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DOE OSTI2020

Atomically dispersed metal–nitrogen–carbon catalysts for fuel cells: advances in catalyst design, electrode performance, and durability improvement

The urgent need to address the high-cost issue of proton-exchange membrane fuel cell (PEMFC) technologies, particularly for transportation applications, drives the development of simultaneously highly active and durable platinum group metal-free (PGM-free) catalysts and electrodes. The past decade has witnessed remarkable progress in exploring PGM-free cathode catalysts for the oxygen reduction reaction (ORR) to overcome sluggish kinetics and catalyst instability in acids. Among others, scientists have identified the newly emerging atomically dispersed transition metal (M: Fe, Co, or/and Mn) and nitrogen co-doped carbon (M–N–C) catalysts as the most promising alternative to PGM catalysts. Here, we provide a comprehensive review of significant breakthroughs, remaining challenges, and perspectives regarding the M–N–C catalysts in terms of catalyst activity, stability, and membrane electrode assembly (MEA) performance. A variety of novel synthetic strategies demonstrated effectiveness in improving intrinsic activity, increasing active site density, and attaining optimal porous structures of catalysts. Rationally designing and engineering the coordination environment of single metal MN x sites and their local structures are crucial for enhancing intrinsic activity. Increasing the site density relies on the innovative strategies of restricting the migration and agglomeration of single metal sites into metallic clusters. Relevant understandings provide the correlations among the nature of active sites, nanostructures, and catalytic activity of M–N–C catalysts at the atomic scale through a combination of experimentation and theory. Current knowledge of the transferring catalytic properties of M–N–C catalysts to MEA performance is limited. Rationally designing morphologic features of M–N–C catalysts play a vital role in boosting electrode performance through exposing more accessible active sites, realizing uniform ionomer distribution, and facilitating mass/proton transports. We outline future research directions concerning the comprehensive evaluation of M–N–C catalysts in MEAs. The most considerable challenge of current M–N–C catalysts is the unsatisfied stability and rapid performance degradation in MEAs. Therefore, we further discuss practical methods and strategies to mitigate catalyst and electrode degradation, which is fundamentally essential to make M–N–C catalysts viable in PEMFC technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2011

Pyramid Resource Center-Green Energy Center

There are currently over 3,500 USA/Canadian landfills listed by the EPA/EC and like numbers in Europe that are producing methane-rich landfill gas (LFG). This gas is typically made up of 50-percent methane (CH4), 35-percent carbon dioxide (CO2), and 2 to 25% nitrogen and oxygen (N2 & O2), plus dozens of dilute contaminants. LFG is classified as a renewable fuel, because it is generated via biological decay of municipal solid waste, a constant byproduct of human activity. To date, most LFG has been allowed to escape into the atmosphere. On account of its high CH4 content, LFG may contribute to climate change, as CH4 is one of the most harmful greenhouse gases with 21 times the global warming potential of CO2. Of the landfills that collect LFG, most simply flare it. In the past decade, some landfills have begun to use LFG for electricity generation or for direct combustion as low Btu gas. Very few landfills upgrade LFG to high Btu gas. A patented CO2 WashTM process developed by Acrion Technologies Inc., and licensed to Firm Green Inc. shows promise as an economically and environmentally sustainable process to recover energy and prevent pollution from landfills. The CO2 WashTM has already been proven at lab-scale. It upgrades LFG, which consists of 50% methane (CH4) + 35% carbon dioxide (CO2) + 2 to 25% nitrogen + oxygen (N2+O2), 1 to 2% water vapor, and dozens of contaminants (which total a few hundred to a few thousand parts per million). CH4, which by itself has an energy content of 1,012 British thermal units (Btu) per standard cubic foot (SCF), is the only component in LFG that contributes to its energy content, which is therefore about 400-550 Btu/SCF. Accordingly, raw LFG is usually referred to as medium-Btu gas. To be salable, it is necessary to remove essentially all the components besides CH4, while keeping the vast majority of the revenue producing CH4. This is high-Btu gas, yielding 850 to 1,000 Btu/SCF. The CO2 WashTM process upgrades LFG to about 930 Btu/SCF, and reduces the contaminants to levels that make it salable as a vehicle fuel in the form of compressed natural gas (CNG).

09 BIOMASS FUELS↗

DOE OSTI2018

Remote Robotic Equipment for Retrieval of High Level Waste to Support Environmental Closure - 18162

The Idaho Cleanup Project contractor, Fluor Idaho, LLC, is responsible for managing HLW calcine stored in the Calcined Solids Storage Facility at the Idaho Nuclear Technology and Engineering Center. The Idaho Nuclear Technology and Engineering Center is a DOE managed facility located at the INL Site in the remote portions of southeast Idaho. In 2016, DOE directed Fluor Idaho to develop and test a full-scale retrieval system to demonstrate the ability to safely retrieve HLW calcine from the Calcined Solids Storage Facility for future retrieval and processing. Removing HLW calcine from the State of Idaho is required by the 1995 Settlement Agreement, which mandates that DOE have HLW calcine ready to be moved out of Idaho by December 2035. Fluor Idaho is currently testing retrieval and transfer concepts to ensure that the Calcined Solids Storage Facility is placed in a final configuration that is protective of human health and the environment after HLW calcine has been removed to the maximum extent practical. This paper describes the full-scale prototype that will be used to prove retrieval and transfer system design capabilities, verify operating parameters, and establish a basis for calcine retrieval efficacy. Additionally, it describes the retrieval (bulk and residual) and visual monitoring systems that were designed and tested in 2017 and will continue to be tested with the full-scale prototype. Testing (i.e., full-scale mockup, bulk retrieval, residual clean-out, and visualization monitoring) performed by Fluor Idaho will demonstrate the maximum extent practical of HLW calcine removal that is achievable. This is critical to (1) establish criteria for ending calcine removal activities, (2) evaluate cost and benefits associated with performing additional removal, and (3) ensure clean up and closure standards under applicable requirements (e.g., statutes, orders, and agreements) are met. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2017

Testing the Accuracy of Data-Driven MHD Simulations of Active Region Evolution

Models for the evolution of the solar coronal magnetic field are vital for understanding solar activity, yet the best measurements of the magnetic field lie at the photosphere, necessitating the development of coronal models which are "data-driven" at the photosphere. We present an investigation to determine the feasibility and accuracy of such methods. Our validation framework uses a simulation of active region (AR) formation, modeling the emergence of magnetic flux from the convection zone to the corona, as a ground-truth data set, to supply both the photospheric information and to perform the validation of the data-driven method. We focus our investigation on how the accuracy of the data-driven model depends on the temporal frequency of the driving data. The Helioseismic and Magnetic Imager on NASA's Solar Dynamics Observatory produces full-disk vector magnetic field measurements at a 12-minute cadence. Using our framework we show that ARs that emerge over 25 hours can be modeled by the data-driving method with only approximately 1 percent error in the free magnetic energy, assuming the photospheric information is specified every 12 minutes. However, for rapidly evolving features, under-sampling of the dynamics at this cadence leads to a strobe effect, generating large electric currents and incorrect coronal morphology and energies. We derive a sampling condition for the driving cadence based on the evolution of these small-scale features, and show that higher-cadence driving can lead to acceptable errors. Future work will investigate the source of errors associated with deriving plasma variables from the photospheric magnetograms as well as other sources of errors, such as reduced resolution, instrument bias, and noise.

Leake, James E.↗

DOE OSTI2018

Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning

Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Typically, this involves developing microkinetic reactor models that are based on parameters obtained from density functional theory and transition-state theory. To reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, linear scaling relations for surface intermediates and transition states have been developed that only depend on a few, typically one or two descriptors, such as the carbon atom adsorption energy. As a result, only the descriptor values have to be computed for various active site models to generate volcano curves in activity or selectivity. Unfortunately, for more complex chemistries the predictability of linear scaling relations is unknown. Also, the selection of descriptors is essentially a trial and error process. Here, using a database of adsorption energies of the surface species involved in the decarboxylation and decarbonylation of propionic acid over eight monometalic transition-metal catalyst surfaces (Ni, Pt, Pd, Ru, Rh, Re, Cu, Ag), we tested if nonlinear machine learning (ML) models can outperform the linear scaling relations in prediction accuracy when predicting the adsorption energy for various species on a metal surface based on data from the rest of the metal surfaces. We found linear scaling relations to hold well for predictions across metals with a mean-absolute error of 0.12 eV, and ML methods being unable to outperform linear scaling relations when the training dataset contains a complete set of energies for all of the species on various metal surfaces. Only when the training dataset is incomplete, namely, contains a random subset of species’ energies for each metal, a currently unlikely scenario for catalyst screening, do kernel-based ML models significantly outperform linear scaling relations. We also found that simple coordinate-free species descriptors, such as bond counts, achieve as good results as sophisticated coordinate-based descriptors. Finally, we propose an approach for automatic discovery of appropriate metal descriptors using principal component analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2011

Landmark Report Analyzes Current State of U.S. Offshore Wind Industry (Fact Sheet)

New report assesses offshore wind industry, offshore wind resource, technology challenges, economics, permitting procedures, and potential risks and benefits. The National Renewable Energy Laboratory (NREL) recently published a new report that analyzes the current state of the offshore wind energy industry, Large-Scale Offshore Wind Power in the United States. It provides a broad understanding of the offshore wind resource, and details the associated technology challenges, economics, permitting procedures, and potential risks and benefits of developing this clean, domestic, renewable resource. The United States possesses large and accessible offshore wind energy resources. The availability of these strong offshore winds close to major U.S. coastal cities significantly reduces power transmission issues. The report estimates that U.S. offshore winds have a gross potential generating capacity four times greater than the nation's present electric capacity. According to the report, developing the offshore wind resource along U.S. coastlines and in the Great Lakes would help the nation: (1) Achieve 20% of its electricity from wind by 2030 - Offshore wind could supply 54 gigawatts of wind capacity to the nation's electrical grid, increasing energy security, reducing air and water pollution, and stimulating the domestic economy. (2) Provide clean power to its coastal demand centers - Wind power emits no carbon dioxide (CO2) and there are plentiful winds off the coasts of 26 states. (3) Revitalize its manufacturing sector - Building 54 GW of offshore wind energy facilities would generate an estimated $200 billion in new economic activity, and create more than 43,000 permanent, well-paid technical jobs in manufacturing, construction, engineering, operations and maintenance. NREL's report concludes that the development of the nation's offshore wind resources can provide many potential benefits, and with effective research, policies, and commitment, offshore wind energy can play a vital role in future U.S. energy markets.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

SIBatt-3D: In-Space/On-Surface 3D Printing of Sodium Ion Batteries from ISRU Materials

Constructed more than 20 years ago, the International Space Station’s primary power system originally used nickel-hydrogen batteries with a lifetime of 6.5 years, until NASA began the process of replacing them in 2016 with lithium-ion batteries with a lifetime of 10 years. The demanding and costly process was accomplished after four flights of the Japanese H-II Transfer Vehicle cargo spacecraft (with a cost of about $10,000 per pound of payload), and 13 different astronauts conducting 14 spacewalks. Besides utilization in the ISS, rechargeable batteries are present in many space applications: they are installed in exploration robots, life support systems and in portable communication devices, to mention some. In this context, this project is focused on the in-space manufacturing of shape-conformable batteries using in-situ resources, and aims to address the NASA’s gaps related to the development of next generation of energy storage devices (TX03), as well as in-space manufacturing and in-situ resource utilization (TX07). The proposed work also tackles the HEOMD’s objectives targeting the in-space additive manufacturing (AM) from Lunar/Martian materials (regolith as AM feedstock) to reinvigorate America’s Human Space Exploration Program (SPD-1). This project is in direct alignment with the STMD’s objectives to demonstrate in-space autonomous manufacturing and assembly of complete systems by 2030, and to enable humans to live and explore in space and on planetary surfaces by 2040 thanks to in-space habitation, infrastructure development and in-situ resource utilization (ST1 and ST5). Manufacturing of shape conformable batteries directly in-space and using in-situ resources would also contribute to reducing the payload weight and volume (TX12) for future missions, thus reducing risk for long term Mars missions where rapid resupply is logistically infeasible. Nowadays, commercial batteries consist of stacked two-dimensional (2D) sheets, which are only manufactured in restricted geometries (cylindrical and coin cell). Evolving from conventional 2D, complex 3D battery architectures have been proven to increase the electrochemical active surface area and ion diffusion path, leading to improved areal energy density and power performance. This tendency was illustrated in our recent in-depth modeling studies by simulating a classical Ragone plot exhibiting the energy-power relationship. Our team demonstrated through modeling that a complex gyroidal 3D printed battery architecture exhibits significantly improved power performances (>150% at the current density of 6C; full discharge in 10 minutes) in comparison to a traditional 3D printed planar geometry. Motivated by these results and as the fabrication of intricate 3D battery design is only possible experimentally thanks to the geometric freedom offered by additive manufacturing (AM), our team has already initiated leveraging thermoplastic material extrusion at the laboratory scale. While 3D printing of batteries is relatively recent (2013), it has witnessed a growing interest during the last recent years, as next-generation shape-conformable 3D batteries can be co-designed with the system. Consequently, dead-volume and mass brought from Earth are minimized, in addition to improved battery performance, in alignment with the aforementioned NASA’s objectives. Further, while this project is specifically dedicated to batteries, it lends itself towards the maturation of in-space manufacturing via 3D printing using in-situ resources, stated in HEOMD and STMD goals.

In-Space Manufacturing↗

NASA NTRS2017

EVA Systems Technology Gaps and Priorities 2017

Performance of Extra-Vehicular Activities (EVA) has been and will continue to be a critical capability for human space flight. Human exploration missions beyond LEO will require EVA capability for either contingency or nominal activities to support mission objectives and reduce mission risk. EVA systems encompass a wide array of products across pressure suits, life support systems, EVA tools and unique spacecraft interface hardware (i.e. EVA Translation Paths and EVA Worksites). In a fiscally limited environment with evolving transportation and habitation options, it is paramount that the EVA community's strategic planning and architecture integration products be reviewed and vetted for traceability between the mission needs far into the future to the known technology and knowledge gaps to the current investments across EVA systems. To ascertain EVA technology and knowledge gaps many things need to be brought together, assessed and analyzed. This includes an understanding of the destination environments, various mission concept of operations, current state of the art of EVA systems, EVA operational lessons learned, and reference advanced capabilities. A combined assessment of these inputs should result in well-defined list of gaps. This list can then be prioritized depending on the mission need dates and time scale of the technology or knowledge gap closure plan. This paper will summarize the current state of EVA related technology and knowledge gaps derived from NASA's Exploration EVA Reference Architecture and Operations Concept products. By linking these products and articulating NASA's approach to strategic development for EVA across all credible destinations an EVA could be done in, the identification of these gaps is then used to illustrate the tactical and strategic planning for the EVA technology development portfolio. Finally, this paper illustrates the various "touch points" with other human exploration risk identification areas including human health and performance.

Johnson, Brian J.↗

DOE OSTI2016

A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

Here, large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely the Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO 2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO 2 , using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO 2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO 2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr -1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.

58 GEOSCIENCES↗

DOE OSTI2024

Development of a Full-Scale Connected U-Net for Reflectivity Inpainting in Spaceborne Radar Blind Zones

CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2001

Thermal Analysis of a Carbon Fiber Rope Barrier for Use in the Reusable Solid Rocket Motor Nozzle Joint-2

This study provides for development and verification of analysis methods used to assess performance of a carbon fiber rope (CFR) thermal barrier system that is currently being qualified for use in Reusable Solid Rocket Motor (RSRM) nozzle joint-2. Modeled geometry for flow calculations considers the joint to be vented with the porous CFR barriers placed in the "open' assembly gap. Model development is based on a 1-D volume filling approach where flow resistances (assembly gap and CFRs) are defined by serially connected internal flow and the porous media "Darcy" relationships. Combustion gas flow rates are computed using the volume filling code by assuming a lumped distribution total joint fill volume on a per linear circumferential inch basis. Gas compressibility, friction and heat transfer are included in the modeling. Gas-to-wall heat transfer is simulated by concurrent solution of the compressible flow equations and a large thermal 2-D finite element (FE) conduction grid. The derived numerical technique loosely couples the FE conduction matrix with the compressible gas flow equations, Free constants that appear in the governing equations are calibrated by parametric model comparison to hot fire subscale test results. The calibrated model is then used to make full-scale motor predictions using RSRM aft dome environments. Model results indicate that CFR thermal barrier systems will provide a thermally benign and controlled pressurization environment for the RSRM nozzle joint-2 primary seal activation.

Clayton, J. Louie↗

NASA NTRS2002

Thermal Analysis of a Carbon Fiber Rope Barrier for Use in the Reusable Solid Rocket Motor Nozzle Joint-2

This study provides development and verification of analysis methods used to assess performance of a carbon fiber rope (CFR) thermal barrier system that is currently being qualified for use in Reusable Solid Rocket Motor (RSRM) nozzle joint-2. Modeled geometry for flow calculations considers the joint to be vented with the porous CFR barriers placed in the 'open' assembly gap. Model development is based on a 1-D volume filling approach where flow resistances (assembly gap and CFRs) are defined by serially connected internal flow and the porous media 'Darcy' relationships. Combustion gas flow rates are computed using the volume filling code by assuming a lumped distribution total joint fill volume on a per linear circumferential inch basis. Gas compressibility, friction and heat transfer are included in the modeling. Gas-to-wall heat transfer is simulated by concurrent solution of the compressible flow equations and a large thermal 2-D finite element (FE) conduction grid. The derived numerical technique loosely couples the FE conduction matrix with the compressible gas flow equations. Free constants that appear in the governing equations are calibrated by parametric model comparison to hot fire subscale test results. The calibrated model is then used to make full-scale motor predictions using RSRM aft dome environments. Model results indicate that CFR thermal barrier systems will provide a thermally benign and controlled pressurization environment for the RSRM nozzle joint-2 primary seal activation.

Clayton, J. Louie↗

NASA NTRS2010

Control Activity in Support of NASA Turbine Based Combined Cycle (TBCC) Research

Control research for a Turbine Based Combined Cycle (TBCC) propulsion system is the current focus of the Hypersonic Guidance, Navigation, and Control (GN&C) discipline team. The ongoing work at the NASA Glenn Research Center (GRC) supports the Hypersonic GN&C effort in developing tools to aid the design of control algorithms to manage a TBCC airbreathing propulsion system during a critical operating period. The critical operating period being addressed in this paper is the span when the propulsion system transitions from one cycle to another, referred to as mode transition. One such tool, that is a basic need for control system design activities, is computational models (hereto forth referred to as models) of the propulsion system. The models of interest for designing and testing controllers are Control Development Models (CDMs) and Control Validation Models (CVMs). CDMs and CVMs are needed for each of the following propulsion system elements: inlet, turbine engine, ram/scram dual-mode combustor, and nozzle. This paper presents an overall architecture for a TBCC propulsion system model that includes all of the propulsion system elements. Efforts are under way, focusing on one of the propulsion system elements, to develop CDMs and CVMs for a TBCC propulsion system inlet. The TBCC inlet aerodynamic design being modeled is that of the Combined-Cycle Engine (CCE) Testbed. The CCE Testbed is a large-scale model of an aerodynamic design that was verified in a small-scale screening experiment. The modeling approach includes employing existing state-of-the-art simulation codes, developing new dynamic simulations, and performing system identification experiments on the hardware in the NASA GRC 10 by10-Foot Supersonic Wind Tunnel. The developed CDMs and CVMs will be available for control studies prior to hardware buildup. The system identification experiments on the CCE Testbed will characterize the necessary dynamics to be represented in CDMs for control design. These system identification models will also be the reference models to validate the CDM and CVM models. Validated models will give value to the tools used to develop the models.

Stueber, Thomas J.↗

DOE OSTI2022

Building a machine learning surrogate model for wildfire activities within a global Earth system model

Abstract. Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90 % higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2=0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

58 GEOSCIENCES↗

DOE OSTI2011

The Global Trigger Processor: A VXS Switch Module for Triggering Large Scale Data Acquisition Systems

The 12 GeV upgrade for Jefferson Lab's Continuous Electron Beam Accelerator Facility requires the development of a new data acquisition system to accommodate the proposed 200 kHz Level 1 trigger rates expected for fixed target experiments at 12 GeV. As part of a suite of trigger electronics comprised of VXS switch and payload modules, the Global Trigger Processor (GTP) will handle up to 32,768 channels of preprocessed trigger information data from the multiple detector systems that surround the beam target at a system clock rate of 250 MHz. The GTP is configured with user programmable Physics trigger equations and when trigger conditions are satisfied, the GTP will activate the storage of data for subsequent analysis. The GTP features an Altera Stratix IV GX FPGA allowing interface to 16 Sub-System Processor modules via 32 5-Gbps links, DDR2 and flash memory devices, two gigabit Ethernet interfaces using Nios II embedded processors, fiber optic transceivers, and trigger output signals. The GTP's high-bandwidth interconnect with the payload modules in the VXS crate, the Ethernet interface for parameter control, status monitoring, and remote update, and the inherent nature of its FPGA give it the flexibility to be used large variety of tasks and adapt to future needs. This paper details the responsibilities of the GTP, the hardware's role in meeting those requirements, and elements of the VXS architecture that facilitated the design of the trigger system. Also presented will be the current status of development including significant milestones and challenges.

Kaneta, Scott↗

DOE OSTI2016

PIXEL ANALYSIS OF PHOTOSPHERIC SPECTRAL DATA. I. PLASMA DYNAMICS

Recent observations of the photosphere using high spatial and temporal resolution show small dynamic features at or below the current resolving limits. A new pixel dynamics method has been developed to analyze spectral profiles and quantify changes in line displacement, width, asymmetry, and peakedness of photospheric absorption lines. The algorithm evaluates variations of line profile properties in each pixel and determines the statistics of such fluctuations averaged over all pixels in a given region. The method has been used to derive statistical characteristics of pixel fluctuations in observed quiet-Sun regions, an active region with no eruption, and an active region with an ongoing eruption. Using Stokes I images from the Vector Spectromagnetograph (VSM) of the Synoptic Optical Long-term Investigations of the Sun (SOLIS) telescope on 2012 March 13, variations in line width and peakedness of Fe i 6301.5 Å are shown to have a distinct spatial and temporal relationship with an M7.9 X-ray flare in NOAA 11429. This relationship is observed as stationary and contiguous patches of pixels adjacent to a sunspot exhibiting intense flattening in the line profile and line-center displacement as the X-ray flare approaches peak intensity, which is not present in area scans of the non-eruptive active region. The analysis of pixel dynamics allows one to extract quantitative information on differences in plasma dynamics on sub-pixel scales in these photospheric regions. The analysis can be extended to include the Stokes parameters and study signatures of vector components of magnetic fields and coupled plasma properties.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Queued Up: Characteristics of Power Plants Seeking Transmission Interconnection As of the End of 2021 [Slides]

Proposed large-scale electric generation and storage projects must apply for interconnection to the bulk power system via interconnection queues. While most projects that apply for interconnection are not subsequently built, data from these queues nonetheless provide a general indicator for mid-term trends in developer interest. Berkeley Lab compiled and analyzed data from all seven ISOs/RTOs in concert with 35 non-ISO utilities, representing an estimated 85% of all U.S. electricity load. We include all "active" projects in these generation interconnection queues through the end of 2021, as well as data on "operational" and "withdrawn" projects where those data are available. We find that the amount of new electric capacity in these queues is growing dramatically, with over 1,400 gigawatts (GW) of total generation and storage capacity now seeking connection to the grid (over 90% of which is for zero-carbon resources like solar, wind, and battery storage). Solar (676 GW) and battery storage (~420 GW) are – by far – the fastest growing resources in the queues; combined they accounted for nearly 85% of new capacity entering the queues in 2021. Substantial wind (247 GW) capacity is also seeking interconnection, 31% of which is for offshore projects (77 GW). In total, about 930 GW of zero-carbon generating capacity is currently seeking transmission access, as is 74 GW of natural gas capacity. Hybrids now comprise a large – and increasing – share of proposed projects, particularly in CAISO and the non-ISO West. 286 GW of solar hybrids (primarily solar+battery) and 19 GW of wind hybrids are currently active in the queues; nearly half of battery storage in the queues is paired with generation. However, much of this proposed capacity will be withdrawn from the queues and not built. Among a subset of queues for which data are available, only 23% of the projects seeking connection from 2000 to 2016 have subsequently been built. Completion percentages appear to be declining and are even lower for wind and solar than other resources. Additionally, wait times are on the rise: for the regions with available data, the typical duration from connection request to commercial operation increased from ~2.1 years for projects built in 2000-2010 to ~3.7 years for those built in 2011-2021.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗