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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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The team of Southwest Research Institute® (SwRI®), ITEA, Sargent & Lundy (S&L), University of Wyoming (UW), Electric Power Research Institute, Inc. (EPRI), General Electric Global Research (GE), designed a research pilot for Flameless Pressurized Oxy-Combustion (FPO), a novel coal technology.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.
The overarching objective of this project is to elucidate the fundamental design rules for microbial division of labor (DOL), the core of ecosystem organization, in the context of artificial yeast—lactic acid bacteria consortia that produce advanced biofuel and chemical from cellulosic biomass. Over the course of four years, we have successfully achieved the objective through three lines of research that combines experiment with mathematical modeling. Specifically, we have shown that compositional and temporal DOL is effective in modulating mixed sugar fermentation by an engineered consortium and uncovered general quantitative criteria for conditions under which microbial DOL outperforms single superbugs for simultaneous utilization of mixed substrates. We also demonstrated, in synthetic consortia of yeast and lactic acid bacteria, that partitioning the labor for substrate breakdown and end-product synthesis can allow an efficient production of 2-butanol that is difficult otherwise for a single yeast strain. Additionally, by systematically probing the stability of an engineered cooperative consortium, we found that the stability of microbial symbiosis is governed by the topological structure of the underlying cellular interactions rather than specific microbial species and that rational modulation of the interactions may facilitate the restoration of collapsed consortia as well as the intervention of target communities. Together, this project has advanced the fundamental knowledge of microbial DOL in terms of its strength in mixed substrate fermentation, production of complex chemicals, and ecological system stability in the context of microbial consortia. It also provides valuable insights into the design, construction and optimization of artificial consortia for the utilization of cellulosic biomass and economic production of biofuel and chemicals.
This grant supported work in high energy theoretical physics on quantum field theory by the Principal Investigator Clay Córdova.
The goal of this project is to develop a highly efficient low-temperature ex-situ Group V diffusion doping approach in the CdCl 2 treated polycrystalline CdTe film using the group V chlorides (i.e., PCl 3 , AsCl 3 , SbCl 3 , and BiCl 3 ) as dopants source. For the first time, our innovative doping strategies through the low-temperature ex-situ Group V doping process in polycrystalline CdSeTe successfully demonstrated a promising power conversion efficiency of ~19% compared to the Cu-doped counterparts. Our newly developed low-temperature ex-situ group V doping allows dedicated control of group V diffusion amount, generates a desirable depth profile, and is completely compatible with commercial CdTe module manufacturing. We built the partnership with the largest CdTe solar module manufacturing in the US, e.g., First Solar, Inc to further develop these low-temperature diffusion doped CdSeTe solar cells. Our technology was granted US Patent No. US11257977B2. In this project, we systematically investigate and characterize the new ex-situ group V doping mechanisms, defect chemistry, microstructure, and device physics in CdTe solar cells by combining experimental and theoretical means to pave the way to achieve lower cost toward $\$$0.02/kWh and higher power conversion efficiency (PCE) toward 25% in polycrystalline CdTe solar technology. This project will benefit the public by providing more affordable solar electricity and provide an effective way to address climate change.