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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 annual supplement on heat pipe technology for 1971 is presented. The document contains 101 references with abstracts and 47 patents. The subjects discussed are: (1) heat pipe applications, (2) heat pipe theory, (3) design, development, and fabrication of heat pipes, (4) testing and operation, (5) subject and author index, and (6) heat pipe related patents.
It is now generally acknowledged that the life science discipline will be the primary beneficiary of Space Station Freedom. The unique facility will permit advances in understanding the consequences of long duration exposure to weightlessness and evaluation of the effectiveness of countermeasures. It will also provide an unprecedented opportunity for basic gravitational biology, on plants and animals as well as human subjects. The major advantages of SSF are the long duration exposure and the availability of sufficient crew to serve as subjects and operators. In order to fully benefit from the SSF, life sciences will need both sufficient crew time and communication abilities. Unlike many physical science experiments, the life science investigations are largely exploratory, and frequently bring unexpected results and opportunities for study of newly discovered phenomena. They are typically crew-time intensive, and require a high degree of specialized training to be able to react in real time to various unexpected problems or potentially exciting findings. Because of the long duration tours and the large number of experiments, it will be more difficult than with Spacelab to maintain astronaut proficiency on all experiments. This places more of a burden on adequate communication and data links to the ground, and suggests the use of AI expert system technology to assist in astronaut management of the experiment. Typical life science experiments, including those flown on Spacelab Life Sciences 1, will be described from the point of view of the demands on the astronaut. A new expert system, 'PI in a Box,' will be introduced for SLS-2, and its applicability to other SSF experiments discussed. (This paper consists on an abstract and ten viewgraphs.)
Abstract Traditionally, FeCrAl alloys played an important role in high-temperature applications due to their ability to form a passive Al oxide film at temperatures above ~ 800 °C. Recently, FeCrAl alloys became of interest for the application of accident tolerant nuclear fuel cladding. This study covers work done at GE Research for better understanding the role of Al, Cr, and Mo in oxidation kinetics and thermodynamics. Several models and commercial prototype alloys have been tested in hydrothermal corrosion autoclave loops, at low temperature steam exposure (~ 400 °C), high temperature steam exposure (~ 1000 °C or higher), and high temperature air exposures. The results provide insights on how chromium and aluminum play a significant role in both high temperature and low temperature oxidation of FeCrAl. Additionally, machine learning tools are used to gain further insights on both predicting future optimized chemistries for balancing the properties of hydrothermal corrosion, low and high temperature steam oxidation, and thermal aging (which is exacerbated due to radiation in a nuclear reactor environment). GE plans to use this framework to further optimize the FeCrAl alloy system for use in nuclear reactor environments. Graphical abstract
Milne Point Field is conducting the first-ever active polymer flood pilot program designed to enhance oil recovery (EOR) of heavy oils on the Alaska North Slope (ANS). The experimental field program consists of two pairs of horizontal injection and production wells deployed in an isolated fault block in the Schrader Bluff reservoir. The EOR method combines polymer flooding and low salinity water flooding to improve recovery. Prior average water cut reduction in polymer EOR fields ordinarily ranges between 10% to 15% for most light oil and heavy oil reservoirs. When comparing other field applications, the long-lasting (24 months) and much lower water cut poses a challenge for numerical history matching when applying typical simulation methods. This abstract discusses two practical new approaches used for history matching of the long-lasting ultralow water cut. These new applications include 1) multiple relative permeability curves with J-function incorporation with low endpoints of relative permeability of water (Krw). This approach integrated the permeability differential adjustment in the geometries of horizontal and vertical directions based on geological review. 2) A specific permeability channel strips along with the horizontal or vertical planar assumption. The current history matching results indicate success in achieving an excellent agreement with the field observed low water-cut.
Abstract Optimizing nitrogen (N) management is an important factor for sustainable perennial biomass systems. However, N application is costly, both financially and environmentally. Our objectives were to determine: (1) N rate and plant spacing effects on yield and yield components of prairie cordgrass swards and (2) fertilizer N replacement value (FNRV) of kura clover in prairie cordgrass-kura clover binary mixtures. Plots were established in Illinois, Minnesota, South Dakota, and Wisconsin, USA, in 2010. Kura clover was transplanted on 30-cm centers in all treatments in which it was a component; prairie cordgrass seedlings were transplanted within the kura clover on 60- and 90-cm centers. Monoculture prairie cordgrass stands were established at the same population densities of mixed stands and fertilized with 0, 75, 150, or 225 kg N ha -1 . Biomass was harvested in the autumn from 2011 to 2013. N (urea), year, plant spacing, and year × plant spacing affected prairie cordgrass production at all locations. Prairie cordgrass yield increased with N application, but the response varied by location. N application tended to increase prairie cordgrass tiller density and consistently increased tiller mass. Prairie cordgrass yield with 0 N was equal to or less than the yield of prairie cordgrass/kura clover mixtures at all locations in 2011 and 2012; however, kura clover provided a FNRV of 25–82 kg N ha -1 to prairie cordgrass in 2013. Kura clover has potential to provide N to prairie cordgrass in binary mixtures of these two species and on land that may not be easily farmed due to wetness.
With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.
Abstract Background Traditional end points used in registrational randomized, controlled trials (RCTs) often do not allow for complete interpretation of the full range of potential clinical outcomes. Desirability of outcome ranking (DOOR) is an approach to the design and analysis of clinical trials that incorporates benefits and risks of novel treatment strategies and provides a global assessment of patient experience. Methods Through a multidisciplinary committee of experts in infectious diseases, clinical trial design, drug regulation, and patient experience, we developed a DOOR end point for infectious disease syndromes and demonstrated how this could be applied to 3 registrational drug trials (ZEUS, APEKS-cUTI, and DORI-05) for complicated urinary tract infections (cUTIs). ZEUS compared fosfomycin to piperacillin/tazobactam, APEKS-cUTI compared cefiderocol to imipenem, and DORI-05 compared doripenem to levofloxacin. Using DOOR, we estimated the probability of a more desirable outcome with each investigational antibacterial drug. Results In each RCT, the DOOR distribution was similar and the probability that a patient in the investigational arm would have a more desirable outcome than a patient in the control arm had a 95% confidence interval containing 50%, indicating no significant difference between treatment arms. DOOR facilitated improved understanding of potential trade-offs between clinical efficacy and safety. Partial credit and subgroup analyses also highlight unique attributes of DOOR. Conclusions DOOR can effectively be used in registrational cUTI trials. The DOOR end point presented here can be adapted for other infectious disease syndromes and prospectively incorporated into future clinical trials.
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Entries for 3632 patents and patent applications for patent citations for the period May 1969 through 1979 are listed. Indexes for subject, inventor, source, number, and accession number, are included.
Entries for 3900 patents and patent applications citations for the period May 1980 through December 1980 are listed. Indexes for subject, invention, source, number, and accession number are included.
For Abstract see A83-12457 (or A8312457/2)
For abstract see A82-30178.
Entries for 4000 patent and patent applications citations for the period May 1969 through June 1983 are listed. Subject, invention, source, number, and accession number indexes are included.
The embedded systems running real-time applications, for which Ada was designed, require their own mechanisms for the management of dynamically allocated storage. There is a need for packages which manage their own internalo structures to control their deallocation as well, due to the performance implications of garbage collection by the KAPSE. This places a requirement upon the design of generic packages which manage generically structured private types built-up from application-defined input types. These kinds of generic packages should figure greatly in the development of lower-level software such as operating systems, schedulers, controllers, and device driver; and will manage structures such as queues, stacks, link-lists, files, and binary multary (hierarchical) trees. Controlled to prevent inadvertent de-designation of dynamic elements, which is implicit in the assignment operation A study was made of the use of limited private type, in solving the problems of controlling the accumulation of anonymous, detached objects in running systems. The use of deallocator prodecures for run-down of application-defined input types during deallocation operations during satellites.
Abstract Can the current successes of global machine learning‐based weather simulators be generalized beyond 2‐week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10‐year simulations with a network trained on output from a physics‐based global atmosphere model using a grid spacing of approximately 110 km and forced by a repeating annual cycle of sea‐surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least 10 years with similarly small climate biases—a prerequisite to wider applicability. With an analysis that combines multiple temporal, spatial, and frequency domain perspectives, we show that ACE faithfully represents the spatiotemporal structure of EAMv2 precipitation and related variables. Finally, we show that a pretrained ACE network is able to adapt to a new global climate model simulation data set with 10 fewer training steps than when starting from random initialization, all while still maintaining low levels of climate bias. Further analysis of these fine‐tuning experiments reveal ACE's intriguing ability to interpolate between distinct global climate models.
This paper presents a reusable design of a data distribution framework for data parallel high performance applications. We are implementing the design in the context of the Chapel high productivity programming language. Distributions in Chapel are a means to express locality in systems composed of large numbers of processor and memory components connected by a network. Since distributions have a great effect on,the performance of applications, it is important that the distribution strategy can be chosen by a user. At the same time, high productivity concerns require that the user is shielded from error-prone, tedious details such as communication and synchronization. We propose an approach to distributions that enables the user to refine a language-provided distribution type and adjust it to optimize the performance of the application. Additionally, we conceal from the user low-level communication and synchronization details to increase productivity. To emphasize the generality of our distribution machinery, we present its abstract design in the form of a design pattern, which is independent of a concrete implementation. To illustrate the applicability of our distribution framework design, we outline the implementation of data distributions in terms of the Chapel language.