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At least 1,027 records · Page 57

A Pacific Ocean general circulation model for satellite data assimilation

A tropical Pacific Ocean General Circulation Model (OGCM) to be used in satellite data assimilation studies is described. The transfer of the OGCM from a CYBER-205 at NOAA's Geophysical Fluid Dynamics Laboratory to a CRAY-2 at NASA's Ames Research Center is documented. Two 3-year model integrations from identical initial conditions but performed on those two computers are compared. The model simulations are very similar to each other, as expected, but the simulations performed with the higher-precision CRAY-2 is smoother than that with the lower-precision CYBER-205. The CYBER-205 and CRAY-2 use 32 and 64-bit mantissa arithmetic, respectively. The major features of the oceanic circulation in the tropical Pacific, namely the North Equatorial Current, the North Equatorial Countercurrent, the South Equatorial Current, and the Equatorial Undercurrent, are realistically produced and their seasonal cycles are described. The OGCM provides a powerful tool for study of tropical oceans and for the assimilation of satellite altimetry data.

Chao, Y.↗

Interpretation of NO(x)/NO(y) observations from AASE-2 using a model of chemistry along trajectories

In situ measurements of NO and NO(y) are used to derive the ratio NO(x)/NO(y) along the flight track of the NASA ER-2 aircraft. Data are presented for two flights at midlatitudes in October 1991 during the Airborne Arctic Stratospheric Expedition-2 (AASE-2). Aerosol particle surface area was concurrently measured. The observations are compared with a photochemical model integrated along back trajectories from the aircraft flight track. Comparison of observations with the model run along trajectories and at a fixed position clearly and quantitatively demonstrates the importance of an air parcel's dynamic history in interpretation of local chemical observations. Comparison of the data with model runs under different assumptions regarding heterogeneous chemistry further reinforces the case for occurrence of the reaction of N2O5 + H2O on sulfate aerosol surfaces in the atmosphere. Finally, comparisons for which relative changes in the model and the data are not consistent caution that our ability to resolve all the observations is not yet complete.

Kawa, S. R.↗

Distributed Simulation for Space Exploration

This viewgraph presentation reviews the use of simulation and modeling in preparation for the planned exploration initiatives. The Exploration Systems Mission Directorate (EMSD) Integrated Modeling and Simulation (IM&S) team strategy encompasses a wide spectrum of simulation and modeling policies and technologies. One prominent technology is distributed simulation. The DIstributed Simulation (DIS),a collaborative simulation project with international participation (US and Japan) is reviewed as an example of distributed simulation development. The Distributed Space Exploration Simulation (DSES) is another example of distributed simulation that is described

Crues, Edwin Z.↗

UAS Integration in the NAS Detect and Avoid Subproject: Kitty Hawk - NASA Collaboration Discussion

NASA is conducting research under the UAS Integration in the NAS Project to develop standards that will enable mid-size and large unmanned aircraft to fly unrestricted in the National Airspace System. As these efforts move into its second phase, NASA is planning a series of flight tests and demonstrations, integrating industry partners' technologies. These events will not only provide valuable data to inform the RTCA Special Committee 228 DAA and C2 MOPS, but also provide an opportunity for the UAS community to test their technologies in a realistic environment. An overview of NASA UAS-NAS research will be presented touching on human systems integration, modeling and simulation and guidance and control. Plans for Flight Test 6 and Systems Integration Operationalization (SIO) will also be presented. The purpose of this meeting is to share with Kitty Hawk, at a high level, UAS-NAS research and discuss potential future collaboration between NASA and Kitty Hawk.

Shively, Jay↗

Integration of the DAYCENT Biogeochemical Model within a Multi-Model Framework

Agricultural residues are the largest near term source of cellulosic 13 biomass for bioenergy production, but removing agricultural residues sustainably 14 requires considering the critical roles that residues play in the agronomic system. 15 Determining sustainable removal rates for agricultural residues has received 16 significant attention and integrated modeling strategies have been built to evaluate 17 sustainable removal rates considering soil erosion and organic matter constraints. 18 However the current integrated model does not quantitatively assess soil carbon 19 and long term crop yields impacts of residue removal. Furthermore the current 20 integrated model does not evaluate the greenhouse gas impacts of residue 21 removal, specifically N2O and CO2 gas fluxes from the soil surface. The DAYCENT 22 model simulates several important processes for determining agroecosystem 23 performance. These processes include daily Nitrogen-gas flux, daily carbon dioxide 24 flux from soil respiration, soil organic carbon and nitrogen, net primary productivity, 25 and daily water and nitrate leaching. Each of these processes is an indicator of 26 sustainability when evaluating emerging cellulosic biomass production systems for 27 bioenergy. A potentially vulnerable cellulosic biomass resource is agricultural 28 residues. This paper presents the integration of the DAYCENT model with the 29 existing integration framework modeling tool to investigate additional environment 30 impacts of agricultural residue removal. The integrated model is extended to 31 facilitate two-way coupling between DAYCENT and the existing framework. The 32 extended integrated model is applied to investigate additional environmental 33 impacts from a recent sustainable agricultural residue removal dataset. The 34 integrated model with DAYCENT finds some differences in sustainable removal 35 rates compared to previous results for a case study county in Iowa. The extended 36 integrated model with DAYCENT also predicts that long term yields will decrease.

09 BIOMASS FUELS↗

Dynamic Analysis of Electrical Power Grid Delivery: Using Prime Mover Engines to Balance Dynamic Wind Turbine Output

This paper presents an investigation into integrated wind + combustion engine high penetration electrical generation systems. Renewable generation systems are now a reality of electrical transmission. Unfortunately, many of these renewable energy supplies are stochastic and highly dynamic. Conversely, the existing national grid has been designed for steady state operation. The research team has developed an algorithm to investigate the feasibility and relative capability of a reciprocating internal combustion engine to directly integrate with wind generation in a tightly coupled Hybrid Energy System. Utilizing the Idaho National Laboratory developed Phoenix Model Integration Platform, the research team has coupled demand data with wind turbine generation data and the Aspen Custom Modeler reciprocating engine electrical generator model to investigate the capability of reciprocating engine electrical generation to balance stochastic renewable energy.

17 WIND ENERGY↗

Electrical Power Grid Delivery Dynamic Analysis: Using Prime Mover Engines to Balance Dynamic Wind Turbine Output

This paper presents an investigation into integrated wind + combustion engine high penetration electrical generation systems. Renewable generation systems are now a reality of electrical transmission. Unfortunately, many of these renewable energy supplies are stochastic and highly dynamic. Conversely, the existing national grid has been designed for steady state operation. The research team has developed an algorithm to investigate the feasibility and relative capability of a reciprocating internal combustion engine to directly integrate with wind generation in a tightly coupled Hybrid Energy System. Utilizing the Idaho National Laboratory developed Phoenix Model Integration Platform, the research team has coupled demand data with wind turbine generation data and the Aspen Custom Modeler reciprocating engine electrical generator model to investigate the capability of reciprocating engine electrical generation to balance stochastic renewable energy.

17 WIND ENERGY↗

Plasma Simulation Program

Many others in the fusion energy and advanced scientific computing communities participated in the development of this plan. The core planning team is grateful for their important contributions. This summary is meant as a quick overview the Fusion Simulation Program's (FSP's) purpose and intentions. There are several additional documents referenced within this one and all are supplemental or flow down from this Program Plan. The overall science goal of the DOE Office of Fusion Energy Sciences (FES) Fusion Simulation Program (FSP) is to develop predictive simulation capability for magnetically confined fusion plasmas at an unprecedented level of integration and fidelity. This will directly support and enable effective U.S. participation in International Thermonuclear Experimental Reactor (ITER) research and the overall mission of delivering practical fusion energy. The FSP will address a rich set of scientific issues together with experimental programs, producing validated integrated physics results. This is very well aligned with the mission of the ITER Organization to coordinate with its members the integrated modeling and control of fusion plasmas, including benchmarking and validation activities. [1]. Initial FSP research will focus on two critical Integrated Science Application (ISA) areas: ISA1, the plasma edge; and ISA2, whole device modeling (WDM) including disruption avoidance. The first of these problems involves the narrow plasma boundary layer and its complex interactions with the plasma core and the surrounding material wall. The second requires development of a computationally tractable, but comprehensive model that describes all equilibrium and dynamic processes at a sufficient level of detail to provide useful prediction of the temporal evolution of fusion plasma experiments. The initial driver for the whole device model will be prediction and avoidance of discharge-terminating disruptions, especially at high performance, which are a critical impediment to successful operation of machines like ITER. If disruptions prove unable to be avoided, their associated dynamics and effects will be addressed in the next phase of the FSP.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparison between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%-40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between - 12% and + 15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.

54 ENVIRONMENTAL SCIENCES↗

Basic Oxygen Furnace: Assessment of Recent Physicochemical Models

Modeling of the basic oxygen furnace (BOF) process, both for online monitoring and fundamental research, has gained importance in steelmaking industry over the past few decades. Especially models integrating fundamental physicochemical relations are appealing. Even though a vast amount of these kind of models and submodels can be found in the literature, no recent review paper is available which thoroughly discusses the most up-to-date BOF modeling methods. This study aims to do so. In the introductory chapters, an overview is given on the assumptions and models for underlying BOF phenomena, which are frequently used in the BOF models and submodels. Focus was put on six models with emphasis on the chemical aspect of the BOF process. For each model, its assumptions are given and subsequently evaluated, highlighting both their strengths and limitations. The six different models are also compared with each other. Finally, opportunities for future research are discussed.

36 MATERIALS SCIENCE↗

Reshaping, Intermixing, and Coarsening for Metallic Nanocrystals: Nonequilibrium Statistical Mechanical and Coarse-Grained Modeling

Self-assembly of supported 2D or 3D nanocrystals (NCs) by vacuum deposition and of 3D NCs by solution-phase synthesis (with possible subsequent transfer to a support) produces intrinsically nonequilibrium systems. Individual NCs can have far-from-equilibrium shapes and composition profiles. The free energy of NC ensembles is lowered by coarsening which can involve Ostwald ripening or Smoluchowski ripening (NC diffusion and coalescence). Preservation of individual NC structure and inhibition of coarsening are key, e.g., for avoiding catalyst degradation. This review focuses on postsynthesis evolution of metallic NCs. Atomistic-level modeling typically utilizes stochastic lattice-gas models to access appropriate time and length scales. However, predictive modeling requires incorporation of realistic rates for relaxation mechanisms, e.g., periphery diffusion and intermixing, in numerous local environments (rather than the use of generic prescriptions). Alternative coarse-grained modeling must also incorporate appropriate mechanisms and kinetics. At the level of individual NCs, we present analyses of reshaping, including sintering and pinch-off, and of compositional evolution in a vacuum environment. Here, we also discuss modeling of coarsening including diffusion and decay of individual NCs and unconventional coarsening processes. We describe high-level modeling integrated with scanning tunneling microscopy (STM) studies for supported 2D epitaxial nanoclusters and developments in modeling for 3D NCs motivated by in situ transmission electron microscopy (TEM) studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

3D-equivariant graph neural networks for protein model quality assessment

Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. We develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method—AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method—AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA.

59 BASIC BIOLOGICAL SCIENCES↗

Quasilocal charges and the generalized Gibbs ensemble in the Lieb-Liniger model

Here, we consider the construction of a generalized Gibbs ensemble composed of complete bases of conserved charges in the repulsive Lieb-Liniger model. We will show that it is possible to construct these bases with varying locality as well as demonstrating that such constructions are always possible provided one has in hand at least one complete basis set of charges. This procedure enables the construction of bases of charges that possess well-defined, finite expectation values given an arbitrary initial state. We demonstrate the use of these charges in the context of two different quantum quenches: a quench where the strength of the interactions in a one-dimensional gas is switched suddenly from zero to some finite value and the release of a one-dimensional cold atomic gas from a confining parabolic trap. While we focus on the Lieb-Liniger model in this paper, the principle of the construction of these charges applies to all integrable models, both in continuum and lattice form.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Hierarchical models of very large problems, dilemmas, prospects, and an agenda for the future

Interdisciplinary approaches to the modeling of global problems are discussed in terms of multilevel cooperation. A multilevel regionalized model of the Lake Erie Basin is analyzed along with a multilevel regionalized world modeling project. Other topics discussed include: a stratified model of interacting region in a world system, and the application of the model to the world food crisis in south Asia. Recommended research for future development of integrated models is included.

Richardson, J. M., Jr.↗

The Global Methane Budget 2000-2012

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (approximately biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modeling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations).For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH4 yr(exp -1), range 540-568. About 60 of global emissions are anthropogenic (range 50-65%). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 TgCH4 yr(exp -1), range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (approximately 64% of the global budget, less than 30deg N) as compared to mid (approximately 32%, 30-60deg N) and high northern latitudes (approximately 4%, 60-90deg N). Top-down inversions consistently infer lower emissions in China (approximately 58 TgCH4 yr(exp -1), range 51-72, minus14% ) and higher emissions in Africa (86 TgCH4 yr(exp -1), range 73-108, plus 19% ) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40% on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_ METHANE_BUDGET_2016_V1.1) and the Global Carbon Project.

Saunois, Marielle↗

Sea Ice Outlook for September 2017: June Report - NASA Global Modeling and Assimilation Office

The GMAO seasonal forecast is produced from coupled model integrations that are initialized every five days, with seven additional ensemble members generated by coupled model breeding and initialized on the date closest to the beginning of the month. The main components of the AOGCM are the GEOS-5 atmospheric model, the MOM4 ocean model, and CICE sea ice model. Forecast fields were re-gridded to the passive microwave grid for averaging.

Sea Ice↗

Sea Ice Outlook for September 2017 July Report - NASA Global Modeling and Assimilation Office

The GMAO seasonal forecast is produced from coupled model integrations that are initialized every five days, with seven additional ensemble members generated by coupled model breeding and initialized on the date closest to the beginning of the month. The main components of the AOGCM are the GEOS-5 atmospheric model, the MOM4 ocean model, and CICE sea ice model. Forecast fields were re-gridded to the passive microwave grid for averaging.

Arctic↗

Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of Escherichia coli Diauxic Growth

ABSTRACT Microbes have adapted to greatly variable environments in order to survive both short-term perturbations and permanent changes. A classical and yet still actively studied example of adaptation to dynamic environments is the diauxic shift of Escherichia coli , in which cells grow on glucose until its exhaustion and then transition to using previously secreted acetate. Here we tested different hypotheses concerning the nature of this transition by using dynamic metabolic modeling. To reach this goal, we developed an open source modeling framework integrating dynamic models (ordinary differential equation systems) with structural models (metabolic networks) which can take into account the behavior of multiple subpopulations and smooth flux transitions between time points. We used this framework to model the diauxic shift, first with a single E. coli model whose metabolic state represents the overall population average and then with a community of two subpopulations, each growing exclusively on one carbon source (glucose or acetate). After introduction of an environment-dependent transition function that determined the balance between subpopulations, our model generated predictions that are in strong agreement with published data. Our results thus support recent experimental evidence that diauxie, rather than a coordinated metabolic shift, would be the emergent pattern of individual cells differentiating for optimal growth on different substrates. This work offers a new perspective on the use of dynamic metabolic modeling to investigate population heterogeneity dynamics. The proposed approach can easily be applied to other biological systems composed of metabolically distinct, interconverting subpopulations and could be extended to include single-cell-level stochasticity. IMPORTANCE Escherichia coli diauxie is a fundamental example of metabolic adaptation, a phenomenon that is not yet completely understood. Further insight into this process can be achieved by integrating experimental and computational modeling methods. We present a dynamic metabolic modeling approach that captures diauxie as an emergent property of subpopulation dynamics in E. coli monocultures. Without fine-tuning the parameters of the E. coli core metabolic model, we achieved good agreement with published data. Our results suggest that single-organism metabolic models can only approximate the average metabolic state of a population, therefore offering a new perspective on the use of such modeling approaches. The open source modeling framework that we provide can be applied to model general subpopulation systems in more-complex environments and can be extended to include single-cell-level stochasticity.

59 BASIC BIOLOGICAL SCIENCES↗