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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Using Machine Learning for Quantum Annealing Accuracy Prediction

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A machine learning model for predicting the minimum miscibility pressure of CO 2 and crude oil system based on a support vector machine algorithm approach

CO 2 enhanced oil recovery (EOR) is a potential way for carbon capture, utilization and storage (CCUS). Though, the effect of CO 2 injection is greatly influenced by the reservoir conditions. Typically, Minimum miscible pressure (MMP) is selected as one of the key parameters for the screening and evaluation of prospective CO 2 flooding. Conventional slim tube test is both accurate and widely accepted but it is inefficient. Existing empirical formulas for MMPs are easy to be used but have been proved inaccurate and unreliable. Machine learning-based methods have great advantages in predicting MMP. However, only predication accuracy is discussed for most models without the screening of the main control factors and further validation of the model reliability. In this paper, a new prediction model based on support vector machine (SVM) was developed for pure/impure CO 2 and crude oil system. This study was based on 147 sets of MMP data from the literature with full information on reservoir temperature, oil composition and gas composition. The main control factors were screened by several statistical methods. Unlike the conventional prediction models that verified by only prediction accuracy, learning curve and single factor control variable analysis are further validated to obtain the optimum model.

02 PETROLEUM↗
DOE OSTI2023

GeoThermalCloud: A Machine Learning Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources

In this 25 minute presentation, we showcase our open source “GeoThermalCloud” tool for identifying hidden geothermal resources using a publicly available dataset for southwestern New Mexico. The presenters include Bulbul Ahmmed and Luke Frash. All of the visuals use source material from LA-UR approved publications and this work falls under the Earth Sciences DUSA. The code shown in this video is already released with LANL approval in open source format on GitHub and DockerHub. The audio in this video includes only material on the topics of geothermal energy and machine learning applied to geothermal energy. The primary machine learning method used is LANL’s Non-negative Matrix Factorization “NMFk” method. Modeling work also mentions LANL’s Geothermal Design Tool “GeoDT” which is another approved open source code that has been released by LANL. This work was performed for DOE Geothermal Technologies Office (DE-EE-3.1.8.1). The host for the released video is intended to be YouTube or a suitable perpetual data repository such as GDR.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Modeling Single-Crystal Battery Materials: From Fundamental Understanding to Performance Evaluation

The performance of rechargeable batteries is fundamentally influenced by the physicochemical properties and microstructural features of their key material components. Recent experimental advancements have highlighted the potential of single-crystal (SC) morphologies to address inherent limitations of polycrystalline (PC) electrodes and solid-state electrolytes, offering tunable charge transport kinetics and improved cell cycling performance. Here, this review examines how state-of-the-art computational modeling, from atomistic and mesoscale to continuum-level approaches, including machine learning methodologies, has been utilized to investigate the critical factors governing the electrochemical behavior of SC battery materials. We explore how predictive modeling can elucidate the processing–structure–property–performance relationships of SC cathodes, anodes, and solid-state electrolytes, with a focus on unique SC characteristics such as crystallographic anisotropy, size effects, and facet-dependent properties. Additionally, we identify limitations in commonly used modeling techniques and discuss strategies to address these challenges. By integrating high-fidelity simulations with experimental insights, this review aims to outline a clear path for the rational design and optimization of SC battery components, paving the way for accelerated advancements in energy storage technologies.

Materials science↗
DOE OSTI2020

Spatially and Temporally Explicit Life Cycle Environmental Impacts of Soybean Production in the U.S. Midwest

Quantifying the spatially and temporally explicit life cycle environmental impacts of crop production is critical for designing sustainable supply chains for biofuel and animal sectors. This study assessed life cycle environmental impacts of soybean production in around 1000 Midwest counties over 9 years. A combination of Environmental Policy Integrated Climate model and process-based life cycle assessment model was used to estimate the spatially and temporally explicit life cycle global warming (GW), eutrophication (EU) and acidification (AD) impacts. Sequentially, a machine learning approach was applied to identify the top influential factors among soil, climate and farming practices for spatially and temporally explicit life cycle environmental impacts. The results indicated that significant variations existed in life cycle environmental impacts among counties and across years. Life cycle GW impacts ranged from -11.4 to 22.0 kg CO2-eq. kg soybean-1, whereas life cycle EU and AD impacts varied by a factor of 302 and 44, respectively. Nitrogen application rates, temperature in March and soil texture were the top influencing factors for the spatial-temporal variations in life cycle GW impacts. In contrast, soil organic content and nitrogen application rate were the top influencing factors for the spatial-temporal variations in life cycle EU and AD impacts.

Soybean, life cycle analysis, sustainability, spat↗
DOE OSTI2025

Machine learning inversion from small-angle scattering for charged polymers

We develop Monte Carlo simulations for uniformly charged polymers and a machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, which can be obtained from small-angle scattering experiments. The polymer is modeled as a chain of fixed-length bonds, where the connected bonds are subject to bending energy, and there is also a screened Coulomb potential for charge interaction between all joints. The bending energy is determined by the intrinsic bending stiffness, and the charge interaction depends on the interaction strength and screening length. All three contribute to the stiffness of the polymer chain and lead to longer and larger polymer conformations. The screening length also introduces a second length scale for the polymer besides the bending persistence length. To obtain the inverse mapping from the structure factor to these polymer conformation and energy-related parameters, we generate a large data set of structure factors by running simulations for a wide range of polymer energy parameters. We use principal component analysis to investigate the intra-polymer structure factors and determine the feasibility of the inversion using the nearest neighbor distance. We employ Gaussian process regression to achieve the inverse mapping and extract the characteristic parameters of polymers from the structure factor with low relative error.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Medium-duty Urban Range Extended Connected Powertrain (Final Scientific Technical Report)

The project goal is to develop and demonstrate a Class 4 delivery vehicle that reduces fuel consumption by 50% or more when compared to an equivalent vehicle with a conventional internal combustion engine (ICE) powertrain driven on a comparable duty cycle. This shall be achieved using a plug-in hybrid electric (PHEV) configuration that optimizes the efficiency of the ICE. The proposed solution aims to be commercially viable for fleets to procure the system without additional incentives. To accomplish this, commercialized light-duty vehicle electric drive components shall be used. A PHEV configuration for Class 4 MD/HD vocational vehicles using commercialized light-duty vehicle electric drive components will require the development of a new topology for the hybrid powertrain applicable to medium-duty vehicles. Prior research indicates that an identified novel power split device could provide a fuel economy improvement potential of up to 51%. Utilizing an advanced battery management algorithm that will allow operation closer to the battery limits, the project will minimize battery capacity to achieve the desired performance results while maintaining the cost target for high commercialization. The solution must be cost effective to purchase and integrate into a Class 4 delivery vehicle, deliver the intended fuel economy improvement, and perform as well, or better, than the current conventional ICE powertrain. MD/HD vehicles typically require higher power, which is one factor that results in high costs for the powerful electric machines typical in larger HEV/PHEV systems. A key element in the project is the use of commercially available Bosch electric drive components (e.g., motors, batteries, and power electronics) from the larger passenger vehicle segment. This leverages the economies of scale to decrease cost and improve reliability beyond current purpose-built low-volume commercial vehicle components. These innovations will result in a game-changing PHEV system that meets DOE’s 50% fuel consumption reduction target, grants the vehicle an uncompromised driving range, and achieves the cost point for widespread adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Advancing Artificial Intelligence with Liquid Argon Neutrino Experiments (Technical Report)

The grant allowed two main contributions: 1) The development of a first successful demonstration of the employment of Optimal Transport in liquid argon time projection chamber neutrino detectors. Optimal Transport, used in other contexts and specifically with LHC calorimetric data, was adapted to address a key particle identification challenge in LArTPCs: the separation of pi0 backgrounds from single-electrons produced in charged-current electron neutrino interactions. The work, leveraging ML methods such as k-nearest-neighbor (kNN) and support-vector-machine (SVM), showed an increase in background rejection of a factor of two or more. Work is now ongoing to incorporate this development in physics analyses for LArTPC experiments and more broadly expand the use of OT in LArTPC detectors including DUNE. This work was done in collaboration with the phenomenology group led by Nathaniel Craig at UCSB. 2) The deployment of NuGraph2, a graph neural network developed for LArTPC reconstruction, in the MicroBooNE experiment. NuGraph2 uses novel graph-neural-network methods on the rather simple LArTPC inputs of reconstructed hits, greatly simplifying the workflow compared to the use of waveform or signal-deconvolved wire ROIs. The network performed particle classification and was shown to address many challenging problems in LArTPC imaging including track-shower separation and the identification of protons and charged pions from primary muons. Our group collaborated with Giuseppe Cerati (FNAL scientist) who is one of the core developers of NuGraph2 to integrate this tool in MicroBooNE’s analysis framework. This consisted in tow key contributions: a) Studying performance on real data, which came with several months of iterations because the MC-trained version of the network was found to show significant bias that our group investigated and addressed. b) Integrating the output hit labeling of NuGraph2 into the existing particle tracking and shower reconstruction code. As a result of this work led by our team NuGraph2 is now enabling a suite of new analyses which benefit from enhanced capabilities and thus broader physics reach. The grant supported primarily the salary of UCSB graduate student Chuyue “Michaelia” Fang as well as partial summer salary support for PI Caratelli. Some funds were used for travel by Michaelia to ML related schools and conferences.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Securing The Future: 2026 Manufacturing & Critical Infrastructure Threat Landscape

This report outlines the current state of manufacturing weaknesses introduced by the complexities of modern environments, including cloud services and Internet of Things (IoT) devices, with particular attention paid to the unique vulnerabilities encountered by SMMs. It also highlights CyManII’s strategic initiatives and collaborative solutions to mitigate these risks and strengthen the cybersecurity posture of the manufacturing ecosystem. Utilizing data from 2025 to inform forward-looking mitigation strategies, this report provides manufacturers with a clear understanding of both current and emerging cybersecurity threats, as well as practical opportunities to strengthen their cyber ecosystems. The following sections detail key vulnerabilities and threat vectors, along with actionable mitigation strategies, many of which have been developed or piloted through CyManII-led efforts. A thorough understanding of these risks and mitigation strategies is essential for manufacturers seeking to strengthen the security and resilience of their manufacturing operations.

3D Printing↗
DOE OSTI2023

Developing Confidence in Machine Learning Results

As the field of deep learning has emerged in recent years, the amount of knowledge and expertise that data scientists are expected to absorb and maintain has correspondingly increased. One of the challenges experienced by data scientists working with deep learning models is developing confidence in the accuracy of their approach and the resulting findings. In this study, we conducted semi-structured interviews with data scientists at a National Laboratory to understand the processes that data scientists use when attempting to develop their models and the ways that they gain confidence that the results they obtained were accurate. These interviews were analysed to provide an overview of the techniques currently used when working with machine learning (ML) models and opportunities for collaboration with human factors researchers to develop new tools are identified.

Baweja, Jessica A.↗
DOE OSTI2023

NREL 25-cm2 High-Pressure Low-Temperature Electrolysis Cell Hardware (Open Source)

This data resource describes an open-source cell hardware that enables low temperature electrolysis (LTE) testing at elevated pressures. Existing commercial options have several downsides when it comes to R&D testing. They are often not designed for repeated reassembly, may not be able to accommodate porous transport layers with different thicknesses, and do not give state-of-the-art performance. Therefore, this hardware was developed specifically with LTE R&D in mind and its design is being made available to the global LTE community. This work was planned and funded by the U.S. Department of Energy's H2NEW consortium (https://h2new.energy.gov/). The hardware design package (.zip file) details the drawings, auxiliary materials, and procedures required to fabricate, assemble, and operate the National Renewable Energy Laboratory's (NREL's) high-pressure low-temperature electrolysis cells. While the hardware itself—end plates, current collectors, flow fields, bolts and washers, tube fittings—is always the same, the assembly and operating procedures may change depending on the active materials being tested, especially for the membrane. Material-specific assembly and operating procedures will be posted to the H2NEW website as they are developed and validated. Disclaimer: The documents and drawings included in this download package describe a design for a low temperature electrolysis hardware that is intended to comply with leak testing according to ASME B31.1. Safe operation at ambient and elevated pressures is the sole responsibility of the end user, which should be evaluated on a case-by-case basis for each individual cell. Factors affecting the sealing capability may depend on, for example, machining quality, cell assembly components, operating conditions, and operating history. Operation at pressure should only be performed on qualified test stands by qualified operators. NREL/ALLIANCE FOR SUSTAINABLE ENERGY, LLC/DOE DISCLAIM ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, AND MAKES NO WARRANTY AS TO THE ACCURACY, COMPLETENESS, OR USEFULNESS OF ANY INFORMATION PROVIDED HEREIN. USE OF THIS PACKAGE IS AT THE USER’S OWN RISK.

08 HYDROGEN↗
DOE OSTI2021

Development of Automated Pipeline for Time-Resolved Link-Wise Vehicular Energy Consumption in the Chattanooga, TN Road Network

The Department of Energy (DOE) has shown strong interest in detecting energy inefficiencies in regional road networks, so as to derive energy consumed at a high spatial temporal resolution. We have developed a workflow to automate the estimation of time-resolved vehicular energy consumption over each link in a road network of interest. The road network used in the current work is centered around the city of Chattanooga, Tennessee and its bordering regions. Utilizing the most mature road network for the Chattanooga, TN region, vehicle speed & count data from TomTom in conjunction with machine learning methods, we have developed an automated pipeline to estimate energy consumption for every link in the network. The first step in the pipeline is ingesting vehicle probe counts and speed estimates from TomTom API. In the next step, the probe counts, speed profiles and other exogenous data (i.e. road types, weather data, ground-truth volume counts and more) were used as input to a supervised learning algorithm to estimate the number of vehicles throughout the entire region for each road segment. These volume estimates were then mapped to a unified road network that contained additional important information such as percentage change in gradient across a link, number of lanes and link lengths that are features in pre-trained single vehicle energy-consumption models available with the RouteE software developed at NREL. The per vehicle energy consumption on each road link predicted using appropriate RouteE vehicular models were multiplied by the volume estimate for the corresponding link over a given time period to predict energy consumed per link for the time interval of interest. Currently, work is underway to improve both the RouteE per vehicle energy estimate and the volume estimates derived from TomTom probe counts. We have also explored the correlation of the link-wise energy estimates with the features of the pre-trained RouteE machine learning model in order to gain insight into what factors contribute most to the link-wise energy consumption.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2017

Developing a human factors toolkit to cope with the multi-project situation - 346

Beside of the challenge from technology improvement and stricter safety review in the area of human factors engineering and integrated human system interface resources design, the pressure from the engineering efficiency and quality continues increasing nowadays while 'one-design-several-sites' is becoming more and more common for the nuclear energy industrial in China. A toolkit based on relational database technology was developed to support the whole process of HFE analysis for designer. A through requirement analysis was conducted. Beside of requirements form technical view of point, the considerations from engineering process, quality management, computer technology and data interface were also involved as a whole. During the implementation, resolutions were developed to solve problems like multi-project, multi-group and so on. Lessons learned during the design and development process are summarized as references for similar projects. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Machine learning for precise hit position reconstruction in Resistive Silicon Detectors

RSDs are LGAD silicon sensors with 100% fill factor, based on the principle of AC-coupled resistive read-out. Signal sharing and internal charge multiplication are the RSD key features to achieve picosecond-level time resolution and micron-level spatial resolution, thus making these sensors promising candidates as 4D-trackers for future experiments. This paper describes the use of a neural network to reconstruct the hit position of ionizing particles, an approach that can boost the performance of the RSD with respect to analytical models. The neural network has been trained in the laboratory and then validated on test beam data. The device-under-test in this work is a 450 μm-pitch matrix from the FBK RSD2 production, which achieved a resolution of about 65 μm at the DESY Test Beam Facility, a 50% improvement compared to a simple analytical reconstruction method, and a factor two better than the resolution of a standard pixel sensor of equal pitch size with binary read-out. The test beam result is compatible with the laboratory ones obtained during the neural network training, confirming the ability of the machine learning model to provide accurate predictions even in environments very different from the training one. Prospects for future improvements are also discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm

Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency and subjective human factors. We developed a novel data-driven geological feature characterization approach based on pre-stack seismic measurements. Our characterization method employs an efficient and accurate machine learning method to extract useful subsurface geologic features automatically. Specifically, we use kernel ridge regression to account for the nonlinear relationship between seismic data and geological features. We moreover employ kernel tricks to avoid the explicit nonlinear mapping and infinite dimension of feature space. Yet, the conventional kernel ridge regression can be computationally prohibitive because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nyström method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate characterization. We provide thorough computational cost analysis to show the efficiency of our new geological feature characterization methods. We validate the performance of our method in characterizing geologic fault zones because faults play an important role in various subsurface applications. Our numerical examples demonstrate that our new characterization method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we demonstrate that our approach yields a speedup ratio on the order of ~102 to ~103 in a multicore computational environment.

58 GEOSCIENCES↗
DOE OSTI2024

Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes

In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They can transform the solving of general nonlinear PDEs into solving quadratic optimization problems with nonlinear, PDE-induced constraints. However, the complexity bottleneck lies in computing with dense kernel matrices obtained from pointwise evaluations of the covariance kernel, and its partial derivatives, a result of the PDE constraint and for which fast algorithms are scarce. The primary goal of this paper is to provide a near-linear complexity algorithm for working with such kernel matrices. We present a sparse Cholesky factorization algorithm for these matrices based on the near-sparsity of the Cholesky factor under a novel ordering of pointwise and derivative measurements. The near-sparsity is rigorously justified by directly connecting the factor to GP regression and exponential decay of basis functions in numerical homogenization. We then employ the Vecchia approximation of GPs, which is optimal in the Kullback-Leibler divergence, to compute the approximate factor. This enables us to compute ϵ-approximate inverse Cholesky factors of the kernel matrices with complexity O(N log d (N/ϵ)) in space and O(N log 2d (N/ϵ)) in time. We integrate sparse Cholesky factorizations into optimization algorithms to obtain fast solvers of the nonlinear PDE. We numerically illustrate our algorithm’s near-linear space/time complexity for a broad class of nonlinear PDEs such as the nonlinear elliptic, Burgers, and Monge-Ampère equations. In summary, we provide a fast, scalable, and accurate method for solving general PDEs with GPs and kernel methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, in this study, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90°C) and medium (90-150°C)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Machine Layout and Performance

Chapter 2 in High-Luminosity Large Hadron Collider (HL-LHC) : Preliminary Design Report. The Large Hadron Collider (LHC) is one of the largest scientific instruments ever built. Since opening up a new energy frontier for exploration in 2010, it has gathered a global user community of about 7,000 scientists working in fundamental particle physics and the physics of hadronic matter at extreme temperature and density. To sustain and extend its discovery potential, the LHC will need a major upgrade in the 2020s. This will increase its luminosity (rate of collisions) by a factor of five beyond the original design value and the integrated luminosity (total collisions created) by a factor ten. The LHC is already a highly complex and exquisitely optimised machine so this upgrade must be carefully conceived and will require about ten years to implement. The new configuration, known as High Luminosity LHC (HL-LHC), will rely on a number of key innovations that push accelerator technology beyond its present limits. Among these are cutting-edge 11-12 tesla superconducting magnets, compact superconducting cavities for beam rotation with ultra-precise phase control, new technology and physical processes for beam collimation and 300 metre-long high-power superconducting links with negligible energy dissipation. The present document describes the technologies and components that will be used to realise the project and is intended to serve as the basis for the detailed engineering design of HL-LHC.

43 PARTICLE ACCELERATORS↗