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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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Case study of a DOE 2021 Housing Innovation Award winning custom home in a marine climate that got a HERS -13 with PV, with 1,804 square feet, highly efficient construction; high-efficiency ductless minisplit heat pumps, appliances, and lighting; triple-pane windows; and 9.9 kW of solar panels on each of the monoplane roofs.
The efficiency of indium phosphide solar cells is limited by high values of surface recombination. The effect of a lattice-matched In(0.52)Al(0.48)As window layer material for InP solar cells, using the numerical code PC-1D is investigated. It was found that the use of InAlAs layer significantly enhances the p(+)n cell efficiency, while no appreciable improvement is seen for n(+)p cells. The conduction band energy discontinuity at the heterojunction helps in improving the surface recombination. An optimally designed InP cell efficiency improves from 15.4 percent to 23 percent AMO for a 10 nm thick InAlAs layer. The efficiency improvement reduces with increase in InAlAs layer thickness, due to light absorption in the window layer.
Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models over the past 3 years, the objective of this work is to produce national data sets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) upgrade measures, or upgrades. "Measures" refers to energy efficiency variables that can be applied to buildings during modeling. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility upgrade applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. This documentation focuses on an upgrade package of three end-use savings shapes upgrades - Window Replacement, Exterior Wall Insulation, and Roof Insulation, which we will refer to collectively as the "High Efficiency Envelope" package. More details on the individual upgrades can be found on the ComStock Measures Documentation page. An upgrade package applies two or more EUSS upgrades to a single building model simulation. Since ComStock is a bottom-up physics-based model, an upgrade package will go beyond aggregating or summing the individual upgrade results and produce novel results by simulating interactions between the upgrades. For example, pairing an envelope upgrade with an electrification upgrade would likely result in higher savings results than the sum of these upgrades individually, and the size of the heating, ventilating, and air conditioning (HVAC) equipment may be reduced if the envelope upgrade reduces the loads significantly.
We are developing a high-pressure Gas Scintillation Proportional Counter (GSPC) for the focus of a balloon-borne hard-x-ray telescope. The device has a total active diameter of 50 mm, of which the central 20 mm only is used, and is filled with xenon + 4% helium at a total pressure of 10 6 Pa giving a quantum efficiency of greater than 85% up to 60 keV. The detector entrance is sealed with a beryllium window, 3-mm thick, which provides useful transmission down to 6 keV, way below the atmospheric cut-off at balloon float altitudes. Scintillation light exits the detector via a UV transmitting window in its base and is registered by a Hamamatsu position-sensitive crossed-grid-readout photomultiplier tube. Initial testing is underway, quantifying light yield and energy resolution. Following that, the spatial resolution and absolute efficiency will be calibrated. Simulations show that a spatial resolution of better than 0.5 mm FWHM should be achievable up to 60 keV, and this is well matched to the angular resolution and plate scale of the mirror system. The energy resolution will be around 5% at 22 keV. Full details of the instrument design and its performance will be presented. A first flight is scheduled for the Fall of 99, on a stratospheric balloon to be launched from Fort Sumner, New Mexico.
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The objectives of Project TECH are: (1) to construct a single family detached dwelling for demonstrating the application of advanced technology and minimizing the requirement for energy and utility services, and (2) to help influence future development in home construction by defining the interaction of integrated energy and water management systems with building configuration and construction materials. Components and methods expected to be cost effective over a 20 year span were studied. Emphasis was placed on the utilization of natural heating and cooling characteristics. Orientation and location of windows, landscaping, natural ventilation, and characteristics of the local climate and microclimate were intended to be used to best advantage. Energy conserving homes are most efficient when design for specific sites, therefore project TECH should not be considered a prototype design suitable for all locations. However, it does provide ideas and analytical methods which can be applied to some degree in all housing.
Establishing a pH difference between the two electrolytes (pH decoupling) of an aqueous redox flow battery (ARFB) enables cell voltages exceeding the 1.23 V thermodynamic water-splitting window, but acid–base crossover penalizes efficiency and lifetime. Here we employ mildly acidic and mildly alkaline electrolytes to mitigate crossover, achieving high round-trip energy efficiency with open circuit voltage >1.7 V. We implemented an acid–base regeneration system to periodically restore electrolytes to their initial pH values. The combined system exhibited capacity fade rate <0.07% per day, round-trip energy efficiency >85% and approximately 99% Coulombic efficiency during stable operation for over a week. Cost analysis shows that the tolerance of acid–base crossover could be increased if the pH-decoupling ARFB achieved a higher voltage output and lower resistance. Finally, this work demonstrates principles for improving lifespan, rate capability and energy efficiency in high-voltage pH-decoupling ARFBs and pH recovery concepts applicable for pH-decoupling systems.
Metal electrocatalysts have been reported to improve the electron transfer kinetics of aqueous redox flow battery electrolytes on various types of carbon electrodes. In this work, we electrodeposited bismuth metal onto a carbon paper anode of a redox flow battery containing our previously reported polyaminocarboxylate-chelated chromium electrolyte. Depositing 0.58 mg cm –2 of bismuth metal enabled an electrochemically reversible electron transfer for the Cr(II)/Cr(III) couple, resulting in a 3.9% voltage efficiency increase over ten cycles at 100 mA cm –2 across an 80% state of charge window, while maintaining >99% current efficiency. The bismuth electrocatalyst provided other improvements, such as a 13% increase in average discharge power density when cycling at 80% energy efficiency, along with a 60% decrease in charge transfer resistance and 12% decrease in the full cell area specific resistance. A chelated bismuth complex was also utilized as a bismuth source for electrodeposition, providing an example of how polyaminocarboxylate ligands can be further implemented into efforts towards improving the energy efficiency of aqueous redox flow batteries.
Because of the complexity of modern buildings—with many interconnected materials, components, and systems—fully electrifying buildings will require targeted R&D and efficient coordination across those material, component, and system levels. Because buildings that consume the smallest amount of energy are easier to electrify, energy efficiency is a crucial step toward fully electrified buildings. Materials advances will play an important role in both reducing the energy intensity of buildings and electrifying their remaining energy use. Materials are currently being explored, discovered, synthesized, evaluated, optimized, and implemented across many building components, including solid-state lighting; dynamic windows and opaque envelopes; cold climate heat pumps; thermal energy storage; heating, ventilating, and air conditioning (HVAC); refrigeration; non-vapor compression HVAC; and more. In this article, we review the current state-of-the-art of materials for various buildings end uses and discuss R&D challenges and opportunities for both efficiency and electrification.
Building Information Modeling (BIM) plays an important role in building design and construction, particularly for achieving energy-efficient retrofits. Building envelope retrofits using panelized prefabricated system, such as those popularized by the Energiesprong program, need accurate as-built dimensions of facade features (windows, doors, etc.) to achieve the desired thermal and air tightness. Traditionally, building surveying is done manually, resulting in a time-consuming and labor-intensive process. Recently, 3D point clouds from terrestrial LiDAR have been used to automate the generation of as-built dimensions of existing buildings. However, automated BIM using LiDAR relies on solving the point cloud semantic segmentation (PCSS) problem. In this work, we propose a robust pipeline for solving the PCSS problem using deep neural networks, focusing on overcoming challenges posed by imbalanced datasets and complex architectural features. We introduce the first high-density, labeled, and validated building envelope point cloud dataset derived from multiple building scans, specifically curated to tackle challenges in facade-level segmentation. Results from the trained neural networks show that advanced attention-based architectures and incorporating radiometry (light intensity and RGB) features significantly boost segmentation accuracy for windows and doors.
This paper presents a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for optimizing power management in microgrids, particularly focusing on external power exchanges among diverse prosumers. DIPPS utilizes a dynamic objective function with a time-varying binary parameter to control the timing of power transfers to the external grid, facilitated by efficient usage of energy storage for surplus renewable power. The microgrid power scheduling problem is modeled as a mixed-integer nonlinear programming (MINLP-PS) and subsequently transformed into a mixed-integer linear programming (MILPPS) optimization through McCormick's relaxation to reduce computational complexity. A predictive window window with 6 data points is solved at an average of 0.92s, a 97.6% improvement over the 38.27s required for the MINLP-PS formulation, implying the numerical feasibility of the DIPPS approach for real-time implementation. Finally, the approach is validated against a static objective using real-world load data across three case studies with different time-varying parameters, demonstrating the ability of DIPPS to optimize power exchanges and efficiently utilize distributed resources while shifting the external power transfers to specified time durations.
New free electron lasers, such as SLAC’s LCLS-II, will provide unique scientific imaging opportunities. In order to fully utilize these facilities, we need to develop detectors with shallow entrance windows that will enable detection of soft x-rays from 250 eV to 1.5 KeV. Achieving adequately shallow entrance windows is challenging because the high temperature anneal needed to activate the dopant also drives the dopant profile deeper, growing the region that is insensitive to soft x-rays. A new microwave annealing technology provides an efficient way to achieve shallow entrance windows in fully depleted high-resistivity silicon sensors. The microwave anneal technique can activate dopants at low substrate temperature, with minimal dopant diffusion, and can be used to fabricate both n-type and p-type entrance windows. SRP and SIMS measurements were used to verify dopant activation with negligible dopant diffusion. We then applied the microwave anneal process to a planar sensor wafer, using the new process to create the backside diode contact. Electrical test of the resulting sensors shows good reverse bias characteristics. The sensors have been bump-bonded to a read-out ASIC and used successfully to measure an Fe-55 x-ray spectrum. Process and device simulations were performed to characterize the quantum efficiency of the entrance window for soft x-rays. This technique is useful for other sensor applications requiring a shallow entrance window, including detectors for UV photons, low energy ions and low energy electrons.
One of the most comprehensive and most effective programs is NECAP, an acronym for NASA Energy Cost Analysis Program. Developed by Langley Research Center, NECAP operates according to heating/cooling calculation procedures formulated by the American Society of Heating, Refrigeration and Air Conditioning Engineers (ASHRAE). The program enables examination of a multitude of influences on heat flow into and out of buildings. For example, NECAP considers traditional weather patterns for a given locale and predicts the effects on a particular building design of sun, rain, wind, even shadows from other buildings. It takes into account the mass of structural materials, insulating values, the type of equipment the building will house, equipment operating schedules, heat by people and machinery, heat loss or gain through windows and other openings and a variety of additional details. NECAP ascertains how much energy the building should require ideally, aids selection of the most economical and most efficient energy systems and suggests design and operational measures for reducing the building's energy needs. Most importantly, NECAP determines cost effectiveness- whether an energy-saving measure will pay back its installation cost through monetary savings in energy bills. thrown off
The two major outcomes from this DOE supported collaboration between UbiQD and NREL were: 1) an expert analysis/modeling of the expected performance and cost of luminescent solar concentrating windows with quantum dot coatings, and 2) critical R&D characterization of materials and device performance analysis using equipment and processes that are too expensive for UbiQD to perform in-house. The latter also included third-party validation of the device performance with an NREL-certified conversion efficiency that was ultimately published. (see ACS Energy Lett. 2018 and recently ACS Appl. Energy Mater. 2020).
We present a method to mitigate the effects of fiber assignment incompleteness in two-point power spectrum and correlation function measurements from galaxy spectroscopic surveys, by truncating small angular scales from estimators. We derive the corresponding modified correlation function and power spectrum windows to account for the small angular scale truncation in the theory prediction. We validate this approach on simulations reproducing the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) with and without fiber assignment. We show that we recover unbiased cosmological constraints using small angular scale truncated estimators from simulations with fiber assignment incompleteness, with respect to standard estimators from complete simulations. Additionally, we present an approach to remove the sensitivity of the fits to high k modes in the theoretical power spectrum, by applying a transformation to the data vector and window matrix. We find that our method efficiently mitigates the effect of fiber assignment incompleteness in two-point correlation function and power spectrum measurements, at low computational cost and with little statistical loss.
The NEAR Mission was launched on February 17, 1996 for a three year cruise to the asteroid 433 Eros. During October, November, and December 1998 cruise measurements with the gamma-ray spectrometer were made at three different escape-window-width settings. These were done in order to understand how the count rate and peak width change as the window width settings change. Analysis of these spectra was completed using the latest version of the spectral analysis program, RobWin. Results as a function of energy were combined with the results from the Schlumberger-Doll Research experiments (described below). Laboratory measurements were needed to confirm efficiency calculations above 6 MeV and to understand the relationship between the full energy peak areas and the areas of the first and second escape peaks as a function of the escape peak widths. A week of measurements was made at Schlumberger-Doll Research using their 14-MeV pulsed neutron generator and large soil samples. Data were collected after adding iron and nickel to the sample to increase the emission of high-energy lines. Approximately 24 hours of data were accumulated at each of three escape peak window widths. These data were analyzed with RobWin. Combining results from the cruise measurements and the laboratory measurements indicated that both data sets had similar energy dependence and that this energy dependence was different from that obtained using standard Monte Carlo calculations. Alternate methods of simulating the response of the detector to changes in the escape window widths are being investigated.
Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.
Studies on quantum algorithms for ground-state energy estimation often assume perfect ground-state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here, we address that problem in two ways: by faster preparation of matrix-product-state (MPS) approximations and by more efficient filtering of the prepared state to find the ground-state energy. We show how to achieve unitary synthesis with a Toffoli complexity about 7 × lower than that in prior work and use that to derive a more efficient MPS-preparation method. For filtering, we present two different approaches: sampling and binary search. For both, we use the theory of window functions to avoid large phase errors and minimize the complexity. We find that the binary-search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about 0.003 . Finally, we estimate the total resources to perform ground-state energy estimation of Fe - S cluster systems, including the Fe Mo cofactor by estimating the overlap of different MPS initial states with potential ground states of the Fe Mo cofactor using an extrapolation procedure. With a modest MPS bond dimension of 4000 , our procedure produces an estimate of approximately 0.9 overlap squared with a candidate ground state of the Fe Mo cofactor, producing a total resource estimate of 7.3 × 10 10 Toffoli gates; neglecting the search over candidates and assuming the accuracy of the extrapolation, this validates prior estimates that have used perfect ground-state overlap. This presents an example of a practical path to prepare states of high overlap in a challenging-to-compute chemical system. Published by the American Physical Society 2025