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Results for “Representativeness Analysis”

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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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Economic evaluation of a solar hot-water-system

Analysis shows economic benefits at six representative sites using actual data from Tempe, Arizona and San Diego, California installations. Model is two-tank cascade water heater with flat-plate collector array for single-family residences. Performances are forecast for Albuquerque, New Mexico; Fort Worth, Texas; Madison, Wisconsin; and Washington, D.C. Costs are compared to net energy savings using variables for each site's environmental conditions, loads, fuel costs, and other economic factors; uncertainty analysis is included.

Source record↗
DOE OSTI2026

buhito

buhito is a Python library for graph analysis and machine learning. Graphs can represent networks with objects as nodes and their relationships as edges. buhito focuses on graphlet methods that study graphs through enumerating their component subgraphs to enable interpretable and fast models of complex systems. The package provides tools for different algorithmic designs for computing, analyzing, and applying graphlets to research problems such as machine learning, data compression, and anomaly detection in graph-structured data. A central feature is performing decomposition data analysis on graphs for machine learning models. Implemented in Python and built upon open-source scientific libraries such as NetworkX, NumPy, and SciPy, buhito provides high-performance methods for researchers exploring the mathematical and computational foundations of graphlet analysis applicable to systems of different sizes.

Pimonova, Yulia↗
DOE OSTI2019

Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

Analysis of reactive-diffusion simulations representing complex mixing processes requires a large number of independent model runs. For each high-fidelity model simulation, the model inputs are varied and the predicted mixing behavior is represented by temporal and spatial changes in species concentration. It is then required to discern how the model inputs (such as diffusivity, dispersion, anisotropy, and velocity field properties) impact the mixing process. This task is challenging and typically involves interpretation of large model outputs representing temporal and spatial changes of species concentration within the model domain. Yet, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. Here, we present an application of an unsupervised ML method (called NTF k ) using Non-negative Tensor Factorization (NTF) coupled with a custom clustering procedure based on k-means to reveal the temporal and spatial features in product concentrations. An attractive and unique aspect of the proposed ML method is that it ensures the extracted features are non-negative, which is important to obtain a meaningful deconstruction of the mixing processes. The ML methodology is applied to a large set of high-resolution finite-element model simulations representing anisotropic reaction-diffusion processes in perturbed vortex-based velocity fields. The applied finite-element method ensures that spatial and temporal species concentration are always non-negative, even in the case of high anisotropic contrasts. The simulated reaction is a fast irreversible bimolecular reaction A+B→C , where species A and B react to form species C. The reactive-diffusion model input parameters that control mixing include properties of the velocity field (such as vortex structures), anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML feature extraction method to produce a meaningful deconstruction of model outputs to discriminate between different physical processes impacting the reactants, their mixing, and the spatial distribution of the product C. The introduced ML analysis allowed us to identify additive temporal and spatial features that characterize mixing behavior. The application of the proposed NTF k approach is not limited to reactive-mixing. NTFk can be readily applied to any observed or simulated datasets that can be represented as tensors (multi-dimensional arrays) and have separable latent signatures or features.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

Off-the-Shelf Real-Time Monitoring of Satellite Constellations in a Visual 3-D Environment

The MSAS Data Monitor is a generic software product that represents the next-generation in real-time data monitoring and analysis tools. It goes beyond conventional text-based displays by representing data in a unique graphical form, conveying system status through the shape, color, motion, and position of graphical objects floating in a three-dimensional cyberspace environment...This paper describes some of the details and benefits of the MSAS monitoring and analysis suite, with primary focus on the Data Monitor.

MSAS↗
DOE OSTI2012

Supercell Depletion Studies for Prismatic High Temperature Reactors

The traditional two-step method of analysis is not accurate enough to represent the neutronic effects present in the prismatic high temperature reactor concept. The long range coupling of the various regions in high temperature reactors poses a set of challenges that are not seen in either LWRs or fast reactors. Unlike LWRs, which exhibit large, localized effects, the dominant effects in PMRs are, for the most part, distributed over larger regions, but with lower magnitude. The 1-D in-line treatment currently used in pebble bed reactor analysis is not sufficient because of the 2-D nature of the prismatic blocks. Considerable challenges exist in the modeling of blocks in the vicinity of reflectors, which, for current small modular reactor designs with thin annular cores, include the majority of the blocks. Additional challenges involve the treatment of burnable poisons, operational and shutdown control rods. The use of a large domain for cross section preparation provides a better representation of the neutron spectrum, enables the proper modeling of BPs and CRs, allows the calculation of generalized equivalence theory parameters, and generates a relative power distribution that can be used in compact power reconstruction. The purpose of this paper is to quantify the effects of the reflector, burnable poison, and operational control rods on an LEU design and to delineate an analysis approach for the Idaho National Laboratory. This work concludes that the use of supercells should capture these long-range effects in the preparation of cross sections and along with a set of triangular meshes to treat BPs, and CRs a high fidelity neutronics computation is attainable.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Surge and Stall Detection Using Acoustic Analysis FOR GAS Turbine Hybrid Cycles

In this article compressor dynamics were studied in a gas turbine - fuel cell hybrid power system having a larger compressor volume than traditionally found in gas turbine systems. This larger compressor volume adversely affects the surge margin of the gas turbine. Industrial acoustic sensors were placed near the compressor to identify when the equipment was getting close to the surge line. Fast Fourier transform (FFT) mathematical analysis was used to obtain spectra representing the probability density across the frequency range (0 - 5000 Hz). Comparison between FFT spectra for nominal and transient operations revealed that higher amplitude spikes were observed during incipient stall at three different frequencies, 900, 1020, and 1800 Hz. These frequencies were compared to the natural frequencies of the equipment and the frequency for the rotating turbomachinery to identify more general nature of the acoustic signal typical of the onset of compressor surge. The primary goal of this acoustic analysis was to establish an online methodology to monitor compressor stability that can be anticipated and avoided.

42 ENGINEERING↗
DOE OSTI2020

Unravelling the diversity of magnetotactic bacteria through analysis of open genomic databases

Magnetotactic bacteria (MTB) are prokaryotes that possess genes for the synthesis of membrane-bounded crystals of magnetite or greigite, called magnetosomes. Despite over half a century of studying MTB, only about 60 genomes have been sequenced. Most belong to Proteobacteria, with a minority affiliated with the Nitrospirae, Omnitrophica, Planctomycetes, and Latescibacteria. Due to the scanty information available regarding MTB phylogenetic diversity, little is known about their ecology, evolution and about the magnetosome biomineralization process. This study presents a large-scale search of magnetosome biomineralization genes and reveals 38 new MTB genomes. Several of these genomes were detected in the phyla Elusimicrobia, Candidatus Hydrogenedentes, and Nitrospinae, where magnetotactic representatives have not previously been reported. Analysis of the obtained putative magnetosome biomineralization genes revealed a monophyletic origin capable of putative greigite magnetosome synthesis. The ecological distributions of the reconstructed MTB genomes were also analyzed and several patterns were identified. These data suggest that open databases are an excellent source for obtaining new information of interest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Analysis of the AHR gene proximal promoter GGGGC-repeat polymorphism in lung, breast, and colon cancer

The aryl hydrocarbon receptor (AhR) regulates expression of numerous genes, including those of the CYP1 gene family. With the goal of determining factors that control AHR gene expression, our studies are focused on the role of the short tandem repeat polymorphism, (GGGGC){sub n}, located in the proximal promoter of the human AHR gene. When luciferase constructs containing varying GGGGC repeats were transfected into cancer cell lines derived from the lung, colon, and breast, the number of GGGGC repeats affected AHR promoter activity. The number of GGGGC repeats was determined in DNA from 327 humans and from 38 samples representing 5 species of non-human primates. In chimpanzees and 3 species of macaques, only (GGGGC){sub 2} alleles were observed; however, in western gorilla, (GGGGC){sub n} alleles with n = 2, 4, 5, 6, 7, and 8 were identified. In all human populations examined, the frequency of (GGGGC){sub n} was n = 4 > 5 ≫ 2, 6. When frequencies of the (GGGGC){sub n} alleles in DNA from patients with lung, colon, or breast cancer were evaluated, the occurrence of (GGGGC){sub 2} was found to be 8-fold more frequent among lung cancer patients in comparison with its incidence in the general population, as represented by New York State neonates. Analysis of matched tumor and non-tumor DNA samples from the same individuals provided no evidence of microsatellite instability. These studies indicate that the (GGGGC){sub n} short tandem repeats are inherited, and that the (GGGGC){sub 2} allele in the AHR proximal promoter region should be further investigated with regard to its potential association with lung cancer susceptibility. - Highlights: • The AHR proximal promoter contains a polymorphism, (GGGGC){sub n}, where n = 4 > 5 ≫ 2, 6 • Matched tumor and non-tumor DNA did not show (GGGGC){sub n} microsatellite instability • AHR promoter activity of a construct with (GGGGC){sub 2} was lower than that of (GGGGC){sub 4} • The frequency of (GGGGC){sub 2} in lung cancer patients was 8-fold higher than in neonates • The (GGGGC){sub 2} allele may be associated with lung cancer susceptibility.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

On Weibull's Spectrum of Nonrelativistic Energetic Particles at IP Shocks: Observations and Theoretical Interpretation

Some interplanetary shocks are associated with short-term and sharp particle flux enhancements near the shock front. Such intensity enhancements, known as shock-spike events (SSEs), represent a class of relatively energetic phenomena as they may extend to energies of some tens of MeV or even beyond. Here we present an SSE case study in order to shed light on the nature of the particle acceleration involved in this kind of event. Our observations refer to an SSE registered on 2011 October 3 at 22:23 UT, by STEREO B instrumentation when, at a heliocentric distance of 1.08 au, the spacecraft was swept by a perpendicular shock moving away from the Sun. The main finding from the data analysis is that a Weibull distribution represents a good fitting function to the measured particle spectrum over the energy range from 0.1 to 30 MeV. To interpret such an observational result, we provide a theoretical derivation of the Weibull spectrum in the framework of the acceleration by “killed” stochastic processes exhibiting power-law growth in time of the velocity expectation, such as the classical Fermi process. We find an overall coherence between the experimental values of the Weibull spectrum parameters and their physical meaning within the above scenario. Hence, our approach based on the Weibull distribution proves to be useful for understanding SSEs. With regard to the present event, we also provide an alternative explanation of the Weibull spectrum in terms of shock-surfing acceleration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Analysis Approach and Data Package for Mayak Public Doses: Data Supporting the Calculation of Representative Doses to Members of the Public from Airborne Releases of 131 I at the Mayak Production Association from 1948 through 1972

Historical activities at facilities producing nuclear materials for weapons released radioactivity into the air and water. Past studies in the United States have evaluated the release, atmospheric transport and environmental accumulation of 131 I from the nuclear facilities at Hanford in Washington State and the resulting dose to members of the public (Farris et al. 1994). A multi-year dose reconstruction effort (Mokrov et al. 2004) is also being conducted to produce representative dose estimates for members of the public living near Mayak, Russia, from atmospheric releases of 131 I at the facilities of the Mayak Production Association. The approach to calculating individual doses to members of the public from historical releases of airborne 131 I has the following general steps: • Construct estimates of releases 131 I to the air from production facilities. • Model the transport of 131 I in the air and subsequent deposition on the ground and vegetation. • Model the accumulation of 131 I in soil, water and food products (environmental media). • Calculate the dose for an individual by matching the appropriate lifestyle and consumption data for the individual to the concentrations of 131 I in environmental media at their residence location. A number of computer codes were developed to facilitate the study of airborne 131 I emissions at Hanford. Of particular interest is DESCARTES code that modeled accumulation of 131 I in environmental media (Miley et al. 1994). In addition, the CIDER computer code estimated annual doses to individuals (Eslinger et al. 1994) using the equations and parameters specific to Hanford (Snyder et al. 1994). Several of the computer codes developed to model 131 I releases from Hanford are general enough to be used for other facilities. Additional codes have been developed, including the new individual dose code CiderF (Eslinger and Napier 2013), and applied to historical releases of 131 I from Mayak. This document provides a data package that identifies computer code runs and associated input and output files prepared for the purpose of calculating doses to members of the public from atmospheric releases of 131 I at the Mayak Production Association for the time period 1948 through 1972.

Mayak↗
DOE OSTI2022

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗
NASA NTRS1978

Iodine imaging using spectral analysis

Existing radiographic imaging systems provide images which represent an integration or averaging over the energy spectrum. In order to provide noninvasive angiography it is necessary to image the relatively small amounts of iodine which are available following an intravenous administration. This is accomplished by making use of the special spectral characteristics of iodine. Two methods will be presented. One involves a special grating for encoding the iodine information in the form of a fine line pattern. This is subsequently decoded to provide images of iodinated structures which are otherwise almost invisible. The second method utilizes a scanned X-ray beam which is rapidly switched in the high energy region. In this region, iodine experiences significant variations in the attenuation coefficient while bone and soft tissue do not. An efficient and accurate X-ray detector can be used with scanned X-ray beams. This provides a high degree of sensitivity enabling the visualization of small vessels containing relatively dilute iodine.

Macovski, A.↗
NASA NTRS1983

A closed-loop principal component analysis of a tetrahedral truss

A tetrahedral truss, representative of a fairly broad class of large space structures, is considered. A method, measuring the dynamical importance of each elastic mode when the structure is under feedback control, is presented. The main conclusion is that the closed-loop importance of each vibration mode depends on the robustness requirements. Also, there appears to be a conflict between precise control of a given node of the structure and stability margin, these two requirements leading to contradictory classifications of the modes by order of importance.

Jonckheere, E. A.↗
DOE OSTI2022

Tritium Fires: Simulation and Safety Assessment

This is the Sandia report from a joint NSRD project between Sandia National Labs and Savannah River National Labs. The project involved development of simulation tools and data intended to be useful for tritium operations safety assessment. Tritium is a synthetic isotope of hydrogen that has a limited lifetime, and it is found at many tritium facilities in the form of elemental gas (T 2 ). The most serious risk of reasonable probability in an accident scenario is when the tritium is released and reacts with oxygen to form a water molecule, which is subsequently absorbed into the human body. This tritium oxide is more readily absorbed by the body and therefore represents a limiting factor for safety analysis. The abnormal condition of a fire may result in conversion of the safer T 2 inventory to the more hazardous oxidized form. It is this risk that tends to govern the safety protocols. Tritium fire datasets do not exist, so prescriptive safety guidance is largely conservative and reliant on means other than testing to formulate guidelines. This can have a consequence in terms of expensive and/or unnecessary mitigation design, handling protocols, and operational activities. This issue can be addressed through added studies on the behavior of tritium under representative conditions. Due to the hazards associated with the tests, this is being approached mainly from a modeling and simulation standpoint and surrogate testing. This study largely establishes the capability to generate simulation predictions with sufficiently credible characteristics to be accepted for safety guidelines as a surrogate for actual data through a variety of testing and modeling activities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2007

Robustness Analysis of Integrated LPV-FDI Filters and LTI-FTC System for a Transport Aircraft

This paper proposes an analysis framework for robustness analysis of a nonlinear dynamics system that can be represented by a polynomial linear parameter varying (PLPV) system with constant bounded uncertainty. The proposed analysis framework contains three key tools: 1) a function substitution method which can convert a nonlinear system in polynomial form into a PLPV system, 2) a matrix-based linear fractional transformation (LFT) modeling approach, which can convert a PLPV system into an LFT system with the delta block that includes key uncertainty and scheduling parameters, 3) micro-analysis, which is a well known robust analysis tool for linear systems. The proposed analysis framework is applied to evaluating the performance of the LPV-fault detection and isolation (FDI) filters of the closed-loop system of a transport aircraft in the presence of unmodeled actuator dynamics and sensor gain uncertainty. The robustness analysis results are compared with nonlinear time simulations.

Khong, Thuan H.↗
DOE OSTI2013

Top-down proteomics reveals a unique protein S-thiolation switch in Salmonella Typimurium in response to infection-like conditions

Characterization of the mature protein complement in cells is crucial for a better understanding of cellular processes on a systems-wide scale. Bottom-up proteomic approaches often lead to loss of critical information about an endogenous protein’s actual state due to post translational modifications (PTMs) and other processes. Top-down approaches that involve analysis of the intact protein can address this concern but present significant analytical challenges related to the separation quality needed, measurement sensitivity, and speed that result in low throughput and limited coverage. Here we used single-dimension ultra high pressure liquid chromatography mass spectrometry to investigate the comprehensive ‘intact’ proteome of the Gram negative bacterial pathogen Salmonella Typhimurium. Top-down proteomics analysis revealed 563 unique proteins including 1665 proteoforms generated by PTMs, representing the largest microbial top-down dataset reported to date. Our analysis not only confirmed several previously recognized aspects of Salmonella biology and bacterial PTMs in general, but also revealed several novel biological insights. Of particular interest was differential utilization of the protein S-thiolation forms S-glutathionylation and S-cysteinylation in response to infection-like conditions versus basal conditions, which was corroborated by changes in corresponding biosynthetic pathways. This differential utilization highlights underlying metabolic mechanisms that modulate changes in cellular signaling, and represents to our knowledge the first report of S-cysteinylation in Gram negative bacteria. The demonstrated utility of our simple proteome-wide intact protein level measurement strategy for gaining biological insight should promote broader adoption and applications of top-down proteomics approaches.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2021

Deployable Wind-Hybrid Power Systems for Defense and Disaster Response Applications

This report presents an analysis of the performance of deployable energy systems comprised of wind energy systems integrated with diesel generators, photovoltaic systems, and battery storage to meet the load requirements of a representative U.S. Army forward operating base. The analysis is conducted using HOMER, a microgrid analysis software that can search through a wide range of parameters to design and optimize microgrid power systems. The search parameters include the system architecture, the wind and solar resources, and the availability of diesel fuel. The results of the analysis measure the relative performance of the different systems and environments in terms of the overall transportation cost to deploy the system and the ability to provide resilience in terms of meeting mission critical loads.

02 PETROLEUM↗
DOE OSTI2013

Assessing the Uncertainty in QUANTEC's Dose–Response Relation of Lung and Spinal Cord With a Bootstrap Analysis

Purpose: To apply a statistical bootstrap analysis to assess the uncertainty in the dose–response relation for the endpoints pneumonitis and myelopathy reported in the QUANTEC review. Methods and Materials: The bootstrap method assesses the uncertainty of the estimated population-based dose-response relation due to sample variability, which reflects the uncertainty due to limited numbers of patients in the studies. A large number of bootstrap replicates of the original incidence data were produced by random sampling with replacement. The analysis requires only the dose, the number of patients, and the number of occurrences of the studied endpoint, for each study. Two dose–response models, a Poisson-based model and the Lyman model, were fitted to each bootstrap replicate using maximum likelihood. Results: The bootstrap analysis generates a family of curves representing the range of plausible dose–response relations, and the 95% bootstrap confidence intervals give an estimated upper and lower toxicity risk. The curve families for the 2 dose–response models overlap for doses included in the studies at hand but diverge beyond that, with the Lyman model suggesting a steeper slope. The resulting distributions of the model parameters indicate correlation and non-Gaussian distribution. For both data sets, the likelihood of the observed data was higher for the Lyman model in >90% of the bootstrap replicates. Conclusions: The bootstrap method provides a statistical analysis of the uncertainty in the estimated dose–response relation for myelopathy and pneumonitis. It suggests likely values of model parameter values, their confidence intervals, and how they interrelate for each model. Finally, it can be used to evaluate to what extent data supports one model over another. For both data sets considered here, the Lyman model was preferred over the Poisson-based model.

62 RADIOLOGY AND NUCLEAR MEDICINE↗