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At least 1,657 records · Page 92

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Optimized Substrate Positioning Enables Switches in the C–H Cleavage Site and Reaction Outcome in the Hydroxylation–Epoxidation Sequence Catalyzed by Hyoscyamine 6β-Hydroxylase

Hyoscyamine 6β-hydroxylase (H6H) is an Fe(II)- and 2-oxoglutarate-dependent (Fe/2OG) oxygenase that catalyzes the last two steps in the biosynthesis of scopolamine, a prolifically administered anti-nausea drug. After its namesake first reaction, H6H couples the newly installed C6-bonded oxygen to C7 to form the epoxide of scopolamine. Oxoiron(IV) (ferryl) intermediates initiate both reactions by cleaving C–H bonds, but it remains unclear how the enzyme switches target site and promotes (C6)O–C7 coupling in preference to C7 hydroxylation in the second step. In one possible epoxidation mechanism, the C6 oxygen would – analogously to mechanisms proposed for the Fe/2OG halogenases and, in the preceding paper, N-acetylnorloline synthase (LolO) – coordinate as alkoxide to the C7–H-cleaving ferryl intermediate to enable alkoxyl coupling to the ensuing C7 radical. Here we provide structural and kinetic evidence that H6H instead exploits the distinct spatial dependencies of competitive C–H-cleavage (C6 vs C7) and C–O-coupling (oxygen rebound vs cyclization) steps to promote the two-step sequence without substrate coordination or repositioning for the epoxidation step. Structural comparisons of ferryl-mimicking vanadyl complexes of wild-type H6H and a variant that preferentially hydroxylates C7 of 6-hydroxyhyoscyamine suggest that only a modest (~ 10°) shift in the Fe–O–H(C7) approach angle is sufficient to change the outcome. Finally, the observation that, in wild-type H6H, 2 H 2 O solvent also increases the C7-hydroxylation:epoxidation ratio by ~ 8-fold implies that the latter outcome requires cleavage of the alcohol O-H bond, which, unlike in the LolO oxacyclization, is not accomplished in advance of C–H cleavage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2003

Operation of MRO's High Resolution Imaging Science Experiment (HiRISE): Maximizing Science Participation

Science return from the Mars Reconnaissance Orbiter (MRO) High Resolution Imaging Science Experiment (HiRISE) will be optimized by maximizing science participation in the experiment. MRO is expected to arrive at Mars in March 2006, and the primary science phase begins near the end of 2006 after aerobraking (6 months) and a transition phase. The primary science phase lasts for almost 2 Earth years, followed by a 2-year relay phase in which science observations by MRO are expected to continue. We expect to acquire approx. 10,000 images with HiRISE over the course of MRO's two earth-year mission. HiRISE can acquire images with a ground sampling dimension of as little as 30 cm (from a typical altitude of 300 km), in up to 3 colors, and many targets will be re-imaged for stereo. With such high spatial resolution, the percent coverage of Mars will be very limited in spite of the relatively high data rate of MRO (approx. 10x greater than MGS or Odyssey). We expect to cover approx. 1% of Mars at approx. 1m/pixel or better, approx. 0.1% at full resolution, and approx. 0.05% in color or in stereo. Therefore, the placement of each HiRISE image must be carefully considered in order to maximize the scientific return from MRO. We believe that every observation should be the result of a mini research project based on pre-existing datasets. During operations, we will need a large database of carefully researched 'suggested' observations to select from. The HiRISE team is dedicated to involving the broad Mars community in creating this database, to the fullest degree that is both practical and legal. The philosophy of the team and the design of the ground data system are geared to enabling community involvement. A key aspect of this is that image data will be made available to the planetary community for science analysis as quickly as possible to encourage feedback and new ideas for targets.

E Eliason↗

NASA NTRS2011

Impact of Optimized land Surface Parameters on the Land-Atmosphere Coupling in WRF Simulations of Dry and Wet Extremes

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty module in NASA's Land Information System (LIS-OPT), whereby parameter sets are calibrated in the Noah land surface model and classified according to the land cover and soil type mapping of the observations and the full domain. The impact of the calibrated parameters on the a) spin up of land surface states used as initial conditions, and b) heat and moisture fluxes of the coupled (LIS-WRF) simulations are then assessed in terms of ambient weather, PBL budgets, and precipitation along with L-A coupling diagnostics. In addition, the sensitivity of this approach to the period of calibration (dry, wet, normal) is investigated. Finally, tradeoffs of computational tractability and scientific validity (e.g.,. relating to the representation of the spatial dependence of parameters) and the feasibility of calibrating to multiple observational datasets are also discussed.

Kumar, Sujay↗

NASA NTRS2012

Hesperian-Amazonian Transition Mid-Latitude Valleys: Markers of a Late Martian Climate Optima?

Recently the inventory of fluvial features that have been dated to the late Hesperian to early Amazonian epoch has increased dramatically, including a reassessment of the ages of the large alluvial fans and deltas (e.g., Eberswalde) to this time period. Mid-latitude Valleys (MLVs) are distinct from the older, more integrated Noachian-Hesperian Valley Networks which are deeply dissected, are generally of much larger spatial extent, and are more degraded. Although some MLVs involve rejuvenation of older Valley Networks, many MLVs are carved into smooth or rolling slopes and intercrater terrain. The MLVs range from a few meters to < 300 m in width, with nearly parallel valley walls and planforms that are locally sinuous. Although the MLVs in Newton and Gorgonum basins extend from the basin rims up to 75 km into the basin interior, most MLVs are shorter and often discontinuous. The occurrence of widespread MLVs suggest the possibility of their formation during one or perhaps more regional to global climatic episodes, possibly due to melting of seasonal to long-term accumulations of snow and ice. Temperatures warm enough to cause extensive melting may have occurred during optimal orbital and obliquity configurations, perhaps in conjunction with intensive volcanism releasing moisture and greenhouse gasses, or as a result of a brief episode of warming from a large impact. The concentration of MLVs to the northern and western basin slopes of Newton and Gorgonum basins suggests a possible aspect control to ice accumulation or melting. MLV activity occurred about at the same time as formation of the major outflow channels. A possible scenario is that delivery of water to the northern lowlands provided, through evaporation and sublimation, water that temporarily accumulated in the mid-southern latitudes as widespread ice deposits whose partial melting formed the MLVs and small, dominantly ice-covered lakes.

Moore, Jeffrey↗

NASA NTRS2017

Navigating the Deployment and Downlink Tradespace for Earth Imaging Constellations

Distributed Spacecraft Missions (DSMs) are gaining momentum in their application to Earth Observation (EO) missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. DSM design includes a much larger number of variables than its monolithic counterpart, therefore, Model-Based Systems Engineering (MBSE) has been often used for preliminary mission concept designs, to understand the trade-offs and interdependencies among the variables. MBSE models are complex because the various objectives a DSM is expected to achieve are almost always conflicting, non-linear and rarely analytical. NASA Goddard Space Flight Center (GSFC) is developing a pre-Phase A tool called Tradespace Analysis Tool for Constellations (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper will describe the different types of constellations that TAT-C’s Tradespace Search Iterator is capable of enumerating (homogeneous Walker, heterogeneous Walker, precessing type, ad-hoc) and their impact on key performance metrics such as revisit statistics, time to global access and coverage. We will also discuss the ability to simulate phased deployment of the given constellations, as a function of launch availabilities and/or vehicle capability, and show the impact on performance. All performance metrics are calculated by the Data Reduction and Metric Computation module within TAT-C, which issues specific requests and processes results from the Orbit and Coverage module. Our TSI is also capable of generating tradespaces for downlinking imaging data from the constellation, based on permutations of available ground station networks - known (default) or customized (by the user). We will show the impact of changing ground station options for any given constellation, on data latency and required communication bandwidth, which in turn determines the responsiveness of the space system.

Nag, Sreeja↗

NASA NTRS2019

Cluster Analysis of IRIS Spectroscopic Line Profiles and SDO/AIA EUV Emission in Observations and RMHD Simulations of the Solar Atmosphere

Spatially-resolved observations from the IRIS and SDO/AIA satellites, especially when coupled with realistic 3D RMHD simulations, are a powerful tool for analysis of processes in the solar chromosphere, transition region, and corona. However, the complexity of the data makes understanding the observations and modeling results difficult. In this work, we apply unsupervised clustering algorithms for analysis of observational and synthetic chromospheric Mg II h&k 2796Å&2803Å and transition region C II 1334Å&1335Å line profiles observed by IRIS, and extreme ultraviolet (EUV) emission observed by SDO/AIA, for various types of problems. The synthetic line profiles are computed for simulations of the quiescent solar atmosphere (using the StellarBox and RH1.5 codes). The K-Means clustering algorithm is applied, and the selection of an optimal number of clusters is supported by the average silhouette width technique. We discuss applications of the line profile clustering method to 1) visualization of computational and observational spectroscopic imaging data; 2) understanding of evolutionary trends and behavior patterns of quiet Sun emission and during solar flares; and 3) recognition of heating events and shock waves.

Sadykov, Viacheslav↗

DOE OSTI2025

Deciphering the Evolution of Current Distribution in Hybrid Silver Vanadium Oxide / Carbon Monofluoride Cathodes within Lithium Primary Batteries

For batteries to function effectively all active material must be accessible requiring both electron and ion transport to each particle. A common approach to generating the needed conductive network is the addition of carbon to create a composite electrode. An alternative approach is the electrochemically induced formation of conductive reaction products where the electrochemically generated materials are in intimate contact with the active material contributing to effective connection of each active particle. Furthermore, this study probes silver vanadium oxide (Ag 2 V 4 O 11 , SVO), carbon monofluoride (CF x ), and hybrid SVO/CF x electrodes in lithium batteries. Ex situ XRD identifies Ag 0 as a reduction product from SVO and LiF from CF x that can be followed as a function of depth-of-discharge (DOD). Spatially-resolved operando energy dispersive x-ray diffraction reveals that the presence of SVO alleviates reaction heterogeneity in the electrodes which are electron transfer limited in the absence of sufficient Ag 0 . Synchrotron X-ray tomography on discharged cathodes reveals the distribution of silver particles where the particles are more closely spaced near the current collector indicating multiple nucleation sites for their formation. Finally, operando isothermal microcalorimetry is used to determine the heat dissipation of the parent and hybrid battery types. Using material enthalpy potentials, we determine the current distribution between the two active materials for the discharging hybrid cathode adding further insight to the diffraction analysis. Taken together, these results provide a comprehensive understanding of hybrid SVO/CF x cathodes and give guidance on optimal compositions that balance power and energy density considerations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Location-Dependent Cobalt Deposition in Smartphone Cells upon Long-Term Fast-Charging Visualized by Synchrotron X-ray Fluorescence

In this work, we investigate the transition-metal dissolution of the layered cathode material LiCoO 2 upon repeated fast-charging of three smartphone batteries from different manufacturers using synchrotron micro X-ray fluorescence (μ-XRF). Using this spatially resolved technique, dissolution of Co and subsequent location-dependent deposition on the anode are observed. μ-XRF mapping of selected parts of the anode electrode sheets, such as electrode folds and edges of the jelly roll, reveals the difference in the way Co is deposited on specific regions of the anode electrode. While some folds show no depositions, edges of the anode show gradually accumulating Co depositions. Furthermore, careful quantification of the dissolved Co reveals that the capacity loss scales with the amount of deposited Co on the anode, that is, total Co loss from within the cathode. Soft X-ray absorption spectroscopy of the Co depositions on the anode shows that Co is mainly deposited in a reduced 2 + state. While optimization of the fast-charging protocol mitigates Li plating on the anode, no significant difference in the amount of deposited Co can be observed between an optimized and a nonoptimized fast-charging algorithm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning

We propose a new machine learning-based method for nowcasting earthquakes to image the time-dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross-correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper, we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near-future seismic hazards.

58 GEOSCIENCES↗

NASA NTRS2016

Streamlining the Design Tradespace for Earth Imaging Constellations

Science Data Processing↗

DOE OSTI2021

Accurate prediction of mega-electron-volt electron beam properties from UED using machine learning

To harness the full potential of the ultrafast electron diffraction (UED) and microscopy (UEM), we must know accurately the electron beam properties, such as emittance, energy spread, spatial-pointing jitter, and shot-to-shot energy fluctuation. Owing to the inherent fluctuations in UED/UEM instruments, obtaining such detailed knowledge requires real-time characterization of the beam properties for each electron bunch. While diagnostics of these properties exist, they are often invasive, and many of them cannot operate at a high repetition rate. Here, we present a technique to overcome such limitations. Employing a machine learning (ML) strategy, we can accurately predict electron beam properties for every shot using only parameters that are easily recorded at high repetition rate by the detector while the experiments are ongoing, by training a model on a small set of fully diagnosed bunches. Applying ML as real-time noninvasive diagnostics could enable some new capabilities, e.g., online optimization of the long-term stability and fine single-shot quality of the electron beam, filtering the events and making online corrections of the data for time-resolved UED, otherwise impossible. This opens the possibility of fully realizing the potential of high repetition rate UED and UEM for life science and condensed matter physics applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1978

Imaging of X-ray aurorae from Spacelab

The Atmospheric X-ray Emission Telescope (AXET) is designed to image and measure the spatial, temporal, and spectral distributions of X-ray aurorae produced in the upper atmosphere by precipitating energetic electrons. The bremsstrahlung-produced X radiation penetrates to stratospheric depths where it can modify ozone, conductivity, and other critical atmospheric parameters. Remote sensing of this radiation from topside will provide vital data on solar-terrestrial relationships and mechanisms that could help trigger tropospheric response to solar activity. The X rays also provide direct information concerning the dynamics of radiation-belt processes within the magnetosphere. AXET is designed to detect sources up to 50 keV and to image them below 25 keV by means of directionally sensitive proportional counters with passive collimators. It will be mounted in the Space Shuttle on a single fixed platform designed to optimize viewing conditions for the anticipated orbits and aspects of early Spacelab missions.

Goldberg, R. A.↗

DOE OSTI2022

Improving deep learning performance for predicting large-scale geological ${{CO}_{2}}$ sequestration modeling through feature coarsening

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO 2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

58 GEOSCIENCES↗

NASA NTRS1999

Recovery of postural equilibrium control following space flight

DSO 605 represents the first large study of balance control following spaceflight. Data collected during DSO 605 confirm the theory that postural ataxia following short duration spaceflight is of vestibular origin. We used the computerized dynamic posturography technique developed by Nashner et al. to study the role of the vestibular system in balance control in astronauts during quiet stance before and after spaceflight. Our results demonstrate unequivocally that balance control is disrupted in all astronauts immediately after return from space. The most severely affected returning crew members performed in the same way as vestibular deficient patients exposed to this test battery. We conclude that otolith mediated spatial reference provided by the terrestrial gravitational force vector is not used by the astronauts balance control systems immediately after spaceflight. Because the postflight ataxia appears to be mediated primarily by CNS adaptation to the altered vestibular inputs caused by loss of gravitational stimulation, we believe that intermittent periods of exposure to artificial gravity may provide an effective in-flight countermeasure. Specifically, we propose that in-flight centrifugation will allow crew members to retain their terrestrial sensory-motor adapted states while simultaneously developing microgravity adapted states. The dual-adapted astronaut should be able to make the transition from microgravity to unit gravity with minimal sensory-motor effects. We have begun a ground based program aimed at developing short arm centrifuge prescriptions designed to optimize adaptation to altered gravitational environments. Results from these experiments are expected to lead directly to in-flight evaluation of the proposed centrifuge countermeasure. Because our computerized dynamic posturography system was able to (1) quantify the postflight postural ataxia reported by crew members and observed by flight surgeons and scientists, (2) track the recovery of normal (preflight) balance control, (3) differentiate between rookie and veteran subjects, and (4) provide normative and clinical databases for comparison, and because our study successfully characterized postflight balance control recovery in a large cross-section of Shuttle crew members, we recommend that this system and protocol be adopted as a standard dependent measure for evaluating the efficacy of countermeasures and/or evaluating the postflight effects of changing mission durations or activities.

Paloski, William H.↗

NASA NTRS2011

Towards an Optimal Noise Versus Resolution Trade-Off in Wind Scatterometry

A scatterometer is a radar that measures the normalized radar cross section sigma(sup 0) of the Earth's surface. Over the ocean this signal is related to the wind via the geophysical model function (GMF). The objective of wind scatterometry is to estimate the wind vector field from sigma(sup 0) measurements; however, there are many subtleties that complicate this problem-making it difficult to obtain a unique wind field estimate. Conventionally, wind estimation is split into two stages: a wind retrieval stage in which several ambiguous solutions are obtained, and an ambiguity removal stage in which ambiguities are chosen to produce an appropriate wind vector field estimate. The most common approach to wind field estimation is to grid the scatterometer swath into wind vector cells and estimate wind vector ambiguities independently for each cell. Then, field wise structure is imposed on the solution by an ambiguity selection routine. Although this approach is simple and practical, it neglects field wise structure in the retrieval step and does not account for the spatial correlation imposed by the sampling. This makes it difficult to develop a theoretically appropriate noise versus resolution trade-off using pointwise retrieval. Fieldwise structure may be imposed in the retrieval step using a model-based approach. However, this approach is generally only practical if a low order wind field model is applied, which may discard more information than is desired. Furthermore, model-based approaches do not account for the structure imposed by the sampling. A more general fieldwise approach is to estimate all the wind vectors for all the WVCs simultaneously from all the measurements. This approach can account for structure of the wind field as well as structure imposed by the sampling in the wind retrieval step. Williams and Long in 2010 developed a fieldwise retrieval method based on maximum a posteriori estimation (MAP). This MAP approach can be extended to perform a noise versus resolution trade-off, and deal with ambiguity selection. This paper extends the fieldwise MAP estimation approach and investigates both the noise versus resolution trade-off as well as ambiguity removal in the fieldwise wind retrieval step. The method is then applied to the Sea Winds scatterometer and the results are analyzed. This paper extends the fieldwise MAP estimation approach and investigates both the noise versus resolution trade-off as well as ambiguity removal in the fieldwise wind retrieval step. The method is then applied to the Sea Winds scatterometer and the results are analyzed.

soil moisture↗

DOE OSTI2024

Imaging the Meissner effect in hydride superconductors using quantum sensors

By directly altering microscopic interactions, pressure provides a powerful tuning knob for the exploration of condensed phases and geophysical phenomena. Here, the megabar regime represents an interesting frontier, in which recent discoveries include high-temperature superconductors, as well as structural and valence phase transitions. However, at such high pressures, many conventional measurement techniques fail. Here we demonstrate the ability to perform local magnetometry inside a diamond anvil cell with sub-micron spatial resolution at megabar pressures. Our approach uses a shallow layer of nitrogen-vacancy colour centres implanted directly within the anvil; crucially, we choose a crystal cut compatible with the intrinsic symmetries of the nitrogen-vacancy centre to enable functionality at megabar pressures. We apply our technique to characterize a recently discovered hydride superconductor, CeH 9 . By performing simultaneous magnetometry and electrical transport measurements, we observe the dual signatures of superconductivity: diamagnetism characteristic of the Meissner effect and a sharp drop of the resistance to near zero. By locally mapping both the diamagnetic response and flux trapping, we directly image the geometry of superconducting regions, showing marked inhomogeneities at the micron scale. Our work brings quantum sensing to the megabar frontier and enables the closed-loop optimization of superhydride materials synthesis.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Coupled Experimental and High-Temperature Discrete-Element Method Modeling Studies of Aluminosilicate Particle Handling in Concentrated Solar Power Environments

Chemically inert, aluminosilicate based particles have been investigated as both a thermal transport and sensible energy storage medium for concentrated solar power facilities. These particles will experience a wide range of operating temperatures (300-1000 K) and handling conditions (dense to dilute falling particle curtains, dense granular flows, or dense structures), requiring specially-designed and optimized infrastructures. The relative influence of collisional and frictional interactions between particles varies based on temperature-dependent particulate properties and greatly impacts the bulk, granular flow behavior. These underlying physics are captured using discrete element method modeling tools. However, this modeling method is computationally expensive as each particle position and interaction is tracked during the simulation. These modeling methods are further complicated by introducing temperature-dependent particle properties, high-temperature radiative exchange, and directional irradiation sources experienced by granular flows in concentrated solar power environments. Coupled experimental and numerical studies of aluminosilicate particles in rotary kilns and dense particle curtains were performed for bulk temperatures up to 1073 K. The three particle types investigated included Carbobead HSP 30 /60, Carbobead CP 30/60, and Granusil 4030. Temperature, spatial, and velocity profile data were extracted from experimental runs using embedded K-type thermocouple probes and particle image velocimetry techniques. Experimental and numerical studies were compared using spatial temperature profiles, velocity fields, and shape profiles of the bulk, granular flows. Numerical models were developed using commercially available discrete element method modeling software, Aspherix®. Existing Aspherix® functionality was expanded by introducing coupled radiative exchange modeling tools. The laboratory-scale rotary kiln was developed to investigate the steady-state heat and mass transfer performance of aluminosilicate particles based on particle type, bulk handling temperature, and wall roughness. The rotational speed of the rotary kiln was varied to control the relative impact of collisional and frictional effects upon the granular flow behavior. Heat and mass transfer performance was categorized based on the Froude number and the observed flow regimes of slipping, rolling, cascading, and centrifuging. Coupled discrete element method modeling studies were used to evaluate the effects of temperature-dependent, particulate mechanical properties upon bulk flow behavior and upon the relative effects of radiative, advective, and/or conductive heat transfer. A high-temperature (< 1073 K) falling particle curtain was similarly fabricated to investigate the heat and mass transfer performance of aluminosilicate particles in particle handling situations dominated by inter-particle collisions. The impact of particle type, flow preheat temperatures (< 1073K), and bulk mass flow rates were investigated upon the particle curtain shape, temperature, and velocity profiles. Coupled discrete element method modeling studies were performed to evaluate the varying impact of temperature-dependent, particulate mechanical properties on the bulk flow behavior and the temperature profile of the particle curtain.

14 SOLAR ENERGY↗

DOE OSTI2023

Supplementary spatial data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison

Collection of the underlying spatially explicit data for Available Land for Cellulosic Biofuel Production: A Supply Chain Centered Comparison. Includes raw biomass yield data and soil carbon sequestration potential data for three types of marginal land for the USA midwest at the field level including field areas. Collection also includes raw land rasters for the three types of marginal land, model parameters for the MILP model used in the study, and results used to generate the figures in the paper.

bioenergy landscape design↗