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DOE OSTI2011

Gaseous dry deposition of atmospheric mercury: A comparison of two surface resistance models for deposition to semiarid vegetation

In the United States, atmospheric mercury (Hg) deposition, from regional and international sources, is the largest contributor to increased Hg concentrations in bodies of water leading to bioaccumulation of methyl mercury in fish. In this work, modeled dry deposition velocities (vd) for gaseous Hg are calculated using two surface resistance parameterizations found in the literature. The flux is then estimated as the product of the species concentration and modeled vd. The calculations utilize speciated atmospheric mercury concentrations measured during an annual monitoring campaign in southern Idaho. Gaseous elemental mercury (GEM) and reactive gaseous mercury (RGM) were monitored with Tekran models 2537A and 1130, respectively. Two anemometers collected meteorological data, including one fast-response three-dimensional sonic anemometer to measure turbulence parameters. For the flux calculation, three resistances are required to model the mechanisms that transport gaseous Hg from the atmosphere to the surface, with the surface resistance being the largest source of error. Results from two surface resistance models are presented. In particular, the downward flux is sensitive to the choice of model and input parameters such as seasonal category and mesophyll resistance. A comparison of annual GEM and RGM fluxes calculated using the two models shows good agreement for RGM (3.2% difference for annual deposition); however, for the low-solubility species of GEM, the models show a 64% difference in annual fluxes, with a range of 32% to 200% in seasonal fluxes. Results indicate the importance of understanding the diurnal variation of the physical processes modeled in the surface resistance parameterization for vd.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2010

Robust emergent climate phenomena associated with the high-sensitivity tail.

Because the potential effects of climate change are more severe than had previously been thought, increasing focus on uncertainty quantification is required for risk assessment needed by policy makers. Current scientific efforts focus almost exclusively on establishing best estimates of future climate change. However, the greatest consequences occur in the extreme tail of the probability density functions for climate sensitivity (the 'high-sensitivity tail'). To this end, we are exploring the impacts of newly postulated, highly uncertain, but high-consequence physical mechanisms to better establish the climate change risk. We define consequence in terms of dramatic change in physical conditions and in the resulting socioeconomic impact (hence, risk) on populations. Although we are developing generally applicable risk assessment methods, we have focused our initial efforts on uncertainty and risk analyses for the Arctic region. Instead of focusing on best estimates, requiring many years of model parameterization development and evaluation, we are focusing on robust emergent phenomena (those that are not necessarily intuitive and are insensitive to assumptions, subgrid-parameterizations, and tunings). For many physical systems, under-resolved models fail to generate such phenomena, which only develop when model resolution is sufficiently high. Our ultimate goal is to discover the patterns of emergent climate precursors (those that cannot be predicted with lower-resolution models) that can be used as a 'sensitivity fingerprint' and make recommendations for a climate early warning system that would use satellites and sensor arrays to look for the various predicted high-sensitivity signatures. Our initial simulations are focused on the Arctic region, where underpredicted phenomena such as rapid loss of sea ice are already emerging, and because of major geopolitical implications associated with increasing Arctic accessibility to natural resources, shipping routes, and strategic locations. We anticipate that regional climate will be strongly influenced by feedbacks associated with a seasonally ice-free Arctic, but with unknown emergent phenomena.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2010

Estimating parameters and uncertainty for three-dimensional flow and transport in a highly heterogeneous sand box experiment.

Heterogeneity plays an important role in groundwater flow and contaminant transport in natural systems. Since it is impossible to directly measure spatial variability of hydraulic conductivity, predictions of solute transport based on mathematical models are always uncertain. While in most cases groundwater flow and tracer transport problems are investigated in two-dimensional (2D) systems, it is important to study more realistic and well-controlled 3D systems to fully evaluate inverse parameter estimation techniques and evaluate uncertainty in the resulting estimates. We used tracer concentration breakthrough curves (BTCs) obtained from a magnetic resonance imaging (MRI) technique in a small flow cell (14 x 8 x 8 cm) that was packed with a known pattern of five different sands (i.e., zones) having cm-scale variability. In contrast to typical inversion systems with head, conductivity and concentration measurements at limited points, the MRI data included BTCs measured at a voxel scale ({approx}0.2 cm in each dimension) over 13 x 8 x 8 cm with a well controlled boundary condition, but did not have direct measurements of head and conductivity. Hydraulic conductivity and porosity were conceptualized as spatial random fields and estimated using pilot points along layers of the 3D medium. The steady state water flow and solute transport were solved using MODFLOW and MODPATH. The inversion problem was solved with a nonlinear parameter estimation package - PEST. Two approaches to parameterization of the spatial fields are evaluated: (1) The detailed zone information was used as prior information to constrain the spatial impact of the pilot points and reduce the number of parameters; and (2) highly parameterized inversion at cm scale (e.g., 1664 parameters) using singular value decomposition (SVD) methodology to significantly reduce the run-time demands. Both results will be compared to measured BTCs. With MRI, it is easy to change the averaging scale of the observed concentration from point to cross-section. This comparison allows us to evaluate which method best matches experimental results at different scales. To evaluate the uncertainty in parameter estimation, the null space Monte Carlo method will be used to reduce computational burden of the development of calibration-constrained Monte Carlo based parameter fields. This study will illustrate how accurately a well-calibrated model can predict contaminant transport.

58 GEOSCIENCES↗

DOE OSTI2014

Constructing Hardware in a Scale Embedded Language

Chisel is a new open-source hardware construction language developed at UC Berkeley that supports advanced hardware design using highly parameterized generators and layered domain-specific hardware languages. Chisel is embedded in the Scala programming language, which raises the level of hardware design abstraction by providing concepts including object orientation, functional programming, parameterized types, and type inference. From the same source, Chisel can generate a high-speed C++-based cycle-accurate software simulator, or low-level Verilog designed to pass on to standard ASIC or FPGA tools for synthesis and place and route.

Bachan, John↗

DOE OSTI2015

Southern Great Plains Ice Nuclei Characterization Experiment Final Campaign Summary

The first ever ice nucleating particle (INP) measurements to be collected at the Southern Great Plains site were made during a period from late April to June 2014, as a trial for possible longer-term measurements at the site. These measurements will also be used to lay the foundation for understanding and parameterizing (for cloud resolving modeling) the sources of these climatically important aerosols as well as to augment the existing database containing this knowledge. Siting the measurements during the spring was intended to capture INP sources in or to this region from plant, soil, dust transported over long distances, biomass burning, and pollution aerosols at a time when they may influence warm-season convective clouds and precipitation. Data have been archived of real-time measurements of INP number concentrations as a function of processing conditions (temperature and relative humidity) during 18 days of sampling that spanned two distinctly different weather situations: a warm, dry and windy period with regional dust and biomass burning influences in early May, and a cooler period of frequent precipitation during early June. Precipitation delayed winter wheat harvesting, preventing intended sampling during that perturbation on atmospheric aerosols. INP concentrations were highest and most variable at all temperatures in the dry period, where we attribute the INP activity primarily to soil dust emissions. Additional offline INP analyses are underway to extend the characterization of INP to cover the entire mixed phase cloud regime from -5°C to -35°C during the full study. Initial comparisons between methods on four days show good agreement and excellent future promise. The additional offline immersion freezing data will be archived as soon as completed under separate funding. Analyses of additional specialized studies for specific attribution of INP to biological and smoke sources are continuing via the National Science Foundation and National Aeronautics and Space Administration funding that helped support instrumentation used for the measurements described herein. Aerosol Observing System aerosol data will be vital to the interpretation and parameterization of results as part of analyses for publications in preparation.

DeMott, PJ↗

DOE OSTI2016

Collaborative Project: Understanding Climate Model Biases in Tropical Atlantic and Their Impact on Simulations of Extreme Climate Events

Recent studies have revealed that among all the tropical oceans, the tropical Atlantic has experienced the most pronounced warming trend over the 20th century. Many extreme climate events affecting the U.S., such as hurricanes, severe precipitation and drought events, are influenced by conditions in the Gulf of Mexico and the Atlantic Ocean. It is therefore imperative to have accurate simulations of the climatic mean and variability in the Atlantic region to be able to make credible projections of future climate change affecting the U.S. and other countries adjoining the Atlantic Ocean. Unfortunately, almost all global climate models exhibit large biases in their simulations of tropical Atlantic climate. The atmospheric convection simulation errors in the Amazon region and the associated errors in the trade wind simulations are hypothesized to be a leading cause of the tropical Atlantic biases in climate models. As global climate models have resolutions that are too coarse to resolve some of the atmospheric and oceanic processes responsible for the model biases, we propose to use a high-resolution coupled regional climate model (CRCM) framework to address the tropical bias issue. We propose to combine the expertise in tropical coupled atmosphere-ocean modeling at Texas A&M University (TAMU) and the coupled land-atmosphere modeling expertise at Pacific Northwest National Laboratory (PNNL) to develop a comprehensive CRCM for the Atlantic sector within a general and flexible modeling framework. The atmospheric component of the CRCM will be the NCAR WRF model and the oceanic component will be the Rutgers/UCLA ROMS. For the land component, we will use CLM modified at PNNL to include more detailed representations of vegetation and soil hydrology processes. The combined TAMU-PNNL CRCM model will be used to simulate the Atlantic climate, and the associated land-atmosphere-ocean interactions at a horizontal resolution of 9 km or finer. A particular focus of the model development effort will be to optimize the performance of WRF and ROMS over several thousand of cores by focusing on both the parallel communication libraries and the I/O interfaces, in order to achieve the sustained throughput needed to perform simulations on such fine resolution grids. The CRCM model will be developed within the framework of the Coupler (CPL7) software that is part of the NCAR Community Earth System Model (CESM). Through efforts at PNNL and within the community, WRF and CLM have already been coupled via CPL7. Using the flux coupler approach for the whole CRCM model will allow us to flexibly couple WRF, ROMS, and CLM with each model running on its own grid at different resolutions. In addition, this framework will allow us to easily port parameterizations between CESM and the CRCM, and potentially allow partial coupling between the CESM and the CRCM. TAMU and PNNL will contribute cooperatively to this research endeavor. The TAMU team led by Chang and Saravanan has considerable experience in studying atmosphere-ocean interactions within tropical Atlantic sector and will focus on modeling issues that relate to coupling WRF and ROMS. The PNNL team led by Leung has extensive expertise in atmosphere-land interaction and will be responsible for improving the land surface parameterization. Both teams will jointly work on integrating WRF-ROMS and WRF-CLM to couple WRF, ROMS, and CLM through CPL7. Montuoro of the TAMU Supercomputing Center will be responsible for improving the MPI and Parallel IO interfaces of the CRCM. Both teams will contribute to the design and execution of the proposed numerical experiments and jointly perform analysis of the numerical experiments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Simulating Aerosol Indirect Effects with Improved Aerosol-Cloud- Precipitation Representations in a Coupled Regional Climate Model

This is a collaborative project among North Carolina State University, Pacific Northwest National Laboratory, and Scripps Institution of Oceanography, University of California at San Diego to address the critical need for an accurate representation of aerosol indirect effect in climate and Earth system models. In this project, we propose to develop and improve parameterizations of aerosol-cloud-precipitation feedbacks in climate models and apply them to study the effect of aerosols and clouds on radiation and hydrologic cycle. Our overall objective is to develop, improve, and evaluate parameterizations to enable more accurate simulations of these feedbacks in high resolution regional and global climate models.

58 GEOSCIENCES↗

DOE OSTI2016

Performance Analysis of Transposition Models Simulating Solar Radiation on Inclined Surfaces: Preprint

Transposition models are widely used in the solar energy industry to simulate solar radiation on inclined photovoltaic (PV) panels. These transposition models have been developed using various assumptions about the distribution of the diffuse radiation, and most of the parameterizations in these models have been developed using hourly ground data sets. Numerous studies have compared the performance of transposition models, but this paper aims to understand the quantitative uncertainty in the state-of-the-art transposition models and the sources leading to the uncertainty using high-resolution ground measurements in the plane of array. Our results suggest that the amount of aerosol optical depth can affect the accuracy of isotropic models. The choice of empirical coefficients and the use of decomposition models can both result in uncertainty in the output from the transposition models. It is expected that the results of this study will ultimately lead to improvements of the parameterizations as well as the development of improved physical models.

14 SOLAR ENERGY↗

DOE OSTI2016

Fast All-Sky Radiation Model for Solar Applications (FARMS): A Brief Overview of Mechanisms, Performance, and Applications: Preprint

Solar radiation can be computed using radiative transfer models, such as the Rapid Radiation Transfer Model (RRTM) and its general circulation model applications, and used for various energy applications. Due to the complexity of computing radiation fields in aerosol and cloudy atmospheres, simulating solar radiation can be extremely time-consuming, but many approximations--e.g., the two-stream approach and the delta-M truncation scheme--can be utilized. To provide a new fast option for computing solar radiation, we developed the Fast All-sky Radiation Model for Solar applications (FARMS) by parameterizing the simulated diffuse horizontal irradiance and direct normal irradiance for cloudy conditions from the RRTM runs using a 16-stream discrete ordinates radiative transfer method. The solar irradiance at the surface was simulated by combining the cloud irradiance parameterizations with a fast clear-sky model, REST2. To understand the accuracy and efficiency of the newly developed fast model, we analyzed FARMS runs using cloud optical and microphysical properties retrieved using GOES data from 2009-2012. The global horizontal irradiance for cloudy conditions was simulated using FARMS and RRTM for global circulation modeling with a two-stream approximation and compared to measurements taken from the U.S. Department of Energy's Atmospheric Radiation Measurement Climate Research Facility Southern Great Plains site. Our results indicate that the accuracy of FARMS is comparable to or better than the two-stream approach; however, FARMS is approximately 400 times more efficient because it does not explicitly solve the radiative transfer equation for each individual cloud condition. Radiative transfer model runs are computationally expensive, but this model is promising for broad applications in solar resource assessment and forecasting. It is currently being used in the National Solar Radiation Database, which is publicly available from the National Renewable Energy Laboratory at http://nsrdb.nrel.gov.

14 SOLAR ENERGY↗

DOE OSTI2019

Support for Next-Generation Ecosystem Experiments (NGEE Arctic) Field Campaign Report

An important challenge for Earth System Models (ESMs) is to represent land surface and subsurface processes and their complex interactions in a warming climate. This is especially important for arctic ecosystems where permafrost extent, topography, hydrology, vegetation, disturbance, and biogeochemistry are inextricably linked. The implications of such linkages include permafrost thaw and deepening of the active layer, microbial decomposition of vulnerable soil organic matter, altered productivity and migration of tall woody shrubs, and watershed-scale changes in surface and groundwater transport and storage. Although ESMs describe some of these interactions for high-latitude ecosystems, their representation requires extensive confrontation with field and laboratory observations to test and improve models, and to use those models to inspire new observations and experiments. The Next-Generation Ecosystem Experiments (NGEE Arctic) is a 10-year project (2012 to 2022) to improve our predictive understanding of carbon-rich arctic system processes and feedbacks to climate. This is achieved through experiments, observations, and synthesis of existing data sets that strategically inform model process representation and parameterization, and that enhance the knowledge base required for model initialization, calibration, and evaluation. One question of special interest to the NGEE Arctic project addresses how above- and below-ground plant functional traits might change across environmental gradients, and what are the consequences for arctic ecosystem C, water, and nutrient fluxes? Arctic plant traits, and their variation in response to changing environmental conditions, will play a key role in the response of tundra ecosystems to warming, permafrost thaw, and the wetter or drier conditions expected in the future. The appropriate representation of these functional traits in models is necessary to accurately represent ecosystem C, water, and nutrient cycling in tundra ecosystems, now and in the future. We characterized the variation in above- and below-ground plant traits in response to varying edaphic and environmental conditions in Utqia&gdot;vik (formerly Barrow), Alaska. These new data, combined with remote-sensing and synthesis activities across the Arctic and the globe, have been used to inform model structure and parameterization of key processes such as photosynthesis, nutrient dynamics, and trait variation across the landscape.

North Slope of Alaska, arctic processes (meteorolo↗

DOE OSTI2019

Real-Time Ice Nuclei Measurements at Oliktok Point Field Campaign Report

The ability of atmospheric particles (aerosols) to serve as heterogeneous ice nuclei (IN) for formation of ice and mixed-phase clouds is one of the least understood microphysical processes resulting in large uncertainties in climate modeling. As stated in the Intergovernmental Panel on Climate Change 2007 report, “The effects of aerosol particles on heterogeneous ice formation are currently insufficiently understood and present another level of challenge for both observations and modeling.” IN can strongly affect cloud radiative properties and precipitation formation. Only a small fraction (often less than 1%) of all aerosol particles act as atmospheric IN. Therefore, it becomes important to identify the active fraction of aerosol particles under a variety of environmental conditions. Proposed IN measurement techniques provide IN concentrations as a function of temperature and humidity conditions, and this unique data set can be analyzed to develop or constrain IN parameterizations that models rely on to calculate primary ice formation. Currently, the processes of ice formation within models are poorly represented, such that climate models often inaccurately partition cloud water between liquid and solid ice. Model studies have shown that uncertainties in cloud phase partitioning induce large uncertainties in the impact of mixedphase clouds on climate change. Arctic clouds composed of ice particles and supercooled liquid droplets at temperatures warmer than -37°C are important for the Arctic’s climate because of their extensive horizontal coverage over long periods, and their impact on the surface radiative balance and thus ice sheet coverage. The complexity of microphysical processes within these clouds and the sensitivity of precipitation to the details of microphysics are not fully understood. In addition, representation of these processes in a variety of cloud models continues to be challenging. Arctic clouds provide unique conditions to understand heterogeneous ice nucleation mechanisms, particularly immersion freezing, and will be the initial focus of the ice nucleation group. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM)-North Slope of Alaska (NSA) site is well equipped with other aerosol measurement (number concentration, size) instruments, and a relationship among long-term IN measurements and aerosol physical and chemical properties under different environmental conditions can be identified and established. The long-term IN measurements will be critical for understanding the ice nucleation process in response to the seasonal variations of aerosol and developing robust ice nucleation parameterizations for cloud and climate models. In the future, IN measurements could also be made at other ARM-NSA sites, specifically, Oliktok Point.

Arctic processes (meteorology), Oliktok Point, aer↗

DOE OSTI2019

Reconstructing high-dimensional Hilbert-valued functions via compressed sensing

We present and analyze a novel sparse polynomial technique for approximating high-dimensional Hilbert-valued functions, with application to parameterized partial differential equations (PDEs) with deterministic and stochastic inputs. Our theoretical framework treats the function approximation problem as a joint sparse recovery problem, where the set of jointly sparse vectors is possibly infinite. To achieve the simultaneous reconstruction of Hilbert-valued functions in both parametric domain and Hilbert space, we propose a novel mixed-norm based `1 regularization method that exploits both energy and sparsity. Our approach requires extensions of concepts such as the restricted isometry and null space properties, allowing us to prove recovery guarantees for sparse Hilbert-valued function reconstruction. We complement the enclosed theory with an algorithm for Hilbert-valued recovery, based on standard forward-backward algorithm, meanwhile establishing its strong convergence in the considered infinite-dimensional setting. Finally, we demonstrate the minimal sample complexity requirements of our approach, relative to other popular methods, with numerical experiments approximating the solutions of high-dimensional parameterized elliptic PDEs.

Dexter, Nick↗

DOE OSTI2020

Sensitivity Study for Forecasting Variables of WRF-Solar Using a Tangent Linear Approach

Integrating solar generation in recent years has highlighted the need for improved accuracy in predicting solar power. Confidence in solar power forecasting can be achieved by designing an ensemble that provides reliable probabilistic information for solar radiation with reduced uncertainty and error. Ideally, ensemble members are created through the optimized perturbation of the initial conditions in numerical weather prediction (NWP) models. Tangent linear models are capable of efficiently investigating the sensitivity of solar radiation to model input parameters because they do not require individual perturbation of each variable. This sensitivity study using tangent linear models provide us the capability to identify the right variables to perturb in an ensemble prediction system. In this study, we developed tangent linear models for WRF-Solar modules that directly impact the computation of solar radiation and the simulation of cloud formation and dissipation including the Fast All-sky Model for Solar Applications (FARMS), the Noah land surface model (LSM), the Thompson microphysics, the Mello-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization, and the Deng scheme for a shallow-convection parameterization. A sensitivity analysis was conducted under various scenarios based on satellite observations and model simulations from the National Solar Radiation Data Base (NSRDB) and WRF-Solar, respectively. Critical forecasting variables that are highly sensitive to the forecasting of global horizontal irradiance (GHI), direct normal irradiance (DNI), cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes were determined using the relevant WRF-Solar module. This study will be used as a guidance on future research leading to high-quality probabilistic solar forecasting. In this presentation, we discuss the validation of tangent linear approach for WRF-Solar modules and illustrate how the sensitivity results are valuable in the improvement of probabilistic solar prediction.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Protein Molecular Dynamics Simulations with Approximate QM: What Can We Learn?

Classical force fields are essential for computer simulations of proteins and are typically parameterized to reproduce secondary and tertiary structure of isolated proteins. However, while protein-protein interactions are ubiquitous in nature, they are not considered in parameterization efforts and are far less understood than isolated proteins. A better characterization of intermolecular interactions is widely recognized as a key to revolutionizing drug and therapeutic developments with high-throughput computational screening. Urgently needed is a critical assessment of the performance of modern protein force fields against first-principles electronic structure methods and experiments. In a daring step toward this goal, we here describe a comparison of peptide folding dynamics as predicted by a molecular mechanics force field on the one hand and by an approximate electronic structure quantum mechanical (QM) method based on density-functional tight-binding (DFTB) on the other. We further compare the dynamics from straightforward DFTB simulations with a near-linear scaling version of DFTB for massively parallel computation based on the fragment molecular orbital (FMO-DFTB) method. We illustrate differences between the phenomenology of the folding dynamics from these three methods for a small model peptide, as well as charge polarization and dynamic fluctuations, point out possible correlations and implications for force field developers, and discuss the lessons learned that might become applicable to future predictive high-throughput computer screening for personalized neoantigen cancer therapy.

Irle, Stephan↗

DOE OSTI2020

Measurement of the Ratio of the Neutron to Proton Structure Functions, and the Three-Nucleon EMC Effect in Deep Inelastic Electron Scattering Off Tritium and Helium-3 Mirror Nuclei

The proton and neutron structure functions Fp 2 and Fn 2, respectively are fundamental to understanding many studies in nuclear physics. They provide important information about quark distributions. For example, the ratio Fn 2/Fp 2 is one of the best measurements to find the ratio of d quark over u quark distribution inside the proton. While the calculations of structure functions and quark distributions are non-perturbative, they can be determined by the parameterization of experimental data. The understanding of Fn 2/Fp 2 and d/u as x -> 1 has a large influence on global fits and parameterization, and can be used to distinguish the non-perturbative models which give different predictions. However, Fn 2/Fp 2 measured using deuteron and hydrogen targets has large nuclear uncertainties at large x, because the nuclear effects in the deuteron become significant at large x. The MARATHON experiment, which ran in spring 2018 using the upgraded 11 GeV Jefferson Lab electron beam, employs a novel method. It performed deep inelastic scattering off tritium and helium-3 mirror nuclei to measure Fn 2/Fp 2 over the range x = 0.17 to x = 0.82. Since tritium and helium-3 are mirror nuclei, theoretical uncertainties largely cancel out in the ratio. The extracted Fn 2/Fp 2 has much smaller uncertainties compared with previous experiments at large x. The MARATHON experiment also provided results on the EMC effect for tritium and helium-3 nuclei. The results are considered essential for understanding the EMC effect. This thesis describes the MARATHON experiment, and presents results for Fn 2/Fp 2, and the EMC effect for tritium and helium-3.

Liu, Hanjie↗

DOE OSTI2021

Machine-Learned Manifold-Based Models for Large Eddy Simulation of Turbulent Combustion

Reduced-order manifold combustion models are commonly used to lower the cost of reacting Large Eddy Simulations (LES) and may be derived either from physical principles as in flamelet models or from data-driven methods like Principal Component Analysis (PCA). In either case, neural networks are increasingly used as part of these models to provide a nonlinear mapping between a small set of pre-defined variables that parameterize the manifold and outputs of interest, such as reaction rates. In this work, we propose a new manifold-based modeling approach that combines the definition of the manifold-parameterizing variables (linear combinations of species), the nonlinear mapping to the outputs, and closure of filtered quantities for LES into the structure of a single neural network. This allows the process used to train the neural network to simultaneously optimize both the functional form of the model and the identities of the inputs to the model. The new approach can flexibly incorporate thermochemical data from any combustion system; if trained on data from 1D flames it can be interpreted as an optimized flamelet model, but it can also be used to learn models from data from more complex configurations. This work presents a priori evaluations of the new approach in both contexts. Evaluation using data from 1D premixed flames demonstrates the physical interpretability of the manifold variables generated by the new approach. Evaluation using data from direct numerical simulations of turbulent flames shows improved predictions relative to either flamelet or PCA-based models in a more complex configuration.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Development, Characterization, and Testing of Solid-State Electrolytes for Batteries

Traditional liquid electrolytes used in lithium (Li) metal batteries (LMBs) have severe safety issues, poor power density, as well as thermal and electrochemical instability that prevents their scaling to newer applications, such as electric vehicles. Using sulfide-based solid-state electrolytes (SSEs) is a viable method to address many of the issues that plague LMBs. However, the key issue preventing the expansion of sulfide-based SSEs is an interplay between poor interfacial stability with the electrodes and massive external stack pressure required to maintain electrode-electrolyte contact. The need for extensive pressure requires large system-level (i.e., battery pack used in an electric vehicle) housing that causes a disconnect between performance at a smaller scale and actual application. Secondary prevention to sulfide-based SSE adoption are difficulties in manufacturing, as toxic deterioration occurs when these materials are exposed to humidity, as well as drastic reduction in SSE performance at low temperature. This work hypothesizes that unique interfacial designs of electrodes and the modification of the sulfide-based SSE will enable reduction of necessary external cell pressure, and therefore support the design of practical LMBs. This will be done through collaboration within Idaho National Laboratory (INL), where manufacturing practices will be parameterized to produce low defect, low porosity SSEs with slight modifications. Next, artificial interfaces will be produced on electrodes to observe the conformality and mass transport properties between the modified SSE and pre-treated anode. External pressure on Li-metal cells will also be parameterized to reduce interfacial impedance while a reduction in the pressure will allot reasonable housing design for larger scale applications. Focus on novelty in this work occurs by modifications to the solid-solid interfaces between electrolyte and electrode, done through doping of the electrolyte and production of artificial interfaces on the Li-metal anode. The modifications at these interfaces, in addition to modified external pressure could identify and address the poor interfacial impedance that hinders high performance SSEs. Fundamental understanding on how to reduce the key hindrances of all-solid-state batteries (ASSBs) will support the advancement for larger scale production and performance, of which will be held in perspective for future works. Beyond battery development, the knowledge gained from process control and defect analysis is applicable to other ceramic systems, such as nuclear fuel coatings and high temperature electrolyzers.

25 ENERGY STORAGE↗

DOE OSTI2023

Development, Characterization, and Testing of Solid-State Electrolytes for Batteries

25 ENERGY STORAGE↗