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All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Shocked Clouds in the Vela Supernova Remnant

Unusually strong high-excitation C I has been detected in eleven lines of sight through the Vela supernova remnant by means of UV absorption-line studies of IUE data. Most of these lines of sight lie near the western edge of the X-ray bright region of the supernova remnant in a spatially distinct band approximately 1deg by 4deg oriented approximately north/south. The high-excitation C I (denoted C I*) is interpreted as evidence of a complex of shocked dense clouds inside the supernova remnant, due to the high pressures indicated in this region. To further analyze the properties of this region of C I*, we present new HIRES-processed IRAS data of the entire Vela SNR. A temperature map calculated from the HIRES IRAS data, based on a two-component dust model, reveals the signature of hot dust at several locations in the SNR. The hot dust is anti-correlated spatially with X-ray emission as revealed by ROSAT, as would be expected for a dusty medium interacting with a shock wave. The regions of hot dust are strongly correlated with optical filaments, supporting a scenario of dense clouds interior to the SNR that have been shocked and are now cooling behind the supernova blast wave. With few exceptions, the lines of sight to the strong C I* pass through regions of hot dust and optical filaments. Possible mechanisms for the production of the anomalously large columns of C I and C I* are discussed. Dense clouds on the back western hemisphere of the remnant may explain the relatively low X-ray emission in the western portion of the Vela supernova remnant due to the slower forward shock velocity in regions where the shock has encountered the dense clouds. An alternate explanation for the presence of neutral, excited state, and ionized species along the same line of sight may be a magnetic precusor that heats and compresses the gas ahead of the shock.

Nichols, Joy S.↗
DOE OSTI2024

DEEPEN: Final 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano

Part of the DEEPEN (DE-risking Exploration of geothermal Plays in magmatic ENvironments) project involved developing and testing a methodology for a 3D play fairway analysis (PFA) for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). This was tested using new and existing geoscientific exploration datasets at Newberry Volcano. This GDR submission includes images, data, and models related to the 3D favorability and uncertainty models and the 2D favorability and uncertainty maps. The DEEPEN PFA Methodology, detailed in the journal article below, is based on the method proposed by Poux & O'brien (2020), which uses the Leapfrog Geothermal software with the Edge extension to conduct PFA in 3D. This method uses all available data to build a 3D geodata model which can be broken down into smaller blocks and analyzed with advanced geostatistical methods. Each data set is imported into a 3D model in Leapfrog and divided into smaller blocks. Conditional queries can then be used to assign each block an index value which conditionally ranks each block's favorability, from 0-5 with 5 being most favorable, for each model (e.g., lithologic, seismic, magnetic, structural). The values between 0-5 assigned to each block are referred to as index values. The final step of the process is to combine all the index models to create a favorability index. This involves multiplying each index model by a given weight and then summing the resulting values. The DEEPEN PFA Methodology follows this approach, but split up by the specific geologic components of each play type. These components are defined as follows for each magmatic play type: 1. Conventional hydrothermal plays in magmatic environments: Heat, fluid, and permeability 2. Superhot EGS plays: Heat, thermal insulation, and producibility (the ability to create and sustain fractures suitable for and EGS reservoir) 3. Supercritical plays: Heat, supercritical fluid, pressure seal, and producibility (the proper permeability and pressure conditions to allow production of supercritical fluid) More information on these components and their development can be found in Kolker et al., (2022). For the purposes of subsurface imaging, it is easier to detect a permeable fluid-filled reservoir than it is to detect separate fluid and permeability components. Therefore, in this analysis, we combine fluid and permeability for conventional hydrothermal plays, and supercritical fluid and producibility for supercritical plays. We also project the 3D favorability volumes onto 2D surfaces for simplified joint interpretation, and we incorporate an uncertainty component. Uncertainty was modeled using the best approach for the dataset in question, for the datasets where we had enough information to do so. Identifying which subsurface parameters are the least resolved can help qualify current PFA results and focus future efforts in data collection. Where possible, the resulting uncertainty models/indices were weighted using the same weights applied to the respective datasets, and summed, following the PFA methodology above, but for uncertainty.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning

The deep operator network (DeepONet) has shown remarkable potential in solving partial differential equations (PDEs) by mapping between infinite-dimensional function spaces using labeled datasets. However, in scenarios lacking labeled data, the physics-informed DeepONet (PI-DeepONet) approach, which utilizes the residual loss of the governing PDE to optimize the network parameters, faces significant computational challenges, particularly due to the curse of dimensionality. This limitation has hindered its application to high-dimensional problems, making even standard 3D spatial with 1D temporal problems computationally prohibitive. Additionally, the computational requirement increases exponentially with the discretization density of the domain. Here, to address these challenges and enhance scalability for high-dimensional PDEs, we introduce the Separable physics-informed DeepONet (Sep-PI-DeepONet). This framework employs a factorization technique, utilizing sub-networks for individual one-dimensional coordinates, thereby reducing the number of forward passes and the size of the Jacobian matrix required for gradient computations. By incorporating forward-mode automatic differentiation (AD), we further optimize computational efficiency, achieving linear scaling of computational cost with discretization density and dimensionality, making our approach highly suitable for high-dimensional PDEs. We demonstrate the effectiveness of Sep-PI-DeepONet through three benchmark PDE models: the viscous Burgers’ equation, Biot’s consolidation theory, and a parameterized heat equation. Our framework maintains accuracy comparable to the conventional PI-DeepONet while reducing training time by two orders of magnitude. Notably, for the heat equation solved as a 4D problem, the conventional PI-DeepONet was computationally infeasible (estimated 289.35 h), while the Sep-PI-DeepONet completed training in just 2.5 h. These results underscore the potential of Sep-PI-DeepONet in efficiently solving complex, high-dimensional PDEs, marking a significant advancement in physics-informed machine learning.

Neural operator↗
NASA NTRS2004

Lamb Wave Stiffness Characterization of Composites Undergoing Thermal-Mechanical Aging

The introduction of new, advanced composite materials into aviation systems requires a thorough understanding of the long term effects of combined thermal and mechanical loading upon those materials. Analytical methods investigating the effects of intense thermal heating combined with mechanical loading have been investigated. The damage mechanisms and fatigue lives were dependent on test parameters as well as stress levels. Castelli, et al. identified matrix dominated failure modes for out-of-phase cycling and fiber dominated damage modes for in-phase cycling. In recent years, ultrasonic methods have been developed that can measure the mechanical stiffness of composites. To help evaluate the effect of aging, a suitably designed Lamb wave measurement system is being used to obtain bending and out-of-plane stiffness coefficients of composite laminates undergoing thermal-mechanical loading. The system works by exciting an antisymmetric Lamb wave and calculating the velocity at each frequency from the known transducer separation and the measured time-of-flight. The same peak in the waveforms received at various distances is used to measure the time difference between the signals. The velocity measurements are accurate and repeatable to within 1% resulting in reconstructed stiffness values repeatable to within 4%. Given the material density and plate thickness, the bending and out-of-plane shear stiffnesses are calculated from a reconstruction of the dispersion curve. A mechanical scanner is used to move the sensors over the surface to map the time-of-flight, velocity, or stiffnesses of the entire specimen. Access to only one side of the material is required and no immersion or couplants are required because the sensors are dry coupled to the surface of the plate. In this study, the elastic stiffnesses D(sub 11), D(sub 22), A(sub 44), and A(sub 55) as well as time-of-flight measurements for composite samples that have undergone combined thermal and mechanical aging for a duration of 10,000 hours are reported.

Seale, Michael D.↗
DOE OSTI2021

Development of Enabling Technologies for Chemical Looping Combustion and Chemical Looping with Oxygen Uncoupling (Final Report)

This report summarizes results from the project, “Development of Enabling Technologies for Chemical Looping Combustion and Chemical Looping with Oxygen Uncoupling,” which evaluated several aspects of dual fluidized bed chemical looping combustion and chemical looping with oxygen uncoupling (CLOU). The objective was to provide tools and enabling technologies to help advance fluidized bed chemical looping technology to pilot, demonstration and commercial scale. One focus area is oxygen carriers, which are key to chemical looping combustion. The copper oxygen carrier-coal ash system was systematically evaluated through a combination of thermodynamic modeling and lab-scale experiments, taking into consideration different oxygen carrier support materials and coal types. A method of mapping “safe” and “risk” zones for different combinations was established, and recommendations for target conditions are provided. In addition, a simple solution for limiting negative influence of some coal ashes, namely adding small amounts of calcium to the system, is proposed. In addition, a novel process for recovering and recycling copper from spent oxygen carriers is proposed. A new approach for design and operation of loop seals in a CLOU system was developed, and involves distinct gas injection points and a short horizontal section to help control the fate of the fluidizing gases. For the air-to-fuel reactor loop seal, the upstream side is fluidized with air, ideally input into the side rather than into the bottom, which prevents uncoupling (reduction) of the oxygen carrier before entering the fuel reactor. Also, alternative for separating oxygen carrier particles and gas in circulating fluidized bed-based chemical looping system is proposed. A new reduced reaction scheme for conversion of coal in copper-based CLOU was developed and implemented into CPFD Software’s Barracuda VR package. The resulting model provides higher fidelity than the baseline, especially when it comes to minor reactions that are part of the overall combustion environment. For in-reactor heat extraction, it was determined in this project that the best way to do that in a dual circulating fluidized-bed system is through heat exchange low in the air reactor. The air reactor is the hotter of the two and for a fast-fluidized circulating bed, the lower, more dense section has a higher heat transfer coefficient and offers more consistent particle-wall contact since it doesn’t have the splashing behavior the top of the bed does. It was determined that just the wall surface area in the lower quarter to third of the air reactor is sufficient to control temperatures in both reactors. Finally, consideration was given to a new concept for CLC, which involves a staged fuel reactor with a different type of oxygen carrier in each stage.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Do chaotic field lines cause fast reconnection in coronal loops?

Over the past decade, Boozer has argued that three-dimensional (3D) magnetic reconnection fundamentally differs from two-dimensional reconnection due to the fact that the separation between any pair of neighboring field lines almost always increases exponentially over distance in a 3D magnetic field. According to Boozer, this feature makes 3D field-line mapping chaotic and exponentially sensitive to small non-ideal effects; consequently, 3D reconnection can occur without intense current sheets. We test Boozer's theory via ideal and resistive reduced magnetohydrodynamic simulations of the Boozer–Elder coronal loop model driven by sub-Alfvénic footpoint motions [A. H. Boozer and T. Elder, Phys. Plasmas 28, 062303 (2021)]. In this work, our simulation results significantly differ from their predictions. The ideal simulation shows that Boozer and Elder under-predict the intensity of current density due to missing terms in their reduced model equations. Furthermore, resistive simulations of varying Lundquist numbers show that the maximal current density scales linearly rather than logarithmically with the Lundquist number.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Generalized Analysis of Dynamic Flow Fouling on Heat Transfer Surfaces

Several energy applications involve flow of untreated heat transfer fluids that contain trace amounts of mineral salts, such as calcium carbonate and calcium sulfate, which show reverse solubility with temperature. The gradual accretion of these salts onto the heat transfer surfaces causes fouling that degrades thermal performance over time. While fouling of bare surfaces has been extensively studied, fouling of nonwetting superhydrophobic or liquid-infused surfaces is less understood, especially under dynamic flow conditions. This study presents a systematic experimental analysis to elucidate dynamic flow fouling of calcium carbonate and calcium sulfate on smooth, superhydrophobic and liquid-infused surfaces in a generalized manner. Here, copper tubes with modified inner surface wettability are fabricated and subjected to forced convection at different flow rates and fouling salt concentrations. Fouling characteristics of the two salts on the different surfaces are examined through microstructural analysis, and quantified in terms of asymptotic fouling resistance that measures the total extent of fouling, and a threshold time, which correlates to the onset of significant fouling. Using Taguchi design of experiments, the study reports, for the first time, closed-form analytical relationships for the asymptotic fouling thermal resistance and threshold time on the Reynolds number, dimensionless concentration of fouling agent and dimensionless infused liquid viscosity that represents the different surface types in a unified manner. It is shown that the analytical model accurately predicts the fouling characteristics in up to about 97% of the data, based on which contour maps of optimum surface designs for minimizing fouling resistance are presented. The results provide information on designing surfaces for reduced fouling or for estimating the fouling characteristics for a given surface in applications.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Seafloor Control on Sea Ice

The seafloor has a profound role in Arctic sea ice formation and seasonal evolution. Ocean bathymetry controls the distribution and mixing of warm and cold waters, which may originate from different sources, thereby dictating the pattern of sea ice on the ocean surface. Sea ice dynamics, forced by surface winds, are also guided by seafloor features in preferential directions. Here, satellite mapping of sea ice together with buoy measurements are used to reveal the bathymetric control on sea ice growth and dynamics. Bathymetric effects on sea ice formation are clearly observed in the conformation between sea ice patterns and bathymetric characteristics in the peripheral seas. Beyond local features, bathymetric control appears over extensive ice-prone regions across the Arctic Ocean. The large-scale conformation between bathymetry and patterns of different synoptic sea ice classes, including seasonal and perennial sea ice, is identified. An implication of the bathymetric influence is that the maximum extent of the total sea ice cover is relatively stable, as observed by scatterometer data in the decade of the 2000s, while the minimum ice extent has decreased drastically. Because of the geologic control, the sea ice cover can expand only as far as it reaches the seashore, the continental shelf break, or other pronounced bathymetric features in the peripheral seas. Since the seafloor does not change significantly for decades or centuries, sea ice patterns can be recurrent around certain bathymetric features, which, once identified, may help improve short-term forecast and seasonal outlook of the sea ice cover. Moreover, the seafloor can indirectly influence cloud cover by its control on sea ice distribution, which differentially modulates the latent heat flux through ice covered and open water areas.

Nghiem, S. V.↗
NASA NTRS2012

G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation in the CONUS and Mexico in support of NASA's Carbon Monitoring System (CMS) and AMIGA-Carb (AMerican Icesat Glas Assessment of Carbon). For NASA's CMS, wall-to-wall G-LiHT data have been acquired over intensive study sites with historic LiDAR datasets, dense inventory data, stem maps and flux tower observations. For AMIGA-Carb, G-LiHT transects have been acquired over ICESat tracks and USDA-FS inventory plots throughout the CONUS, and similar data will be acquired in Mexico during 2013. This talk will highlight recent science results from continental-scale transects landscape-scale deployments of G-LiHT, as well as seasonal forest dynamics from repeat pass G-LiHT acquisitions.

Cook, Bruce↗
DOE OSTI2026

Spatiotemporal Thermal Coupling in VO 2 Device Arrays

Correlated oxides such as VO 2 exhibit an electrically driven insulator–metal transition (IMT) that underlies their promise for neuromorphic and memory devices. Yet the IMT is not a uniform bulk process but a spatiotemporal phenomenon in which local heating nucleates filaments, contracts or dissolves them with the electric field, and couples to the environment. In this work, we directly image the VO 2 IMT dynamics by mid-wave infrared, thermography synchronized with electrical transport, resolving device temperature with micrometer spatial and microsecond temporal resolution. At the single-device level, we capture the full cycle of filament nucleation, contraction, and relaxation during current/voltage-driven resistive switching. At the array level, we show that heat propagates across etched gaps with an effective length scale of ∼131 µm, enabling cooperative behaviors among electrically isolated devices. Short-range distanced devices exhibit mutual filament attraction and sequential dissolution, while long-range distanced devices differentiate into distinct roles: drivers that initiate switching, cooperative responders that undergo assisted self-oscillations, and passive reporters that record the thermal field. Furthermore, these results reframe thermal crosstalk, long regarded as parasitic, as an intrinsic coupling channel and design principle for organizing collective switching behaviors, with direct implications for emergent circuit functionality in neuromorphic and unconventional computing architectures.

coupling↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1994

Multiple outflows in the bipolar planetary nebula M1-16: A molecular line study

Extensive observations of the molecular gas in the young, compact planetary nebula M1-16 have been made, using the Swedish-ESO-Submillimeter Telescope. A map of the CO J = 2-1 emission shows that the molecular envelope contains both a slow and a fast outflow with expansion velocities of 19 km/s and greater than 34 km/s, respectively. The slow outflow is mildly elliptical, while the fast molecular outflow is bipolar. This fast outflow is roughly aligned with the very fast outflows recently found in the optical, while the long axis of the slow elliptical outflow is roughly orthogonal to the optical outflow axis. The kinematic timescales for the CO fast outflow and the optical very fast outflow agree closely, supporting the view that the former represents material in the slow outflow accelerated by the very fast outflow. The kinematic signature of a disk expanding with about 15.5 km/s can also be seen in the CO J = 2-1 data. The mass-loss rate (a) for the slow outflow is greater than or equal to 2.8 x 10(exp -5) solar mass/yr and possibly as large as 9 x 10(exp -5) solar mass/yr, (b) for the fast outflow is greater than or equal to 5 x 10(exp -6) solar mass/yr, and (c) for the very fast optically visible outflow is approximately equal 5 x 10(exp -7) solar mass/yr. The disk mass is approximately equal 6 x 10(exp -3) solar mass. Grain photoelectric heating results in temperatures of 20-70 K in molecular gas of the slow outflow. The (13)C/(12)C abundance ratio in M1-16 is found to be 0.33, quite possibly the highest found for any evolved object. Upper limits for the (18)O/(16)O and (17)O/(16)O ratios were found to be consistent with the values found in AGB stars. A search for other molecular species in M1-16 resulted in the detection of the high-excitation species HCN, CN, (13)CN, HCO(+), and H(13)CO(+) and possibly N2H(+). Both the HCO(+)/HCN and CN/HCN line-intensity ratios are enhanced, the former by a very large factor, over the values found in the envelopes of AGB stars, probably as a result of enhancement of the CN and HCO(+) abundances due to photochemistry induced by the stellar UV. The CS J = 2-1, SiO J = 2-1 (v = 0), and SiS J = 6-5 lines were not detected to low levels. For the high-excitation molecules, adequate collisional excitation of rotational levels and survival against photodissociation by the UV radiation requires significant clumping of the molecular gas into clumps with H2 densities approximately 10(exp 5)/cu cm. The IRAS fluxes of M1-16, assuming negligible contribution from line emission, imply the presence of about (1.7-0.4) x 10(exp -3) solar mass of cool dust (temperature around 50 K) and a smaller quantity, (2.7-3.1) x 10(exp -6) solar mass, of warmer dust (temperature around 125 K) for a power-law emissivity index p = 1-2. The evolutionary nature of M1-16 cannot be explained by existing single-star models of post-AGB evolution. The very high (13)C/(12)C abundance ratio in M1-16 suggests a possible evolutionary connection between M1-16 and the rare class of J-type silicate-carbon stars which also have high (13)C/(12)C ratios and are thought to be binary systems with accretion disks.

Sahai, Raghvendra↗
NASA NTRS2016

3-D Flash Lidar Performance in Flight Testing on the Morpheus Autonomous, Rocket-Propelled Lander to a Lunar-Like Hazard Field

For the first time, a 3-D imaging Flash Lidar instrument has been used in flight to scan a lunar-like hazard field, build a 3-D Digital Elevation Map (DEM), identify a safe landing site, and, in concert with an experimental Guidance, Navigation, and Control (GN&C) system, help to guide the Morpheus autonomous, rocket-propelled, free-flying lander to that safe site on the hazard field. The flight tests served as the TRL 6 demo of the Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT) system and included launch from NASA-Kennedy, a lunar-like descent trajectory from an altitude of 250m, and landing on a lunar-like hazard field of rocks, craters, hazardous slopes, and safe sites 400m down-range. The ALHAT project developed a system capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The Flash Lidar is a second generation, compact, real-time, air-cooled instrument. Based upon extensive on-ground characterization at flight ranges, the Flash Lidar was shown to be capable of imaging hazards from a slant range of 1 km with an 8 cm range precision and a range accuracy better than 35 cm, both at 1-delta. The Flash Lidar identified landing hazards as small as 30 cm from the maximum slant range which Morpheus could achieve (450 m); however, under certain wind conditions it was susceptible to scintillation arising from air heated by the rocket engine and to pre-triggering on a dust cloud created during launch and transported down-range by wind.

Roback, Vincent E.↗
NASA NTRS2012

Large-Eddy / Reynolds-Averaged Navier-Stokes Simulations of a Dual-Mode Scramjet Combustor

Numerical simulations of reacting and non-reacting flows within a scramjet combustor configuration experimentally mapped at the University of Virginia s Scramjet Combustion Facility (operating with Configuration A ) are described in this paper. Reynolds-Averaged Navier-Stokes (RANS) and hybrid Large Eddy Simulation / Reynolds-Averaged Navier-Stokes (LES / RANS) methods are utilized, with the intent of comparing essentially blind predictions with results from non-intrusive flow-field measurement methods including coherent anti-Stokes Raman spectroscopy (CARS), hydroxyl radical planar laser-induced fluorescence (OH-PLIF), stereoscopic particle image velocimetry (SPIV), wavelength modulation spectroscopy (WMS), and focusing Schlieren. NC State's REACTMB solver was used both for RANS and LES / RANS, along with a 9-species, 19- reaction H2-air kinetics mechanism by Jachimowski. Inviscid fluxes were evaluated using Edwards LDFSS flux-splitting scheme, and the Menter BSL turbulence model was utilized in both full-domain RANS simulations and as the unsteady RANS portion of the LES / RANS closure. Simulations were executed and compared with experiment at two equivalence ratios, PHI = 0.17 and PHI = 0.34. Results show that the PHI = 0.17 flame is hotter near the injector while the PHI = 0.34 flame is displaced further downstream in the combustor, though it is still anchored to the injector. Reactant mixing was predicted to be much better at the lower equivalence ratio. The LES / RANS model appears to predict lower overall heat release compared to RANS (at least for PHI = 0.17), and its capability to capture the direct effects of larger turbulent eddies leads to much better predictions of reactant mixing and combustion in the flame stabilization region downstream of the fuel injector. Numerical results from the LES/RANS model also show very good agreement with OH-PLIF and SPIV measurements. An un-damped long-wave oscillation of the pre-combustion shock train, which caused convergence problems in some RANS simulations, was also captured in LES / RANS simulations, which were able to accommodate its effects accurately.

Fulton, Jesse A.↗
DOE OSTI2023

TRACER-Coastal Urban Boundary-Layer Interactions with Convection (TRACER-CUBIC) Field Campaign Report

To better understand the complicated web of processes governing convective cloud life cycle and aerosol-convection interactions, the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility supported deployment of a variety of advanced atmospheric measurement systems to the greater Houston, Texas, area from 1 October 2021 to 30 September 2022 as part of the Tracking Aerosol Convection Interactions Experiment (TRACER). Houston was selected as a study area because isolated convection and a variety of aerosol conditions are common in this region. This one-year ARM Mobile Facility (AMF) deployment featured a four-month intensive operational period (IOP) during summer 2022 (1 June–30 September). The ARM instrumentation was deployed at three sites along an east-west transect from La Porte, Texas to an ancillary site in a less-polluted rural region southwest of downtown Houston (Figure 1). At the La Porte Site, which is located near the Houston ship channel in an area that experiences significant pollution, the first ARM Mobile Facility (AMF1) was deployed. During the IOP, the ARM tethered balloon system (TBS) operated at the ancillary site. The second-generation C-Band Scanning ARM Precipitation Radar (CSAPR) operated near Pearland, Texas, roughly halfway between the Laporte and ancillary sites. As part of the TRACER- Coastal Urban Boundary-Layer Interactions with Convection (CUBIC) project, three boundary-layer profiling systems) were deployed along a north-south transect spanning from the University of Houston Coastal Center to the Aldine site north of downtown Houston (also blue dot in Figure 1) during the TRACER IOP. These systems included the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory CLAMPS2 (C2), which was deployed at the UHCC, the University of Wisconsin SPARC, which was deployed at the ARM CSAPR site near Pearland (orange diamond in middle of map in Figure 1), and the University of Oklahoma CLAMPS1 (C1), which was deployed at Aldine. These three systems have been successfully operated in various field campaigns, providing data sets that collectively offer new insights into atmospheric-boundary-layer (ABL) processes, sea-breeze (SB) circulations, and convection initiation (CI). For the TRACER IOP window, these systems ran continuously between 1 June and 26 September, 2022. Due to commitments to NOAA projects, the Doppler lidar at the UHCC site was not available until 24 June 2022. The CLAMPS and SPARC profiling systems are self-contained platforms that have benefited from several years of development and deployment. Instruments and data processing were maintained remotely, which made the 4-month deployment for the TRACER-CUBIC IOP period possible. The same basic instrument configuration comprises each system: a scanning Doppler wind lidar for flow characterization and passive profiler(s) for characterizing planetary-boundary-layer (PBL) thermodynamic properties. Each platform includes a Halo Streamline Doppler wind lidar, an Atmospheric Emitted Radiance Interferometer (AERI), and a surface meteorology station. CLAMPS1 and CLAMPS2 each also include a microwave radiometer (MWR, Figure 1d). The TRACER-CUBIC hypotheses included (i) Interactions of SB and urban circulations and how they affect the PBL structure in the Houston environment, causing spatially (horizontally and vertically) and temporally highly variable flow patterns, (ii) heat, moisture, and aerosol transport and mixing depend on these flow dynamics, and (iii) an improved understanding of the flow patterns and PBL structure are critical for investigating the processes leading to CI. To test these hypotheses, the project aimed at (i) characterizing SB circulations and their impacts on the diurnal evolution of the structure of the ABL, (ii) studying the evolution of Houston’s complex urban boundary layer, and (iii) identifying effects of urban-induced circulations on pre-convective environments. TRACER-CUBIC observations generally provide good coverage during the summer IOP. Initial screening of the data indicates a good number of cases with bay-breeze (BB) and/or SB signatures, local CI, and interesting boundary-layer features such as strong nocturnal low-level jets (LLJs, Table 1). The numbers listed in rows 3-5 in this table will be further updated as part of ongoing in-depth analyses and systematic identification of local circulations and CI events. More detailed information about the data availability and quality for each instrument is provided in the “readme” files that were submitted to the ARM Data Center along with each archived data sets. These “readme” files also provide instrument descriptions, information about the data collection and processing procedures, data formats, and any additional information relevant for further data analysis.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

The classification of the Arctic Sea ice types and the determination of surface temperature using advanced very high resolution radiometer data

The accurate quantification of new ice and open water areas and surface temperatures within the sea ice packs is a key to the realistic parameterization of heat, moisture, and turbulence fluxes between ocean and atmosphere in the polar regions. Multispectral NOAA advanced very high resolution radiometer/2 (AVHRR/2) satellite images are analyzed to evaluate how effectively the data can be used to characterize sea ice in the Bering and Greenland seas, both in terms of surface type and physical temperature. The basis of the classification algorithm, which is developed using a late wintertime Bering Sea ice cover data, is that frequency distributions of 10.8- micrometers radiances provide four distinct peaks, represeting open water, new ice, young ice, and thick ice with a snow cover. The results are found to be spatially and temporally consistent. Possible sources of ambiguity, especially associated with wider temporal and spatial application of the technique, are discussed. An ice surface temperature algorithm is developed for the same study area by regressing thermal infrared data from 10.8- and 12.0- micrometers channels against station air temperatures, which are assumed to approximate the skin temperatures of adjacent snow and ice. The standard deviations of the results when compared with in situ data are about 0.5 K over leads and polynyas to about 0.5-1.5 K over thick ice. This study is based upon a set of in situ data limited in scope and coverage. Cloud masks are applied using a thresholding technique that utilizes 3.74- and 10.8- micrometers channel data. The temperature maps produced show coherence with surface features like new ice and leads, and consistency with corresponding surface type maps. Further studies are needed to better understand the effects of both the spatial and temporal variability in emissivity, aerosol and precipitable atmospheric ice particle distribution, and atmospheric temperature inversions.

Massom, Robert↗
NASA NTRS1996

Soot Imaging and Measurement

Soot, sometimes referred to as smoke, is made up primarily of the carbon particles generated by most combustion processes. For example, large quantities of soot can be seen issuing from the exhaust pipes of diesel-powered vehicles. Heated soot also is responsible for the warm orange color of candle flames, though that soot is generally consumed before it can exit the flame. Research has suggested that heavy atmospheric soot concentrations aggravate conditions such as pneumonia and asthma, causing many deaths each year. To understand the formation and oxidation of soot, NASA Lewis Research Center scientists, together with several university investigators, are investigating the properties of soot generated in reduced gravity, where the absence of buoyancy allows more time for the particles to grow. The increased time allows researchers to better study the life cycle of these particles, with the hope that increased understanding will lead to better control strategies. To quantify the amount of soot present in a flame, Lewis scientists developed a unique imaging technique that provides quantitative and qualitative soot data over a large field of view. There is significant improvement over the single-point methods normally used. The technique is shown in the sketch, where light from a laser is expanded with a microscope objective, rendered parallel, and passed through a flame where soot particles reduce the amount of light transmitted to the camera. A filter only allows light at the wavelength of the laser to pass to the camera, preventing any extraneous signals. When images of the laser light with and without the flame are compared, a quantitative map of the soot concentration is produced. In addition to that data, a qualitative image of the soot in the flame is also generated, an example of which is displayed in the photo. This technique has the potential to be adapted to real-time process control in industrial powerplants.

Source record↗
NASA NTRS1996

Stable light isotope biogeochemistry of hydrothermal systems

The stable isotopic composition of the elements O, H, S and C in minerals and other chemical species can indicate the existence, extent, conditions and the processes (including biological activity) of hydrothermal systems. Hydrothermal alteration of the 18O/16O and D/H values of minerals can be used to detect fossil systems and delineate their areal extent. Water-rock interactions create isotopic signatures which indicate fluid composition, temperature, water-rock ratios, etc. The 18O/16O values of silica and carbonate deposits tend to increase with declining temperature and thus help to map thermal gradients. Measurements of D/H values can help to decipher the origin(s) of hydrothermal fluids. The 34S/32S and 13C/12C values of fluids and minerals reflect the origin of the S and C as well as oxygen fugacities and key redox processes. For example, a wide range of 34S/32S values which are consistent with equilibration below 100 degrees C between sulfide and sulfate can be attributed to sulfur metabolizing bacteria. Depending on its magnitude, the difference in the 13C/12C value of CO2 and carbonates versus organic carbon might be attributed either to equilibrium at hydrothermal temperatures or, if the difference exceeds 1% (10/1000), to organic biosynthesis. Along the thermal gradients of thermal spring outflows, the 13C/12C value of carbonates and 13C-depleted microbial organic carbon increases, principally due to the outgassing of relatively 13C-depleted CO2.

Review, Tutorial↗