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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 three basic methods of weight prediction (fixed-fraction, statistical correlation, and point stress analysis) and some of the computer programs that have been developed to implement them are discussed. A modified version of the WAATS (Weights Analysis of Advanced Transportation Systems) program is presented, along with input data forms and an example problem.
This report documents a study performed to investigate the requirements for criticality safety benchmark experiments for high-assay, low-enriched uranium (HALEU) fuel in transportation applications. In this work, an exploratory application model, the “Pebble Tanker,” was developed to represent TRISO fuel in a transportation scenario for an analysis of the validation basis in industrial quantities. An aspect of the criticality validation process involves assessing the “similarity” between application and experimental benchmark systems through an integral index parameter evaluation. Here, this includes propagating nuclear data uncertainties and calculating a correlation coefficient (hereinafter referred to as “c k ”) to evaluate the similarity of benchmark experiments compared with the application Pebble Tanker model. Finding sufficient critical benchmark experiments allows for the evaluation of bias and bias uncertainty, thus determining the upper subcritical limit (USL) of the transportation package. A target k eff of ~0.94 was used in this work to establish appropriate modeling conditions, reflecting a reasonable estimate for a USL. Two container models were investigated: one with the Hermes-type pebble and one with the Pebble Bed Modular Reactor (PBMR)–type pebble. The models were simplified, considering only fuel, containment structure, and either water or air. This allows a focus on the underlying physics of applications involving TRISO fuel pebbles using the Pebble Tanker model. A crucial consideration is the transport package's ability to safely hold pebbles while flooded, maintaining subcritical conditions. Tools available in the SCALE 6.3.1 suite—the CSAS6-Shift, TSUNAMI-3D-Shift, and TSUNAMI-IP sequences—were employed for neutronics and sensitivity and uncertainty (S/U) analysis of the Pebble Tanker. Findings demonstrated sufficient available critical experiment benchmarks to perform a validation of the Pebble Tanker in the most reactive state, i.e., when the Tanker is flooded.
Artificial Satellite Analysis Program (ASAP) is general orbit-predicting computer program incorporating sufficient orbit-modeling accuracy for design and planning of missions and analysis of maneuvers. Suitable for study of planetary-orbit missions with spacecraft trajectories of reconnaissance (flyby) and exploratory (mapping) nature. Not written for specific mission and intended use for almost any planetary orbiting mission. Written in FORTRAN 77.
A methodology for Hazard Extraction and Analysis of Trends (HEAT) is proposed and conducted on a data set of wildfire incident response forms, known as ICS-209-PLUS.The HEAT processes: (1) extract a set of hazards from a data set, (2) calculate hazard-relevant metrics in a primary analysis, (3) analyze trends over time in metrics using timeseries, and (4) examine potential explanations for metric trends using a secondary analysis. Hazards are extracted from narrative data in the ICS-209-PLUS based on a framework previously developed by the authors, using natural language processing. Metrics examined for each hazard include operational time to occurrence, rate of occurrence, frequency, and severity. Primary results include a taxonomy of hazards present in the data set with relevant quantitative metrics. The most frequent hazards identified are environmental and include hazardous terrain. Most hazards occur on average between 35-55% containment. Incidents with hazards tend to have a higher average severity score when compared to the average score for all incidents. Time series of the metrics and relevant predictors, including fire characteristics, fire intensity, and operations, are created to facilitate further analysis. Secondary results used to determine which factors best predict hazard frequency include a correlation matrix and regression analysis. These findings are relevant to safety for current, as well as emerging wildfire operations, and are an exploratory first step in developing historical data-driven risk assessment models.
This report provides an initial study for producing reduced-order models (ROMs) of pebble-bed high temperature gas reactor (HTGR) models for the purposes of design optimization. As an initial study, this work is meant to be exploratory---identifying useful workflows and methods for ROM generation---and not meant to be a catch-all analysis of HTGR ROM generation and usage for optimization. This report summarizes three tasks performed in Fiscal Year 2022: 1) the creation of HTGR model, 2) the sensitivity analysis of model design parameters, and 3) an introduction to ROM generation techniques. The representative HTGR model created in this work is a multiphysics equilibrium-core using the BlueCRAB (comprehensive reactor analysis bundle) reactor analysis application, coupling four physical phenomena: neutronics, streamline depletion, porous flow thermal hydraulics, and pebble heat conduction. Part of the model creation was identifying some design parameters and quantities of interest that are relevant in an optimization analysis and adjustable in the model. The sensitivity analysis utilized a polynomial chaos expansion methodology to compute global sensitivity metrics. This analysis showed that thermal hydraulics parameters and quantities of interest had a relatively small impact on simulation results. Finally, the ROM generation work involved exploring three different ROM methodologies: polynomial regression, a Gaussian process, and artificial neural networks. Using a cross-validation technique to characterize ROM performance, the Gaussian process and single-layer artificial neural networks showed the most promising results. Overall, this study was insightful and the lessons learned will be invaluable for the eventual development of an HTGR design optimization workflow.
P-MartCancer is a new interactive web-based software environment that enables biomedical and biological scientists to perform in-depth analyses of global proteomics data without requiring direct interaction with the data or with statistical software. P-MartCancer offers a series of statistical modules associated with quality assessment, peptide and protein statistics, protein quantification and exploratory data analyses driven by the user via customized workflows and interactive visualization. Currently, P-MartCancer offers access to multiple cancer proteomic datasets generated through the Clinical Proteomics Tumor Analysis Consortium (CPTAC) at the peptide, gene and protein levels. P-MartCancer is deployed using Azure technologies (http://pmart.labworks.org/cptac.html), the web-service is alternatively available via Docker Hub (https://hub.docker.com/r/pnnl/pmart-web/) and many statistical functions can be utilized directly from an R package available on GitHub (https://github.com/pmartR).
Electrification of end-use services in the transportation, buildings, and industrial sectors coupled with decarbonization of electricity generation has been identified as one of the key pathways to achieving a low-carbon future in the United States. By lowering the carbon intensity of the electricity generation and substituting electricity for higher-emissions fossil fuels in end-use sectors, significant reductions in carbon dioxide emissions can be achieved. This report describes a preliminary analysis that examines the potential impacts of widespread electrification on the U.S. energy sector. We develop a set of exploratory scenarios under which electrification is aggressively pursued across all end-use sectors and examine the impacts of achieving these electrification levels on electricity load patterns, total fossil energy consumption, carbon dioxide emissions, and the evolution of the U.S. power system.
The ability to fine tune gene expression has created the field of metabolic pathway optimization and balancing where a variety of factors affecting flux balance are carefully modulated to improve product titers, yields and productivity. Using a library of isopropyl β-D- 1 -thiogalactopyranoside (IPTG)-inducible mutant T 7 promoters of varied strength a combinatorial method was developed for transcriptional balancing of the violacein pathway. Violacein biosynthesis involves a complex five-gene pathway that is an excellent model for exploratory metabolic engineering efforts into pathway regulation and control due to many colorful intermediates and side products allowing for easy analysis and strain comparison. Upon screening approximately 4% of the total initial library, several high-titer mutants were discovered that resulted in up to a 63-fold improvement over the control strain. With further fermentation optimization, titers were improved to 1829 ± 46 mg/L; a 2.6-fold improvement in titer and a 30-fold improvement in productivity from previous literature reports.
Results are summarized of a common scientific probe study to explore the atmospheres of Saturn and Uranus. This was a three-month follow-on effort to the Outer Planet Entry Probe System study. The report presents: (1) a summary, conclusions and recommendations of this study, (2) parametric analysis conducted to support the two system definitions, (3) common Saturn/Uranus probe system definition using the Science Advisory Group's exploratory payload and, (4) common Saturn/Uranus probe system definition using an expanded science complement. Each of the probe system definitions consists of detailed discussions of the mission, science, system and subsystems including telecommunications, data handling, power, pyrotechnics, attitude control, structures, propulsion, thermal control and probe-to-spacecraft integration. References are made to the contents of the first three volumes where it is feasible to do so.
Our article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. Furthermore, these challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.
The feasibility of using a solid-state photo-imaging system to obtain resolution imagery from a Pioneer-type spinning spacecraft in future exploratory missions to the outer planets is discussed. Evaluation of the photo-imaging system performance, based on electrical video signal analysis recorded on magnetic tape, shows that the signal-to-noise (S/N) ratios obtained at low spatial frequencies exceed the anticipated performance and that measured modulation transfer functions exhibited some degradation in comparison with the estimated values, primarily owing to the difficulty in obtaining a precise focus of the optical system in the laboratory with the test patterns in close proximity to the objective lens. A preliminary flight model design of the photo-imaging system is developed based on the use of currently available phototransistor arrays. Image quality estimates that will be obtained are presented in terms of S/N ratios and spatial resolution for the various planets and satellites. Parametric design tradeoffs are also defined.
Neuromorphic computers are hardware systems that mimic the brain’s computational process phenomenology. This is in contrast to neural network accelerators, such as the Google TPU or the Intel Neural Compute Stick, which seek to accelerate the fundamental computation and data flows of neural network models used in the field of machine learning. Neuromorphic computers emulate the integrate and fire neuron dynamics of the brain to achieve a spiking communication architecture for computation. While neural networks are brain-inspired, they drastically oversimplify the brain’s computation model. Neuromorphic architectures are closer to the true computation model of the brain (albeit, still simplified). Neuromorphic computing models herald a 1000x power improvement over conventional CPU architectures. Sandia National Labs is a major contributor to the research community on neuromorphic systems by performing design analysis, evaluation, and algorithm development for neuromorphic computers. Space-based remote sensing development has been a focused target of funding for exploratory research into neuromorphic systems for their potential advantage in that program area; SNL has led some of these efforts. Recently, neuromorphic application evaluation has reached the NA-22 program area. This same exploratory research and algorithm development should penetrate the unattended ground sensor space for SNL’s mission partners and program areas. Neuromorphic computing paradigms offer a distinct advantage for the SWaP-constrained embedded systems of our diverse sponsor-driven program areas.
Recent technological advances in airplane construction techniques and materials allow for the production of aerodynamic surfaces without significant waviness and roughness, permitting long runs of natural laminar flow (NLF). The present research effort seeks to refine and validate computational design tools for use in the design of axisymmetric and nonaxisymmetric natural-laminar-flow bodies. The principal task of the investigation involves fuselage body shaping using a computational design procedure. Analytical methods were refined and exploratory calculations conducted to predict laminar boundary-layer on selected body shapes. Using a low-order surface-singularity aerodynamic analysis program, pressure distribution, boundary-layer development, transition location and drag coefficient have been obtained for a number of body shapes including a representative business-aircraft fuselage. Extensive runs of laminar flow were predicted in regions of favorable pressure gradient on smooth body surfaces. A computational design procedure was developed to obtain a body shape with minimum drag having large extent of NLF.
The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require devising unsupervised learning methods for data mining and extraction of the latent features, and non-negative matrix factorization (NMF) is one of the prominent such methods. NMF is based on compute-intense non-convex constrained minimization, which, for large datasets requires fast and distributed algorithms. However, current parallel implementations of NMF fail to estimate the number of latent features. In practice, identifying these features is both difficult and significant for pattern recognition and latent feature analysis, especially for large dense matrices. Here, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model determination while scaling nearly linearly across multiple processors for large data. Further, we employ DnMFk to determine the number of hidden features from a terabyte matrix.
Understanding the structure, functions, activities and dynamics of microbial communities in natural environments is one of the grand challenges of 21st century science. To address this challenge, over the past decade, numerous technologies have been developed for interrogating microbial communities, of which some are amenable to exploratory work (e.g., high-throughput sequencing and phenotypic screening) and others depend on reference genes or genomes (e.g., phylogenetic and functional gene arrays). Here, we provide a critical review and synthesis of the most commonly applied “open-format” and “closed-format” detection technologies. We discuss their characteristics, advantages, and disadvantages within the context of environmental applications and focus on analysis of complex microbial systems, such as those in soils, in which diversity is high and reference genomes are few. In addition, we discuss crucial issues and considerations associated with applying complementary high-throughput molecular technologies to address important ecological questions.
The current downward trend in funding for U.S. defense systems seems to be on a collision course with the state of the practice in systems engineering, which typically results in the increased pace and scale of capabilities and resultantly increased cost of complex national defense systems. Recent advances in the state of the art in systems engineering methodology can be leveraged to address this growing challenge. The present work leverages advanced constructs and methods for early-phase conceptual design of complex systems, when committed costs are still low and management influence is still high. First, a literature review is presented of the topics relevant to this work, including approaches to the design of affordable systems, assumptions and methods of exploratory modeling, and enabling techniques to help mitigate the computational challenges involved. The types, purposes, and limits of early-phase, exploratory models are then elucidated. The RSC-based Method for Affordable Concept Selection (RMACS) is described, which comprises nine processes in the three main thrusts of information gathering, evaluation, and analysis. The method is then applied to a naval ship case example, described as the Next-Generation Combat Ship, with representational information outputs and discussions of affordability with respect to each process. The ninth process, Multi-Era Analysis (MERA), is introduced and explicated, including required and optional informational components, temporal and change-related considerations, required and optional activities involved, and the potential types of outputs from the process. The MERA process is then applied to a naval ship case example similar to that of the RMACS application, but with discrete change options added to enable a tradespace network. The seven activities of the MERA process are demonstrated, with the salient outputs of each given and discussed. Additional thoughts are presented on MERA and RMACS, and 8 distinct areas are identified for further research in the MERA process, along with a brief description of the directions that such research might take. It is concluded that the affordability of complex systems can be better enabled through a conceptual design method that incorporates MERA as well as metrics such as Multi-Attribute Expense, Max Expense, and Expense Stability. It is also found that affordability of changeable systems can be better enabled through the use of existing path-planning algorithms in efficient evaluation and analysis of long-term strategies. Finally, it is found that MERA enables the identification and analysis of path-dependent considerations related to designs, epochs, strategies, and change options, in many possible futures.
The Tropospheric Trace Gas and Airborne Measurements Group's (TTGAMG) efforts on NASA GTE (Global Tropospheric Experiment) PEM (Pacific Exploratory Mission) West A & B field campaign primarily involved the acquisition of NO, NO2 and NO(y) measurements, as well as the subsequent analysis and interpretation of the data base obtained during the PEM West field campaign. These investigations focused on the distribution of trace gases, sources and sinks of ozone, ozone producing precursors with a heavy emphasize on ozone's photochemical state, and the partitioning of the molecules within the NO(y) family over the north western Pacific Ocean. The two components of PEM West were focused on observing air masses as they reached the Asian Continent (PEM West A) or as the air mass departed the Asian Continent (PEM West B). NO(x) concentrations play a pivotal role in controlling the photochemical lifetime of ozone in these environments, and understanding the NO(x) species partitioning is paramount. The transport of NO(x) into the regions, in the form of longer lived NO(y) family members, was examined in relation to the comparison of natural occurring sources of NO(x) (i.e., lightning and stratosphere/troposphere exchange) to those produced as a result of anthropogenic activity (i.e., biomass burning and aircraft emissions). The TTGAMG's measurements of NOx and NO(y), in conjunction with other investigators' measurements of PAN (H. B. Singh's group) and HNO3 (R. W. Talbot's group), have been used to assess the total reactive odd nitrogen levels over the study regions, the partitioning of the reactive odd nitrogen species in their various forms, and the usefulness of the NO, measurement and its measurement technique. The TTGAMG's primary PEM West objectives were the characterization of the factors controlling the distribution and fate of reactive odd nitrogen compounds over the western Pacific Ocean and an analysis of the concentration of various trace gases in the troposphere as the air mass aged by both dynamical mixing and photochemical processes in the troposphere.
Structural equation modeling is used in statistical applications as both confirmatory and exploratory modeling to test models and to suggest the most plausible explanation for a relationship between the independent and the dependent variables. Although structural analysis cannot prove causation, it can suggest the most plausible set of factors that influence the observed variable. Here, we apply structural model analysis to the annual mean Arctic surface air temperature from 1900 to 2012 to find the most effective set of predictors and to isolate the anthropogenic component of the recent Arctic warming by subtracting the effects of natural forcing and variability from the observed temperature. We also find that anthropogenic greenhouse gases and aerosols radiative forcing and the Atlantic Multidecadal Oscillation internal mode dominate Arctic temperature variability. Finally, our structural model analysis of observational data suggests that about half of the recent Arctic warming of 0.64 K/decade may have anthropogenic causes.