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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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DOE OSTI2024

Utah FORGE 5-2615: Determination and Analysis of Thermo-poromechanical Response of Fractured Rock - 2024 Annual Workshop Presentation

This is a presentation on the Determination and Modeling-Informed Analysis of Thermo-poromechanical Response of Fractured Rock for Application to FORGE by the University of Oklahoma, presented by Ahmad Ghassemi. This video presentation discusses how to improve understanding and control of coupled thermoporomechanical (or thermo-hydro-mechanical-THM) processes in reservoir development, and to demonstrate its role in interpretations of the fracture closure pressure. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 13-15, 2024.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

System and method for structural characterization of materials by supervised machine learning-based analysis of their spectra

A method of supervised machine learning-based spectrum analysis information, using a neural network trained with spectrum information, to identify a specified feature of a given material, a system for supervised machine learning-based spectrum analysis, and a method of training a neural network to analyze spectrum data. The method of supervised machine learning-base spectrum analysis comprises inputting into the neural network spectrum data obtained from a sample of the given material; and the neural network processing the spectrum data, in accordance with the training of the neural network, and outputting one or more values for the specified feature of the sample of the material. In an embodiment, the training set of data includes x-ray absorption spectroscopy data for the given material. In an embodiment, the training set of data includes electron energy loss spectra (EELS) data.

Frenkel, Anatoly↗
DOE OSTI2024

GeoBridge: Connecting Communities to Geothermal Information and Opportunities: Preprint

The geothermal community is well established with long-standing events, organizations, and tools that are known across the geothermal community. But many of these tools and resources are located behind pay walls, require memberships, or are otherwise difficult to find, especially for people looking to join the geothermal community. These barriers to access can prevent outsiders from discovering valuable geothermal resources, limiting the geothermal community's potential for collaboration with other communities, such as clean energy entrepreneurs looking to expand into geothermal energy. The Department of Energy's (DOE) GeoBridge serves to bring these communities together by acting as a single, publicly accessible, searchable portal that facilitates easy access to available geothermal knowledge and information. It works to expand and diversify the pool of geothermal stakeholders by providing in-roads to geothermal information and community resources. It helps build a stronger geothermal community; one inclusive of individuals and groups from a variety of different backgrounds, including potential investors and start-up companies looking to accelerate innovation in geothermal technologies. By linking communities to geothermal information, analysis and expertise, GeoBridge serves as a launch point, directing interested parties to existing data and tools, events, educational resources, STEM programs, permitting and regulatory information, and other resources that can be used to evaluate, promote, and discover geothermal opportunities.

access↗
DOE OSTI2025

GeoBridge: Connecting Communities to Geothermal Information and Opportunities

The geothermal community is well established with long-standing events, organizations, and tools that are known across the geothermal community. But many of these tools and resources are located behind pay walls, require memberships, or are otherwise difficult to find, especially for people looking to join the geothermal community. These barriers to access can prevent outsiders from discovering valuable geothermal resources, limiting the geothermal community's potential for collaboration with other communities, such as clean energy entrepreneurs looking to expand into geothermal energy. The Department of Energy's (DOE) GeoBridge serves to bring these communities together by acting as a single, publicly accessible, searchable portal that facilitates easy access to available geothermal knowledge and information. It works to expand and diversify the pool of geothermal stakeholders by providing in-roads to geothermal information and community resources. It helps build a stronger geothermal community; one inclusive of individuals and groups from a variety of different backgrounds, including potential investors and start-up companies looking to accelerate innovation in geothermal technologies. By linking communities to geothermal information, analysis and expertise, GeoBridge serves as a launch point, directing interested parties to existing data and tools, events, educational resources, STEM programs, permitting and regulatory information, and other resources that can be used to evaluate, promote, and discover geothermal opportunities.

access↗
DOE OSTI2023

LA100 Equity Strategies. Chapter 3: Community-Guided Energy Equity Strategies

The LA100 Equity Strategies project synthesizes community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles' clean energy transition. Grounded in the analysis of past and ongoing energy inequities and engagement with underserved communities, the project presents community-guided strategies that aim to operationalize recognition and procedural justice. Chapters 1 and 2 target the problem space - the causal factors, impact areas, and values affecting LA's energy justice landscape. This chapter threads those themes through to begin focusing on the solution space. We examine community-identified problems and solutions through the lens of recognition and procedural justice, presenting analysis and strategies that form the basis for more equitable outcomes in LA's energy transition. In our listening sessions, underserved Angelenos highlighted the need to transition away from the status quo one participant described as "transactional extraction of information to check the box. To say yes, we engaged." She asked the LA Department of Water and Power (LADWP) to approach her community with respect and transparency, stating, "We consider you all to be experts in your community, and we'd like to authentically engage with you in the decision-making process. So, I do think there needs to be some intentional actions for that rapport building and that trust building." The community-informed analysis and strategies described in this chapter, which are foundational to the LA100 Equity Strategies project, rise to the challenge of engaging authentically to build rapport, establish relationships of respect, and meaningfully involve Angelenos in the decision-making process. LADWP is already making concerted efforts to redress a disproportionate distribution of investments in physical infrastructure and energy efficient technologies in Los Angeles. This chapter concentrates on the challenge to further rectify past and ongoing inequities in the social, cultural, and institutional scaffolding of Los Angeles. We examine community-guided strategies to tackle this challenge, informed by community input on how all Angelenos can equitably access green jobs and affordable, safe, and resilient energy services, technologies, and programs. These actionable strategies can help move energy equity programs from plans to applied practices, supporting LADWP in launching a just and equitable clean energy transition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Effect of Impurities on the Redox Properties of Goethite

Iron oxide minerals regulate the flux of electrons in the environment and are important hosts for trace and minor, yet critical, elements. Here, we present the first evidence of a direct link between the local coordination environments of Ni and Zn and the redox properties of their host phase goethite (α-FeOOH), the most abundant Fe(III) (oxyhydr)oxide at Earth’s surface. Here, we used aqueous redox measurements to show that the redox potential E H 0 , and hence the mineral’s stability, follows the order: pure goethite ≥ Zn-goethite > Ni-goethite. Parallel X-ray absorption and scattering measurements demonstrate, using quantum-informed analysis, that the local coordination environment of the smaller impurity, Ni, causes more bulk strain energy than Zn, which nearly accounts for the difference in E H 0 between Ni- and Zn-goethite. Our theory-informed, experimental study reveals how two common impurities affect the stability of goethite with implications for the biogeochemical reactivity of Fe(III) (oxyhydr)oxide in mediating elemental and electron fluxes in the environment.

42 ENGINEERING↗
DOE OSTI2024

Source term analysis of FeCrAl accident tolerant fuel using MELCOR

It has been established that incremental improvements in beyond design basis accident performance can be achieved through accident tolerant fuel (ATF). However, they have the potential to recover margin with respect to conventional fuel and therefore enhance plant economics through uprate or cycle length increase. To realize this potential, it is necessary to quantify the reduction in source term due to use of ATF, and correspondingly how this is affected by increasing the cycle length and/or burnup. This requires development of a risk informed analysis methodology for ATF under high burnup conditions, which is being developed within LWRS. To this end, a MELCOR simulation of FeCrAl ATF in a recovered Large Break LOCA (LBLOCA) scenario has been developed, using a model based on the Zion Pressurized Water Reactor (PWR). The new user defined material capability, along with the inclusion of detailed neutronics- and depletion-derived parameters such as core power distribution, decay heat behavior, and fission product inventories, allows a more detailed simulation of FeCrAl clad material properties and behavior than has previously been possible using MELCOR. These detailed FeCrAl results were compared with a zircaloy clad model to investigate the differences between the two clad types and quantify the benefits of FeCrAl ATF with an 18-month cycle. Next, the fuel cycle with ATF was extended to 24 months, to determine whether any additional safety margin provided by FeCrAl ATF could be leveraged to implement high burnup FeCrAl-clad fuel while retaining the same operating and safety limits as current zircaloy-clad fuel. For the particular scenario analyzed, the delay to fuel failure from using ATF was of the same order as the LOCA recovery time, and hence significant in reducing fission product release, with the 24-month FeCrAl core performing better than the 18-month zircaloy core. It is noted that for other transients, the reduction in release due to using FeCrAl may be less significant. Furthermore, the material model developed here can be used in such further studies in support of determining the overall source term.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Energy Storage Grand Challenge: Energy Storage Market Report

As part of the Department of Energy's (DOE) Energy Storage Grand Challenge (ESGC), DOE intends to synthesize and disseminate best available energy storage data, information, and analysis to inform decision-making and accelerate technology adoption. The ESGC Roadmap provides options for addressing technology development, commercialization, manufacturing, valuation, and workforce challenges to position the United States for global leadership in the energy storage technologies of the future. This report provides a baseline understanding of the numerous, dynamic energy storage markets that fall within the scope of the ESGC via an integrated presentation of deployment, investment, and manufacturing data from the best, publicly-available sources. This report covers the following energy storage technologies: lithium ion batteries, lead acid batteries, pumped storage hydropower, comrpessed air energy storage, redox flow batteries, hydrogen, building thermal energy storage, and select long duration energy storage technologies. Not all energy storage technologies and markets could be addressed in this report. Due to the wide array of energy technologies, market niches, and data availability issues, this market report only included a select group of technologies. For example, thermal energy storage technologies are very broadly defined and cover a wide range of potential markets, technology readiness levels, and primary energy sources. In other areas, data scarcity necessitates a greater understanding of future applications and emerging science. Future efforts will update data presented in this report and be expanded to include other energy storage technologies. This data-driven assessment of the current status of energy storage markets is essential to track progress toward the goals described in the Energy Storage Grand Challenge and inform the decision-making of a broad range of stakeholders. At the same time, gaps identified through the development of this report can point to areas where further data collection and analysis could provide an even greater level of understanding of the full range of markets and technologies. Finally, numerous complementary analyses are planned, underway, or completed that will provide a deeper understanding of the specific technologies and markets covered at a high-level in this report.

25 ENERGY STORAGE↗
DOE OSTI2024

Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling

Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.

13 HYDRO ENERGY↗
DOE OSTI2021

Quantifying the Long‐Range Coupling of Electronic Properties in Proteins with ab initio Molecular Dynamics**

Abstract The delicate interplay of covalent and non‐covalent interactions in proteins is inherently quantum mechanical and highly dynamic in nature. To directly interrogate the evolving nature of the electronic structure of proteins, we carry out 100‐ps‐scale ab initio molecular dynamics simulations of three representative small proteins with range‐separated hybrid density functional theory. We quantify the nature and length‐scale of the coupling of residue‐specific charge probability distributions in these proteins. While some nonpolar residues exhibit expectedly narrow charge distributions, most polar and charged residues exhibit broad, multimodal distributions. Even for nonpolar residues, we observe sequence‐specific deviations corresponding to charge accumulation or depletion that would be challenging to capture in a fixed charge force field. We quantify the effect of residue‐residue interactions on charge distributions first with linear cross‐correlations. We then show how additional insight can be gained from evaluating the mutual information of charge distributions. We show that a significant number of residues couple most strongly with residues that are distant in both sequence and space over a range of secondary structures including α‐helical, β‐sheet, disulfide bridging, and lasso motifs. The mutual information analysis is necessary to capture coupling between some polar and charged residues that would be otherwise missed.

Yang, Zhongyue↗
DOE OSTI2021

Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency

Recent advances in machine learning and computer vision brought to light technologies and algorithms that serve as new opportunities for creating intelligent and efficient manufacturing systems. In this study, the real-time monitoring system of manufacturing workflow for the Smart Connected Worker (SCW) is developed for the small and medium-sized manufacturers (SMMs), which integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems. Specifically, object detection and text recognition models are investigated and adopted to ameliorate the labor-intensive machine state monitoring process, while artificial neural networks are introduced to enable real-time energy disaggregation for further optimization. The developed system achieved efficient supervision and accurate information analysis in real-time for prolonged working conditions, which could effectively reduce the cost related to human labor, as well as provide an affordable solution for SMMs. The competent experiment results also demonstrated the feasibility and effectiveness of integrating machine learning technologies into the realm of advanced manufacturing systems.

42 ENGINEERING↗
DOE OSTI2021

Pangenomics reveals alternative environmental lifestyles among chlamydiae

Chlamydiae are highly successful strictly intracellular bacteria associated with diverse eukaryotic hosts. Here we analyzed metagenome-assembled genomes of the “Genomes from Earth’s Microbiomes” initiative from diverse environmental samples, which almost double the known phylogenetic diversity of the phylum and facilitate a highly resolved view at the chlamydial pangenome. Chlamydiae are defined by a relatively large core genome indicative of an intracellular lifestyle, and a highly dynamic accessory genome of environmental lineages. We observe chlamydial lineages that encode enzymes of the reductive tricarboxylic acid cycle and for light-driven ATP synthesis. We show a widespread potential for anaerobic energy generation through pyruvate fermentation or the arginine deiminase pathway, and we add lineages capable of molecular hydrogen production. Genome-informed analysis of environmental distribution revealed lineage-specific niches and a high abundance of chlamydiae in some habitats. Together, our data provide an extended perspective of the variability of chlamydial biology and the ecology of this phylum of intracellular microbes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗
DOE OSTI2023

Diel, seasonal, and inter-annual variation in carbon dioxide effluxes from lakes and reservoirs

Abstract Accounting for temporal changes in carbon dioxide (CO 2 ) effluxes from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of flux measurements of CO 2 based on the eddy covariance method from 13 lakes and reservoirs in the Northern Hemisphere, and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO 2 flux at all sites. By accounting for diel variation, only 11% of site-months were net daily sinks of CO 2 . Annual CO 2 emissions had an average of 25% (range 3%–58%) interannual variation. Similar to studies on streams, nighttime emissions regularly exceeded daytime emissions. Biophysical regulations of CO 2 flux variability were delineated through mutual information analysis. Sample analysis of CO 2 fluxes indicate the importance of continuous measurements. Better characterization of short- and long-term variability is necessary to understand and improve detection of temporal changes of CO 2 fluxes in response to natural and anthropogenic drivers. Our results indicate that existing global lake carbon budgets relying primarily on daytime measurements yield underestimates of net emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Constraining neutrino-nucleon form factors with charged-current scattering at the Electron-Ion Collider

Next-generation neutrino oscillation experiments such as the Deep Underground Neutrino Experiment require percent-level knowledge of neutrino-nucleon interaction cross sections. The nucleon axial form factor 𝐹 𝐴 ⁡(𝑄 2 ), parametrized by the axial mass 𝑀 𝐴 , is the dominant source of uncertainty in the quasielastic channel, and the parity-violating structure function 𝑥⁢𝐹 3 is poorly constrained on free nucleons. We propose using charged-current (CC) electron-proton scattering at the Electron-Ion Collider (EIC) to address both problems simultaneously. The measurement exploits three key features of the EIC: (1) helicity-selective electron bunches provide in situ electromagnetic background rejection; (2) a longitudinally polarized proton target enables extraction of 𝐹 𝐴 ⁡(𝑄 2 ) through the target-spin asymmetry 𝐴 𝑈⁢𝐿 ; and (3) the 𝑦-distribution leverage in CC deep inelastic scattering (DIS) separates 𝐹 2 and 𝑥⁢𝐹 3 on a free proton, without nuclear corrections. Using a Fisher information analysis at $\sqrt{𝑠}$ =141 GeV with 500 fb −1 of integrated luminosity, we project the Cramér-Rao statistical floor of 𝛿⁢𝑀 𝐴 ≈0.03 GeV (3%). Incorporating first-order realistic detector effects, such as zero-degree calorimeter acceptance, 𝑄 2 smearing (5%), and background noise from helicity subtraction, the projected sensitivity is severely background-limited due to the small signal-to-background ratio (𝑆/𝐵 ≈ 3 ×10 −4 ) in the elastic channel. Achieving competitive sensitivity (𝛿⁢𝑀 𝐴 ≈ 0.14 GeV) would require ∼10 −7 background suppression, 3 orders of magnitude beyond current projections. The CC DIS 𝑦 distribution provides subpercent extraction of 𝑥⁢𝐹$^{𝑊^{−}}_{3}$ over 0.05 < 𝑥 < 0.5, representing the most robust electroweak measurement in the near term.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Ensuring statistical reproducibility of ocean model simulations in the age of hybrid computing

Novel high performance computing systems that feature hybrid architectures require large scale code refactoring to unravel underlying exploitable parallelism. Such redesign can often be accompanied with machine-precision changes as the order of computation cannot always be maintained. For chaotic systems like climate models, these round-off level differences can grow rapidly. Systematic errors may also manifest initially as machine-precision differences. Isolating genuine round off level differences from such errors remains a challenge. Here, we apply two-sample equality of distribution tests to evaluate statistical reproducibility of the ocean model component of US Department of Energy's Energy Exascale Earth System Model (E3SM). A 2-year control simulation ensemble is compared to a modified ensemble as a test case - after a known non-bit-for-bit change in a model component is introduced - to evaluate the null hypothesis that the two ensembles are statistically indistinguishable. To quantify the false negative rates of these tests, we conduct a formal power analysis using a targeted suite of short simulation ensembles. The ensemble suite contains several perturbed ensembles, each with a progressively different climate than the baseline ensemble - obtained by perturbing the magnitude of a single model tuning parameter, the Gent and McWilliams κ, in a controlled manner. The null hypothesis is evaluated for each of perturbed ensembles using these tests. The power analysis informs on the detection limits of the tests for given ensemble size allowing model developers to evaluate the impact of an introduced non-bit-for-bit change to the model.

Mahajan, Salil↗
DOE OSTI2023

High-Fidelity Numerical Investigation on Elucidating Sodium Heat Transfer Characteristics for 37-Pin Wire-Wrapped Fuel Bundle in the PLANDTL Facility

This study involved a Reynolds-averaged Navier-Stokes- (RANS-) based computational fluid dynamics (CFD) analysis of the 37-pin wire-wrapped fuel bundle of the PNC Plant dynamics test loop (PLANDTL) facility. Previously, mainly the hydrodynamic phenomena of the wire-wrapped fuel bundle were analyzed, but the present study additionally included heat transfer analysis through conjugate heat transfer. The main purpose of the study was to benchmark the experimental data of the PLANDTL 37-pin wire-wrapped fuel bundle to investigate the heat transfer phenomena. In addition, the aim was to verify the accuracy of the RANS-based CFD analysis method using the STAR-CCM+ simulation software in comparison with the experimental data. The grid used for verification was an innovative grid system consisting of hexahedra using Fortran-based code. The development of the RANS-based CFD methodology included grid sensitivity analysis, turbulence model sensitivity analysis, and turbulent Prandtl number sensitivity analysis. Information on the temperature, mass flow rate, and area of the CFD results for each subchannel was provided for the top of the heated section and is expected to serve as a reference for future studies aiming to perform the validation and verification of a PLANDTL facility. In addition, the dependence of the peak temperature on the azimuth angle of each pin was analyzed.

97 MATHEMATICS AND COMPUTING↗