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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Design, scaling and cost evaluations of circulating fluidized-bed systems for biomass pyrolysis

To generate updated and transparent capital cost estimates for biomass fast pyrolysis equipment, refinery fluidized catalytic cracking design and sizing principles are examined and extended to pyrolysis of woody biomass. Capital costs for the sized equipment are estimated with process-industry software. A one-dimensional flow simulation with pyrolysis kinetics is leveraged to validate the fluidization conditions and thermal energy balance. After successful sizing and a system cost estimate of $\$$2.8 M (in 2016 US$\$$) at the biorefinery scale of 1000 metric tons per day (MTD), these methods were exercised for even smaller equipment at the distributed pyrolysis scale with modifications to the process design constraints, and not directly comparable with the 1000 MTD case, arriving at capital cost estimates of $\$$1.2 M for a 500 ton/day system and $\$$0.9 M for a 200 ton/day system. Here in this work, It is noted that this work only estimates purchased equipment costs at the ±50% accuracy level; there are significant other custom factors applicable to each installation based on location, maturity, scale, complexities during installation, engineering and licensing costs, etc. that need to be added on to these estimates to derive investment costs.

09 BIOMASS FUELS↗
DOE OSTI2021

Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques

Predicting the properties of grain boundaries poses a challenge because of the complex relationships between structural and chemical attributes both at the atomic and continuum scales. Grain boundary systems are typically characterized by parameters used to classify local atomic arrangements in order to extract features such as grain boundary energy or grain boundary strength. The present work utilizes a combination of high-throughput atomistic simulations, macroscopic and microscopic descriptors, and machine-learning techniques to characterize the energy and strength of silicon carbide grain boundaries. Additionally, a diverse data set of symmetric tilt and twist grain boundaries are described using macroscopic metrics such as misorientation, the alignment of critical low-index planes, and the Schmid factor, but also in terms of microscopic metrics, by quantifying the local atomic structure and chemistry at the interface. These descriptors are used to create random-forest regression models, allowing for their relative importance to the grain boundary energy and decohesion stress to be better understood. Results show that while the energetics of the grain boundary were best described using the microscopic descriptors, the ability of the macroscopic descriptors to reasonably predict grain boundaries with low energy suggests a link between the crystallographic orientation and the resultant atomic structure that forms at the grain boundary within this regime. For grain boundary strength, neither microscopic nor macroscopic descriptors were able to fully capture the response individually. However, when both descriptor sets were utilized, the decohesion stress of the grain boundary could be accurately predicted. These results highlight the importance of considering both macroscopic and microscopic factors when constructing constitutive models for grain boundary systems, which has significant implications for both understanding the fundamental mechanisms at work and the ability to bridge length scales.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Functionalized Porous Polymer Networks as High-Performance PFAS Adsorbents

Toxic per- and polyfluoroalkyl substances (PFAS) are now found in nearly every water source on the planet. Exposure to these molecules can have negative health consequences, but the low concentration of PFAS relative to other solutes in water makes their removal challenging. Adsorbents offer a promising treatment route, but often exhibit low selectivities and removal capacities, as well as slow kinetics. The performance in these metrics can be improved by chemically optimizing PFAS binding sites and maximizing PFAS-adsorbent interactions. To explore how to achieve this, a porous polymer network solid (PPN-6, also known as PAF-1) was postsynthetically modified with various chemical moieties capable of leveraging unique combinations of electrostatic, hydrogen-bonding, hydrophobic, and fluorophilic interactions with PFAS molecules. Batch adsorption experiments and computational studies revealed that electrostatic and hydrogen-bonding interactions drive short-chain PFAS adsorption, while hydrophobic and fluorophilic interactions improve long-chain PFAS adsorption. In complex water matrices, a combination of electrostatic and fluorophilic interactions led to the greatest total PFAS removal. The best-performing material, functionalized with a fluorinated alkylammonium (PPN-6-FNDMB), selectively adsorbs PFAS with high capacity (up to 4.0 mmol/g) and rapid kinetics (equilibrium reached in <30 s). Furthermore, PPN-6-FNDMB outperforms several commercial adsorbents, achieving near-complete removal of 21 different PFAS from a groundwater sample collected at a US Air Force base. The PFAS could subsequently be desorbed from PPN-6-FNDMB, concentrating them by a factor of over 50 times. The recycled PPN-6-FNDMB could then be reused with minimal losses in long-chain PFAS adsorption capacity over four cycles.

Pezoulas, Ethan R↗
DOE OSTI2026

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

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

Artificial neural networks↗
DOE OSTI2023

Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation

Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its cells and complexity of the interactions. Generative models can provide more rapid sample production, but currently require significant effort to optimize performance for specific detector geometries, often requiring many models to describe the varying cell sizes and arrangements, without the ability to generalize to other geometries. Here, we develop a geometry-aware autoregressive model, which learns how the calorimeter response varies with geometry, and is capable of generating simulated responses to unseen geometries without additional training. The geometry-aware model outperforms a baseline unaware model by over 50% in several metrics such as the Wasserstein distance between the generated and the true distributions of key quantities which summarize the simulated response. A single geometry-aware model could replace the hundreds of generative models currently designed for calorimeter simulation by physicists analyzing data collected at the Large Hadron Collider. This proof-of-concept study motivates the design of a foundational model that will be a crucial tool for the study of future detectors, dramatically reducing the large upfront investment usually needed to develop generative calorimeter models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Validation of Synthetic U.S. Electric Power Distribution System Data Sets

There is a strong need for synthetic yet realistic distribution system test data sets that are as diverse, large, and complex to solve as real systems. Such data sets can facilitate the development of advanced algorithms and the assessment of emerging distributed energy resources while avoiding the need to acquire proprietary critical infrastructure or private data. Such synthetic data sets, however, are useful only if they are realistic enough to look and behave similarly to actual systems. This paper presents a comprehensive framework for validating synthetic distribution data sets using a three-pronged statistical, operational, and expert validation approach. Furthermore, it also presents a set of statistical and operational metric targets for achieving realistic data sets based on detailed characterization of more than 10,000 real U.S. utility feeders. The paper demonstrates the use of the proposed validation approach to validate three large-scale synthetic data sets developed by the authors representing Santa Fe, New Mexico; Greensboro, North Carolina; and the San Francisco Bay Area, California.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Regional Variation in Light-Duty Plug-in Electric Vehicle Emissions

Automobile electrification is viewed as one of the key requirements for deep decarbonization of transportation. However, the exact magnitude of decarbonization from switching from a combustion of petroleum-based fuels to grid-derived electricity is a complex issue. Variations in local grid mixes lead to differing carbon intensities of electricity in different regions. This complexity is compounded by geographical differences in vehicle characteristics. This analysis considers these geographic differences to produce a historical assessment of the fuel-cycle carbon emissions of plug-in electric vehicles (PEVs) in the United States from 2011 to 2021. We find that PEVs in the United States decreased in electricity-derived carbon intensity, from 187 grams per mile to 110 grams per mile from 2011 to 2021 due to improvements in the electric grid and vehicle efficiency. Through 2021, PEVs emitted a total of 13.6 million metric tons of greenhouse gas (GHG) emissions in the United States, including both emissions from electricity and gasoline, while driving a total of 100 billion miles. Of this total, 8.39 million metric tons was due to electricity consumption. Over the same distance, comparable gasoline vehicles would have emitted between 29 and 41 million metric tons of GHG, leading to a reduction of 15 to 27 million metric tons. Relative to national-level assessments that do not factor in this regional difference, these electric vehicles resulted in 13% greater reductions in GHG emissions than previously calculated. In the future, announced goals to further decarbonize the electricity sector will continue to reduce the emissions of PEVs, including reducing emissions rates for vehicles already on the road, further improving the benefits relative to the unchanging emissions of contemporaneous gasoline vehicles.

02 PETROLEUM↗
NASA NTRS1985

Application of axisymmetric analogue for calculating heating in three-dimensional flows

A rapid, approximate method has been developed for calculating the heating rates on three-dimensional vehicles such as the Space Shuttle Orbiter and other advanced reentry configurations. The method is based on the axisymmetric analogue for three-dimensional boundary layers. It uses information obtained from a three-dimensional inviscid flowfield solution, such as HALIS, to calculate inviscid surface streamlines along which approximate heating rates are calculated independent of what happens along other streamlines. Three-dimensional effects are included through the metric coefficient that describes the divergence or convergence of streamlines. Boundary-layer edge properties are obtained from the inviscid flowfield solution by interpolating in the inviscid flowfield at a distance equal to the boundary-layer thickness away from the wall. This accounts, approximately, for the variable boundary-layer edge entropy. Using this method, heating calculations can be made along a typical streamline in a few seconds. This method has been used to accurately predict heating rates for simple shapes such as a spherically blunted cone and more complex shapes such as the Shuttle Orbiter for a variety of wind-tunnel and flight conditions. A unique feature of the method is its ability to accurately predict heating rates on the Shuttle Orbiter wing.

Hamilton, H. H.↗
NASA NTRS2022

Quantifying Spatial Drought Propagation Potential in North America Using Complex Network Theory

Droughts have a dominant three-dimensional (3-D) spatiotemporal structure typically spanning hundreds of kilometers and often lasting for months to years. Here, we introduced a novel framework to explore the 3-D structure of the evolution of droughts based on network theory concepts. The proposed framework is applied to identify critical source regions responsible for large-scale drought onsets during 1901–2014 for the North American continent using the Standardized Precipitation Evaporation Index (SPEI). We built a spatial network connecting the drought onset timings for the North American continent. Using a spatially weighted network partitioning algorithm, the whole continent is then classified into regional spatial drought networks (RSN), where droughts are more likely to propagate within these regional systems. Finally, a customized network metric was applied to identify locations (source regions) where the drought onsets further propagate to other areas within the regional spatial network. Our results indicated that the West coast, Texas coastal region, and Southeastern Arkansas as major source regions through which atmospheric drought propagates to Western, South Central, and Eastern North America. The formation of drought source regions are due to presence of high pressure ridges and anomalous wind patterns. Furthermore, our results indicate that the drought propagation from these source regions may be due to inadequate moisture transport. The proposed framework can help to develop an early warning detection system for droughts and other spatially extensive extreme events such as heatwaves and floods.

Goutam Konapala↗
DOE OSTI2021

A fast reduced model for a shell-and-tube based latent heat thermal energy storage heat exchanger and its application for cost optimal design by nonlinear programming

Numerical simulation of latent heat thermal energy storage (LHTES) systems plays a fundamental role in studying the physical process and guiding the engineering design. Discretization of the PDEs describing the nonlinear solidification/melting process of phase change materials (PCMs) leads to a large-scale complex dynamics system, where the system behavior depends on a set of parameters. In a design setting, repeated model evaluations are required over the set of parameters results in significant computational burden. In this paper, an explicit analytic solution was built for the propagation of the solidification front in a cylindrical coordinate. The analytic solution approach is further employed to develop a low computational reduced model (RM) as a module for a shell-and-tube based LHTES heat exchanger. The levelized Cost of Energy (LCOE) is used as a design metric and the RM model is used to apply system-level constraints in the nonlinear programming formulation that facilitates efficient global optimal design of the PCM properties, flow conditions and tube geometries. The use of LCOE as the design metric prevents over design of the heat transfer rate and also establishes a fair ground for evaluation of different thermal storage technologies and their integrated applications with other systems. Optimal results showed that a higher effectiveness results in a higher LCOE; the velocity of the HTF and the length of the channel are highly correlated with each other; both larger PCM latent energy and conductivity result in lower LCOE.

25 ENERGY STORAGE↗
DOE OSTI2025

Demonstration of a 1820 channel multiplexer for transition-edge sensor bolometers

The scalability of most transition-edge sensor arrays is limited by the multiplexing technology, which combines their signals over a reduced number of wires and amplifiers. Here, in this Letter, we present and demonstrate a multiplexer design optimized for transition-edge sensor bolometers with 1820 sensors per readout unit, a factor of two more than the previous state-of-the-art. The design is optimized for cosmic microwave background imaging applications, and it builds on previous microwave superconducting quantum interference device multiplexers by doubling the available readout bandwidth to the full 4–8 GHz octave. Evaluating the key performance metrics of yield, sensitivity, and crosstalk through laboratory testing, we find an end-to-end operable detector yield of 78%, a typical nearest-neighbor crosstalk amplitude of ∼0.4%, and a median white noise level of 83 pA/$\sqrt{\textrm{Hz}}$ due to the multiplexer, corresponding to an estimated contribution of 4% to the total system noise for a ground-based cosmic microwave background telescope. Additionally, we identify a possible path toward reducing resonator loss for future designs with reduced noise. We expect these developments to alleviate the system complexity, cryogenic requirements, and cost of future large arrays of low temperature detectors.

cosmic microwave background↗
DOE OSTI2026

PowerMappeR: Power-Optimized Mapping of SNNs onto ReRAM Crossbars coupled via Packet-Switched NoCs

Many recent efforts in developing hardware-accelerated spiking neural networks (SNNs) are characterized by deep co-design between algorithms, architectures, and devices. Architectural advances overcome device constraints by coupling together many small resistive-RAM (ReRAM) crossbars via a network-on-chip (NoC) for neuromorphic component operation. Concurrently, improved SNN training methods increase accuracy and structural sparsity in networks despite growing problem sizes. Finally, compilers leverage these attributes to minimize area and inter-crossbar communication while mapping large SNNs to sophisticated architectures. However, for compiler-driven co-design to realize increasingly complex and profitable optimizations, a compile-time view of power consumption is critical. We present PowerMappeR to express and optimize over mapping-, architecture-, and device-specific power consumption information. By modeling the dynamic power of well-established components, we develop an integer linear programming (ILP)-based, encoding-agnostic, parametric power estimation model. Using this model, we demonstrate practical improvements in area and inter-crossbar communication by 0%–9.5% and 1.4%–5.1%, respectively. We also limit hotspot formation during optimization, achieving comparable or better results in targeted metrics with up to 96.4%–97.1% restriction of hotspot magnitude. Finally, we introduce profile-guided formulations to reduce worst-case and expected-case hotspot magnitude by 40.7%–69.5% and 40.6%–56.3%, respectively. Optimizing worst-case hotspot magnitude incidentally improves expected-case magnitude by 10.85%–33.45%. Reciprocally, optimizing expected-case magnitude incidentally improves worst-case magnitude by 4.33%–39.87%. Validation against hardware simulators confirms that PowerMappeR can decrease dynamic power consumption by 12.6%–27.3%.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗
DOE OSTI2024

Modeling Spatial Asymmetries in Teleconnected Extreme Temperatures

Abstract Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.

Krock, Mitchell L.↗
DOE OSTI2024

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, in this paper, we describe and illustrate PeakQC’s functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A network approach for multiscale catchment classification using traits

Abstract. The classification of river catchments into groups with similar biophysical characteristics is useful to understand and predict their hydrological behavior. The increasing availability of remote sensing and other large-scale geospatial datasets has enabled the use of advanced data-driven approaches to classify catchments using traits such as topography, geology, climate, land cover, land use, and human influence. Unsupervised clustering algorithms based on the Euclidean distance are commonly used for trait-based classification but are not suitable for highly dimensional data. In this study we present a new network-based method for multi-scale catchment classification, which can be applied to large datasets and used to determine the traits associated with different catchment groups. In this framework, two networks are analyzed in parallel: the first being where the nodes are traits and the second being where the nodes are catchments. In both cases, edges represent pairwise similarity, and a network cluster detection algorithm is used for the classification. The trait network is used to investigate redundancy in the trait data and to condense this information into a small number of interpretable categories. The catchments network is used to classify the catchments into clusters and to identify representative catchments for the different groups using the degree centrality metric. We apply this method to classify 9067 river catchments across the contiguous United States at both regional and continental scales using 274 non-categorical traits. At the continental scale, we identify 25 interpretable trait categories and 34 catchment clusters of sizes greater than 50. We find that catchments with similar trait categories are typically located in the same region, with different spatial patterns emerging among clusters dominated by natural and anthropogenic traits. We also find that the catchment clusters exhibit distinct hydrological behavior based on an analysis of streamflow indices. This network approach provides several advantages over traditional means of classification, including better separation of clusters, the use of alternate similarity metrics that are more suitable for highly dimensional data, and reducing redundancy in the trait information. The paired catchment–trait networks enable analysis of hydrological behavior using the dominant trait categories for each catchment cluster. The approach can be used at multiple spatial scales since the network topologies adjust automatically to reflect the trait patterns at the scale of investigation. Finally, the representative catchments identified as hub nodes in the network can be used to guide transferable observational and modeling strategies. The method is broadly applicable beyond hydrology for classification of other complex systems that utilize different types of trait datasets.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Apparent depth/apparent diameter relation for lunar craters

Photogrammetric measurements from Apollo metric-quality pictures ensure high accuracy of apparent depth (R sub a) and apparent diameter (D sub a) data for fresh lunar craters. R sub a is a direct function of D sub a and the R sub a/D sub a distribution inflects sharply at an apparent diameter of about 15 km. Each of the two resulting subgroups of craters requires two separate least-squares fits. For simple lunar craters (less than 15 km across), the ratio of R sub a to D sub a is about 0.2; the ratio for complex (larger) craters ranges from 0.1 to 0.01. Apparent depth does not exceed 3400 m, regardless of crater size, possibly as a result of isostasy, substrate layering, or curvature of the moon. R sub a/D sub a differences for mare versus terra craters are either absent or are equivocal. The ratio of apparent diameter to rim-crest diameter (D sub a/D sub r) averages 0.83 for smaller craters and 0.86 for larger craters, indicating another simple-to-complex contrast in crater morphology on the moon, and confirms a prediction by R. B. Baldwin for simple craters. Close similarity of D sub a/D sub r for terrestrial experimental craters and simple lunar craters is especially consistent with impact origin for the moon's craters.

Pike, R. J.↗
NASA NTRSDate not supplied

Development and Initial Validation of HFBP-EM Short-Form Surveys

The Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite includes self-report surveys designed to assess Behavioral Medicine (BMed) and Team risks for future exploration class missions. Survey data was collected during the Human Exploration Research Analog (HERA) Campaign 4 (C4), HERA Campaign 5 (C5), and during SIRIUS19 missions in the Russian Ground Based Experiment Complex, NEK. Crew time in spaceflight analogs and in spaceflight is typically limited. Extensive self-report surveys can be a significant burden to participants and are not operationally acceptable for spaceflight. The purpose of this project was to comprehensively assess the reliability and validity of the standard-length HFBP-EM surveys, identify acceptable short forms to administer during HERA C6, examine initial validation evidence for the proposed short form measures, and identify key measurement gaps in HFBP-EM coverage. In this presentation, we will report reliability and validity metrics of standard form data, describe the proposed new short forms, and present initial psychometric analyses supporting the use of these short forms.

S T Bell↗
NASA NTRSDate not supplied

Advances in Integrally Formed Metallic Structures

NASA Langley Research Center (LaRC) is applying flow forming technology to aerospace aluminum alloys for the manufacture of integrally stiffened cylinders that meet aircraft fuselage or launch vehicle specifications. High-strength 2xxx series alloys, in various temper/product form conditions, are being compared with medium-strength, very formable AA 6061 as the benchmark. Screening of candidate materials traditionally relies on empirical forming trials, but more recently on metrics derived from constant nominal strain rate tensile testing. In this work, the established hierarchy of formability metrics is adopted as the baseline; tensile area reduction, work hardening exponent, and modulus of resilience. The suite of metrics can be expanded based on phenomenological and mechanistic considerations of plastic flow instabilities to incorporate anisotropy ratio, rate of work hardening, and dynamic strain aging effects. Separate research thrusts at LaRC are aimed at formulating a protocol for scalable production of fully formed, defect-free components: 1) quantifying the extended set of formability metrics from constant and jump strain rate tensile tests with digital image correlation (DIC); 2) using statistically designed experiments on a vertical spin/flow forming facility (WF VUD-600®) to isolate the effect of convenient process variables, such as axial feed rate and thickness reduction; and 3) predicting the effect of feed ratio (roller translation relative to mandrel rotation speeds) on material response via process modeling using the DEFORM® simulation software. Current results will be discussed in terms of correlating intrinsic material properties with the bulk workability characteristics to successfully fabricate cylinders with complex cross-sections.

Aluminum↗