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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Distributed Sensor Coordination for Advanced Energy Systems

The ability to collect key system level information is critical to the safe, efficient and reliable operation of advanced energy systems. With recent advances in sensor development, it is now possible to push some level of decision making directly to computationally sophisticated sensors, rather than wait for data to arrive to a massive centralized location before a decision is made. This type of approach relies on networked sensors (called “agents” from here on) to actively collect and process data, and provide key control decisions to significantly improve both the quality/relevance of the collected data and the associating decision making. The technological bottlenecks for such sensor networks stem from a lack of mathematics and algorithms to manage the systems, rather than difficulties associated with building and deploying them. Indeed, traditional sensor coordination strategies do not provide adequate solutions for this problem. Passive data collection methods (e.g., large sensor webs) can scale to large systems, but are generally not suited to highly dynamic environments, such as advanced energy systems, where crucial decisions may need to be reached quickly and locally. Approaches based on local decisions on the other hand cannot guarantee that each agent performing its task (maximize an agent objective) will lead to good network wide solution (maximize a network objective) without invoking cumbersome coordination routines. There is currently a lack of algorithms that will enable self-organization and blend the efficiency of local decision making with the system level guarantees of global decision making, particularly when the systems operate in dynamic and stochastic environments. In this work we addressed this critical gap and provided a comprehensive solution to the problem of sensor coordination to ensure the safe, reliable, and robust operation of advanced energy systems. The differentiating aspect of the proposed work is in shifting the focus towards “what to observe” rather than “how to observe” in large sensor networks, allowing the agents to actively determine both the structure of the network and the relevance of the information they are seeking to collect. In addition to providing an implicit coordination mechanism, this approach allows the system to be reconfigured in response to changing needs (e.g., sudden external events requiring new responses) or changing sensor network characteristics (e.g., sudden changes to plant condition). Outcome Summary: All milestones associated with this project have been completed. In particular, private sensor objective functions were developed which are aligned with the global objective function, sensor effectiveness has been improved by using “sensor teams,” system efficiency has been improved by 30% using difference evaluation functions, we have demonstrated system reconfigurability for 20% changes in system conditions, we have demonstrated extreme scalability of our proposed algorithm, we have demonstrated that sensor networks can overcome disruptions of up to 20% in network conditions, and have demonstrated system reconfigurability to 20% changes in system conditions in hardware-based simulations. This final report summarizes how each of these milestones was achieved, and gives insight into future research possibilities past the work which has been completed. The following publications support these milestones [6, 8, 9, 10, 16, 18, 19].

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2023

Marin County Wildfires Ii: Improving Fire Suppression Modeling to Inform Fire Prevention and Suppression Decisions in Marin County, Ca

A future of increased wildfires requires greater integration of spatial analysis and local knowledge of emergency responders. We examine the application of a Potential Operational Delineations (PODs) framework for strategic pre-fire planning in Marin County. PODs are spatial units for wildfire management that combine predictive modeling and local firefighter expertise to identify potential control locations as unit boundaries and assess the difficulty of suppression within units. Additionally, this project explores the integration of road networks and social vulnerability to assess environmental justice in evacuation safety. This project constitutes a novel application of the PODs framework as it integrates expertise from Marin County senior firefighters with a Fireline Location Model (FLM) to achieve POD definition and uses a Suppression Difficulty Score (SDS) to rank each POD. The FLM uses network analysis and hydrologic modeling to identify key roads and ridgelines as boundaries and combines them with expert knowledge, in the form of workshops, to construct PODs. Once identified, PODs are classified using SDS, which includes processed inputs such as LiDAR-derived aboveground biomass, ECOSTRESS Evaporative Stress Index, land use cover type from Sentinel-2 Imagery, and a digital elevation model. Environmental justice for evacuation safety incorporated three key road metrics such as connectivity, travel area, and exit capacity, the Social Vulnerability Index from the Center for Disease Control, and cell coverage to determine a final Evacuation Difficulty Score. Results indicate a strong link between road networks as primary POD boundaries, with ridgelines and waterways as secondary and tertiary locations. Specifically, we find 78.5% of expertise-identified POD boundaries align with FLM-determined boundaries. More validation is needed to support this process; however, initial results signal a feasible framework to integrate expertise and spatial analysis in local level strategic fire planning

Wildfire modeling↗
DOE OSTI2017

Measurements of pore-scale flow through apertures

Pore-scale aperture effects on flow in pore networks was studied in the laboratory to provide a parameterization for use in transport models. Four cases were considered: regular and irregular pillar/pore alignment with and without an aperture. The velocity field of each case was measured and simulated, providing quantitatively comparable results. Two aperture effect parameterizations were considered: permeability and transmission. Permeability values varied by an order of magnitude between the cases with and without apertures. However, transmission did not correlate with permeability. Despite having much greater permeability the regular aperture case permitted less transmission than the regular case. Moreover, both irregular cases had greater transmission than the regular cases, a difference not supported by the permeabilities. Overall, these findings suggest that pore-scale aperture effects on flow though a pore-network may not be adequately captured by properties such as permeability for applications that are interested in determining particle transport volume and timing.

58 GEOSCIENCES↗
DOE OSTI2012

0.22 THz wideband sheet electron beam traveling wave tube amplifier: Cold test measurements and beam wave interaction analysis

In this paper, we describe micro-fabrication, RF measurements, and particle-in-cell (PIC) simulation modeling analysis of the 0.22 THz double-vane half period staggered traveling wave tube amplifier (TWTA) circuit. The TWTA slow wave structure comprised of two sections separated by two sever ports loaded by loss material, with integrated broadband input/output couplers. The micro-metallic structures were fabricated using nano-CNC milling and diffusion bonded in a three layer process. The 3D optical microscopy and SEM analysis showed that the fabrication error was within 2-3 {mu}m and surface roughness was measured within 30-50 nm. The RF measurements were conducted with an Agilent PNA-X network analyzer employing WR5.1 T/R modules with a frequency range of 178-228 GHz. The in-band insertion loss (S{sub 21}) for both the short section and long section (separated by a sever) was measured as {approx}-5 dB while the return loss was generally around {approx}-15 dB or better. The measurements matched well with the S-matrix simulation analysis that predicted a 3 dB bandwidth of {approx}45 GHz with an operating frequency at 220 GHz. However, the measured S{sub 21} was {approx}3 dB less than the design values, and is attributed to surface roughness and alignment issues. The confirmation measurements were conducted over the full frequency band up to 270 GHz employing a backward wave oscillator (BWO) scalar network analyzer setup employing a BWO in the frequency range 190 GHz-270 GHz. PIC simulations were conducted for the realistic TWT output power performance analysis with incorporation of corner radius of 127 {mu}m, which is inevitably induced by nano-machining. Furthermore, the S{sub 21} value in both sections of the TWT structure was reduced to correspond to the measurements by using a degraded conductivity of 10% International Annealed Copper Standard. At 220 GHz, for an elliptic sheet electron beam of 20 kV and 0.25 A, the average output power of the tube was predicted to be reduced from 90 W (for ideal conductivity/design S-parameters) to 70 W (for the measured S-parameters/inferred conductivity) for an average input power of 50 mW. The gain of the tube remains reasonable: {approx}31.4 dB with an electronic efficiency of {approx}1.4%. The same analysis was also conducted for several frequencies between 190 GHz-260 GHz. This detailed realistic PIC analysis demonstrated that this nano-machined TWT circuit has slightly reduced S-parameters and output power from design, but within an acceptable range and still have promising output power, gain, and band width as required. Thus, we expect to meet the specifications of 1000 W-GHz for the darpa program goals.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

3D Printing of Liquid Crystal Elastomer Foams for Enhanced Energy Dissipation Under Mechanical Insult

Polymer foams are an essential class of lightweight materials used to protect assets against mechanical insults, such as shock and vibration. Two features are important to enhance their energy absorption characteristics: the foam structure and the matrix phase mechanical behavior. This study investigates novel approaches to control both of these features to enhance the energy absorption capability of flexible lattice foams. First, we consider 3D printing via digital light processing (DLP) as a method to control the foam mesostructure across a suite of periodic unit cells. Second, we introduce an additional energy dissipation mechanism in the solid matrix phase material by 3D printing the lattice foams with polydomain liquid crystal elastomer (LCE), which undergo a mechanically induced phase transition under large strains. This phase transition is associated with LC mesogen rotation and alignment and provides a second mechanism for mechanical energy dissipation in addition to the viscoelastic relaxation of the polymer network. Here, we contrast the 3D printed LCE lattices with conventional, thermomechanically near-equivalent elastomer lattice foams to quantify the energy-absorbing enhancement the LCE matrix phase provides. Under cyclic quasi-static uniaxial compression conditions, the LCE lattices show dramatically enhanced energy dissipation in uniaxial compression compared to the non-LCE equivalent foams printed with a commercially available photocurable elastomer resin. The lattice geometry also plays a prominent role in determining the energy dissipation ratio between the LCE and non-LCE foams. We show that when increasing the lattice connectivity, the foam deformation transitions from bending-dominated to stretching-dominated deformations, which generates higher axial strains in the struts and higher energy dissipation in the lattice foam, as stretching allows greater mesogen rotation than bending. The LCE foams demonstrate superior energy absorption during the repeated dynamic loading during drop testing compared with the non-LCE equivalent foams, demonstrating the potential of LCEs to enhance physical protection systems against mechanical impact.

3D printing↗
DOE OSTI2016

On the Path to SunShot. Utility Regulatory and Business Model Reforms for Addressing the Financial Impacts of Distributed Solar on Utilities

Net-energy metering (NEM) has helped drive the rapid growth of distributed PV (DPV) but has raised concerns about electricity cost shifts, utility financial losses, and inefficient resource allocation. These concerns have motivated real and proposed reforms to utility regulatory and business models. This report explores the challenges and opportunities associated with such reforms in the context of the U.S. Department of Energy's SunShot Initiative. Most of the reforms to date address NEM concerns by reducing the benefits provided to DPV customers and thus constraining DPV deployment. Eliminating NEM nationwide, by compensating exports of PV electricity at wholesale rather than retail rates, could cut cumulative DPV deployment by 20% in 2050 compared with a continuation of current policies. This would slow the PV cost reductions that arise from larger scale and market certainty. It could also thwart achievement of the SunShot deployment goals even if the initiative's cost targets are achieved. This undesirable prospect is stimulating the development of alternative reform strategies that address concerns about distributed PV compensation without inordinately harming PV economics and growth. These alternatives fall into the categories of facilitating higher-value DPV deployment, broadening customer access to solar, and aligning utility profits and earnings with DPV. Specific strategies include utility ownership and financing of DPV, community solar, distribution network operators, services-driven utilities, performance-based incentives, enhanced utility system planning, pricing structures that incentivize high-value DPV configurations, and decoupling and other ratemaking reforms that reduce regulatory lag. These approaches represent near- and long-term solutions for preserving the legacy of the SunShot Initiative.

14 SOLAR ENERGY↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Chemiomics: Network Reconstruction and Kinetics of Port Wine Aging

Network reconstruction (NR) has proven to be useful in the detection and visualization of relationships among the compounds present in a Port wine aging data set. This view of the data provides a considerable amount of information with which to understand the kinetic contexts of the molecules represented by peaks in each chromatogram. The aim of this paper was to use NR together with the determination of kinetic parameters to extract more information about the mechanisms involved in Port wine aging. The volatile compounds present in samples of Port wines spanning 128 years in age were measured with the use of GC-MS. After chromatogram alignment, a peak matrix was created, and all peak vectors were compared to one another to determine their Pearson correlations over time. A correlation network was created and filtered on the basis of the resulting correlation values. Some nodes in the network were further studied in experiments on Port wines stored under different conditions of oxygen and temperature in order to determine their kinetic parameters. The resulting network can be divided into three main branches. The first branch is related to compounds that do not directly correlate to age, the second branch contains compounds affected by temperature, and the third branch contains compounds associated with oxygen. Compounds clustered in the same branch of the network have similar expression patterns over time as well as the same kinetic order, thus are likely to be dependent on the same technological parameters. Network construction and visualization provides more information with which to understand the probable kinetic contexts of the molecules represented by peaks in each chromatogram. Finally, the approach described here is a powerful tool for the study of mechanisms and kinetics in complex systems and should aid in the understanding and monitoring of wine quality.

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

Learning to identify semi-visible jets

We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible) particles. Such objects are challenging to identify due to the complex nature of jets and the alignment of the momentum imbalance from the dark particles with the jet axis, but such jets do not yet benefit from the construction of dedicated theoretically-motivated jet substructure observables. A deep network operating on jet constituents is used as a probe of the available information and indicates that classification power not captured by current high-level observables arises primarily from low-p T jet constituents.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or “suspects,” were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer’s brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Nuclear Fabrication Consortium

This report summarizes the activities undertaken by EWI while under contract from the Department of Energy (DOE) Office of Nuclear Energy (NE) for the management and operation of the Nuclear Fabrication Consortium (NFC). The NFC was established by EWI to independently develop, evaluate, and deploy fabrication approaches and data that support the re-establishment of the U.S. nuclear industry: ensuring that the supply chain will be competitive on a global stage, enabling more cost-effective and reliable nuclear power in a carbon constrained environment. The NFC provided a forum for member original equipment manufactures (OEM), fabricators, manufacturers, and materials suppliers to effectively engage with each other and rebuild the capacity of this supply chain by : Identifying and removing impediments to the implementation of new construction and fabrication techniques and approaches for nuclear equipment, including system components and nuclear plants. Providing and facilitating detailed scientific-based studies on new approaches and technologies that will have positive impacts on the cost of building of nuclear plants. Analyzing and disseminating information about future nuclear fabrication technologies and how they could impact the North American and the International Nuclear Marketplace. Facilitating dialog and initiate alignment among fabricators, owners, trade associations, and government agencies. Supporting industry in helping to create a larger qualified nuclear supplier network. Acting as an unbiased technology resource to evaluate, develop, and demonstrate new manufacturing technologies. Creating welder and inspector training programs to help enable the necessary workforce for the upcoming construction work. Serving as a focal point for technology, policy, and politically interested parties to share ideas and concepts associated with fabrication across the nuclear industry. The report the objectives and summaries of the Nuclear Fabrication Consortium projects. Full technical reports for each of the projects have been submitted as well.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Estimation, UQ, and Sensitivity Analysis for a New Diagnostic Setup [Slides]

This is for an early career talk I am giving at the Association for Women in Math’s Research Symposium. It provides a general overview of three projects: the first focuses on velocity measurements from optical laser diagnostics, the second focuses on probe alignment and location (metrology), and the third is my Site-Directed Research and Development (SDRD) which focuses on neural networks for image datasets. All three projects have strong uncertainty quantification components.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗
NASA NTRS2010

NASA Tech Briefs, October 2010

Topics covered include: Hybrid Architecture Active Wavefront Sensing and Control; Carbon-Nanotube-Based Chemical Gas Sensor; Aerogel-Positronium Technology for the Detection of Small Quantities of Organic and/or Toxic Materials; Graphene-Based Reversible Nano-Switch/Sensor Schottky Diode; Inductive Non-Contact Position Sensor; High-Temperature Surface-Acoustic-Wave Transducer; Grid-Sphere Electrodes for Contact with Ionospheric Plasma; Enabling IP Header Compression in COTS Routers via Frame Relay on a Simplex Link; Ka-Band SiGe Receiver Front-End MMIC for Transponder Applications; Robust Optimization Design Algorithm for High-Frequency TWTs; Optimal and Local Connectivity Between Neuron and Synapse Array in the Quantum Dot/Silicon Brain; Method and Circuit for In-Situ Health Monitoring of Solar Cells in Space; BGen: A UML Behavior Network Generator Tool; Platform for Post-Processing Waveform-Based NDE; Electrochemical Hydrogen Peroxide Generator; Fabrication of Single, Vertically Aligned Carbon Nanotubes in 3D Nanoscale Architectures; Process to Create High-Fidelity Lunar Dust Simulants; Lithium-Ion Electrolytes Containing Phosphorous-Based, Flame-Retardant Additives; InGaP Heterojunction Barrier Solar Cells; Straight-Pore Microfilter with Efficient Regeneration; Determining Shear Stress Distribution in a Laminate; Self-Adjusting Liquid Injectors for Combustors; Handling Qualities Prediction of an F-16XL-Based Reduced Sonic Boom Aircraft; Tele-Robotic ATHLETE Controller for Kinematics - TRACK; Three-Wheel Brush-Wheel Sampler; Heterodyne Interferometer Angle Metrology; Aligning Astronomical Telescopes via Identification of Stars; Generation of Optical Combs in a WGM Resonator from a Bichromatic Pump; Large-Format AlGaN PIN Photodiode Arrays for UV Images; Fiber-Coupled Planar Light-Wave Circuit for Seed Laser Control in High Spectral Resolution Lidar Systems; On Calculating the Zero-Gravity Surface Figure of a Mirror; Optical Modification of Casimir Forces for Improved Function of Micro- and Nano-Scale Devices; Analysis, Simulation, and Verification of Knowledge-Based, Rule-Based, and Expert Systems; Core and Off-Core Processes in Systems Engineering; Digital Reconstruction Supporting Investigation of Mishaps; and Template Matching Approach to Signal Prediction.

Source record↗
DOE OSTI2026

CHESS 2025: Orthorectified airborne RGB imagery from NEON AOP surveys

This dataset provides Level 1 (L1) and Level 3 (L3) orthorectified Red-Green-Blue (RGB) imagery collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). This high-resolution imagery is a photographic record of red, green, and blue visible light from sunlight reflected off of the Earth’s surface. The data comprise full-color images of the ground surface and are primarily intended to provide context to imaging spectroscopy and light detection and ranging (LiDAR) data. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. RGB images were acquired using the PhaseOne IXM-RS150F high-resolution digital camera onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). The package data include both an L1 product comprising one camera frame per file and an L3 mosaic aligned to the Universal Transverse Mercator (UTM) Zone 13N grid and the World Geodetic System (WGS) 84 projection. Both products are provided in geotif (.tif) format at 0.1 m ground resolution. The bulk of the imagery was collected during the main CHESS field campaign from June 13 to July 15, 2025. Additional images of a portion of the Upper Taylor (UPTA) domain were collected on September 18, 2025, to fill gaps in imagery identified after the main campaign was complete. RGB camera imagery is not radiometrically calibrated, and therefore pixel values should not be exploited for scientific analysis. Pixel values have undergone a manual adjustment to enhance feature identification. The imagery is rigorously geolocated which does allow for reliable geometric information to be retrieved. To generate the orthorectified imagery, the NEON AOP camera captured visible spectrum in red, green, and blue bands. The raw images were then processed using NEON’s camera orthorectification workflow. A boresight calibration flight was made to build a complete camera, distortion, and alignment model. Color balance/white balance and exposure correction were applied to the raw RGB images. The corrected images were orthorectified by ray-tracing image pixels to a lidar-derived digital surface model (DSM) mesh using the refined camera model, outputting orthorectified raster pixels on a regular grid. Flightline-level data were mosaicked by selecting per-pixel contributions from overlapping orthorectified images using line-of-sight (LOS) zenith angle minimization to reduce edge distortions. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2013

Networks of silicon nanowires: A large-scale atomistic electronic structure analysis

Networks of silicon nanowires possess intriguing electronic properties surpassing the predictions based on quantum confinement of individual nanowires. Employing large-scale atomistic pseudopotential computations, as yet unexplored branched nanostructures are investigated in the subsystem level as well as in full assembly. The end product is a simple but versatile expression for the bandgap and band edge alignments of multiply-crossing Si nanowires for various diameters, number of crossings, and wire orientations. Further progress along this line can potentially topple the bottom-up approach for Si nanowire networks to a top-down design by starting with functionality and leading to an enabling structure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach

The escalating energy demand and the adverse environmental impacts of fossil-fuel use necessitate a shift towards cleaner and renewable alternatives. Concentrated Solar Power (CSP) technology emerges as a promising solution, offering a carbon-free alternative for power generation. The efficiency and profitability of CSP depend on the Direct Normal Irradiance (DNI) component of solar radiation; hence, accurate DNI forecasting can help optimize CSP plants’ operations and performance. The unpredictable nature of weather phenomena, particularly cloud cover, introduces uncertainty into DNI projections. Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. This research addresses the challenge by presenting a novel approach that formulates DNI prediction as a multi-class classification problem, departing from conventional regression-based methods. The primary objective of this classification framework is to identify optimal periods aligning with specific operational thresholds for CSP plants, contributing to enhanced dispatch optimization strategies. We model the DNI classification problem using four advanced deep neural networks – rectified linear unit (ReLU) networks, 1D residual networks (ResNets), bidirectional long short-term memory (BiLSTM) networks, and transformers – achieving accuracies up to 93.5% without requiring meteorological parameters.

14 SOLAR ENERGY↗
DOE OSTI2018

r-Process nucleosynthesis from three-dimensional jet-driven core-collapse supernovae with magnetic misalignments

We investigate r-process nucleosynthesis in three-dimensional general relativistic magnetohydrodynamic simulations of jet-driven supernovae resulting from rapidly rotating, strongly magnetized core-collapse. We explore the effect of misaligning the pre-collapse magnetic field with respect to the rotation axis by performing four simulations: one aligned model and models with 15°, 30°, and 45° misalignments. The simulations we present employ a microphysical finite-temperature equation of state and a leakage scheme that captures the overall energetics and lepton number exchange due to post-bounce neutrino emission and absorption. We track the thermodynamic properties of the ejected material with Lagrangian tracer particles and analyse its composition with the nuclear reaction network SKYNET. By using different neutrino luminosities in post-processing the tracer data with SKYNET, we constrain the impact of uncertainties in neutrino luminosities. We find that, for the aligned model considered here, the use of an approximate leakage scheme results in neutrino luminosity uncertainties corresponding to a factor of 100–1000 uncertainty in the abundance of third peak r-process elements. Our results show that for misalignments of 30° or less, r-process elements are robustly produced as long as neutrino luminosities are reasonably low (≲ 5 × 10 52 erg s -1 ). For a more extreme misalignment of 45°, we find the production of r-process elements beyond the second peak significantly reduced. We conclude that robust r-process nucleosynthesis in magnetorotational supernovae requires a progenitor stellar core with a large poloidal magnetic field component that is at least moderately (within ~30°) aligned with the rotation axis.

79 ASTRONOMY AND ASTROPHYSICS↗