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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Selected advances in small-angle scattering and applications they serve in manufacturing, energy and climate change

Innovations in small-angle X-ray and neutron scattering (SAXS and SANS) at major X-ray and neutron facilities offer new characterization tools for researching materials phenomena relevant to advanced applications. For SAXS, the new generation of diffraction-limited storage rings, incorporating multi-bend achromat concepts, dramatically decrease electron beam emittance and significantly increase X-ray brilliance over previous third-generation sources. This results in intense X-ray incident beams that are more compact in the horizontal plane, allowing significantly improved spatial resolution, better time resolution, and a new era for coherent-beam SAXS methods such as X-ray photon correlation spectroscopy. Elsewhere, X-ray free-electron laser sources provide extremely bright, fully coherent, X-ray pulses of <100 fs and can support SAXS studies of material processes where entire SAXS data sets are collected in a single pulse train. Meanwhile, SANS at both steady-state reactor and pulsed spallation neutron sources has significantly evolved. Developments in neutron optics and multiple detector carriages now enable data collection in a few minutes for materials characterization over nanometre-to-micrometre scale ranges, opening up real-time studies of multi-scale materials phenomena. SANS at pulsed neutron sources is becoming more integrated with neutron diffraction methods for simultaneous structure characterization of complex materials. In this paper, selected developments are highlighted and some recent state-of-the-art studies discussed, relevant to hard matter applications in advanced manufacturing, energy and climate change.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantification of Off-Channel Inundated Habitat for Pacific Chinook Salmon ( Oncorhynchus tshawytscha ) along the Sacramento River, California, Using Remote Sensing Imagery

Off-channel areas are one of the most impacted aquatic habitats by humans globally, as extensive agricultural and urban development has limited them to roughly 10% of historical extent. This is also true for California’s Sacramento River Valley, where historically frequent widespread inundation has been reduced to a few off-channel water bodies along the mid-Sacramento River. This remaining shallow-water habitat provides crucial ecological benefits to multiple avian and fish species, but especially to floodplain-adapted species such as Chinook salmon (Oncorhynchus tshawytscha). Characterizing spatiotemporal off-channel dynamics, including inundation extent and residence time, is fundamental to better understanding the intrinsic value of such habitats and their potential to support recovery actions. Remote sensing techniques have been increasingly used to map surface water at regional and local scales, with improved resolutions. As such, this study maps off-channel inundation areas and describes their temporal dynamics by analyzing pixel-based time- series of multiple water indices, modified Normalized Difference Water Index (mNDWI) and the Automated Water Extraction Index (AWEI), generated from LandSat-8 and Sentinel-2 data between 2013–2021. Quantified off-channel area was similar with each water index and method used, but improved performance was associated with Sentinel-2 products and AWEI index to identify wetted areas under lower mainstem discharges. Results indicate an uneven distribution of off-channel habitat in the study area, with limited inundated areas in upstream reaches (<16% of total off-channel area for greater flows). In addition, much less habitat exists for flows under 400 m 3 /s, an important migration cue for endangered winter-run Chinook salmon, limiting juvenile access to areas with enhanced rearing conditions. Off-channel habitat residence times averaged between 7 and 16 days, primarily defined by the rate of receding flows, with rapid flow recession providing marginal off-channel habitat. This study shows reasonable performance of moderate resolution LandSat-8 and Sentinel-2 remote sensing imagery to characterize shallow-water inundated habitat in higher-order rivers, and as a method to inform restoration and native fish recovery efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Continuous snow depth and temperature measurements from dense network of above-ground distributed temperature profiling systems from 2021-09-23 to 2024-08-23, Seward Peninsula, Alaska

The dataset contains temperature measurements from distributed temperature profiling (DTP) systems (Dafflon et al., 2022; Wielandt et al., 2022; Wang et al., 2024a; Fiolleau et al., 2024) deployed vertically above the ground surface at a large number of locations from 2021 to 2024. The research is designed to improve understanding of the local heterogeneity in snow depth and snow thermal insulation dynamics, as well as their interactions in a discontinuous permafrost region (Wang et al., 2025). The DTP systems were deployed at 96 locations in a watershed along the Nome-Teller road at mile marker 27 (T27) and at 54 locations on a hillslope along the Kougarok road at mile marker 64 (K64) in the Seward Peninsula, Alaska. The probe location information is stored in Probe_locations_T27.csv and Probe_locations_K64.csv. Temperature measurements were recorded at 15-minute intervals using high-precision digital sensors (accuracy: ±0.1°C, resolution: 0.0078°C). The temperature probes, either 1.4 m or 1.6 m long, contain sensors spaced every 5 cm or 10 cm along their length. The temperature data are stored in compressed files following the format: DTP_snow_air_temperature_(site)_(start)_(end).zip, where site is either T27 or K64, and start and end represent the time series period. Within each ZIP file, individual CSV files are named by probe ID and contain temperature records at different heights above the ground surface.This dataset also includes derived snow depth time series over three snow seasons, estimated from temperature measurements. Snow depth was estimated by identifying the consecutive sensor pair that exhibited the largest drop in high-frequency temperature fluctuations (detailed in the methods). These data are stored in: Snow_depths_flags_(site)_(start)_(end).csv, which includes snow depth time series and corresponding quality flags (defined in the methods) from different probes. Additionally, the dataset includes derived metrics and supporting measurements at selected locations over two snow seasons, contributing to the manuscript of Wang et al., 2025. These locations were chosen based on the availability of high-quality snow depth time series during both seasons. The additional data include: (1) Air temperature proxies measured from the top sensors on the pole when they were not buried by snow, stored in Air_temperature_proxies_(site)_(start)_(end).csv (2) Ground interface temperature, recorded at 3 cm above the ground, stored in Ground_interface_temperature_(site)_(start)_(end).csv (3) Site characteristics, including vegetation height, elevation, and the topographic position index (TPI) within a 50 m radius, stored in Selected_probe_locations_gps_vegheight_tpi_elevation_(site).csv. These metrics were derived from 1 m resolution summer LiDAR-based digital elevation models and digital surface models from Singhania et al., 2023, DOI:10.5440/1832016. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv.This dataset is an updated version of a previous archive (Wang et al., 2024b, DOI: 10.15485/2475020), incorporating multiple seasons and improved snow depth estimation. Please note that due to large amount of information present in this dataset, many specificities associated with the acquisition of snow temperature, air temperature proxy and estimation of snow depth, and the future archiving of additional datasets on the soil temperature, thaw depth and soil characteristics at these locations, the author would welcome being contacted by people planning to use this dataset.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Water chemistry in flume channel and hyporheic zone (i.e., porewater) associated with: “Rethinking Aerobic Respiration in the Hyporheic Zone Under Variation in Carbon and Nitrogen Stoichiometry”

Dissolved oxygen (DO), total organic carbon (TOC), total nitrogen (TN), molecular data for organic matter, and biochemical reactions for surface water and porewater (i.e., hyporheic zone) collected from a water recirculating flume located at the University of Texas, Austin. The flume contained real river water from Lower Colorado River(Austin, TX) and clean sand. Hyporheic exchange in the flume was induced through The study aims to understand relationships between aerobic metabolism of organic matter and molecular characteristics of organic matter, such as thermodynamic signature and nitrogen content, through the extent of the hyporheic zone at 10 cm- resolution, and through time. During the experiment, organic matter (dry leaves) was added to the flume and removed after 24 hours. The water samples were collected before the addition of leaves, at the time of removal of leaves, and at hour 72. The water samples were analyzed using ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) and total organic carbon (TOC) and total nitrogen (TN) analysis. Dissolved oxygen content throughout the surface water and the hyporheic zone of the flume was measured with a large planar optode. This data package is associated with the publication ’ Rethinking Aerobic Respiration in the Hyporheic Zone Under Variation in Carbon and Nitrogen Stoichiometry’ published in Environmental Science and Technology (Turețcaia et al., 2023 https://doi.org/10.1021/acs.est.3c04765). The dataset is comprised of five folders (1) Diss_O2_pic, (2) input_files (3) output_files; (4) python_code; and (5) R_code . Diss_O2_pic contains siximages of dissolved oxygen distribution in a bedform at hours 0, 24, and 72 of the experiment conducted in a large recirculation flume. Images are in separate R and G channels (i.e., RGB). The input_files contains (1) a csv file with FTICR peaks identified within each sample, (2) a csv file with molecular information pertinent to FTICR data with Gibbs free energy calculations adjusted for environmental temperature, (3) a csv file containing concentrations of non-purgeable organic carbon measured throughout the experiment , (4) a csv file containing concentrations of total nitrogen measured throughout the experiment, (5) a csv file containing total biochemical reactions (i.e., transformations) identified in the dataset, (6) a csv containing transformation profiles, and (7) a csv file containing transformations with formulas, and (8) a jpg file with schematic representation of locations for sample collection. The output_files contains (1) and xlsx file containing percent biochemical reactions containing nitrogen identified across all 39 sample, (2) a csv file of merged FTICR data and molecular information files, (3) a csv files containing average Gibbs free energy within sampling domains and at each sampling location, (4) a csv file with average concentrations of dissolved oxygen across sampling locations at hour 0, (5) a csv file with average concentrations of dissolved oxygen across sampling locations at hour 24, (6) a csv file with average concentrations of dissolved oxygen across sampling locations at hour 72, (7) a csv file with percent chemical classes identified across sampling locations at hour 0, (8) a csv file with percent chemical classes identified across sampling locations at hour 24, (9) a csv file with percent chemical classes identified across sampling locations at hour 72, and (10) a csv file containing percent nitrogen containing biochemical reactions identified across sampling locations at hours 0, 24, and 72. The python_code contains seven ipynb files which are Jupyter Notebooks used for data analysis and figures generation. The R_code contains 3 R files with R code used for data analysis and figures generation. This data package contains the processed data used in the associated manuscript. This data has not been previously published.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters including growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High-precision Measurement of the 16 O($n, n'γ$) Cross Section using $γ$-ray Detection in Liquid Scintillators with H 2 O and BeO Targets

The 16 O($n, n'γ$) reaction was measured at the Los Alamos Neutron Science Center white neutron source using γ-ray detection in liquid scintillators present in the upper hemisphere of the Correlated Gamma-Neutron Array for sCattering (CoGNAC). Separate measurements of this reaction were performed using H 2 O and BeO targets in successive years. The unique high energies of γ rays emitted from the 16 O($n, n'γ$) reaction facilitated a clean selection of this reaction from threshold to 9.8 MeV incident neutron energy without the need for precise measurements of the γ-ray energy or the scattered neutrons. The precise time resolution of the liquid scintillator detectors was then exploited to obtain high-resolution incident neutron energy measurements, and good agreement was obtained between the H 2 O and BeO results reported here. The dominant literature data sets for this reaction have systematic differences between them, but the present results improve upon the neutron energy resolution of earlier measurements and show important discrepancies in recent data. Finally, tentative data are also shown up to 20 MeV incident neutron energy but are potentially subject to improved understanding of the relative γ-ray and α decay branches from 16 O excited states.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters

In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

AI-enabled high-resolution scanning coherent diffraction imaging

Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Capturing recovered images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. Our results demonstrate the practical application of machine learning to recover high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages. Furthermore, by overcoming the constraints of iterative model-based methods, we can significantly relax sampling constraints on data acquisition while still producing an excellent image of the sample. Besides drastically accelerating acquisition and analysis, this capability has profound implications for the imaging of dose sensitive, dynamic, and extremely voluminous samples.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A multi-channel x-ray temporal diagnostic for measurement of time-resolved electron temperature in cryogenic deuterium–tritium implosions at OMEGA

Electron-temperature (T e ) measurements in implosions provide valuable diagnostic information, as Te is unaffected by residual flows and other non-thermal effects unlike ion temperature inferred from a fusion product spectrum. In OMEGA cryogenic implosions, measurement of T e (t) can be used to investigate effects related to time-resolved hot-spot energy balance. The proposed diagnostic utilizes five fast-rise (~15 ps) scintillator channels with distinct x-ray filtering. Furthermore, titanium and stepped aluminum filtering were chosen to maximize detector sensitivity in the 10 keV–20 keV range, as it has been shown that these x rays have similar density and temperature weighting to the emitted deuterium–tritium fusion neutrons. Initial data collected using a prototype nosecone on the existing neutron temporal diagnostic demonstrate the validity of this diagnostic technique. The proposed system will be capable of measuring spatially integrated T e (t) with 20 ps time resolution and <10% uncertainty at peak emission in cryogenic DT implosions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Phased plan for the implementation of the time-resolving magnetic recoil spectrometer on the National Ignition Facility (NIF)

The time-resolving magnetic recoil spectrometer (MRSt) is a transformative diagnostic that will be used to measure the time-resolved neutron spectrum from an inertial confinement fusion implosion at the National Ignition Facility (NIF). It uses a CD foil on the outside of the hohlraum to convert fusion neutrons to recoil deuterons. An ion-optical system positioned outside the NIF target chamber energy-disperses and focuses forward-scattered deuterons. A pulse-dilation drift tube (PDDT) subsequently dilates, un-skews, and detects the signal. While the foil and ion-optical system have been designed, the PDDT requires more development before it can be implemented. Therefore, a phased plan is presented that first uses the foil and ion-optical systems with detectors that can be implemented immediately—namely CR-39 and hDISC streak cameras. These detectors will allow the MRSt to be commissioned in an intermediate stage and begin collecting data on a reduced timescale, while the PDDT is developed in parallel. A CR-39 detector will be used in phase 1 for the measurement of the time-integrated neutron spectra with excellent energy-resolution, necessary for the energy calibration of the system. Streak cameras will be used in phase 2 for measurement of the time-resolved spectrum with limited spectral coverage, which is sufficient to diagnose the time-resolved ion temperature. Simulations are presented that predict the performance of the streak camera detector, indicating that it will achieve excellent burn history measurements at current yields, and good time-resolved ion-temperature measurements at yields above 3 × 10 17 . The PDDT will be used for optimal efficiency and resolution in phase 3.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

The advancing wave front on a sloping channel covered by a rod canopy following an instantaneous dam break

The drag coefficient Cd for a rigid and uniformly distributed rod canopy covering a sloping channel following the instantaneous collapse of a dam was examined using flume experiments. The measurements included space x and time t high resolution images of the water surface h(x, t) for multiple channel bed slopes So and water depths behind the dam Ho along with drag estimates provided by sequential load cells. Using these data, an analysis of the Saint-Venant equation (SVE) for the front speed was conducted using the diffusive wave approximation. An inferred Cd=0.4 from the h(x, t) data near the advancing front region, also confirmed by load cell measurements, is much reduced relative to its independently measured steady-uniform flow case. This finding suggests that drag reduction mechanisms associated with transients and flow disturbances are more likely to play a dominant role when compared to conventional sheltering or blocking effects on Cd examined in uniform flow. The increased air volume entrained into the advancing wave front region as determined from an inflow–outflow volume balance partly explains the Cd reduction from unity.

Mechanics↗
DOE OSTI2023

OPEN leaf : an open‐source cloud‐based phenotyping system for tracking dynamic changes at leaf‐specific resolution in Arabidopsis

SUMMARY The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., thedark genome). High‐throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation ofOPEN leaf, an open‐source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing.OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf‐specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high‐throughput screens to identify and characterize previously unidentified phenotypes in a leaf‐specific time‐dependent manner. Moreover, the modular and open‐source design ofOPEN leafallows seamless integration of additional sensors based on users and experimental needs.

Plant Sciences↗
DOE OSTI2022

Incoherent range-based holography of far-distant objects using LIDAR

Los Alamos National Laboratory has developed an incoherent, long-range, sub-centimeter resolution (LIDAR) with which we achieve centimeter-scale reflection holography at extremely long ranges. The system consists of a pulsed laser and photon-counting receiver. This combination yields round-trip time of flight data to illuminate parts of the object of interest. The aggregation of these data for many LIDAR pulses yields a plot with a range on the X axis and reflectance on the Y axis, which we refer to as a range profile. Observing that the range profile is a projection of the reflection map of the object onto the view vector, we collect profiles from a variety of viewing angles and invert these data to form an image. We adapt imaging algorithms from the field of computer aided tomography to suit our application and present results from imaging demonstrations at a 10 km range.

Hoffmann, Mitchell P. (ORCID:0000000170551324)↗
DOE OSTI2020

Development of Monitoring Techniques for Binderjet Additive Manufacturing of Silicon Carbide Structures

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear components to be assembled into a fully functional microreactor core. Compared with traditional manufacturing technologies, AM technologies allow (1) observation of the manufacturing process at a much higher resolution in real-time using in situ monitoring technologies to capture the sensor signature that scientifically describes each event occurring over time and space and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on binder jetting in situ process monitoring and associated data analytics results, as well as sample placement and tracking for the subsequent chemical vapor infiltration (CVI) process. Examples are provided to illustrate the progress. Elements of the Digital Thread and data management are discussed in the main document, and an extensive supplemental material section is provided detailing the Digital Platform, as well as its implementation and components. In conclusion the path forward for the next fiscal year is discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Mesh-Sequenced Realizations for Evaluation of Subgrid-Scale Models for Turbulent Combustion

This paper develops a new approach for analysis of subgrid closures for turbulent combustion as modeled using direct quadrature (finite-rate chemistry) techniques. The approach, termed multiresolution analysis through mesh-sequenced realizations (MRA-MSR), conducts simultaneous, constrained large-eddy simulations on a set of hierarchically coarsened meshes. Furthermore, the availability of underlying fine-mesh (subgrid) data corresponding to coarse-mesh locations allows a clearer assessment of the effects of unresolved fluctuations on apparent reactivity. A key to MRA-MSR is the correlation of eddy structures at coarser mesh levels, which is facilitated by the transfer of filtered fine-mesh velocity information. A seven-mesh MRA-MSR hierarchy using three resolution levels is applied to one of the Sydney bluff-body stabilized methane–hydrogen flames. Analysis of the simultaneously evolved data at different resolution levels reveals several interesting trends. First, at high Damköhler numbers, there is clear evidence of attenuation of apparent reactivity due to the effects of unresolved fluctuations. Secondly, single-point, single-time filtered density functions of a normalized subgrid Damköhler number show a characteristic beta probability density function (PDF) form and display evidence of scale similarity. Interrogation of the MRA-MSR database also shows that the recently-proposed least-squares minimization (LSM) turbulence–chemistry interaction model can account for the observed diminishment in reactivity at high Damköhler numbers but cannot reduce scatter significantly. A new form of the LSM model, which makes use of the normalized subgrid Damköhler number beta PDF distribution, performs slightly better than the original model, illustrating the potential of MRA-MSR both in assessing existing closure concepts and in developing new ones.

42 ENGINEERING↗
DOE OSTI2026

Boundary Layer Exploration of Aerosols and Clouds ON Ships (BEACONS)

BEACONS aims to demonstrate autonomous deployment of a shipborne system that routinely observes atmospheric, cloud, and aerosol properties. These observations enable scientists to study aerosol-cloud interactions, advancing energy resilience and the Department of Energy's mission to improve model prediction. Key Project Outcomes Autonomous deployment of aerosol and cloud measurement systems aboard a Pasha Hawaii Marjorie C commercial ship. Six-month campaign for continuous, high-resolution aerosol and cloud data collection with minimal crew intervention. Development and testing of instruments in two phases, advancing from essential to complex systems. AI-ready processed data on the BEACONS Data Platform for near-real-time data access. Validation of aerosol-cloud interaction hypotheses for improved modeling. Establishment of best practices for instrument deployment in marine environments. Scientific collaboration to support the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) mission.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Long- and short-term temporal variability in cloud condensation nuclei spectra over a wide supersaturation range in the Southern Great Plains site

Abstract. When aerosol particles seed the formation of liquid water droplets in the atmosphere, they are called cloud condensation nuclei (CCN). Different aerosols will act as CCN under different degrees of water supersaturation (relative humidity above 100 %), depending on their size and composition. In this work, we build and analyze a best-estimate CCN spectrum product, tabulated at ∼ 45 min resolution, generated using high quality data from seven independent instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The data product spans a large supersaturation range, from 0.0001 % to ∼ 30 %, and time period of 5 years, from 2009–2013, and is available on the ARM data archive. We leverage this added statistical power to examine relationships that are unclear in smaller datasets. Our analysis is performed in three main areas. First, probability distributions of many aerosol and CCN metrics are found to exhibit skewed log-normal distribution shapes. Second, clustering analyses of CCN spectra reveal that the primary drivers of CCN differences are aerosol number size distributions, rather than hygroscopicity or composition, especially at supersaturations above 0.2 %, while also allowing for a simplified understanding of seasonal and diurnal variations in CCN behavior. The predictive ability of using limited hygroscopicity data with accurate number size distributions to estimate CCN spectra is investigated, and the uncertainties of this approach are estimated. Third, the dynamics of CCN spectral clusters and concentrations are examined with cross-correlation and autocorrelation analyses. We find that CCN concentrations change rapidly on the timescale of 1–3 h, with some conservation beyond that which is greatest for the lower supersaturation region of the spectrum.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Combustion Feature Characterization using Computer Vision Diagnostics within Rotating Detonation Combustors

Rotating detonation engines (RDEs) theoretically achieve pressure gain by continuously propagating detonation waves in a cylindrical annulus. Research aims to implement them in gas turbines for improved propulsion and power generation efficiency. Current diagnostic methods, such as point measurements and optical diagnostics, face limitations due to high data acquisition rates required to analyze rapid detonation interactions. In contrast, image classification and time series classification achieve real-time capabilities with rates of 4 and 5 Hz, respectively. Object detection offers the highest time-step resolution at 20 μsec, while real-time methods require longer resolutions of 80 msec, highlighting the advancements in diagnostic capabilities for RDEs.

May, Kristyn Johnson↗