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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) Solar Sounding Rocket Campaign – Calibration and Performance

The Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) is a sounding rocket experiment that is designed to observe, for the first time, soft X-ray spectra of high-temperature, low-emission plasma of coronal structures spatially resolved along a narrow slit.MaGIXS observation involves a set of high temperature spectral lines in soft X-rays from 0.5 - 2.0 keV from an active region core,which will extend the DEM coverage from 3MK to 10MK constraining the slope of the DEM fall-off. The novel instrument designincludes a Wolter-I type telescope and a 3-optic grazing-incidence spectrometer. The spectrometer consists of a finite conjugatemirror pair and a blazed planar, varied line spaced grating, which disperses the rays on to a CCD and provides a high spatial andspectral resolution. Component level instrument testing, integration of the instrument and end-to-end X-ray calibration are carriedout using the X-ray and Cryogenic Facility (XRCF) at NASA Marshall Space Flight Center. MaGIXS is scheduled for launch in2021. We will present the results of X-ray calibration tests for MaGIXS and discuss the expected inflight performance throughdifferent solar observation scenarios.

P. S. Athiray↗
NASA NTRS2021

Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modelling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850-1900, using an observationally-based historical warming estimate of 0.8°C between 1850-1900 and 1995-2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above pre-industrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

Climate↗
NASA NTRS2023

Uncertainty in Land-Use Adaptation Persists Despite Crop Model Projections Showing Lower Impacts Under High Warming

Climate change is expected to impact crop yields and alter resource availability. However, the understanding of the potential of agricultural land-use adaptation and its costs under climate warming is limited. Here, we use a global land system model to assess land-use-based adaptation and its cost under a set of crop model projections, including CO2 fertilization, based on climate model outputs. In our simulations of a low-emissions scenario, the land system responds through slight changes in cropland area in 2100, with costs close to zero. For a high emissions scenario and impacts uncertainty, the response tends toward cropland area changes and investments in technology, with average adaptation costs between −1.5 and +19 US$05 per ton of dry matter per year. Land-use adaptation can reduce adverse climate effects and use favorable changes, like local gains in crop yields. However, variance among high-emissions impact projections creates challenges for effective adaptation planning.

Agriculture↗
NASA NTRS2024

The Impact of Decarbonization on Particulate Soiling of Solar Panels

Climate researchers have examined many impacts of climate change on energy supply and demand under various scenarios. However, the effect of changing particulate deposition onto solar panel surfaces on solar power production efficiency (i.e., soiling) has not been studied. We therefore characterize probabilistic outcomes across multiple climate models and scenarios. We find large current regional losses (up to 40% without manual cleaning, up to 20% with monthly cleaning and rain removal) in generation that grow slightly under a high-emission scenario, largely due to regional increases in windblown dust. In contrast, under a low-emissions scenario, potential production increases significantly (2–8% interquartile range with only rain removal) due to reduced soiling, especially in regions of Asia and Africa where anthropogenic aerosols are major contributors to soiling. Projected changes vary widely across models in many dusty areas outside of the Sahara and Arabia. Differences can also be large in regions dominated by anthropogenic aerosols, such as Nigeria, eastern China, and northern India, where the full range across modeled potential power production changes extends from −1 to +11% for the end of the century (without manual cleaning), underscoring the need to consider multiple climate models. With large increases in projected solar power deployment, the relatively small potential production increases reported here could nevertheless represent a large dividend in additional energy production. Hence, reductions in air pollution attributable to decarbonization could provide positive feedback under which a greater deployment of solar power (or other renewables) increases the production of solar power, facilitating the transition to renewable energy.

renewable energy↗
DOE OSTI2020

Flexible Nuclear Energy for Clean Energy Systems

Clean low-emission energy systems will increasingly need flexible energy technologies tailored for an individual country's energy resources, infrastructure, and social objectives. While solutions may be unique, the diversity of experience from various countries can help create innovative approaches. Hybrid renewable and flexible nuclear energy systems are a solution explored by the countries and organizations of the Nuclear Innovation: Clean Energy Future (NICE Future) initiative of the Clean Energy Ministerial. We found that operational, product, and deployment flexibility could help expand clean energy systems globally.

Clean Energy Ministerial↗
DOE OSTI2020

High-Performance Windows -- More than just a Pretty Hole in the Wall

As more stringent building energy codes and better insulation products combine to yield better performing walls, window efficiency is coming into sharper focus. The U.S. Department of Energy has supported several projects through its national laboratories to improve the thermal performance of windows. At Pacific Northwest National Laboratory (PNNL) in Richland, Washington, the PNNL Lab Homes, two fully monitored identical side-by-side manufactured homes, have been used to test the performance of several window improvements including triple-pane windows, storm windows with low-emissivity (low-e) coatings, smart automated interior insulated shades, and exterior shading products. Findings will be presented on these studies, along with impacts. For example, PNNL’s Lab Home research on low-e storm windows has helped to support a new ENERGY STAR certification, industry standards, and utility incentives. Preliminary findings will also be presented on current Lab Homes experiments focusing on exterior shades. The session will also present findings from related field studies and a market assessment of emerging high-efficiency windows technologies and discuss some of PNNL’s planned field studies that will focus on validating the benefits and costs of the emerging thin triple-pane high-R window.

windows, high-r windows, insulating windows, windo↗
DOE OSTI2021

Clean Energy in Oil & Gas Consortium: Compressor Station Case Study

JISEA conducted analysis of technical and economic potential of incorporating low-emissions energy sources into various oil and gas operations in different locations to assist the oil and gas industry with decision-making. The case study analysis presented here evaluated renewable energy opportunities at an all-electric compressor station located in Texas.

Baker Hughes↗
DOE OSTI2022

Compatibility Of Steels At 450°-650°C In Supercritical CO2 With O2 And H2O Additions

Direct-fired supercritical CO2 (sCO2) power cycles are being commercialized to revolutionize fossil energy as a low-emission power source. However, the cycle will increase O2 and H2O in the sCO2 and the implications of these additions have not been fully studied, particularly for lower cost steels that are needed in the lower temperature segments of the plant. Representative 9 and 12%Cr ferritic-martensitic (FM) steels and conventional and advanced austenitic steels were evaluated at 450-650°C to determine the maximum use temperatures in sCO2 with 1%O2 and 0.1%H2O at 300 bar. Compared to research grade (low impurity) sCO2 in indirect-fired cycles, the mass gains and scale thickness were not significantly changed for FM steels: both formed thick duplex Fe-rich scales. For stainless steels, higher mass gains were observed in all cases with increased Fe-rich oxide nodule formation. After 1000h at 650°C, the measured bulk C content was high for all of the steels with the addition of impurities suggesting a lower maximum operating temperature for steels. The post-exposure impact of the environment on room temperature tensile properties also will be discussed.

Pint, Bruce↗
DOE OSTI2021

DOE Zero Energy Ready Home Case Study HIA 2021: Insight Homes, The Brenner, Felton, DE

Case study of a DOE 2021 Housing Innovation Award winning custom home in a mixed-humid climate that got a HERS 51 without PV, with 1,940 square feet, MERV 13 filters and low-emission paints, adhesives, and carpeting; plus a sealed, conditioned crawl space, and a high-efficiency gas furnace and heat pump.

Building America, residential construction, home b↗
DOE OSTI2021

Dynamic Simulation of a Sub-Critical Coal Fired Power Plant

In order to address the demanding operating conditions for remaining coal-fired power plants, a dynamic model and a suite of tools have been developed for studying load cycling and to find optimization opportunities. A sub-critical steam cycle power plant was modeled in a flow-sheet modeling tool, APROS™. The model represented the firing system, economizer, evaporator, superheat, and reheat systems. Four loads from 100% TMCR to 25% TMCR were calibrated and tested such that low-to-high cycling could be studied. The model was run through various load cycles; one of which is presented here. This modeling is a prototype for general use in developing cutting edge controls products and for maximizing economic, low-emissions, and efficient operation of the existing coal power fleet.

Braun, Timothy↗
DOE OSTI2022

Transforming Energy Through Sustainable Mobility: Expanding Low-Carbon Transportation R&D Solutions

As the nation's premier laboratory for cutting-edge transportation decarbonization research and development solutions, the National Renewable Energy Laboratory (NREL) pioneers the creation and deployment of sustainable mobility technologies and strategies, with a focus on slashing transportation sector greenhouse gas emissions and combatting climate change. Trucks, planes, cargo ships, and other difficult-to-decarbonize vehicles are part of this essential transition for the transportation sector, which is currently the nation's largest source of the greenhouse gas emissions. NREL provides the scientific building blocks needed to spur innovation through multifaceted analysis, research, and engineering. This work acts as a catalyst to help industry bring affordable, high-performance, energy-efficient, and low-emission modes of transport and related infrastructure to market sooner. Our researchers collaborate closely with academic, government, and industry partners to design better batteries, drivetrains, and engines. They develop technologies for high-power charging, thermal management, energy storage, and power electronics. They are also reimagining fuels and combustion while creating sustainable lightweight materials. Unbiased expert guidance - backed by credible data and analysis, tools, and scientific rigor - empowers partners to make informed decisions about sustainable transportation. NREL recognizes that communities with limited mobility options face reduced access to employment opportunities, health care, and education, lowering overall quality of life. Alongside partners, NREL experts are creating transportation solutions that meet community-identified needs and increase mobility equity in historically underserved and overburdened communities. Rather than providing a one-size-fits-all solution, we take an interdisciplinary approach to mobility equity that considers the needs and challenges of diverse groups, maximizing benefits at the individual, community, and societal levels.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Embedding Equity into Community EV Readiness Planning

When planning to incorporate electric vehicle (EV) infrastructure into your community, there are opportunities to weave in equity considerations. This will streamline the approval process for federal funding while ensuring no community member is left behind during this nationwide transition to low-emissions transportation options.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Performance Investigation of High-temperature Latent Heat Storage for Integration with Advanced Nuclear Reactors

Integrating thermal energy storage (TES) with advanced nuclear reactors enhances the flexible use of nuclear energy, facilitating its expansion beyond conventional electricity generation. High-temperature TES, capable of storing heat above 400 degree C, emerges as a vital carbon-free energy solution for decarbonizing industrial sector, especially when combined with low-emission energy sources like advanced nuclear reactors. Idaho National Laboratory (INL) has recently developed a novel design for high-temperature latent heat storage system, called Heat pipe Integrated Thermal Battery (HITB), and efforts are underway to experimentally demonstrate the concept. HITB employs liquid-metal heat pipes to establish thermal linkage between TES and nuclear systems without direct fluid exchange, minimizing the potential risk of the integrated systems such as cross-contamination. HITB is designed to achieve high charging and discharging efficiency as well as high energy storage density by employing metal alloys as Phase Change Material (PCM). Initial proof-of-concept experiments are being undertaken using an aluminum alloy (Al59%-Mg35%-Zn6%). This paper discusses current progress of the HITB project at INL, seeking to develop high-temperature TES for versatile integration with advanced nuclear reactors, and shares insights from the performance evaluations via numerical modeling and analysis.

25 ENERGY STORAGE↗
DOE OSTI2024

Integrating Variable Renewable Energy Into the Grid: Key Issues

To foster sustainable, low-emission development, many countries are establishing ambitious renewable energy targets for their electricity supply. The variability of solar and wind compared to traditional sources, coupled with growing demand for electricity, requires changes to power system planning and operations to meet these targets. Grid integration is the practice of developing efficient ways to deliver variable renewable energy (VRE), primarily wind and solar, to the grid. Good integration methods maximize the cost-effectiveness of incorporating VRE while maintaining or increasing system stability and reliability. To inform decarbonization strategies, policymakers, regulators, and system operators consider a variety of costs and opportunities associated with VRE integration, which can be organized into five topics: New renewable energy generation and transmission; Power system reliability; Transmission and distribution coordination; Cross-sectoral decarbonization opportunities; Energy equity and justice.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

This is the conference paper accompanying an oral presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2025

Combined TDLAS and chemiluminescence imaging in a flat flame burner operated with NH3/H2 blends

Ammonia is a promising hydrogen carrier due to its favorable storage and transport characteristics. However, its direct use in combustion systems is limited by low flammability and potential for high nitrogen oxide emissions. To better understand ammonia combustion, researchers conducted experiments using a flat flame burner and measured species profiles using tunable-diode-laser-absorption-spectroscopy and chemiluminescence imaging. They tested three flame conditions with different ammonia-hydrogen blends and oxygen levels. The results were compared to simulations using various kinetic mechanisms, including one that accounts for excited species chemistry. The goal is to provide direct information about species profiles in a simple system, isolating chemical kinetics from fluid dynamic effects, which can inform the development of more efficient and low-emission combustion systems using ammonia.

ammonia combustion↗
DOE OSTI2025

Flame Characterization of a Hydrogen Micromix Burner using Experimental and Numerical Methods - Extended Abstract

Micromix combustion presents a promising solution for integrating hydrogen into gas turbine combustors, offering rapid and compact air-fuel mixing that effectively shortens the combustion zone and significantly reduces nitrogen oxides (NOX) emissions. A major challenge in hydrogen combustion systems is flashback, which is mitigated through short mixing lengths and high flow velocities. This study investigates three fuel injector designs – zero swirl, low swirl, and high swirl – for a 6-mm inner diameter nozzle, where hydrogen is injected through six 0.3 mm diameter holes in a jet-in-crossflow configuration relative to the airstream. Numerical simulations indicate stable flame formation, moderate peak flame temperatures, and subsequent low NOx potential for all designs. These injectors have been fabricated using additive manufacturing, and upcoming experiments will explore flame structure and NOx behavior across varying fuel injection angles, equivalence ratios and pre-mixer velocities. These efforts aim to advance the development of low-emission hydrogen micromix combustion.

hydrogen↗
DOE OSTI2025

Potential of deep learning methods to enhance satellite-based monitoring of nuclear power plants focusing on remote operation evaluations

The anticipated expansion of the nuclear industry and the deployment of new nuclear reactors (200 + GW of new nuclear capacity by 2050) require the development of monitoring systems that align with safety and security concerns, providing enhanced evaluation capabilities. A remote monitoring system using satellites and deep learning techniques was evaluated for its ability to detect anomalies and capture various features of nuclear reactors independently of the conditions on the ground. Satellite images of current operational and under-construction nuclear power plants were collected from Google Earth Pro as a surrogate database. Subsequently, five datasets were created from the collected images. Transfer learning technique was used for several classification tasks utilizing VGG16, ResNet50V2, Xception, DenseNet121, and MobileNetV2 pre-trained models. In the first task, the capability of the monitoring system to detect abnormal conditions or processes in a nuclear power plant was investigated. In the second task, the ability to capture operational features remotely was examined. As an example, for the purposes of this study, these features included classifying reactors based on type, power range, or onsite condition. Several evaluation metrics were used to compare the performance of the pre-trained models and the overall monitoring system. Here, the evaluation results demonstrated that deep learning techniques and pre-trained models applied to satellite images have the potential to facilitate further and expand capabilities in monitoring systems to assess plant operation details.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗