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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An Active Oxygen Electrode for Proton-Conducting Solid Oxide Electrolysis Cells with High Faradaic Efficiency

Addressing the challenges posed by inferior electrochemical performance at low temperatures and the uncertain Faradaic efficiency (FE) represents a pivotal undertaking in the development of high performance and efficient proton-conducting solid oxide electrolysis cells (P-SOECs). In this work, a novel oxygen electrode material BaCo0.8Zr0.1Zn0.1O3-d (BCZZ) is first designed and synthesized. At 600 °C, P-SOECs with BCZZ oxygen electrode achieve an electrolysis current density of 1.98 A cm-2 with an ˜90% FE at 1.3 V. Utilizing 1-inch P-SOECs as a reliable platform, the effect of extrinsic operating conditions (i.e., steam concentration, voltage, current density, and temperature) and intrinsic properties of P-SOECs (i.e., electrolyte material and electrolyte thickness) on FE are further systemically investigated, both experimentally and theoretically.

08 - HYDROGEN↗
DOE OSTI2026

Calibration of urban building energy model using smart meter data for district peak load prediction

Urban building energy modeling (UBEM) is a powerful approach to assessing baseline building energy performance and retrofits with new technologies across building stocks in cities. However, the accuracy of UBEM is often constrained by the limited availability of reliable data about building characteristics and operations, such as envelope efficiency levels, HVAC system performance, and end-use load patterns. Existing research has performed UBEM calibration using annual or monthly energy consumption data, which falls short when higher-resolution time series applications are needed, such as peak load prediction for utility operation planning. This study presents a new framework for calibrating building energy models at urban scale using smart meter data, targeting the accurate prediction of summer peak electricity loads to support robust grid planning. The framework first integrates various data sources to enhance baseline input assumptions for building models, and then calibrates the baseline models through a pattern-matching approach. A case study using CityBES and two years of AMI data from over 9000 residential customers in Portland, Oregon, demonstrated the workflow and its effectiveness. The calibrated models achieved a daily peak load mean absolute percentage error of 2.6 % during the heatwave in the calibration year, and 2.0 % in the validation year using another year of AMI data. Using the calibrated models, we analyzed the demand flexibility potential of the district building stock as an application of UBEM calibration. The findings affirm the appropriate use of UBEM for peak electric load forecasting and demand side management at the utility distribution system level.

AMI data↗
DOE OSTI2023

Desiccants enabling energy-efficient buildings: A review

Buildings account for about 39% of the total energy consumption in the United States. Developing highly energy-efficient and environmentally friendly systems that are either actively or passively integrated into buildings plays a crucial role in decarbonizing the building sector. Among various technologies, desiccant-based energy systems have received particular attention in recent years due to their unique advantages, such as being thermally driven using low-grade waste or solar energies and being reliable over a wide range of operating ranges. Extensive research efforts have been exerted on desiccant-enabled technologies at both material and system levels aiming to increase their performance and achieve high technological readiness levels. The present review paper comprehensively discusses research works made at the system level. It overviews desiccant-based air conditioning systems, desiccant-based humidity pumps, desiccant-based thermal energy storage systems, and desiccant-based appliances. The study identifies challenges and opportunities to accelerate the commercialization of desiccant-enabled technologies. It is found that desiccant materials offer great promise to improve energy efficiency and functionality of future buildings through decoupling the latent and sensible cooling loads in air conditioning systems, humidity pumps integrated into building facades, and next-generation appliances. However, commercial viability and widespread acceptance of desiccant-based systems have been hampered by several major obstacles, including liquid and air flow mal-distribution and inferior thermo-physical properties of desiccant materials resulting in low ab/adsorption and regeneration rates and bulky/costly systems.

25 ENERGY STORAGE↗
DOE OSTI2026

WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs With Physics‐Informed Neural Networks

Accurate representation of pumping wells is essential for reliable reservoir characterization and simulation of operational scenarios in subsurface flow models. Physics-informed neural networks (PINNs) are emerging as a promising alternative to numerical models for reservoir modeling, offering seamless integration of monitoring data and governing physical equations. However, existing PINN-based studies face major challenges in capturing fluid pressure near wells when using a source/sink term, particularly during the early stages after pumping begins. We address this problem by introducing WellPINN, a workflow in which an initially trained PINN infers fluid pressure across the entire reservoir domain using a large equivalent well radius. This initial PINN solution is then locally refined around the well by a set of subdomain PINNs that are trained for smaller equivalent well radii. Continuity across these subdomain interfaces as well as at the initial condition is ensured by hard-constraining each PINN on its subdomain boundary. Our results demonstrate WellPINN as the first workflow of its kind to focus on accurate inference of fluid pressure from pumping rates throughout the entire injection period, significantly advancing the potential of PINNs for inverse modeling and operational scenario simulations. All data and code for this paper are openly available at https://doi.org/10.20350/DIGITALCSIC/17260.

58 GEOSCIENCES↗
DOE OSTI2021

Proton beam quality enhancement by spectral phase control of a PW-class laser system

We report on experimental investigations of proton acceleration from solid foils irradiated with PW-class laser-pulses, where highest proton cut-off energies were achieved for temporal pulse parameters that varied significantly from those of an ideally Fourier transform limited (FTL) pulse. Controlled spectral phase modulation of the driver laser by means of an acousto-optic programmable dispersive filter enabled us to manipulate the temporal shape of the last picoseconds around the main pulse and to study the effect on proton acceleration from thin foil targets. The results show that applying positive third order dispersion values to short pulses is favourable for proton acceleration and can lead to maximum energies of 70 MeV in target normal direction at 18 J laser energy for thin plastic foils, significantly enhancing the maximum energy compared to ideally compressed FTL pulses. The paper further proves the robustness and applicability of this enhancement effect for the use of different target materials and thicknesses as well as laser energy and temporal intensity contrast settings. We demonstrate that application relevant proton beam quality was reliably achieved over many months of operation with appropriate control of spectral phase and temporal contrast conditions using a state-of-the-art high-repetition rate PW laser system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Predicting the chemical reactivity of organic materials using a machine-learning approach

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system. In configuring devices such as batteries or solar cells, not only the functionality of individual constituting materials such as electrodes or electrolyte but also an appropriate combination of materials which do not undergo unwanted side reactions is critical in ensuring their reliable performance in long-term operation. While the universal theory that can predict the general chemical reactivity between materials is long awaited and has been the subject of studies with a rich history, traditional ways proposed to date have been mostly based on simple electronic properties of materials such as electronegativity, ionization energy, electron affinity and hardness/softness, and could be applied to only a small group of materials. Moreover, prediction has often been far from accurate and has failed to offer general implications; thus it was practically inadequate as a selection criterion from a large material database, i.e. data-driven material discovery. Herein, we propose a new model for predicting the general reactivity and chemical compatibility among a large number of organic materials, realized by a machine-learning approach. As a showcase, we demonstrate that our new implemented model successfully reproduces previous experimental results reported on side-reactions occurring in lithium–oxygen electrochemical cells. Furthermore, the mapping of chemical stability among more than 90 available electrolyte solvents and the representative redox mediators is realized by this approach, presenting an important guideline in the development of stable electrolyte/redox mediator couples for lithium–oxygen batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Radiation-resilient InAs quantum dot lasers

Displacement damage from particle radiation increases the threshold current of semiconductor lasers, impeding the deployment of photonic systems in harsh radiation environments. Replacing conventional quantum well (QW) gain regions with quantum dots (QDs) has been shown to suppress the radiation-induced increase in threshold current owing to three-dimensional carrier confinement, which effectively segregates charge carriers away from radiation-induced defects. However, radiation-induced change in threshold current is also affected by extrinsic design elements of a laser, such as photon lifetime, and therefore provides only a partial assessment of the radiation hardness. In this work, we advance the understanding of the radiation resiliency of InAs QD lasers by extracting the change in the nonradiative recombination rate after radiation exposure, which provides a more fundamental assessment of the intrinsic radiation hardness of InAs QD gain regions. We find that carriers that populate InAs QD states have a nonradiative recombination rate that is over an order-of-magnitude less sensitive to neutron radiation relative to carriers in InGaAsP QWs. InAs QD lasers must be designed such that carriers primarily occupy QD states, preventing carrier spillover into surrounding barrier layers to obtain the maximum benefit. Furthermore, we show that enhanced radiation hardness is observed in InAs QD lasers grown on both highly lattice-mismatched silicon substrates and native GaAs substrates, further broadening the appeal of InAs QD lasers as reliable light sources for photonic systems operating in harsh radiation environments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Glass-contact refractory of the nuclear waste vitrification melters in the United States: a review of corrosion data and melter life

The performance of the refractory lining in glass melters used for nuclear waste vitrification is critical to the melter reliability for long-term continuous operation. Monofrax® K-3, a high Cr 2 O 3 fused cast refractory material, has been widely used to build the liners of nuclear waste glass melters in the United States. Corrosion behaviour of Monofrax® K-3 refractory has been evaluated based on crucible-scale testing, inspection of the refractory components following scaled melter testing, and inspections of the Defense Waste Processing Facility (DWPF) melter refractory after service. The literature generally consists of empirical models based on short-term testing to describe refractory corrosion dependence on glass composition. Corrosion data from tests with longer testing times, at various temperatures, in the presence of molten salts, and with different redox reactions in the plenum atmosphere exist, may be insufficient to provide accurate refractory service life estimates. Additionally, the corrosion data collected under actual and scaled melter operating conditions are limited. Recommendations to achieve more direct correlation between the laboratory refractory corrosion data predictions and the observed melter service life are discussed to allow for more accurate predictions of the useful life of melter refractory linings.

Jin, Tongan↗
DOE OSTI2023

Detection of False Data Injection Attacks in Battery Stacks Using Input Noise-Aware Nonlinear State Estimation and Cumulative Sum Algorithms

Grid-scale battery energy storage systems (BESSs) are vulnerable to false data injection attacks (FDIAs), which could be used to disrupt state of charge (SoC) estimation. Inaccurate SoC estimation has negative impacts on system availability, reliability, safety, and the cost of operation. In this article a combination of a Cumulative Sum (CUSUM) algorithm and an improved input noise-aware extended Kalman filter (INAEKF) is proposed for the detection and identification of FDIAs in the voltage and current sensors of a battery stack. The series-connected stack is represented by equivalent circuit models, the SoC is modeled with a charge reservoir model and the states are estimated using the INAEKF. Further, the root mean squared error of the states’ estimation by the modified INAEKF was found to be superior to the traditional EKF. By employing the INAEKF, this article addresses the research gap that many state estimators make asymmetrical assumptions about the noise corrupting the system. Additionally, the INAEKF estimates the input allowing for the identification of FDIA, which many alternative methods are unable to achieve. The proposed algorithm was able to detect attacks in the voltage and current sensors in 99.16% of test cases, with no false positives. Utilizing the INAEKF compared to the standard EKF allowed for the identification of FDIA in the input of the system in 98.43% of test cases.

25 ENERGY STORAGE↗
DOE OSTI2025

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05) .

Marfo, William [Univ. of Texas, El Paso, TX (Unite↗
DOE OSTI2025

ACTIVE

The Automated Control Testbed for Integration, Verification, and Emulation (ACTIVE) framework is a software platform designed to support the optimized operation and management of a wide range of building types. It enables the development, testing, and validation of diverse control strategies, including AI-based, rule-based, and model-based approaches. The platform facilitates a seamless transition from simulation-based evaluation of control strategies to real-world field validation and deployment. ACTIVE supports the full building management lifecycle, encompassing data acquisition and management, system monitoring, optimized control, adaptive learning services, device dispatch and coordination, as well as advanced analytics and visualization. Together, these capabilities provide an integrated environment for improving building performance, operational efficiency, reducing energy cost, and reliability.

Smith, Robert [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2023

Addressing Data Center Cooling Needs through the Use of Subsurface Thermal Energy Storage Systems

This study aims to evaluate the feasibility of addressing the cooling needs for information technology (IT) equipment in data centers by using reservoir thermal energy storage (RTES) to provide reliable and sustainable low-temperature fluid. This project focuses on the technical viability of operating such a system in Houston, Texas, which is a representative location for crypto mining data centers. An analysis has been performed to investigate the technical feasibility with climate data for Houston. Results show that data centers on the scale of 30 MW, and operating at a temperature of 27 degrees C, can be reliably cooled by a combined RTES and dry coolers setup. A techno-economic analysis will be performed and energy/water saving benefits will be quantified in the future. In addition, the study will be extended to other representative data center locations with different climates and geographical locations.

cooling↗
DOE OSTI2020

Abstract for CRADA among the National Energy Technology Laboratory, National Technology & Engineering Solutions of Sandia, The Regents of the University of California, and Tri-State Generation and Transmission Association, Inc. (AGMT-0855)

The objective of this CRADA is to facilitate cooperation among Tri-State Generation and Transmission Association, Inc. and collaborators of the Institute for the Design of Advanced Energy Systems (IDAES), including the National Energy Technology Laboratory, Sandia National Laboratories, and Lawrence Berkeley National Laboratory. The goal is to develop steady-state and dynamic optimization models of power plants. Tri-State will provide data related to the design and operation of power plants, which will be used to validate the IDAES team’s models and tools. These models will provide insight into potential improvements which could improve heat rate, reduce operations and maintenance costs, and/or improve reliability of the existing fleet of coal plants in the United States.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Development of Gyrosole™ and Solar Thermal HVAC Technology for 20-60kW Applications, Final Report, CRADA No. TC02164.0

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Sol Xorce LLC to develop LLNL’s Gyrosole™ solar thermal energy system. The original scope of the CRADA project was to develop LLNL’s Gyrosole™thermal-powered motor technology for solar thermal energy system use, and was originally entitled, "Development of Gyrosole™ Solar Thermal Technology for 20-60 kW Applications". The scope and title of the project were later modified under Amendment One to the CRADA. The technical objective of this CRADA project was to develop Gyrosole™ motor technology so that it is marketplace-ready, and to produce a motor prototype that: Produces 50kW power at >22% efficiency; Operates oil-free; Has sufficient reliability and lifetime to be suitable for both commercial and residential applications; Costs less than $100 per kW installed to produce at scale; and, Can be combined cost-effectively to produce 200kW. The motor prototype would be integrated into model systems that provide electricity generation in combination with air/water heating, heat-pump driven cooling, desiccant dehumidification, and light industrial heat supply.

14 SOLAR ENERGY↗
DOE OSTI2021

Impact of High Penetration Distributed Energy Resources on the Bulk Electric System

The growth of distributed energy resources (DERs), mostly generation using intermittent renewable energy sources, along with the retirement of central generation (mostly conventional power plants using fossil-fuel-based resources) has implications for the steady-state and dynamic performance of the bulk electric system (BES). Among the DERs connected to the distribution system, photovoltaic (PV) systems are the most prevalent and have been installed at an increasing rate. Until recently, the penetration levels of DERs have not been high enough to create significant impacts on the reliability and security of power system operation. However, in recent years, the penetration levels of DERs have risen to a level that their impacts on the bulk electric system should be considered in a detailed fashion in planning and operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Risk-Informed Analysis for Enhanced Resilient Nuclear Power Plant with Initiatives including ATF, FLEX, and Advanced Battery Technology

This report documents the activities performed by the Idaho National Laboratory (INL) during fiscal year (FY) 2021 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk-Informed Systems Analysis (RISA) Pathway, Enhanced Resilient Plant (ERP) Systems research. The purpose of the RISA Pathway research and development is to support plant owner-operator decisions with the aim to improve the economics, reliability, and maintain the high levels of safety of current nuclear power plants over periods of extended plant operations. The concept of ERP refers to the combinations of accident-tolerant fuel (ATF), optimal use of diverse and flexible coping strategy (FLEX), enhancements to plant components and systems, the incorporation of augmented or new passive cooling systems, the advanced battery technology with extended capacity, as well as improved fuel cycle efficiency. The objective of the ERP research effort is to use the RISA methods and toolkit in industry applications, including methods development and early demonstration of technologies, in order to enhance existing reactors? safety features and to substantially reduce operating costs through risk-informed approaches to plant design modifications to the plant and their characterization. The ERP R&D efforts in FY 2021 are focused on three industry initiatives, including accident-tolerant fuel (ATF), optimal use of diverse and flexible coping strategy (FLEX), and advanced battery technology with extended capacity.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Considerations of Radiation-Hardened Electronics for Alpha Detectors in Molten Salt Reactors

Along with advanced generations of reactors quickly becoming reality, the need for instrumentation to provide reliable information for their safety and operation is also becoming relevant. Instrumentation-wise, molten salt reactors (MSRs) represent an especially challenging case in which detectors must survive extreme temperatures, high amounts of radiation, and a highly corrosive coolant. Adding to the complexity, tight material control and accounting must be maintained with any special nuclear material (SNM), a practice made more difficult by the nature of a loop of flowing fuel. Alpha spectroscopy offers a unique window into investigating the isotopic concentrations of actinides within just such a molten salt environment. The characteristic α emissions of specific actinide isotopes in the salt enable quantification of their concentrations. Semiconductor devices based on wide bandgap materials such as silicon carbide and gallium oxide can withstand the extreme temperatures within MSRs and offer good chemical stability with the corrosive salt. Collaborators at The Ohio State University are fabricating and testing a β gallium oxide α particle sensor that could identify actinide concentrations within the fuel salt to help improve the material control and accounting required for complicated system inherent to MSRs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Powering the Blue Economy Foundational Research & Development Topics Reliability for Marine Energy Power Systems

Ocean observing platforms are deployed across the world’s oceans to collect meteorological and oceanographic data. Current technology makes use of solar panels and battery banks to provide power to these platforms, which can be challenging at high latitudes due to lack of solar insolation and cold temperatures. Marine renewable energy (MRE) generation offers a potential improvement in consistent power generation throughout the year for this use on ocean observation buoys. This report analyzes existing reliability of high latitude coastal weather buoys and tsunami detection buoys to determine performance standards that MRE generation sources must meet to become viable power sources for these remote systems. Using measurements recorded from the U.S. National Data Buoy Center’s coastal weather buoys and tsunami detection buoys, the analysis calculated the historic data availability – or the percentage of time a measurement is recorded – to evaluate the reliability of ocean observation buoys. The results show that coastal weather buoys operate at 74% data availability and the tsunami detection systems operate at an average 72% data availability. The results describe the total reliability of the system, including many different types of failures not associated with the power system such as mooring failures or equipment failures caused by extreme weather. Power system outages may only account for a small portion of the system failures. Reliability is an important design criterion for the power system of ocean observation platforms. Future power systems that would be designed for these buoys, such as an MRE generator, need to have a minimum reliability of 72% and 74% to meet the overall performance of the weather buoys and tsunami buoys, respectively. However, power system reliability likely needs to be much higher than overall system reliability, so that power system outages do not negatively impact the overall performance of the buoy.

42 ENGINEERING↗