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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Trends in best-in-class energy-efficient technologies for room air conditioners

Improving the efficiency of room air conditioners (RACs) could provide significant energy and associated emissions savings, particularly in emerging economies with hot climates where the cooling demand is expected to increase dramatically. To help accelerate efficiency improvements, this study identifies “best-in-class” high-efficiency RAC components and products. The findings show that manufacturers tend to minimize manufacturing costs by using RAC designs that are readily available or standardized to their production, and they share components across various models. High-efficiency RAC models use advanced compressor technologies optimized at a low frequency, large heat exchangers with thermodynamically effective materials and designs, highly efficient direct current fan motors, advanced metering devices, and smart sensors for temperature and humidity control. Recently RAC manufacturers have been improving seasonal efficiency – better reflecting part-load operation – especially via variable-speed (inverter) drives for compressor motors. Recent highest-efficiency RAC models use low global warming potential (GWP) refrigerants, having transitioned from conventional high-GWP refrigerants, in regions where RACs that use these low-GWP refrigerants are commercially available. Recently demonstrated innovative technologies show trends toward smart hybrid designs, evaporative cooling, and solid-state materials beyond the conventional vapor-compression technology. This information could help policymakers improve their RAC market-transformation programs to align with the most-efficient global technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Carbon–Mineral Slurry Electrodes for Energy-Efficient Lithium Leaching from Low-Grade Clay Feedstocks

Lithium extraction from clay deposits is typically hampered by low lithium grades and the high capital and energy demands of conventional processing. We report an ambient temperature electrochemical leaching method that bypasses high-temperature roasting and concentrated-acid leaching, directly liberating 93% of lithium from low-grade hectorite in 24 h at 1 V, with 77% Faradaic efficiency, an energy intensity of 3.91 MWh/t lithium carbonate equivalent (LCE). Utilizing a carbon-mineral composite slurry electrode strategy we achieve electron-driven lattice deconstruction through iron oxidation coupled with proton uptake to drive lithium deintercalation, offering a new strategy for low-temperature critical-metal recovery. A preliminary cost analysis highlights pathways to achieving cost intensities below $3000/t LCE. Challenges remain in improving current density, extraction kinetics, and demonstrating process scalability, however, this work establishes a foundation for effective electrochemical lithium extraction from abundant but previously uneconomical clay deposits.

Haddad, Andrew Z↗
DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗
DOE OSTI2019

Efficacy of occupancy-based smart ventilation control strategies in energy-efficient homes in the United States

Proper ventilation of residences is essential for occupant health and comfort, and is responsible for a significant portion of energy consumption in homes. This study examines a method for providing adequate ventilation in homes while reducing energy consumption and peak demand: “smart” control of ventilation through sensing of occupancy and modulation of ventilation fans. We first conducted a detailed simulation study of advanced California homes with several occupancy-based ventilation control strategies. We then look at how general these results are nationally through a second simulation campaign in 15 ASHRAE climate zones. All simulations compared equivalent indoor air quality situations and assessed energy savings benefits. A key difference from previous demand-controlled ventilation strategies is that our study includes the effects of building related contaminants that are continuously emitted, irrespective of occupancy status, consistent with the requirements in ASHRAE Standard 62.2-2016. Under this new assumption, it is very difficult to extract substantial energy savings using only occupancy sensing. For the baseline strategy, savings were less than 10% of ventilation energy and sometimes negative in all cases analyzed other than leakier 2-story homes. Addition of a pre-occupancy flush period increases savings somewhat, but savings are still less than 15% other than in 2-story leakier homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Designing Energy-Efficient Quantum Computers Through Prediction and Reduction of Cooling Requirements for Cryogenic Electronics

Quantum computing has been identified as a “wild card” by the International Energy Agency in predicting future global data center energy usage. This is primarily because both uncertainty in the extent to which quantum computing will be adopted, and uncertainty in the power consumption of individual quantum data centers. Unlike the classical counterparts, quantum computers need to be maintained at near absolute zero, requiring energy-intensive cryogenic cooling systems. Therefore, as quantum computers scale up from existing 50 qubit technology demonstrations to the 10,000 to 100,000 qubit systems that will be able to solve complex problems, the energy consumption of both the electronics and the required cooling systems will also increase. To predict this scaling, this work analyzes the energy requirements for both computation and cooling of quantum hardware. We show that the energy requirements for cooling of quantum computers is determined by several computing system parameters, including the number and type of physical qubits, the operating temperature, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. The energy requirements can then be found based on thermal system parameters such as cooling efficiency and cryostat heat transfer. Analysis of these parameters shows that the energy required for cooling is significantly larger than that required for computation, a reversal from energy usage patterns seen in conventional computing. The results and discussions provide a road-map for creating energy efficient quantum computers through the selection of computer architectures and cryogenic system configurations that minimize cooling requirements.

energy efficiency↗
DOE OSTI2018

Final Report: TIP-337 Home Battery System for Cybersecure Energy Efficiency and Demand Response

Many challenges related to energy use and grid participation face the residential building sector and utilities that serve our homes today. To reduce energy consumption, increase grid service participation, and improve homeowner benefits, a solution is needed that can adapt itself to each home and homeowner/occupant, that can deliver both building and grid services with reliability and high availability, that can automate these operations to minimize cost and complexity of deployment, and that can provide both home data privacy and grid cybersecurity. We hypothesize that customer-oriented home automation can mutually satisfy home occupant/owner needs, reduce energy consumption, and deliver reliable grid services. This project seeks to develop innovative technology solutions that prove this hypothesis. The Home Battery System (HBS) is a technology package comprised of connected 'smart' appliances, rooftop solar photovoltaics, a home battery, and a coordinating smart controller. This system is envisioned, developed and demonstrated by the National Renewable Energy Laboratory (NREL), Bosch, ESCRYPT and Colorado State University (CSU). It is the result of three years of research by our diverse team. NREL developed the home automation controller, and performed simulation and laboratory evaluations of the HBS. Bosch developed and delivered most of the connected appliances used in the project, and provided technical and commercialization guidance. ESCRYPT led cybersecurity analysis and developed the cybersecurity layer. CSU provided leadership on preference elicitation methodologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

CoNNECT: Citizen Engagement for Energy Efficient Comunities

The CoNNECT system is a home energy management software system developed to provide the public and electric utilities access to timely, useful, and actionable information about energy usage (that is, usage data for electricity, water, gas, and others). CoNNECT for the public provides the utility customers access to historical usage data; permits visual correlation between weather and consumption patterns; compares customers’ consumption data to that of their peers (customers living in houses of similar size, age, and household size); and provides information for actions using generic appliance data. Utility customers can customize their information for action by contributing data about their appliances. CoNNECT for the utility provides electric utility analysts access to additional data, such as building envelope data, which they naturally do not have access to; permits easy decision making through the generation of usage “heat-maps” for global comparison and assessment; and permits the clustering of customers based on user-defined criteria.

Omitaomu, Olufemi↗
DOE OSTI2015

Personalized Energy Reduction Cyber-Physical System (PERCS): A gamified end-user platform for energy efficiency and demand response.

The mission of the Personalized Energy Reduction Cyber-physical System (PERCS) is to create new possibilities for improving building operating efficiency, enhancing grid reliability, avoiding costly power interruptions, and mitigating greenhouse gas emissions. PERCS proposes to achieve these outcomes by engaging building occupants as partners in a user-centered smart service platform. Using a non-intrusive load monitoring approach, PERCS uses a single sensing point in each home to capture smart electric meter data in real time. The household energy signal is disaggregated into individual load signatures of common appliances (e.g., air conditioners), yielding near real-time appliance-level energy information. Users interact with PERCS via a mobile phone platform that provides household- and appliance-level energy feedback, tailored recommendations, and a competitive game tied to energy use and behavioral changes. PERCS challenges traditional energy management approaches by directly engaging occupant as key elements in a technological system.

Sintov, Nicole↗
DOE OSTI2012

Data Center Energy Efficiency Measurement Assessment Kit Guide and Specification

A portable and temporary wireless mesh assessment kit can be used to speed up and reduce the costs of a data center energy use assessment and overcome the issues with respect to shutdowns. The assessment kit is comprised of temperature, relative humidity, and pressure sensors. Also included are power meters that can be installed on computer room air conditioners (CRACs) without intrusive interruption of data center operations. The assessment kit produces data required for a detailed energy assessment of the data center.

Data, Center, Energy, Efficiency, Measurement, Ass↗