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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

NG-CT Peaker Upgrades with Bottoming Cycle: Preliminary Analysis

The National Energy Technology Laboratory (NETL) has conducted a preliminary analysis exploring a significant opportunity to increase the nation's power grid capacity and reliability. The study focuses on retrofitting under-utilized natural gas combustion turbines (NG-CTs), often called "peaker" plants, with a bottoming cycle to capture waste heat and generate additional electricity. This analysis shows that upgrading these existing assets could add approximately 26,250 MW of new power capacity across the United States. A detailed study of the PJM Interconnection, the nation's largest grid operator, confirmed the benefits of these upgrades. Key findings for the PJM region include: - A median increase in the plants' capacity factor by 17 percentage points. - A projected 36.2% median reduction in the levelized cost of electricity (LCOE) for the upgraded plants, with nearly 88% of them expected to break even. - An average reduction in the regional wholesale price of electricity by approximately 3.0% in the year 2030. - A significant boost to grid reliability, with a 46% reduction in projected Loss of Load Hours (LOLH). These findings suggest that retrofitting peaker plants is a promising and economically viable strategy to enhance grid performance, reduce electricity costs, and meet future demand without the challenges associated with building entirely new facilities.

bottoming cycle↗
DOE OSTI2023

Commissioning, Performance, and Effect of the Quench Current-Boosting Device on a Dedicated Superconducting Magnet

Superconducting magnet training is one of the accelerator related issues attracting attention due to significant operational costs and time budget associated to it. It is especially worrisome that magnets based on the “next-generation” Nb 3 Sn technology are affected by long training. While various efforts are underway to better understand and resolve the problem a parallel path could also be investigated, a path bypassing the issue. Following the concept of fast induced over-current during magnet powering, FNAL has developed an upgradable capacitor-based device to discharge through a superconducting magnet at quench detection or operator chosen time. The 0.4 F/1 kV device has been tested on a 1-m-long dipole-coil in a “mirror” magnet configuration and conclusive results on magnet training elimination have been observed. Here in this paper we discuss the main characteristics of the device, compare simulated response and actual performance, elaborate on test drivers and outcomes. Next steps and perspectives for future use are debated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Long-Term Stability in Perovskite Solar Cells Through Atomic Layer Deposition of Tin Oxide

Robust contact schemes that boost stability and simplify the production process are needed for perovskite solar cells (PSCs). We codeposited perovskite and hole-selective contact while protecting the perovskite to enable deposition of SnOx/Ag without the use of a fullerene. The SnOx, prepared through atomic layer deposition, serves as a durable inorganic electron transport layer. Tailoring the oxygen vacancy defects in the SnOx layer led to power conversion efficiencies (PCEs) of >25%. Our devices exhibit superior stability over conventional p-i-n PSCs, successfully meeting several benchmark stability tests. They retained >95% PCE after 2000 hours of continuous operation at their maximum power point under simulated AM1.5 illumination at 65degrees C. Additionally, they boast a certified T97 lifetime exceeding 1000 hours.

c60↗
DOE OSTI2020

Operation and Optimization of Microwave-Heated Continuous-Flow Microfluidics

Microwave (MW) technology can be powerful for electrification and process intensification but limited fundamental understanding of scalability and design principles hinders its effective use. In this work, we build a continuous-flow microreactor inside a commercial single-mode MW applicator and the corresponding computational fluid dynamics model to simulate the temperature profile. The model is in good agreement with experiments for various microreactor dimensions and operating conditions. The model indicates that MW heating is greatly influenced by reactor geometry as well as the operating parameters. We observe a strong correlation between parameters and develop a gradient boost regression tree model to predict the outlet temperature accurately. This model is then applied to optimize the dimensions and operating conditions to maximize the outlet temperature and energy efficiency, resulting in a Pareto optimal. We demonstrate computationally and experimentally that it is possible to surpass the Pareto optimal and achieve an energy efficiency of ~90% or greater at temperatures relevant for liquid-phase chemistry via salting of the solvent. The present methodology can be applied to other complex MW reactors. Lastly, the combined numerical and experimental approach provides insights into and a framework for scale-up and optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Consensus International Solar Resource Standards and Best Practices Development

Standardization and best practices of data sets and models enable the industry to develop widely accepted protocols adapted to various stages of solar project development and operations. In collaboration with the International Energy Agency Photovoltaic Power Systems Programme Task 16, the National Renewable Energy Laboratory (NREL) developed and regularly updates the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications. In collaboration with ASTM's radiometry subcommittee, NREL leads the development of various radiometric standards. These contribute to increased accuracy in the measurement of the solar resource available to conversion systems and boost the development and improvement of radiative models. These models can then provide the long-term geographic or climatic distribution of solar radiation for areas where measured solar data are not available. Further, these standards play a preeminent role in all aspects of solar energy projects, including standard conditions, methods and instrumentation, accelerated testing, and service lifetime of materials.

ASTM↗
DOE OSTI2025

Analysis of Two Models for the Angular Structure of the Outflows Producing the Swift/XRT “Larger-angle Emission” of Gamma-Ray Bursts

The quasi-instantaneous emission from a relativistic surface endowed with a Lorentz factor that decreases away from the outflow symmetry axis can naturally explain the three phases observed by Swift X-Ray Telescope (XRT) in gamma-ray bursts (GRBs) and their afterglows (GRB tail, afterglow plateau, and postplateau) based only on the angular change of the relativistic Doppler boost across the outflow surface. We develop further the analytical formalism of the “larger-angle emission” model for the case of “n-exponential” outflows (where the Lorentz factor Γ dependence of the angular location θ is Γ ~ exp{-(θ/θ c ) n }), and compare its ability to account for the X-ray emission of XRT afterglows relative to that of “power-law” outflows (Γ ∼ θ −g ). Power-law outflows yield longer afterglow plateaus, followed by slower postplateau flux decays than n-exponential outflows, features which may be used in identifying which type of angular structure is at work in a given afterglow. Identifying the Γ(θ) angular structure that accommodates XRT light curves is slightly complicated by the fact that the afterglow X-ray light curve is also determined by how two characteristics of the comoving-frame emission spectrum (peak energy $E'_p$ and peak intensity $i'_p$) change with the angular location or, equivalently, with the Lorentz factor. Here, we assume power-law Γ dependences of those spectral characteristics and find that, unlike power-law outflows, n-exponential outflows cannot account for plateaus with a temporal dynamical range larger than 100 (2 dex in logarithmic space). To capture all the information contained in XRT afterglow measurements (0.3–10 keV unabsorbed flux and effective spectral slope), we calculate 0.3 and 10 keV light curves using a broken-power-law emission spectrum of peak energy and low- and high-energy slopes that are derived from the effective slope measured by XRT. This economical peak energy determination is found to be consistent with the results of more expensive spectral fits. The angular distributions of the Lorentz factor, comoving frame peak energy, and peak intensity (Γ(θ), $E'_p$(θ), $i'_p$(θ)) constrain the (yet-to-be determined) convolution of various features of the production of relativistic jets by solar-mass black holes and of their propagation through the progenitor/circumburst medium, while the $E'_p$(Γ) and $i'_p$(Γ) dependences may constrain the GRB dissipation mechanism and the GRB emission process.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

CO 2 to Fuels Through Novel Electrochemical Catalysis (Final Report)

Electrochemical upgrading of CO 2 into hydrocarbon fuels via CO 2 –H 2 O co-electrolysis using solid oxide electrolysis cells (SOECs) presents an encouraging approach to cost-effective storage of renewable power. In this study, we harness the unique property of protonic ceramic electrolysis cells (PCEC) and demonstrate direct electro-catalytic production of CH 4 from CO 2 –H 2 O in a unit-cell stack configuration. The unit-cell stack was developed to evaluate the scalability and the applicability of PCECs from a broader system-level viewpoint. Tradeoffs between operating conditions and performance metrics were explored and thoroughly characterized. An exceptional CH 4 -yield ratio of 34.6% from only CO 2 –H 2 O reactants was achieved under an electrolysis current of 1 A cm –2 at 450 °C. With exhaust-H 2 recycle, this value was boosted to greater than 70%. In addition, the electrochemical co-conversion of CO 2 –H 2 O offers a higher CH 4 -yield ratio compared to traditional thermo-catalytic conversion of CO 2 –H 2 under certain operating conditions, indicating possible electrochemical promotion of catalytic CO 2 methanation. The demonstrated good performance is a promising result for further scale-up and for the practical utilization of electrochemical CO 2 upgrade using PCECs. Techno-economic analyses reveal operating conditions that yield cost-competitive levelized cost of fuel production.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Microgrid's Role in Enhancing the Security and Flexibility of City Energy Systems

Smart cities depend on flexible and secure energy systems to ensure resilient power for critical infrastructure; however, recent weather-related events and cyberattacks have highlighted weaknesses in our energy systems, with the potential for widespread economic and security impacts. As stated by the Executive Office of the President, "the resilience of the US electric grid is a key part of the nation's defense against severe weather." To address the energy delivery security challenge, microgrids are rising as a viable solution that enhances the flexibility and resilience of the distribution grid and boosts the reliability of the local supply for the end-user. Traditionally, high capital investment has been a barrier to large-scale adoption of microgrid technology. Understanding the flexibility and resilience benefits of microgrids and accounting for the associated value streams can make the microgrid's proposition economically viable. In this chapter, microgrids' utility and their potential to serve as a flexible and resilient resource for the utility grid by providing capabilities such as peak shaving, demand response, and frequency regulation is presented. Moreover, other value streams, such as (1) their ability to island during a disaster and sustain critical loads which makes them a robust resilience solution for end-users, in the event of the utility grid outage and (2) microgrids also provide a flexible platform for integrating distributed energy resources in conjunction with storage and conventional generation technologies, strengthen microgrid's role in reducing the over-arching goal of emission reduction. Given the myriad of benefits associated with microgrids, we present strategies which can be employed for making microgrid itself resilient against physical and cyberthreats by employing hardware, software, and personnel training solutions to operate the microgrid before, during, and after a potential disaster. This chapter, thus, provides a holistic study of the microgrid as a resilience resource, for the utility grid, and a self-contained end-user for the end-user.

cyber-physical system↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Highly transparent silanized cellulose aerogels for boosting energy efficiency of glazing in buildings

Abstract To maintain comfortable indoor conditions, buildings consume ~40% of the energy generated globally. In terms of passively isolating building interiors from cold or hot outdoors, windows and skylights are the least-efficient parts of the building envelope because achieving simultaneously high transparency and thermal insulation of glazing remains a challenge. Here we describe highly transparent aerogels fabricated from cellulose, an Earth-abundant biopolymer, by utilizing approaches such as colloidal self assembly and procedures compatible with roll-to-roll processing. The aerogels have visible-range light transmission of 97–99% (better than glass), haze of ~1% and thermal conductivity lower than that of still air. These lightweight materials can be used as panes inside multi-pane insulating glass units and to retrofit existing windows. We demonstrate how aerogels boost energy efficiency and may enable advanced technical solutions for insulating glass units, skylights, daylighting and facade glazing, potentially increasing the role of glazing in building envelopes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

High-performance windows improve thermal survivability of occupants during cold snaps

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Krelling, Amanda F↗
DOE OSTI2021

A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys

Abstract The class of 9–12% Cr ferritic-martensitic alloys (FMA) and austenitic stainless steels have received considerable attention due to their numerous applications in high temperature power generation industries. To design high strength steels with prolonged service life requires a thorough understanding of the long-term properties, e.g., creep rupture strength, rupture life, etc., as a function of the chemical composition and processing parameters that govern the microstructural characteristics. In this article, the creep rupture strength of both 9–12% Cr FMA and austenitic stainless steel has been parameterized using curated experimental datasets with a gradient boosting machine. The trained model has been cross validated against unseen test data and achieved high predictive performance in terms of correlation coefficient ( $$R^{2} > 0.98 $$ R 2 > 0.98 for 9–12% Cr FMA and $$R^{2} > 0.95 $$ R 2 > 0.95 for austenitic stainless steel) thus bypassing the need for additional comprehensive tensile test campaigns or physical theoretical calculations. Furthermore, the feature importance has been computed using the Shapley value analysis to understand the complex interplay of different features.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Harnessing Machine Learning to Predict MoS 2 Solid Lubricant Performance

Physical vapor deposited (PVD) molybdenum disulfide (MoS 2 ) solid lubricant coatings are an exemplar material system for machine learning methods due to small changes in process variables often causing large variations in microstructure and mechanical/tribological properties. Here, in this work, a gradient boosted regression tree machine learning method is applied to an existing experimental data set containing process, microstructure, and property information to create deeper insights into the process-structure–property relationships for molybdenum disulfide (MoS 2 ) solid lubricant coatings. The optimized and cross-validated models show good predictive capabilities for density, reduced modulus, hardness, wear rate, and initial coefficients of friction. The contribution of individual deposition variables (i.e., argon pressure, deposition power, target conditioning) on coating properties is highlighted through feature importance. The process-property relationships established herein show linear and non-linear relationships and highlight the influence of uncontrolled deposition variables (i.e., target conditioning) on the tribological performance.

MoS2↗
DOE OSTI2024

An Experimental Investigation of Sintered Particle Effect on Heat Transfer Performance in an “Annular Flow” Evaporation Tube

Wicking structures have been widely used within passive heat transfer devices with high heat fluxes, such as heat pipes, to enhance their thermal performance. While wicking structures promote capillary pumping of the working fluid and thin film evaporation, they can result in capillary evaporation and further enhance the evaporation heat transfer. In this study, a 0.5 mm thick layer of 105 µm sintered copper particles was added to the inner wall of a copper tube, aiming to form an “annular flow” and enhance the heat transfer characteristics by taking advantage of thin film and capillary evaporation. Acetone was chosen as the working fluid, and the performance of an evaporation tube was tested for power inputs of 10, 30, 50, and 70 W. For each power input, trials were run at inclination angles varying from -90 deg to 90 deg to investigate the capillary effects. The temperature measurements showed that the temperature distribution along the evaporation tube is always downward sloping, meaning the temperature at the fluid inlet is larger than the outlet. In conclusion, results show that an “annular flow” formed by a thin layer of sintered particles can promote thin film and capillary evaporation and, therefore, boost the evaporation heat transfer coefficient.

42 ENGINEERING↗
DOE OSTI2020

Naphthalene‐Diimide‐Based Ionenes as Universal Interlayers for Efficient Organic Solar Cells

Abstract Self‐doping ionene polymers were efficiently synthesized by reacting functional naphthalene diimide (NDI) with 1,3‐dibromopropane ( NDI‐NI ) or trans ‐1,4‐dibromo‐2‐butene ( NDI‐CI ) via quaternization polymerization. These NDI‐based ionene polymers are universal interlayers with random molecular orientation, boosting the efficiencies of fullerene‐based, non‐fullerene‐based, and ternary organic solar cells (OSCs) over a wide range of interlayer thicknesses, with a maximum efficiency of 16.9 %. NDI‐NI showed a higher interfacial dipole (Δ), conductivity, and electron mobility than NDI‐CI , affording solar cells with higher efficiencies. These polymers proved to efficiently lower the work function ( WF ) of air‐stable metals and optimize the contact between metal electrode and organic semiconductor, highlighting their power to overcome energy barriers of electron injection and extraction processes for efficient organic electronics.

Liu, Ming↗
DOE OSTI2020

Naphthalene‐Diimide‐Based Ionenes as Universal Interlayers for Efficient Organic Solar Cells

Abstract Self‐doping ionene polymers were efficiently synthesized by reacting functional naphthalene diimide (NDI) with 1,3‐dibromopropane ( NDI‐NI ) or trans ‐1,4‐dibromo‐2‐butene ( NDI‐CI ) via quaternization polymerization. These NDI‐based ionene polymers are universal interlayers with random molecular orientation, boosting the efficiencies of fullerene‐based, non‐fullerene‐based, and ternary organic solar cells (OSCs) over a wide range of interlayer thicknesses, with a maximum efficiency of 16.9 %. NDI‐NI showed a higher interfacial dipole (Δ), conductivity, and electron mobility than NDI‐CI , affording solar cells with higher efficiencies. These polymers proved to efficiently lower the work function ( WF ) of air‐stable metals and optimize the contact between metal electrode and organic semiconductor, highlighting their power to overcome energy barriers of electron injection and extraction processes for efficient organic electronics.

Liu, Ming↗