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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Analyses of Longitudinal Mode Combustion Instability in J-2X Gas Generator Development

The National Aeronautics and Space Administration (NASA) and Pratt & Whitney Rocketdyne are developing a liquid oxygen/liquid hydrogen rocket engine for future upper stage and trans-lunar applications. This engine, designated the J-2X, is a higher pressure, higher thrust variant of the Apollo-era J-2 engine. The contract for development was let to Pratt & Whitney Rocketdyne in 2006. Over the past several years, development of the gas generator for the J-2X engine has progressed through a variety of workhorse injector, chamber, and feed system configurations on the component test stand at the NASA Marshall Space Flight Center (MSFC). Several of the initial configurations resulted in combustion instability of the workhorse gas generator assembly at a frequency near the first longitudinal mode of the combustion chamber. In this paper, several aspects of these combustion instabilities are discussed, including injector, combustion chamber, feed system, and nozzle influences. To ensure elimination of the instabilities at the engine level, and to understand the stability margin, the gas generator system has been modeled at the NASA MSFC with two techniques, the Rocket Combustor Interaction Design and Analysis (ROCCID) code and a lumped-parameter MATLAB(TradeMark) model created as an alternative calculation to the ROCCID methodology. To correctly predict the instability characteristics of all the chamber and injector geometries and test conditions as a whole, several inputs to the submodels in ROCCID and the MATLAB(TradeMark) model were modified. Extensive sensitivity calculations were conducted to determine how to model and anchor a lumped-parameter injector response, and finite-element and acoustic analyses were conducted on several complicated combustion chamber geometries to determine how to model and anchor the chamber response. These modifications and their ramification for future stability analyses of this type are discussed.

Hulka, J. R.↗
NASA NTRS2015

Completing the Feedback Loop: The Impact of Chlorophyll Data Assimilation on the Ocean State

In anticipation of the integration of a full biochemical model into the next generation GMAO coupled system, an intermediate solution has been implemented to estimate the penetration depth (1Kd_PAR) of ocean radiation based on the chlorophyll concentration. The chlorophyll is modeled as a tracer with sources-sinks coming from the assimilation of MODIS chlorophyll data. Two experiments were conducted with the coupled ocean-atmosphere model. In the first, climatological values of Kpar were used. In the second, retrieved daily chlorophyll concentrations were assimilated and Kd_PAR was derived according to Morel et al (2007). No other data was assimilated to isolate the effects of the time-evolving chlorophyll field. The daily MODIS Kd_PAR product was used to validate the skill of the penetration depth estimation and the MERRA-OCEAN re-analysis was used as a benchmark to study the sensitivity of the upper ocean heat content and vertical temperature distribution to the chlorophyll input. In the experiment with daily chlorophyll data assimilation, the penetration depth was estimated more accurately, especially in the tropics. As a result, the temperature bias of the model was reduced. A notably robust albeit small (2-5 percent) improvement was found across the equatorial Pacific ocean, which is a critical region for seasonal to inter-annual prediction.

MERRA-OCEAN↗
NASA NTRS2017

Contribution of Sub-Mesoscales to the Vertical Velocity: The Omega-Equation

The oceans ability to regulate carbon dioxide depends on biogeochemical processes that are influenced strongly by eddies. Eddy-resolving simulations have shown that sub-mesoscales (SM) (110 km) generate the highest magnitude vertical velocity and that mesoscales (M) also enhance their contribution to the vertical velocity but to a lesser extent. In this study, we consider the question: can analogous results be obtained using the less numerically demanding -equation? Previously, this question has not been answered because of two reasons: 1) the canonical Hoskins form of the - equation does not include the buoyancy vertical fluxes caused by M and SM; and 2) Giordani et al. (2016) showed how to include an arbitrary vertical buoyancy flux, but no parameterizations were available for the M and SM vertical fluxes. How- ever, the latter are now available together with their assessments, so we consider SM because they make the largest contribution to the vertical velocity. The resulting vertical velocity depends on the extent of the SM regime, the horizontal buoyancy gradient (representing baroclinic instabilities), and the SM eddy kinetic energy. The vertical velocity depends in a linear manner on the wind stress and it may exhibit seasonal variations. The wind stress has two effects on the -equation: indirectly via its contribution to the sub-mesoscale buoyancy flux and directly through the wind stress itself. The results of our sensitivity analysis highlight the range of SM-induced vertical velocities obtained using different input data.

oceans↗
NASA NTRSDate not supplied

Thermal Modeling of a Novel Air-Cooled Temperature Swing Adsorption Compressor (AC-TSAC)

Contaminant removal technology, such as the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS), is critical to Environmental Close-Loop Life Support Systems (ECLSS), which enable humans to live and work in outer space. The CDRA includes an adsorbent bed, which absorbs CO2 and releases it to a Sabatier reactor for water production when thermally cycled. An effective system would heat and cool the adsorbent quickly and uniformly to maximize the amount of CO2 removed from the cabin. Air-Cooled Temperature Swing Adsorption Compressors (AC-TSAC) are promising because they are a simple alternative to more mechanically-complex compressors currently used on the ISS. The current investigation addresses the thermal modeling of potential AC-TSAC designs. Design trades include bed geometry (rectangular vs. cylindrical), bed structural material, and configuration of heat pipes within the bed. Also investigated is AC-TSAC performance sensitivity to the thermal conductivity of the adsorbent material and the heat pipes, the input power for heating, and various heat loss mechanisms. The results from these studies will inform the design of the next generation of the AC-TSAC, and the thermal modeling results will be validated through testing.

Hannah Alpert↗
DOE OSTI2018

UNCERTAINTY QUANTIFICATION OF CALCULATED FUEL TEMPERATURE FOR AGR-3/4 IRRADIATION EXPERIMENT

The third in a series of Advanced Gas Reactor (AGR) experiments conducted at the Idaho National Laboratory, AGR-3/4, was completed in April 2014, resulting in irradiation of the tristructural isotropic fuel for 369 effective full power days. A number of designed-to-fail fuel particles were embedded along the central line of each irradiated compact to provide a known source of fission products for subsequent transport through the compact matrix and structural graphite materials. The fuel temperature in each capsule during irradiation, a significant factor in the release and transport of fission products, was maintained within a predefined range representative of a high temperature gas reactor. Fuel temperatures were not measured directly because contact between a thermocouple and the fuel could lead to unwanted particle failures. An ABAQUS-based finite element heat transfer code, calibrated based on temperatures measured in the surrounding graphite rings, was used to predict fuel temperatures during irradiation. To quantify the uncertainty of calculated fuel temperatures, thermal model parameters with high sensitivity and/or large uncertainty were identified. Propagation of model parameter uncertainty and sensitivity is then used to quantify the overall uncertainty of calculated temperatures. Quantification of input uncertainties over the extended irradiation period of the AGR-3/4 experiment is a challenging task due to changes in capsule thermal properties and geometry caused by exposure to neutron irradiation at high temperatures. In the absence of direct measurements or complete knowledge about these changes, inputs to thermal model and associated uncertainties are estimated based on modeling assumptions and expert judgment. Unplanned events that occurred during irradiation contribute to input uncertainties as well. For example, shrinkage of fuel compact and graphite rings led to significant increase in the gap size uncertainties. This paper focuses on uncertainty quantification of calculated fuel temperature for AGR-3/4 capsule thermal models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Sensitivity and Uncertainty Quantification of Transition Scenario Simulations

This report documents the first collective attempt at developing and applying capabilities to quantify uncertainties, assess parametric sensitivities, and optimize multiple parameters and metrics in fuel cycle simulations generated by the SA&I Campaign. To do this, external codes that were designed to perform sensitivity analysis and uncertainty quantification (SA&UQ) needed to be coupled to the SA&I Campaign’s nuclear fuel cycle simulators (NFCS). In FY20, two approaches were pursued: 1) coupling Cyclus to an ORNL-internal code called MOT (Metaheuristic Optimization Tool) and 2) coupling DYMOND to the opensource SA&UQ tool kit Dakota. The primary objective of having these NFCS/SA&UQ coupled capabilities is to better inform DOE-NE and other stakeholders on the results generated from the NFCS. For a given set of fuel cycle strategies, policies, and technology assumptions that make up a fuel cycle scenario, these NFCS have traditionally been used by the SA&I Campaign to provide quantitative answers in terms of year-by-year mass flows, infrastructure requirements, costs, etc. With these newly developed coupled capabilities, the SA&I Campaign can now efficiently simulate hundreds or thousands of these scenarios, sample large ranges of parameters and assumptions, and use the unique features of the SA&UQ tools to process the data. This enables providing answers with known and propagated uncertainties, determining the sensitivity of important metrics to different parameters and assumptions, quantifying how much fuel cycle and technology parameters impact each other, and producing optimized fuel cycle strategies for single and multiple variables. To demonstrate these new capabilities, the Cyclus/MOT was used to model several scenarios ranging from simple fleet retirements to transitions to advanced reactors. Specifically, for a transition scenario from LWRs to SFRs and advanced LWRs, uncertainty quantification, sensitivity analysis, and optimization studies were applied to cases involving single and multiple parameter (input) and single and multiple metric (output) variations. In addition, a similar transition scenario was modeled to demonstrate how to optimize the reprocessing capacity parameter to minimize two performance metrics while taking into account uncertainties from two other parameters. Lastly, a depletion module based on SCALE/ORIGEN was added in Cyclus to simulate the third scenario that was designed to quantify the impact of the modeling assumption that all LWR used nuclear fuel have the same burnup. The newly developed DYMOND/Dakota capability was also applied to a transition scenario from the existing fleet to small modular reactors and fast reactors. This particular scenario involves not only explicit isotopic depletion via ORIGEN-2, but also includes multirecycling and utilizing the criticality search feature to determine the fresh fuel composition of recycled fuel, a feature unique to the DYMOND NFCS. A large database of simulations were run with 4 main parameters that were sampled: start date of reprocessing, reprocessing capacity, energy demand growth rate, and advanced reactor share of the fleet. The 4 main metrics were uranium consumption, enrichment requirements, waste generation, and levelized cost of electricity using data from the Cost Basis Report. The demonstrated SA&UQ results include those that inform on how to choose parameters to avoid “failed” scenarios, Sobol’ indices that inform on the importance of various parameters individually and synergistically, and Analysis of Variance (ANOVA) studies that decompose parameter ranges into groups and informs on whether variations are statistically significant.

Feng, B.↗
DOE OSTI2023

Validating Mixtures of 233 U, 235 U, and 239 Pu for the Sum-of-Fractions Method

The Sum-of-Fractions method is a technique used to assure that homogeneous mixtures of fissile and fissionable isotopes are below a minimum margin of k eff or reactivity. Current work by Pacific Northwest National Laboratory examines different mixtures of 233 U, 235 U, and 239 Pu to determine critical mass limits for mixtures of transuranic actinides lacking a validation basis. To provide a validation basis for these limits, the work presented here describes the results of a sensitivity and uncertainty analysis of various mixtures of these isotopes in various concentrations moderated and reflected by light water and polyethylene. The TSUNAMI-1D sequence in the SCALE code system was used to generate sensitivity coefficients for three different concentrations of mixtures of 233 U, 235 U, and 239 Pu. The TSUNAMI-IP sequence was then used for similarity assessment (c k ) with critical benchmark experiment sensitivity data files (SDFs) from the Oak Ridge National Laboratory Verified, Archived Library of Inputs and Data and the Nuclear Energy Agency SDF database. The VADER sequence in SCALE was used for statistical testing and to generate upper subcritical limits from the data to develop a basis for validating critical mass limits.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Tapsolver: A Python Package For The Simulation And Analysis Of Tap Reactor Experiments

TAPsolver is a python package, which automates TAP simulation and analysis routines. TAPsolver is built around the python packages FEniCS and Dolfin-Adjoint, which help take advantage of model adjoints to provide automatic derivatives. TAPsolver is flexible, with reaction mechanisms and rate constants that can be set through input files that allow users to take advantage of the different functionalities, which include sensitivity analyses, parameter optimization and uncertainty quantification.

Kunz, MatthewR.↗
DOE OSTI2023

Improved Modeling of Waste Isolation Pilot Plant Disposal Room Porosity

Disposal rooms at the Waste Isolation Pilot Plant (WIPP) contain waste and gas, and their porosity evolves over time. This report presents several improvements to the disposal room porosity model and presents new porosity predictions for use in future WIPP Performance Assessment activities. The improvements pertain to three sub-models: the geomechanical model, the waste compaction model, and the gas pressurization model. The impacts of each major improvement were quantified and the new porosity predictions were shown to be both mesh and domain size converged. Also, sensitivity studies on the disposal room horizon, clay seam friction coefficients, and homogenized waste representation were performed to support assumptions in the disposal room porosity model. To compare the legacy and new porosity predictions, the simulation results were plotted as a response surface, where gas pressure and time are inputs and porosity is the output. The new porosity response surface is insensitive to pressures beneath lithostatic pressure and highly sensitive to pressures above lithostatic pressure. The legacy porosity response surface, on the other hand, has moderate porosity gradients over all pressures. The new porosity response surface has a stronger scientific foundation than the legacy surface and may now be used for Compliance Decision Analyses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Silicon on-chip bandpass filters for the multiplexing of high sensitivity photonic crystal microcavity biosensors

A method for the dense integration of high sensitivity photonic crystal (PC) waveguide based biosensors is proposed and experimentally demonstrated on a silicon platform. By connecting an additional PC waveguide filter to a PC microcavity sensor in series, a transmission passband is created, containing the resonances of the PC microcavity for sensing purpose. With proper engineering of the passband, multiple high sensitivity PC microcavity sensors can be integrated into microarrays and be interrogated simultaneously between a single input and a single output port. The concept was demonstrated with a 2-channel L55 PC biosensor array containing PC waveguide filters. The experiment showed that the sensors on both channels can be monitored simultaneously from a single output spectrum. Less than 3 dB extra loss for the additional PC waveguide filter is observed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Development and application of two-step uncertainty propagation and sensitivity analysis methodology for fast reactor safety analysis

Uncertainty quantification (UQ) in nuclear reactors for transients is directly linked with safety assessment through the cross-sections uncertainties, provided as a covariance matrix, which are propagated through the reactor system to output of interest pertaining to reactor safety, such as peak temperatures in fuel/clad/coolant. Using a two-step approach, uncertainties are first quantified and propagated from basic input variables (such as reaction cross-sections) to intermediate quantities (such as reactivity feedback coefficients) through lattice level calculations. Uncertainties of intermediate quantities (from the first step) are then propagated through the system transient calculations, in the second step, to obtain uncertainties on reactor safety output parameters of interest. The scope of this work consists of Uncertainty Quantification & Propagation of nuclear data uncertainties that are highly correlated through unprotected transient overpower and unprotected loss of flow to assess their impact on core safety parameters. This two-step approach in the presence of covariance renders the sensitivity analysis very challenging. In fact, usually the sensitivity analysis is restricted to each step, which limits its application since the sensitivities between the system output quantities and the basic input variables are difficult to obtain. Here, in this work, we address this issue by proposing a simple, general methodology to combine the sensitivity indices obtained in each step by assuming the model behavior being linear. For the first step Generalized Perturbation theory based indices are used while in the second step the recently studied Johnson indices. The uncertainty quantification and sensitivity methodologies discussed here are demonstrated on a generic LFR design which is based on the 500 MWth demonstration Lead-cooled fast reactor (DLFR) using oxide fuel, developed by Westinghouse Electric Company (WEC).

42 - ENGINEERING↗
DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Quantum optimality of photon counting for temperature measurement of thermal astronomical sources

Using the quantum Cramér–Rao bound from quantum estimation theory, we derive a fundamental quantum limit on the sensitivity of a temperature measurement of a thermal astronomical source. This limit is expressed in terms of the source temperature T{sub s}, input spectral bandwidth Δν, and measurement duration T, subject to a long measurement time assumption TΔν≫1. It is valid for any measurement procedure that yields an unbiased estimate of the source temperature. The limit agrees with the sensitivity of direct detection or photon counting, and also with that of the ideal radiometer in the regime kT{sub s}/hν{sub 0}≫1 for which the Rayleigh–Jeans approximation is valid, where ν{sub 0} is the center frequency at which the radiometer operates. While valid across the electromagnetic spectrum, the limit is especially relevant for radio astronomy in this regime, since it implies that no ingenious design or technological improvement can beat an ideal radiometer for temperature measurement. In this connection, our result refutes the recent claim of a radio astronomy technique with much-improved sensitivity over the radiometer.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2019

Air Pollution Inputs to the Mojave Desert By Fusing Surface Mobile and Airborne in Situ and Airborne and Satellite Remote Sensing: A Case Study of Interbasin Transport With Numerical Model Validation

Deserts are fragile and highly sensitive ecosystems that increasingly are affected by upwind urban areas and industrial activities. The Los Angeles Basin (LAB) contributes to poor air quality in downwind deserts including the Mojave Desert. Few studies have investigated potential air pollution inputs to the Mojave, whose fragile ecosystem includes endangered plant and animal species. Data were collected on 19 August 2015 by a mobile air quality laboratory, AMOG (AutoMObile trace Gas) Surveyor, that observed inputs can arise from the LAB as well as the San Joaquin Valley (SJV), California. The campaign used a strong methane (CH4) plume as a tracer for the downwind fate of emissions from Bakersfield area petroleum production and also measured ozone (O3). Additional in situ concurrent airborne GHG and O3 data were collected by AJAX - Alpha Jet Atmospheric eXperiment. Both AMOG and AJAX measure winds. Mojave Desert air quality was very poor (visibility ~4 km). Based on the winds, an additional source was inferred beyond the LAB and SJV Basins. Numerical transport modeling and analysis of aerosol lidar data collected the same day by the Cloud Profiling LiDAR onboard the Earth Research-2 stratospheric airplane demonstrated that fires in Northern California were responsible, with prevailing winds transporting air southwards along the eastern Sierra Nevada Range (Bishop Valley) to the Mojave. Whereas the southern and eastern Mojave are impacted by SJV and LAB outflow, the north Mojave generally avoids these inputs. This study shows it can be affected by even distant wildfires, which likely will increase in occurrence and intensity from climate change. Thus, regulatory efforts to reduce air quality impacts on the endangered Mojave ecosystem must include wildfires and also account for the significant differences between different regions of the Mojave. Currently, there is a paucity of studies, highlighting the critical need for field research.

Ira Leifer↗
NASA NTRS1993

A Numerical Simulation Study to Develop an Acceptable Wake Encounter Boundary for a B737-100 Airplane

The National Aeronautics and Space Administration (NASA) is conducting research with the goal of enabling safe improvements in the capacity of the nation's air transportation system. The wake-vortex upset hazard is an important factor in establishing the minimum safe spacing between aircraft during landing and take-off operations, thus impacting airport capacity. A batch simulation study was conducted to assess the sensitivity of various safe landing criteria in the development of an acceptable wake encounter boundary. A baseline six-degree-of-freedom simulation of a B737-100 airplane was modified to include a wake model and the vortex-induced forces and moments. The guidance and control input for the airplane was provided by an auto-land system. The wake strength and encounter geometry were varied. A sensitivity study was also conducted to assess the effects of encounter modeling methods and accuracy.

Vicroy, Dan D.↗
NASA NTRS1968

Thermal short improves sensitivity of cryogenically cooled maser

In-line, quarter-wave thermal short cools the center conductor of the signal-input coaxial transmission line to a cryogenically cooled traveling wave maser. It reduces both the thermal noise contribution of the coaxial line and the heat leak through the center conductor to the maser at 4.4 degrees K.

Clauss, R. C.↗
NASA NTRSDate not supplied

Quantifying the Sensitivity of Condition Incidence Parameters in the Evidence Library

One approach to quantifying spaceflight risk at NASA makes use event driven probabilistic techniques. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is such a tool that estimates medical risk metrics via simulation and enables optimization of medical resources subject to mission constraints [1]. Previous analyses have informed medical set composition, exercise countermeasures, and water intake, where each analysis quantifies the risk associated with proposed variations in system design. As future mission profiles extend beyond Low-Earth Orbit (LEO) and lengthen in duration, understanding these risks and contributing factors is critical. MEDPRAT employs Monte Carlo sampling techniques to simulate missions and track the occurrence of medical events. These events follow fault-tree-like progressions through levels of severity and mitigation via medical treatment to many possible outcomes and these are reported throughout the mission. Making this possible, are the medical databases that contain evidence gathered by the Human Research Program (HRP). Quantifying the impact of uncertainty or variability in the input data is an important step in evaluating the credibility of modeling and simulation results. In this work, we investigate the sensitivity of medical risk metrics with respect to the condition incidence parameters within the Evidence Library (EL) [2] as the medical database input for MEDPRAT. The medical conditions, contained in the EL, are equipped with incidence rates that describe the likelihood that the condition will occur. These incidence rates reflect historical spaceflight data or when appropriate, terrestrial data. In this presentation, we will explore how uncertainty in these rates propagate to the medical risk described by MEDPRAT. These results identify the conditions and parameters with the largest contribution to medical risks.

Ian Lim↗
DOE OSTI2016

Climate Change Feedbacks from Interactions Between New and Old Carbon

Priming effects, or responses of SOM decomposition rates to inputs of new, labile carbon (C), have the potential to dramatically alter projections of ecosystem C storage. Priming effects occur in most ecosystems, are significant in magnitude, and are highly sensitive to global changes. Nevertheless, our mechanistic understanding of priming effects remains poor, and this has prevented the inclusion of these dynamics into current Earth system models (ESMs). We conducted two manipulative experiments in the field to quantify how priming effects influence SOM dynamics. Specifically, we asked: To what extent do inputs of “new” root-derived carbon (C) influence “older” C in SOM, and are the magnitude and direction of these effects sensitive to climate? We addressed these questions within the Boston-Area Climate Experiment - an old-field ecosystem that has been subjected to three precipitation treatments (ambient, -50%, and +50% of each precipitation event during the growing season) and four warming treatments (from ambient to +4°C) since 2008. In the first experiment, we installed root and fungal ingrowth cores into the plots. Each core was filled with SOM that had an isotopic signature (of its C compounds) that differed from the vegetation in the plots such that inputs of “new” C from roots/fungi could be quantified using the change in isotopic signatures of C in the cores. Further, we used cores with different mesh sizes to isolate root vs. mycorrhizal fungal inputs. We found that belowground C fluxes were dominated by root inputs (as opposed to mycorrhizal inputs), and that root-derived inputs were greatest in the plots subjected to experimental warming. Given that that the warming-induced increase in belowground C flux did not result in a net increase in soil C, we conclude that the warming treatment likely enhanced priming effects in these soils. In the second experiment, we experimentally dripped dissolved organic C compounds into soils in the BACE plots to simulate root-derived C fluxes. Specifically, we constructed artificial roots attached to an automated peristaltic pump to deliver the compounds to soil semi-continuously during the peak of the growing season. We found that changes in exudate quality had small but significant effects on microbial activities, often interacting with N availability and temperature-induced changes. These results further underscore the importance of priming effects, especially under warming conditions. Collectively, our results provide some of the first field-based estimates of how soil moisture and temperature can directly and indirectly alter root-induced changes in SOM dynamics. This exploratory project lays the groundwork for further research on priming that incorporates effects of plant species and microbial communities to global changes. Such information should enable the development of more mechanistic and better predictive models of SOM decomposition under increased greenhouse gas levels, with the ultimate goal of reducing the level of uncertainty in projections of future climate.

54 ENVIRONMENTAL SCIENCES↗