Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “WATER TABLES”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26

Towards an Enhanced Droplet Activation Scheme for Multi-Moment Bulk Microphysics Schemes

Initial droplet spectra produced upon activation impact the ensuing chain of microphysical processes andtherefore play a crucial role in cloud evolution. This work re-examines dependencies of newly formed clouddroplet size distribution (CDSD) characteristics on environmental and aerosol properties via parcel model simulationsthat serve as the basis for a multi-moment bulk microphysics droplet activation scheme suitable for acloud-resolving model (CRM). It is found that applying a fixed size threshold to define activated droplets versusemploying physical considerations can lead to erroneous activation and overly broad CDSDs for high aerosolconcentration and weak updraft conditions. Aerosol distributions characterized by larger median sizes and/orincreased solubility can result in greater activated droplet numbers, whereas impacts of these parameters onCDSD spectral width depend on both aerosol number concentration and updraft velocity. An expansion of theactivation scheme to include CDSD spectral width is proposed to aid efforts to extend high-order momentprediction to cloud droplet categories in CRMs as well as better represent variability in the activation process onthe cloud scale.simulations to investigate the regime dependence of the relative dispersion(d)1 of newly activated CDSDs, where d is the ratio of dropletradius standard deviation (σ) to the mean radius (r ). C16 demonstratedthat increasing Na resulted in increasing (decreasing) d values via reducedcondensational narrowing (spectral broadening) rates within theAL (UL) regime, with d values peaking in the TR regime. Their findingssuggest a similar regime dependence for d as R09 noted for Nc and helpexplain reportedly conflicting relationships between Na and CDSDspectral characteristics (cf. Hudson and Noble, 2014; Liu et al., 2014),although the applicability of these results within bulk microphysicalschemes was not addressed.Simulating aerosol-cloud interactions with CRMs employing bulkmicrophysics requires that the scheme minimally predict two CDSDparameters, namely mass and number concentrations, and represent thedroplet activation process. Various activation schemes aim to determineNc from aerosol and environmental properties and include analyticalexpressions (e.g., Abdul-Razzak et al., 1998; Morrison et al., 2005) aswell as lookup tables (LUTs) based on detailed parcel model calculations(e.g., Saleeby and Cotton, 2004, hereafter SC04; Segal and Khain,2006; Thompson and Eidhammer, 2014). Expressions to diagnose CDSDspectral width from Nc (Grabowski, 1998; Liu et al., 2006; Morrison andGrabowski, 2007) or cloud water content (Geoffroy et al., 2010) havealso been developed, although more robust methods to obtain CDSDspectral width upon activation are presently lacking. This latter point isrelevant for triple-moment (3 M) bulk microphysics that aim to predictdistribution spectral width alongside number and mass concentrations(e.g., Loftus et al., 2014; Milbrandt and Yau, 2005).The current work extends the findings of C16 to the current LUTbasedaerosol activation scheme used in the Regional AtmosphericModeling System (RAMS) (Cotton et al., 2003; SC04; Saleeby and vanden Heever, 2013, hereafter SvdH13) and additionally examinesaerosol size and solubility impacts on newly activated CDSD properties.Because early cloud development processes such as condensationalgrowth, evaporation, and droplet self-collection depend on and impactCDSD spectral width (Hudson and Yum, 1997; Seifert and Beheng 2001;Lu and Seinfeld, 2006; Igel and van den Heever, 2017), an expansion ofthe activation LUTs to include CDSD spectral width is proposed as apreliminary step for extending 3M prediction to CDSDs in CRMs forimproved simulations of aerosol-cloud interactions.2. MethodologyThe current RAMS two-moment microphysics module determinesthe fractional number of aerosol particles that activate to cloud dropletsfrom five-dimensional LUTs based on model predicted air temperature(T), w, Na, and the geometric median radius (rg) and soluble fraction (ε)of the aerosol size distribution (SvdH13). These LUTs are created offlineusing a one-dimensional Lagrangian adiabatic parcel model (Feingoldand Heymsfield, 1992; Heymsfield and Sabin, 1989; SC04) to simulateexplicit droplet activation and initial CDSD growth for a range of ambientatmospheric conditions [T, w] and binned lognormal aerosol sizedistributions given by= ⎡⎣ ⎢− ⎤⎦ ⎥N r Nr π σr rσ( )2 lnexp[ln( / )]2(ln )aggg22 (1)where r is the dry aerosol particle bin radius and σg is the geometricstandard deviation of the distribution. As the parcel model simulationsfocus on the activation process, other processes such as coalescence,sedimentation, and mixing are not considered. Details of the parcelmodel can be found in SC04 and SvdH13, and only a brief description isprovided here. At the onset of parcel model calculations, the initiallydry aerosol particles in all bins first deliquesce and reach theirequilibrium diameters in a sub-saturated environment based on theKöhler equation for solution droplets. The parcel is then lifted at a fixedupward velocity w, and particle growth by vapor diffusion, along withconcurrent changes in the ambient environment, are iteratively computedusing the Variable-coefficient Ordinary Differential Equation(VODE) solver (Brown et al., 1989). The time resolution of these calculationsis determined within the VODE solver, and the frequency atwhich the solver is called is controlled by a longer model time stepbased on fixed upward parcel displacement increments (Δz) at thespecified w (Δt=Δz/w). Model calculations proceed until the parcelreaches a height 50m beyond the level of maximum saturation ratio(Smax) or total parcel displacement exceeds 2 km. Upon model termination,Smax and the fractional number of aerosols (factv) resulting innewly formed cloud droplets, defined as particles having diameters of atleast 2 μm, are cataloged in the LUTs according to the specified T, w, Na,rg, and ε parameter values.A critical point regarding the creation of these LUTs is the use of afixed minimum diameter (Dmin) to define cloud droplets in the parcelmodel, which can produce erroneous CDSD characteristics, particularlywithin the UL regime. For aerosol distributions with large rg valuesunder low SS conditions, for example, deliquesced aerosols within thelarge tail of the distribution can exceed 2 μm in diameter yet remainunactivated as ‘haze’ particles (Levin and Cotton, 2009; McFigganset al., 2006). For this study, aerosol particles activate to cloud dropletsbased on the critical diameter Dcrit as a function of parcel supersaturationratio (Sr) as in R09:D = σ MS RTρ83 ln( ) critsol wr w (2)where σsol is the surface tension of a solution droplet, Mw and ρw are themolar mass and density of liquid water, respectively, and R is theuniversal gas constant. Additionally, at relatively large w values withinthe AL regime, Nc stabilizes shortly after reaching supersaturation.However, parcel ascent and condensational growth continue beyondthe level of Smax, potentially causing additional narrowing of the CDSD.In the current work, model calculations terminate upon reaching Smaxas changes in Nc are negligible with continued ascent (Peng et al., 2007;R09).Parcel model simulations are performed to examine the sensitivitiesof CDSD characteristics to w, Na, rg, and ε, with the ranges for theseparameters listed in Table 1. Aerosols are assumed to be a mix of solubleand insoluble material of equal density, specified by ε, where fullysoluble aerosols correspond to ammonium sulfate with hygroscopicityparameter κ=0.61 (Petters and Kreidenweis, 2007). FollowingSvdH13, aerosol geometric standard deviation is fixed at σg=1.8, andaerosol distributions (Eq. 1) are partitioned into 100 logarithmicallyspacedbins spanning a size range specific to each rg value. For all simulations,Δz=1 m, and initial values of relative humidity, air temperatureand pressure are set to RH=0.99, T=10 °C and p=900 hPa,respectively.

Loftus, Adrian M.↗

Characteristics of the GOES I-M imager and sounder

The key features and operational characteristics of the thermal imaging and sounding instruments included into the next-generation GOES spacecraft (GOES I-M) are described. The GOES Imager's censor module has five spectral channels, including an eigth-element visible channel, three IR channels, and a water-vapor channel. The GOES Sounder's detector and filter arrangement makes use of four spectral bands: long-wave, mid-wave, short-wave, and visible. Tables of the Imager and the Sounder sensing performance characteristics are presented together with diagrams of the Imager optic parts and the Imager and the Sounder field and scan patterns.

Koenig, Edward W.↗

Soyuz 22 Return Samples: Assessment of Air Quality Aboard the International Space Station

Three mini-grab sample containers (m-GSCs) were returned aboard Soyuz 22 because of concerns that new air pollutants were present in the air and these were getting into the water recovery system. The Total Organic Carbon Analyzer had been giving increasing readings of total organic carbon (TOC) in the potable water, and it was postulated that an increased load into the system was responsible. The toxicological assessment of 3 m-GSCs from the ISS is shown in Table 1. The recoveries of the 3 standards (as listed above) from the GSCs averaged 103, 95 and 76%, respectively. Recovery from formaldehyde control badges were 90 and 91%.

Jams, John T.↗

Assessment of Air Quality in the International Space Station (ISS) and Space Shuttle Based on Samples Returned Aboard STS-ll1 (UF2) in June 2002

The toxicological assessments of grab sample canisters (GSCs) and 2 solid sorbent air samplers (SSASs) returned aboard STS-111 are reported. Analytical methods have not changed from earlier reports. Surrogate standard recoveries from the GSCs were 86-106% and 62% to 136 % from the SSASs; 2 tubes with low surrogate recoveries were not reported. Pressure tracking indicated no leaks in the canisters during analysis. Recoveries from lab and trip controls for formaldehyde analyses ranged from 87 to 96%. The two general criteria used to assess air quality are the total-non-methane-volatile organic hydrocarbons (NMVOCs) and the total T-value (minus the CO2 and formaldehyde contributions). Because of the inertness of Freon 218 (octafluoropropane, OFP), Its contribution to the NMVOC is subtracted and tabulated separately. Control of atmospheric alcohols is important to the water recovery system engineers, hence total alcohols (including acetone) are also shown for each sample. Because formaldehyde is quantified from sorbent badges, its concentration is listed separately. The table shows that the air quality in general was acceptable for crew respiration; however, certain values shown in bold require further explanation. The 1.05 T value on 2/28/02 was caused by an unusually high measurement ofhexamethylcyc1otrisiloxane (T value = 0.50), which is not a concern. The MPLM T value of 1.42 and the alcohol level of 7.5 mg/cu m were due to an overall polluted atmosphere, which was expected at first entry. The major T-value component was carbon monoxide at a contribution of 0.44 units. Since the crew was only exposed momentarily to the polluted atmosphere, no health effects are expected. The formaldehyde value of 0.060 mg/cu m found in the Lab sample from 3/27/02 is cause for concern because the Lab consistently shows higher concentrations of formaldehyde than the SM and occasionally the concentrations are above the acceptable guideline. Levels of OFP have remained low, suggesting that no further leaks of the SM air conditioner have occurred.

James, John T.↗

Transient desorption of water vapor - A potential source of error in upper atmosphere rocket experiments

Results of measurements of the outgassing rates of samples of materials and surface finishes used on the outer skins of rocket-borne experiment packages in simulated rocket ascents. The results showed outgassing rates for anodized aluminum in the second minute of flight which are two to three orders of magnitude higher than those given in typical tables of outgassing rates. The measured rates for aluminum with chromate conversion surface coatings were also abnormally high. These abnormally high initial rates fell quickly after about five to ten minutes to values comparable with those in the published literature. It is concluded that anodized and chromate conversion coatings on the aluminum outer surfaces of a sounding rocket experiment package will cause gross distortion of the true water vapor environment.

Kendall, B. R. F.↗

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a collection of objects stored within an RDate file, stateWaterRightsHarmonized.RData. Each object either describes how one should interact with the other objects, or contains the harmonized water rights data. Here is each object described in detail: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. The column headers for each data frame are: waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. basinNum - The alpha-numeric identifier of the WMA the record belongs to. priorityDate - The priority date associated with the right. allocatedFlow - The maximum flow of the allocation in cubic feet per second (ft3s-1). source - Whether the right is for surface water or groundwater. origWaterUse - The original stated water use from the state. waterUse - The water use category under the unified use categories established here. rightsByState_surface: A list of the cleaned surface water rights collected from each state. See rightsByState_ground for more details on how the data is formatted. fullRightsRecs: A list of the combined groundwater and surface water records for each state. Essentially, these lists are the merging of rightsByState_ground and rightsByState_surface by state. See rightsByState_ground for more details on how the data is formatted. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page.

Economics↗

CORPSE model with litter decomposition parameters derived from the LIDET dataset

This is a version of the CORPSE model (Carbon, Organisms, Rhizosphere and Protection in the Soil Environment, Sulman et al. 2014) that uses litter decomposition parameters derived from a modified Monte Carlo simulation using the LIDET litter decomposition dataset (Long-term Intersite Decomposition Experiment Team, Harmon 2013). The code also includes the Baseline parameters, and the eight other best parameter sets identified in a modified Monte Carlo simulation. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of files:The folder "Input Files" contains one folder for each LIDET site with data necessary to run the model. Note that "(site)" in the filenames below indicates where the LIDET site code appears (see Table 1 for site codes). Data streams include: CORPSE_full_spinup_litter.csv, CORPSE_full_spinup_rhizo.csv, CORPSE_full_spinup_bulk.csv, litterbag_init_100g_6spp.csv: initial C and N (kg C or N/m2) pool values for each soil layer, the litterbag_init_100_6spp.csv file is for the litterbag layer and is the same file for all sites. All initial C and N files have the same columns (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 soilT (site) DOY274start.csv: Average daily soil temperature (oC) interpolated from previously calculated monthly values used in DayCent LIDET simulations (Bonan et al., 2013). soilT (site) DOY274start.csv: Average daily soil volumetric water content (VWC) scalar interpolated from previously calculated monthly values used in DayCent LIDET simulations (Bonan et al., 2013). litter production.csv: Average daily litter production values for each site, data sources listed in Table S3 of related publication. litter (site) CN.csv: C:N ratio for each species from LIDET dataset (Table 2, Harmon 2013). (site).csv: Table indicating number of observations for each species decomposed at each site. Instructions: Save the model code ("CORPSE_LIDET.R") and "Input Files" folder in the same folder. Also make a folder for the model output (e.g., "results_Baseline") in the same folder. Set the working directory (setwd) in the model code to the folder with the files saved in step #1. Select the parameter set to use for the litter and litterbag compartments, comment out all other parameter sets. Run code. Output will be saved in the folder made in step 1. Output destination can be changed as necessary in code section called "Running the model." Table 1 LIDET sites and site codes used in model files. Site Code - Site AND - H.J. Andrews Experimental Forest BNZ - Bonanza Creek Experimental Forest BSF - Blodgett Research Forest CDR - Cedar Creek Natural History Area CPR - Central Plains Experimental Range HBR - Hubbard Brook Experimental Forest HFR - Harvard Forest JUN - Juneau KBS - Kellogg Biological Station KNZ - Konza Prairie Research Natural Area NWT - Niwot Ridge/Green Lakes Valley OLY - Olympic National Park OLY Conifer forest SEV - Sevilleta National Wildlife Refuge SMR - Santa Margarita Ecological Reserve UFL - University of Florida VCR - Virginia Coast Reserve Table 2 LIDET species and species codes used in model files (6 common species). Species - Species Code Sugar maple (Acer saccharum) - ACSA Drypetes (Drypetes glauca) - DRGL Red pine (Pinus resinosa) - PIRE Chestnut oak (Quercus prinus) - QUPR Western redcedar (Thuja plicata) - THPL Wheat (Triticum aestivum) - TRAE References:Bonan, G. B., Hartman, M. D., Parton, W. J., & Wieder, W. R. (2013). Evaluating litter decomposition in earth system models with long-term litterbag experiments: an example using the Community Land Model version 4 (CLM4). Global Change Biology, 19(3), 957-974. https://doi.org/https://doi.org/10.1111/gcb.12031 Harmon, M. (2013). LTER Intersite Fine Litter Decomposition Experiment (LIDET), 1990 to 2002. Long-Term Ecological Research. Forest Science Data Bank, Corvallis, OR. [Data set]. Accessed http://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023. https://doi.org/10.6073/pasta/f35f56bea52d78b6a1ecf1952b4889c5. Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E., & Pacala, S. W. (2014). Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nature Climate Change, 4, 1099 - 1102. https://doi.org/10.1038/nclimate2436

Juice, Stephanie↗

Analytic error analysis of cross section interpolation methods in nodal diffusion codes - I : Theory

This paper discusses two cross section interpolation methods commonly found in popular nodal codes; the partial derivatives and multiple tables models. The motivation for choosing a model, and thus a case matrix structure, is a trade off between accuracy and computational cost. Due to decades of experience, there are default structures that are sufficient for current light water reactor analysis. However, this is not necessarily the case for advanced reactor designs. Therefore, it is advantageous to understand the sources of error in cross section interpolation models so that the quality of a case matrix may be improved. A mathematical framework for these models is presented in this work that provides a more rigorous connection between the nuclear engineering field's cross section interpolation methods and the broader mathematical field of function approximation. The two cross section models examined in this paper were found to utilize Lagrange interpolation and are a subset of Lagrange tensor products. Classical results of Lagrange polynomial error analysis were then applied to the partial derivative and multiple tables models to derive expressions for the total point-wise error. The analytical results classify the total error into two parts: the model form error and interpolation error. Finally, based on our observations, a better foundation for improving the quality of a case matrix is proposed. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Projections of Declining Surface-Water Availability for the Southwestern United States

16 of the CMIP5 models had all the data needed for this work for at least one simulation that was continuous from 1950 to 2040. Details of the models analyzed here are provided in Table S1. The model data analyzed here are available at http://strega.ldeo.columbia.edu:81/expert/home/.naomi/.AR5/.v2/.historical:rcp85/.mmm16/ a. Assessing the climatology of the models Despite increases in horizontal resolution of many models compared to their CMIP3 counterparts none of these models can adequately resolve the topography of the south west United States, such as the Sierra Nevada and Rocky Mountains and the associated orographic precipitation. This requires that caution be used when interpreting the results presented here. To assess the ability of the models to simulate the current hydroclimate, in Figure S1 we show the observed (from the Global Precipitation Climatology Centre gridded rain gauge data, (1)) monthly climatology of precipitation and the same for all the models and the multimodel mean for the California-Nevada, Colorado headwaters and Texas regions. The GPCC data uses rain gauges only and interpolates to regular grids of which we used the 1◦ by 1◦ one. Details of the data set can be found in (2). While the models apparently overestimate precipitation in California and Nevada the seasonal cycle with wet winters and dry summers is very well represented. It is also possible that the rain gauge observations are biased low by inadequately sampling the higher mountain regions. How ever the models might also be expected to underestimate orographic precipitation due to inadequate horizontal resolution. The 25 models are also too wet in the Colorado headwaters region but correctly represent the quite even distribution though the year. The bimodal distribution of precipitation in Texas, with peaks in May and September, and the absolute amounts, are well modeled but with the September peak too weak. The positive precipitation bias translates into a positive runoff bias for the Colorado headwaters as also shown in Figure S1. Here the observed runoff values are taken from simulations of the Variable Infiltration Capacity (VIC) land surface-hydrology model (3) forced by observed meteorology (5) that were conducted as part of the North American Land Data Assimilation System project phase 2 ( (NLDAS-2), http://www.emc.ncep.noaa.gov/mmb/nldas/. Runoff for California-Nevada is better simulated but there is a positive bias over Texas despite no strong precipitation bias. To check whether regional climate models better simulate P and runoff in these regions we analyzed the historical simulation with the Regional Climate Model version 3 driven by the National Centers for Environmental Prediction-Department of Energy Reanalysis 2 available from the North American Regional Climate Change Assessment Program (http://www.narccap.ucar.edu). This model configuration retained these biases in P and runoff although they were reduced in amplitude. Given these varying biases we plot P and P − E changes in actual values but apply the simplest bias correction possible to the runoff and soil moisture values and show the modeled changes in terms of percentages of the 20th Century model climatologies. A thorough assessment of the simulation of North American climate in CMIP5 models is conducted in Sheffield at al. (North American Climate in CMIP5 Experiments. Part I: Evaluation of 20th Century Continental and Regional Climatology, manuscript submit ted to J. Climate, available at http://www.climate.noaa.gov/index.jsp?pg=./cpo pa/ mapp/cmip5 publications.html). Sheffield et al. analyze the climatology of precipitation, surface air temperature, low level winds, moisture fluxes, runoff etc. and conclude that the main features of the hydrological cycle, including characteristics of the atmospheric moisture balance and its seasonality, are captured in the CMP5 models subject to biases in total precipitation amounts. We chose to use all available models instead of selecting some and rejecting others based on an assessment of model realism. This is in accord with the suggestions of Mote et al. for CMIP3 (4) but future work needs to revisit this matter for the case of the CMIP5 ensemble.

Seager, Richard↗

HarDWR - Harmonized Water Rights Records

For a detailed description of the database of which this record is only one part, please see the HarDWR meta-record. Here we present a new dataset of western U.S. water rights records. This dataset provides consistent unique identifiers for each spatial unit of water management across the domain, unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of 7 broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of intersectoral water allocation changes through water markets (Grogan et al., in review). Specifically, the data were formatted for use as input to a process-based hydrologic model, WBM, with a water rights module (Grogan et al., in review). While this specific study motivated the development of the database presented here, U.S. west water management is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al, 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. west. The raw downloaded data for each state is described in Lisk et al. (in review), as well as here. The dataset is a series of various files organized by state sub-directories. The first two characters of each file name is the abbreviation for the state the in which the file contains data for. After the abbreviation is the text which describes the contents of the file. Here is each file type described in detail: XXFullHarmonizedRights.csv: A file of the combined groundwater and surface water records for each state. Essentially, this file is the merging of XXGroundwaterHarmonizedRights.csv and XXSurfaceWaterHarmonizedRights.csv by state. The column headers for each of this type of file are: state - The name of the state the data comes from. FIPS - The two-digit numeric state ID code. waterRightID - The unique identifying ID of the water right, the same identifier as its state uses. priorityDate - The priority date associated with the right. origWaterUse - The original stated water use(s) from the state. waterUse - The water use category under the unified use categories established here. source - Whether the right is for surface water or groundwater. basinNum - The alpha-numeric identifier of the WMA the record belongs to. CFS - The maximum flow of the allocation in cubic feet per second (ft3s-1). Arizona is unique among the states, as its surface and groundwater resources are managed with two different sets of boundaries. So, for Arizona, the basinNum column is missing and instead there are two columns: surBasinNum - The alpha-numeric identifier of the surface water WMA the record belongs to. grdBasinNum - The alpha-numeric identifier of the groundwater WMA the record belongs to. XXStatePOD.shp: A shapefile which identifies the location of the Points of Diversion for the state's water rights. It should be noted that not all water right records in XXFullHarmonizedRights.csv have coordinates, and therefore may be missing from this file. XXStatePOU.shp: A shapefile which contains the area(s) in which each water right is claimed to be used. Currently, only Idaho and Washington provided valid data to include within this file. XXGroundwaterHarmonizedRights.csv: A file which contains only harmonized groundwater rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. XXSurfaceWaterHarmonizedRights.csv: A file which contains only harmonized surface water rights collected from each state. See XXFullHarmonizedRights.csv for more details on how the data is formatted. Additionally, one file, stateWMALabels.csv, is not stored within a sub-directory. While we have referred to the spatial boundaries that each state uses to manage its water resources as WMAs, this term is not shared across all states. This file lists the proper name for each boundary set, by state. For those whom may be interested in exploring our code more in depth, we are also making available an internal data file for convenience. The file is in .RData format and contains everything described above as well as some minor additional objects used within the code calculating the cumulative curves. For completeness, here is a detailed description of the various objects which can be found within the .RData file: states: A character vector containing the state names for those states in which data was collected for. More importantly, the index of the state name is also the index in which that state's data can be found in the various following list objects. For example, if California is the third index in this object, the data for California will also be in the third index for each accompanying list. rightsByState_ground: A list of data frames with the cleaned ground water rights collected from each state. This object holds the the data that is exported to created the xxGroundwaterHarmonizedRights.csv files. rightsByState_surface: A list of data frames with the cleaned surface water rights collected from each state. This object holds the the data that is exported to created the xxSurfaceWaterHarmonizedRights.csv files. fullRightsRecs: A list of the combined groundwater and surface water records for each state. This object holds the the data that is exported to created the xxFullHarmonizedRights.csv files. projProj: The spatial projection used for map creation in the beginning of the project. Specifically, the World Geodetic System (WGS84) as a coordinate reference system (CRS) string in PROJ.4 format. wmaStateLabel: The name and/or abbreviation for what each state legally calls their WMAs. h2oUseByState: A list of spatial polygon data frames which contain the area(s) in which each water right is claimed to be used. It should be noted that not all water right records have a listed area(s) of use in this object. Currently, only Idaho and Washington provided valid data to be included in this object. h2oDivByState: A list of spatial points data frames which identifies the location of the Point of Diversion for the state's water rights. It should be noted that not all water right records have a listed Point of Diversion in this object. spatialWMAByState: A list of spatial polygon data frames which contain the spatial WMA boundaries for each state. The only data contained within the table are identifiers for each polygon. It is worth reiterating that Arizona is the only state in which the surface and groundwater WMA boundaries are not the same. wmaIDByState: A list which contains the unique ID values of the WMAs for each state. plottingDim: A character vector used to inform mapping functions for internal map making. Each state is classified as either "tall" or "wide", to maximize space on a typical 8x11 page. The code related to the creation of this dataset can be viewed within HarDWR GitHub Repository/dataHarmonization.

Economics↗

U-50Zr Microstructure and Property Assessment for LWR Applications

This project provides an initial assessment and research recommendations of U-50 wt.%Zr (U-50Zr) for its use as a light water reactor (LWR) fuel. This work based on the outcome of a recently funded exercise on the failure mode effects and analysis (FMEA) and the phenomena identification and ranking table (PIRT) to investigate high level nuclear fuel qualification metrics as derived by the United States Nuclear Regulatory Commission.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Physical properties of the satellites of Saturn

The photometric and bulk parameters of the known and suspected satellites of Saturn are presented in updated tables and are compared. The surface compositions of all the satellites are discussed in terms of modern photometry and spectroscopy; most if not all of the inner bodies have water frost surfaces, but the outer three satellites have surfaces of unknown composition. The few reliable mass values of some inner satellites, together with the best current values for the satellite radii, suggest mean densities representative of bulk compositions dominated by frozen volatiles, though Titan may have a substantial volume fraction of silicates. The special case of Iapetus is considered in the light of new studies of its two distinct faces and polar cap.

Cruikshank, D. P.↗

The surfaces and interiors of Saturn's satellites

The photometric and bulk parameters of the known and suspected satellites of Saturn are presented in updated tables and are compared. The surface compositions of all the satellites are discussed in terms of modern photometry and spectroscopy; most, if not all, of the inner bodies have water frost surfaces, but the outer three satellites have surfaces of unknown composition. The few reliable mass values of some inner satellites, together with the best current values for the satellite radii, suggest mean densities representative of bulk compositions dominated by frozen volatiles, though Titan may have a substantial volume fraction of silicates. The special case of Iapetus is considered in the light of new studies of its two distinct faces and polar cap.

Cruikshank, D. P.↗

Measurements of Humidity in the Atmosphere and Validation Experiments (Mohave, Mohave II): Results Overview

The Measurements of Humidity in the Atmosphere and Validation Experiments (MOHAVE, MOHAVE-II) inter-comparison campaigns took place at the Jet Propulsion Laboratory (JPL) Table Mountain Facility (TMF, 34.5(sup o)N) in October 2006 and 2007 respectively. Both campaigns aimed at evaluating the capability of three Raman lidars for the measurement of water vapor in the upper troposphere and lower stratosphere (UT/LS). During each campaign, more than 200 hours of lidar measurements were compared to balloon borne measurements obtained from 10 Cryogenic Frost-point Hygrometer (CFH) flights and over 50 Vaisala RS92 radiosonde flights. During MOHAVE, fluorescence in all three lidar receivers was identified, causing a significant wet bias above 10-12 km in the lidar profiles as compared to the CFH. All three lidars were reconfigured after MOHAVE, and no such bias was observed during the MOHAVE-II campaign. The lidar profiles agreed very well with the CFH up to 13-17 km altitude, where the lidar measurements become noise limited. The results from MOHAVE-II have shown that the water vapor Raman lidar will be an appropriate technique for the long-term monitoring of water vapor in the UT/LS given a slight increase in its power-aperture, as well as careful calibration.

lidar↗

The Effect of Temperature on the Preservation of Volatile-Rich Lunar Samples

Introduction. The Moon’s south pole is a high-priority target for human exploration and scientific study. This interest is, in part, due to the presence of Permanently Shadowed Regions (PSRs), which could contain high concentrations of unique volatiles at cryogenic temperatures [1]. Returned samples from PSRs may include a unique combination of rocks, regolith, and volatile species, providing unprecedented insights into the history of the Solar System and the potential for resource utilization on the Moon. However, because PSR samples are cryogenic up-on collection, lunar polar sample return will eventually require cold stowage for the journey from the Moon to Earth. Without cold stowage, PSR sample return will likely result in phase changes and chemical reactions within the volatile component of the sample, which would negatively impact the resulting scientific studies of those samples. This abstract summarizes the initial results from an ongoing characterization of analog PSR samples at a range of temperatures, with the goal of defining the temperatures needed for a flight cold stowage freezer. Background. Based on remote sensing observations of the Moon [2], south polar PSRs range in temperature from ~120K for small and/or shallow PSRs to ~20K at the most extreme locations in large, deep PSRs. A range of volatiles have been hypothesized to exist at the surface or subsurface of the lunar poles [3-5 and others]. This hypothesis was verified when the LCROSS mission impacted the <50-K PSR in the crater Cabeus, detecting a range of volatiles from water to low condensation temperature species such as H2S and methane [6]. Species such as H2S and ammonia (also detected by LCROSS) are also highly reactive, and increase the likelihood of chemical reactions at elevated (non-cryogenic) temperatures. At the Johnson Space Center’s Planetary Exploration and Astromaterials Research Laboratory (JSC-PEARL), we have developed a volatile-bearing lunar simulant that incorporates several of the species detected by LCROSS [Table 1] mixed cryogenically with the USGS Lunar Highlands Type (LHT) regolith simulant. The new volatile-regolith simulant will be used to assess the degree of sample alteration at room temperature, -20°C, -80°C, and -196°C (liquid nitro-gen), over a two-week period. Room temperature samples represent those likely to be returned during initial missions without cold stowage, -20°C provides an analog to Apollo cold curated samples, -80°C is the temperature of multiple flight payload freezers (e.g., MELFI), and -196°C is analogous to lunar PSRs. Two weeks is an approximation of the time between sample collection and Earth return for initial Artemis missions. Over this period of time, sample head-space gases will be analyzed using a Universal Gas Analyzer (UGA, a type of mass spectrometer) coupled with a Baratron pressure sensor. After testing, the regolith component of the simulant will be purged of volatiles and preserved for future electron beam and/or FTIR analysis. Experimental Procedure. Volatile-regolith simulants will be produced as an initial homogenous batch; this batch will then be distributed into aliquots (gas chromatography/GC vials or cryo vials), ensuring that each sample has the same starting composition and conditions [Fig. 1]. In addition to the “full” simulant shown in Table 1, less complex simulant compositions will be used as baseline and control samples [Table 2]. Aliquots will be produced in triplicate for each simulant composition, storage temperature, and date of sampling. Headspace gases in all Day 0 samples will be analyzed by the UGA immediately. Cold storage samples for future analytical days will be placed in freezers appropriate to their target temperatures (-20°C, -80°C, -196°C). For ambient-temperature samples, regolith and regolith-water samples will be stored in a fume hood, while the full simulant will be stored in a sealed Parr vessel for safety; no other simulants (RWCM/ RWCM+) will be stored at ambient temperature for this test. Samples will be analyzed by UGA in this manner on each Analysis Day outlined in Table 2. Analytical Data. The UGA measures the partial pressures in a single sample aliquot over a set mass range of 0-105 atomic mass units (AMU) [Figure 2]; this set range was selected to slightly exceed the mass of the highest-mass expected reaction product (H2SO4). The Baratron complements the UGA by measuring the total pressure in the headspace of a sample vial. Coupled together, the quantitative abundances of gases will be monitored throughout the test. UGA analyses of the triplicate samples for each storage temperature, day, and simulant composition will be averaged, and standard deviations for each will be calculated. The compositions of starting species (shown in Table 1) will be characterized as a function of time, and the presence of any new compounds (reaction products) will be monitored as well. Total pressures will be recorded for each sample analysis, and any samples that show signs of leakage (e.g., a significant reduction in pressure or simulant volatiles) will be discarded. Anticipated Results. Testing is planned to begin in January 2022. The resulting data will allow compositional and phase changes in the volatile component of the simulants to be determined. Both the reduction in initial compounds and the addition of reaction products are expected to be observed. In addition, the relative efficacy of the different temperatures at pre-serving the initial composition of the simulants will be quantified. Finally, the regolith component of each sample will be argon-purged and stored in a controlled environment for future laboratory analysis. Compositional and morphological changes in the regolith are expected for samples above 0°C. This test will be repeated three times over the course of 2022. Understanding the effect of temperature on both the volatile and regolith components of analog lunar materials will allow requirements for a cold stowage freezer to be developed. The implementation of cold stowage for lunar polar missions will maximize the preservation of returned samples, enabling ground-breaking lunar and Solar System volatiles science for decades to come.

J L Mitchell↗

A User Guide to PARET/ANL

PARET was originally created in 1969 at what is now Idaho National Laboratory (INL), to analyze reactivity insertion events in research and test reactor cores cooled by light or heavy water, with fuel composed of either plates or pins. The use of PARET is also appropriate for fuel assemblies with curved fuel plates when their radii of curvatures are large with respect to the fuel plate thickness. The PARET/ANL version of the code has been developed at Argonne National Laboratory (ANL) under the sponsorship of the U.S. Department of Energy/NNSA since the inception of the Reactor Conversion Program. Since Reduced Enrichment for Research and Test Reactors (RERTR) began in 1978, PARET/ANL has been benchmarked to experimental data including SPERT testing, and used to determine the expected transient behavior of a large number of reactors regardless of enrichment both inside and outside the Reactor Conversion Program. This document provides the pertinent information for the use of PARET/ANL Version 7.6. PARET/ANL models the various fueled regions of a reactor core as channels. Each of these channels consists of a single flat fuel plate/pin (including cladding and, optionally, a gap) with water coolant on each side. In slab geometry the coolant channels for a given fuel plate are of identical dimensions (mirror symmetry), but they can be of different thickness in each channel. There can be many channels, but each channel is independent and coupled only through reactivity feedback effects to the whole core. The time-dependent differential equations that represent the system are replaced by an equivalent set of finite-difference equations in space and time, which are integrated numerically. PARET/ANL uses fundamentally the same numerical scheme as RELAP5 for the time-integration of the point-kinetics equations. The one-dimensional thermal-hydraulic model includes temperature-dependent thermal properties of the solid materials, such as heat capacity and thermal conductivity, as well as the transient heat production and heat transfer from the fuel meat to the coolant. Temperature- and pressure-dependent thermal properties of the coolant such as enthalpy, density, thermal conductivity, and viscosity are also used in determining parameters such as friction factors and heat transfer coefficients. The code first determines the steady-state solution for the initial state. Then the solution of the transient is obtained by integration in time and space. Multiple heat transfer, DNB and flow instability correlations are available. The code was originally developed to model reactors cooled by an open loop, which was adequate for rapid transients in pool-type cores. An external loop model appropriate for Miniature Neutron Source Reactors (MNSR’s) was also added to PARET/ANL to model natural circulation within the vessel, heat transfer from the vessel to pool and heat loss by evaporation from the pool. PARET/ANL also contains models for decay heat after shutdown, control rod reactivity versus time or position, time-dependent pump flow, and loss-of-flow event with flow reversal as well as logic for trips on period, power, and flow. Feedback reactivity effects from coolant density changes and temperature changes are represented by tables. Feedback reactivity from fuel heat-up (Doppler Effect) is represented by a four-term polynomial in powers of fuel temperature. Photo-neutrons produced in beryllium or in heavy water may be included in the point-kinetics equations by using additional delayed neutron groups.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Assessment of BQ-9000 Biodiesel Properties for 2020 (CRADA CRD-15-593)

Biodiesel producers in the United States and Canada can voluntarily participate in the industry's BQ-9000 quality assurance program. This is the fourth in a series of reports documenting biodiesel quality from participating producers. Participants in the BQ-9000 program were requested to voluntarily provide data to a third-party team. This team anonymized and randomized the data prior to providing to the National Renewable Energy Laboratory (NREL) for analysis and reporting. The critical quality parameters analyzed are: sodium and potassium, calcium and magnesium, phosphorus, flash point and alcohol control, water and sediment, cloud point, acid number, free and total glycerin, monoglycerides, sulfur, oxidation stability, and cold soak filterability test (CSFT). The statistical analysis for these parameters is presented in Table ES-1.

09 BIOMASS FUELS↗

Output Data: gcam_usa_v5p3_im3

Output databases and tables supporting the publication: Khan, Z., Zhao, M., Ahsan, H., Wolfram, P., Vernon, C., Rice, J., Iyer, G., Binsted, M., Snyder, A., Graham, N., Kyle, P., 2023. Coevolution of future water, energy and land systems across the United States in response to national and global socioeconomic, climate, and energy policy drivers. (In progress) Earths Future, DOI: XXX with further details available at the meta-repository: https://immm-sfa.github.io/khan-etal_2023_im3gcamusa Folder: gcam_usa_im3_outputs (GCAM USA v 5.3 used for IM3 experiment Output Databases): database_rcp45cooler_ssp3.tar.gz database_rcp45cooler_ssp5.tar.gz database_rcp45hotter_ssp5.tar.gz database_rcp45hotter_ssp5.tar.gz database_rcp85cooler_ssp3.tar.gz database_rcp85cooler_ssp5.tar.gz database_rcp85hotter_ssp5.tar.gz database_rcp85hotter_ssp5.tar.gz Folder: gcam_usa_im3_gcamextractor_outputs (gcamextractor post-processing of the GCAM databases for specific downstream models ): dataGCAM_cerf.tar.gz (For CERF Model) dataGCAM_demeter.tar.gz (For Demeter model) dataGCAM_go.tar.gz (For GO model) gcamDataTable.tar.gz (All IM3 scenarios)

Climate Change↗