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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Systems and Implementation: Integrating Unmanned Aircraft Systems into Physical Protection Systems at Fixed Sites and During Transportation

Physical protection systems, and response forces in particular, are designed to prevent an adversary from successfully completing a malevolent act against a facility or transport operations. Timely detection and assessment of any potential adversary action against a target is an essential element of materials security. The timely detection and assessment must then be followed-up by a capable and timely response that might be enhanced with the additional situational awareness provided by unmanned aircraft systems (UAS). The United States Department of Energy’s National Nuclear Security Administration Office of International Nuclear Security has been exploring capabilities provided by UAS to support response force operations within the physical protection system. UAS have the potential to provide response force commanders and operators with situational awareness in assessing adversary locations and actions as well as the locations of responders. UAS may be utilized for area searches ahead of responder pathways to identify potential threats and to provide situational awareness of areas not normally covered by cameras (such as areas outside the fence line outside at fixed facilities). In addition, UAS can provide real-time information to transportation convoy teams that pass through constantly changing public access environments. This paper will provide operational recommendations to be addressed when integrating UAS into existing physical protection systems at fixed sites and during transport. Recommendations will include aspects of the following: needs analysis; tactics and techniques to support detection and assessment as well as response force deployment; remote pilot selection, qualifications, training, and currency; UAS selection criteria; UAS laws and regulations; possible cost sharing with other facility operations; and on-scene emergency management.

Stockwell, Brandon↗
DOE OSTI2026

Hydrogen Education for a Decarbonized Global Economy (H 2 EDGE) (Final Technical Report)

The energy sector is undergoing rapid growth and transformation, creating urgent demand for a skilled workforce to support emerging technologies and resilient systems. Hydrogen is an emerging solution for “hard to abate” sectors, including heavy industry and transportation that also provides optionality for the electric system and enables long‑duration energy storage. As hydrogen scales from traditional industrial uses to new applications, success depends on knowledgeable engineers, technicians, planners, and operators safely deploying hydrogen technologies across the full value chain: production, delivery, storage, and end use. The H 2 EDGE initiative, funded by the U.S. Department of Energy’s Hydrogen and Fuel Cell Technologies Office from 2020 to 2025, was launched to prepare people for careers hydrogen and related technologies. Through its interactive approach, H 2 EDGE equipped stakeholders with knowledge and tools to build a reliable and sustainable energy future together. H 2 EDGE built a national network of academic and industry partners to deliver modular training, share open-access curricula, and engage diverse talent pipelines. The program leveraged EPRI’s extensive industry membership and expanded on the train-the-trainer model of the GridEd program to connect educators, employers, and community organizations across all regions of the U.S. New methods were introduced to more systematically map hydrogen competencies and assess curricula using traditional and AI-assisted approaches. Outcomes included delivery of professional short courses, developing university curriculum, sponsoring faculty and student projects, and engaging stakeholders through workshops, site tours, and webinars.

08 HYDROGEN↗
DOE OSTI2026

Deposition Height Prediction in Directed Energy Deposition

Using 316L stainless steel as a model material, reduced-order models are developed to predict capture efficiency, deposition height, and site-specific hardness in directed energy deposition. Capture efficiency is predicted over a 15 to 55 pct range using a dimensionless number derived from processing conditions and thermophysical properties. Deposition height is predicted over a 0.3 to 1.3 mm range without in situ sensing or prior training data, using two models based on the same mass and energy-balance principles. Predictions are compared with machine learning approaches. A quantitative relationship links deposition height, primary dendrite arm spacing (PDAS), and hardness: heights of 0.3 to 1.1 mm correspond to PDAS values of 2.7 to 5.1 µm and Vickers hardness (HV) of 160 to 219. Thinner layers cool more rapidly, producing finer microstructures and higher hardness. Samples fabricated with in situ variations in deposition height exhibited up to 55 HV differences between thick and thin regions, demonstrating that local control of deposition height enables predictive, site-specific hardness within a single build. These results establish deposition height prediction as a pathway for a priori process design and property control in directed energy deposition for 316L stainless steel.

Kunkel, William [Univ. of Wisconsin, Madison, WI (↗
DOE OSTI2025

Multi-Label Classification with Constraint-Based Learning for Hierarchical Consistency

We explore the limitations of traditional crossentropy loss in a hierarchical multi-label classification setting and introduce a novel loss function. This function is designed to integrate hierarchical constraints directly into the training process. By incorporating such constraints into the loss, our approach slightly improves the logical consistency of predictions in structured domains. We demonstrate the efficacy of our approach through experiments on primary site and histology classification by using electronic pathology reports. These results show that our proposed hierarchical loss function enhances the model's ability to produce predictions that are logically consistent with the natural data hierarchies, and it slightly improves predictive accuracy. Our framework may be extended to other hierarchical domains, however the performance gains are context specific.

Shivanna, Abhishek [ORNL] (ORCID:0009000665228593)↗
DOE OSTI2026

Intern Poster Session 08/13: Autonomous Nuclear Robotics: Applications in nuclear waste inspection and hot cell experiments

The nuclear industry is experiencing renewed interest in autonomous robotics, yet most deployed systems remain teleoperated with limited autonomy. This work presents two contributions toward fully autonomous nuclear robotic systems: autonomous waste inspection at the Hanford Site and an autonomous hot cell laboratory framework. Inspections of Hanford's underground waste storage tanks are performed manually at significant cost and personnel exposure. We developed a reinforcement-learning (RL) training pipeline for a custom-built inspection arm. In parallel, we are designing an autonomous laboratory framework for post-irradiation examination in hot cells at the Specimen Preparation Laboratory (SPL) that integrates computer vision, task and motion planning, hardware execution, and operator-in-the-loop control. These systems demonstrate a path toward safer, more efficient nuclear operations by reducing human exposure while maintaining rigorous human oversight at critical decision points.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction

Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating the specific residues that compose the interface of protein complexes. In recent years, new machine learning and other algorithmic approaches have been proposed to solve this problem. However, little effort has been made in finding better training datasets to improve the performance of these methods. With the aim of vindicating the importance of the training set compilation procedure, in this work we present BIPSPI+, a new version of our original server trained on carefully curated datasets that outperforms our original predictor. We show how prediction performance can be improved by selecting specific datasets that better describe particular types of protein interactions and interfaces (e.g. homo/hetero). In addition, our upgraded web server offers a new set of functionalities such as the sequence-structure prediction mode, hetero- or homo-complex specialization and the guided docking tool that allows to compute 3D quaternary structure poses using the predicted interfaces. BIPSPI+ is freely available at https://bipspi.cnb.csic.es.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Surrogate models for development of unconventional shale reservoirs by an integrated numerical approach of hydraulic fracturing, flow and geomechanics, and machine learning

We develop well-completion surrogate models by taking an integrated workflow of hydraulic fracturing, flow, geomechanics, and machine learning simulation. There are three steps in the proposed workflow. First, history-matching processes are conducted with the field data including pumping and production data for characterization. Second, full-physics simulation is performed with various parameters of the field development (e.g., cluster spacing, clusters per stage, pumping rates and times, amount of proppant, and well spacing) to generate multiple simulation results by changing the parameters of the completion design with well-known hydraulic fracturing, reservoir, geomechanics simulators to calculate fracture geometry, reservoir depressurization, induced stress changes. The workflow is demonstrated over a field in the Southern Midland Basin. Here, we take two completion scenarios: a single well case followed by a multi-well case. Finally, a Long Short-Term Memory (LSTM) machine learning algorithm is employed to create surrogate models that can replicate the full-physics simulation results. Furthermore, results show that the trained models applied in the single well and multi-well cases for a particular geological system can provide good accuracy close to those provided by full-physics simulations. Specifically, the site-specific surrogate models can predict fracture parameters (length, height, and surface area) and cumulative production accurately with computational efficiency, suggesting our proposed workflow can be used as a pragmatic tool for expediting the well completion optimization process.

Geomechanics↗
DOE OSTI2025

Automated Construction of Artificial Lattice Structures with Designer Electronic States

Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM tip, constrains the exploration of diverse configurations and limits the size of the designed features. In this study, we present a reinforcement learning (RL)-based framework for creating artificial structures by spatially manipulating carbon monoxide (CO) molecules on a copper substrate by using the STM tip. The automated workflow combines molecule detection and manipulation, employing deep-learning-based object detection to locate CO molecules and linear assignment algorithms to allocate these molecules to designated target sites. We initially perform molecule maneuvering based on randomized parameter sampling for sample bias, tunneling current set point, and manipulation speed. This data set is then structured into an action trajectory used to train an RL agent. The model is subsequently deployed on the STM for real-time fine-tuning of the manipulation parameters during structure construction. Our approach incorporates path-planning protocols coupled with active drift compensation to enable atomically precise fabrication of structures with significantly reduced human input while realizing larger-scale artificial lattices with the desired electronic properties. Furthermore, using our approach, we demonstrate the automated construction of an extended artificial graphene lattice and confirm the existence of a characteristic Dirac point in its electronic structure. Further challenges regarding the RL-based structural assembly scalability are discussed.

Algorithms↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗
DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Echo state network for coarsening dynamics of charge density waves

An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer. Compared with other recurrent neural networks, one great advantage of ESN is the simplicity of its training process. Yet, despite the seemingly restricted learnable parameters, ESN has been shown to successfully capture the spatial-temporal dynamics of complex patterns. Here we build an ESN to model the coarsening dynamics of charge-density waves (CDWs) in a semiclassical Holstein model, which exhibits a checkerboard electron density modulation at half-filling stabilized by a commensurate lattice distortion. The inputs to the ESN are local CDW order parameters in a finite neighborhood centered around a given site, while the output is the predicted CDW order of the center site at the next time step. Special care is taken in the design of couplings between hidden layer and input nodes to ensure lattice symmetries are properly incorporated into the ESN model. Since the model predictions depend only on CDW configurations of a finite domain, the ESN is scalable and transferrable in the sense that a model trained on dataset from a small system can be directly applied to dynamical simulations on larger lattices. Furthermore, our work opens avenues for efficient dynamical modeling of pattern formations in functional electron materials.

2-dimensional systems↗
DOE OSTI2020

Project 57 Air Monitoring Report: (Jan 1 - Dec 31 2018)

During the late 1950s, the Atomic Energy Commission (AEC) (now the U.S. Department of Energy [DOE]) conducted a series of safety experiments to determine if a nuclear device subjected to a large conventional explosives detonation would result in a nuclear yield. The AEC obtained temporary use of a large portion of western Emigrant Valley from the U.S. Department of Defense (DOD) for one of these experiments: Project 57. Following the Project 57 safety experiment, the AEC fenced the contaminated area and returned control of the surrounding land to the DOD. Emigrant Valley is part of the Nevada Test and Training Range (NTTR). For safety and security reasons, access to the NTTR is controlled using both physical (i.e., fences) and administrative (e.g., signs and postings) controls. Therefore, the public cannot access the Project 57 site and there are no known human receptors that routinely access the site. Project 57 was detonated on April 24, 1957, in Emigrant Valley approximately 13 mi (21 km) northeast of the north end of Yucca Flat. This test was undertaken to develop: (1) a means of estimating immediate distribution and long-term redistribution of plutonium dispersed during a nonnuclear detonation; (2) biomedical evaluation techniques for use in likely plutonium-laden environments; (3) methods of decontaminating ground areas, pavements, and building materials; and (4) alpha survey instruments and field monitoring procedures to promptly estimate contaminant deposition. Although the test did not result in the fission of nuclear materials, it did disseminate plutonium across the ground surface. Various radiological surveys have been performed in the area since Project 57 was conducted. The AEC delineated the area containing radioactive material based on radioactivity surveys performed shortly after the test was conducted. A designated Contamination Area (CA), marked by a fence, was established later. The distribution of americium-241 (Am-241) in the area was determined again in a 1997 flyover, which showed Am-241 ranging from as much as 70,000 counts per second (cps) at ground zero to background (<70 cps). This survey also documented Am-241 radioactivity on the ground surface beyond the east side CA fence at levels of up to 150 cps. In 2007, the DOE expanded the CA by posting “Contamination Area” signs 200 ft to 400 ft (60 m to 120 m) beyond the original fence, which formed a new CA boundary.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Surface Molecular Chemistry in Solar Fuel Research

Through this research, we aim to combine transition metal catalysts with light-absorbing semiconductors for use in efficient solar energy conversion. Upon receiving photogenerated electrons from the semiconductors, the transition metal catalysts could accelerate the reduction of carbon dioxide into energy-rich fuels. We have deposited cobalt complexes, including cobalt macrocycles and single cobalt catalysts, onto different semiconductors that demonstrated targeted activities in photochemical carbon dioxide reduction. The structures and catalytic mechanisms of the surface cobalt catalysts were investigated using advanced techniques, including X-ray absorption spectroscopy at DOE user facilities. Our research provides new strategies to enable effective coupling between molecularly defined catalytic sites with heterogeneous surfaces for converting solar energy into chemicals and fuels. Research results obtained through this project have been disseminated through journal publications and conference presentation. In addition, this project provided important interdisciplinary training opportunities for two postdoctoral scholars, four graduate researchers, and one high school student. It also allowed us to establish successful State-National Laboratory partnerships with Brookhaven National Lab and Argonne National Lab.

14 SOLAR ENERGY↗
DOE OSTI2025

High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning

Abstract The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high‐resolution (∼37 km 2 ) differentiable models (a type of hybrid model): one with implicit, unit‐hydrograph‐style routing and another with explicit Muskingum‐Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process‐based equations to provide a structural backbone, which were trained simultaneously (“end‐to‐end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash‐Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long‐standing modeling challenge. The Muskingum‐Cunge version further improved performance for basins >10,000 km 2 . Overall, our results show how neural‐network‐based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next‐generation NWM. We also provide a CONUS‐scale hydrologic data set for further evaluation and use.

Song, Yalan [Civil and Environmental Engineering T↗
DOE OSTI2020

Tribal Renewable Energy: Bishop Paiute Tribe, Residential Solar Program Phase II (Final Report)

The project consisted of the design, installation, inspection, and interconnection of 35 grid-tied, solar electric systems, totaled 123 kW rated capacity, on qualified existing low-income single-family homes located within the Bishop Paiute Reservation, which provide at least 65-79% savings in displaced electricity. Tribal job trainees were hired for each installation, gaining valuable job experience. Additionally, each homeowner was educated on energy efficiency and renewable energy. The overall project goal was to deploy clean energy systems in order to achieve the Bishop Paiute Tribe’s long-term goals of energy self-sufficiency, environmental protection, and better lives for our Tribal members and community. The energy displaced was 82.4 percent of total electricity used (exceeding estimated 65-79%)or over 170,000 kWh/year generating approximately $1.29 million worth of power for low-income families over their lifespans, while eliminating an estimated 2,640 tons of greenhouse gas emissions. This reduction makes a significant difference on the reservation giving more families money to spend on other essential items, while reducing their carbon footprint in this beautiful mountain community. Also, implementing these 35 systems provided a approximately 132 hours of paid solar installation work for tribal members. Training and good paying jobs are scarce on the reservation and solar is the fastest growing industry in CA and this training offered our members a real chance to learn and then get paid. The program is very significant to the family who qualified, as most families fall below the federal poverty guidelines, and many are living paycheck to paycheck. Overall, the triple impact of the Bishop Paiute Tribe Residential Solar Program Phase II— affordable energy for low-income families, on-site clean energy production, and hands-on solar installation jobs for local workers—these all help build the Tribe’s energy, economic, environmental, and social self-sufficiency and sovereignty amongst the most needy on the Reservation

14 SOLAR ENERGY↗
DOE OSTI2021

CyProduct: A software tool for accurately predicting the byproducts of human cytochrome P450 metabolism

In silico metabolism prediction is a cheminformatic task of autonomously predicting the set of metabolic byproducts produced from a specified molecule and a set of enzymes or reactions. Here we describe a novel machine-learned in silico cytochrome P450 (CYP450) metabolism prediction suite, called CyProduct, that accurately predicts metabolic byproducts for a specified molecule and a human CYP450 isoform. It includes three modules: (1) CypReact, a tool that predicts if the query compound reacts with a given CYP450 enzyme; (2) CypBoM, a tool that accurately predicts the “bond site” of the reaction (i.e., which specific bonds within the query molecule react with the CYP isoform); and (3) MetaboGen, a tool that generates the metabolic byproducts based on CypBoM’s bond-site prediction. CyProduct predicts metabolic biotransformation products for each of the nine most important human CYP450 enzymes. CypBoM uses an important new concept called “Bond of Metabolism” (BoM), which extends the traditional “Site of Metabolism" (SoM) by specifying the information about the set of chemical bonds that is modified or formed in a metabolic reaction (rather than the specific atom). We created a BoM database for 3487 CYP450-mediated Phase I reactions, then used this to train the CypBoM Predictor to predict the reactive bond locations on substrate molecules. CypBoM Predictor’s cross-validated Jaccard score for reactive bond prediction ranged from 0.380 to 0.452 over the nine CYP450 enzymes. Over variants of a test set of 72 known CYP450 substrates and 30 non-reactants, CyProduct outperformed the other packages -- including ADMET Predictor, BioTransformer and GLORY -- by an average of 200% (wrt Jaccard score) in terms of predicting metabolites. The CyProduct suite and the datasets are freely available at https://bitbucket.org/wishartlab/cyproduct/src/master/.

Machine learning, Cytochrome P450, Metabolism pred↗
DOE OSTI2025

ELM–Wet: Inclusion of a Wet–Landunit With Sub–Grid Representation of Eco–Hydrological Patches and Hydrological Forcing Improves Methane Emission Estimations in the E3SM Land Model (ELM)

Wetlands are the largest emitters of biogenic methane (CH 4 ) and represent the highest source of uncertainty in global CH 4 budgets. Here, we aim to improve the realism of wetland representation in the U.S. Department of Energy's Exascale Earth System Model land surface model, ELM, thereby reducing uncertainty of CH 4 flux predictions. We develop an updated version, ELM-Wet, where we activate a separate landunit for wetlands that handles multiple wetland-specific eco-hydrological patch functional types. We introduce more realistic hydrological forcing through prescribing site-level constraints on surface water elevation, which allows resolving different sustained inundation depth for different patches, and if data exists, prescribing inundation depth. We modified the calculation of aerenchyma transport diffusivity based on observed conductance per leaf area for different vegetation types. We use Bayesian Optimization to parameterize CO 2 and CH 4 fluxes in the developed wet-landunit. Site-level simulations of a coastal non-tidal freshwater wetland in Louisiana were performed with the updated model. Eddy covariance observations of CO 2 and CH 4 fluxes from 2012 to 2013 were used to train the model and data from 2021 were used for validation. Patch-specific chamber flux observations and observations of CH 4 concentration profiles in the soil porewater from 2021 were used for evaluation of the model performance. Our results show that ELM-Wet reduces the model's CH 4 emission root mean squared error by up to 33% and is able to represent inter-daily CO 2 and CH 4 flux variability across the wetland's eco-hydrological patches, including during periods of extreme dry or wet conditions.

54 ENVIRONMENTAL SCIENCES↗