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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↗

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↗

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↗

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↗

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↗

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↗

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↗

Utilizing a Novel Acoustic Backscatter Array to Characterize Waste Consolidation and Settling in a Horizontal Flow Clarifier - 16051

As part of a collaboration between the University of Leeds, Sellafield Ltd and MMI Engineering Ltd, bespoke acoustic instrumentation and numerical simulation methods have been established to optimize nuclear legacy waste transfers. Specifically, a new ultrasonic measurement system has been developed, allowing suspended particle concentration and consolidation to be measured remotely. This technology has been combined with computational fluid dynamics (CFD) modelling of settling particulate systems, permitting full characterization, simulation and prediction of sludge separation processes. Presented here are experimental and numerical results on the transport and settling behavior of non-active nuclear-analogue suspensions in two flow geometries: gravitational settling of glass dispersions in a 250-liter column; and transport and sedimentation of flocculated calcite in a close-to-full-scale clarifier (9 m{sup 3} total volume and 4.8 m{sup 2} length). In addition, details of the design, construction and calibration of a custom-built Ultrasonic Array Research Platform (UARP) are described. Settling column results suggested that particle concentration changes could be simulated with good accuracy by a CFD model (developed using the open source OpenFOAM framework) when compared to measured samples. Large-scale clarifier trials were used to assess the effectiveness of the UARP controller with an attached in situ acoustic backscatter array (ABA). The ABA consisted of 8 transducers angled down at 20 deg. to the vertical, pulsing at 2-2.5 MHz in cycles (covering 1.8 m total depth). In comparison to a range of sampled concentrations across the length of the clarifier, results confirmed that the UARP-ABA system was highly successful at characterizing these complex flows over several hours of operation, allowing visualization of depth-wise bulk density changes and bed build-up over time. Collectively, this work highlights the capability of these tools to monitor and optimize a number of current waste transfer operations at Sellafield, with further potential applications for solid-liquid separations across waste, water treatment and minerals engineering industries. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Bio-inspired MOF-based Catalysts for Lignin Valorization.

Lignin is a potentially plentiful source of renewable organics, with %7E50Mtons/yr produced by the pulp/paper industry and 200-300 Mtons/yr projected production by a US biofuels industry. This industry must process approximately 1 billion tons of biomass to meet the US Renewable Fuel goals. However, there are currently no efficient processes for converting lignin to value-added chemicals and drop-in fuels. Lignin is therefore an opportunity for production of valuable renewable chemicals, but presents staggering technical and economic challenges due to the quantities of material involved and the strong chemical bonds comprising this polymer. Aggressive chemistries and high temperatures are required to degrade lignin without catalysts. Moreover, chemical non-uniformity among lignins leads to complex product mixtures that tend to repolymerize. Conventional petrochemical approaches (pyrolysis, catalytic cracking, gasification) are energy intensive (400-800 degC), require complicated separations, and remove valuable chemical functionality. Low-temperature (25-200 degC) alternatives are clearly desirable, but enzymes are thermally fragile and incompatible with liquid organic compounds, making them impractical for large-scale biorefining. Alternatively, homogeneous catalysts, such as recently developed vanadium complexes, must be separated from product mixtures, while many heterogenous catalysts involve costly noble metals. The objective of this project is to demonstrate proof of concept that an entirely new class of biomimetic, efficient, and industrially robust synthetic catalysts based on nanoporous Metal- Organic Frameworks (MOFs) can be developed. Although catalytic MOFs are known, catalysis of bond cleavage reactions needed for lignin degradation is completely unexplored. Thus, fundamental research is required that industry and most sponsoring agencies are currently unwilling to undertake. We introduce MOFs infiltrated with titanium and nickel species as catalysts for the C-O bond hydrogenolysis in model compounds, which mimic the b-O-4, a-O-4, and 4-O-5 linkages of natural lignin. The versatile IRMOF-74(n) series is proposed as a platform for creating efficient hydrogenolysis catalysts as it not only displays tunable pore sizes, but also has the required thermal and chemical stability. The catalytic C-O bond cleavage occurs at 10 bar hydrogen pressure and temperatures as low as 120 degC. The conversion efficiency of the aromatic ether substrates into the corresponding hydrocarbons and phenols varies as PhCH 2 CH 2 OPh > PhCH 2 OPh > PhOPh (Ph = phenyl), while the catalytic activity generally follows the following trend Ni@IRMOF-74>Ti@IRMOF-74>IRMOF-74. Conversions as high as 80%, coupled with good selectivity for hydrogenolysis vs. hydrogenation, highlight the potential of MOF-based catalysts for the selective cleavage of recalcitrant aryl-ether bonds found in lignin and other biopolymers. This project supports the DOE Integrated Biorefinery Program goals, the objective of which is to convert biomass to fuels and high-value chemicals, by addressing an important technology gap: the lack of low-temperature catalysts suitable for industrial lignin degradation. Biomass, which is %7E30 wt% lignin, constitutes a potentially major source of platform chemicals that could improve overall profitability and productivity of all energy-related products, thereby benefiting consumers and reducing national dependence on imported oil. Additionally, DoD has a strong interest in low-cost drop-in fuels (Navy Biofuel Initiative) and has signed a Memorandum of Understanding with DOE and USDA to develop a sustainable biofuels industry.

Allendorf, Mark D.↗

IEA Bioenergy Task 40 Sustainable International Bioenergy Trade: Securing Supply and Demand (Country Report 2014—United States)

Logistical barrier are tied to feedstock harvesting, collection, storage and distribution. Current crop harvesting machinery is unable to selectively harvest preferred components of cellulosic biomass while maintaining acceptable levels of soil carbon and minimizing erosion. Actively managing biomass variability imposes additional functional requirements on biomass harvesting equipment. A physiological variation in biomass arises from differences in genetics, degree of crop maturity, geographical location, climatic events, and harvest methods. This variability presents significant cost and performance risks for bioenergy systems. Currently, processing standards and specifications for cellulosic feedstocks are not as well-developed as for mature commodities. Biomass that is stored with high moisture content or exposed to moisture during storage is susceptible to spoilage, rotting, spontaneous combustion, and odor problems. Appropriate storage methods and strategies are needed to better define storage requirements to preserve the volume and quality of harvested biomass over time and maintain its conversion yield. Raw herbaceous biomass is costly to collect, handle, and transport because of its low density and fibrous nature. Existing conventional, bale-based handling equipment and facilities cannot cost-effectively deliver and store high volumes of biomass, even with improved handling techniques. Current handling and transportation systems designed for moving woodchips can be inefficient for bioenergy processes due to the costs and challenges of transporting, storing, and drying high-moisture biomass. The infrastructure for feedstock logistics has not been defined for the potential variety of locations, climates, feedstocks, storage methods, processing alternatives, etc., which will occur at a national scale. When setting up biomass fuel supply chains, for large-scale biomass systems, logistics are a pivotal part in the system. Various studies have shown that long-distance international transport by ship is feasible in terms of energy use and transportation costs, but availability of suitable vessels and meteorological conditions (e.g., winter time in Scandinavia and Russia) need to be considered. However, local transportation by truck (both in biomass exporting and importing countries) may be a high-cost factor, which can influence the overall energy balance and total biomass costs.

09 BIOMASS FUELS↗

Queued Up: Characteristics of Power Plants Seeking Transmission Interconnection As of the End of 2022 [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 2022, 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 2,000 gigawatts (GW) of total generation and storage capacity now seeking connection to the grid (over 95% of which is for zero-carbon resources like solar, wind, and battery storage). Solar (947 GW) and battery storage (~680 GW) are – by far – the fastest growing resources in the queues; combined they accounted for over 80% of new capacity entering the queues in 2022. Substantial wind (300 GW) capacity is also seeking interconnection, 38% of which is for offshore projects (113 GW). In total, about 1,250 GW of zero-carbon generating capacity is currently seeking transmission access, as is 82 GW of natural gas capacity. Hybrids projects (co-locating multiple generation and/or storage types) comprise a large – and increasing – share of proposed projects, particularly in CAISO and the non-ISO West. 457 GW of solar hybrids (primarily solar+battery) and 24 GW of wind hybrids are currently active in the queues; over 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 21% of the projects (and 14% of capacity) seeking connection from 2000 to 2017 have been built as of the end of 2022. Additionally, interconnection wait times are on the rise: The typical duration from connection request to commercial operation increased from <2 years for projects built in 2000-2007 to nearly 4 years for those built in 2018-2022 (with a median of 5 years for projects built in 2022).

24 POWER TRANSMISSION AND DISTRIBUTION↗

How Well Can We Represent the Spectrum of Convective Clouds in a Climate Model? Comparisons between Internal Parameterization Variables and Radar Observations

Current climate models cannot resolve individual convective clouds, and hence parameterizations are needed. The primary goal of convective parameterization is to represent the bulk impact of convection on the gridbox-scale variables. Spectral convective parameterizations also aim to represent the key features of the subgrid-scale convective cloud field such as cloud-top-height distribution and in-cloud vertical velocities in addition to precipitation rates. Ground-based radar retrievals of these quantities have been made available at Darwin, Australia, permitting direct comparisons of internal parameterization variables and providing new observational references for further model development. A spectral convective parameterization [the convective cloud field model (CCFM)] is discussed, and its internal equation of motion is improved. Results from the ECHAM–HAM model in single-column mode using the CCFM and the bulk mass flux Tiedtke–Nordeng scheme are compared with the radar retrievals at Darwin. The CCFM is found to outperform the Tiedtke–Nordeng scheme for cloud-top-height and precipitation-rate distributions. Radar observations are further used to propose a modified CCFM configuration with an aerodynamic drag and reduced entrainment parameter, further improving both the convective cloud-top-height distribution (important for large-scale impact of convection) and the in-cloud vertical velocities (important for aerosol activation). This study provides a new development in the CCFM, improving the representation of convective cloud spectrum characteristics observed in Darwin. This is a step toward an improved representation of convection and ultimately of aerosol effects on convection. It also shows how long-term radar observations of convective cloud properties can help constrain parameters of convective parameterization schemes.

54 ENVIRONMENTAL SCIENCES↗

Advanced Instrumentation, Information and Control (II&C) Research and Development Facility Buildout and Project Execution of LWRS II&C Pilot Projects 1 and 3

The U.S. Department of Energy (DOE) is sponsoring research, development, and deployment on light water reactor sustainability (LWRS), in which the Idaho National Laboratory (INL) is working closely with nuclear utilities to develop technologies and solutions to help ensure the safe operational life extension of current reactors. As technologies are introduced that change the operation of the plant, the LWRS pilot projects can help identify their best-advanced uses and help demonstrate the safety of these technologies. In early testing of operator performance given these emerging technologies will ensure the safety and usability of systems prior to large-scale deployment and costly verification and validation at the plant. The aim of these collaborations, demonstrations, and approaches are intended to lessen the inertia that sustains the current status quo of today's II&C systems technology, and to motivate transformational change and a shift in strategy to a long-term approach to II&C modernization that is more sustainable. Research being conducted under Pilot Project 1 regards understanding the conditions and behaviors that can be modified, either through process improvements and/or technology deployment, to improve the overall safety and efficiency of outage control at nuclear facilities. The key component of the research in this pilot project is accessing the delivery of information that will allow researchers to simulate the control room, outage control center (OCC) information, and plant status data. The simulation also allows researchers to identify areas of opportunity where plant operating status and outage activities can be analyzed to increase overall plant efficiency. For Pilot Project 3 the desire is to demonstrate the ability of technology deployment and the subsequent impact on maximizing the 'Collective Situational Awareness' of the various stakeholders in a commercial nuclear power plant. Specifically, the desire is to show positive results in plant status control, information management, knowledge management, and 'Real-Time-Truth' as it relates to the current plant conditions. The following report includes two attachments; each attachment represents Pilot Project 1 and 3. The two attachments also provide a report on two distinct milestones that were completed and are described below: M3L11IN06030307 - Complete initiation of two pilot projects. Complete initiation of pilot projects on real-time configuration management and control to overcome limitations with existing permanent instrumentation and real-time awareness of plant configurations; two candidate projects that consider low-cost wireless technology for in situ configuration monitoring and candidate technologies and an information architecture for outage management and control will be initiated with utilities. M3L11IN06030309 - Complete data collection, R&D plans, and agreements needed to conduct the two pilot projects. Complete data collection conducted at pilot project utilities to support real-time configuration management and outage control center pilot studies conducted; R&D plan for pilot projects produced and needed agreements established to support R&D activities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Design and Study of Inductively Coupled Plasma Chamber Components Using the SupRISE Test Device at DIII-D

The DIII-D National Fusion Facility aims to increase the auxiliary heating power for the tokamak by upgrading the Neutral Beam Injection (NBI) system. In collaboration with North Carolina State University, the conventional ‘arc and filament’ NBI ion sources will be converted to inductively coupled plasma (ICP) sources which utilize radio frequency (RF) coupling to maximize reliability for high power operation. In support of this initiative, a full scale test device (SupRISE, Superior Radiofrequency Ion Source Experiment) is currently under construction at the DIII-D Facility. In preparation for the construction of a full scale prototype that can be installed on the DIII-D NBI system, experiments on SupRISE will be conducted to determine the optimal RF frequency for high power coupling, Faraday shield slit configuration, and ICP chamber length. SupRISE is comprised of an approximately 30 x 70 cm quartz dielectric vessel with an internal Faraday shield enclosed in a secondary vacuum chamber to ensure structural stability of the dielectric. Actively cooled front and back plates are designed to reduce the thermal stresses on the plasma facing components and mate with the existing accelerator used by the NBI system at DIII-D. 50 kW of RF power will be coupled to the plasma through the quartz over a variable frequency range of 4-8 MHz to sustain a plasma density of ∼ 10 18 m −3 for a 10 s ON, 210 s OFF duty cycle. Various modelling efforts have been employed to simulate the thermal and stress profiles over the primary components of the SupRISE device as well as the inductance behavior of the RF antenna. These simulation results and the final design for SupRISE will be presented. An additional reduced-scale predecessor ICP source (called RISE) has been used to develop a predictive match model that will be applied to frequency optimization studies on SupRISE. Furthermore, the outcomes of this research and complementary efforts at North Carolina State University are essential for the incorporation of ICP NBI positive ion sources at the DIII-D facility.

DIII-D↗

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

59 BASIC BIOLOGICAL SCIENCES↗

Validation of SAS4A/SASSYS-1 for predicting steady-state single-phase natural circulation

The validation of system analysis codes for nuclear reactor systems is required for the development and application of these computational tools. Designed as a comprehensive system analysis code for advanced nuclear reactors, SAS4A/SASSYS-1 requires validation of its physics model for capturing single-phase natural circulation behavior. To support the validation of SAS4A/SASSYS-1, high-precision experiments are performed capturing steady-state single-phase natural circulation on a scaled facility with comprehensive instrumentation. In this work, dedicated tests are performed quantifying the critical modeling parameters, and a single-phase natural circulation benchmark dataset is obtained with well-documented uncertainty and comprehensive facility description. The validation is then performed against the dataset examining the capability of SAS4A/SASSYS-1 in simulating steady-state single-phase natural circulation. The experimental facility is modeled in the candidate code. Solution verification is performed using Richardson-extrapolation-based estimators which quantify and restrict numerical errors from discretization. Input uncertainty provided by the benchmark dataset is forward propagated through the candidate code, quantifying the output uncertainty in a Monte Carlo approach. The composition of the output uncertainty is also quantified through a variance-based sensitivity analysis. With the uncertainty quantified for each individual condition, a detailed comparison between the simulation results and experimental data is performed covering the whole dataset. The results show consistent agreement for all primary parameters. The current validation activity provides a valuable benchmark dataset for the validation of system analysis codes in capturing single-phase natural circulation and demonstrates satisfactory prediction capability of SAS4A/SASSYS1 for steady-state single-phase natural circulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Ultra-fast Boriding for Improved Energy Efficiency and Reduced Emissions in Materials Processing Industries (Final Report)

High-temperature treatment processes (such as nitriding, carburizing, and boronizing) are well-established and used widely by numerous industrial sectors to achieve superior mechanical and tribological properties on all kinds of engineering components made out of ferrous materials. Unfortunately, these traditional processes are very slow and energy intensive, and they produce large amounts of CO 2 emissions and liquid/solid wastes to deal with during and after each process run. From economic and environmental points of views, it is highly desirable to develop and implement novel surface treatment technologies that are quick, cheap, clean, and energy efficient. The main objective of this project was to develop an industrial-scale ultra-fast boriding process that realizes all of these desirable attributes. The other objectives of the project were to demonstrate the superior performance, properties, and durability of borided surfaces under relevant test conditions and to transfer optimized technology to industry for large-scale practice. Major research activities included process design, modeling, product optimization, and scale-up. This project has achieved all of its stated objectives, and the industrial-scale boriding process has been successfully transferred to industry. Specifically, all of the original tasks have been completed, and the milestones have been met by scaling up from a small proof-of-concept system (which could only handle about 0.25 kg melt) to a commercial-scale boriding unit that can handle up to 4,000 kg melt. Mechanical and tribological testing of borided test samples and industrial parts has also been carried out, and their superior performance over a broad range of test conditions has been validated. With the large-scale, ultra-fast boriding process, we could achieve thick, hard, and uniform boride layers on all kinds of steel components, even after only 5 min of treatment time. We could also attain excellent uniformity in thickness over the entire exposed surfaces — even in the very sharp corners or interior sections of odd-shaped workpieces. Boriding of numerous nonferrous materials was also demonstrated, and thick boride layers (up to 300 µm in thickness) were achieved on their outer surfaces. With the use of a large-scale boriding unit at Argonne National Laboratory, our research team has successfully treated large numbers of industrial components in a batch-to-batch operation without creating any in-house gaseous emissions or producing solid and/or liquid wastes. With the use of this large unit, our team achieved surface hardness values of 15 to 45 GPa, depending on the type of metal or alloy being treated. The thick boride layers attained on steel components consisted of FeB, Fe 2 B, and Fe 3 B phases, and their hardness values ranged from 16 to 22 GPa. Layers consisting of Fe 2 B phase alone are also possible with this process, and in the case of nonferrous materials. Overall, because of the much superior property and performance improvements in treated products, much lower energy consumption, and nearly zero environmental waste and emissions, the demonstrated boriding technology may find broad applications in many cross-cutting industries. Mechanical wear, abrasion, erosion, corrosion, and scuffing are often life-limiting factors in mechanical systems that are used in manufacturing, transportation, aerospace, agricultural, earth-moving, and mining equipment; but the machine components treated by ultra-fast boriding can effectively prevent such degradations. In this project, the ultra-fast boriding was proven to be most ideal for the treatment of iron and many other ferrous alloys, like stainless steel. We have shown through our experimental studies that our method can also be used for the treatment of many nonferrous metals and alloys. In the case of superalloys and Ni 3 Al intermetallic materials, more than a 300-µm thick boride layer can be achieved with our boriding furnace within 15 to 30 min (which results in five times the hardness of the base materials). Pre-carburized steels and nonferrous alloys can also be treated by the new boriding technique. Overall, very thick (more than a quarter millimeter) and hard surface layers produced by ultra-fast boriding are expected to have a positive impact on the performance and durability of all types of machine components that are subject to corrosive, erosive, abrasive, and adhesive wear. Since the focus of our project was primarily on the large-scale boriding of ferrous materials, in this report, we will broadly cover the results from various ferrous materials that were used in our experiments. Another transformational feature of ultra-fast boriding is that it does not create any greenhouse gas emissions or solid and/or liquid wastes to deal with during or after the treatment; hence, it is an ultra-green technology (no need for expensive waste disposal and/or treatment facilities). It may eliminate the use of natural gas and other carbon-based fuels as the primary energy source in the heat treatment industry (which is the most common practice at present), thus, minimizing the CO 2 , NO x , CO, and SO x emissions that are generated in large quantities by current heat treatment practices. The main ingredient used during boriding is borax, which is a natural mineral. Our demonstrated and scaled-up technology can now be used in a wide range of manufacturing and transportation applications. It can deliver thick and uniform boride layers of more than 200 µm in steels, if needed, over the entire exposed surfaces — even in the very sharp corners or interior sections of workpieces (which are typical of gears, tools, pipes, and other components). Beneath the distinct boride layers is an equally-thick boron diffusion layer or zone, which is comparable to the diffusion zones achieved by carburizing and nitriding processes. In the cases of traditional boriding, nitriding, and/or carburizing methods, achieving such layer thickness and case depths would have taken several hours, and these surfaces would not have achieved the same levels of hardness and other desirable properties as they do with ultra-fast boriding. Some of the key stakeholders of ultra-fast boriding technology include companies that are active in heat-treatment, thermal-diffusion, and hard-coating processes; metal-forming, machining, and other manufacturing processes; and technologies related to transportation (including original equipment manufacturers for autos), mining and oil exploration, agricultural and heavy equipment. Bodycote Thermal Processing was the cost-sharing industrial partner and licensee in this project. They also provided test components and technical and commercial information pertaining to industrial components that are most suitable for ultra-fast boriding. Bodycote also coordinated some of the field tests and metallurgical examinations. Overall, ultra-fast boriding is a transformational technology that can complement, if not replace, many of the energy- and carbon-intensive surface treatment processes that are in use today, such as conventional boriding, carburizing, nitriding, carbo-nitriding, and physical and chemical vapor deposition. In our project, the developed technology was scaled-up and proven to be an ultra-green, ultra-efficient process that can drastically reduce costs, increase productivity, and improve the performance and reliability of machine components used in cross-cutting industrial applications.

Erdemir, Ali↗

Discovering New Geothermal Systems In The Great Basin Region, Western USA: An Integrated Approach For Establishing Geothermal Play Fairways

The Great Basin of the western USA is capable of producing much greater amounts of geothermal energy than the current ~670 MW from ~25 power plants. Most geothermal resources in this region are blind, and thus the favorable characteristics for geothermal activity must be synthesized and methodologies developed to discover new commercial-grade systems (>130oC). The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity as a means of identifying promising areas for new development. In the Nevada play fairway project, nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new geothermal potential map of 96,000 km2. These parameters were grouped into subsets and individually weighted to delineate rankings for heat and local, intermediate, and regional permeability, which collectively defined the play fairways. From the regional map, 24 highly prospective areas, including known undeveloped systems and unknown potential blind systems, were identified for further analysis. Five particularly promising sites were then selected for detailed studies. Multiple techniques, including geologic mapping, Quaternary fault analysis, 2-m temperature surveys, gravity surveys, LiDAR, geochemical studies, seismic reflection analysis, and 3D modeling, were employed in these areas to define likely sites for high permeability and define highly prospective drilling targets. Local and intermediate permeability models were revised to reflect results of detailed analyses and generate new detailed play fairway maps. Lessons learned include: 1) initially identified sites commonly include multiple favorable settings at a finer scale; 2) promising sites in Cenozoic basins cannot be recognized without detailed geophysical surveys; and 3) play fairway analysis is critical at multiple scales, providing a means to select regional prospects as well as vectoring into drilling targets at individual sites. Based on our detailed studies, several potential new, high temperature systems were discovered. However, TG drilling is needed to confirm commercial grade resources.

15 GEOTHERMAL ENERGY↗