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A Method for Determining Bulk Density, Material Density, and Porosity of Melter Feed During Nuclear Waste Vitrification
Glassmelting efficiency largely depends on heat transfer to reacting glass batch (melter feed), which in turn is influenced by the bulk density (ρ b ) and porosity (ϕ) of the reacting feed as functions of temperature ( T ). Neither ρ b ( T ) nor ϕ( T ) functions are readily accessible from direct measurements. For the determination of ρ b , we monitored the profile area of heated feed pellets and calculated the pellet volume using numerical integration. For the determination of ϕ, we measured the material density of feeds quenched at various stages of conversion via pycnometry and then computed the feed density at heat‐treatment temperature using thermal expansion values of basic feed constituents.
Soil bulk density and texture data collected during field survey associated with NEON AOP survey, East River, CO 2018
The purpose of soil core sampling was to determine soil bulk density, volumetric water content, and soil texture across the sites that were sampled in association with the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) in 2018. Soil cores were collected from surface (2.5-7.5 cm) and subsurface (50-55cm) depths at sampling sites across East River, Washington Gulch, Slate River, and Coal Creek watersheds in Gunnison, Colorado during the summer of 2018. Surface samples were weighed for determination of soil water content prior to dry down for bulk density calculations. For all samples, we measured dry weight and weight of < 2mm fraction and conducted soil textural analysis. We provide metadata for the samples, raw data files, python code that was used to calculate bulk density, water content, and texture fraction densities from primary datasets, and a final csv file with the calculated soil bulk density, volumetric water content, and soil texture data. Additional associated site metadata and vegetation fractional cover information can be found in the associated data release.
Recalculation of Soil Bulk Density Used in the Scorpius MCNP6 Model
The soil composition used in the Scorpius MCNP6.2 (Ref. 1) model was recently recalculated. Originally, it was “determined using the United States Geological Survey (USGS) Mercury Core Library and the Nevada National Security Site U.S. Geological Survey Databases (NNSS USGS) and Nevada National Security Site Petrographic, Geochemical, and Geophysical Database (NNSS PGG),” but the details had been lost. Reference 2 documents the updated soil composition determination. Reference 2 estimated the grain density of the soil instead of the bulk density. This report corrects that error. I am indebted to Garrett Euler (EES-17) for reading Ref. 2, pointing out its errors, and guiding me through the correct calculations. This report is organized as follows. For completeness, the composition calculations from Ref. 2 are repeated: Sec. II discusses the data that are reported in the PGG Access database, and Sec. III uses the data in the PGG Access database to compute the composition of the soil. Section IV estimates the bulk density of the soil. Section V evaluates the soil wall thickness in the Scorpius model. Section VI estimates the effect of the soil density change on previously calculated results. Section VII presents a summary and conclusions. Input files and output files are listed in appendices.
Predicting the evolution of biomass bulk density through feedstock preprocessing: Discrete element modeling, regression analysis, and pilot-scale validation
Bulk density is an important material property of biomass feedstocks, influencing handling, storage, transport costs, and conversion efficiency. In this study, predictive regression models for loose and tapped bulk densities of Alamo and Cave-in-Rock switchgrass are developed using a comprehensive dataset generated via calibrated bonded-sphere discrete element method (DEM) simulations. Here, a key contribution of this study is the use of a DEM-based approach, which correlates density with moisture content and particle size distribution parameters and enables analysis across a continuous particle size range, overcoming limitations of purely experimental data. For comparison, regression models are also developed using only experimental data from pilot-scale runs at the Biomass Feedstock National User Facility at Idaho National Laboratory. Validation against pilot-scale data showed reasonable prediction accuracy for both model types, particularly for smaller particle sizes (post-secondary grinding). While the experimental model showed slightly better performance matching the validation data in some cases, the DEM-based model benefits from a much larger dataset, reduced predictor multicollinearity, and continuous parameter coverage, highlighting the utility of validated simulation models for developing robust predictive tools for biomass preprocessing applications.
Effects of dry bulk density and particle size fraction on gas transport parameters in variably saturated landfill cover soil
Highlights: > The effects of soil physical properties on gas transport parameters were investigated. > Higher values of D{sub p} and k{sub a} exhibited in the '+gravel' than the '-gravel' fraction at same soil-air content ({epsilon}). > Recent power law models for D{sub p} (WLR) and k{sub a} (RPL) were modified. > Model parameters were linearly related to easily measurable dry bulk density ({rho}{sub b}). - Abstract: Landfill sites are emerging in climate change scenarios as a significant source of greenhouse gases. The compacted final soil cover at landfill sites plays a vital role for the emission, fate and transport of landfill gases. This study investigated the effects of dry bulk density, {rho}{sub b}, and particle size fraction on the main soil-gas transport parameters - soil-gas diffusivity (D{sub p}/D{sub o}, ratio of gas diffusion coefficients in soil and free air) and air permeability (k{sub a}) - under variably-saturated moisture conditions. Soil samples were prepared by three different compaction methods (Standard and Modified Proctor compaction, and hand compaction) with resulting {rho}{sub b} values ranging from 1.40 to 2.10 g cm{sup -3}. Results showed that D{sub p} and k{sub a} values for the '+gravel' fraction (<35 mm) became larger than for the '-gravel' fraction (<2 mm) under variably-saturated conditions for a given soil-air content ({epsilon}), likely due to enhanced gas diffusion and advection through less tortuous, large-pore networks. The effect of dry bulk density on D{sub p} and k{sub a} was most pronounced for the '+gravel' fraction. Normalized ratios were introduced for all soil-gas parameters: (i) for gas diffusivity D{sub p}/D{sub f}, the ratio of measured D{sub p} to D{sub p} in total porosity (f), (ii) for air permeability k{sub a}/k{sub a,pF4.1}, the ratio of measured k{sub a} to k{sub a} at 1235 kPa matric potential (=pF 4.1), and (iii) for soil-air content, the ratio of soil-air content ({epsilon}) to total porosity (f) (air saturation). Based on the normalized parameters, predictive power-law models for D{sub p}({epsilon}/f) and k{sub a}({epsilon}/f) models were developed based on a single parameter (water blockage factor M for D{sub p} and P for k{sub a}). The water blockage factors, M and P, were found to be linearly correlated to {rho}{sub b} values, and the effects of dry bulk density on D{sub p} and k{sub a} for both '+gravel' and '-gravel' fractions were well accounted for by the new models.
EFFECTIVE POROSITY IMPLIES EFFECTIVE BULK DENSITY IN SORBING SOLUTE TRANSPORT
The concept of an effective porosity is widely used in solute transport modeling to account for the presence of a fraction of the medium that effectively does not influence solute migration, apart from taking up space. This non-participating volume or ineffective porosity plays the same role as the gas phase in single-phase liquid unsaturated transport: it increases pore velocity, which is useful towards reproducing observed solute travel times. The prevalent use of the effective porosity concept is reflected by its prominent inclusion in popular texts, e.g., de Marsily (1986), Fetter (1988, 1993) and Zheng and Bennett (2002). The purpose of this commentary is to point out that proper application of the concept for sorbing solutes requires more than simply reducing porosity while leaving other material properties unchanged. More specifically, effective porosity implies the corresponding need for an effective bulk density in a conventional single-porosity model. The reason is that the designated non-participating volume is composed of both solid and fluid phases, both of which must be neglected for consistency. Said another way, if solute does not enter the ineffective porosity then it also cannot contact the adjoining solid. Conceptually neglecting the fluid portion of the non-participating volume leads to a lower (effective) porosity. Likewise, discarding the solid portion of the non-participating volume inherently leads to a lower or effective bulk density. In the author's experience, practitioners virtually never adjust bulk density when adopting the effective porosity approach.
Bulk density, grain size, carbon, and nitrogen content of floodplain fine sediment along the East River, Colorado collected from 2014 to 2017
The dataset is comprised of grain size, bulk density, carbon and nitrogen content of soils sampled on the floodplain of the East River 4 to 11 km downstream of Gothic, CO near Crested Butte. This dataset was collected to characterize the sedimentological properties and carbon inventory of the East River floodplain. The data help quantify the flux and storage of sediment and carbon between the East River and the alluvial floodplain bounding the river. The samples were collected between 2014 and 2017. The soil samples come from a total of 156 locations and were sampled in the upper 40 to 60 cm of the floodplain and in abandoned channel segments or oxbows. Not all samples were analyzed for all properties, but 26 samples were analyzed for bulk density, full particle size distribution, carbon, and nitrogen content. An additional 48 samples were analyzed for particle size fraction greater than and less than 2 mm and total organic content determined by loss on ignition. The dataset is comprised of a total of six separate data files based on the sample type and analysis and another file lists the sample identifications and locations of samples with a complete suite of analyses.
Bulk density, grain size, carbon content and isotopes, and nitrogen content of floodplain sediment along the Koyukuk River, Alaska collected in Summer 2018.
The dataset is comprised of grain size, bulk density, carbon and nitrogen content, stable carbon isotopes, and radiocarbon content of soils sampled on the floodplain of the Koyukuk River near Huslia, AK. This dataset was collected to characterize sediment properties and floodplain organic carbon stocks in the discontinuous permafrost floodplain of the Koyukuk River. The data help quantify the flux and storage of sediment and carbon between the Koyukuk River and the surrounding floodplain as the river migrates laterally. The samples were collected in June 27 - July 9, 2018. The soil samples come from a total of 24 locations and were sampled in the upper 490 cm of the river cutbanks, point bars, and floodplain. Samples were analyzed for bulk density, full particle size distribution, nitrogen content and carbon content and isotopes, though not all samples were included in each analysis. An additional 10 samples of floodplain woody debris were measured for radiocarbon content. Data, detailed methods, analyses, and interpretations were previously published at: Douglas, M. M., Lingappa, U. F., Lamb, M. P., Rowland, J. C., West, A. J., Li, G., et al. (2021). Impact of river channel lateral migration on microbial communities across a discontinuous permafrost floodplain. Applied and Environmental Microbiology, 87(20), AEM.01339-21. https://doi.org/10.1128/AEM.01339-21 Douglas, M. M., Li, G. K., Fischer, W. W., Rowland, J. C., Kemeny, P. C., West, A. J., et al. (2022). Organic carbon burial by river meandering partially offsets bank erosion carbon fluxes in a discontinuous permafrost floodplain. Earth Surface Dynamics, 10(3), 421–435. https://doi.org/10.5194/esurf-10-421-2022 The dataset is comprised of a total of three separate data files based on the sample type and analysis, including a file that lists the sample locations, permafrost presence, and geomorphic landform.
Estimating Compressional Velocity and Bulk Density Logs in Marine Gas Hydrates Using Machine Learning
Compressional velocity (Vp) and bulk density (ρb) logs are essential for characterizing gas hydrates and near-seafloor sediments; however, it is sometimes difficult to acquire these logs due to poor borehole conditions, safety concerns, or cost-related issues. We present a machine learning approach to predict either compressional Vp or ρb logs with high accuracy and low error in near-seafloor sediments within water-saturated intervals, in intervals where hydrate fills fractures, and intervals where hydrate occupies the primary pore space. We use scientific-quality logging-while-drilling well logs, gamma ray, ρb, Vp, and resistivity to train the machine learning model to predict Vp or ρb logs. Of the six machine learning algorithms tested (multilinear regression, polynomial regression, polynomial regression with ridge regularization, K nearest neighbors, random forest, and multilayer perceptron), we find that the random forest and K nearest neighbors algorithms are best suited to predicting Vp and ρb logs based on coefficients of determination (R2) greater than 70% and mean absolute percentage errors less than 4%. Given the high accuracy and low error results for Vp and ρb prediction in both hydrate and water-saturated sediments, we argue that our model can be applied in most LWD wells to predict Vp or ρb logs in near-seafloor siliciclastic sediments on continental slopes irrespective of the presence or absence of gas hydrate.
High Efficiency m-plane LEDs on Low Defect Density Bulk GaN Substrates
Solid-state lighting is a key technology for reduction of energy consumption in the US and worldwide. In principle, by replacing standard incandescent bulbs and other light sources with sources based on light-emitting diodes (LEDs), ultimate energy efficiency can be achieved. The efficiency of LEDs has improved tremendously over the past two decades, however further progress is required for solid- state lighting to reach its full potential. The ability of an LED at converting electricity to light is quantified by its internal quantum efficiency (IQE). The material of choice for visible LEDs is Gallium Nitride (GaN), which is at the basis of blue-emitting LEDs. A key factor limiting the performance of GaN LEDs is the so-called efficiency droop, whereby the IQE of the LED decreases significantly at high current density. Despite decades of research, efficiency droop remains a major issue. Since high-current operation is necessary for practical lighting applications, reducing droop is a major challenge for the scientific community and the LED industry. Our approach to solving the droop issue is the use of newly available low-defect-density bulk GaN non-polar substrates. In contrast to the standard foreign substrates (sapphire, silicon carbide, silicon) used in the industry, we have employed native bulk GaN substrates with very low defect, density, thus ensuring exquisite material quality and high IQE. Whereas all commercial LEDs are grown along the c-plane crystal direction of GaN, we have used m-plane non-polar substrates; these drastically modify the physical properties of the LED and enable a reduction of droop. With this approach, we have demonstrated very high IQE performance and low droop. Our results focused on violet and blue LEDs. For these, we have demonstrated very high peak IQEs and current droops of 6% and 10% respectively (up to a high current density of 200A.cm-2). All these results were obtained under electrical operation. These high IQE and low droop values are in line with the program’s milestones. They demonstrate that bulk non-polar GaN substrates represent a disruptive technology for LED performance. Application of this technology to real-world products is feasible, provided that the cost of GaN substrates is compatible with the market’s requirement.
Bulk-Density Representations of Branched Planar Ice Crystals: Errors in the Polarimetric Radar Variables
Abstract Recent interest in interpreting polarimetric radar observations of ice and evaluating microphysical model output with these observations has highlighted the importance of accurately computing the scattering of microwave radiation by branched planar ice crystals. These particles are often represented as spheroids with uniform bulk density, reduced from that of solid ice to account for the complex, nonuniform structure of natural branched crystals. In this study, the potential errors that arise from this assumption are examined by comparing scattering calculations of branched planar crystals with those of homogeneous, reduced-density plate crystals and spheroids with the same mass, aspect ratio, and maximum dimension. The results show that this assumption leads to significant errors in backscatter cross sections at horizontal and vertical polarization, specific differential phase (KDP), and differential reflectivity (ZDR), with the largest ZDR errors for ice crystals with the most extreme aspect ratios (<0.01) and effective densities < 250 kg m−3. For example, the maximum errors in X-band ZDR are 4.5 dB for 5.6-mm branched planar crystals. However, substantial errors are present at all weather radar frequencies, with resonance scattering effects at Ka and W band amplifying the low-frequency errors. The implications of these results on the interpretation of polarimetric radar observations and forward modeling of the polarimetric radar variables from microphysical model output are discussed.
Reliability of AlGaN/GaN high electron mobility transistors on low dislocation density bulk GaN substrate: Implications of surface step edges
To enable gaining insight into degradation mechanisms of AlGaN/GaN high electron mobility transistors, devices grown on a low-dislocation-density bulk-GaN substrate were studied. Gate leakage current and electroluminescence (EL) monitoring revealed a progressive appearance of EL spots during off-state stress which signify the generation of gate current leakage paths. Atomic force microscopy evidenced the formation of semiconductor surface pits at the failure location, which corresponds to the interaction region of the gate contact edge and the edges of surface steps.
How does uncertainty of soil organic carbon stock affect the calculation of carbon budgets and soil carbon credits for croplands in the U.S. Midwest?
Cropland carbon budget depicts the amount of carbon flowing in and out of agroecosystems and the changes in carbon stocks of soil and living biomass during the same period. Soil carbon credit is the additional change in soil carbon stock under certain farming practices compared with the business-as-usual practices. Accurately calculating cropland carbon budget and soil carbon credit is critical to assessing climate change mitigation potential in agroecosystems. The calculation of cropland carbon budget and soil carbon credit is sensitive to local soil and climatic conditions, especially initial soil organic carbon (SOC) stock, which is determined by both SOC concentration (SOC%) and bulk density (Bulk_Density). SOC stock data are either from soil sampling or gridded public survey data. In agroecosystem models, SOC stock data are a key model input for quantifying cropland carbon budget and soil carbon credit. However, various types and degrees of uncertainties exist in SOC stock datasets, which propagate to the quantification of SOC stock change. In particular, a large discrepancy is found in two widely used SOC stock datasets — Rapid Carbon Assessment dataset (RaCA) and Gridded Soil Survey Geographic Database (gSSURGO) — in the U.S. Midwest, with a relative difference (quantified using Normalized Root Mean Square Error, NRMSE) of 48.0% for 0–30 cm SOC stock between the two datasets. It remains largely unclear how uncertainty in SOC stocks affects the calculation of cropland carbon budget and soil carbon credit. To address this question, we used a well-validated process-based agroecosystem model, ecosys, to assess the impacts of SOC stock uncertainty on carbon budget and soil carbon credit calculation in the U.S. Midwestern corn-soybean rotation systems. Our results reveal the following findings: (1) A sizable discrepancy exists in simulated cropland carbon budget between using gSSURGO and using RaCA for their SOC% and Bulk_Density as model inputs, with a Pearson correlation coefficient (r) of only 0.4 for simulated change of SOC stock (ΔSOC) using these two different soil datasets. (2) Simulated cropland carbon budget components were more sensitive to initial SOC% than to Bulk_Density. For example, the upper and lower quartiles of multi-year averaged ΔSOC were –29.8 and 4.8 gC/m 2 /year for the selected counties respectively, with an uncertainty of 13.7 and 0.7 gC/m 2 /year induced by uncertainties in initial SOC% and Bulk_Density, respectively. (3) Both simulated ΔSOC and its uncertainty were negatively correlated with initial SOC%, whereas ΔSOC was negatively correlated with air temperature, and ΔSOC uncertainty was positively correlated with air temperature. (4) The uncertainty of calculated soil carbon credits was much smaller compared with the uncertainty of calculated absolute carbon budgets assuming the same SOC stock uncertainty level in the inputs. Specifically, in our assessment comparing planting cover crops vs no cover crop, the uncertainty of calculated soil carbon credits induced by initial SOC% uncertainty was less than 4% (relative to the quantified value of the soil carbon credits) for 90% of the cases. Our analysis highlights that high accuracy measurement of SOC% as inputs is needed for the calculation of cropland carbon budgets; however, soil carbon credit quantification is much less sensitive to the initial SOC% inputs, and the current publicly available soil datasets (e.g., gSSURGO) are largely suitable for the calculation of soil
Computational modeling of latent-heat-storage in PCM modified interior plaster
The latent heat storage systems represent a promising way for decrease of buildings energy consumption with respect to the sustainable development principles of building industry. The presented paper is focused on the evaluation of the effect of PCM incorporation on thermal performance of cement-lime plasters. For basic characterization of the developed materials, matrix density, bulk density, and total open porosity are measured. Thermal conductivity is accessed by transient impulse method. DSC analysis is used for the identification of phase change temperature during the heating and cooling process. Using DSC data, the temperature dependent specific heat capacity is calculated. On the basis of the experiments performed, the supposed improvement of the energy efficiency of characteristic building envelope system where the designed plasters are likely to be used is evaluated by a computational analysis. Obtained experimental and computational results show a potential of PCM modified plasters for improvement of thermal stability of buildings and moderation of interior climate.
NGEE Arctic Plant Traits: Soil Cores, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, 2016
Soil cores were collected from twelve vegetation biomass plots at the Kougarok hillslope in late July of 2016. The sampled plots were located across six ecotypes present at this site (n=2 replicates per ecotype). Soil cores were separated into depth intervals in the field and frozen for transport to Oak Ridge National Laboratory. Laboratory sample processing of these soils generated data on soil texture, pH, gravimetric water content, bulk density, bulk soil %C and %N. Water extracts were performed on subsamples for determination of extractable total nitrogen, inorganic nitrogen, organic nitrogen, phosphorus, and dissolved organic carbon. This dataset contains 1 csv, and 1 pdf.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Effects of thermal treatment on energy density and hardness of torrefied wood pellets
Here, three types of wood pellets samples, including two types of commercial pellets and one type of lab-made control pellets were torrefied in a fixed bed unit to study the effect of thermal pretreatment on the quality of wood pellets. The quality of wood pellets was mainly characterized by the pellet density, bulk density, higher heating value, Meyer hardness, saturated moisture uptake, volumetric energy density, and energy yield. Results showed that torrefaction significantly decreased the pellet density, hardness, volumetric energy density, and energy yield. The higher heating value increased and the saturated moisture content decreased after torrefaction. In view of the lower density, lower hardness, lower volumetric energy density, and energy yield of torrefied pellets, it is recommended that biomass should be torrefied and then compressed to make strong pellets of high hydrophobicity and volumetric energy density.