US DOE Scientific-Technical R&D Work to Address Safety Assessments of Spent Nuclear Fuel Storage and Transportation .
Abstract not provided.
Saltzstein, Sylvia↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Improving Solar and Solar+Storage Screening Techniques to Reduce Utility Interconnection Time and Costs (Final Technical Report)
Residential PV installations have increased rapidly over the last decade, and the increased application volume has caused permitting delays and lower overall adoption rates. In this project, we developed and evaluated whether data-driven secondary modeling and screening techniques can help utilities assess customer applications more accurately than traditional screening shortcuts. Secondary topologies are predicted using decision trees and commonly available information, such as service transformer, customer, and street locations. Conductors were predicted using a logistic regression method based on real world object (RWO) types, service transformer ratings, conductor length, and distance to transformer. After developing the combined primary and secondary distribution network model, hosting capacity results were used to train a random forest model to predict the pass/fail likelihood of a customer application. Powerflow based models with predicted secondaries and data-driven methods both increased the screening success rate, relative to common utility heuristics, by as much as 55 percentage points. Data-driven screening techniques were described by one utility as a "right-sized" approach for residential customers given the low-risk of small errors and the high-cost of accurate modeling.
14 SOLAR ENERGY↗
Hypothesis testing via AI: Generating physically interpretable models of scientific data with machine learning (Full Technical Report)
Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.
97 MATHEMATICS AND COMPUTING↗
Feedstock Carbon Intensity Calculator (FD-CIC): Users’ Manual and Technical Documentation
The carbon intensities (CIs) of biofuels are determined with the life cycle analysis (LCA) technique, which accounts for the energy/material uses and emissions during the complete supply chain of biofuel including feedstock production and fuel conversion stages. Regulatory agencies such as California Air Resources Board (CARB) adopts LCA to calculate biofuel CIs. The Low Carbon Fuel Standard (LCFS) program developed by CARB allows individual biofuel conversion facilities to submit their own biofuel CIs with their facility input data and incentivizes the reduction in the CI specific to that particular facility compared to a reference fuel’s CI (Liu et al 2020). Such an incentive program has driven innovations in biorefineries to reduce their greenhouse gas (GHG) emissions by linking their revenue directly to its CI score through LCFS credit trading. Besides the biofuel conversion stage, different farming practices for feedstock growth can result in significant CI variations for feedstocks, thus for biofuels. To provide evidence-based research findings, the U.S. Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E) has supported the Systems Assessment Center of the Energy Systems and Infrastructure Analysis Division at Argonne National Laboratory to examine CI variations of different farming practices to grow agricultural crops for biofuel production. Meanwhile, the ARPA-E has launched the Systems for Monitoring and Analytics for Renewable Transportation Fuels from Agricultural Resources and Management (SMARTFARM) program to develop technologies and data platforms that enable an accurate measurement of key farming parameters that can help robust accounting of the GHG benefits of sustainable, low-carbon agronomic practices at farm level.
54 ENVIRONMENTAL SCIENCES↗
Modular Hybrid Solid State Transformer (H-SST) for Next Generation Flexible and Adaptable Large Power Transformer (LPT) (Final Technical Report)
A 500 kVA HSST, the world first of such system, has been successfully developed. The HSST has the capability to regulate the power flow of the transmission or distribution power lines, or provide voltage regulation. The HSST design is modular therefore it has the ability to be connected in series of parallel to form a large power transformer with smart controllability. The project has enabled a high TRL level prototype be demonstrated at University of Texas at Austin which can lead to additional commercialization and demonstration opportunities. A large number of students were trained under this program that will benefit the power grid industry. A number of publications are published at well known international conferences and journals, further accomplish the mission of knowledge development and dissemination.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Arctic Shrub Expansion, Plant Functional Trait Variation, and Effects on Belowground Carbon Cycling (Final Technical Report)
Terrestrial ecosystems are undergoing dramatic changes in response to climate warming, and these changes are expected to feedback to the atmosphere, potentially altering the trajectory of future climate change. Feedbacks from Arctic ecosystems are a major concern because the Arctic is projected to warm significantly in the 21 st century and because >50% of global belowground organic carbon is stored in permafrost and overlying soils. Warming-driven release of this carbon could drastically increase atmospheric greenhouse gas concentrations and accelerate climate warming. Plant communities are also responding to warming, as evidenced by the widely documented increase in woody-shrub growth and “greening” across much of the Arctic tundra biome. This vegetation shift may offset or amplify warming by altering carbon cycling. The direction and magnitude of shrub effects remain highly uncertain, however, due to limited understanding of the consequences of shrub expansion for belowground carbon cycling and simplification of these relationships in models. The major shrubs expanding in the Arctic (Betula, Salix, and Alnus) vary widely with respect to aboveground and belowground traits (e.g., tissue production and chemistry, rooting depth, microbial symbionts), and may also exhibit substantial intraspecific variation in these traits in response to environmental conditions. Such variation is likely to have profound implications for soil carbon cycling. The overarching goal of this project was to improve process-based understanding of the influence of shrub expansion on carbon cycling to enable improved representation of carbon dynamics in ecosystem and Earth system models. We investigated how plant functional traits vary among shrub genera, respond to environmental conditions, and affect belowground carbon and nutrient cycling by quantifying relationships among functional traits and biogeochemical cycling along edaphic gradients nested within a climate gradient in the Alaskan tundra. We found consistent differences in leaf and root traits among shrub genera and between shrubs and a widespread sedge species, indicating diverse nutrient acquisition strategies and belowground impacts among different arctic shrubs. We also found striking differences in trait values among individuals within the same species or genera within sites. Soil parameters were more important than climate parameters for predicting size and leaf trait variation, and root trait responses were less dependent on climate overall. For all but one root trait, including parameters representing aboveground traits improved the predictive ability of models. These results demonstrate that tundra shrub traits vary considerably at local scales and soil factors drive this variation, especially belowground. Furthermore, leveraging information about aboveground traits and soil conditions can improve predictions of how belowground traits will respond to climate change. Despite these differences, soil carbon and nitrogen pools in the active layer did not vary among plots dominated by different shrub or sedge genera. Instead, pool sizes generally decreased from warmer to colder sites, consistent with a productivity gradient. Patterns of isotopic N composition indicate that shrubs tighten nitrogen cycling via nitrogen resorption or immobilization of shrub litter. Overall, these results suggest that further identifying the specific shrub genera in the tundra landscape will ultimately provide better predictions of belowground dynamics across the changing arctic. We also performed simulation experiments with the Terrestrial Ecosystem Model (TEM) incorporated in the Predictive Ecosystem Analyzer (PEcAn) framework, treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We performed simulations across different types of tundra, including shrub tundra. One key finding was that both model sensitivity and uncertainty to a given parameter could vary within the same type of tundra, but in a different geographical location, such as over the climate gradient of shrub tundra described above. We organized a special session at the annual meeting of the Ecological Society of America in August 2019 to disseminate our results, refine recommendations for model improvement, and initiate collaborations to implement these recommendations in existing models of tundra carbon dynamics at ecosystem to Earth system scales. Our results support DOE near-term priorities by providing mechanistic insights into the role of vegetation change in the terrestrial carbon cycle in a region that is inadequately represented in Earth system models. Current models reduce the complexity of Arctic vegetation to a small number of plant functional types (PFTs). This approach implicitly assumes that each PFT represents the average ecological function of its constituent species, thus ignoring the effects of trait variation on biogeochemical cycling and potentially leading to large uncertainty in the sign and magnitude of ecosystem feedbacks to climate. By quantifying variation of plant functional traits across broad gradients of climatic and edaphic conditions and elucidating the linkages of such variation with carbon and nutrient cycling, our results illustrate the need and create a foundation for further developing trait-based modeling approaches that allow the traits of PFTs to vary as a function of environmental conditions. These approaches should improve the capacity of simulation models to offer insights into ecosystem carbon dynamics associated with novel plant communities in a rapidly changing Arctic.
54 ENVIRONMENTAL SCIENCES↗
Non-Technical Barriers to Geothermal Development in California and Nevada
Geothermal project development in the United States may be subject to numerous permits, authorizations, and other regulatory requirements at the federal, state, and local level, which are necessary to address potential environmental and resource impacts at geothermal project sites. This report presents the findings of our study, including an analysis of federal, state, and local geothermal regulatory and permitting processes, case studies analyzing site-specific attributes that may impact project development at four selected geothermal project sites, an analysis of cost and timeline implications for geothermal project development, and the results of a qualitative study focused on inter-agency coordination and collaboration efforts between federal, state, and/or local agencies for geothermal projects located in California and Nevada. Our analysis found that development timelines may be impacted by multiple federal and state environmental review processes and duplicative permitting requirements as well as coordination efforts between numerous federal, state, and local agencies involved in issuing authorizations and permits necessary for project development. In addition, projects in California and Nevada may face site-specific environmental challenges due to the presence of sensitive resources (e.g., biological species and species habitat, cultural resources) that may result in project construction delays. Our study results also indicate that protracted geothermal development timelines caused by delays in acquiring necessary permits and environmental reviews may result in loss of generated electricity revenue and additional financing costs, which may increase economic uncertainty associated with project development. Through our qualitative analysis, we found that utilization of best practices, including tiering to existing environmental review documents and developing memoranda of understanding, which clearly delineate agency roles and responsibilities may reduce overall project timelines, costs, and uncertainties associated with geothermal project development in California and Nevada.
15 GEOTHERMAL ENERGY↗
LCOE reduction through proactively optimized monitoring of PV Systems (Final Technical Report)
The project demonstrates the value proposition for a high-resolution monitoring system (HRMS) with diagnostic-prognostic capability and determine its impact on LCOE. The HRMS differs from conventional monitoring systems in a number of ways. First it will include the capability to automatically measure IV curves at the string and module levels. This provides a much richer view into the DC performance of the PV system and allows classification of many typical failure and degradation modes. Second, it will incorporate software capable of quantifying power and energy losses in the field as well as define the location and mechanism of the power and energy loss. Moreover, we aimed to deliver a prognostic system that is capable of predicting certain failures before they occur and giving system operators the opportunity to more efficiently plan operations and maintenance (O&M) activity in order to lower costs and increase yield over the life of the system. The research identifies cost targets required for different monitoring stages, including at the string combiner, at the individual string, and at the module level, to lower the levelized cost of energy (LCOE). Finally, a comprehensive guide determines the value PV monitoring brings to PV field operations. The guide assists plant operators in maximizing value from existing plants and identify the trade-offs of different monitoring solutions for future plants depending on the size, location, and expected system lifetime.
14 SOLAR ENERGY↗
PVInsight (Final Technical Report)
Data generated from real-world photovoltaic (PV) systems represent significant opportunities for the industry–from digital operations and maintenance to real-time planning and forecasting. However, these data also come with substantial, unique challenges. A particular challenge is the analysis of unlabeled PV performance data, which we define as time-series measurements of real power production (or sometimes current or voltage) that do not have corresponding meteorological measurements (irradiance, temperature, etc.) or system configuration information (sometimes called system metadata). These challenges are amplified in the distributed rooftop sector, in which data quality, completeness, and metadata can be very poor.
14 SOLAR ENERGY↗
Photoconversion in Disordered Semiconductors: Spatial, Spectral, and Temporal Insights through Nonlinear Microscopy (Final Technical Report)
Conversion of solar photons to useable energy requires two processes to occur successfully in a light-harvesting material: 1) the initial photoexcited state must efficiently produce excited states that are free to move, and 2) those states must be transported to a new location where they can do useful work before any potential is lost as heat. The core focus of this project examined the spatial and energetic dynamics of these two primary events of photoconversion in solution-processed semiconducting materials. The electronic properties of these systems are complex and strongly influenced by the cumulative interaction of local morphology and composition, along with defects, interfaces, and trap sites. It is therefore important to interrogate these systems at the local level, where structural and compositional heterogeneity can be precisely correlated to variation in functionality. Utilizing ultrafast laser spectroscopy coupled together with optical microscopy, this project examined how excited states and free charges spatially and energetically evolve on length scales between 10 nanometers and 10 microns and on timescales from 50 femtoseconds to 1 nanosecond. Insights gained from measurements made on these characteristic length and time scales provided new insight into how macroscopic functionality in disordered photovoltaic materials emerges from the interplay of nanoscale and mesoscale interactions.
14 SOLAR ENERGY↗
High Performance Circuit Pastes for Solid Oxide Fuel Cell Applications: Final Technical Report
Using a combination of in-plane electrical conductivity measurements, electrical contact resistance measurements, tensile fracture tests, double shear lap fracture tests, rapid thermal cycling adhesion tests, in situ wetting angle measurements, 3D X-ray tomography, reduction-oxidation (RedOx) cycling, scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDAX), Density Functional Theory (DFT) modeling, Molecular Dynamics (MD) modeling, and Phase Field modeling, this project determined that a new brazing technique developed by the PIs (specifically the use of nickel particles to direct the wetting and spreading of molten silver) could be used to produce well-adhered, >97% dense, electrically-conductive circuits, current collectors, and/or electrical contacts on a variety of ceramic and ceramic-coated substrates (specifically lanthanum strontium manganite, yttria stabilized zirconia, sapphire, Cr2O3-passivated stainless steel, and Al2O3-passivated stainless steel). Compared to other silver-based alternatives, Ag:Ni led to less manufacturing defects and was more tolerant of the extreme environments (isothermal high-temperature aging) and occasional abuse (rapid thermal cycling and RedOx cycling) encountered during Solid Oxide Fuel Cell and/or Solid Oxide Electrolysis Cell operation. Further, in some situations (such as when to produce Ag:Ni electrical contacts to Al2O3-protected stainless steel) the Ni particles also improved operation by acting as chemical getters that removed electrically-insulating, surface-segregating substrate impurities (i.e. by lowering the contact resistance with the underlying substrate).
36 MATERIALS SCIENCE↗
Corrected Moments in Antenna Coordinates (CMAC) X-SAPR Technical Report
In 2010 the Atmospheric Radiation Measurement user facility procured 3- and 5-cm wavelength radars for documenting the macrophysical, microphysical, and dynamical structure of precipitating systems. In order to maximize the scientific impact, the program supported the development of an application chain to correct for various phenomena in order to retrieve the “point” values of moments of the radar spectrum and polarimetric measurements. This report details the motivation, science, and progress to date as well as charting a path forward.
54 ENVIRONMENTAL SCIENCES↗
A Systematic, Polynomial-Cost Approach to Exact Correlation Energies (Final Technical Report)
The full configuration interaction (FCI) wave function provides the exact solution to the Schrödinger equation in a given basis set. While FCI is intractable due to exponential computational costs, the many-body expansion (or the method of increments) can reduce scaling to a low-order polynomial with system size. This project entailed advances in the incremental FCI (iFCI) approach, designed to allow iFCI to reach larger system sizes and maintain its intrinsic high accuracy. This document describes advances in solvers for iFCI, strategies to treat multiple charge and spin states, and virtual state management methods to reduce memory requirements. Overall, this project allows iFCI to correlate (for the first time) 142 valence electrons in 444 orbitals in a realistic model of a transition metal complex.
74 ATOMIC AND MOLECULAR PHYSICS↗
Final Technical Report for "Cloud-based Low-Scaling Quantum Chemistry Simulations for Materials"
The objective of phase I was to develop the basic infrastructure for performing efficient hybrid DFT calculations on solid materials with a turnaround time amenable to the use in large-scale data-driven approaches. To fulfill the goal we have developed a novel algorithm that significantly reduces the cost of exchange matrix formation. The algorithm does so by making use of three ingredients (a) interpolative decomposition of the electron integrals (b) robust pseudospectral method and (c) occ-RI approach. Our published work demonstrates that the algorithm is orders of magnitude faster than any other hybrid-DFT method and is on the order of only 3-4 times slower than pure DFT. All steps of the algorithm developed during phase I can be accelerated to the point that, for large enough systems, the diagonalization of the Fock matrix will become the most expensive step. To treat such large systems we have created an interface to the ASCR-funded PEXSI library. The PEXSI method is used to compute the density matrix directly from the Fock matrix in a manner that preserves the sparsity of the local representation.
Shiozaki, Toru↗
Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+) Version 5.0 Technical Reference Manual
The HyRAM+ software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen, natural gas, and autogas systems. HyRAM+ is designed to facilitate the use of state-of-the-art models to conduct robust, repeatable assessments of safety, hazards, and risk. HyRAM+ integrates deterministic and probabilistic models for quantifying leak sizes and rates, predicting physical effects, characterizing hazards (thermal effects from jet fires, overpressure effects from delayed ignition), and assessing impacts on people. HyRAM+ is developed at Sandia National Laboratories to support the development and revision of national and international codes and standards, and to provide developed models in a publicly-accessible toolkit usable by all stakeholders. This document provides a description of the methodology and models contained in HyRAM+ version 5.0. The most significant change for HyRAM+ version 5.0 from HyRAM+ version 4.1 is the ability to model blends of different fuels. HyRAM+ was previously only suitable for use with hydrogen, methane, or propane, with users having the ability to use methane as a proxy for natural gas and propane as a proxy for autogas/liquefied petroleum gas. In version 5.0, real natural gas or autogas compositions can be modeled as the fuel, or even blends of natural gas with hydrogen. These blends can be used in the standalone physics models, but not yet in the quantitative risk assessment mode of HyRAM+.
03 NATURAL GAS↗
Exploring Quantized Axion Electrodynamics in Magnetic Topological Insulator Multilayer Heterostructures (Final Technical Report)
The current program is focusing on investigating the properties of the high Chern number quantum anomalous Hall (QAH) effect in magnetic topological insulator (TI) multilayer heterostructures and the exploration of the topological magnetoelectric effect in thick magnetic TI films/heterostructures. Our program includes both experimental and theoretical efforts on this topic.
36 MATERIALS SCIENCE↗
The BREKTRIA 500 – A Breakthrough in Technology and Power Density for an Advanced 500kW Utility-Scale String Inverter (Final Technical Report)
The primary goal of this development project, planned to be 36 months in duration, was to create a 500kW utility-scale string inverter, demonstrating an advanced hybrid architecture that would achieve unprecedented high power density, and bring the inverter to the stage of production readiness. Specific key objectives for the 500kW utility-scale string inverter were: Create a 500kW 3-phase 600Vac inverter that was similar in overall size to the existing 250kW string inverters in the market, thereby demonstrating dramatically increased power density; Achieve a cost of goods, including all manufacturing-related costs and overheads, at or below 2.5¢/Wac ($12,500); Demonstrate full-power operation at a 45-50C ambient temperature with no power de-rating; Accommodate a PV array DC input of up to 1MWdc, aka a DC/AC Ratio of 2.0; Demonstrate peak efficiency greater than 99% at any operating dc voltage, and a CEC weighted average efficiency greater than or equal to 98.5%. The motivation for this project was to leapfrog the competition by creating the world’s most powerful string inverter, utilizing an innovative topology and achieving a step change increase in power density. Achieving the goals of the project would have enabled Yaskawa Solectria Solar to demonstrate its technology leadership, manufacture the utility-scale string inverter in the company’s facilities in Illinois, and bring to the market a highly-differentiated and compelling product to help the company grow its share in the large and growing utility market segment. During the course of this project, BREK Electronics’ hybrid architecture inverter was taken from an early-stage 125kW prototype, to a more mature and successfully demonstrated power stage at twice the power. Two 250kW power stages were planned to build the 500kW inverter. Significant progress was made in the inverter controls, achieving a clean AC sinewave and closed-loop operation into the grid, and improved efficiency by control of the high-speed switching. Further, the updated 250kW power stage was on track to achieve full power operation at an ambient temperature of 50C. At the time of the project’s closure in September 2022, global semiconductor supply remained limited, with shortages creating dramatic swings in both availability and price. Given this situation, our ability to predict cost-of-goods two years out with any accuracy or confidence was limited. As a result, the probability of attaining the target cost of 2.5¢/Wac for the 500kW inverter remained uncertain at the closure of the project. This project successfully demonstrated the potential for the advanced hybrid architecture inverter that emerged from a decade of university research on Si IGBTs and SiC mosfets, leading to this unique inverter topology. The team has taken the first steps toward an evolutionary advancement in inverter technology that remains to be fully realized.
14 SOLAR ENERGY↗