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Capture coefficient of highly directed flow of gaseous carbon dioxide and water vapor on cryogenic quartz crystal microbalance
Results of sustained-load environmental crack growth threshold tests performed on six spacecraft pressure vessel alloys are presented. The alloys were Inconel 718, 6Al-4V titanium, A-286 steel, AM-350 stainless steel, cryoformed AISI 301 stainless steel; and cryoformed AISI 304L steel. The test environments for the program were air, pressurized gases of hydrogen, oxygen, nitrogen, and carbon dioxide, and liquid environments of distilled water, sea water, nitrogen tetroxide, hydrazine, aerozine 50, monomethyl hydrazine, and hydrogen peroxide. Surface flaw type specimens were used with flaws located in both base metal and weld metal.
This UNT Senior design team was tasked by NASA to develop a variable conductance thermal radiator prototype for CO2 deposition for deep space transit. NASA selects university teams every year to partake in the X-HAB Academic Innovation Challenge, with this year’s number of teams being six. Air Revitalization is a crucial system for any space travel, be it for Low Earth Orbit, such as the International Space Station, or for deep space transit. Current systems, such as the Carbon Dioxide Removal Apparatus aboard the ISS, require upkeep and maintenance, which cannot be done on long distance space missions. For the past several years, NASA has done research on Cryogenic systems for Carbon Dioxide removal. These systems operate on the fact that Carbon Dioxide freezes at a higher temperature than Oxygen and Nitrogen, so Carbon Dioxide can be frozen out of the cabin atmosphere without the use of filters, which degrade over time. To cool the cabin air down to a temperature where Carbon Dioxide freezes, Stirling cryocoolers have been used, which have shown promise in the hope of Carbon Dioxide deposition for Cabin Air Revitalization. Cryogenic systems are much more reliable but require significant energy input to operate. Physical systems, such as radiators, have generally not been used for this task, as there is a need to be able to “turn off” the rejection of heat to allow the frozen carbon dioxide to be collected. However, with working fluids pumped through a physical radiator, that aspect of operation can be achieved. The goal of this challenge is to determine the effectiveness of a variable conductance thermal radiator that can reject heat to deep space, without the use of a dedicated cryocooler to remove energy from the cabin air. The proposed design uses piping, hot and cold working fluids, and non-condensable gas to absorbl heat from the cabin air on one side of the radiator and reject the heat to deep space by means of thermal radiation. As well, the system will allow for the recovery of deposited Carbon Dioxide. The UNT X-HAB 2021 team will create a model radiator and test its performance with simulated heat sources and sinks and extrapolate those data points to analyze for real world conditions.
To identify promising exoplanets for atmospheric characterization and to make the best use of observational data, a thorough understanding of their atmospheres is needed. 3D general circulation models (GCMs) are one of the most comprehensive tools available for this task and will be used to interpret observations of temperate rocky exoplanets. Due to parameterization choices made in GCMs, they can produce different results, even for the same planet. Employing four widely-used exoplanetary GCMs - ExoCAM, LMD-G, ROCKE-3D and the UM - we continue the TRAPPIST-1 Habitable Atmosphere Intercomparison by modeling aquaplanet climates of TRAPPIST-le with a moist atmosphere dominated by either nitrogen or carbon dioxide. Although the GCMs disagree on the details of the simulated regimes, they all predict a temperate climate with neither of the two cases pushed out of the habitable state. Nevertheless, the inter-model spread in the global mean surface temperature is non-negligible: 14 K and 24 K in the nitrogen and carbon dioxide dominated case, respectively. We find substantial inter-model differences in moist variables, with the smallest amount of clouds in LMDGeneric and the largest in ROCKE-3D. ExoCAM predicts the warmest climate for both cases and thus has the highest water vapor content and the largest amount and variability of cloud condensate. The UM tends to produce colder conditions, especially in the nitrogen-dominated case due to a strong negative cloud radiative effect on the day side of TRAPPIST-le. Our study highlights various biases of GCMs and emphasizes the importance of not relying solely on one model to understand exoplanet climates.
Rivers have a significant role in global carbon and nitrogen cycles, serving as a nexus for nutrient transport between terrestrial and marine ecosystems. Although rivers have a small global surface area, they contribute substantially to worldwide greenhouse gas emissions through microbially mediated processes within the river hyporheic zone. Despite this importance, research linking microbial and viral communities to specific biogeochemical reactions is still nascent in these sediment environments. To survey the metabolic potential and gene expression underpinning carbon and nitrogen biogeochemical cycling in river sediments, we collected an integrated data set of 33 metagenomes, metaproteomes, and paired metabolomes. We reconstructed over 500 microbial metagenome-assembled genomes (MAGs), which we dereplicated into 55 unique, nearly complete medium- and high-quality MAGs spanning 12 bacterial and archaeal phyla. We also reconstructed 2,482 viral genomic contigs, which were dereplicated into 111 viral MAGs (vMAGs) of >10 kb in size. As a result of integrating gene expression data with geochemical and metabolite data, we created a conceptual model that uncovered new roles for microorganisms in organic matter decomposition, carbon sequestration, nitrogen mineralization, nitrification, and denitrification. We show how these metabolic pathways, integrated through shared resource pools of ammonium, carbon dioxide, and inorganic nitrogen, could ultimately contribute to carbon dioxide and nitrous oxide fluxes from hyporheic sediments. Further, by linking viral MAGs to these active microbial hosts, we provide some of the first insights into viral modulation of river sediment carbon and nitrogen cycling.
Venusian and Martian nitrogen and carbon dioxide atmosphere as combustible medium for metallic powders, examining heat evolution per unit mass
Future long-duration missions will require a sustainable supply of food to support human crews. The spaceflight cabin environment often contains very high concentrations of CO 2 due to release of CO 2 by astronauts that is not completely scrubbed from the cabin, and it is therefore crucial to understand plant responses to elevated CO 2 (eCO 2 ) environments. Much focus has been given to changes in plant photosynthetic and performance parameters in response to eCO 2 , but the effects of eCO 2 on nitrogen (N) uptake are poorly understood. Shoot nitrate reduction may be reduced at eCO 2 , likely due to less reductant available for nitrate reduction because of reduced photorespiration and increased carbon fixation 1. Relative growth rate can be reduced at eCO 2 when N is provided only as nitrate and can be unaffected when N is supplied as ammonium. However, N uptake in response to eCO 2 has, to our knowledge, not yet been studied. An increased ‘preference’ for plants to take up N as ammonium at eCO 2 could have important implications for growth in the space environment, where N is currently only supplied as nitrate. In this study, novel stable isotope approaches were used in conjunction with hydroponics and isotope ratio mass spectrometry to determine the effect of eCO 2 on N preference for ammonium or nitrate in spring barley and lettuce when both N forms are provided equally. Several varieties of spring barley displayed increased ammonium preference at eCO 2 (720 ppm) compared to ambient CO 2 (410 ppm), though this was not true for all varieties. In most cases, increases in ammonium preference were driven by increases in ammonium uptake at eCO 2 and not decreases in nitrate uptake. Current research is assessing whether similar responses are observed in the candidate space crop lettuce, at levels of CO 2 like those observed on ISS (3000 ppm), and these results will also be presented. This work will enable the development of optimized nutrient regimes for candidate crops in space environments and the selection of crop varieties adapted to eCO 2 environments. Plants adapted to ammonium nutrition may play a role in future plant-based bioregenerative life support systems with higher ammonium concentrations due to waste recycling. Moreover, this research will further our understanding of plant responses to the eCO 2 environment brought about by climate change, allowing the development of future-proof crops that will help to maintain food security.
The present Mars atmosphere is relatively thin and cold. It is not at all like that which is presumed to have been responsible for the formation of valley networks and the heavy erosion of craters during the earliest epochs of martian history. An important goal of Mars exploration is to try to understand the properties of the early atmosphere, the initial inventory of volatiles at the planet's surface, the processes by which the atmosphere and climate have evolved over time, and the current location of volatiles presumed to have been in the atmosphere in the earlier times. The current status of understanding of the escape of volatiles to space over geologic time and the resulting fractionation of isotopes of stable atoms remaining in the atmosphere are described, and a scenario for volatile abundance and evolution that is consistent with the available information on the escape and fractionation of each species is constructed. In particular, the evolution of hydrogen, carbon, oxygen, and nitrogen, as contained in atmospheric (and non-atmospheric) water, carbon dioxide, and molecular nitrogen, is examined.
A FORTRAN IV subprogram called GASP is discussed which calculates the thermodynamic and transport properties for 10 pure fluids: parahydrogen, helium, neon, methane, nitrogen, carbon monoxide, oxygen, fluorine, argon, and carbon dioxide. The pressure range is generally from 0.1 to 400 atmospheres (to 100 atm for helium and to 1000 atm for hydrogen). The temperature ranges are from the triple point to 300 K for neon; to 500 K for carbon monoxide, oxygen, and fluorine; to 600 K for methane and nitrogen; to 1000 K for argon and carbon dioxide; to 2000 K for hydrogen; and from 6 to 500 K for helium. GASP accepts any two of pressure, temperature and density as input conditions along with pressure, and either entropy or enthalpy. The properties available in any combination as output include temperature, density, pressure, entropy, enthalpy, specific heats, sonic velocity, viscosity, thermal conductivity, and surface tension. The subprogram design is modular so that the user can choose only those subroutines necessary to the calculations.
Continuum source molecular beam to provide highly directed gas flows for determining capture coefficient of gaseous carbon dioxide and water vapor
Hydrogen, N, O, Kr methane and carbon dioxide gas chromatographic examination, discussing life detection device for Mars landing
Computer programs for calculation of thermodynamic and transport properties of eight cryogenic fluids
Effects of N level (15 to 30 mM), time of N increase (14 to 28 days after planting), and planting density (1163 to 2093 plants/m2) were determined for crop yield responses of dwarf, rapid-cycling brassica (Brassica napus L., CrGC 5-2, Genome: ACaacc). Crops were grown in solid-matrix hydroponic systems and under controlled-environment conditions, including nonsupplemented (ambient) or elevated CO2 concentrations (998 +/- 12 micromoles mol-1). The highest seed yield rate obtained (4.4 g m-2 day-1) occurred with the lowest N level (15 mM) applied at the latest treatment time (day 28). In all trials, CO2 enrichment reduced seed yield rate and harvest index by delaying the onset of flowering and senescence and stimulating vegetative shoot growth. The highest shoot biomass accumulation rate (55.5 g m-2 day-1) occurred with the highest N level (30 mM) applied at the earliest time (day 14). Seed oil content was not significantly affected by CO2 enrichment. Maximum seed oil content (30% to 34%, dry weight basis) was obtained using the lowest N level (15 mM) initiated at the latest treatment time (day 28). In general, an increase in seed oil content was accompanied by a decrease in seed protein. Seed carbohydrate, moisture, and ash contents did not vary significantly in response to experimental treatments. Effects of N level and time of N increase were consistently significant for most crop responses. Planting density was significant only under elevated CO2 conditions.
The International Civil Aviation Organization (ICAO), the U.S. Environmental Protection Agency (EPA), local environmental groups, and the public have become increasingly concerned over damage to local air quality from aircraft emissions and the impact of producing greenhouse gases. The NASA Glenn Research Center has been working to develop revolutionary technologies to minimize environmentally harmful engine emissions, such as nitrogen oxides, carbon dioxide, aerosols, and particulates. The two objectives of UEET are (1) to develop technologies to reduce nitrogen oxide (NOx) emissions by 70 percent below 1996 ICAO regulations and (2) to decrease carbon dioxide emissions (CO2) by dramatically increasing performance and efficiency. High temperature engine materials, ultra-low-NOx combustor designs, efficient, highly loaded turbomachinery, and propulsion-airframe integration analysis are technologies being developed at Glenn to meet these goals. Technology developed in the previous Advanced Subsonic Technology Program is being put into commercial production for large and regional aircraft to reduce NOx emissions 50 percent below 1996 ICAO regulations for landing and takeoff cycles. UEET will take the technology to the next quantum leap-reducing emissions to 70 percent below the ICAO regulations level. In addition, NASA-developed research will significantly reduce carbon monoxide, unburned hydrocarbons, and corresponding cruise NOx levels for the next generation of aircraft engines. Glenn's UEET research will be useful across the whole range of flight: subsonic, supersonic, and hypersonic. It will improve the subsonic transportation that the public depends on, contribute to supersonic commercial aircraft, improve military aircraft, and contribute to the design of a future hypersonic vehicle. These technologies are contributing to a better quality of life on Earth.
Aircraft operations need to meet the combined requirements of safety, efficiency, capacity and reduced environmental impact. Aircraft routes can be made efficient by flying wind optimal routes. However, the desire to reduce the impact of aviation emissions and contrails may result in trajectories, which deviate from wind optimal trajectories leading to extra fuel use. The lifetime associated with different emissions and contrails varies from a few hours to several hundred years. The impact of certain gases depends on the amount and location of the emission, and the decision-making horizon, in years, when the impact is estimated. The Absolute Global Temperature Potential (AGTP) is used as a metric to measure the combined effects of emissions and contrails. This paper extends earlier work by the authors to include the effect of oxides of nitrogen in the development of aircraft trajectories to reduce the combined effects of carbon dioxide, oxides of nitrogen (NOX) and contrails. The methodology is applied to air traffic in the continental US. The paper shows the trade-offs between reducing emissions and the cost of extra fuel using a fuel sensitivity index, defined as the reduction in AGTP per kg of fuel. The paper shows the performance of the optimization strategy for decision intervals of 10, 25 and 100 years. Based on the simplified models, the inclusion of NOX emissions has a slight influence on the minimal climate impact trajectories when the decision horizons are around 25 years.
Spectroscopic investigation of Venus atmosphere to determine nitrogen, oxygen, carbon dioxide, water vapor, and other components
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
This repository contains the code and data necessary to run the FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms, and Protection) model with litter decomposition parameters derived from a modified Monte Carlo simulation that used the Long-term Intersite Decomposition Experiment Team dataset. 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: FUNBioCROP_LIDET Study.Rmd R code with FUN-BioCROP model that can be run with 10 different sets of parameters for litter decomposition (Baseline, LIDET, or eight other best parameter sets identified in the modified Monte Carlo simulation. CORPSE Functions_Bioenergy_V2.R Code with CORPSE model functions, called by FUNBioCROP_LIDET Study.Rmd Model Input Data: bulk.csv, bulk_till.csv, rhizo.csv, rhizo_till.csv, litter.csv Initial C and N (kg C or N/m2) pool values for each soil compartment, final values from spin up. All five 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 Model Input Data: FluxTower_AvgSoilT.csv: Average daily soil temperature (oC) at 10 cm depth at University of Illinois Urbana-Champaign (UIUC) Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: FluxTower_AvgSoilVWC.csv: Average daily soil volumetric water content (VWC) at 10 cm depth at UIUC Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: input_CCS_LIDET Study.csv: This file has daily data to run FUN-BioCROP (Column - Description - Units): yr - calendar year - year doy - day of year (1 to 365) (no leap year) - day anpp - aboveground NPP (DayCent) - kg C/m2/day bnpp - belowground NPP (DayCent) - kg C/m2/day aglivc - live aboveground biomass carbon (DayCent) - kg C/m2 bglivcj - live juvenile fine root biomass carbon (DayCent) - kg C/m2 bglivcm - live mature fine root biomass carbon (DayCent) - kg C/m2 aglivn - live aboveground biomass nitrogen (DayCent) - kg N/m2 bglivnj - live juvenile fine root biomass nitrogen (DayCent) - kg N/m2 bglivnm - live mature fine root biomass nitrogen (DayCent) - kg N/m2 nyr - simulation year - year cult - indicates a cultivation event (0 or 1) crop - indicates a new crop (0 or 1) fert - indicates a fertilizer event (0 or 1) frst - indicates the first day of the growing season (0 or 1) harv - indicates a harvest event (0 or 1) last - indicates the end of the growing season (0 or 1) croptype - crop type (0=none; 1=alfalfa; 2=corn; 3=grass clover pasture; 4=soybean; 5=wheat) cropsrl - crop specific root length - mm/g root cultrhizmix - fraction of rhizosphere mixed with bulk soil during cultivation (0.0-1.0) - fraction cultlitmix - fraction of litter mixed with bulk soil during cultivation (0.0-1.0) - fraction harvremov - fraction of above ground biomass removed during harvest (0.0-1.0) - fraction fertamt - fertilization amount - g N/m2 lifehist - plant life history (0 = annual, 1 = perennial) froot_turnover_c - amount of C in fine root turnover - kg C/m2 froot_turnover_n - amount of N in fine root turnover - kg N/m2 agrd_turnover_c - amount of C in aboveground biomass turnover - kg C/m2 agrd_turnover_n - amount of N in aboveground biomass turnover - kg N/m2 leaf_litter_fastfrac - Fast decomposing fraction of leaf litter (0.0-1.0) - fraction root_litter_fastfrac - Fast decomposing fraction of root litter (0.0-1.0) - fraction root_diameter - root diameter - mm root_length - root length - mm root/m2 rhizo_frac - fraction of total soil volume that is rhizosphere (0.0 - 1.0) - fraction date - date in format YYYY-MM-DD Instructions: Save the model code ("FUN-BioCROP_LIDET Study.Rmd") and accompanything files (data streams and CORPSE function code) in the same folder. In model code "Chunk 3: Load CORPSE Data Streams" set the working directory (setwd) to the folder with the files saved in step #1. In "Chunk 5: Define LIDET parameter sets" select the litter decomposition parameter set to be used in the run, and comment out all other sets. If changing any parameter values, edit them in "Chunk 6: Load parameters." Run all chunks up to and including "Chunk 10: Prepare Data for Export." In "Chunk 11: Export Output Data" edit data frames for export and filenames, as necessary. "Chunk 12: Graph Total Soil C" makes a figure of C remaining over the model run period. Description of each model chunk (in file FUN-BioCROP_LIDET Study.Rmd): Chunk 1: Remove all functions, clear memory. Removes all functions from R environment, clears the memory. Chunk 2: Load Packages. Loads packages necessary to run the code. Chunk 3: Load CORPSE Data Streams. Sets the working directory and loads the data files necessary to run CORPSE. Chunk 4: Load CORPSE Functions. Loads the R script with CORPSE functions from the working directory, "CORPSE Functions_Bioenergy_V2.R". Chunk 5: Define LIDET parameter sets. Has ten different parameter sets for litter decomposition tested in this study: Baseline parameters, LIDET parameters, and the other 8 best performing parameter sets identified in the modified Monte Carlo. To run the model, all but one parameter set must be commented out. Chunk 6: Load Parameters. Loads all fixed parameters to run the model. Data frame with definitions of parameters is in the CORPSE function script "CORPSE Functions_Bioenergy_V2.R" Chunk 7: Prepare Data Streams. Takes data streams loaded in Chunk 3 and puts them in the format necessary to run the model. The model is coded to run at least two sites at a time, so if only one site is being run it must be run in duplicate. Individual data tables of daily values are created in this chunk from the input data file. Chunk 8: Set Initial Conditions. Creates data tables of soil C and N pools for each soil compartment (rhizo_till, rhizo, bulk_till, bulk, litter) and loads initial values into the data tables. Creates lists for each soil compartment to hold model output. Chunk 9: Load FUN Data and Set Up Matrices. Uses DayCent data to calculate FUN input data: root and leaf N demand, total N demand, plant CN, leaf N available for retranslocation, and litter production. Creates matrices for FUN model outputs. Chunk 10: Run Model. Runs the model. Chunk 11: Prepare Data for Export. Combines data from each day saved as lists into data frames for each soil compartment. Adds values from all soil compartments together to calculate total soil values, creates separate data frames for each soil C and N pool (e.g., protected slow C) for the total soil value. Adds different C and N pools together to calculate total soil C and N for all layers. Creates data frame of ratio of protected to unprotected SOC. Organizes FUN data for export. Chunk 12: Export Results. Exports CSV files of model results to the working directory. Chunk 13: Graph Total Soil C. Makes figure of C remaining over time. Related Links: Original FUN-BioCROP model: https://github.com/BrzostekEcologyLab/FUN-BioCROP LIDET dataset: https://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023