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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 objective of this study is to improve the stability and efficiency of thin solar cells with emphasis on a-Si:H devices. The research project was broken down into three main phases. The first involves designing and building a UHV glow discharge system; the second involves making good quality films and eventually efficient cells; the final phase will be analytical.
The evolution of high-temperature, creep-fatigue, life-prediction methods used for cyclic crack initiation is traced from inception in the late 1940's. The methods reviewed are material models as opposed to structural life prediction models. Material life models are used by both structural durability analysts and by material scientists. The latter use micromechanistic models as guidance to improve a material's crack initiation resistance. Nearly one hundred approaches and their variations have been proposed to date. This proliferation poses a problem in deciding which method is most appropriate for a given application. Approaches were identified as being combinations of thirteen different classifications. This review is intended to aid both developers and users of high-temperature fatigue life prediction methods by providing a background from which choices can be made. The need for high-temperature, fatigue-life prediction methods followed immediately on the heels of the development of large, costly, high-technology industrial and aerospace equipment immediately following the second world war. Major advances were made in the design and manufacture of high-temperature, high-pressure boilers and steam turbines, nuclear reactors, high-temperature forming dies, high-performance poppet valves, aeronautical gas turbine engines, reusable rocket engines, etc. These advances could no longer be accomplished simply by trial and error using the 'build-em and bust-em' approach. Development lead times were too great and costs too prohibitive to retain such an approach. Analytic assessments of anticipated performance, cost, and durability were introduced to cut costs and shorten lead times. The analytic tools were quite primitive at first and out of necessity evolved in parallel with hardware development. After forty years more descriptive, more accurate, and more efficient analytic tools are being developed. These include thermal-structural finite element and boundary element analyses, advanced constitutive stress-strain-temperature-time relations, and creep-fatigue-environmental models for crack initiation and propagation. The high-temperature durability methods that have evolved for calculating high-temperature fatigue crack initiation lives of structural engineering materials are addressed. Only a few of the methods were refined to the point of being directly useable in design. Recently, two of the methods were transcribed into computer software for use with personal computers.
Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.
During the core collapse of massive stars, the formation of the proto-neutron star is accompanied by the emission of a significant amount of mass energy (~0.3M ⊙ ) in the form of neutrinos. This mass-energy loss generates an outward-propagating pressure wave that steepens into a shock near the stellar surface, potentially powering a weak transient associated with an otherwise-failed supernova. We analytically investigate this mass-loss-induced wave generation and propagation. Heuristic arguments provide an accurate estimate of the amount of energy contained in the outgoing sound pulse. We then develop a general formalism for analysing the response of the star to centrally concentrated mass loss in linear perturbation theory. To build intuition, we apply this formalism to polytropic stellar models, finding qualitative and quantitative agreement with simulations and heuristic arguments. We also apply our results to realistic pre-collapse massive star progenitors (both giants and compact stars). Our analytic results for the sound pulse energy, excitation radius, and steepening in the stellar envelope are in good agreement with full time-dependent hydrodynamic simulations.We show that prior to the sound pulses arrival at the stellar photosphere, the photosphere has already reached velocities ~20–100 per cent of the local sound speed, thus likely modestly decreasing the stellar effective temperature prior to the star disappearing. Our results provide important constraints on the physical properties and observational appearance of failed supernovae.
There is an array of building technologies whose operation–either passively or actively–holds the potential to improve resilience for building occupants and owners, utilities, communities, and other financial interests. Recent decades have witnessed enhanced resilience to major disruptions, as well as significant improvements in the energy efficiency of building technologies. However, these dual goals have often been pursued independently of each other. Similar levels of energy efficiency can be realized by a range of different technologies. For example, buildings can be kept cool in summer through passive ventilation, more efficient heating, ventilation, and air conditioning (HVAC), higher quality walls and windows, advanced control systems, and intelligent analytics enabled by IoT technology. However, each of these technologies realizes energy efficiency through a different mechanism, and those mechanisms may contribute to resilience in non-uniform ways. One purpose of this workshop was the identification of specific energy efficiency and load flexibility characteristics, followed by an assessment of how those characteristics map onto the resilience space. In June 2019, Oak Ridge National Laboratory hosted a two-day workshop to explore how and where the two areas overlap, complement, or interfere with each other. A diverse group of experts assembled to discuss how to mutually address goals for improving the efficiency and resilience of our nation’s buildings. While this workshop only considered energy efficiency and load flexibility technologies that were realized through behind-the-meter technologies, all forms of resilience were considered. That is, the impact of these technologies on building occupants, building owners, business operators, utilities, communities, and all other relevant stakeholders were within scope.
This report documents the efforts and analyses conducted for the Applied Studies and Technology (AS&T) Ancillary Work Plan (AWP) project titled Evaluation of Pre- and Post- Redevelopment Groundwater Sample Laboratory Analyses from Selected LM Groundwater Monitoring Wells. This effort entailed compiling an inventory of nearly 500 previous well redevelopment events at 16 U.S. Department of Energy Office of Legacy Management (LM) sites, searching the literature for impacts of well redevelopment on groundwater sample quality, and—the focus of this report—evaluating the impacts of well redevelopment on field measurements and sample analytical results. Study Catalyst Monitoring well redevelopment, the surging or high-volume pumping of a well to loosen and remove accumulated sediment and biological build-up from a well, is considered an element of monitoring well maintenance that is implemented periodically during the lifetime of the well to mitigate its gradual deterioration. Well redevelopment has been conducted fairly routinely at a few LM sites in the western United States (e.g., the Grand Junction office site and the Gunnison processing site in Colorado), but at most other sites in this region it is not a routine practice. Also, until recently (2014–2015), there had been no specific criteria for implementing well redevelopment, and documentation of redevelopment events has been inconsistent. A catalyst for this evaluation was the self-identification of these inconsistencies by the Legacy Management Support contractor. As a result, in early 2015 Environmental Monitoring Operations (EMO) staff began collecting and documenting additional field measurements during well redevelopment events. In late 2015, AS&T staff undertook an independent internal evaluation of EMO's well redevelopment records and corresponding pre- and post-well-redevelopment groundwater analytical results. Study Findings Although literature discussions parallel the prevailing industry-wide assumption that well redevelopment is necessary to increase production or to extend the life of a well, no data in the literature indicate that redevelopment affects chemical signatures in monitoring wells. The comprehensive evaluation undertaken for this study also yielded no evidence that redevelopment has any quantifiable or predictable effect on groundwater sample quality. Both short-term and long-term changes in groundwater chemistry were assessed relative to preceding and subsequent well redevelopment events. Although short-term changes in specific conductance or contaminant concentrations likely attributable to well redevelopment were found at some sites, these cases were limited to a small subset of wells in which chemical stratification had been observed. Redevelopment causes mixing of the well water, resulting in short-term impacts, but not in a consistent direction. Long-term groundwater concentration trends of uranium, the primary contaminant of concern at most LM Uranium Mill Tailings Radiation Control Act (UMTRCA) sites, were evaluated for all 16 sites addressed in this study. Based on the data evaluated, there are no apparent impacts of well redevelopment on uranium trends. In most cases where deviations in trends were found, these could be attributed to water level fluctuations or other factors. A few exceptions were found where spikes or marked decreases in uranium concentrations could potentially be related to the preceding well redevelopment, but apparent impacts were neither predictable nor quantifiable. Summary and Recommendations A catalyst for this project was a concern that there was a need for strict criteria for a program-wide approach to well redevelopment at LM sites. There was also an implicit question as to whether well redevelopment was a necessary practice that was being overlooked at some sites. The primary objective of this study was to determine if there are significant differences in laboratory analytical results between pre- and post-redevelopment groundwater samples. Results of this evaluation indicate that this is not the case—groundwater concentrations of uranium, the primary contaminant of concern at most LM UMTRCA sites, generally remained unchanged pre- and post-well-redevelopment. The literature supports redevelopment of monitoring and municipal wells if signs of reduced productivity, biofouling, sediment buildup, or other conditions potentially affecting long-term well integrity are observed. In these cases, use of a downhole camera to examine the condition of the well screen and casing may be useful. However, based on the data sets examined for this study, there is no evidence that well redevelopment is needed in order to obtain samples that have the same chemical concentrations as those in the groundwater. To conclusively demonstrate that point—that is, to define chemical effects—the underlying mechanisms have to be understood. For example, if biofouling is observed in a well and is considered a potential cause of spurious or invalid chemical data, appropriate hypothesis-testing ethodology should be used to test the validity of this claim. Until late 2014, the onset of this project, there was no standard procedure for documenting well redevelopment events; some (perhaps many) had not been captured in the historical record. EMO has made notable progress in this regard since 2015, having established a format for documenting well redevelopment events and associated field measurements, as well as a data repository for capturing those records. This policy should be continued to ensure that all well redevelopment events and associated field observations are recorded and easily tracked.
Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of "Decomposition Functional Types" (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing models to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top-down expert opinion, but ultimately developed using bottom-up, data-driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present and discuss an example clustering analysis to show how model-produced annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from already-existing PFTs. A similar analysis, incorporating observational data, could form a basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in-depth analyses combining open data with high-performance computing; rigorous testing of analytical results; and planning by the global modeling community for decoupling decomposition from fixed site data. These are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR at large scales.
We present results of prebiotic organic synthesis in shock-compressed reducing mixtures of simple ices from quantum molecular dynamics simulations extended to close to chemical equilibrium timescales. Given the relative abundance of carbon in reduced forms in astrophysical ices as well as the tendency of these mixtures to form complex hydrocarbons under the presence of external stimuli, it is possible that cometary impacts on a planetary surface could have yielded a larger array of prebiotic organic compounds than previously investigated. We find that the high pressures and temperatures due to shock compression yield a large assortment of carbon- and nitrogen-bonded extended structures that are highly reactive with short molecular lifetimes. Expansion and cooling causes these materials to break apart and form a wide variety of stable, potentially life-building compounds, including long-chain linear and branched hydrocarbons, large heterocyclic compounds, and a variety of different amines and exotic amino acids. Lastly, our results help provide a bottom-up understanding of hydrocarbon impact synthesis on the early Earth and its role in producing life-building molecules from simple starting materials.
Oak Ridge National Laboratory (ORNL) was competitively awarded the “Maximum Energy Efficiency Building Research Laboratory” (MAXLAB) project under the American Recovery and Reinvestment Act (ARRA). As part of that project, the design and construction of two flexible research platforms (FRP) was included to provide new building test apparatus at ORNL. The two FRPs are designed to be capable of being outfitted as test buildings that are representative of typical construction methods, materials, and geometry used in the light commercial market. These test buildings will be heavily instrumented and will provide ORNL’s industry partners a means of assessing new HVAC, control, and envelope solutions under realistic conditions, in a low financial risk environment. The long-term data collected at the facility can be used to validate analytic models which can then be used by developers to refine their potential products and reduce delivery time to market. This paper outlines the FRP concept, long term research plan and the challenges in the design and construction of the base research apparatus and test buildings.
A copolymer and water/ethanol solvent solution capable of binding with airborne contaminants or potential airborne contaminants, such as biological weapon agents or toxic particulates, coagulating as the solvent evaporates, and adhering the contaminants to a surface so as to inhibit the re-suspension of such contaminants. The solution uses a water or ethanol/water mixture for the solvent, and a copolymer having one of several functional group sets so as to have physical and chemical characteristics of high adhesion, low viscosity, low surface tension, negative electrostatic charge, substantially neutral pH, and a low pKa. Use of the copolymer solution prevents re-aerosolization and transport of unwanted, reactive species thus increasing health and safety for personnel charged with decontamination of contaminated buildings and areas.
There are new tools available to you in Phenix for interpreting cryo-EM maps. You can automatically sharpen (or blur) a map with phenix.auto_sharpen and you can segment a map with phenix.segment_and_split_map. If you have overlapping partial models for a map, you can merge them with phenix.combine_models. If you have a protein-RNA complex and protein chains have been accidentally built in the RNA region, you can try to remove them with phenix.remove_poor_fragments. You can put these together and automatically sharpen, segment and build a map with phenix.map_to_model.
Background: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. Objective: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. Methods: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user’s understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. Results: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. Conclusions: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.
In recent years, considerable attention has been directed toward improving aircraft fuel efficiency. Analytical studies and research with wind tunnel models have demonstrated that the high inherent efficiency of low speed turboprop propulsion systems may now be extended to the Mach .8 flight regime of today's commercial airliners. This can be accomplished with a propeller, employing a large number of thin highly swept blades. The term Prop-Fan has been coined to describe such a propulsion system. In 1983 the NASA-Lewis Research Center contracted with Hamilton Standard to design, build and test a near full scale Prop-Fan, designated the Large Scale Advanced Prop-Fan (LAP). This report provides a detailed description of the LAP program. The assumptions and analytical procedures used in the design of Prop-Fan system components are discussed in detail. The manufacturing techniques used in the fabrication of the Prop-Fan are presented. Each of the tests run during the course of the program are also discussed and the major conclusions derived from them stated.
In this paper the filament winding process was analyzed to relate process variables to structural parameters. An analytical model has been developed for a filament wound preform by determining the necessary geometric and kinematic conditions. An equation of motion of winding was developed to predict layer by layer geometry using the mandrel shape as the initial boundary condition. This model provides the laws of motion of the traverse stroke relative to the mandrel to ensure that the filament laydown is of the predetermined geodesic path over the mandrel, that the winding density is distributed over the mandrel in the prearranged manner, and that the winding build is being formed to reproduce the preassigned shape or contour of the preform. From this analysis, the winding process parameters can be controlled accordingly to produce any given shape. The analytical model was used to produce computer simulation of the winding process. The dynamic simulation of the winding process visualizes and inspects the layer by layer path of the tow and provides process variables required to produce preforms of optimum structural parameters.
To achieve ambitious decarbonization goals it is critical that buildings operate to their full potential. Commercial HVAC systems, however, experience a wide range of operational faults, adversely affecting energy consumption, occupant comfort, and maintenance costs. Analytical tools such as fault detection & diagnostics (FDD) software identify and help diagnose these types of sensing, mechanical, or control-related faults. While significant energy savings has been documented for FDD, along with limited-scale studies on technical capabilities, there is a lack of empirical data on faults being reported by FDD tools. With FDD deployment accelerating significantly over the past decade there is an opportunity to gather and analyze data on commercial HVAC operational problems at an unprecedented scale. Such data could address many questions such as: [a] What faults are most commonly reported?; and [b] How does fault reporting vary by time of year and other possible drivers? A recent study into FDD fault reporting amassed the largest U.S. dataset of commercial HVAC air-side fault records, drawn from multi-year monitoring across over 60,000 pieces of HVAC equipment. The results of this study provide granular data on fault reporting for over 90 unique fault types. In this paper we provide an overview of the research process and highlight key findings and lessons learned. This study presents an extraordinary level of detail on FDD fault reporting characteristics across many climate zones and building types. Armed with these new insights, commercial building industry stakeholders can make better informed decisions when designing, configuring, and operating commercial HVAC systems.
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With the recent emergence of large-scale knowledge dis- covery, data mining and social network analysis, irregular applications have gained renewed interest. Classic cache-based high-performance architectures do not provide optimal performances with such kind of workloads, mainly due to the very low spatial and temporal locality of the irregular control and memory access patterns. In this paper, we present a multi-node, multi-core, fine-grained multi-threaded shared-memory system architecture specifically designed for the execution of large-scale irregular applications, and built on top of three pillars, that we believe are fundamental to support these workloads. First, we offer transparent hardware support for Partitioned Global Address Space (PGAS) to provide a large globally-shared address space with no software library overhead. Second, we employ multi-threaded multi-core processing nodes to achieve the necessary latency tolerance required by accessing global memory, which potentially resides in a remote node. Finally, we devise hardware support for inter-thread synchronization on the whole global address space. We first model the performances by using an analytical model that takes into account the main architecture and application characteristics. We describe the hardware design of the proposed cus- tom architectural building blocks that provide support for the above- mentioned three pillars. Finally, we present a limited-scale evaluation of the system on a multi-board FPGA prototype with typical irregular kernels and benchmarks. The experimental evaluation demonstrates the architecture performance scalability for different configurations of the whole system.
This project supports development of the next generation, integrated energy management infrastructure (EMS) able to incorporate advance visualization of behind-the-meter distributed resource information and probabilistic renewable energy generation forecasts to inform real-time operational decisions. The project involves end-users and active feedback from an Utility Advisory Team (UAT) to help inform how information can be used to enhance operational functions (e.g. unit commitment, load forecasting, Automatic Generation Control (AGC) reserve monitoring, ramp alerts) within two major EMS platforms. Objectives include: Engaging utility operations personnel to develop user input on displays, set expectations, test and review; Developing ease of use and timeliness metrics for measuring enhancements; Developing prototype integrated capabilities within two operational EMS environments; Demonstrating an integrated decision analysis platform with real-time wind and solar forecasting information and timely distributed resource information; Seamlessly integrating new 4-dimensional information into operations without increasing workload and complexities; Developing sufficient analytics to inform and confidently transform and adopt new operating practices and procedures; Disseminating project lessons learned through industry sponsored workshops and conferences;Building on collaborative utility-vendor partnership and industry capabilities