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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.

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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Learning Coagulation Processes With Combinatorial Neural Networks

Abstract Simulating the evolution of a coagulating aerosol or cloud of droplets in a key problem in atmospheric science. We present a proof of concept for modeling coagulation processes using a novel combinatorial neural network (CombNN) architecture. Using two types of data from a high‐detail particle‐resolved aerosol simulation, we show that CombNN models outperform standard neural networks and are competitive in accuracy with traditional state‐of‐the‐art sectional models. These CombNN models could have application in learning coarse‐grained coagulation models for multi‐species aerosols and for learning coagulation models from observed size‐distribution data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Predicting Correlation Coefficients for Monte Carlo Eigenvalue Simulations

Monte Carlo methods are most often considered as a reference for neutron transport simulations since very limited approximations are made about nuclear data and system geometry. To report uncertainty of any tally evaluated as generation averages, the sample variance is divided by the number of active generations, which is based on the assumption that the neutron generations are independent. Correlation effects between neutrons in multiplying systems, particularly when performing power iteration to evaluate eigenvalues have been observed in previous work. Neglecting the correlation effect results in an underestimate of uncertainty reported by Monte Carlo calculations. Previous work has also proposed methods to predict the underestimation ratio. Yamamoto et al expanded the fission source distribution with diffusion equation modes, performed numerical simulation of the AR (autoregressive) process of the expansion coefficients and used the correlation of the AR process to predict that of the Monte Carlo eigenvalue simulation. Sutton applied the discretized phase space (DPS) approach to predict the underestimation ratio but the method cannot predict the ratio when one neutron generates offspring in different phase space regions or generates a random number of offspring. This paper presents a method to predict the correlation effect with the model of multi-type branching processes (MBP). The method requires simulations for one generation of neutrons without knowing the source distribution and can predict the underestimation ratio for the cases where the traditional DPS approach does not work. The generation-to-generation correlation determines the convergence rate of active generations, the bias of variance estimator for each generation and the underestimation ratio of variance estimator for tallies averaged over active generations. The generation-to-generation correlation is characterized by the Auto-Correlation Coefficients (ACC) between tallies from different generations. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Distribution System Segmented Model Simplification with Independent Dynamically Changing End-Use Loads

With more emerging technologies in power distribution systems, to support quasi-static time-series (QSTS) simulation over short time frames, accurately modeling the dynamic behavior of end-use load under various conditions is im- portant. However, time and cost can be a burden for the QSTS simulation of thousands of houses representing dynamic load behaviors on large distribution feeders. There are two main contributions of this paper in order to reduce the QSTS simulation complexity. First, the paper presents a method to stochasti- cally model the dynamic behavior of end-use load for QSTS simulations. Second, using the stochastically modeled load behavior as an input to a distribution sys- tem, and building on our previous QSTS simulation simplification/acceleration method that relies on uniform load behavior, this paper presents a segment substitution approach for the simplification of distribution system models with independent dynamically changing end-use loads. Our numerical results show that compared to GridLAB-D, a software tool that simulates end-use load be- havior through physical models, the proposed stochastic modeling approach achieved an 89.3% simulation time reduction for end-use loads, while accurately capturing the temporally changing total load behavior. Moreover, the QSTS simulation of a large test distribution system through OpenDSS showed that the proposed simplification method decreased the feeder simulation time by a factor of three at less than 0.3% voltage error. Compared to our previous method with independent dynamic loads, the voltage error is reduced by a factor of four.

Power distribution system modeling, stochastic pro↗
DOE OSTI2022

Cooperative multivalent receptor binding promotes exposure of the SARS-CoV-2 fusion machinery core

The molecular events that permit the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to bind and enter cells are important to understand for both fundamental and therapeutic reasons. Spike proteins consist of S1 and S2 domains, which recognize angiotensin-converting enzyme 2 (ACE2) receptors and contain the viral fusion machinery, respectively. Ostensibly, the binding of spike trimers to ACE2 receptors promotes dissociation of the S1 domains and exposure of the fusion machinery, although the molecular details of this process have yet to be observed. We report the development of bottom-up coarse-grained (CG) models consistent with cryo-electron tomography data, and the use of CG molecular dynamics simulations to investigate viral binding and S2 core exposure. We show that spike trimers cooperatively bind to multiple ACE2 dimers at virion-cell interfaces in a manner distinct from binding between soluble proteins, which processively induces S1 dissociation. We also simulate possible variant behavior using perturbed CG models, and find that ACE2-induced S1 dissociation is primarily sensitive to conformational state populations and the extent of S1/S2 cleavage, rather than ACE2 binding affinity. These simulations reveal an important concerted interaction between spike trimers and ACE2 dimers that primes the virus for membrane fusion and entry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Coupled Process/Device Modeling and Point Defect Engineering of Cu(In,Ga)Se 2 Solar Cells

Point defects directly impact solar cell device performance by limiting the carrier lifetime. In this work, density functional theory calculations are first used to determine the formation energy and diffusion energy barriers of dominant defects in Cu(In,Ga)Se 2 . Next, continuum reaction–diffusion models are developed to analyze the redistribution of defects during manufacturing processes. We estimate defect capture cross sections using a first-principles-based approach. Here, these cross sections are combined with our calculated defect profiles and trap energy levels to parameterize a Shockley–Read–Hall recombination model, which we implement into a device simulator to predict carrier lifetimes and device performance. In that way, a predictive technology computer aided design model is built to predict and optimize the performance of Cu(In,Ga)Se 2 solar cells.

14 SOLAR ENERGY↗
DOE OSTI2019

Parameterization of Vertical Profiles of Governing Microphysical Parameters of Shallow Cumulus Cloud Ensembles Using LES with Bin Microphysics

Shallow convection is a subgrid process in cloud-resolving models for which their grid box is larger than the size of small cumulus clouds (Cu). At the same time such Cu substantially affect radiation properties and thermodynamic parameters of the low atmosphere. The main microphysical parameters used for calculation of radiative properties of Cu in cloud-resolving models are liquid water content (LWC), effective droplet radius, and cloud fraction (CF). In this study, these parameters of fields of small, warm Cu are calculated using large-eddy simulations (LESs) performed using the System for Atmospheric Modeling (SAM) with spectral bin microphysics. Despite the complexity of microphysical processes, several fundamental properties of Cu were found. First, despite the high variability of LWC and droplet concentration within clouds and between different clouds, the volume mean and effective radii per specific level vary only slightly. Second, the values of effective radius are close to those forming during adiabatic ascent of air parcels from cloud base. These findings allow for characterization of a cloud field by specific vertical profiles of effective radius and of mean liquid water content, which can be calculated using the theoretical profile of adiabatic liquid water content and the droplet concentration at cloud base. Using the results of these LESs, a simple parameterization of cloud-field-averaged vertical profiles of effective radius and of liquid water content is proposed for different aerosol and thermodynamic conditions. These profiles can be used for calculation of radiation properties of Cu fields in large-scale models. The role of adiabatic processes in the formation of microstructure of Cu is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

ACDC (Automated Campbell Diagram Code) [SWR-26-042]

This application provides a web-based graphical user interface to generating Campbell Diagrams and visualizing mode shapes for OpenFAST turbine models. Determining the aeroelastic stability and dynamic characteristics of wind turbines is a critical step in turbine design and analysis. Historically, extracting natural frequencies and mode shapes from OpenFAST—the industry-standard whole-turbine simulation code—has been a fragmented and tedious process. It required manual model configuration, command-line linearization execution, and complex post-processing via proprietary scripts to handle rotating-frame dynamics. To address these workflow bottlenecks, we present the Automated Campbell Diagram Code (ACDC), an open-source graphical software tool developed by the National Laboratory of the Rockies (NLR) under the DOE-funded Distributed Wind Aeroelastic Modeling (dWAM) project. ACDC streamlines the end-to-end linearization and stability analysis workflow into a single, intuitive cross-platform application. The software guides users through OpenFAST model configuration, definition of operating points, and the automated execution of steady-state trim and linearization simulations. Under the hood, ACDC automates the complex mathematical post-processing steps required for rotating systems, including Multi-Blade Coordinate (MBC) transformations, eigenanalysis, and advanced modal tracking utilizing the Modal Assurance Criterion (MAC) and spectral clustering. Finally, ACDC processes these results to automatically generate Campbell diagrams and features a robust 3D visualization engine to animate full-system mode shapes. By eliminating the reliance on external post-processing environments and manual data manipulation, ACDC significantly accelerates dynamic analysis and lowers the barrier to entry for wind energy researchers and engineers.

Summerville, Brent [National Laboratory of the Roc↗
DOE OSTI2016

Behavior of Bilayer Leaflets in Asymmetric Model Membranes: Atomistic Simulation Studies

Spatial organization within lipid bilayers is an important feature for a range of biological processes. Leaflet compositional asymmetry and lateral lipid organization are just two of the ways in which membrane structure appears to be more complex than initially postulated by the fluid mosaic model. This raises the question of how the phase behavior in one bilayer leaflet may affect the apposing leaflet and how one begins to construct asymmetric model systems to investigate these interleaflet interactions. In this paper, we report on all-atom molecular dynamics simulations (a total of 4.1 μs) of symmetric and asymmetric bilayer systems composed of liquid-ordered (Lo) or liquid-disordered (Ld) leaflets, based on the nanodomain-forming POPC/DSPC/cholesterol system. We begin by analyzing an asymmetric bilayer with leaflets derived from simulations of symmetric Lo and Ld bilayers. In this system, we observe that the properties of the Lo and Ld leaflets are similar to those of the Lo and Ld leaflets in corresponding symmetric systems. However, it is not obvious that mixing the equilibrium structures of their symmetric counterparts is the most appropriate way to construct asymmetric bilayers nor that these structures will manifest interleaflet couplings that lead to domain registry/antiregistry. We therefore constructed and simulated four additional asymmetric bilayer systems by systematically adding or removing lipids in the Ld leaflet to mimic potential density fluctuations. We find that the number of lipids in the Ld leaflet affects its own properties, as well as those of the apposing Lo leaflet. Collectively, the simulations reveal the presence of weak acyl chain interdigitation across bilayer leaflets, suggesting that interdigitation alone does not contribute significantly to the interleaflet coupling in nonphase-separated bilayers of this chemical composition. Finally, however, the properties of both leaflets appear to be sensitive to changes in in-plane lipid packing, possibly providing a mechanism for interleaflet coupling by modulating local density and/or curvature fluctuations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Incorporating the Acclimation of Photosynthesis and Leaf Respiration in the Noah‐MP Land Surface Model: Model Development and Evaluation

Abstract Realistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long‐term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C ( V cmax,25 ) and the leaf respiration rate at 25°C ( R 25 )) following increasingly well‐understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco‐Evolutionary Optimality (EEO)‐based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT‐specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5‐year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed R 25 and V cmax,25 vary substantially both temporally and spatially within the same PFT ( C.V. >20%). Our EEO‐based scheme captures 62% of the temporal and 70% of the spatial variations in V cmax,25 (73% and 54% of the variations in R 25 ). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread in r (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO 2 uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters.

Ren, Yanghang [Department of Earth System Science ↗
DOE OSTI2023

A Novel Algorithm for CAD to CSG Conversion in McCAD

Modeling and simulation lie at the heart of the design process of any nuclear application. An accurate representation of the radiation environment ensures not only the feasibility of new technologies, but it also aids in operation, maintenance, and even decommissioning. With increasingly complex designs, high-fidelity models have become a necessity for design maturity. McCAD has been under development for many years at Karlsruhe Institute of Technology (KIT) to facilitate the process of generating suitable models for nuclear analyses. In this paper, an overview of the major advances in the new version of the code is presented. A novel conversion algorithm has proven to be robust in significantly reducing the processing time to generate radiation transport models, making it easier to iterate on design details. A first-of-a-kind capability to generate hierarchical void cells is also discussed with preliminary analysis showing performance gains for particle tracking.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

A Simulation Framework for Battery Cell Impact Safety Modeling Using LS-DYNA

The development process of electrified vehicles can benefit significantly from computer-aided engineering tools that predict themultiphysics response of batteries during abusive events. A coupled structural, electrical, electrochemical, and thermal model framework has been developed within the commercially available LS-DYNA software. The finite element model leverages a three-dimensional mesh structure that fully resolves the unit cell components. The mechanical solver predicts the distributed stress and strain response with failure thresholds leading to the onset of an internal short circuit. In this implementation, an arbitrary compressive strain criterion is applied locally to each unit cell. A spatially distributed equivalent circuit model provides an empirical representation of the electrochemical responsewith minimal computational complexity.The thermalmodel provides state information to index the electrical model parameters, while simultaneously accepting irreversible and reversible sources of heat generation. The spatially distributed models of the electrical and thermal dynamics allow for the localization of current density and corresponding temperature response. The ability to predict the distributed thermal response of the cell as its stored energy is completely discharged through the short circuit enables an engineering safety assessment. A parametric analysis of an exemplary model is used to demonstrate the simulation capabilities.

25 ENERGY STORAGE↗
DOE OSTI2016

Analysis of non-equilibrium phenomena in inductively coupled plasma generators

This article addresses the modeling of non-equilibrium phenomena in inductively coupled plasma discharges. In the proposed computational model, the electromagnetic induction equation is solved together with the set of Navier-Stokes equations in order to compute the electromagnetic and flow fields, accounting for their mutual interaction. Semi-classical statistical thermodynamics is used to determine the plasma thermodynamic properties, while transport properties are obtained from kinetic principles, with the method of Chapman and Enskog. Particle ambipolar diffusive fluxes are found by solving the Stefan-Maxwell equations with a simple iterative method. Two physico-mathematical formulations are used to model the chemical reaction processes: (1) A Local Thermodynamics Equilibrium (LTE) formulation and (2) a thermo-chemical non-equilibrium (TCNEQ) formulation. In the TCNEQ model, thermal non-equilibrium between the translational energy mode of the gas and the vibrational energy mode of individual molecules is accounted for. The electronic states of the chemical species are assumed in equilibrium with the vibrational temperature, whereas the rotational energy mode is assumed to be equilibrated with translation. Three different physical models are used to account for the coupling of chemistry and energy transfer processes. Numerical simulations obtained with the LTE and TCNEQ formulations are used to characterize the extent of non-equilibrium of the flow inside the Plasmatron facility at the von Karman Institute. Each model was tested using different kinetic mechanisms to assess the sensitivity of the results to variations in the reaction parameters. A comparison of temperatures and composition profiles at the outlet of the torch demonstrates that the flow is in non-equilibrium for operating conditions characterized by pressures below 30 000 Pa, frequency 0.37 MHz, input power 80 kW, and mass flow 8 g/s.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Application of a temporal multiscale method for efficient simulation of degradation in PEM Water Electrolysis under dynamic operating conditions

Hydrogen is emerging as a vital energy carrier, driven by the need to reduce carbon emissions. Proton Electrolyte Membrane Water Electrolysis (PEMWE) enables hydrogen production under fluctuating renewable power conditions but requires improved understanding and stability of the anode catalyst layer under dynamic operating conditions, especially with low noble metal loadings. Long-term degradation experiments are both time-consuming and costly; therefore, a systematic, model-aided approach is essential. In the present work, a temporal multiscale method is applied to reduce the computational effort of simulating long-term degradation processes in PEMWE, with an exemplary focus on catalyst dissolution. A mechanistic model incorporating the oxygen evolution reaction, catalyst dissolution, and hydrogen permeation from the cathode to the anode was hypothesized and implemented. In this way, the local periodicity of transport and reaction processes in dynamic PEMWE operation, which influence the gradual degradation of the catalyst layer, is captured. The temporal multiscale method significantly reduces the computational effort of simulation, decreasing processing time from hours to mere minutes. This efficiency gain is attributed to the limited evolution of Slow-Scale variables during each period of time P of the Fast-Scale variables. Consequently, simulation is required only until local periodicity is achieved within each Slow-Scale time step. Hence, the fully resolved dynamic problem is decoupled into these two scales, employing a heterogeneous multiscale technique. The developed approach effectively accelerates parameter estimation and predictive simulations, supporting systematic modeling of PEMWE degradation under dynamic conditions.

08 HYDROGEN↗
DOE OSTI2026

A Generalized Grain-Scale Model for the Non-Plasma and Plasma-Assisted Hydrogen Direct Reduction of Iron Ore

Direct Reduction of Iron ore using hydrogen (H-DRI) is a promising pathway towards efficient steelmaking and accurate predictive models are a necessity for scale-up and optimization of this technology. However, accurate models of this process remain limited because existing models oversimplify grain-scale phenomena, such as nonlinearity inside grain, self-sufficient porosity, surface reactions, and the role of plasma species. These phenomena are important for flash steelmaking and plasma-assisted H-DRI processes. To address this need, we present a phenomenological model for simulating H-DRI at the scale of a single micron-sized grain of the iron ore. We call this the Transient Reactive Grain Model (TRGM). TRGM incorporates key physical process: gas species transport, a chemical kinetics of material conversion, nanopore structural evolution and, adsorption-desorption surface kinetics at the reactive nanopore surface. The important contribution of this work is that the model provides a dependence on different reductant species, specifically hydrogen atoms versus molecules, so that role of hydrogen plasma reduction can be clarified compared to the use of pure hydrogen gas reduction. TRGM predictions agree well with experimental data for both molecular H2 reduction of Fe2O3 and plasma hydrogen reduction of Fe3O4. Results reveal species concentration gradients with a diffuse reaction zone, and enhanced hydrogen diffusion at the grain outer surface due to evolving porosity. These findings challenge common assumptions in existing models, including sharp reaction fronts, quasi-steady diffusion and kinetics, and the neglect of surface chemistry. As a generalized grain-scale model for H-DRI processes, TRGM has practical applications in flash steelmaking and in-flight reduction using both molecular and plasma hydrogen.

08 HYDROGEN↗
DOE OSTI2025

Extending SST vanadis to Add SIMT Functional Units

Sandia National Laboratories is currently investigating scalable architectural simulation capabilities, with a focus on simulating and evaluating highly scalable supercomputers for high-performance computing applications. This exploration is driven by the shift toward more specialized forms of compute and the need for a more diverse set of accurate models. This project will explore the use of General-Purpose Graphical Processing Units (GPGPUs) in high-performance computing using both physical systems and new simulator models – traditional GPUs as well as tightly-coupled SIMT accelerators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Validation of gamma-ray detection techniques for safeguards monitoring at natural uranium conversion facilities

Recent IAEA circulars and policy papers have sought to implement safeguards when any purified aqueous uranium solution or uranium oxides suitable for isotopic enrichment or fuel fabrication exists. Under the revised policy, IAEA Policy Paper 18, the starting point for nuclear material under safeguards was reinterpreted, suggesting that purified uranium compounds should be subject to safeguards procedures no later than the first point in the conversion process. In response to this technical need, a combination of simulation models and experimental measurements were employed to develop and validate concepts of nondestructive assay monitoring systems in a natural uranium conversion plant (NUCP). In particular, uranyl nitrate (UO 2 (NO 3 ) 2 ) solution exiting solvent extraction was identified as a key measurement point (KMP), where gamma-ray spectroscopy was selected as the process monitoring tool. The Uranyl Nitrate Calibration Loop Equipment (UNCLE) facility at Oak Ridge National Laboratory was employed to simulate the full-scale operating conditions of a purified uranium-bearing aqueous stream exiting the solvent extraction process in an NUCP. Nondestructive assay techniques using gamma-ray spectroscopy were evaluated to determine their viability as a technical means for drawing safeguards conclusions at NUCPs, and if the IAEA detection requirements of 1 significant quantity (SQ) can be met in a timely way. This work investigated gamma-ray signatures of uranyl nitrate circulating in the UNCLE facility and evaluated various gamma-ray detector sensitivities to uranyl nitrate. These detector validation activities include assessing detector responses to the uranyl nitrate gamma-ray signatures for spectrometers based on sodium iodide, lanthanum bromide, and high-purity germanium detectors. The results of measurements under static and dynamic operating conditions at concentrations ranging from 10–90 g U/L of natural uranyl nitrate are presented. A range of gamma-ray lines is examined, including attenuation for transmission measurement of density and concentration. It was determined that transmission-corrected gamma-ray spectra provide a reliable way to monitor the 235 U concentration of uranyl nitrate solution in transfer pipes in NUCPs. Furthermore, existing predictive and analysis methods are adequate to design and realize practical designs. The 137 Cs transmission source employed in this work is viable but not optimal for 235 U densitometry determination. Validated simulations assessed the viability of 133 Ba and 57Co as alternative densitometry sources. All three gamma-ray detectors are viable for monitoring natural uranium feed; although high-purity germanium is easiest to interpret, it is, however, the least attractive as an installation instrument. Overall, for monitoring throughput in a facility such as UNCLE, emulating the uranium concentration and pump speeds of the Springfields conversion facility in the United Kingdom, an uncertainty of less than 0.17% is required in order to detect the diversion of 1 SQ of uranyl nitrate through changes in uranium concentration over an accountancy period of one year with a detection probability of 50%. As a result, calibrated gamma-ray detection systems are capable of determining the concentration of uranium content in NUCPs, it is only in combination with verifiable operator declarations and supporting data, such as flow rate and enrichment, that safeguards conclusions can be drawn.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2019

Neutron Star Mergers Might Not Be the Only Source of r -process Elements in the Milky Way

Probing the origin of r -process elements in the universe represents a multidisciplinary challenge. We review the observational evidence that probes the properties of r -process sites, and address them using galactic chemical evolution simulations, binary population synthesis models, and nucleosynthesis calculations. Our motivation is to define which astrophysical sites have significantly contributed to the total mass of r -process elements present in our Galaxy. We found discrepancies with the neutron star (NS–NS) merger scenario. When we assume that they are the only site, the decreasing trend of [Eu/Fe] at [Fe/H] > -1 in the disk of the Milky Way cannot be reproduced while accounting for the delay-time distribution (DTD) of coalescence times ( ∝t -1 ) derived from short gamma-ray bursts (GRBs) and population synthesis models. Steeper DTD functions ( ∝t -1.5 ) or power laws combined with a strong burst of mergers before the onset of supernovae (SNe) Ia can reproduce the [Eu/Fe] trend, but this scenario is inconsistent with the similar fraction of short GRBs and SNe Ia occurring in early-type galaxies, and it reduces the probability of detecting GW170817 in an early-type galaxy. One solution is to assume an additional production site of Eu that would be active in the early universe, but would fade away with increasing metallicity. If this is correct, this additional site could be responsible for roughly 50% of the Eu production in the early universe before the onset of SNe Ia. Rare classes of supernovae could be this additional r -process source, but hydrodynamic simulations still need to ensure the conditions for a robust r -process pattern.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Collaborative Project: Building improved optimized parameter estimation algorithms to improve methane and nitrogen fluxes in a climate model

Soils in natural and managed ecosystems and wetlands are well known sources of methane, nitrous oxides, and reactive nitrogen gases, but the magnitudes of gas flux to the atmosphere are still poorly constrained. Thus, the reasons for the large increases in atmospheric concentrations of methane and nitrous oxide since the preindustrial time period are not well understood. The low atmospheric concentrations of methane and nitrous oxide, despite being more potent greenhouse gases than carbon dioxide, complicate empirical studies to provide explanations. In addition to climate concerns, the emissions of reactive nitrogen gases from soils are important to the changing nitrogen balance in the earth system, subject to human management, and may change substantially in the future. Thus improved modeling of the emission fluxes of these species from the land surface is important. Currently, there are emission modules for methane and some nitrogen species in the Community Earth System Model’s Community Land Model (CLM-ME/N); however, there are large uncertainties and problems in the simulations, resulting in coarse estimates. In this proposal, we seek to improve these emission modules by combining state-of-the-art process modules for emissions, available data, and new optimization methods. In earth science problems, we often have substantial data and knowledge of processes in disparate systems, and thus we need to combine data and a general process level understanding into a model for projections of future climate that are as accurate as possible. The best methodologies for optimization of parameters in earth system models are still being developed. In this proposal we will develop and apply surrogate algorithms that a) were especially developed for computationally expensive simulations like CLM-ME/N models; b) were (in the earlier surrogate optimization Stochastic RBF) demonstrated to perform very well on computationally expensive complex partial differential equations in earth science with limited numbers of simulations; and, c) will be (as part of the proposed research) significantly improved both by adding asynchronous parallelism, early truncation of unsuccessful simulations, and the improvement of both serial and parallel performance by the use of derivative and sensitivity information from global and local surrogate approximations S(x) . The algorithm development and testing will be focused on the CLM-ME/N model application, but the methods are general and are expected to also perform well on optimization for parameter estimation of other climate models and other classes of continuous multimodal optimization problems arising from complex simulation models. In addition, this proposal will compile available datasets of emissions of methane, nitrous oxides and reactive nitrogen species and develop protocols for site level comparisons with the CLM-ME/N. Once the model parameters are optimized against site level data, the model will be simulated at the global level and compared to atmospheric concentration measurements for the current climate, and future emissions will be estimated using climate change as simulated by the CESM. This proposal combines experts in earth system modeling, optimization, computer science, and process level understanding of soil gas emissions in an interdisciplinary team in order to improve the modeling of methane and nitrogen gas emissions. This proposal thus meets the requirements of the SciDAC RFP, by integrating state-of-the-art computer science and earth system to build an improved earth system model.

54 ENVIRONMENTAL SCIENCES↗