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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ecological connectivity and in-kind mitigation in a regulatory decision framework: A case study with an amphibian habitat specialist

Ecological connectivity is critical to the survival and long-term viability of populations but is often overlooked in regulatory frameworks. We integrated landscape-level processes into a mitigation strategy for impacts to aquatic resources on the U.S. Department of Energy (DOE) Oak Ridge Reservation (ORR) in eastern Tennessee. Wetlands on the ORR, which contain significant breeding populations of the imperiled four-toed salamander (Hemidactylium scutatum) and tubercled rein orchid (Platanthera flava var. herbiola), will be impacted by construction of an environmental waste disposal facility under the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA). Here, we used a modified Kepner-Tregoe decision analysis to select general mitigation options that balanced regulatory requirements and interest group perspectives. We emphasized habitat connectivity through models that prioritized an area's importance to natural area connectivity (centrality) and maintenance of population structure for an affected habitat specialist (four-toed salamanders). We also emphasized in-kind mitigation through the preservation and enhancement of ecologically similar resources and the translocation and establishment of a new subpopulation of four-toed salamanders elsewhere on the ORR. We ultimately released over 500 juvenile salamanders that originated from the impacted site into the chosen mitigation wetlands. By doing so under the constraints of a time-sensitive CERCLA remediation effort and exceeding its substantive requirements, this work underscores feasibility. Ecological connectivity and the conservation of species that are not afforded explicit regulatory processes can be effectively and efficiently integrated into environmental decision-making and land use planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

EVALUATION OF HRA METHODOLOGIES FOR APPLICATION IN SDP WORK

This study critically evaluates human reliability analysis (HRA) methodologies applicable to regulatory probabilistic safety assessment (PSA) model, with a particular focus on their role in supporting the significance determination process (SDP) in nuclear safety assessment. Firstly, three widely utilized HRA methods – IDHEAS-ECA, SPAR-H, and ASEP/THERP – were qualitatively and quantitatively assessed. Qualitative assessments were conducted using attributes from the NEA/CSNI/R(2015)1 report, while quantitative evaluations employed regression and correlation analyses to compare predicted human error probabilities (HEPs) against empirical data. Results reveal distinct strengths, for example, IDHEAS-ECA’s robust predictive accuracy and K-HRA’s alignment with operational practices. In addition, dependency analysis and recovery analysis were critically evaluated. For dependency analysis, the methods’ handling of inter-task dependencies and their impact on HEPs were examined, while recovery analysis highlighted strategies for mitigating failure events. Furthermore, strategies were proposed to evaluate performance-shaping factors under conditions of reduced human performance, such as stress, fatigue, or cognitive overload, addressing specific challenges faced in SDP evaluations. Human errors from KINS’s operational performance information system event reports were evaluated as a case study. This study identifies gaps and provides actionable insights to ensure their validity and applicability in SDP HRA applications. This paper is a part of research conducted by KINS, and it should be noted that this result does not represent the regulatory position of KINS.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Preliminary System Interface Failure Mode Analysis for Two Advanced Reactors

This study explores two distinct Balance of Plant (BOP) configurations: the Rankine cycle for a sodium-cooled fast reactor (SFR) and the Brayton cycle for a gas-cooled reactor (GCR). As representative designs, the Power Reactor Innovative Small Module (PRISM) by GE Hitachi Nuclear Energy was selected for the SFR, while the Gas Turbine Modular Helium Reactor (GT MHR) by General Atomics was chosen for the gas-cooled reactor. Both configurations were adapted to deliver high-quality heat for industrial applications. A Failure Modes and Effects Analysis (FMEA) was conducted for each system to identify critical failure modes affecting key components. This study marks the first phase of a two-step design optimization approach, integrating FMEA into the design process. Based on the analysis, design modifications and mitigation strategies were proposed to enhance system resilience. The second phase, to be detailed in a subsequent report, will focus on the role of the control system in mitigating these failures. The FMEA serves as a foundation for defining the control topology, ensuring system resilience against component failures that could compromise essential functions, such as electricity and heat production.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

The potential for New Mexico basalts to sequester CO 2 with a focus on critical element mobility

This study evaluates the potential of New Mexico basaltic rocks and mafic mine waste for CO₂ sequestration via mineral carbonation. Batch experiments up to 200 days at 40°C simulated CO₂-bearing fluid interactions with various crushed basaltic materials at different grain sizes. Results showed that 70–80% of injected CO₂ was mineralized, with the finest grain sizes showing the most rapid and effective drawdown. The experiments also assessed the mobility of critical metals (Cu, Ni, Zn, Co), revealing trends tied to mineral dissolution and secondary mineral formation. The findings support the dual potential of mine waste for CO₂ sequestration and critical element recovery, informing future carbon capture and resource upcycling strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Evaluating CO 2 mitigation strategies in SAF biorefineries: Techno-economic and life cycle analysis

The aviation sector requires scalable decarbonization strategies, and lignocellulosic sustainable aviation fuel (SAF) represents a promising pathway. This study comparatively evaluates the techno-economic analysis and life cycle assessment (LCA) of three CO 2 management strategies integrated within a U.S.-based gasification–Fischer–Tropsch SAF biorefinery: (i) catalytic hydrogenation of captured CO 2 to methanol, (ii) geological CO 2 sequestration, and (iii) mineralization to sodium bicarbonate (NaHCO 3 ). Techno-economic analysis indicates that methanol synthesis requires approximately 26% higher capital investment and 33% higher operating costs than mineralization. Although methanol co-production generates the highest gross revenue, NaHCO 3 production reduces the SAF minimum selling price by approximately 38% relative to both methanol synthesis and geological sequestration pathways, reflecting a more balanced cost allocation through mineral co-product valorization. Geological sequestration lowers operating costs by nearly 50% compared with methanol synthesis but remains highly dependent on carbon credit mechanisms. LCA reveals substantial divergence in climate performance. Relative to methanol synthesis, sequestration improves net greenhouse gas performance by approximately 163%, transitioning the system from net-positive to net-negative emissions. Mineralization further enhances carbon mitigation, achieving roughly 85% greater carbon reduction than sequestration and over sixfold improvement relative to methanol synthesis within the defined system boundary. Sensitivity analysis identified hydrogen price, co-product market value, and process emissions as dominant drivers. Under baseline assumptions, CO 2 mineralization is found to offer the most balanced pathway.

Carbon capture and storage↗
DOE OSTI2025

Techno-economic evaluation of emission control configurations for AMP/PZ-based post-combustion CO 2 capture

Minimizing the environmental impacts of amine-based post-combustion carbon capture technologies is essential for regulatory compliance and public acceptance. Experimental campaigns at RWE's CO 2 capture pilot plant in Niederaussem using CESAR1 demonstrated that combining existing emission abatement technologies can substantially reduce the concentration of amines and degradation products in the CO 2 -depleted flue gas to below the detection limit of an infrared spectrometer. The campaign confirmed that a proprietary dry bed technology (OEASE aerozone™) or a second water wash can reduce AMP and PZ emissions to below 1 mg/Nm 3 . However, an acid or chemically active wash downstream of the water wash is necessary to reduce NH 3 emissions to very low levels (<2 mg/Nm 3 ). Combining a dry bed with an acid wash significantly reduces both the required acid solution and the resulting acid waste. The techno-economic analysis indicates that implementing emission mitigation technologies provides substantial environmental benefits for the CESAR1 process, with only a marginal increase (<1 €/tCO 2 ) in both the carbon capture cost (CCC) and CO 2 avoidance cost (CAC). The additional capital cost associated with extra column packing is offset by operational cost savings from reduced solvent losses. For stringent emission permits targeting NH 3 , a configuration combining a dry bed upstream of the water wash followed by an acid wash achieves the best balance between emission control and cost efficiency. This configuration results in a Levelized Cost of Electricity (LCOE) of 143 €/MWh, a CCC of 45.4 €/tCO 2 , and a CAC of 88.4 €/tCO 2 .

AMP↗
DOE OSTI2025

DESI 2024 VI: cosmological constraints from the measurements of baryon acoustic oscillations

We present cosmological results from the measurement of baryon acoustic oscillations (BAO) in galaxy, quasar and Lyman-α forest tracers from the first year of observations from the Dark Energy Spectroscopic Instrument (DESI), to be released in the DESI Data Release 1. DESI BAO provide robust measurements of the transverse comoving distance and Hubble rate, or their combination, relative to the sound horizon, in seven redshift bins from over 6 million extragalactic objects in the redshift range 0.1 < z < 4.2. To mitigate confirmation bias, a blind analysis was implemented to measure the BAO scales. DESI BAO data alone are consistent with the standard flat ΛCDM cosmological model with a matter density Ω m =0.295±0.015. Paired with a baryon density prior from Big Bang Nucleosynthesis and the robustly measured acoustic angular scale from the cosmic microwave background (CMB), DESI requires H 0 =(68.52±0.62) km s -1 Mpc -1 . In conjunction with CMB anisotropies from Planck and CMB lensing data from Planck and ACT, we find Ω m =0.307± 0.005 and H 0 =(67.97±0.38) km s -1 Mpc -1 . Extending the baseline model with a constant dark energy equation of state parameter w, DESI BAO alone requirew=-0.99 +0.15 -0.13 . In models with a time-varying dark energy equation of state parametrised by w 0 and w a , combinations of DESI with CMB or with type Ia supernovae (SN Ia) individually prefer w 0 > -1 and w a < 0. This preference is 2.6σ for the DESI+CMB combination, and persists or grows when SN Ia are added in, giving results discrepant with the ΛCDM model at the 2.5σ, 3.5σ or 3.9σ levels for the addition of the Pantheon+, Union3, or DES-SN5YR supernova datasets respectively. For the flat ΛCDM model with the sum of neutrino mass ∑ m ν free, combining the DESI and CMB data yields an upper limit ∑ m ν < 0.072 (0.113) eV at 95% confidence for a ∑ m ν > 0 (∑ m ν > 0.059) eV prior. These neutrino-mass constraints are substantially relaxed if the background dynamics are allowed to deviate from flat ΛCDM.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

An AI-Based 3D Bat Movement Tracking System at Wind Energy Facilities Using Multi-Thermal Video Cameras

The poster at the 15th Wind Wildlife Research Meeting discusses how to leverage the potential of real-time thermal-imaging methodologies in quantifying nocturnal bat activities at wind turbines, using 3D computer vision techniques within a deep learning framework. This innovation enables the automatic detection and classification of bats, birds, and insects in thermal-imaging videos captured at wind turbine sites, facilitating efficient and accurate data analysis for enhanced understanding and mitigation of bat-wind turbine interactions.

AI↗
DOE OSTI2025

An AI-Based 3D Bat Movement Tracking System at Wind Energy Facilities Using Multi-Thermal Video Cameras

The talk at the NAWEA Wind Tech 2024 conference discusses how to leverage the potential of real-time thermal-imaging methodologies in quantifying nocturnal bat activities at wind turbines, using 3D computer vision techniques within a deep learning framework. This innovation enables the automatic detection and classification of bats, birds, and insects in thermal-imaging videos captured at wind turbine sites, facilitating efficient and accurate data analysis for enhanced understanding and mitigation of bat-wind turbine interactions.

AI↗
DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗
DOE OSTI2025

Utility-Scale Operational Consequences for Solar Grid Services

This report delves into the critical aspects of grid services provided by solar inverter-based resources (IBRs), with an emphasis on the evolving landscape of microgrids, virtual power plants (VPPs), aggregators, and distributed energy resource management systems (DERMS). As the energy sector undergoes a transformative shift towards more decentralized and resilient grid architectures, understanding the multifaceted risks associated with these technologies becomes paramount. The report categorizes these risks into organizational, technical, and procedural domains, providing a thorough risk assessment framework that stakeholders can utilize to anticipate and mitigate potential issues. In addressing the increasing complexity of grid interconnections, the report highlights the importance of Cyber-Informed Engineering (CIE). By embedding engineering controls and cybersecurity measures into the early stages of system design, this approach aims to fortify grid infrastructure against emerging cyber threats. The analysis includes an exploration of best practices and strategies for integrating CIE principles to enhance grid security and resilience. To provide practical insights, the report conducts a detailed consequence analysis of various grid services and cyber mitigations that can be applied through the interconnection process. This analysis evaluates the potential impacts of different failure modes and vulnerabilities, offering a clear understanding of the consequences that could arise from disruptions within the energy grid. The findings are further enriched by a series of case studies that illustrate real-world scenarios and lessons learned from past incidents. Through this comprehensive examination of grid services and their criticality, the report aims to prepare industry professionals with the knowledge and tools necessary to navigate the complexities of modern energy systems. By providing a comprehensive approach that includes risk assessment, cybersecurity, and consequence analysis, solar stakeholders can more effectively guarantee the reliability, efficiency, and security of the energy grid.

14 SOLAR ENERGY↗
DOE OSTI2024

Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.

domain adaptation↗
DOE OSTI2024

Component Criticality in BESS for Cybersecurity

Battery energy storage systems (BESS), inverters, and associated digital equipment are integral pieces of interdependent energy delivery systems. When considering the supply chain security of such systems, there is often a misplaced focus on the origin of energy generation and storage materials like battery cells, overlooking the more significant cyber risks that stem from digital power electronics control systems. Part of this misplaced focus is due to the relative costs of these components, with more attention given to expensive raw materials rather than the impactful digital elements themselves. The Idaho National Laboratory (INL) is addressing this gap in supply chain security through a systems-of-systems approach that considers the impact various components in digital energy systems can have should misoperation occur. Therefore, INL assigns a cyber criticality score based on quantitative analysis, which enables informed prioritization of mitigations and allows operators to reduce risk in light of the prevalence of a BESS and associated systems foreign supply chain.

25 ENERGY STORAGE↗
DOE OSTI2025

Consequence Based Framework for Deployment of Cloud Solutions in the Digital Energy Transition

This study proposes a framework for evaluating cloud computing deployment in the electric sector, focusing on the digital transition of energy systems. It assesses the implications of cloud technology adoption, particularly in terms of security, operational resilience, and efficiency. The paper introduces a method for consequence-driven risk analysis, enabling utilities to prioritize and mitigate potential threats effectively. It also discusses the shared responsibility model in cloud computing, highlighting the need for collaborative security efforts. The research aims to provide utilities with a strategic assessment tool for cloud adoption, emphasizing the importance of security culture in enhancing cloud computing's role in critical infrastructure.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Consequence Based Framework for Deployment of Cloud Solutions in the Digital Energy Transition

This study proposes a framework for evaluating cloud computing deployment in the electric sector, focusing on the digital transition of energy systems. It assesses the implications of cloud technology adoption, particularly in terms of security, operational resilience, and efficiency. The paper introduces a framework for consequence-driven applied risk analysis, enabling utilities to prioritize and mitigate potential threats effectively, and responsibly deploy cloud applications. It also discusses the shared responsibility model in cloud computing, highlighting the need for collaborative security efforts. The research aims to provide utilities with a strategic assessment tool for cloud adoption, emphasizing the importance of security culture in enhancing cloud computing's role in critical infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

An assessment of China’s methane mitigation potential and costs and uncertainties through 2060

Abstract China, the world’s largest methane emitter, is increasingly focused on methane mitigation in support of its climate goals, but gaps exist in the understanding of key methane sources, as well as mitigation opportunities and their associated uncertainties. We use a bottom-up modeling approach with updated methane emission projections and abatement cost analysis to account for additional sources, uncertainties, and mitigation measures in China’s energy and agricultural sectors. Here we show the significant cost-effective potential for reducing methane emissions in China by 2030, with 660 million tonnes of carbon dioxide equivalent possible with average negative abatement costs of US$6.40 per tonne CO 2 e. Most of this potential exists in the energy sector, particularly coal mining, but the greater potential will shift towards agriculture by 2060. Aquaculture and biochar applications in rice cultivation have net economic benefits but need greater support for deployment, while new mitigation measures will be needed for remaining emissions from enteric fermentation, rice cultivation, and wastewater.

Science & Technology - Other Topics↗
DOE OSTI2025

Climate-focused Life Cycle Assessments of Biochar Production by an ARTi Pyrolysis Reactor and an Air Burners CharBoss® Air Curtain Incinerator

This report presents a limited, dynamic, consequential life cycle assessment (LCA) to compare the climate impacts of two biochar production methods using wood as a feedstock. The two methods are a pyrolysis reactor supplied by ARTi (Des Moines, IA, https://www.arti.com/) and a T26 CharBoss® air curtain incinerator supplied by Air Burners, Inc. (Palm City, FL, https://airburners.com/). The underlying LCA methodology is described in a chapter by Singh et al. (2024) and implemented in the form of a workbook freely available as online Supplementary Material for the chapter. For the convenience of the reader, a pre-print version of the relevant portions of Singh et al. (2024) is attached as Appendix A to this report. Specific assumptions and calculations to obtain the input parameters used in this LCA for each production method are described in the Methodology section below. This implementation of the LCA considers emissions associated with biomass loading, comminution and conversion, biochar decay in soil, and the production and use of bioenergy generated during the conversion process. The LCA is “limited” in that upstream emissions associated with biomass production, harvest, transportation, and land-use change, as well as embodied emissions in equipment and facilities are not considered. Similarly, downstream emissions from biochar transport and incorporation into soil (i.e., tillage), and the impact of biochar soil amendments on soil greenhouse gas (GHG) emissions (other than CO 2 from biochar decay in soil), soil organic carbon stocks, crop response, and surface albedo are not considered. As the intent is to compare different biochar production methods in a simple unbiased manner, the primary alternative biomass pathway for the LCA is immaculate combustion, which is the hypothetical instantaneous and complete conversion of carbon in the biomass to CO 2 at time zero without generation of any other greenhouse gases or aerosols (GHGAs) or any useful bioenergy. Use of this pathway provides relative values for the production methods and, when the embodied emissions are similar and the same feedstock is used, these relative values are reasonable approximations for those attained with a full LCA.

09 BIOMASS FUELS↗
DOE OSTI2025

Constraining primordial non-Gaussianity with DESI 2024 LRG and QSO samples

We analyse the large-scale clustering of the Luminous Red Galaxy (LRG) and Quasar (QSO) sample from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). In particular, we constrain the primordial non-Gaussianity (PNG) parameter f NL loc via the large-scale scale-dependent bias in the power spectrum using 1,631,716 LRGs (0.6 < z < 1.1) and 1,189,129 QSOs (0.8 < z < 3.1). This new measurement takes advantage of the enormous statistical power at large scales of DESI DR1 data, surpassing the latest data release (DR16) of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). For the first time in this kind of analysis, we use a blinding procedure to mitigate the risk of confirmation bias in our results. We improve the model of the radial integral constraint proposing an innovative technique allowing the correction through the window matrix convolution. We also carefully test the mitigation of the dependence of the target selection on the photometry qualities by incorporating an angular integral constraint contribution to the window function, and validate our methodology with the blinded data. Finally, combining the two samples, we measure f NL loc = -3.6 -9.1 +9.0 at 68% confidence, where we assume the universality relation for the LRG sample and a recent merger model for the QSO sample about the response of bias to primordial non-Gaussianity. Adopting the universality relation for the PNG bias in the QSO analysis leads to f NL loc = 3.5 -7.4 +10.7 at 68% confidence. Due to restricted selection in the LRG sample, the inclusion of the LRGs allows for 10% improvement. This measurement is the most precise determination of primordial non-Gaussianity using large-scale structure to date, surpassing the latest result from eBOSS by a factor of 2.3.

79 ASTRONOMY AND ASTROPHYSICS↗