighting systems. When the auto-correction routine is triggered, it will overwrite the control setpoints or other variables (via BACnet or other protocol) to implement the intended changes. These algorithms are able to automatically correct the faults or improve the operation associated with an incorrectly programmed schedule, override manual control, sensor bias, control hunting, rogue zone, and less aggressive setpoints/setpoints setback. The paper will also discuss the implementation of the auto-correction algorithms in FDD software products.

Lin, Guanjing↗

DOE OSTI2025

An open control sequence specification to scale building demand flexibility via analytics software

For over two decades, researchers and practitioners have showcased the ability of large commercial buildings to provide grid services by shedding or shifting load. Various utility demand response (DR) and virtual power plant (VPP) programs throughout the United States are presently utilizing these demand-side resources. However, growth of these programs have been limited, in part due to the high cost necessary to integrate the DR control strategies into the building automation system (BAS). Implementing these strategies involves adjusting control sequences, necessitating dozens of hours of customized programming per building, limiting their adoption to large organizations and progressive owners. Recent efforts by researchers and industry have demonstrated the capability of energy management and information systems (EMIS), originally designed for fault detection and diagnostics, to interface with existing BAS and perform supervisory control to optimize building operations. While these approaches are quickly being adopted by industry, demand flexibility (DF) control strategies remain limited in product offerings. One of the challenges is the lack of documented best-practice DF sequences, despite the rich literature on field implementations. This paper develops a new open-specification for a zone-based temperature adjustment shed strategy for commercial building HVAC systems, describing the specification’s implementation in two EMIS tools in both experimental and field settings. Both implementations successfully reduced electric load by at least 40% on average during the called event, while maintaining temperature limits. This study’s detailed process from specification to deployment shows the potential for scalability as well as highlights challenges related to integration with heterogeneous BAS products.

Granderson, Jessica↗

DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗

NASA NTRS2013

Self Diagnostic Accelerometer Ground Testing on a C-17 Aircraft Engine

The self diagnostic accelerometer (SDA) developed by the NASA Glenn Research Center was tested for the first time in an aircraft engine environment as part of the Vehicle Integrated Propulsion Research (VIPR) program. The VIPR program includes testing multiple critical flight sensor technologies. One such sensor, the accelerometer, measures vibrations to detect faults in the engine. In order to rely upon the accelerometer, the health of the accelerometer must be ensured. Sensor system malfunction is a significant contributor to propulsion in flight shutdowns (IFSD) which can lead to aircraft accidents when the issue is compounded with an inappropriate crew response. The development of the SDA is important for both reducing the IFSD rate, and hence reducing the rate at which this component failure type can put an aircraft in jeopardy, and also as a critical enabling technology for future automated malfunction diagnostic systems. The SDA is a sensor system designed to actively determine the accelerometer structural health and attachment condition, in addition to making vibration measurements. The SDA uses a signal conditioning unit that sends an electrical chirp to the accelerometer and recognizes changes in the response due to changes in the accelerometer health and attachment condition. In an effort toward demonstrating the SDAs flight worthiness and robustness, multiple SDAs were mounted and tested on a C-17 aircraft engine. The engine test conditions varied from engine off, to idle, to maximum power. The two SDA attachment conditions used were fully tight and loose. The newly developed SDA health algorithm described herein uses cross correlation pattern recognition to discriminate a healthy from a faulty SDA. The VIPR test results demonstrate for the first time the robustness of the SDA in an engine environment characterized by high vibration levels.

Tokars, Roger P.↗

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↗

NASA NTRS2007

POD Model Reconstruction for Gray-Box Fault Detection

Proper orthogonal decomposition (POD) is the mathematical basis of a method of constructing low-order mathematical models for the "gray-box" fault-detection algorithm that is a component of a diagnostic system known as beacon-based exception analysis for multi-missions (BEAM). POD has been successfully applied in reducing computational complexity by generating simple models that can be used for control and simulation for complex systems such as fluid flows. In the present application to BEAM, POD brings the same benefits to automated diagnosis. BEAM is a method of real-time or offline, automated diagnosis of a complex dynamic system.The gray-box approach makes it possible to utilize incomplete or approximate knowledge of the dynamics of the system that one seeks to diagnose. In the gray-box approach, a deterministic model of the system is used to filter a time series of system sensor data to remove the deterministic components of the time series from further examination. What is left after the filtering operation is a time series of residual quantities that represent the unknown (or at least unmodeled) aspects of the behavior of the system. Stochastic modeling techniques are then applied to the residual time series. The procedure for detecting abnormal behavior of the system then becomes one of looking for statistical differences between the residual time series and the predictions of the stochastic model.

Park, Han↗

DOE OSTI2022

A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring

Automating the task of fault detection and diagnosis is crucial in the effort to reduce the operation and maintenance cost in the nuclear industry. This paper describes a physics-based approach for system-level diagnosis in thermal-hydraulic systems in nuclear power plants. The inclusion of physics information allows for the creation of virtual sensors, which provide improved fault diagnosis capability. The physics information also serves to better constrain diagnostic solutions to the physical domain. As a demonstration, various test cases for fault diagnosis in a high-pressure feedwater system were considered. The use of virtual sensors allows constructing performance models for two first-point feedwater heaters which would not have been possible otherwise due to the limited sensor set. Real-time plant data provided by a utility partner were used to assess the diagnostic approach. The detection of an abnormal event immediate after a plant startup pointed to faulty behaviors in the two first-point feedwater heaters. Further, this double-blind fault diagnosis was subsequently confirmed by the plant operator. In addition, several simulated sensor fault events demonstrated the capability of our algorithms in detecting and discriminating sensor faults.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Testing and validating SMDS algorithms implemented in the cloud

Pacific Northwest National Laboratory (PNNL) provided technical assistance to NorthWrite Inc. under the Small Business Vouchers (SBV) Pilot. NorthWrite delivered services to owners of small commercial buildings, using a cloud-based service to monitor, control, and optimize building operations, saving energy and reducing operating costs for the owners while ensuring that occupant comfort needs were consistently met. NorthWrite had a longstanding desire to add a new suite of diagnostic capabilities to their service offering and had been trying, without success, to incorporate several diagnostic algorithms published by PNNL. These algorithms arise from research previously supported by BTO. Through the SBV awarded to NorthWrite, PNNL made available technical knowledge regarding the derivation and application of the following sets of algorithms for use in the NorthWrite Cloud-based service delivery system: • algorithms for monitoring rooftop packaged air conditioners and heat pumps (often referred to as rooftop units or RTUs) and diagnosing faults in these units, • advanced algorithms for automated fault detection and diagnosis of other equipment found in small buildings, and • algorithms for identifying, prioritizing, and assessing the economic effectiveness of implementing energy saving measures in small commercial buildings based on sensed data. The PNNL researchers involved were the original developers of these algorithms, had a unique understanding of the derivation of the algorithms, and had the source data used for this derivation. Further, the PNNL researchers had extensive experience using these algorithms in the laboratory, unique experience applying these algorithms in real-world small commercial buildings and could solve a number of key problems that NorthWrite and other users faced in using these algorithms at scale. In addition, PNNL had recently installed a pair of RTUs in a laboratory setting that were instrumented and connected with data acquisition systems that provided a unique test rig for validating the algorithms before NorthWrite began to deploy their new services in the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2001

Automating Trend Analysis for Spacecraft Constellations

Spacecraft trend analysis is a vital mission operations function performed by satellite controllers and engineers, who perform detailed analyses of engineering telemetry data to diagnose subsystem faults and to detect trends that may potentially lead to degraded subsystem performance or failure in the future. It is this latter function that is of greatest importance, for careful trending can often predict or detect events that may lead to a spacecraft's entry into safe-hold. Early prediction and detection of such events could result in the avoidance of, or rapid return to service from, spacecraft safing, which not only results in reduced recovery costs but also in a higher overall level of service for the satellite system. Contemporary spacecraft trending activities are manually intensive and are primarily performed diagnostically after a fault occurs, rather than proactively to predict its occurrence. They also tend to rely on information systems and software that are oudated when compared to current technologies. When coupled with the fact that flight operations teams often have limited resources, proactive trending opportunities are limited, and detailed trend analysis is often reserved for critical responses to safe holds or other on-orbit events such as maneuvers. While the contemporary trend analysis approach has sufficed for current single-spacecraft operations, it will be unfeasible for NASA's planned and proposed space science constellations. Missions such as the Dynamics, Reconnection and Configuration Observatory (DRACO), for example, are planning to launch as many as 100 'nanospacecraft' to form a homogenous constellation. A simple extrapolation of resources and manpower based on single-spacecraft operations suggests that trending for such a large spacecraft fleet will be unmanageable, unwieldy, and cost-prohibitive. It is therefore imperative that an approach to automating the spacecraft trend analysis function be studied, developed, and applied to missions such as DRACO with the intent that mission operations costs be significantly reduced. The goal of the Constellation Spacecraft Trend Analysis Toolkit (CSTAT) project is to serve as the pathfinder for a fully automated trending system to support spacecraft constellations. The development approach to be taken is evolutionary. In the first year of the project, the intent is to significantly advance the state of the art in current trending systems through improved functionality and increased automation. In the second year, the intent is to add an expert system shell, likely through the adaptation of an existing commercial-off-the-shelf (COTS) or government-off-the-shelf (GOTS) tool to implement some level of the trending intelligence that humans currently provide in manual operations. In the third year, the intent is to infuse the resulting technology into a near-term constellation or formation-flying mission to test it and gain experience in automated trending. The lessons learned from the real missions operations experience will then be used to improve the system, and to ultimately incorporate it into a fully autonomous, closed-loop mission operations system that is truly capable of supporting large constellations. In this paper, the process of automating trend analysis for spacecraft constellations will be addressed. First, the results of a survey on automation in spacecraft mission operations in general, and in trending systems in particular will be presented to provide an overview of the current state of the art. Next, a rule-based model for implementing intelligent spacecraft subsystem trending will be then presented, followed by a survey of existing COTS/GOTS tools that could be adapted for implementing such a model. The baseline design and architecture of the CSTAT system will be presented. Finally, some results obtained from initial software tests and demonstrations will be presented.

Davis, George↗

NASA NTRS2006

Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.

Kobayashi, Takahisa↗

NASA NTRS1987

An evaluation of a real-time fault diagnosis expert system for aircraft applications

A fault monitoring and diagnosis expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in fault monitoring, fault diagnosis, and recovery planning. The present implementation of this concept performs monitoring and diagnosis for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.

Schutte, Paul C.↗

NASA NTRS1979

Algorithms for the Automated Power Systems Management

The system breadboard for the demonstration of Automated Power Systems Management (APSM) functions has been designed, fabricated, and is in the final stages of verification and testing. APSM functions fall into categories of fault detection and correction, commanding system and subsystem test and diagnoses, relay position monitoring, data acquisition and processing, and power management. All these functions are accomplished through software residing in 8-bit microprocessors dedicated to each group of Viking Orbiter power breadboard subassemblies and a 16-bit microprocessor serving as the central processor for the power system. This paper describes key monitoring, diagnostic, and control algorithms used in the APSM breadboard.

Moser, R. L.↗

DOE OSTI2020

Advanced control sequences and FDD technology. Just shiny objects, or ready for scale?

Innovations in commercial building control sequences (ASHRAE guideline 36) and fault detection and diagnostics (FDD) technology have the potential to transform existing building operational efficiency by realizing whole-building level savings on the order of 15% and higher. However, several technical and market barriers related to up-front estimation of achievable savings, verifying implementation, and ensuring persistence are slowing down their adoption as a core offering through diverse owner-funded initiatives and rate-payer funded programs. This paper describes a suite of solutions to overcome these barriers to program delivery of advanced HVAC controls integrated with FDD energy management and information system (EMIS) technology. First, the Guideline 36 conformance test offers an automated method to confirm correct sequence implementation by the manufacturer, before field deployment. A successful test of a controller was conducted using a new manufacturer-independent hardware-in-the-loop testbed demonstrating the viability and scalability of this approach. Second, the Guideline 36 energy savings calculator uses modeling paired with high-level user inputs to estimate building-specific savings potential, target cost effective implementation and minimize uncertainty. Initial results indicate the baseline control sequences significantly impact energy savings, however, as much as 50% savings may be possible for the worst-case baseline scenario. Third, a functional specification provides minimum recommended EMIS-FDD capabilities, and best practices to integrate the technology into organizational practice and ensure that advanced control sequences provide persistent savings.

Pritoni, Marco↗

DOE OSTI2020

Virtual Metering for Monitoring Building Energy Consumption

The United States Department of Energy (DOE) has standard metering requirements of commercial buildings for optimizing energy performance. The guiding principles are to continuously track and optimize energy performance and install building-level meters for electricity, natural gas, and steam. Some buildings at Los Alamos National Laboratory (LANL) have physical submeters monitoring their energy consumption, but these meters have proven to be unreliable. And, in most cases, replacing them has proven to be a slow process. Installing new submeters also requires a temporary lockout of the circuit on which they are being installed. Many buildings at LANL contain laboratories with ongoing experiments or data centers, which makes an equipment power outage nearly impossible to plan. This inability to plan power outages results in long-term submeter failures. Although most submeters are eventually replaced, failures lead to missing consumption data for some unpredictable, extended time. A building automation system (BAS) is a system that provides control and monitoring on a building to maintain the operational performance of the building and occupancy comfort. Many buildings at LANL currently have a BAS, and all new renovations and installs will include installing a BAS if one does not already exist. The intended purpose for a BAS is primarily to monitor the health and efficiency of a building; however, it is also possible to calculate equipment power and energy consumption using BAS information. This project aims to use virtual meters to monitor building energy consumption as a cost-effective and minimally labor-intensive alternative to installing physical submeters. The fault detection and diagnostics tool, SkySpark, provides a centralized database for all the data from the various BAS that are active at LANL. This data includes the information that is needed to create virtual meters for heating, ventilating, and air conditioning (HVAC) systems in most buildings, including heating and cooling loads. 9 This report begins with a detailed summary of the project, including the reasoning, procedure, and results. The specific processes of creating the various virtual meters are then identified. Then the limitations are discussed. And, lastly, the results and future potential are presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Advanced Monitoring and Control in the ANL METL Facility Using an Engineering Digital Twin

The potential benefits of using an engineering digital twin to achieve greater autonomy for monitoring and control functions in advanced reactors was investigated for the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The METL sodium purification system served as a representative system as it requires significant human in-the-loop interaction to accomplish its design function. The objective was to demonstrate how real-time operation could be automated while preserving oversight of the operator for ensuring that the system design functions are met. A digital twin model of the purification system was developed for both the cold trap purification loop and plugging meter diagnostic loop using information from the METL piping and instrumentation diagram (P&ID). Automated monitoring and diagnosis of component degradation in the METL facility was demonstrated in tests using the PRO-AID health monitoring software with the digital twin model incorporated in the library of components. Component failures were introduced and were successfully diagnosed in real time. These tests serve to demonstrate an advanced monitoring capability able to differentiate sensor degradation from component degradation, to generate a rank ordering of probabilities of different failure mechanisms that serves to circumvent the false alarm problem with current anomaly detection methods, and how facility monitoring can be transformed from anomaly detection to identification of a specific fault. Automated control of the purification system was demonstrated through simulations that exercised a model predictive controller designed using the digital twin model. Results of these simulations compared favorably with experimental data showing very good reference tracking response with negligible overshoot. In conclusion, these pilot tests and simulations successfully demonstrated the use of a digital twin for improved automation of monitoring and control. It was shown how the digital twin enables switching between control modes from cold trap operation where impurities are removed to plugging meter operation where impurity concentrations are measured.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Connected Loads – Grid Connected Appliances: Deployment IoT Solution for Fault Detection and Diagnostics

As one of the most energy-intensive end-uses in the commercial buildings sector, supermarkets consume around 50 kWh/ft 2 ( or 537.6 kWh/m 2 ) of electricity annually, or more than 2 million kWh of electricity per year for a typical store. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40–60% of its total electricity usage and is equivalent to about 2–3% of the total energy consumed by commercial buildings in United States, or around 0.5 quadrillion Btu (or 0.53 quadrillion KJ). Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Current supermarket refrigeration systems rely on high global warming potential hydrofluorocarbon refrigerants. Reducing refrigerant usage or using environment friendly alternatives can result in significant climate benefits. Transcritical CO2 refrigeration systems have attracted more attention in recent years because of their zero-carbon emission advantages compared with traditional refrigerants. These systems are widely used in commercial buildings such as supermarkets. The refrigeration system can also be adapted to handle flexible building loads and be integrated into grid response transactive control to balance the supply and demand of the electric grid. Even minor improvements in the efficiency and operational reliability of supermarket refrigeration systems can create significant value in terms of saving energy, improving food quality, protecting the environment, reducing carbon footprint, and enhancing electric grid resilience. For decarbonization, the new administration has set a target to reduce greenhouse gas emissions by 50– 52% by 2030 and targeting a carbon-neutral economy by 2050. For electrification, the goal is to achieve 100% clean electricity by 2035. Such decarbonization and electrification in the building sector require that energy consumption in buildings be reduced significantly. Therefore, the building sector must continuously adopt new technologies to achieve its energy and carbon emission goals. One of the most fundamental technologies is the Internet of Things (IoT). IoT has proven to be an effective solution for the building domain, including building information/energy modeling, smart buildings, etc. Although much progress has been made in the development of IoT-based building energy systems, there is still a lack of reliable, scalable, and affordable IoT-based automated fault and degradation diagnostics (AFDDs) solutions. Such solutions would enable deployment of advanced algorithms into real systems to archive the projected energy benefits. This study reviews existing IoT solutions developed for building energy– related application and developed a simple but effective AFDD IoT deployment solution, including developing a suitable IoT architecture and conducting easy and scalable deployment by leveraging a common cloud-based IoT service.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗