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NASA NTRS2023

The Impact (Influence) of Stakeholder Participation in the Logic Modeling Process: A Case Study from NASA Goddard Space Flight Center

This is a three-paper dissertation that responds to a call for empirical research on stakeholder influence in the implementation of evaluation recommendations. The literature is consistent but mostly theoretical: stakeholder participation should positively influence and increase the probability of implementation (see e.g., Christie, 2011; Cousins and Earl, 1992); Fetterman, 2004). In response, I use two Goddard Space Flight Center engineering technical training and development programs to build a causal pathway argument supporting my proposition that stakeholder influence in the logic modeling process impacts organizational decision-making and change. I first introduce the logic modeling process as a tool to involve stakeholders in program and evaluation design (paper 1), then use the data collection process to implement the collaboratively constructed logic model outcomes (paper 2), and finally, I triangulate the data using the Toal (2009) Evaluation Involvement Scale (paper 3). I do this to “increase the validity of the findings” (Noble & Heale, 2019, p. 67) and to garner potential mechanisms. I found that stakeholder participation in the logic modeling process positively influenced the use of data to inform recommendations which in turn positively influenced implementation of evaluation recommendations. I identified the process itself as the mechanism and the key decision points as links or influencers. Potential factors included power and interest (measured by role) and time-on-job.

Organizational Decision-Making↗

DOE OSTI2016

Bottom-up holographic approach to QCD

One of the most known result of the string theory consists in the idea that some strongly coupled gauge theories may have a dual description in terms of a higher dimensional weakly coupled gravitational theory — the so-called AdS/CFT correspondence or gauge/gravity correspondence. The attempts to apply this idea to the real QCD are often referred to as “holographic QCD” or “AdS/QCD approach”. One of directions in this field is to start from the real QCD and guess a tentative dual higher dimensional weakly coupled field model following the principles of gauge/gravity correspondence. The ensuing phenomenology can be then developed and compared with experimental data and with various theoretical results. Such a bottom-up holographic approach turned out to be unexpectedly successful in many cases. In the given short review, the technical aspects of the bottom-up holographic approach to QCD are explained placing the main emphasis on the soft wall model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

DOE OSTI2019

Pathways to Carbon Liberation: A Systems Approach to Understanding Carbon Transformations and Losses from Thawing Permafrost

Our objective in this project was to discover how microbial communities mediate the fate of carbon in thawing permafrost under climate change. We proposed a systems approach integrating (a) molecular microbial and viral ecology, (b) molecular organic chemistry and stable and radiocarbon isotopes, and (c) state-of-the-art modeling, along a chronosequence of permafrost thaw in subarctic Sweden. The fate of carbon (C) in thawing permafrost is an outstanding challenge of modern biogeochemistry and climate change. Permafrost C pools are large (~1700 PgC), and C dynamics of thawing permafrost complex: old C decomposes as it is liberated from thawing permafrost as CO 2 or CH 4 , with a significant fraction cycling through lake sediments, even as new C accumulates due to thaw-initiated ecological succession. Our work is allowing better prediction of the net effect of these processes. Microbes mediate C dynamics in thawing permafrost, but a mechanistic understanding of how to scale microbial population dynamics, genomic potential, and expression to ecosystem-scale processes has been missing. A key question was: What is the interplay of microbial communities and organic matter chemical structure in the decomposition/preservation of organic C across a thaw gradient? And intriguingly, what (if any) is the role of phage (viruses that infect prokaryotic cells) in mediating these processes? Viruses appear to play a large role in driving oceanic function, but these phenomena are virtually unstudied in terrestrial systems. This endeavor linking microbial and viral dynamics, organic geochemistry and trace gas production will improve models of C cycling in permafrost systems, and clarify the fate of C under future climates. Technical Approach – We conducted a study at Stordalen Mire, Sweden, along a permafrost thaw chronosequence encompassing permafrost palsas and their initial collapse stage, thawing bog sites (dominated by Sphagnum moss), fully-thawed and inundated fen sites (dominated by Eriophorum spp or Carex spp.), and lakes. Our project used cutting edge technologies in both biogeochemistry and molecular microbial ecology to advance systems biology research on microbial carbon cycling through: (a) systems-level mapping of chemical states and ages of organic matter (via FT-ICR-MS and 14 C analysis) along thaw gradients to associated microbial communities, biochemical potentials, and activities (via meta-genomics, -transcriptomics, –proteomics, and viral genomics), and to CO 2 and CH 4 fluxes; (b) experimental incubations using Quantum Dot Probing, to test key hypotheses, arising from (a), about particular microbially-driven biochemical degradation pathways; (c) bioinformatics designed to simultaneously enhance the DOE Knowledgebase, and (d) integrated ecosystem C-cycle modeling testable by soil organic chemical and microbial data. Results from this project spanned 4 categories: (i) integrative across disciplines, and disciplinarily-focused in (ii) microbiology, (iii) biogeochemistry, and (iv) modeling. In integrated understanding of the system, we answered the questions posed above, mapping resident microbes to carbon transformations to reveal a carbon-processing shift with permafrost thaw, and discovering abundant, diverse and habitat-specific viruses that appear to be impacting carbon cycling, while also relating microbial communities to modeled processes. In microbiology-specific papers, we dug into viromes, microbial community networks, and informatic methods, while also introducing a new permafrost-associated phylum and its ecogenomic context. Relevance to DOE FOA - With a focus on discovering how microbial communities mediate the fate of old and new carbon in permafrost systems with climate change, our work directly responds to the call for “–omics driven basic research on the contribution of... microbial communities to C cycling processes in terrestrial ecosystems.” We bridge all three of the focal areas of the call, including (i) “Systems biology studies”, and development of –omics approaches (ii) “to investigate microbial community functional processes”, and (iii) “for imaging and analysis of microbially-mediated carbon cycling.”

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1971

A study of alternative methods for reclaiming oxygen from carbon dioxide and water by a solid-electrolyte process for spacecraft applications

Two alternative technical approaches were studied for application of an electrochemical process using a solid oxide electrolyte (zirconia stabilized by yttria or scandia) to oxygen reclamation from carbon dioxide and water, for spacecraft life support systems. Among the topics considered are the advisability of proceeding to engineering prototype development and fabrication of a full scale model for the system concept, the optimum choice of method or approach to be carried into prototype development, and the technical problem areas which exist.

Source record↗

DOE OSTI2023

Challenges and opportunities of machine learning control in building operations

Machine learning control (MLC) is a highly flexible and adaptable method that enables the design, modeling, tuning, and maintenance of building controllers to be more accurate, automated, flexible, and adaptable. The research topic of MLC in building energy systems is developing rapidly, but to our knowledge, no review has been published that specifically and systematically focuses on MLC for building energy systems. Here this paper provides a systematic review of MLC in building energy systems. We review technical papers in two major categories of applications of machine learning in building control: (1) building system and component modeling for control, and (2) control process learning. We identify MLC topics that have been well-studied and those that need further research in the field of building operation control. We also identify the gaps between the present and future application of MLC and predict future trends and opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1931

Effect of Turbulence in Wind-Tunnel Measurements

This paper gives some quantitative measurements of wind tunnel turbulence and its effect on the air resistance of spheres and airship models, measurements made possible by the hot wire anemometer and associated apparatus in its original form was described in Technical Report no. 320 and some modifications are presented in an appendix to the present paper. One important result of the investigation is a curve by means of which measurements of the air resistance of spheres can be interpreted to give the turbulence quantitatively. Another is the definite proof that the discrepancies in the results on the N. P. L. Standard airship models are due mainly to differences in the turbulences of the wind tunnels in which the tests were made. An attempt is made to interpret the observed results in terms of the boundary layer theory and for this purpose a brief account is given of the physical bases of this theory and of conceptions that have been obtained by analogy with the laws of flow in pipes.

Dryden, H L↗

DOE OSTI2020

Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area

Several companies, universities, and national laboratories are developing urban-scale energy modeling that allows the creation of a digital twin of buildings for the simulation and optimization of real-world, city-sized areas. Prior to simulation-based assessment, a baseline of savings for a set of utility-defined use cases was established to clarify the initial business case for specific energy efficient building technologies. In partnership with a municipal utility, 178,337 OpenStudio and EnergyPlus models of buildings in the utility’s 1400 km 2 service area were created, simulated, and assessed with measures for quantifying energy, demand, cost, and emissions reductions of each building. The method of construction and assumptions behind these models is discussed, definitions of example measures are provided, and distribution of savings across the building stock is provided under a maximum technical adoption scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

GeoVision Analysis: Exploration Task Force Report

The GeoVision study effort included ground-breaking, detailed research on current and future market conditions and geothermal technologies in order to forecast and quantify the electric and non-electric deployment potentials under a range of scenarios, in addition to their impacts on the Nation’s jobs, economy and environment. Coordinated by the U.S. Department of Energy’s (DOE’s) Geothermal Technologies Office (GTO), the GeoVision study development relied on the collection, modeling, and analysis of robust datasets through seven national laboratory partners, which were organized into eight technical Task Force groups. The purpose of this report is to provide a central repository for the research conducted by the Exploration Task Force. The Exploration Task Force consists of four individuals representing three national laboratories: Patrick Dobson (task lead) and Christine Doughty of Lawrence Berkeley National Laboratory, Anna Wall of National Renewable Energy Laboratory, Travis McLing of Idaho National Laboratory, and Chester Weiss of Sandia National Laboratories. As part of the GeoVision analysis, our team conducted extensive scientific and financial analyses on a number of topics related to current and future geothermal exploration methods. The GeoVision Exploration Task Force complements the drilling and resource technology investigations conducted as part of the Reservoir Maintenance and Development Task Force. The Exploration Task Force however has focused primarily on early stage R&D technologies in exploration and confirmation drilling, along with an evaluation of geothermal financing challenges and assumptions, and innovative “blue-sky” technologies. This research was used to develop geothermal resource supply curves (through the use of GETEM) for use in the ReEDS capacity expansion modeling that determines geothermal technology deployment potential. It also catalogues and explores the large array of early-stage R&D technologies with the potential to dramatically reduce exploration and geothermal development costs, forming the basis of the GeoVision Technology Improvement (TI) scenario. These modeling topics are covered in detail in Potential to Penetration task force report. Most of the research contained herein has been published in peer-reviewed papers or conference proceedings and are cited and referenced accordingly. The sections that follow provide a central repository for all of the research findings of the Exploration and Confirmation Task Force. In summary, it provides a comprehensive discussion of Engineered Geothermal Systems (EGS) and associated technology challenges, the risks and costs of conducting geothermal exploration, a review of existing government efforts to date in advancing early-stage R&D in both exploration and EGS technologies, as well as a discussion of promising and innovative technologies and implementation of blue-sky concepts that could significantly reduce costs, lower risks, and shorten the time needed to explore and develop geothermal resources of all types.

15 GEOTHERMAL ENERGY↗

DOE OSTI2019

Advanced Simulation Tools for Muon-Based Accelerators

The project developed new software tools incorporating the most accurate theoretical calculations and experimental data available for crucial and not-yet-considered physics processes specific to muon accelerators. These new tools were applied to modeling muon ionization cooling channels for a muon collider or a neutrino factory, the facilities that have the potential to discover and explore new exciting fundamental physics but require the development of demanding technologies and innovative concepts. G4beamline, COSY Infinity, and WARP computer codes are particularly well-suited for addressing muon accelerator modeling problems. G4beamline was chosen for its flexibility and modularity, and for being one of the de facto standard codes used by the muon accelerator community. COSY was selected for speed, ability to take into account high-order nonlinear effects essential for the large beams under consideration, and availability of built-in optimization tools. WARP was chosen for its space charge calculation capabilities, speed and flexibility of implementing new lattice elements such as RF cavities. This final technical report summarizes the efforts on implementing advanced new tools in these computer codes addressing the shortcomings in modeling muon physics processes and using these tools to model muon ionization cooling channels.

43 PARTICLE ACCELERATORS↗

NASA NTRS2008

Applied Space Systems Engineering: Manage Technical Data - Chapter 17

Effective space systems engineering (SSE) is conducted in a fully electronic manner. Competitive hardware, software, and system designs are created in a totally digital environment that enables rapid product design and manufacturing cycles, as well as a multitude of techniques such as modeling, simulation, and lean manufacturing that significantly reduce the lifecycle cost of systems. Because the SSE lifecycle depends on the digital environment, managing the enormous volumes of technical data needed to describe, build, deploy, and operate systems is a critical factor in the success of a project. This chapter presents the key aspects of Technical Data Management (TDM) within the SSE process. It is written from the perspective of the System Engineer tasked with establishing the TDM process and infrastructure for a major project. Additional perspectives are reflected from the point of view of the engineers on the project who work within the digital engineering environment established by the TDM toolset and infrastructure, and from the point of view of the contactors who interface via the TDM infrastructure. Table 17.1 lists the TDM process as it relates to SSE.

Kent, Peter↗

DOE OSTI2017

Ceria-based model catalysts: fundamental studies on the importance of the metal–ceria interface in CO oxidation, the water–gas shift, CO 2 hydrogenation, and methane and alcohol reforming

Model metal/ceria and ceria/metal catalysts have been shown to be excellent systems for studying fundamental phenomena linked to the operation of technical catalysts. In the last fifteen years, many combinations of well-defined systems involving different kinds of metals and ceria have been prepared and characterized using the modern techniques of surface science. So far most of the catalytic studies have been centered on a few reactions: CO oxidation, the hydrogenation of CO 2 , and the production of hydrogen through the water–gas shift reaction and the reforming of methane or alcohols. By using model catalysts it is been possible to examine in detail correlations between the structural, electronic and catalytic properties of ceria–metal interfaces. In situ techniques (X-ray photoelectron spectroscopy, X-ray absorption spectroscopy, infrared spectroscopy, scanning tunneling microscopy) have been combined to study the morphological changes under reaction conditions and investigate the evolution of active phases involved in the cleavage of C–O, C–H and C–C bonds. Several studies with model ceria catalysts have shown the importance of strong metal–support interactions. Generally, a substantial body of knowledge has been acquired and concepts have been developed for a more rational approach to the design of novel technical catalysts containing ceria.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Defense Waste Processing Facility Nitric- Glycolic Flowsheet Chemical Process Cell Chemistry: Part 2

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2021

Defense Waste Processing Facility Nitric-Glycolic Flowsheet Chemical Process Cell Chemistry: Part 2

The conversions of nitrite to nitrate, the destruction of glycolate, and the conversion of glycolate to formate and oxalate were modeled for the Nitric-Glycolic flowsheet using data from Chemical Process Cell (CPC) simulant runs conducted by Savannah River National Laboratory (SRNL) from 2011 to 2016. The goal of this work was to develop empirical correlation models to predict these values from measurable variables from the chemical process so that these quantities could be predicted a-priori from the sludge or simulant composition and measurable processing variables. The need for these predictions arises from the need to predict the REDuction/OXidation (REDOX) state of the glass from the Defense Waste Processing Facility (DWPF) melter. This report summarizes the work on these correlations based on the aforementioned data. Previous work on these correlations was documented in a technical report covering data from 2011-2015. This current report supersedes this previous report. Further refinement of the models as additional data are collected is recommended. The glass REDOX depends on the concentrations of nitrate and manganese (oxidants), and of glycolate, formate, oxalate, carbon, and antifoam (reductants) in the melter feed. The waste sludge contains nitrite, nitrate, manganese (Mn), and oxalate. Virtually all of the nitrite is converted to nitrate or NO+NO 2 +N 2 O gases in the CPC. The portion of the nitrite converted to nitrate increases the amount of nitrate in the sludge. The amount of glycolate in the final melter feed depends on the amount of the glycolic acid feed that is destroyed. Similarly, the amounts of formate and oxalate formed during the decomposition of glycolic acid are required. The material balance on carbon was found to not close in most cases. Generally, there was less carbon at the end of testing compared to the inputs. The most uncertain product variable was glycolate, so material balances were performed where the glycolate concentration was adjusted, usually upward, to close the balance. Correlation versus the original, as-measured, data was generally poor, but correlation against the material balance adjusted values was greatly improved. It was also shown that the correlation of the measured REDOX versus the predicted REDOX was much better when the material balance adjusted glycolate values were used. Three data series were primarily used during the regressions of the data; these series were 1) Sludge Batch 9 NG flowsheet simulant runs NG51-62 (SB9-NG); 2) Scaled Runs + Bounding Hydrogen Runs (SR+BH); and 3) Runs GN43-50 and 57 (43-50,57). The glycolate destruction was found to correlate with acid stoichiometry (AS), percent reducing acid (PRA), and for some data series, headspace to simulant volume ratio (HSV), mercury (Hg), and nitrate. Although glycolate destruction for pairs of data series (e.g., [SB9-NG] and [SR+BH]) were found to depend on HSV, the combination of all three data series was not found to have significant dependence on this variable. The best model for glycolate destruction depended on AS, nitrate, and Hg. This model predicted the product glycolate compositions of the data to within 92-106%. The conversion of glycolate to formate was high when noble metals and Hg were not present, with values up to 100%. When noble metals and Hg were present, this conversion ranged from zero to 7%, and was dependent on AS. Lower AS gave higher conversions to formate. The conversion to oxalate was found to depend on the AS and the initial concentration of nitrite. An alternative fit versus AS and the form of ruthenium (Ru) used is a possible alternative. This fit was somewhat less statistically significant. This second model predicts that more oxalate is formed when Ru-nitrosyl nitrate is used rather than Ru chloride. The conversion of glycolate to oxalate ranged from zero to 6%. The conversion of nitrite to nitrate depended primarily on AS and PRA, with HSV and Hg being significant when these variables were varied. For multiple series of data, nitrite was also needed to SRNL-STI-2017-00172 5HYLVLRQ viL distinguish between data series, and the effect of HSV became insignificant. The best model for nitrite to nitrate conversion depended on AS, PRA, nitrite, and Hg. The 95% confidence intervals on the predicted values of glycolate destruction, glycolate to oxalate conversion, and nitrite to nitrate conversion were used to determine the uncertainty in the predicted REDOX when starting with only the composition of the sludge, AS, and PRA. Using the 95% confidences on an individual value (that is the confidence in getting a particular value for one single test as opposed to what the mean would be for multiple tests), the uncertainty in the predicted REDOX was calculated. The uncertainty in the actual product composition glycolate, oxalate, formate, and nitrate concentrations translated to an uncertainty in the REDOX value of ±0.1,which is approximately the uncertainty claimed in the REDOX model itself.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

ONRAMP, ORNL Nuclear Resources Analysis and Modeling Portfolio

Many teams from US and international organizations, government, industry, research organizations, and academic institutions have established near-term technical exchanges and long-term partnerships with ORNL, realizing previously unforeseen innovations and efficiencies. Through ONRAMP, we invite you to learn more about the rich resources available at ORNL that can enhance your current approaches. Learn what is possible using customized, advanced techniques.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Final Technical and Scientific Report: Advanced Control of the Azura Wave Energy Device

The objective of the project was to develop a model predictive controller and associated hydraulic PTO for the Azura wave energy device to improve the annual energy production (AEP) and reduce the levelized cost of electricity (LCOE) of the device. Performance of the controller and hydraulic PTO were validated in a 1/15th scale wave tank test performed at the University of Maine. Three controllers, with three corresponding PTOs, were developed and tested as part of the project. The three controllers are an ideal MPC controller paired with an ideal PTO, an MPC controller with a heuristic paired with a directional force control hydraulic PTO, and a preliminary study of an MPC controller applied to a variable force hydraulic PTO. Experimental data was used to estimate the controller paired with the directional force control would increase the annual energy production of the Azura at WETS by 12%, with a corresponding reduction in LCOE of 10.8%. Preliminary testing of the variable force hydraulic PTO paired with a MPC shows this configuration has the potential to increase the AEP of the Azura at WETS by at least 33%. Further design and development work is necessary to develop the PTO technology to achieve these gains. Additionally, all control schemes require additional work to address the short-term wave prediction method needed to implement the control. In addition to validation of these control methods, NWEI demonstrated careful design of a wave tank test model, PTO, and instrumentation yields high quality test data, and the ability to test advanced control algorithms and include complex PTO dynamics via hardware-in-the-loop testing techniques.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Agricultural Impacts on Nitrogen Cycling: Climate and Air Pollution (Final Technical Report)

We have successfully added a parameterization of ammonia emissions from synthetic fertilizer and manure from agriculture to Earth System Models. We have evaluated how emissions change in the future and the processes driving these changes. We have examined the impact of agricultural ammonia emissions on present-day air quality and have evaluated the impact of resolving the emissions with high temporal resolution. We have also examined the impact of how Earth System Models treat the cycling of emissions in soils. Our code, the Flows of Agricultural Nitrogen – version 2 – model (FANv2) has been run in both the CLM framework and the ELM framework. To my knowledge the code has not yet been fully implemented into ELM. Volatilization of ammonia (NH3) from fertilizers and livestock wastes forms a significant pathway of nitrogen losses in agricultural ecosystems and constitutes the largest source of atmospheric emissions of NH3. We have provided a major update to the process model FAN (Flow of Agricultural Nitrogen), which evaluates NH3 emissions interactively within an Earth system model; in this work, the Community Earth System Model (CESM) is used. The updated version (FANv2) includes a more detailed treatment of both physical and agricultural processes, which allows the model to differentiate between the volatilization losses from animal housings, manure storage, grazed pastures, and the application of manure and different types of mineral fertilizers. The modeled ammonia emissions are first evaluated at a local scale against experimental data for various types of fertilizers and manure, and they are subsequently run globally to evaluate NH3 emissions for 2010–2015 based on gridded datasets of fertilizer use and livestock populations. Comparison of regional emissions shows that FANv2 agrees with previous inventories for North America and Europe and is within the range of previous inventories for China. However, due to higher NH3 emissions in Africa, India, and Latin America, the global emissions simulated by FANv2(48 Tg N) are 30 %–40 % higher than in the existing inventories.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Probing photosynthesis by altering chloroplast proteins (Final Technical Report)

The goal of this project was to explore the effect of altering the carbon fixing enzyme Rubisco in the model C3 plant tobacco on photosynthesis and plant growth and development. We introduced the gene sequences encoding Rubisco from a Limonium species and from a red alga into tobacco and found that the enzyme did not assemble properly, undoubtedly requiring additional assembly factors from the source organism. We then developed a method to make single amino-acid changes into the Rubisco subunit that is encoded by the chloroplast genome. We used the method to investigate the role of particular amino acid residues in kinetic properties of Rubisco. Finally, we improved a bacterial expression system that has allowed assembly of active tobacco Rubisco in E. coli. We used the improved system to investigate the kinetic properties of Rubisco enzymes that contain only one type of Rubisco small subunit along with the large subunit.

59 BASIC BIOLOGICAL SCIENCES↗