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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Molten Salt vs. Liquid Sodium Receiver Selection Using the Analytic Hierarchy Process

An international team led by NREL analyzed the favorability of two alternative liquid receiver designs for a 700+ degrees C receiver under the Gen3 CSP Liquid Pathway project. The competing liquid heat transfer fluids were a ternary chloride salt and liquid-metal sodium. The team applied a facilitated analytic hierarchy process to arrive at a recommended alternative and set a path forward for the project. The AHP criteria were formulated, weighted, and scored by the project leadership team and technical advisory committee consisting of energy industry and CSP experts. The six-month process culminated with a two-day workshop where the sodium alternative was deemed to have both a significantly higher benefit (19.3%) and a lower LCOE (11.4%), with only a slightly higher risk (~3%) than the salt alternative. Consequently, a sodium-receiver design was selected for the Liquid Pathway project, where it will be used to charge a two-tank chloride salt thermal energy storage system.

central receiver↗
DOE OSTI2021

Molecular dynamics of the early stages of high-temperature corrosion

In this work, we performed molecular dynamics simulations of the high-temperature oxidation of metal alloys composed of Al, Cr, and Fe and compared their behavior with that of pure Fe. The metal alloy elements (Al and Cr) segregated to the surface during oxidation, producing a lower stress gradient at the metal/environment interface compared to pure Fe. We have found that the lowered stress gradients produced in the alloy material appear to play a key role in the development of corrosion. Interfaces with lower stress gradients have reduced rates of H 2 O adsorption, especially for the ferritic (bcc) alloys. The diffusivity of oxygen and hydrogen drops more rapidly for the interfaces with reduced stress gradients. The stress gradient is also diminished when the gas pressure is increased, indicating that the Fe-Cr-Al alloy system is more resistant to oxidation than pure Fe at higher pressures. Therefore, we conclude that the lower stress gradients at the alloy/environment interface reduce the stress concentration and can slow down the rate of the initial oxide scale growth. We also compared bcc and fcc alloys with pure Fe based on our three evaluation criteria (peak stress, stress gradient, and summation of stress in the oxide scale). We found that the alloys have lower values under the three criteria compared to pure Fe. The bcc alloy has the best score under a water rich environment and the fcc alloy is proven to be better for peak stress and summation of stress in the oxide scale under an oxygen rich environment. For surface segregation to occur, we find that a minimum content of Al or Cr in the near-surface region must be achieved. We also learned that the role of Al is more important than that of Cr in terms of corrosion resistant behavior at relatively higher temperatures for the Fe-Cr-Al ternary alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.

42 ENGINEERING↗
DOE OSTI2024

Dataset of mechanically induced thermal runaway measurement and severity level on Li-ion batteries

The deployment of Li-ion batteries covers a wide range of energy storage applications, from mobile phones, e-bikes, electric vehicles (EV) and stationary energy storage systems. However, safety issue such as thermal runaway is always one of the most important concerns to prevent Li-ion batteries from further market penetration. A standardized single-side indentation test protocol was developed to mechanically induce an internal short-circuit. The cell voltage, compressive load, indenter stroke, and temperature at the indentation point are measured in time series. The test data of each cell, along with cell parameters such as dimensions, mass, chemistry, state of charge (SOC), capacity, are integrated together to calculate a thermal runaway severity score from 0 to100. Complete data collection process including the original measured record, test method, severity score calculation scheme is presented in this article. The thermal runaway severity analysis and the more than 100 tested Li-ion battery records provide a good data source for further comparison and ranking of thermal runaway risks.

25 ENERGY STORAGE↗
DOE OSTI2025

Multi-criteria thermal resilience certification scheme for indoor built environments during heat waves

With climate change, the indoor built environment is expected to significantly influence the occupant's safety and well-being. A novel multi-criteria thermal resilience certification scheme for indoor built environments during extreme heat events is proposed in this paper. The certification scheme considers overheating, thermal comfort, heat stress, and hygrothermal discomfort in built environments. These criteria are quantified using key performance indicators like indoor overheating degree, hours of exceedance, wet-bulb globe temperature, and heat index, respectively. This scheme is developed based on existing best practices like standards, rating systems, and literature. The scheme is implemented on a benchmark building energy performance model for detached post-World War II dwellings in Belgium as a case study using weather data measured from the City of Brussels. The indoor overheating in the reference dwelling is assessed with a static threshold of 27 °C for the bedrooms and adaptive thresholds for other areas. The analysis found that the building performance is within the defined threshold levels throughout the heat wave duration for all criteria. Therefore, the reference dwelling got a maximum attainable score of four points and is rated five-star for thermal resilience during heat waves. The proposed certification scheme is intended as a standardized framework and highlights the need for further revisions in building performance policies and guidelines.

Climate change↗
DOE OSTI2022

ppdx : Automated modeling of protein–protein interaction descriptors for use with machine learning

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein–protein interactions. The descriptors can be used to predict various properties of interest, such as protein–protein binding affinities, or inhibitory concentrations (IC 50 ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC 50 of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates — and to do so with enough efficiency at large scale. In this work, we propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.

42 ENGINEERING↗
DOE OSTI2024

Applications of fuzzy logic and best-worst method for tritium sensor selection

Accurate assessment of tritium as a fuel source is critical in fusion reactions, necessitating effective sensor evaluation methods. This study investigates a multi-criteria decision-making framework for selecting tritium sensors, integrating fuzzy logic to enhance decision quality. Initial attempts at applying fuzzy logic were found to be too elementary and failed to capture the complexity of multi-criteria selection; this prompted a refined approach that incorporated expert insights and advanced ranking techniques for sensor evaluation. The research used a two-stage methodology. In the first stage, important criteria and sub-criteria for sensor performance were identified and defined. These criteria were then weighted and scored using a fuzzy best-worst method, drawing upon expert opinions to ensure relevance and validity. The second stage involved interpreting information about varying sensors to rank them based on their overall criteria scores, encouraging the selection of the most suitable options. The result of the study is a proposed method for effective sensor selection in fusion reactors, which in turn will significantly improve the reliability of tritium monitoring in fusion applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Pyrocumulonimbus Events Over British Columbia in 2017: An Ensemble Model Study of Parameter Sensitivities and Climate Impacts of Wildfire Smoke in the Stratosphere

Abstract The Pyrocumulonimbus (pyroCb) events over British Columbia in 2017 were observed in the lower stratosphere for about 8–10 months after the smoke injections. Several previous studies used global climate models to investigate the physical parameters for the 2017 pyroCb events, but the conclusions show strong model dependency. In this study, we use the Energy Exascale Earth System Model (E3SM) and complete an ensemble of runs exploring three injection parameters: smoke aerosol mass, the percentage of black carbon within the smoke aerosols, and plume injection height. Additionally, we consider the heterogeneous reaction of ozone and primary organic matter. According to the satellite daily observed aerosol optical depth (AOD), we find that the best ensemble member is the simulation with 0.4 Tg of smoke, 3% of which is black carbon, a 13.5 km smoke injection height, and a 10 −5 probability factor of the heterogeneous reaction. Besides AOD, we examine the ensemble score based on the metrics used in previous studies: the metrics of the extinction coefficient at 18 km altitude and the maximum plume height. The conclusion of the best estimate of the injection parameters for 2017 pyroCb events shows strong not only model but also evaluation metric dependency. We use the Random Forest machine learning technique to quantify the relative importance of each parameter in accurately simulating the 2017 pyroCb events and find that the injection height is the most critical feature, no matter which metric is used to score the ensemble members.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

SeqMask: Behavior Extraction Over Cyber Threat Intelligence Via Multi-Instance Learning

Abstract Identification and extraction of Tactics, Techniques and Procedures (TTPs) for Cyber Threat Intelligence (CTI) restore the full picture of cyber attacks and guide the analysts to assess the system risk. Existing frameworks can hardly provide uniform and complete processing mechanisms for TTPs information extraction without adequate knowledge background. A multi-instance learning approach named SeqMask is proposed in this paper as a solution. SeqMask extracts behavior keywords from CTI evaluated by the semantic impact, and predicts TTPs labels by conditional probabilities. Still, the framework has two mechanisms to determine the validity of keywords. One using expert experience verification. The other verifies the distortion of the classification effect by blocking existing keywords. In the experiments, SeqMask reached 86.07% and 73.99% in F1 scores for TTPs classifications. For the top 20% of keywords, the expert approval rating is 92.20%, where the average repetition of keywords whose scores between 100% and 90% is 60.02%. Particularly, when the top 65% of the keywords were blocked, the F1 decreased to about 50%; when removing the top 50%, the F1 was under 31%. Further, we also validate the possibility of extracting TTPs from full-size CTI and malware whose F1 are improved by 2.16% and 0.81%.

Ge, Wenhan↗
DOE OSTI2023

Uncovering novel liquid organic hydrogen carriers: a systematic exploration of chemical compound space using cheminformatics and quantum chemical methods

We present a comprehensive, in silico-based discovery approach to identifying novel liquid organic hydrogen carrier (LOHC) candidates using cheminformatics methods and quantum chemical calculations. We screened over 160 billion molecules from ZINC15 and GDB-17 chemical databases for structural similarity to known LOHCs and employed a data-driven selection criterion connecting molecular features with dehydrogenation enthalpy. This scoring criterion effectively predicts dehydrogenation enthalpies from SMILES strings, streamlining the LOHC screening process. After rigorous screening and down-selection, we compiled a database of 3000 dehydrogenation reactions for the most promising LOHC candidates, setting the stage for future selection based on kinetics and catalysis. This work demonstrates the significant impact of integrating quantum chemistry and cheminformatics in materials discovery, accelerating the selection process while reducing experimental efforts and time. By proposing new molecules as prospective LOHC candidates, our study provides a valuable resource for researchers and engineers in the development of advanced LOHC systems and showcases a successful approach for high-throughput discovery, contributing to more efficient and sustainable energy storage solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Systemic racial disparities in funding rates at the National Science Foundation

Concerns about systemic racism at academic and research institutions have increased over the past decade. Here, we investigate data from the National Science Foundation (NSF), a major funder of research in the United States, and find evidence for pervasive racial disparities. In particular, white principal investigators (PIs) are consistently funded at higher rates than most non-white PIs. Funding rates for white PIs have also been increasing relative to annual overall rates with time. Moreover, disparities occur across all disciplinary directorates within the NSF and are greater for research proposals. The distributions of average external review scores also exhibit systematic offsets based on PI race. Similar patterns have been described in other research funding bodies, suggesting that racial disparities are widespread. The prevalence and persistence of these racial disparities in funding have cascading impacts that perpetuate a cumulative advantage to white PIs across all of science, technology, engineering, and mathematics.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

A Data-Driven Framework for Automated Detection of Aircraft-Generated Signals in Seismic Array Data Using Machine Learning

Abstract Ground motions associated with aircraft overflights can cover a significant portion of the seismic data collected by shallowly emplaced seismometers, such as new nodal and Distributed Acoustic Sensing systems. This article describes the first published framework for automated detection of aircraft on single channel and multichannel seismic data. The seismic data are converted to spectrograms in a sliding time window and classified as aircraft or nonaircraft in each window using a deep convolutional neural network trained with analyst-labeled data. A majority voting scheme is used to convert the output from the sequence of sliding time windows onto a decision time sequence for each channel and to combine the binary classifications on the decision time sequences across multiple channels. Precision, recall, and F-score are used to quantify the detection performance of the algorithm on nodal data using fourfold time-series cross validation. By applying our framework to data from the Sage Brush Flats nodal array in Southern California, we provide a benchmark performance and demonstrate the advantage of using an array of sensors.

Geochemistry & Geophysics↗
DOE OSTI2025

Generative AI models for learning flow maps of stochastic dynamical systems in bounded domains

Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in learning SDEs, existing learning methods are not applicable to SDEs in bounded domains because they cannot accurately capture the particle exit dynamics. We present a unified hybrid data-driven approach that combines a conditional diffusion model with an exit prediction neural network to capture both interior stochastic dynamics and boundary exit phenomena. Our ML model consists of two major components: a neural network that learns exit probabilities using binary cross-entropy loss with rigorous convergence guarantees, and a training-free diffusion model that generates state transitions for non-exiting particles using closed-form score functions. The two components are integrated through a probabilistic sampling algorithm that determines particle exit at each time step and generates appropriate state transitions. Here, the performance of the proposed approach is demonstrated via three test cases: a one-dimensional simplified problem for theoretical verification, a two-dimensional advection-diffusion problem in a bounded domain, and a three-dimensional problem of interest to magnetically confined fusion plasmas.

Bounded domains↗
DOE OSTI2023

Integrated Land Suitability Assessment for Depots Siting in a Sustainable Biomass Supply Chain

A sustainable biomass supply chain would require not only an effective and fluid transportation system with a reduced carbon footprint and costs, but also good soil characteristics ensuring durable biomass feedstock presence. Unlike existing approaches that fail to account for ecological factors, this work integrates ecological as well as economic factors for developing sustainable supply chain development. For feedstock to be sustainably supplied, it necessitates adequate environmental conditions, which need to be captured in supply chain analysis. Using geospatial data and heuristics, we present an integrated framework that models biomass production suitability, capturing the economic aspect via transportation network analysis and the environmental aspect via ecological indicators. Production suitability is estimated using scores, considering both ecological factors and road transportation networks. These factors include land cover/crop rotation, slope, soil properties (productivity, soil texture, and erodibility factor) and water availability. This scoring determines the spatial distribution of depots with priority to fields scoring the highest. Two methods for depot selection are presented using graph theory and a clustering algorithm to benefit from contextualized insights from both and potentially gain a more comprehensive understanding of biomass supply chain designs. Graph theory, via the clustering coefficient, helps determine dense areas in the network and indicate the most appropriate location for a depot. Clustering algorithm, via K-means, helps form clusters and determine the depot location at the center of these clusters. An application of this innovative concept is performed on a case study in the US South Atlantic, in the Piedmont region, determining distance traveled and depot locations, with implications on supply chain design. The findings from this study show that a more decentralized depot-based supply chain design with 3depots, obtained using the graph theory method, can be more economical and environmentally friendly compared to a design obtained from the clustering algorithm method with 2 depots. In the former, the distance from fields to depots totals 801,031,476 miles, while in the latter, it adds up to 1,037,606,072 miles, which represents about 30% more distance covered for feedstock transportation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Convolutional neural networks for intra-hour solar forecasting based on sky image sequences

Accurate and timely solar forecasts play an increasingly critical role in power systems. Compared to longer forecasting timescales, very short-term solar forecasting has lagged behind in both research and practice. In this paper, we propose deep convolutional neural networks (CNNs) to provide operational intra-hour (10-minute-ahead to 60-minute-ahead) solar forecasts. We develop two CNN structures inspired by a widely-used CNN architecture. The CNNs are tailored to our solar forecasting regression tasks and rely solely on sky image sequences. Case studies based on six years of data (over 150,000 data points) demonstrate that the best CNN model has forecast skill scores of 20%-39% over the naive persistence of cloudiness benchmark, even at these very short timescales. The CNNs also have consistently superior performance when compared to shallow machine learning models with meteorological predictors, where the improvement averages around 7%. The sensitivity analyses show that the sky image length, resolution, and weather conditions have impacts on the deep learning model accuracy. In our intra-hour problem with specific setups, two sky images with a 10-minute 128 x 128 resolution yield the most accurate forecasts. Current limitations, future work, and deployment challenges and solutions are also discussed.

14 SOLAR ENERGY↗
DOE OSTI2022

Deep learning categorization of infrasound array data

Here we develop a deep learning-based infrasonic detection and categorization methodology that uses convolutional neural networks with self-attention layers to identify stationary and non-stationary signals in infrasound array processing results. Using features extracted from the coherence and direction-of-arrival information from beamforming at different infrasound arrays, our model more reliably detects signals compared with raw waveform data. Using three infrasound stations maintained as part of the International Monitoring System, we construct an analyst-reviewed data set for model training and evaluation. We construct models using a 4-category framework, a generalized noise vs non-noise detection scheme, and a signal-of-interest (SOI) categorization framework that merges short duration stationary and non-stationary categories into a single SOI category. We evaluate these models using a combination of k-fold cross-validation, comparison with an existing “state-of-the-art” detector, and a transportability analysis. Although results are mixed in distinguishing stationary and non-stationary short duration signals, f-scores for the noise vs non-noise and SOI analyses are consistently above 0.96, implying that deep learning-based infrasonic categorization is a highly accurate means of identifying signals-of-interest in infrasonic data records.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

PTM‐Psi : A python package to facilitate the computational investigation of p ost‐ t ranslational m odification on p rotein s tructures and their i mpacts on dynamics and functions

Abstract Post‐translational modification (PTM) of a protein occurs after it has been synthesized from its genetic template, and involves chemical modifications of the protein's specific amino acid residues. Despite of the central role played by PTM in regulating molecular interactions, particularly those driven by reversible redox reactions, it remains challenging to interpret PTMs in terms of protein dynamics and function because there are numerous combinatorially enormous means for modifying amino acids in response to changes in the protein environment. In this study, we provide a workflow that allows users to interpret how perturbations caused by PTMs affect a protein's properties, dynamics, and interactions with its binding partners based on inferred or experimentally determined protein structure. This Python‐based workflow, called PTM‐Psi , integrates several established open‐source software packages, thereby enabling the user to infer protein structure from sequence, develop force fields for non‐standard amino acids using quantum mechanics, calculate free energy perturbations through molecular dynamics simulations, and score the bound complexes via docking algorithms. Using the S ‐nitrosylation of several cysteines on the GAP2 protein as an example, we demonstrated the utility of PTM‐Psi for interpreting sequence–structure–function relationships derived from thiol redox proteomics data. We demonstrate that the S ‐nitrosylated cysteine that is exposed to the solvent indirectly affects the catalytic reaction of another buried cysteine over a distance in GAP2 protein through the movement of the two ligands. Our workflow tracks the PTMs on residues that are responsive to changes in the redox environment and lays the foundation for the automation of molecular and systems biology modeling.

59 BASIC BIOLOGICAL SCIENCES↗