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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Modeling and Simulation of Air-Source CO2 Heat Pump Water Heater

Carbon dioxide (CO2) has been widely used as working fluid for the vapor-compression refrigeration systems in large marine device. Due to the potential energy efficiency and the favorable environmental properties of CO2 as a working fluid, CO2 heat pump water heater (HPWH) systems are regarded a promising technology for centralized domestic hot water (DHW) heating in residential and commercial buildings. However, there is still at the early stage of appropriately optimizing and improving the energy performance of CO2 HPWH. This requires CO2 HPWH simulation tools capable of capturing the accurate impact of the emerging compressor, throttle device, and heat exchanger technology on CO2 heat transfer and energy efficiency. In this study, high efficiency components (compressors, pumps, fans, heat exchangers) were identified and applied to the state-of-art CO2 HPWH designs and analyzed their performance by using numerical simulation. This was done by simulating the performance of CO2 HPWH using ACMODEL design model combined with the component models developed at Oak Ridge National Laboratory (ORNL) for orifice tube, map-based compressor, and tube-in-tube gas cooler. ACMODEL is an equipment design model for CO2-based air conditioners and heat pumps developed by Purdue University to account for the details of each component. The simulated CO2 HPWH performance was then compared with the heat pump water heater using conventional refrigerants.

Gao, Zhiming↗
DOE OSTI2022

Performance Assessment of High-Efficiency Refrigerated Display Cases with Low-GWP Refrigerants: Preprint

As California policy makers enforce new regulations such as SB 1383 to achieve state greenhouse gas (GHG) reduction goals, it will be critical to understand the energy efficiency implications of low global warming potential (GWP) refrigerants in commercial refrigeration applications. The goal of this research project, funded by Commonwealth Edison was to assess the energy performance of two high efficiency medium-temperature, self-contained, reach-in display cases that utilized natural refrigerant propane (R-290), and hydrofluorocarbon (HFC) drop-in hydrofluoroolefin R-513a, respectively. Performance of these display cases were compared to a baseline fixture using HFC (R-134a) under equal conditions in a controlled environment chamber. These display cases were selected due to their widespread use in convenience stores, and small supermarkets. The high efficiency cases were equipped with energy efficient lighting, improved panel insulation, high efficiency evaporator and condenser fan motors, and high effectiveness heat exchangers. The test method used in this project was foundationally inspired by the ANSI/ASHRAE 72-2018 standard method to evaluate each case under equivalent conditions. However, minor modifications to this methodology were implemented to better represent more realistic operation of the units. While maintaining target product temperature, the energy efficient fixtures containing R-290 and R-513a consumed 61.8% to 32.6% less energy, respectively, than a baseline case using R-134a refrigerant. The daily energy consumption of the R-290, R-513a, and baseline display cases at the upper limit of environmental conditions was 4.30, 7.59, and 11.26 kWh/day, respectively.

alternative refrigerants↗
DOE OSTI2023

Modeling Assessment of Residential Air-to-Water Heat Pumps Coupled with Cooling Thermal Storage

This study explored the performance and operating cost viability of air-to-water heat pumps (AWHPs) coupled with thermal energy storage (TES) in efficient new residential construction. AWHPs are an emerging technology in this country, but offer promise in terms of high efficiency, fully contained and factory charged outdoor refrigeration system, and hydronic delivery capabilities, which facilitates zoning, ducts in conditioned space, and TES integration for summer load-shifting. Although this AWHP+TES strategy is not yet mainstream, the authors feel that in ten years as decarbonization efforts proceed and TOU rates become more common, strategies such as this will be more accessible. Validated EnergyPlus simulation models were developed based on detailed monitoring data collected over several years at Pacific Gas and Electric's CVRH laboratory test homes located in Stockton, California. One of the CVRH test homes (1,962 ft 2 two-story) had been testing various AWHP systems and configurations over the past six years. The validated model was then updated with high efficiency IECC ZERH envelope and component requirements for climate zones 1-5, including ducts in conditioned space thermal distribution. Simulations were completed for the 1,962 ft 2 home in each climate zone for a minimum efficiency ASHP, an AWHP coupled with a fan coil, and an AWHP coupled with TES sized to eliminate summer on-peak compressor operation. To maintain consistency in reporting energy use estimates, all cases were run with a similar indoor thermostat control strategy to pre-cool the house below the nominal 76 degrees Fahrenheit set point prior to the on-peak period and float slightly above the set point during the peak period. The AWHP+TES configuration was controlled to alternately condition the indoor space or to charge the TES tanks prior to the beginning of the on-peak. Three composite TOU rates were developed based on existing TOU rates across the U.S. to provide differing economic scenarios to evaluate customer bill impacts throughout the summer. Two of the TOU rates had short three-hour peak periods, while the third rate had a longer seven-hour duration peak period. AWHP modeling projections were based on the observed field performance of the Chiltrix CX34 variable speed unit. Other products on the market or entering the market in the near term would likely perform differently.

25 ENERGY STORAGE↗
DOE OSTI2023

Optimization of a Residential Air Source Heat Pump using Refrigerants with GWP <150 for Improved Performance and Reduced Emission

Using low-GWP refrigerants can reduce the Green House Gas (GHG) emission of heat pump systems. Heat exchangers and compressors are the key components and have a prominent impact on system performance, significant research is devoted to reducing the cost of the heat exchangers while achieving the same or better system performance with refrigerant charge reduction.To better understand the environmental impacts of optimized systems with low-GWP refrigerants, Life Cycle Climate Performance (LCCP) evaluation method was used to evaluate the direct and indirect emissions of the system over the course of its lifetime from manufacturing to disposal. The DOE/ORNL Heat Pump Design Model (HPDM) is used to evaluate the performance of heat pumps. Multi-objective optimizations using Particle Swarm Optimization (PSO) algorithm are performed on a 3-ton R410A residential air source heat pump on market. Seven R410A alternatives, i.e., R32, R454B, R454C, R455A, R457A, R1234yf and R1234ze(E) are investigated. The last five fluids have GWP lower than 150.As a result, 5.5%-12.8% seasonal energy efficiency ratio 2 (SEER2) improvement is achieved, and the optimized systems reduce life cycle CO2 emission by 8.5%-28.6% with GWP lower than 150 refrigerants. The optimal heat exchangers can fit into the original R410A fan-coil units; therefore, the proposed design method establishes a production and installation path to produce cost-effective low-GWP heat pumps easily accepted by end users.

Li, Zhenning↗
DOE OSTI2023

Air Classification of Forest Residue for Tissue and Ash Separation Efficiency

The goal of this Case Study was to evaluate the performance of air classification of logging residues toward meeting conversion CMAs for carbon and ash contents, as compared to the static status quo Base Case system in which the residues are first dried and then ground in a hammer mill with a 6 mm screen and fines less than 1.18 mm are removed. Also considered were moisture and ash impacts on throughput and Overall Operating Effectiveness (OOE), as well as delivered feedstock cost and minimum fuel selling price (MFSP). Laboratory data on the impacts of fan speed and moisture content on the separation efficiency of soil ash, needles and bark from white wood were received from FCIC Subtask 5.2: Preprocessing, High Temperature Conversion Preprocessing (Jordan Klinger and Tiasha Bhattacharjee, INL). Average throughput and energy consumption data were obtained from the Bioenergy Feedstock National User Facility (BFNUF) (Neal Yancey, INL) for the same air classifier. These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Feedstock-Conversion Interface Consortium. Because the Base Case status quo system utilizes drying prior to grinding, we modeled the Case Study with drying prior to air classification and subsequent grinding of the separated white wood to isolate the individual quality and cost impacts of air classification relative to the Base Case system.

CMA↗
DOE OSTI2023

Robotic inspection device

Robotic devices that can be utilized on pipes of any material and of a variety of pipe diameters are provided. The robotic device utilizes a ducted fan to create the normal forces needed to adhere to any part of a pipe. The chassis of the device can be segmented to allow for application on various diameter pipes.

McDaniel, Dwayne E.↗
DOE OSTI2020

Pyrolysis of Radioactive Spent Resins in the PRIME Installation - 20190

Montair and Belgoprocess developed the pilot PRIME (Pyrolysis Resins In Mobile Electric) Installation for treatment of spent resins in one single reactor without pre-treatment and without intermixing of other waste, allowing for easy traceability and isotopic characterization of the end product. Both low level waste (LLW, < 2 mSv/h) and medium level waste (MLW, > 2 mSv/h) can be treated in the installation. The PRIME pilot installation is designed to treat 2 batches of 100 liter of saturated ion exchange resins over a period of 24 hours. A dosing unit with screw feeder is used for the dosing of the saturated ion exchange resins to the pyrolysis reactor. This completely new developed innovative pyrolysis reactor contains a special design mechanical stirrer and can operate at a temperature of up to 600 deg. C under inert conditions. As a result, the organic molecules will decompose at an accelerated rate until only the mineralized material remains. The lower temperature and the absence of oxygen also prevent the breakdown of ion exchange resins into (powdery) ash, which is a considerable advantage during further handling. Gases that are released during drying and pyrolysis are treated in an also newly developed thermal oxidizer, which uses an innovative, electrically heated design instead of a traditional natural gas- or fuel oil secondary combustion chamber design. After passing through the thermal oxidizer, the gases are cooled down to saturation temperature and treated in the wet scrubbing system. An extraction fan keeps the system under negative pressure to prevent the possibility of any off-gas leakage out of the system. A series of tests were performed successfully with wet cationic resins, anionic resins and a mixture of both. The results of these tests are described in the presentation. For intellectual property protection, the system and process of pyrolysis of organic waste and the system of thermal oxidation of pyrolysis gases described in this paper are patent pending. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Impact of Dry Cooler Air-Side Performance on a sCO2 Power Cycle for a CSP Application

Uncertainty around the design and control of the supercritical CO2 power cycle must be reduced before this technology can be implemented for large-scale grid support. To better understand the day-to-day performance of an sCO2 cycle, off-design performance calculations must be included for all power block components, and performance assumptions must be removed. This study has expanded the modeled scope to include the air-side performance for the dry cooler and has incorporated discretized heat transfer calculations for both streams through the pre-cooler to better predict off-design performance. This study considered a recompression Brayton cycle in a concentrating solar power application. The cycle model utilized fixed sCO2 turbomachinery maps for the main compressor, recompressor, and expander operating to supply approximately 10 MW gross at the design point. Fixed vendor-supplied fan curves were used to calculate the air-side performance of the dry cooler. The primary heater was modeled considering both the sCO2 and heat transfer fluid streams. Off-design performance was predicted for an ambient temperature range of 0-55℃, a HTF temperature range of 705-735℃, and a HTF mass flow range of 50-105% of the design point value. To understand the importance of modeling the air-side performance, the cycle off-design performance was also calculated using a constant CO2 outlet temperature assumption and a constant approach temperature assumption for the dry cooler. Results show that using these assumptions can significantly alter the power output and cycle efficiency predictions.

14 SOLAR ENERGY↗
DOE OSTI2024

Building Automation

In this chapter, we focus on both need-based and vision-based control innovation for building automation for HVAC systems. After providing a brief background that describes different HVAC architectures and products, we describe the control challenges and opportunities from a needs-based point of view, at the equipment level, the systems level, and the building level. At the equipment level, increased use of continuously variable actuation such as variable speed compressors and fans, requires application of robust multivariable control that must consider various types of nonlinear behavior across increasingly larger operating envelopes. The objective at this level is to achieve robust and energy efficient operation. At the system level, coupled and interactive dynamics among subsystems are increasingly important to consider, and functional integration of these subsystems, along with set points and operating schedules are designed to minimize energy consumption and improve occupant comfort. At the building level, issues such as integration among other building systems, such as the building envelope and the electric grid, offer opportunities to develop new types of demand response and grid-interactive behaviors and technologies. At the higher levels, system dynamics and robust stability are less of an issue, similar to process control applications, while optimization and integration are more important, although dynamic stability is increasing an issue at the higher levels too. From a vision-driven innovation point of view, we describe three areas that will require sustained research efforts and will eventually impact building automation: Digital Twins, Model Predictive Control, and Grid Interactive Buildings.

Bortoff, Scott↗
DOE OSTI2024

Performance Evaluation of Various Configurations for Domestic Refrigerators with R600a

The increasing global focus on mitigating global warming has driven a change in refrigerant selection for domestic refrigerators. While most existing refrigerators use R-134a as their working fluid, there is a gradual shift towards R600a in newly manufactured products to minimize environmental impact. In order to design a more energy-efficient isobutane refrigerator, various configurations, including both single-evaporator and dual-evaporator cycles, were investigated. The study was conducted using a high-fidelity simulation platform, the Heat Pump Design Model (HPDM), developed and experimentally validated by the US DOE/ORNL. This platform includes detailed segment-to-segment heat exchanger model, fan model, and compressor model, utilizing refrigerant properties from REFPROP 10. A quasi-steady-state approach was employed to simulate the transient behavior and performance of a domestic refrigerator. The performance of various configurations was compared, and the effects of the refrigerant properties and operating conditions were also analyzed and discussed.

Yang, Cheng-Min↗
DOE OSTI2024

System Modeling of Frost/Defrost Cycles in Heat Pumps

Accurately modeling frost/defrost cycles is crucial for guiding heat exchanger design, appropriately sizing the compressor, and quantifying seasonal energy consumption for heating. While previous modelling studies of frosting cycle have predominantly focused on the component level of fin-and-tube coils, there has been limited development of models for system defrosting cycles. This study enhances the existing knowledge by improving a detailed, segment-to-segment heat exchanger model to simulate frost/defrost cycles for both fin-and-tube coils and microchannel heat exchangers. Furthermore, it integrates this component-level model into a steady-state/quasi-steady-state system modeling framework to simulate complete frost/defrost processes, i.e. the ORNL Heat Pump Design Model (HPDM). This comprehensive approach will unveil differences in coil types, and their interactions with the outdoor fan. It will also predict energy consumption patterns and time intervals associated with these processes.

Shen, Bo↗
DOE OSTI2021

Persistent Acoustic Sensing for Monitoring A Reactor Facility - Oral Presentation

Measurements over the past few years, taking place within the Multi-Informatics for Nuclear Operations Scenarios (MINOS) project, show that infrasound and low-frequency acoustic monitoring can detect, and often quantify, activities that occur on-site at a large research reactor. Observable activities include: crane translation, lifting, and lowering; differentiation between loaded and unloaded crane operations; access door opening and closing; vehicle operations; and cooling tower fan operation. Advanced data analytic and spectral feature extraction methods can be used to interpret selected signatures to reach a deeper understanding of different reactor activities. These measurements are being conducted using a network of smartphones that continuously operate in conjunction with cloud-based architectures. A recent addition to the system is the development and deployment of a real-time, cloud-based analytic framework that supports near-real-time alarming which can facilitate tip-and-cue protocols. This paper will present recent research advances in this area including studies to explore the transferability of learned parameters from one smartphone to another, the detectability of events from multiple sensors at different locations simultaneously, and the feasibility of porting analytical tools directly to the smartphones to allow edge computing with optimized configurations.

42 ENGINEERING↗
DOE OSTI2021

Persistent Acoustic Sensing for Monitoring A Reactor Facility

Measurements over the past few years, taking place within the Multi-Informatics for Nuclear Operations Scenarios project, show that infrasound and low-frequency acoustic monitoring can detect, and often quantify, activities that occur on-site at the High Flux Isotope Reactor. Observable activities include: crane translation, lifting, and lowering; differentiation between loaded and unloaded crane operations; access door opening and closing; vehicle operations; and cooling tower fan operations. Advanced data analytic and spectral feature extraction methods can be used to interpret selected signatures to reach a deeper understanding of different reactor activities. These measurements are being conducted using a network of smartphones that continuously operate in conjunction with cloud-based architectures. A recent addition to the system is the development and deployment of a real-time, cloud-based analytic framework that supports near-real-time alarming which can facilitate tip-and-cue protocols. This paper presents recent research in this area including the use of low-frequency acoustics to tip-off transfer operations and cue a more comprehensive analysis by other sensor networks.

42 ENGINEERING↗
DOE OSTI2023

A Simulation Framework for Analyzing the Impact of Stochastic Occupant Behaviors on Demand Flexibility in Typical Commercial Buildings

As one of the primary users of the electric grid, buildings and building equipment, including heating, ventilation, and air conditioning (HVAC) systems, can be leveraged to provide the flexible demand needed to balance the grid. Typical strategies to achieve demand flexibility are to reduce electricity use during peak or critical periods by shutting down equipment or relaxing system setpoints, which will inevitably impact the occupants’ comfort. When occupants feel uncomfortable, they may take actions to regain their comfort, and some of those actions (such as turning on a personal fan) may have a negative impact on meeting the demand response goal. Therefore, it is important to incorporate occupant behaviors into the assessment ofthe building demand flexibility potential. In this study, a simulation framework that includes simulation of zone thermal loads, an HVAC system, and occupant behaviors, was developed to investigate the impact of occupant behaviors on demand flexibility. A case study was conducted using a small office model from the U.S. Department of Energy (DOE) Commercial Prototype Building Models to simulate the building envelope and zone loads. An agent-based occupant thermal behavior model was adapted to forecast occupants’ thermal comfort and their resulting thermal behaviors. An artificial neural network (ANN) based airflow model trained from a computational fluid dynamics (CFD) model of the zone was adopted to better predict the ambient environment of each occupant. An air-source heat pump simulation model that was calibrated from a real two-stage air-source heat pump system was used as the HVAC system. A typical load shedding event during peak hours was studied. Repeated simulations were conducted to capture the stochastic effects of occupant behaviors. The interplay between the demand flexibility, occupant comfort and behavior were analyzed by evaluating key performance indicators, including the energy use, occupant discomfort duration, and occupant behavior duration during the peak period. The results suggest that this framework can be used to analyze typical commercial buildings and their HVAC systems in terms ofdemand flexibility potential under the impact of occupant behaviors.

Chen, Zhelun↗
DOE OSTI2025

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy↗
DOE OSTI2024

Proactive and Reactive Thermal Comfort Behaviors

The expansion of renewable electricity generation, growing demands due to electrification, greater prevalence of working from home, and increasing frequency and severity of extreme weather events, will place new demands on the electric supply and distribution grid. Broader adoption of demand response programs (DRPs) for the residential sector may help meet these challenges; however, experience shows that occupant overrides in DRPs compromises their effectiveness. There is a lack of formal understanding of how discomfort, routines, and other motivations affect DRP overrides and other related human building interactions (HBI). This paper reports preliminary findings from a study of 20 households in Colorado and Massachusetts, US over three months. Participants responded to ecological momentary assessments (EMA) triggered by thermostat interactions and at random times throughout the day. EMAs included Likert-scale questions of thermal preference, preference intensity, and changes to 7 different activity types that could affect thermal comfort, and an opened ended question about motivations of such actions. Twelve tags were developed to categorize motivation responses and analyzed statistically to identify associations between motivations, preferences, and HBI actions. Reactions to changes in the thermal environment were the most frequently observed motivation (118 of 220 responses). On the other hand, almost half (47%) responses were at least partially motivated by non-thermal factors, suggesting limited utility for occupant behavior models founded solely on thermal comfort. Changes in activity level and clothing were less likely to be reported when EMAs were triggered by thermostat interactions, while fan interactions were more likely. Windows, shades, and portable heater interactions had no significant dependence on how the EMA was triggered. These results suggest that better understanding of motivations for HBI may improve effectiveness of demand response programs.

Pathak, Maharshi↗
DOE OSTI2021

LBNL-BELLA Center Monoenergetic Photon Source Experiments

This document outlines measurements made by Idaho National Laboratory (INL) staff at the Berkeley Lab Laser Accelerator (BELLA) Center. Measurements of a mono-energetic Thomson photon beam were made with various INL instruments. The photon beam was generated by a laser-plasma accelerator system. This project is referred to by the name Laser Wake. Key Lawrence Berkeley National Laboratory (LBNL) personnel involved include Liona Fan-Chiang, Cameron Geddes, Anthony Gonsalves, Robert Jacob, Tobias Ostermayr, and Hai-En Tsai.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

Detecting Process Equipment Failures Using Acoustic Data and Machine Learning

Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗