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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Role of Hydrogen as Fuel in Decarbonizing US Clinker Manufacturing for Cement Production: Costs and CO2 Emissions Reduction Potentials

As a low-carbon fuel, feedstock, and energy source, hydrogen is expected to play a vital role in the decarbonization of high-temperature process heat during the pyroprocessing steps of clinker production in cement manufacturing. However, to accurately assess its potential for reducing CO2 emissions and the associated costs in clinker production applications, a techno-economic analysis and a study of facility-level CO2 emissions are necessary. Assuming that up to 20% hydrogen can be blended in clinker fuel mix without significant changes in equipment configuration, this study evaluates the potential reduction in CO2 emissions (scopes 1 and 2) and cost implications when replacing current carbon-intensive fuels with hydrogen. Using the direct energy substitution method, we developed an Excel-based model of clinker production, considering different hydrogen–blend scenarios. Hydrogen from steam methane reformer (gray) and renewable-based electrolysis (green) are considered as sources of hydrogen fuel for blend scenarios of 5%–20%. Metrics such as the cost of cement production, facility-level CO2 emissions, and cost of CO2 avoided were computed. Results show that for hydrogen blends (gray or green) between 5% and 20%, the cost of cement increases by 0.6% to 16%, with only a 0.4% to 6% reduction in CO2 emissions. When the cost of CO2 avoided was computed, the extra cost required to reduce CO2 emissions is $229 to $358/ metric ton CO2. In summary, although green hydrogen shows promise as a low-carbon fuel, its adoption for decarbonizing clinker production is currently impeded by costs.

Okeke, Ikenna J.↗
DOE OSTI2024

Using Computer Simulations to Optimize Biofuel Production

The DOE strives to ensure America's security and prosperity by addressing energy challenges. NREL shares this goal and tries to achieve a clean energy world. Fossil fuels are problematic for both organizations. Using them endangers American security. Their supply is finite and burning them causes environmental damage. Biofuels are a good alternative to fossil fuels. They are renewably produced on American soil and can lower greenhouse gas emissions. Also, cars and planes need no costly mechanical adjustments to use biofuels. However, the fuels themselves are expensive. For my SULI project, I reduced the cost of biofuels by optimizing the production process through computer simulations. Existing simulations were accurate but slow. One simulation takes up to eight hours, and researchers must do hundreds. My solution reduces the computing time. I treated the biomass particles in the simulation as one-dimensional. That simplified the simulation equations, making them easier for the computer to solve. Still, biomass particles are three-dimensional. The 1D assumption was wrong and produced inaccurate results. To maintain accuracy while increasing speed, I developed a method to convert 1D simulation results into usable 3D data. I adjusted the 1D simulation until the output matched the 3D results for a specific environment. I found out how much the simulation changed when the environment changed. Machine learning algorithms defined a relationship between 1D and 3D data for all environments. This lets scientists convert fast 1D simulation results into valid 3D data.

1D↗
DOE OSTI2024

UN-SiC TRISO Post Irradiation Examination Developmental Work

As part of efforts to strengthen INL?s post irradiation analysis capabilities with non-Advanced Gas Reactor (AGR) tristructural isotropic (TRISO) fuels, two developmental activities were conducted. The first activity was to determine how best to analyze uranium nitride TRISO fuel kernels using electron probe microanalysis, while the second activity focused on developing a method to deconsolidate TRISO fuel particles that have been encased in a silicon carbide matrix. Because these two activities were unrelated, they have been presented separately in this report. Initial EPMA analyses showed nitrogen contents that far exceeded the concentration expected for UN--a line compound. Further examination showed that current literature values for the mass absorption coefficient (MAC) for the N ka X-ray absorbed by U ranged from approximately 1600 to 9500, with most values tending toward 9500. Measuring UN with five different progressively increasing accelerating voltages followed by using the modeling program xMAC suggests the actual MAC is approximately 2115. Additional MAC modifications were required to produce reasonable analytical results. Because of the inaccuracies of necessary MAC coefficients, UN analysis via scanning electron microscopy (SEM) is likely to produce inaccurate results. This is because SEM software does not typically allow the user to alter MACs. Tests have been performed to examine the feasibility of an electrochemical technique to liberate irradiated TRISO fuel from a SiC matrix without damaging the outer pyrolytic carbon layer of the fuel particle. The method is performed by electrochemically exposing the SiC to magnesium metal forming Mg2Si and C. Following exposure, the small SiC samples showed slight mass increases with no evidence of conversion to Mg2Si and C nor obvious degradation of the SiC samples.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Co-Processing Fast Pyrolysis Bio-Oils and Hydrothermal Liquefaction Biocrudes in Fluid Catalytic Cracking and Hydroprocessing in Refineries

Co-processing of biogenic feedstocks within the existing petroleum infrastructure represents an opportunity to incorporate large volumes of bio-carbon into transportation fuels. However, upgrading intermediate liquids from biomass and waste to final products efficiently and economically, while minimizing impacts to existing refinery infrastructure, remains a notable barrier to commercialization. The co-processing project, Bio-oil Co-processing with Refinery Streams, aimed to generate foundational knowledge for processing renewable intermediates in petroleum refineries and offer co-processing strategies, as well as develop new methods for biogenic carbon tracking and measurement.

bio-oil↗
DOE OSTI2025

Graph-based Simulation Framework for Power Resilience Estimation and Enhancement

The increasing frequency of extreme weather events poses significant risks to power distribution systems, leading to widespread outages and severe economic and social consequences. This paper presents a novel simulation framework for assessing and enhancing the resilience of power distribution networks under such conditions. Resilience is estimated through Monte Carlo simulations, which simulate extreme weather scenarios and evaluate the impact on infrastructure fragility. Due to the proprietary nature of power network topology, a distribution network is synthesized using publicly available data. To generate the weather scenarios, an extreme weather generation method is developed. To enhance resilience, renewable resources such as solar panels and energy storage systems (batteries in this study) are incorporated. A customized Genetic Algorithm is proposed to determine the optimal locations and capacities for solar panels and battery installations, maximizing resilience while balancing cost constraints. Experiment results demonstrate that on a large-scale synthetic distribution network with more than 300,000 nodes and 300,000 edges, the proposed framework can efficiently evaluate the resilience, and enhance the resilience through the installations of distributed energy resources (DERs), providing utilities with valuable insights for community-level power system resilience estimation and enhancement.

Wang, Xuesong [Wayne State Univ., Detroit, MI (Uni↗
DOE OSTI2025

DEVELOPMENT AND APPLICATION OF RISK ANALYSIS TOOLKIT FOR PLANT RESOURCE OPTIMIZATION

This paper presents the development of methods and tools that are being designed to optimize plant operations (e.g., maintenance/replacement schedules and optimal maintenance postures for plant components) in a manner that is more cost effective than current approaches and makes better use of available component health and cost data. These methods include both data- and model-based optimization methods. Model-based optimization methods directly include reliability and cost models to determine an optimal plant operational strategy. We consider gradient-based and evolutionary (based on genetic algorithms) optimization methods. The second class of methods target more specific use cases (e.g., project schedule optimization) and are not based on reliability models directly, but they require specific component reliability and cost data. This class of methods is based on variants of the knapsack problem with an aim to determine an optimal project schedule that maximizes the overall NPV. This paper also presents multi-objective methods designed to identify an optimal maintenance posture based on a Pareto frontier analysis. Rather than dictating the “right” tradeoff (i.e., identify the absolute best posture), we show how it is possible to perform a trade space exploration approach (i.e., identify value and costs of several postures and let the analysis account for desired value and cost metrics). This is performed by identifying maintenance postures that maximize value (e.g., system availability) and minimize operational costs, i.e., the Pareto frontier in a value-cost trade space. For all these methods we present detailed applicative examples that show their validity from a decision-making perspective.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Characterization of Oxygen Anode Film Studies

Annual report on the Anode Films project. The objectives of this project are to: * Characterize the films that develop on the anode during electrolytic oxide reduction (OR) of UO2 fuel * Develop operando methods to remove these films and preclude their formation

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Calibration and Performance of Germanium High Voltage Detectors for SuperCDMS SNOLAB

As SuperCDMS SNOLAB is getting ready to search for low mass dark matter particles, using cryogenic Ge and Si detectors, a set of six of the new SuperCDMS High Voltage (HV) detectors (four Ge and two Si) were tested in the Cryogenic Underground TEst facility (CUTE) at SNOLAB. This provided the first opportunity to gain experience with this new detector type and assess their performance thoroughly under low background conditions. Here we describe the SuperCDMS HV detector concept and discuss some of the newly developed analysis methods and approaches. Focusing on the Ge detectors, we investigate the detector performance under voltage bias (up to 90 V), exercise the low energy (keV to sub-keV range) calibration based on the electron capture peaks generated by the decay of $^{71}$Ge, assess the detector resolution, and demonstrate the unexpected (and encouraging) ability of these detectors to also measure high energy interactions in the hundreds of keV range with good resolution (better than 3% at 356 keV).

Albakry, M. F. [British Columbia U.; TRIUMF]↗
DOE OSTI2024

Tools And Methods to Analyze Plant Outage Schedule and Assist Schedulers in Improving Outage Resilience

Refueling outages of nuclear power plants (NPPs) are considered one of the most critical phases throughout the plant lifetime. In such instances, tens of thousands of activities (e.g., maintenance, surveillance) are performed in a short amount of time (typically 2-3 weeks unless major backfitting or modernization projects are carried out) by a large number of crews (e.g., electricians, mechanics) that are hired as contractors. As a consequence, a plant outage can be expensive not only in terms of costs (e.g., contractor labor, material), but also in terms of loss generation since the plant is taken off the grid during the full outage duration (an indicative metric is about 1.2M$/day of loss of revenue). Thus, there is a continuous need to decrease the economic impact of outages on plant finances. This can be done by: decreasing the frequency of plant outages (e.g., from 18 to 24 months), reducing the time to complete the outage, and reducing the risk of outage delays. The Optimization of Outage Activities project under the Risk Informed Systems Analysis Pathway (RISA) sponsored by Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program focuses on developing tools and methods to support NPPs with outage schedule optimization. The developed tools and methods are designed to analyze plant outage schedule with the goal of identify critical elements in the schedule that might pose a high risk of delays. These methods and tools can be considered resource-centric in the sense that they address outage challenges as a resource optimization problem. In this context, resources are either time and crews; outage delays occurs when either (or both) resources are insufficient to complete the set of tasks assigned at a specific time instant of the outage. This report provides details on how plant resources (time and crews) can be allocated in such a way that delays are minimized. In this respect, two classes of methods have been developed: the first one focuses on the time resource and how variability of the time to complete outage tasks may impact outage delays. The second one integrates available resources to assess when dailies activities should be performed such that the risk of outage delays are minimized.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Enhanced Boundary Layer Height Detection Using Ceilometer, Surface Meteorology, and Radiation Products With a Random Forest Ensemble Method

This study develops and evaluates a Random Forest (RF) model for estimating planetary boundary layer height (PBLH) using 9 years of data from the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) user facility, with potential application in the NOAA Surface Radiation (SURFRAD) Network. The model integrates ceilometer, surface meteorology, and radiation measurements, and is trained using thermodynamic PBLH estimates derived from radiosondes. This approach aims to bridge gaps between aerosol-based and thermodynamic-based PBLH estimates. The RF model outperformed traditional methods during daytime and better captured transition periods, demonstrating improved accuracy and robustness. At ARM SGP, it showed a substantial reduction in both bias and RMSE, with a bias near zero (−4.9 m) compared with traditional Haar Wavelet (HW) (70.9 m) and Vaisala BL-View software (124.1 m), and an RMSE of 303.2 m, lower than both BL-View (566.9 m) and HW (404.6 m). During daytime hours, RF consistently outperformed both alternatives, maintaining lower bias and RMSE across all periods. At a second evaluation site, RF achieved the lowest overall RMSE (323.7 m), similar to HW (326.4 m) and significantly better than BL-View (738.3 m). However, all models showed reduced accuracy under stable nighttime conditions, limiting the reliability of PBLH estimates. Key predictors for the model included the lifting condensation level height (LCLH), aerosol gradients, and month for seasonal variability. The study underscores the potential of integrating machine learning with multiple data sets such as surface energy and thermodynamic data to advance PBLH estimation.

boundary layer height↗
DOE OSTI2024

Scattering Calorimeter FY24 Deliverable Report

A simulation-based method has been developed to prototype new detector designs for nuclear data measurements utilizing neutron scattering. This method uses representative physics inputs for signal and background generation, full detector resolution smearing benchmarked by experimental data, and a neutron beam timing simulation to produce analyzable output like a physical measurement. A test case has been studied using a hybrid time-of-flight calorimeter detector for scattering cross-section measurements on 239 Pu with 1-5 MeV incident monoenergetic neutrons. Data analysis methods have been developed to perform event-level particle reconstruction and reaction channel discrimination. This analysis has been used to estimate the capability of the test detector to perform simultaneous scattering and fission cross section measurements, as well as its ability to provide neutron spectra and particle angular information.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Development of a TSR-based method for understanding structural relationships of cofactors and local environments in photosystem I

All chemical forms of energy and oxygen on Earth are generated via photosynthesis where light energy is converted into redox energy by two photosystems (PS I and PS II). There is an increasing number of PS I 3D structures deposited in the Protein Data Bank (PDB). The Triangular Spatial Relationship (TSR)-based algorithm converts 3D structures into integers (TSR keys). A comprehensive study was conducted, by taking advantage of the PS I 3D structures and the TSR-based algorithm, to answer three questions: (i) Are electron cofactors including P700, A -1 and A 0 , which are chemically identical chlorophylls, structurally different? (ii) There are two electron transfer chains (A and B branches) in PS I. Are the cofactors on both branches structurally different? (iii) Are the amino acids in cofactor binding sites structurally different from those not in cofactor binding sites? The key contributions and important findings include: (i) a novel TSR-based method for representing 3D structures of pigments as well as for quantifying pigment structures was developed; (ii) the results revealed that the redox cofactor, P700, are structurally conserved and different from other redox factors. Similar situations were also observed for both A -1 and A 0 ; (iii) the results demonstrated structural differences between A and B branches for the redox cofactors P700, A -1 , A 0 and A 1 as well as their cofactor binding sites; (iv) the tryptophan residues close to A 0 and A 1 are structurally conserved; (v) The TSR-based method outperforms the Root Mean Square Deviation (RMSD) and the Ultrafast Shape Recognition (USR) methods. The structural analyses of redox cofactors and their binding sites provide a foundation for understanding the unique chemical and physical properties of each redox cofactor in PS I, which are essential for modulating the rate and direction of energy and electron transfers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Chemistry↗
DOE OSTI2025

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Arasteh, Kourosh [Lawrence Livermore National Labo↗
DOE OSTI2025

CLPNets: Coupled Lie–Poisson neural networks for multi-part Hamiltonian systems with symmetries

To accurately compute data-based prediction of Hamiltonian systems, it is essential to utilize methods that preserve the structure of the equations over time. We consider a particularly challenging case of systems with interacting parts that do not reduce to pure momentum evolution. Such systems are essential in scientific computations, such as discretization of a continuum elastic rod, which can be viewed as the group of rotations and translations $SE(3)$. The evolution involves not only the momenta but also the relative positions and orientations of the particles. The presence of Lie group-valued elements, such as relative positions and orientations, poses a problem for applying previously derived methods for data-based computing. We develop a novel method of data-based computation and complete phase space learning of such systems. We follow the original framework of SympNets (Jin et al., 2020) and LPNets (Eldred et al., 2024), building the neural network from phase space mappings that preserve the Lie–Poisson structure. We derive a novel system of mappings that are built into neural networks describing the evolution of such systems. We call such networks Coupled Lie–Poisson Neural Networks, or CLPNets. We consider increasingly complex examples for the applications of CLPNets, starting with the rotation of two rigid bodies about a common axis, progressing to the free rotation of two rigid bodies, and finally to the evolution of two connected and interacting $SE(3)$ components, describing the discretization of an elastic rod into two elements. Our method preserves all Casimir invariants to machine precision, preserves energy to high accuracy, and shows good resistance to the curse of dimensionality, requiring only a few thousand data points for all cases studied (three to eighteen dimensions). Additionally, the method is highly economical in memory requirements, requiring only about 200 parameters for the most complex case considered.

Data-based modeling↗
DOE OSTI2025

Projected Operational Energy Life Cycle Data Development: 2025 Update

This report documents the data sources and methods used to develop the energy life cycle data for use by the National Institute of Standards and Technology (NIST) Engineering Laboratory (EL) to be used for the development of measurement science and incorporation into decision-support tools for evaluating building and facility capital investments. The data are posted separately under DOI 10.18141/2575194.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗