Search NASA⌕ Search

SEARCH · Search NASA

Results for “Input”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Optimization-based control using input convex neural networks

Input convex neural networks (ICNNs) are a family of deep learning models where the outputs are constructed to be convex functions of the inputs. By parameterizing system models using ICNNs, optimization-based control problems can be solved as convex optimization problems, leading to improved performance and robustness. This work proposes a novel framework where the control objective function and constraints are modelled using ICNNs. A case study of optimization-based control with output constraints is conducted on a process with input multiplicity and nonminimum phase behavior. The simulation results demonstrate improved economic yield compared with normal neural networks. Additionally, the input convexity formulation is compared with simple regularization techniques, and unique benefits such as improved data efficiency and robustness of the proposed formulation are shown. Finally, by explicitly incorporating prior knowledge about convexity, this framework provides a good balance between the universal approximation power of deep learning and computational feasibility required by control.

42 ENGINEERING↗
DOE OSTI2020

Calculating the divided differences of the exponential function by addition and removal of inputs

We introduce a method for calculating the divided differences of the exponential function by means of addition and removal of items from the input list to the function. Our technique exploits a new identity related to divided differences recently derived by F. Zivcovich. We show that upon adding an item to or removing an item from the input list of an already evaluated exponential, the re-evaluation of the divided differences can be done with only O(sn) floating point operations and O(sn) bytes of memory, where [z 0 ,...,z n ] are the inputs and s ∝ max i,j |z i – z j |. We demonstrate our algorithm’s ability to deal with input lists that are orders-of-magnitude longer than the maximal capacities of the current state-of-the-art. Here, we discuss in detail one practical application of our method: the efficient calculation of weights in the off-diagonal series expansion quantum Monte Carlo algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Relationship between laser energy input, microstructures and magnetic properties of selective laser melted Fe-6.9%wt Si soft magnets

Highlights: • Effect of laser energy input on magnetic properties of SLM Fe-6.9%wt.Si is described; • Magnetic properties can be described as function of as-built microstructure after stress-relief post-treatment; • Samples with density >99% and strong crystallographic texture along BD have the best magnetic properties; • At very high laser energy, a strong cube texture forms that deteriorates the magnetic properties. - Abstract: Selective Laser Melting (SLM), a powder-bed Additive Manufacturing technology, can be used in combination with high temperature post-annealing to produce high-silicon steel parts characterised by quasi-static magnetic properties comparable to those of commercial electrical steel. However, the role of the as-built microstructure on the magnetic properties is still unexplored. Therefore, in this study the effect of the energy input of the processing laser on the magnetic properties of the material is investigated. The magnetic properties are determined for 4 mm-high rings obtained using three laser energy input values that provide a good compromise in terms of porosity and crack formation. The best magnetic properties are obtained for the rings built using a value of energy input that produces a strong fibrous crystallographic texture, in which one of the crystallographic <001> axes is preferentially aligned along the build direction. Whether the magnetic properties change with sample direction as a result of the crystallographic texture will be the subject of future research.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization

Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application’s source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. Here, we implement our approach in the XSCOPE framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. XSCOPE is able to identify inputs that trigger exceptions in about 73% of the tested functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Getting to the root of the problem: Soil carbon and microbial responses to root inputs within a buried paleosol along an eroding hillslope in southwestern Nebraska, USA

Large quantities of soil carbon (C) can persist within paleosols for millennia due to burial and subsequent isolation from plant-derived inputs, atmospheric conditions, and microbial activity at the modern surface. Erosion exposes buried soils to modern root-derived C influx via root exudation and root turnover, thus stimulating microbial activity leading to SOC decomposition and accumulation through organo-mineral stabilization of modern C. With this study we aim to quantify how modern root-derived C inputs impact paleosol C decomposition and stabilization across varying degrees of isolation from modern surface conditions in southwestern Nebraska, USA, where hillslope erosion is bringing a buried Late-Pleistocene-early Holocene paleosol (the “Brady Soil”) closer to the modern surface. We collected Brady Soil samples from 0.2m, 0.4m, and 1.2m below the modern surface and conducted two lab-based incubations. Soils were amended with either (1) a lab-synthesized mixture of low molecular weight compounds (12 atom% 13 C), or (2) 13 C enriched root residues (92 atom% 13 C), in 30-day and 240-day incubation experiments, respectively. Here we determined microbial responses to synthetic root exudates and residues by partitioning the 13 C label from Brady Soil C, including measurements of total, root, and primed C respiration, microbial biomass C (MBC), microbial C use efficiency (CUE). To assess the capacity of isolated paleosols to accrue modern plant C, we used Nano-scale Secondary Ion Mass Spectrometry imaging. We found that: (1) adding root-derived C inputs primed Brady Soil C across all depths, and was mediated by depth and composition of root additions; (2) root-derived C inputs stimulated microbial biomass C (MBC) growth similarly across depths, but the magnitude of CUE and MBC varied by chemistry of root-derived additions; (3) new particulate organic matter was incorporated into mineral-associated pools over time; (4) material from the added root residues was found in association with bacterial cells and fungal hyphae as well as with soil aggregate and mineral surfaces. Our study shows that paleosols defy expectations of C content and reactivity with depth, and changes in land cover and climate will expose buried paleosols to modern surface conditions, increasing respired C. This work highlights the importance of evaluating the role resurfacing buried soils through landscape change plays in C cycle feedbacks to the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

SparcleQC: Automated Input File Creation for QM/MM Studies of Protein:Ligand Complexes

SparcleQC is a Python package that, given a protein:ligand complex in the Protein Data Bank (PDB) file format, can create quantum mechanics/molecular mechanics (QM/MM)-like input files for the electronic structure theory packages PSI4, QChem, and NWChem. The resulting input files include quantum mechanical representations of the ligand and a small section of the protein, surrounded by point charges that represent the rest of the protein. Creation of these QM/MM input files includes cutting and capping the QM subregion, obtaining point charges for the protein, and adjusting charges at the QM/MM boundary; and each of these tasks are automated by the software. In this article, we describe the details of SparcleQC’s procedure, show examples of the Python API, and explain additional features that are helpful in protein:ligand interaction studies. Finally, we show that SparcleQC enables automated preparation of input files for QM/MM calculations, which can return can return accurate interaction energies in minutes, while a fully quantum mechanical computation on the protein:ligand complex could take days, if it is even possible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Global nitrogen deposition inputs to cropland at national scale from 1961 to 2020

Nitrogen (N) deposition is a significant nutrient input to cropland and consequently important for the evaluation of N budgets and N use efficiency (NUE) at different scales and over time. However, the spatiotemporal coverage of N deposition measurements is limited globally, whereas modeled N deposition values carry uncertainties. Here, we reviewed existing methods and related data sources for quantifying N deposition inputs to crop production on a national scale. We utilized different data sources to estimate N deposition input to crop production at national scale and compared our estimates with 14 N budget datasets, as well as measured N deposition data from observation networks in 9 countries. We created four datasets of N deposition inputs on cropland during 1961–2020 for 236 countries. These products showed good agreement for the majority of countries and can be used in the modeling and assessment of NUE at national and global scales. One of the datasets is recommended for general use in regional to global N budget and NUE estimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An Investigation into High Voltage Spiral Generators Utilizing Thyristor Input Switches

High voltage nanosecond pulses are widely used in scientific research, but their wider adoption in industry requires compact, cost effective and easy to use generators to be developed. This paper presents modelling and experimental investigations into one method of producing such pulses – a spiral generator with a solid-state, thyristor-based input switch. It includes how the pulses are formed within the spiral, why a high speed input switch is required, how the geometry of the spiral dictates its output characteristics, and the effects of different loads. Using thyristors, often connected in series to increase the operating voltage of the spiral, enables the spiral generators to have low jitter, high repetition rate, and long lifetime. Modelling of the circuit used a combination of telegraph equations to account for the wave propagation along the spiral and a lumped circuit exchanging charge between the spiral, the input switch, and the load. The model is verified by detailed experimental results with the relative error being < 10% in most cases. Here, the output voltage pulse was often observed to have an initial peak of much lower magnitude than the subsequent peak(s) – which can only be fully explained by considering wave propagation effects. Lower input switch inductance, shorter switching time, larger mean diameter of the spiral, and increasing the width of the copper tape that makes up the spiral can all increase the voltage multiplication efficiency. Though increasing the number of turns that make up the spiral can increase the output voltage, it can also lower the multiplication efficiency. By understanding the effects of different geometries the spiral can be optimized to drive different loads - three applications of such spiral generators are then presented: pulses with 10 kV amplitude and 10 kHz repetition rate for driving DBD plasma, pulses with amplitude of 10 kV and 10 kV/ns rising rate for triggering of advanced solid state switches, and pulses with -50 kV amplitude and 50 ns rising time for triggering high current gas switches through field distortion.1 Index Terms—spiral generator, nanosecond pulses, telegraph equations, series-connected thyristor module, pulse forming process

42 ENGINEERING↗
DOE OSTI2025

Historical Land Management Alters New Soil Carbon Inputs by Annual and Perennial Bioenergy Crops

Bioenergy and bioproduct markets are expanding to meet demand for climate-friendly goods and services. Perennial biomass crops are particularly well suited for this goal because of their high yields, low input requirements, and potential to increase soil carbon (C). However, it is unclear how much C is allocated into belowground pools by perennial bioenergy crops and whether the belowground benefits vary with nitrogen (N) fertilizer inputs. Using in situ 13 C pulse-chase labeling, we tested whether the sterile perennial grass Miscanthus × giganteus (miscanthus) or annual maize transfers more photosynthetic C to belowground pools. The experiment took place at two sites in Central and Northwest (NW) Iowa with different management histories and two nitrogen (N) fertilizer rates (0 and 224 kg N ha −1 year −1 ) to determine if the fate of plant-derived soil C depends on soil fertility and crop type (perennial or annual). Maize allocated a greater percentage of total new 13 C to roots than miscanthus, but miscanthus had greater new 13 C in total and belowground plant biomass. We found strong interactions between site and most soil measurements—including new 13 C in mineral and particulate soil organic matter (SOM) pools—which appears to be driven by differences in historical fertilizer management. The NW Iowa site, with a history of manure inputs, had greater plant-available nutrients (phosphorus, potassium, and ammonium) in soils, and resulted in less 13 C from miscanthus in SOM pools compared to maize (approximately 64% less in POM and 70% less in MAOM). In more nutrient-limited soils (Central site), miscanthus transferred 4.5 times more 13 C than maize to the more stable mineral-associated SOM pool. Our results suggest that past management, including historical manure inputs that affect a site's soil fertility, can influence the net C benefits of bioenergy crops.

13C↗
DOE OSTI2024

Rhizosphere microbial community structure in high-producing, low-input switchgrass families

Switchgrass ( Panicum virgatum L.) is a native, low-input North American perennial crop primarily grown for bioenergy, livestock forage, and industrial fiber. To achieve no-input switchgrass production that meets biomass needs, several switchgrass genotypes have been identified that have a low or negative response to nitrogen fertilizer, i . e ., the biomass accumulation with added nitrogen is less than or equal to that when grown without nitrogen. In order to improve the viability of low-input switchgrass production, a more detailed understanding of the biogeochemical mechanisms active in these select genotypes is needed. 16S and ITS amplicon sequencing and qPCR of key functional genes were applied to switchgrass rhizospheres to elucidate microbial community structure in high-producing, no-input switchgrass families. Rhizosphere microbial community structure differed strongly between sites, and nitrogen responsiveness.

Stonoha-Arther, Christina (ORCID:0000000263964507)↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

User input verification and test driven development in the NJOY21 nuclear data processing code

Before physically-meaningful data can be used in nuclear simulation codes, the data must be interpreted and manipulated by a nuclear data processing code so as to extract the relevant quantities (e.g. cross sections and angular distributions). Perhaps the most popular and widely-trusted of these processing codes is NJOY, which has been developed and improved over the course of 10 major releases since its creation at Los Alamos National Laboratory in the mid-1970’s. The current phase of NJOY development is the creation of NJOY21, which will be a vast improvement from its predecessor, NJOY2016. Designed to be fast, intuitive, accessible, and capable of handling both established and modern formats of nuclear data, NJOY21 will address many issues that many NJOY users face, while remaining functional for those who prefer the existing format. Although early in its development, NJOY21 is quickly providing input validation to check user input. By providing rapid and helpful responses to users while writing input files, NJOY21 will prove to be more intuitive and easy to use than any of its predecessors. Furthermore, during its development, NJOY21 is subject to regular testing, such that its test coverage must strictly increase with the addition of any production code. This thorough testing will allow developers and NJOY users to establish confidence in NJOY21 as it gains functionality. This document serves as a discussion regarding the current state input checking and testing practices of NJOY21.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Physics Briefing Book [Input for the European Strategy for Particle Physics Update 2020]

The European Particle Physics Strategy Update (EPPSU) process takes a bottom-up approach, whereby the community is first invited to submit proposals (also called inputs) for projects that it would like to see realised in the near-term, mid-term and longer-term future. National inputs as well as inputs from National Laboratories are also an important element of the process. All these inputs are then reviewed by the Physics Preparatory Group (PPG), whose role is to organize a Symposium around the submitted ideas and to prepare a community discussion on the importance and merits of the various proposals. The results of these discussions are then concisely summarised in this Briefing Book, prepared by the Conveners, assisted by Scientific Secretaries, and with further contributions provided by the Contributors listed on the title page. This constitutes the basis for the considerations of the European Strategy Group (ESG), consisting of scientific delegates from CERN Member States, Associate Member States, directors of major European laboratories, representatives of various European organizations as well as invitees from outside the European Community. The ESG has the mission to formulate the European Strategy Update for the consideration and approval of the CERN Council.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Procedure for Creating, Reviewing, and Submitting Input Files into the Benchmark Library

Los Alamos National Laboratory (LANL) has a number of criticality benchmark input files, modeled for use with MCNP, developed and maintained by various organizations including the Nuclear Criticality Safety (NCS) Division, XCP-3, and XCP-5. The purpose of this procedure is to standardize input file creation, review, and maintenance. This procedure incorporates existing practices used by IRSN as documented in the original report and the translation from French in addition to practices used by the LANL NCS Division as documented in. The report relates to input files used for the validation of the continuous energy Monte Carlo code MCNP6 but is general enough to be extended to other codes. This report describes the procedure on how to prepare and use benchmarks (taken mainly from the ICSBEP Handbook) for the validation of calculation routes. In particular, this report describes the procedure to follow in the creation of the input files and their integration in the experimental validation databases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A Comparison of Adverse Meteorology Inputs for Emergency Management and Safety Basis Dispersion Modeling Applications at SRS

The MELCOR Accident Consequence Calculation System (MACCS), used for safety basis accident analyses at SRS, implements adjustment factors to consider the increased vertical turbulence associated with local surface roughness and the increased lateral turbulence associated with time-based plume meander. The two models used for SRS emergency planning radiological consequence assessment, i.e., Hotspot and Puff/Plume, currently do not have an option to input or apply diffusion adjustments for meander or surface roughness. Adverse meteorology conditions at SRS have been determined as E stability class and 1.3 meters per second wind speed for ground level releases. This is the input used for emergency planning models. Contrastingly safety basis analyses use a different input, representing adverse meteorology values as F stability class and wind speeds of 1.3 meters per second (for a ground level release). This results in an apparent discrepancy between the two approaches. In this report we document the differing modeling assumptions, discuss the logic behind each modeling approach used to represent local roughness and plume meander conditions, and numerically compare dispersion coefficients and concentration estimates obtained from each set of model inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Going under the hood of MontePy: A python API for MCNP input files [Slides]

MontePy is an open-source python software library for reading, editing, and writing MCNP input files. This presentation was given to a university research group interested in it. MontePy provides an object-oriented interface for working with these MCNP input files. It uses a lexer and parser system in order to create a concrete syntax tree representing the input from the input file.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Nutrient Inputs Stimulate Mercury Methylation by Syntrophs in a Subarctic Peatland

Climate change dramatically impacts Arctic and subarctic regions, inducing shifts in wetland nutrient regimes as a consequence of thawing permafrost. Altered hydrological regimes may drive changes in the dynamics of microbial mercury (Hg) methylation and bioavailability. Important knowledge gaps remain on the contribution of specific microbial groups to methylmercury (MeHg) production in wetlands of various trophic status. Here, we measured aqueous chemistry, potential methylation rates (k meth ), volatile fatty acid (VFA) dynamics in peat-soil incubations, and genetic potential for Hg methylation across a groundwater-driven nutrient gradient in an interior Alaskan fen. We tested the hypotheses that (1) nutrient inputs will result in increased methylation potentials, and (2) syntrophic interactions contribute to methylation in subarctic wetlands. We observed that concentrations of nutrients, total Hg, and MeHg, abundance of hgcA genes, and rates of methylation in peat incubations (k meth ) were highest near the groundwater input and declined downgradient. hgcA sequences near the input were closely related to those from sulfate-reducing bacteria (SRB), methanogens, and syntrophs. Hg methylation in peat incubations collected near the input source (FPF2) were impacted by the addition of sulfate and some metabolic inhibitors while those down-gradient (FPF5) were not. Sulfate amendment to FPF2 incubations had higher k meth relative to unamended controls despite no effect on k meth from addition of the sulfate reduction inhibitor molybdate. The addition of the methanogenic inhibitor BES (25 mM) led to the accumulation of VFAs, but unlike molybdate, it did not affect Hg methylation rates. Rather, the concurrent additions of BES and molybdate significantly decreased k meth , suggesting a role for interactions between SRB and methanogens in Hg methylation. The reduction in k meth with combined addition of BES and molybdate, and accumulation of VFA in peat incubations containing BES, and a high abundance of syntroph-related hgcAsequences in peat metagenomes provide evidence for MeHg production by microorganisms growing in syntrophy. Collectively the results suggest that wetland nutrient regimes influence the activity of Hg methylating microorganisms and, consequently, Hg methylation rates. Our results provide key information about microbial Hg methylation and methylating communities under nutrient conditions that are expected to become more common as permafrost soils thaw.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Energy Input Flux in the Global Quiet-Sun Corona

We present first results of a novel technique that provides, for the first time, constraints on the energy input flux at the coronal base ( r ∼ 1.025 R {sub ⊙}) of the quiet Sun at a global scale. By combining differential emission measure tomography of EUV images, with global models of the coronal magnetic field, we estimate the energy input flux at the coronal base that is required to maintain thermodynamically stable structures. The technique is described in detail and first applied to data provided by the Extreme Ultraviolet Imager instrument, on board the Solar TErrestrial RElations Observatory mission, and the Atmospheric Imaging Assembly instrument, on board the Solar Dynamics Observatory mission, for two solar rotations with different levels of activity. Our analysis indicates that the typical energy input flux at the coronal base of magnetic loops in the quiet Sun is in the range ∼0.5–2.0 × 10{sup 5} (erg s{sup −1} cm{sup −2}), depending on the structure size and level of activity. A large fraction of this energy input, or even its totality, could be accounted for by Alfvén waves, as shown by recent independent observational estimates derived from determinations of the non-thermal broadening of spectral lines in the coronal base of quiet-Sun regions. This new tomography product will be useful for the validation of coronal heating models in magnetohydrodinamic simulations of the global corona.

79 ASTRONOMY AND ASTROPHYSICS↗