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

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Experimental study on particle formation in a pressurized oxy-fuel combustor using a novel in-situ optical instrument

Particle formation in pressurized oxy-fuel combustion can be fundamentally different from oxy-fuel combustion at atmospheric pressure. Under high pressures, the rate of char gasification with CO2 and H2O increases significantly and dominates the char reactions particularly at high temperatures. Also, the extent of fragmentation varies. This could result in a very different particle size distribution from that of air-fired combustion. To accurately obtain the particle size distribution in a pressurized oxy-fuel combustor without sampling bias, the particle measurement should be made at the system pressure. Very few published studies have accomplished this, and never under the conditions reported herein. In this work, we fire PRB coal in a 100 kWth staged pressurized oxy-fuel combustor (SPOC) under two pressures, 15 and 2 bara. A sampling probe (oil-cooled) is installed at the combustor outlet and the aerosol flow are sampled isokinetically. We utilize a state-of-the-art optical analyzer (Malvern Insitec) with a novel high-pressure and high-temperature flow cell to measure the particle size distribution and concentration in-situ in a sampled flow, which contains moist/corrosive flue gas under the process pressure and a controlled temperature that is kept above the dew point. The results show that the pressure can significantly influence the particle size distribution and concentration from the oxy-fuel combustor.

Cheng, Mao↗
DOE OSTI2024

Reduced‐Order Modeling for Linearized Representations of Microphysical Process Rates

Abstract Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Simulating a Mesoscale Convective System Using WRF with a New Spectral Bin Microphysics: Part 1: Hail vs Graupel

A modified Fast Spectral Bin Microphysics scheme (FSBM-2) embedded into the Weather Research and Forecasting Model (WRF) is used to simulate a mesoscale deep convective system observed during the Midlatitude Continental Convective Clouds Experiment (MC3E). FSBM-2 uses modified source codes as compared to the current FSBM (FSBM-1). In contrast to FSBM-1, FSBM-2 can simulate hail of several cm in diameter and includes additional processes such as spontaneous breakup of raindrops and aerosol regeneration by drop evaporation. It is shown that allowing large hail particles of diameters exceeding about 1 cm substantially increases the agreement between the simulated and observed squall-line structures both in the convective and stratiform regions. In contrast, if graupel particles are used to represent high-density hydrometeors in convective areas, the ratio of convective-to-stratiform areas diverges from the ratio seen in observations and maximum radar reflectivities are substantially underestimated. Analysis of snow size distributions in the stratiform area shows an important link between the ice crystals formed by homogeneous freezing in the convective area to ice particle number concentration in the stratiform region. Simulated raindrop size distributions in the stratiform area below the melting level from FSBM-2 show a good agreement with observations. The regeneration of CCN by droplet evaporation and detrainment of these CCN to the stratiform region increases the concentration of CCN there up to 8-9 km altitude; the additional CCN penetrate clouds and produce new droplets, leading to some convection intensification. Key words: Mesoscale convective systems (MCS), squall lines, spectral bin microphysics, large hail, in-cloud nucleation, size distributions of hydrometeors in MCS.

Shpund, Kobby↗
DOE OSTI2023

A riming‐dependent parameterization of scattering by snowflakes using the self‐similar Rayleigh–Gans approximation

Abstract Riming is a key process of precipitation formation in ice‐containing clouds, but quantifying riming from observations is challenging, limiting our ability to evaluate the riming process in numerical weather models. One challenge for radar observations is that riming changes both the physical properties (mass, area cross‐section) and scattering properties of ice particles. These changes need to be implemented consistently as a function of riming in radar forward operators, which are required for retrievals and model evaluation in observation space. In this study, mass–size, cross‐section area–size, and backscattering cross‐section relations are developed as a function of the normalized rime mass for aggregates composed of various monomer types (columns, dendrites, needles, plates, and rosettes). The proposed framework allows us to simulate scattering properties of aggregated ice particles consistently as a function of riming in retrievals and radar forward operators. The parameterizations are developed from a large data set of simulated rimed aggregates of different sizes and monomer crystal types. The backscattering cross‐section parameterization (the “riming‐dependent parameterization”) is evaluated for radar frequencies of 35.6 and 94.0 GHz and is based on the Self‐Similar Rayleigh–Gans approximation (SSRGA), which is increasingly used to calculate microwave scattering of ice crystals and snowflakes. Compared with parameterizations from the literature that do not consider riming, the riming‐dependent parameterization leads to significantly smaller biases in terms of backscattering cross‐section. When using the particle masses and scattering properties of the individual particles simulated by the aggregation and riming model as a reference, the bias of our parameterization is below 1 dB when integrating over an exponential particle size distribution with sizes from 0.1–10 mm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Structures of fluoride containing aluminosilicate low activity nuclear waste glasses: A molecular dynamics simulations study

Fluorine and other halides commonly exist in nuclear waste forms, and due to their volatile nature, halide retention poses an issue affecting waste loading during vitrification. The compositional effect on fluorine incorporation in aluminosilicate glasses is investigated through molecular dynamics simulations. Oxygen and fluorine coordination numbers around glass former and modifier cations, bond angle distributions, and medium range structure features such as Q n distributions, ring size distributions and neutron diffraction structure factors were calculated. It was found that fluorine has higher preference to bond to Ca 2+ than to Na + , both in the melt and the glass, and there is no Si-F bond formation in the glass but they do exist in the melt. Consequently, CaO for Na 2 O substitution can be an effective way to help fluorine retention without significantly changing the glass chemistry. Furthermore, these results thus provide insights on fluorine incorporation in the aluminosilicate nuclear waste glasses and the strategy on how to improve fluorine retention both in the glass and the melt.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Role of Ice Splintering on Microphysics of Deep Convective Clouds Forming Under Different Aerosol Conditions: Simulations Using the Model With Spectral Bin Microphysics

Observations during the Ice in Clouds Experiment-Tropical (ICE-T) field experiment show that the ice particles concentration in a developing deep convective clouds at the level of T = -15 °C reached about 500 L -1 , that is, many orders higher than that of ice-nucleating particle. To simulate microphysics of these clouds, the 2-D Hebrew University Cloud model (HUCM) with spectral bin microphysics was used in which two main types of ice multiplication mechanisms were included in addition to the Hallet-Mossop mechanism. In the first ice multiplication mechanism ice splinters form by drop freezing and drop-ice collisions. Ice multiplication of this type dominates during developing stage of cloud evolution, when liquid water content is significant. At later stage when clouds become nearly glaciated, ice crystals are produced largely by ice splintering during ice-ice collisions (the second ice multiplication mechanism). Simulations show that droplet size distributions, as well as size distributions of ice particles, agree well with the measurements during ICE-T. Simulations with different cloud condensation nuclei concentrations show the existence of the “optimum” cloud condensation nuclei concentration (or droplet concentration), at which concentration of ice splinters reaches maximum. In these simulations ice nucleation caused by the direct formation of ice crystals upon ice-nucleating particles, as well as the Hallett-Mossop process, has a negligible contribution to the ice crystal concentration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics

Tree growth and survival differ strongly between canopy trees (those directly exposed to overhead light), and understory trees. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. The integration of remote sensing and ground-based data enables this determination and measurements of how canopy and understory trees differ in structure and dynamics. Here we analyzed 2 cm resolution RGB imagery collected by a Remotely Piloted Aircraft System (RPAS), also known as drone, together with two decades of bi-annual tree censuses for 2 ha of old growth forest in the Central Amazon. We delineated all crowns visible in the imagery and linked each crown to a tagged stem through field work. Canopy trees constituted 40% of the 1244 inventoried trees with diameter at breast height (DBH) > 10 cm, and accounted for ~70% of aboveground carbon stocks and wood productivity. The probability of being in the canopy increased logistically with tree diameter, passing through 50% at 23.5 cm DBH. Diameter growth was on average twice as large in canopy trees as in understory trees. Growth rates were unrelated to diameter in canopy trees and positively related to diameter in understory trees, consistent with the idea that light availability increases with diameter in the understory but not the canopy. The whole stand size distribution was best fit by a Weibull distribution, whereas the separate size distributions of understory trees or canopy trees > 25 cm DBH were equally well fit by exponential and Weibull distributions, consistent with mechanistic forest models. The identification and field mapping of crowns seen in a high resolution orthomosaic revealed new patterns in the structure and dynamics of trees of canopy vs. understory at this site, demonstrating the value of traditional tree censuses with drone remote sensing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Sediment Mineralogy Data Review for the Hanford Central Plateau

The compendium of Hanford site sediment mineralogy data provided in this report was initiated in 2012 to support flux mitigation technologies and real-time monitoring that could limit contaminant fluxes to the groundwater in the Central Plateau. Recent efforts in advancing spectral induced polarization as a method for monitoring contaminant transformations for active biogeochemical remedies have underscored the need for a published compendium of sediment characterization data. Although the data are primarily focused on the mineralogical composition of Hanford sediments, other soil measurements such as particle size distribution such as particle size distribution and carbon content are provided. The data have been reproduced from the original reports and are provided here for information only (FIO).

58 GEOSCIENCES↗
DOE OSTI2021

Intercomparison of Aerosol Instrumental Responses Using AEROTRAK, SMPS, and APS in a Simulated Building

In this work, three aerosol instruments were intercompared by using data collected from field campaigns in a simulated building environment during the last quarter of 2020 from October to December at Oak Ridge National Laboratory. The three aerosol instruments—AeroTrak Model 9110, Scanning Mobility Particle Spectrometer (SMPS) 3080L + 3025A, and Aerodynamic Particle Sizer (APS) 3320—were manufactured by TSI Inc. to perform in situ measurements of particle number concentration in the air. These instruments were co-located in one room in which an aerosol generator was used to produce a defined aerosol population for the instruments to sample and detect in each campaign. The SMPS measured aerosol particles with diameters from 0.01 to 0.7 µm, APS measured aerosol particles with diameters from 0.5 to 20 µm, and AeroTrak measured aerosol particles with diameters from 0.1 to 10 µm. The results suggest that although AeroTrak is fast and easy to use, it does not provide measurements comparable with those obtained by SMPS and APS. The differences in particle counts were orders of magnitude in size (i.e., the AeroTrak data were unreasonably small in the tested scenarios). The results clearly indicate that AeroTrak should not be used in ambient applications in which the level of particle concentration is expected to be high. This would cause difficulty to a campaign in which the dilution factor is unknow a priori. The high particle concentration could also lead to an erroneous aerosol size distribution (e.g., a bimodal size distribution was measured as opposed to a single-mode one). The number concentration limit that AeroTrak is expected to run reliably (i.e., with a counting coincidence error less than ±10%) is approximately three to four per cubic centimeter. This suggests that the instrument is best used in a clean room environment, not in an ambient or indoor application, such as the Flexible Research Platform campaigns.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Coastal Cloud Chemistry during EPCAPE Field Campaign Report

For this campaign, Principal Investigator Markus Petters deployed a suite of instruments inside Lynn Russell’s container that was sited at Mt. Soledad during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) campaign. Funding for this activity was provided by National Science Foundation Award 2410536. The instrument rack (Figure 1) included a differential mobility analyzer, a condensation particle counter, a portable optical particle spectrometer, and a continuous flow diffusion cloud condensation nuclei counter (CCN). The instruments sampled from a counterflow virtual impactor (CVI) inlet and an isokinetic inlet. The measurements provided aerosol size distribution, size-resolved CCN activity, and denuded versus undenuded CCN activity of aerosols and cloud drop residuals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The influence of cooling rate on condensation of iron, aluminum, and uranium oxide nanoparticles

Fundamental observations of particle size distributions are needed to develop models that predict the fate and transport of radioactive materials in the atmosphere following a nuclear incident. The extent of material transport is influenced by the time scales of particle formation processes (e.g., condensation, coagulation). In this study, we investigated the influence of cooling time scales on size distributions of uranium, aluminum, and iron oxide particles that are synthesized separately under identical run conditions inside the controlled environment of an argon plasma flow reactor. Two distinct temperature distributions are imposed along the flow reactor by varying the argon flow rate downstream of the plasma torch. The vaporized reactants of uranium, aluminum, and iron are cooled from about 5000K to 1000K before they are collected on silicon wafers for ex situ scanning electron microscope analysis. The microscope images show that the sizes of the largest aluminum and iron oxide particles heavily depend on the cooling time scales, whereas significant size variation with cooling rate is not observed for uranium oxide particles. In addition, the size distribution of aluminum oxide particles exhibits the broadest range among all three metal oxides studied. We performed simulations of particle size distributions using a kinetic model that couples gas phase oxidation chemistry with particle formation processes, including nucleation, condensation, and coagulation. The model results demonstrate the strong sensitivity of particle size distribution to different cooling histories (i.e., temperature vs residence time) along the flow reactor. In conclusion, the kinetic model also helps identify directions for future research to improve the predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Drop clustering and drop size correlations from holographic imagery suggest cloud droplet spectral broadening via entrainment-mixing

The question of how droplets rapidly grow large enough to initiate collision-coalescence has persisted for decades. Many theories explaining the production of sufficiently large drops (i.e., those in the “bottleneck” size range; ∼ 25–50 µm diameters) involve drop clustering on millimeter scales. A novel method is introduced to evaluate drop clustering trends particle-by-particle (i.e., the number/proximity of neighboring drops for given droplets; defined as drop clustering fields) which are diagnosed relative to drops within their shared drop environments – in contrast to previous studies which diagnose drop clustering of defined sample volumes, or in terms of absolute length scales. Specifically, this study evaluates the statistical likelihood that drops of a given size are associated with either a significant number of neighboring drops, or are significantly isolated from neighboring drops. Observations are acquired from the HOLODEC during the Cloud System Evolution in the Trades campaign, which sampled subtropical marine clouds. The HOLODEC measures drop size distributions and the 3D spatial coordinates of droplets. Results show drops within the bottleneck size range (diameters of ∼ 25–50 µm) are most likely to be significantly isolated from neighboring drops. This “isolated large drop trend” is primarily observed at subsaturated conditions, suggesting entrainment is the contributing factor. Holograms associated with this trend are more likely to have broader drop size distributions, larger maximum drop sizes and overly regions where precipitation reaches the lowest altitudes from the sampled cloud, suggesting entrainment-mixing drop size distribution broadening is a relevant precipitation-initiation mechanism.

D'Alessandro, John J. [Univ. of Washington, Seattl↗
DOE OSTI2024

Cloud response to co-condensation of water and organic vapors over the boreal forest

Abstract. Accounting for the condensation of organic vapors along with water vapor (co-condensation) has been shown in adiabatic cloud parcel model (CPM) simulations to enhance the number of aerosol particles that activate to form cloud droplets. The boreal forest is an important source of biogenic organic vapors, but the role of these vapors in co-condensation has not been systematically investigated. In this work, the environmental conditions under which strong co-condensation-driven cloud droplet number enhancements would be expected over the boreal biome are identified. Recent measurement technology, specifically the Filter Inlet for Gases and AEROsols (FIGAERO) coupled to an iodide-adduct chemical ionization mass spectrometer (I-CIMS), is utilized to construct volatility distributions of the boreal atmospheric organics. Then, a suite of CPM simulations initialized with a comprehensive set of concurrent aerosol observations collected in the boreal forest of Finland during spring 2014 is performed. The degree to which co-condensation impacts droplet formation in the model is shown to be dependent on the initialization of temperature, relative humidity, updraft velocity, aerosol size distribution, organic vapor concentration, and the volatility distribution. The predicted median enhancements in cloud droplet number concentration (CDNC) due to accounting for the co-condensation of water and organics fall on average between 16 % and 22 %. This corresponds to activating particles 10–16 nm smaller in dry diameter that would otherwise remain as interstitial aerosol. The highest CDNC enhancements (ΔCDNC) are predicted in the presence of a nascent ultrafine aerosol mode with a geometric mean diameter of ∼ 40 nm and no clear Hoppel minimum, indicative of pristine environments with a source of ultrafine particles (e.g., via new particle formation processes). Such aerosol size distributions are observed 30 %–40 % of the time in the studied boreal forest environment in spring and fall when new particle formation frequency is the highest. To evaluate the frequencies with which such distributions are experienced by an Earth system model over the whole boreal biome, 5 years of UK Earth System Model (UKESM1) simulations are further used. The frequencies are substantially lower than those observed at the boreal forest measurement site (< 6 % of the time), and the positive values, peaking in spring, are modeled only over Fennoscandia and the western parts of Siberia. Overall, the similarities in the size distributions between observed and modeled (UKESM1) are limited, which would limit the ability of this model, or any model with a similar aerosol representation, to project the climate relevance of co-condensation over the boreal forest. For the critical aerosol size distribution regime, ΔCDNC is shown to be sensitive to the concentrations of semi-volatile and some intermediate-volatility organic compounds (SVOCs and IVOCs), especially when the overall particle surface area is low. The magnitudes of ΔCDNC remain less affected by the more volatile vapors such as formic acid and extremely low- and low-volatility organic compounds (ELVOCs and LVOCs). The reasons for this are that most volatile organic vapors condense inefficiently due to their high volatility below the cloud base, and the concentrations of LVOCs and ELVOCs are too low to gain significant concentrations of soluble mass to reduce the critical supersaturations enough for droplet activation to occur. A reduction in the critical supersaturation caused by organic condensation emerges as the main driver of the modeled ΔCDNC. The results highlight the potential significance of co-condensation in pristine boreal environments close to sources of fresh ultrafine particles. For accurate predictions of co-condensation effects on CDNC, also in larger-scale models, an accurate representation of the aerosol size distribution is critical. Further studies targeted at finding observational evidence and constraints for co-condensation in the field are encouraged.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Ultra‐Uniform Nanocrystalline Materials via Two‐Step Sintering

Abstract Nanocrystalline materials with superior properties are of great interest. Much is discussed about obtaining nanograins, but little is known about maintaining grain‐size uniformity that is critical for reliability. An especially intriguing question is whether it is possible to achieve a size distribution narrower than what Hillert theoretically predicted for normal grain growth, a possibility suggested—for growth with a higher growth exponent—by the generalized mean‐field theory of Lifshitz, Slyozov, Wagner (LSW), and Hillert but never realized in practice. Following a rationally designed two‐step sintering route, it has been made possible in bulk materials by taking advantage of the large growth exponent in the intermediate sintering stage to form a uniform microstructure despite residual porosity, and freezing the grain growth thereafter while continuing densification to reach full density. The bulk dense Al 2 O 3 ceramic thus obtained has an average grain size of 34 nm and a size distribution much narrower than Hillert's prediction. Bulk Al 2 O 3 with a grain‐size distribution narrower than the particle‐size distribution of starting powders is also demonstrated, as are highly uniform bulk engineering metals (refractory Mo and W‐Re alloy) and complex functional ceramics (BaTiO 3 ‐based alloys with superior dielectric strength and energy capacity).

Dong, Yanhao↗
DOE OSTI2020

Laser Disdrometer Quantities (LDQUANTS) and Video Disdrometer Quantities (VDISQUANTS) Value-Added Products Report

Disdrometers are useful instruments that measure the distribution of raindrop sizes (drop size distribution: DSD) and the associated rainfall rates/accumulation as those raindrops fall to the ground. These DSDs are a quantity of interest to members of the modeler and observational communities. However, use of disdrometer data for model evaluation, radar monitoring, or other activities requires careful quality control and processing for key DSD properties of interest (e.g., the number concentration of drops) to ensure appropriate physical (scattering, fall speed) assumptions. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Laser Disdrometer Quantities and Video Disdrometer Quantities Value-Added Product (LDQUANTS/VDISQUANTS VAP) uses standard methods from Tokay et al. (e.g., 2013, 2014) to filter drops with unrealistic fall speeds. Further, it estimates several microphysical/geophysical quantities of parameterized DSDs (gamma or exponential assumption type fitting methods) as in previous disdrometer studies and ARM long-term efforts (e.g., Testud et al. 2001, Giangrande et al. 2014, Thompson et al. 2015). Disdrometers are also beneficial for cross-checks with other instrumentation, including rain gauges and radars. To support research interests and related radar monitoring activities, this product calculates radar equivalent quantities, including dual polarization radar quantities (e.g., Z, Differential Reflectivity ZDR, etc.), using T-Matrix scattering and additional wavelength, temperature, and drop shape assumptions (e.g., Thurai et al. 2007). All of these efforts allow disdrometer data sets to become more useful and easily handled by modeling and observational studies, or routine instrumentation checks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Department of Transportation PHMSA Aerosol Spray Project: Phase-0

This report discusses the work conducted at Sandia National Laboratories during the preliminary phase (Phase-0) of the Department of Transportation (DOT) Pipeline and Hazardous Materials Safety Administration (PHMSA) project tasked to better understand what happens to a high-pressure liquid canister or vessel during a fire in which an accidental instantaneous release occurs. Phase 0 had two main objects: (1) to establish a methodology for the high temperature and pressure spray vessel release and (2) to characterize the spray droplet formation. For this test series, diagnostic equipment such as the Malvern Spraytec, high-speed photometric cameras, pencil gauges, and thermocouples were used. Additionally, the effect of surface tension on the particle size distribution was evaluated with 1-liter of distilled water and with the same including the addition of 0.5% soap/water mixture. Results from this effort indicated that the plume formed from the instantaneous release in all conditions primarily included two distinct particle size distributions including 217-312µm and 0.53µm on average. Soapy water formed a higher concentration of smaller mist (0.53µm) than distilled water, potentially due to decreased surface tension with the added surfactant. Though results showed good correlation to certain particle size distributions it is encouraged to conduct additional tests like this with a wider range of liquids with different properties, which could affect the overall particle size distribution when released.

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

Responses of Drying-rewetting (Transient Soil Moisture) and Steady State Soil Moisture Incubation on Soil Organic Carbon Dynamics in Three US Soils, 2017

This data set contains measurements of soil characteristics (aggregate size distribution and mean size, total aggregate associated carbon, extractable organic C, and microbial biomass C), microbial respiration, and soil metabolite concentrations from a transient and steady soil moisture incubation experiment using soils of different textures (sandy, loamy, and clayey). The study investigated mechanisms driving the Birch effect (increased carbon mineralization pulses with wetting following a drying period) in differing soil textures. Three different soils of distinctly different textures were collected from 0-15cm depth in Georgia (sandy, 2017-05-01), Missouri (loamy, 2017-06-14), and Texas (clayey, December 2017). Soils were incubated for 140 days with destructive harvests done on days 1, 29, 33, 56, 112, 116, and 140 in transient soil moisture incubation and on days 1, 33, 116, and 140 in steady state soil moisture incubation. This dataset contains six data files in comma separate (.csv) format. Additional metadata are provided: six data dictionaries and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

1-Methyladenosine concentration↗