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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
NASA NTRS2019

Measuring Metapopulation Characteristics of the Southeastern Beach Mouse on Kennedy Space Center using a Dynamic Habitat Occupancy Approach

The Cape Canaveral Barrier Island Complex (CCBIC) represents the largest (72 km) continuous coastal habitat supporting the core population of the threatened southeastern beach mouse (Peromyscus polionotus niveiventris) in Florida. Despite protection throughout the subspecies’ remaining range, storm activity during the past two decades has de-stabilized coastal dune and coastal strand habitat required for beach mouse survival. Our long-term goal is to provide information to the management process to ensure a sustainable population on the CCBIC in light of sea-level rise, extreme storm events, and continued land development supporting the multi-user spaceport.Because beach mice populations are affected by storms (hurricanes, tropical storms, nor’easters), it is important to understand the species-habitat relations during periods with and without storm impacts. Conventional wisdom is that coastal dune and coastal strand are the preferred habitats, but coastal scrub habitat may be important because it is less affected by storm impacts (e.g., overwash, escarpment, sand deposition, etc.) and thus may serve as a refugia. In addition, genetic studies have shown some evidence for reduced dispersal between regions on CCBIC (Kalkvik et al., 2012; Zimmerman et al., 2015). We compared site occupancy dynamics for coastal dune, coastal strand, and coastal scrub habitat over three quarters (seasons) and determined the pattern of spatial correlation in habitat occupancy dynamics for locations at a range of distances in coastal dune, coastal strand, and coastal scrub habitats along the Kennedy Space Center (KSC) coastline (Figures1 and 2). Although habitat occupancy is very useful for understanding species-habitat relationships,in its simplest form it ignores differences in abundance between sites. Because information on abundance may inform management efforts and provide knowledge regarding status of the population, we tested measuring abundance state based on the number of detections in tracking tubes. Small mammal populations often fluctuate in a cyclic manner over time. These fluctuations make populations more susceptible to extinction and make trend detection more difficult. Efficient methods of measuring abundance across time and space are needed for monitoring trends.The purpose of this project was to:1.Estimate southeastern beach mouse habitat occupancy parameters in coastal dune, coastal strand, and coastal scrub habitats using a combination of tracking tubes and traps; 2.Model the spatial relationships of the effect of habitat (coastal dune, coastal strand, and coastal scrub) on parameters as a function of distance; and 3.Compare the reliability and efficiency of our survey methods and evaluate the potential to develop an abundance index.

Eric Douglas Stolen↗
DOE OSTI2020

What's in a Name? Developing a Standardized Taxonomy for HVAC System Faults

Faults occurring in heating, ventilation and air-conditioning (HVAC) systems have significantly negative impacts on building energy consumption, occupant comfort, and indoor air quality. In the past thirty years, extensive research has been conducted on fault detection and diagnostics (FDD) methods, and there are now dozens of commercially available FDD software tools. Growing adoption of FDD tools has the potential to generate a massive and useful data set on fault characteristics. However, the lack of a unifying taxonomy is a significant barrier to efficient analysis and evaluation of FDD outputs. Therefore, there is a strong need to develop a robust taxonomy which can better represent and interpret FDD output data. This paper documents the development of a unifying taxonomy for HVAC system faults in commercial buildings, with initial focus on air handling units, variable air volume terminal units, and roof top unit systems. The developed fault taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model as tools to better understand and support root cause analysis for HVAC faults. A variable air volume terminal unit is used as an example to demonstrate the application of the developed fault taxonomy. The taxonomy has short-term application in a major U.S. study on fault prevalence, and promises longer term benefits to FDD software developers and building operators by creating a foundation for improved approaches to identifying and resolving HVAC faults.

Chen, Yimin↗
DOE OSTI2020

Effects of trapped charge on the signal formation and detection efficiency for subsequent pulses in irradiated silicon sensors

Envisaged high energy physics experiments like the Future Circular Collider require unprecedented radiation hardness of the detectors, as well as short readout time due to high luminosity and occupancy. Silicon has proven to be extremely radiation hard. Sufficient signals can be recorded even at fluences close to 1 ∙ 1 0 16 n e q / cm 2 . Here, the signal formation was studied in silicon strip sensors, irradiated and annealed until the phenomenon of charge multiplication occurred. ATLAS12EC R0 mini sensors were tested by means of edge TCT measurements at temperatures around –20 °C. It was observed that the detection of subsequent signals separated up to several microseconds is altered by the charge trapped during the preceding pulses. The effects of trapped charge on the electrical configuration of a sensor are well known as a polarization or pumping effect in larger band-gap materials like diamond, but is often neglected for silicon at this relatively high temperature. The investigation of the effect created by trapped charges in silicon sensors using subsequent pulses allows to gain information on important parameters such as de-trapping times. Furthermore, it shows a severe impact on the sensor performance in a pile-up scenario. The irradiation fluence and hence the effective doping concentration, the temperature and the amount of initially created charge have a large impact on this phenomenon. The measurements presented help to characterize this phenomenon; particular attention was paid to the application point of view.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

The CMOS Pseudo-Thyristor: a zero-static current discriminator circuit

We report a very low power discriminator circuit for pixelized detectors, called the Pseudo-Thyristor is described in this document. It is a positive feedback topology using regular PMOS and NMOS field-effect transistors (FET's) with zero static current. When a small charge is injected into the circuit, it flips rapidly due to the positive feedback and outputs a logic transition for further digitization. Simulation shows that in a 65 nm process, it is possible to achieve a detecting threshold below 5 fC while maintain the average power consumption below 10 micro-Watts when the hit occupancy is <10% for 40 MHz operation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems

Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. Here, this paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

East Tennessee Technology Park 2006 Illness and Injury Surveillance Report

The U.S. Department of Energy’s (DOE) commitment to assuring the health and safety of its workers includes the conduct of illness and injury surveillance activities that provide an early warning system to detect health problems among workers. The Illness and Injury Surveillance Program monitors illnesses and health conditions that result in an absence, occupational injuries and illnesses, and disabilities and deaths among current workers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Gaining Real-Time Water Leak Detection

Devens Reserve Forces Training Area is a United States Army Reserve (USAR) Installation that struggles with severe water leaks, often causing significant damage to the facility and requiring major renovation. Traditional water use is highly dependent on occupancy, so it can be difficult to benchmark a facility’s water use. It can be exceptionally difficult when occupancy is transient and/or varies. Pacific Northwest National Laboratory (PNNL) collaborated with Devens to implement real-time monitoring of their water consumption by utilizing the smart meter data from their existing 23 water meters. PNNL created a simple algorithm to calculate hourly water consumption and trigger an alert to be instantly emailed to Devens’ personnel when there appears to be a water leak in any building with a smart water meter. Here, this approach is expected to save hundreds of thousands of dollars in unnecessary water consumption costs and damages from leaks and was implemented with little-to-no costs or service disruptions. Next steps for this project include slow leak detection through nighttime monitoring and to extrapolate this water leak approach to the remainder 360 water meters on USAR’s Enterprise Building Control System so USAR sites across the country can be instantly notified of potential water leaks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Tuning of charge density wave transitions in LaAu x Sb 2 by pressure and Au stoichiometry

Two charge density wave transition can be detected in La Au Sb 2 at ~ 110 and ~ 90 K by careful electrical transport measurements. Whereas control of the Au site occupancy in La Au x Sb 2 (for 0.9 ≲ x ≲ 1.0 ) can suppress each of these transitions by ~ 80 K , the application of hydrostatic pressure can completely suppress the lower transition by ~ 7.5 kbar and the upper transition by ~ 17 kbar . Here, clear anomalies in the resistance as well as the magnetoresistance are observed to coincide with the pressures at which the charge density wave transitions are driven to zero.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Occupational and dietary differences in hydroxylated and methoxylated PBDEs and metals in plasma from Puget Sound, Washington, USA region volunteers

BACKGROUND: Electronic waste (E-waste) recycling is a rapidly growing occupation in the USA with the potential for elevated exposure to flame retardants and metals associated with electronic devices. We previously measured polybrominated diphenyl ethers (PBDEs) in plasma from E-waste workers and found them similar to non-E-waste workers. OBJECTIVES: This study focused on structurally related PBDE derivatives, the hydroxylated (OH-PBDEs) and methoxylated (MeO-PBDEs) forms along with metals known to occur in E-waste. Humans can metabolize PBDEs and some MeO-PBDEs into OH-PBDEs, which is a concern due to greater health risks associated with OH-PBDEs METHODS: We measured 32 different OH-PBDEs and MeO-PBDEs in plasma samples provided by 113 volunteers living in the greater Puget Sound region of Washington State, USA. We measured 14 metals in a subset of 10 E-waste and 10 non-E-waste volunteers. Volunteers were classified based on occupational and dietary habits: work outdoors and weekly consume = 5 seafood servings (outdoor), electronic waste recycling (E-waste) or non-specific indoor occupations (indoor). A two-week food consumption diary was obtained from each volunteer prior to blood sampling. RESULTS: OH-PBDEs were detected in all volunteers varying between 0.27 – 102 ng/g/g-lipid. The MeO-PBDEs were detected in most, but not all volunteers varying between 0 – 60.4 ng/g/g-lipid. E-waste recyclers had OH-PBDE and MeO-PBDE plasma levels that were similar to the indoor group. The outdoor group had significantly higher levels of MeO-PBDEs, but not OH-PBDEs. Comparison of plasma concentrations of BDE-47 with its known hydroxylated metabolites suggested OH-PBDE levels were likely determined by biotransformation and at least two subpopulations identified differing in their apparent rates of OH-PBDE formation. The metals analysis indicated no significant differences between E-waste workers and non-E-waste workers. CONCLUSIONS: Our results indicate E-waste workers do not have elevated plasma levels of these contaminants relative to non-E-waste workers with similar dietary habits.

Schultz, Irvin↗
NASA NTRS2011

High-Temperature Thermometer Using Cr-Doped GdAlO3 Broadband Luminescence

A new concept has been developed for a high-temperature luminescence-based optical thermometer that both shows the desired temperature sensitivity in the upper temperature range of present state-of-the-art luminescence thermometers (above 1,300 C), while maintaining substantial stronger luminescence signal intensity that will allow these optical thermometers to operate in the presence of the high thermal background radiation typical of industrial applications. This objective is attained by using a Cr-doped GdAlO3 (Cr:GdAlO3) sensor with an orthorhombic perovskite structure, resulting in broadband luminescence that remains strong at high temperature due to the favorable electron energy level spacing of Cr:GdAlO3. The Cr:GdAlO3 temperature (and pressure) sensor can be incorporated into, or applied onto, a component s surface when a non-contact surface temperature measurement is desired, or alternatively, the temperature sensor can be attached to the end of a fiber-optic probe that can then be positioned at the location where the temperature measurement is desired. In the case of the fiber-optic probe, both the pulsed excitation and the luminescence emission travel through the fiber-optic light guide. In either case, a pulsed light source provides excitation of the luminescence, and the broadband luminescence emission is collected. Real-time temperature measurements are obtain ed using a least-squares fitting algorithm that determines the luminescence decay time, which has a known temperature dependence established by calibration. Due to the broad absorption and emission bands for Cr:GdAlO3, there is considerable flexibility in the choice of excitation wavelength and emission wavelength detection bands. The strategic choice of the GdAlO3 host is based on its high crystal field, phase stability, and distorted symmetry at the Cr3+ occupation sites. The use of the broadband emission for temperature sensing at high temperatures is a key feature of the invention and is novel since broadband luminescence emission normally shows severe thermal quenching. The tightly bound AlO6 octahedra in GdAlO3 results in a larger energy barrier to nonradiative decays than in other materials and therefore makes using broadband emission for temperature sensing possible at high temperatures. This approach results in a substantial increase in temperature capability. For example, the most commonly used Cr-doped crystal used for luminescence-based temperature measurements, ruby, has only been demonstrated up to 600 C, whereas the Cr:GdAlO3 optical thermometer under development has already been shown to exhibit useful luminescence up to 1,300 C. Because GdAlO3 is non-reactive and is stable in harsh, high-temperature environments, sensors composed of Cr:GdAlO3 will be very well suited for remote high-temperature measurements in engine or industrial environments where its intense high-temperature luminescence will stand out above significant thermal radiation background levels.

Eldridge, Jeffrey↗
NASA NTRS2009

Initial Concept for Terminal Area Conflict Detection, Alerting, and Resolution Capability on or Near the Airport Surface

The Next Generation Air Transportation System (NextGen) concept for 2025 envisions the movement of large numbers of people and goods in a safe, efficient, and reliable manner. The NextGen will remove many of the constraints in the current air transportation system, support a wider range of operations, and deliver an overall system capacity up to 3 times that of current operating levels. In order to achieve the NextGen vision, research is necessary in the areas of surface traffic optimization, maximum runway capacity, reduced runway occupancy time, simultaneous single runway operations, and terminal area conflict prevention, among others. The National Aeronautics and Space Administration (NASA) is conducting Collision Avoidance for Airport Traffic (CAAT) research to develop technologies, data, and guidelines to enable Conflict Detection and Resolution (CD&R) in the Airport Terminal Maneuvering Area (ATMA) under current and emerging NextGen operating concepts. In this report, an initial concept for an aircraft-based method for CD&R in the ATMA is presented. This method is based upon previous NASA work in CD&R for runway incursion prevention, the Runway Incursion Prevention System (RIPS). CAAT research is conducted jointly under NASA's Airspace Systems Program, Airportal Project and the Aviation Safety Program, Integrated Intelligent Flight Deck Project.

Green, David F.↗
NASA NTRS2013

Initial Concept for Terminal Area Conflict Detection, Alerting, and Resolution Capability On or Near the Airport Surface, Version 2.0

The Next Generation Air Transportation System (NextGen) concept for 2025 envisions the movement of large numbers of people and goods in a safe, efficient, and reliable manner. The NextGen will remove many of the constraints in the current air transportation system, support a wider range of operations, and deliver an overall system capacity up to 3 times that of current operating levels. In order to achieve the NextGen vision, research is necessary in the areas of surface traffic optimization, maximum runway capacity, reduced runway occupancy time, simultaneous single runway operations, and terminal area conflict prevention, among others. The National Aeronautics and Space Administration (NASA) is conducting Collision Avoidance for Airport Traffic (CAAT) research to develop technologies, data, and guidelines to enable Conflict Detection and Resolution (CD&R) in the Airport Terminal Maneuvering Area (ATMA) under current and emerging NextGen operating concepts. The term ATMA was created to reflect the fact that the CD&R concept area of operation is focused near the airport within the terminal maneuvering area. In the following, an initial concept for an aircraft-based method for CD&R in the ATMA is presented. This method is based upon previous NASA work in CD&R for runway incursion prevention, the Runway Incursion Prevention System (RIPS).

Otero, Sharon D.↗
DOE OSTI2024

IoT-Based Comfort Control and Fault Diagnostics System for Energy-Efficient Homes

This project studies an Internet of Things (IoT)-based comfort control and fault diagnostics system (referred as iComfort in this report) for energy-efficient homes. The system delivers an occupant-comfort-oriented thermal environment adaptive to fault scenarios and achieves HVAC energy savings in a cost-effective and straightforward way. This smart iComfort home system consists of the following key features. 1) Cost-effectiveness and scalability of the entire hardware and software system: The system includes low-cost temperature, humidity, and airflow sensors, and a Raspberry Pi-based local hub that interfaces with the cloud and IoT-enabled devices. The cost is low, not only for sensors, but also the costs associated with sensor installation, system setup and commissioning, data communication and storage, and data analytics (e.g., the development of automated fault detection and diagnosis (AFDD), as well as adaptive control strategies that are both computationally efficient and practical to implement). 2) Energy performance and user satisfaction: The system delivers user satisfaction and energy savings. This includes a) ease of use, b) optimal occupant thermal comfort, and c) accurate system feedback (e.g., low false alarm of AFDD strategies). 3) Favorable demonstrated prototype performance: The prototype tested at the Pacific Northwest National Laboratory (PNNL) Lab Homes demonstrates the accuracy of fault detections and diagnoses and shows thermal comfort improvement and energy savings through adaptive and optimal HVAC operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Learning-based CO 2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation

There have been increasing concerns over the air quality inside buildings as high levels of bio-effluents can cause nausea, dizziness, headaches, and fatigue to the people working in those spaces. First published in 2004 as Standard 62.1, ASHRAE Standard 62.2-2019 requires highly occupied spaces to implement heating, ventilation, and air conditioning (HVAC) that can dilute contaminants produced by occupants. In this regard, occupant-centric ventilation control has been regarded as an effective practice to maintain a satisfactory indoor air quality (IAQ) when dealing with highly variable occupancy environments. However, few established models in current literature and practice consider dynamic occupancy behavior and adaptive IAQ control. To address this gap, a dynamic indoor CO2 model is constructed using machine learning algorithms to forecast CO2concentrations across a range of forecasting horizons. Herein, we tuned and compared six state-of-the-algorithms—including Support Vector Machine, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. The algorithms’ performances are validated using CO 2 and historical meteorological data collected from a campus classroom with a variable occupancy rate. Simulation results showed that Multilayer Perceptron can strongly predict the volatile CO 2 behavior and also outperforms other algorithms in terms of accuracy. Furthermore, a control strategy capable of modeling and detecting dynamic patterns of CO 2 level is utilized to modulate the ventilation rate in real-time and also reduce the energy consumption. The proposed controller reduced the HVAC fan’s energy consumption by 51.4% and provide ventilation as needed per the ASHRAE standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1994

Size estimation of complexes consisting of hairpin DNAs bound to an assembler-strand

In connection with a model of a primitive translation based entirely on nucleic acids the structural requirements and interactions of small nucleic acids were studied. As shown previously hairpins bind to a complementary single-strand by base pairs of their loop nucleotides if the loop contains at least five nucleotides (Baumann et al 1987). Here, the question is approached to which degree a single-strand, the assembler-strand, is occupied with hairpin molecules. A gapless occupation would allow the close proximity necessary for the assumed peptide bond formation by hairpins bearing an amino acid at their 3'-end. Oligo(dC)(sub n), n = 12, 15, 18, and the investigated hairpins form complexes which are detectable under nondenaturing electrophoretic conditions. The size range is estimated to be 3-4 hairpin molecules to one assembler molecule when oligo(dC)(sub n), n = 15, 18, is used. Due to the limitation of the method, dependence of the migration velocity on the shape which is unique and thus limits the use of marker nucleic acids, and due to the uncertainty if the ends of the assembler form base pairs, a gapless occupation of the assembler is conceivable but not proven.

Baumann, Ulrich↗
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↗