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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DFAT: A web-based toolkit for estimating demand flexibility in building-to-grid integration

Demand Flexibility Assessment Tool (DFAT) is an open source web-based tool that estimates the demand flexibility potential of common control strategies in commercial buildings. The toolkit features a demand flexibility estimation tool that contains two calculators, basic and advanced, based on the level of input of customer data. The basic version calculates demand shed metrics for the control strategy “global temperature adjustment” and “cycle on/off compressors” using customer building information, local weather data, and electrical meter data. The advanced version, which uses detailed HVAC equipment data, calculates demand flexibility metrics for control strategies such as static pressure reset, global temperature adjustment, and cycle on/off compressors. In addition to the demand flexibility estimation tool, this toolkit offers a benchmarking tool that helps facility operators, aggregators, and utility resource managers assess demand flexibility opportunities, quantify/verify performance, and compare their performance against that of their peers.

Leong, Michael↗
DOE OSTI2014

Fibroblasts maintained in 3 dimensions show a better differentiation state and higher sensitivity to estrogens

Cell differentiation and response to hormonal signals were studied in a 3D environment on an in-house generated mouse fibroblast cell line expressing a reporter gene under the control of estrogen responsive sequences (EREs). 3D cell culture conditions were obtained in a Rotary Cell Culture System; (RCCS™), a microgravity based bioreactor that promotes the aggregation of cells into multicellular spheroids (MCS). In this bioreactor the cells maintained a better differentiated phenotype and more closely resembled in vivo tissue. The RCCS™ cultured fibroblasts showed higher expression of genes regulating cell assembly, differentiation and hormonal functions. Microarray analysis showed that genes related to cell cycle, proliferation, cytoskeleton, migration, adhesion and motility were all down-regulated in 3D as compared to 2D conditions, as well as oncogene expression and inflammatory cytokines. Controlled remodeling of ECM, which is an essential aspect of cell organization, homeostasis and tissue was affected by the culture method as assessed by immunolocalization of β-tubulin. Markers of cell organization, homeostasis and tissue repair, metalloproteinase 2 (MMP2) and its physiological inhibitor (TIMP4) changed expression in association with the relative formation of cell aggregates. The fibroblasts cultured in the RCCS™ maintain a better responsiveness to estrogens, measured as expression of ERα and regulation of an ERE-dependent reporter and of the endogenous target genes CBP, Rarb, MMP1 and Dbp. Our data highlight the interest of this 3D culture model for its potential application in the field of cell response to hormonal signals and the pharmaco-toxicological analyses of chemicals and natural molecules endowed of estrogenic potential. - Highlights: • We here characterized the first cell line derived from an estrogen reporter mouse. • In the RCCS cells express an immortalized behavior but not a transformed phenotype. • The RCCS provides a system for maintaining cells in more physiological conditions. • RCCS-cultured fibroblasts showed higher hormonal sensitivity to estradiol. • This bioreactor is a novel 3D model to be applied to pharmacotoxicological studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Use of a Stereochemical Strategy To Probe the Mechanism of Phenol-Soluble Modulin $α$3 Toxicity

Phenol-soluble modulin α3 (PSMα3) is a cytotoxic peptide secreted by virulent strains of Staphylococcus aureus . Here, we used a stereochemical strategy to examine the mechanism of PSMα3-mediated toxicity. One hypothesis is that PSMα3 toxicity requires fibril formation; an alternative is that toxicity is caused by soluble forms of PSMα3, possibly oligomeric. We find that the unnatural enantiomer (D residues) displays cytotoxicity comparable to that of L-PSMα3. Racemic PSMα3 is similarly toxic to enantiopure PSMα3 (L or D) under some conditions, but the toxicity is lost under conditions that cause racemic PSMα3 to aggregate. A crystal structure of racemic PSMα3-NH2 displays an α-helical secondary structure and a packing pattern that is reminiscent of the cross-α arrangement recently discovered in crystals of L-PSMα3. Our data suggest that the cytotoxicity of PSMα3 does not depend on stereospecific engagement of a target protein or other chiral macromolecule, an observation that supports a mechanism based on membrane disruption. In addition, our data support the hypothesis that toxicity is exerted by a soluble form rather than an insoluble fibrillar form.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Atomistic structural ensemble refinement reveals non-native structure stabilizes a sub-millisecond folding intermediate of CheY

The dynamics of globular proteins can be described in terms of transitions between a folded native state and less-populated intermediates, or excited states, which can play critical roles in both protein folding and function. Excited states are by definition transient species, and therefore are difficult to characterize using current experimental techniques. We report an atomistic model of the excited state ensemble of a stabilized mutant of an extensively studied flavodoxin fold protein CheY. We employed a hybrid simulation and experimental approach in which an aggregate 42 milliseconds of all-atom molecular dynamics were used as an informative prior for the structure of the excited state ensemble. The resulting prior was then refined against small-angle X-ray scattering (SAXS) data employing an established method (EROS). The most striking feature of the resulting excited state ensemble was an unstructured N-terminus stabilized by non-native contacts in a conformation that is topologically simpler than the native state. We then predict incisive single molecule FRET experiments, using these results, as a means of model validation. Our study demonstrates the paradigm of uniting simulation and experiment in a statistical model to study the structure of protein excited states and rationally design validating experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Workplace Charge Management with Aggregated Building Loads

This paper describes a workplace charge management system developed to control plug-in electric vehicle charging stations based on aggregated building loads. A system to collect information from drivers was also developed for better charge management performance since the present AC charging station standard does not provide battery state of charge information. First, simulations with uncontrolled charging data were conducted to investigate several scenarios and control methods, and then one method with the most power curtailment during peak load was selected for verification tests. This paper illustrates load reduction test results for 36 charging stations and real-time campus net load data.

25 ENERGY STORAGE↗
DOE OSTI2016

TH-EF-BRB-06: Implementation of a Modulated-Arc Total Body Irradiation (TBI) Technique Using the RayStation Treatment Planning System

Purpose: To develop a clinical workflow for delivering a modulated-arc total body irradiation (TBI) with RayStation scripting. This technique uses arc fields with the patient lying at floor level on a padded table and is validated through measurements taken on a custom-made TBI phantom. Methods: Treatment planning was performed for a retrospective cohort of eight patients with a diverse range of heights and body types. Each was replanned using an open-field dual arc method, with the patient in supine and prone positions on the floor of the vault. All plans were optimized using Raystation Planning 4.7.2.5 (RaySearch Laboratories, Stockholm, Sweden), with 200 cGy prescribed to the 95% of the body contour − 5mm. This results in an open-field beam that sweeps craniocaudally across the length of the patient. The technique is validated with measurements at 10 cm intervals in a custom-milled, 5 cm thick acrylic phantom. A centrally located CC13 ion chamber and a Mobile MOSFET (Best Medical Canada, Ottawa, ON) detector array were used to measure dose. Supine and prone arcs for each patient were consecutively delivered, and the aggregate dose at each point was compared to the planned dose calculated in the phantom. Results: The ion chamber measurements differed from the planned dose by an average of .5%, with a standard deviation of 2.1%. All measured data for the MOSFETS were within 10% of the corresponding planned dose except for two outlying points. The standard deviation of dose differences across the entire cohort was 4.0%. Most significant discrepancies occurred either in inhomogeneous regions with large gradients, or at inferior points where beam angle was steepest. Conclusion: We have confirmed that the planned dose is well matched to our measurements within 10% for this method of planning and delivery. We are currently incorporating this technique into our clinical workflow. This work is supported by RaySearch.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1985

Integrated risk/cost planning models for the US Air Traffic system

A prototype network planning model for the U.S. Air Traffic control system is described. The model encompasses the dual objectives of managing collision risks and transportation costs where traffic flows can be related to these objectives. The underlying structure is a network graph with nonseparable convex costs; the model is solved efficiently by capitalizing on its intrinsic characteristics. Two specialized algorithms for solving the resulting problems are described: (1) truncated Newton, and (2) simplicial decomposition. The feasibility of the approach is demonstrated using data collected from a control center in the Midwest. Computational results with different computer systems are presented, including a vector supercomputer (CRAY-XMP). The risk/cost model has two primary uses: (1) as a strategic planning tool using aggregate flight information, and (2) as an integrated operational system for forecasting congestion and monitoring (controlling) flow throughout the U.S. In the latter case, access to a supercomputer is required due to the model's enormous size.

Mulvey, J. M.↗
DOE OSTI2019

Measuring Degradation of Fielded Systems on an Ongoing Basis with RdTools

The solar investment community needs up-to-date and data-derived system degradation rates to use in financial models that calculate the risk and expectation of energy revenue. To date, the majority of financial models choose degradation rates from past studies that are limited and may not be representative of future solar assets (systems) they are financing. As the solar industry expands in all sectors (residential, commercial, and utility), there is a growing amount of energy production available to analyze. With the help of the open source degradation analysis code (RdTools) it is now possible to derive degradation on an ongoing basis and continuously provide up-to-date durability statistics to the investment community. Ongoing and accurate statistics of fielded photovoltaic systems allows financial stakeholders to constrain energy revenue projections, lower financial risk, and increase the bankability of solar. This poster will present considerations and software engineering best practices to encourage data owners to measure degradation of their own photovoltaic assets on an ongoing basis. This poster will also preview an upcoming DOE-funded kWh Analytics degradation project which involves applying RdTools analysis on kWh Analytics’ industry database, aggregating results across variables such as weather, hardware, system configurations etc., and sharing the aggregated results with the broader community.

14 SOLAR ENERGY↗
DOE OSTI2016

Polarimetric radar and aircraft observations of saggy bright bands during MC3E

Polarimetric radar observations increasingly are used to understand cloud microphysical processes, which is critical for improving their representation in cloud and climate models. In particular, there has been recent focus on improving representations of ice collection processes (e.g., aggregation, riming), as these influence precipitation rate, heating profiles, and ultimately cloud life cycles. However, distinguishing these processes using conventional polarimetric radar observations is difficult, as they produce similar fingerprints. This necessitates improved analysis techniques and integration of complementary data sources. Furthermore, the Midlatitude Continental Convective Clouds Experiment (MC3E) provided such an opportunity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Chemical crystallography by serial femtosecond X-ray diffraction

Abstract Inorganic–organic hybrid materials represent a large share of newly reported structures, owing to their simple synthetic routes and customizable properties 1 . This proliferation has led to a characterization bottleneck: many hybrid materials are obligate microcrystals with low symmetry and severe radiation sensitivity, interfering with the standard techniques of single-crystal X-ray diffraction 2,3 and electron microdiffraction 4–11 . Here we demonstrate small-molecule serial femtosecond X-ray crystallography (smSFX) for the determination of material crystal structures from microcrystals. We subjected microcrystalline suspensions to X-ray free-electron laser radiation 12,13 and obtained thousands of randomly oriented diffraction patterns. We determined unit cells by aggregating spot-finding results into high-resolution powder diffractograms. After indexing the sparse serial patterns by a graph theory approach 14 , the resulting datasets can be solved and refined using standard tools for single-crystal diffraction data 15–17 . We describe the ab initio structure solutions of mithrene (AgSePh) 18–20 , thiorene (AgSPh) and tethrene (AgTePh), of which the latter two were previously unknown structures. In thiorene, we identify a geometric change in the silver–silver bonding network that is linked to its divergent optoelectronic properties 20 . We demonstrate that smSFX can be applied as a general technique for structure determination of beam-sensitive microcrystalline materials at near-ambient temperature and pressure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Energy consumption and charging load profiles from long-haul truck electrification in the United States

Abstract The urgent need to decarbonize the transportation sector combined with falling battery prices has spurred industry and policy interest in long-haul truck electrification. The charging behavior and resulting loads from electrified long-haul freight trucks are crucial for the smooth operation of the electric grid and have far-reaching environmental impacts (e.g., greenhouse gas and other air pollutant emissions). However, the aggregate energy impact of a fleetwide shift to electrified long-haul freight trucking has not been explored. This study combines electric truck design scenarios, bottom-up truck weight modeling, vehicle energy modeling, large-scale truck traffic data, and simulation of likely operation and charging behaviors to estimate end-use energy consumption and location-specific hourly charging loads for a national fleet of long-haul electric trucks. Relative to a fleet of future diesel trucks, electrification would reduce direct end-use energy consumption by 0.9 × 10 18 J (0.9 quadrillion BTU), but electrification might increase life cycle energy consumption depending on the electricity source. The electricity required to charge long-haul electric trucks is equivalent to five percent of annual electricity consumption in the United States (US). The simulated truck charging loads peak during the day across the US grid regions, but the charging peaks’ exact timing is sensitive to when trucks are dispatched for operation. The load shapes suggest that electric trucks’ charging loads can coincide with peaks in solar power generation, and planning could enable on- or off-site integration between truck charging stations and renewable electricity generation.

Tong, Fan (ORCID:0000000346613956)↗
DOE OSTI2020

A High-Throughput Solver for Marginalized Graph Kernels on GPU

Here, we present the design and optimization of a solver for efficient and high-throughput computation of the marginalized graph kernel on General Purpose GPUs. The graph kernel is computed using the conjugate gradient method to solve a generalized Laplacian of the tensor product between a pair of graphs. To cope with the large gap between the instruction throughput and the memory bandwidth of the GPUs, our solver forms the graph tensor product on-the-fly without storing it in memory. This is achieved by using threads in a warp cooperatively to stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse. Warps across a thread block can further share tiles via the shared memory to increase data reuse. We exploit the sparsity of the graphs hierarchically by storing only non-empty tiles using a coordinate format and nonzero elements within each tile using bitmaps. We propose a new partition-based reordering algorithm for aggregating nonzero elements of the graphs into fewer but denser tiles to further exploit sparsity. We carry out extensive theoretical analyses on the graph tensor product primitives for tiles of various density and evaluate their performance on synthetic and real-world datasets. Our solver delivers three to four orders of magnitude speedup over existing CPU-based solvers such as GraKeL and GraphKernels. The capability of the solver enables kernel-based learning tasks at unprecedented scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Fleet Utilization

A key goal of NextGen Profiles' fleet utilization study was to conduct a comprehensive, strategic, and standardized assessment of the operational behavior and utilization patterns across EV and EVSE production-ready fleets. These data-driven insights were intended to inform current fleet management strategies and support future infrastructure planning, ensuring the effective adoption and adaptation of the growing EV fleet market. The study applied a series of metrics defined in NextGen Profiles to evaluate diverse fleet operations across various use cases, emphasizing trends in charging, routing, and other critical behaviors. The fleet utilization dataset includes these three sets of metrics from 17 EV fleets, each consisting of a wide range of vehicle types and operational categories, as well as two EVSE fleets. Data were collected from a variety of sources and reformatted into a unified structure before metric computation, ensuring consistency and comparability across all fleets. To protect confidentiality, all fleet metadata are anonymized, and the publicly released metric datasets are aggregated to an hourly cadence.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1991

Kinematic and precipitation structure of a small, long-lived mesoscale convective system

A comprehensive analysis using conventional single Doppler radar analyses, analysis of multiparameter (CP-2) data, network sounding and surface data, and GOES satellite data is presented to characterize the internal kinematic and precipitation structure of a small, long-lived mesoscale convective system. The data pertain to the Cooperative Huntsville Meteorological experiment conducted over northern Alabama and central Tennessee during June and July of 1986. The mesoscale convective system pulsations visible in the IR patterns were associated with the development of closely interacting aggregates of convective elements. The aggregates were about 20 km in diameter, attained echo tops of about 13 km, and were dominated by a single relatively intense convective element. The measured difference in radial velocity across the upper portion of these elements was about 20 m/s over a distance of about 10 km.

Knupp, Kevin R.↗
NASA NTRS2016

Optimum Strategies for Selecting Descent Flight-Path Angles

An information processing system and method for adaptively selecting an aircraft descent flight path for an aircraft, are provided. The system receives flight adaptation parameters, including aircraft flight descent time period, aircraft flight descent airspace region, and aircraft flight descent flyability constraints. The system queries a plurality of flight data sources and retrieves flight information including any of winds and temperatures aloft data, airspace/navigation constraints, airspace traffic demand, and airspace arrival delay model. The system calculates a set of candidate descent profiles, each defined by at least one of a flight path angle and a descent rate, and each including an aggregated total fuel consumption value for the aircraft following a calculated trajectory, and a flyability constraints metric for the calculated trajectory. The system selects a best candidate descent profile having the least fuel consumption value while the fly ability constraints metric remains within aircraft flight descent flyability constraints.

Wu, Minghong G.↗
NASA NTRS2019

Detecting Nighttime Fire Combustion Phase by Hybrid Application of Visible and Infrared Radiation from Suomi NPP VIIRS

An accurate estimation of biomass burning emissions is 1 partially limited by the lack of knowledge of fire burning phase (smoldering vs. flaming). In recent years, several fire detection products have been developed to provide information of fire radiative power (FRP), location, size, and temperature of fire pixels, but no information regarding fire burning phase is retrieved. The Day-Night band (DNB) aboard Visible Infrared Imaging Radiometer Suite (VIIRS) is sensitive to visible light from flaming fires in nighttime scenes. In contrast, VIIRS 4 m moderate resolution band #13 (M13), though capable of detecting fires at all phases, has no direct sensitivity for discerning fire phase. However, the hybrid usage of VIIRS DNB and M-bands data is hampered by their different scanning technology and spatial resolution. In this study, we present a novel method to rapidly and accurately resample DNB pixel radiances to the footprint of M-band pixels, accounting for onboard detector aggregation schemes and bowtie effect removals. The visible energy fraction (VEF) is subsequently introduced as an indicator of fire burning phase VEF is calculated as the ratio of visible light power (VLP) to FRP for each fire pixel retrieved from the VIIRS 750 m active fire product. A global distribution of VEF values, and thereby the fire phase, is quantitatively obtained, showing smaller VEF values in regions with mostly smoldering wildfires, such as peatland fires in Indonesia, larger VEF values in regions with flaming wildfires over grasslands and savannas in the sub-Sahelian region, and the largest VEF values associated with gas flaring in the Middle East. Mean VEF for different land cover types or regions is highly correlated with modified combustion efficiency (MCE). These results, together with a case study of the 2018 California Camp Fire, show that the VEF has the potential to be an indicator of fire combustion phase for each fire pixel, appropriate for estimating emission factors at the satellite pixel level.

Jun Wang↗
DOE OSTI2015

The Case for the Use of Active Social Media in Nonproliferation and Nuclear Security

A great amount of attention and consideration is being directed at possible applications of social media in many challenging areas. The use of social media has already shown its importance in the area of disaster response, where, each citizen is essentially acting as a sensor in reporting local conditions. In the aggregate, valuable information is obtained to enable a more effective response as well as provide timely information to those in the disaster area. No one needs to be trained to understand what constitutes a disaster, so a social media data stream from the public is literally always active and ready to engage. A similar but more focused approach is the use of crowdsourcing for science, where specific challenges in areas such as mathematics, astronomy, and biology are posted to social media and solved by the crowd.

Schanfein, Mark J.↗