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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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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs

Crewmembers returning from long-duration space flight face significant challenges due to the microgravity-induced inappropriate adaptations in balance/ sensorimotor function. The Neuroscience Laboratory at JSC is developing a method based on stochastic resonance to enhance the brain s ability to detect signals from the balance organs of the inner ear and use them for rapid improvement in balance skill, especially when combined with balance training exercises. This method involves a stimulus delivery system that is wearable/portable providing imperceptible electrical stimulation to the balance organs of the human body. Stochastic resonance (SR) is a phenomenon whereby the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular non-zero level of noise. This phenomenon of SR is based on the concept of maximizing the flow of information through a system by a non-zero level of noise. Application of imperceptible SR noise coupled with sensory input in humans has been shown to improve motor, cardiovascular, visual, hearing, and balance functions. SR increases contrast sensitivity and luminance detection; lowers the absolute threshold for tone detection in normal hearing individuals; improves homeostatic function in the human blood pressure regulatory system; improves noise-enhanced muscle spindle function; and improves detection of weak tactile stimuli using mechanical or electrical stimulation. SR noise has been shown to improve postural control when applied as mechanical noise to the soles of the feet, or when applied as electrical noise at the knee and to the back muscles.

Bloomberg, Jacob↗
NASA NTRS2015

Municipality Level Simulations of Dengue Fever Incidence in Puerto Rico Using Ground Based and Remotely Sensed Climate Data

Dengue fever (DF) is caused by a virus transmitted between humans and Aedes genus mosquitoes through blood feeding. In recent decades incidence of the disease has drastically increased in the tropical Americas, culminating with the Pan American outbreak in 2010 which resulted in 1.7 million reported cases. In Puerto Rico dengue is endemic, however, there is significant inter‐annual, intraannual, and spatial variability in case loads. Variability in climate and the environment, herd immunity and virus genetics, and demographic characteristics may all contribute to differing patterns of transmission both spatially and temporally. Knowledge of climate influences on dengue incidence could facilitate development of early warning systems allowing public health workers to implement appropriate transmission intervention strategies. In this study, we simulate dengue incidence in several municipalities in Puerto Rico using population and meteorological data derived from ground based stations and remote sensing instruments. This data was used to drive a process based model of vector population development and virus transmission. Model parameter values for container composition, vector characteristics, and incubation period were chosen by employing a Monte Carlo approach. Multiple simulations were performed for each municipality and the results were compared with reported dengue cases. The best performing simulations were retained and their parameter values and meteorological input were compared between years and municipalities. Parameter values varied by municipality and year illustrating the complexity and sensitivity of the disease system. Local characteristics including the natural and built environment impact transmission dynamics and produce varying responses to meteorological conditions.

Remote Sensing↗
DOE OSTI2017

Use of Groundwater Flow, Solute Transport, and Geochemical Modeling To Evaluate Long-Term Nitrate Plume Concentrations Following Phreatophyte Source Control - 17262

The Monument Valley, Arizona, Processing Site is a former uranium mill site within the Navajo Nation in northeastern Arizona. Milling activities between 1955 and 1968 resulted in contamination of groundwater in the shallow alluvium at the site. Remedial action was completed under Title I of the Uranium Mill Tailings Radiation Control Act of 1978. Site remediation activities between 1992 and 1994 removed surficial source material and other site-related contamination, but contamination of deeper soil, primarily by nitrate, remained between the excavation depth and the water table. Residual nitrate in these soils, from uranium ore processing at the mill, has continued to be a source of groundwater contamination. To evaluate the potential for native phreatophytes to control remaining source material, four-wing salt-bush and black grease-wood were planted in 2000 and 2005 within the footprints of the former tailings piles. The objectives of the plantings were to increase nitrate removal from soil water through root uptake and, because phreatophyte water usage is expected to equal or exceed annual precipitation, to halt nitrate loading to groundwater. Recent evaluation showed more than an 80% decrease in soil nitrate concentrations and no effective groundwater recharge in the planting area. Numerical groundwater flow, solute transport, and geochemical modeling were performed to evaluate the long-term response of the existing nitrate plume to the phreatophyte source control measures. This modeling will help determine if 100-year natural flushing is a viable compliance strategy for the nitrate plume. Results showed that nitrate concentrations in the alluvial aquifer are expected to drop below the standard of 10 mg/L as N in approximately 70 years as a result of the phreatophyte planting. Companion sensitivity analysis was also performed to better understand how uncertainty in groundwater flow model and solute transport model inputs would impact cleanup times. Even considering input uncertainty, nitrate concentrations are expected to reach the standard in the alluvial aquifer within 100 years. Recognizing that model predictions are not absolute, but represent the general potential for an event to occur, the model was used to identify key sampling locations to verify that the nitrate plume will attenuate as predicted. This evaluation identified locations that can be used to assess the viability of the modeling predictions within the next 50 years. If the source diminishes or no longer exists, modeling predicts that concentrations of nitrate and ammonium in these wells should show significant declines over the next 10-55 years. If downward concentration trends do not occur as predicted, other remedial strategies may need to be evaluated to meet groundwater standards within the specified 100-year duration. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Investigation of Heterogeneity Effects on Calculated Sensitivities of the ZPPR-15B Assemblies

In past years, sensitivity studies of ZPR and ZPPR assemblies were carried out with deterministic codes for several purposes, such as uncertainty estimation of calculated parameter values, cross section adjustment, representativity analysis. etc. However, these sensitivity studies used simplified R-Z models of ZPR/ZPPR assemblies where the multi-group cross section data was obtained from the homogenized material compositions of each reactor zone. The main reason for this simplification was the lack of a deterministic path able to compute sensitivity coefficients for complicated 3D geometries in transport theory. Also, each ZPR and ZPPR assembly consists of several different drawers each containing different plate types and plate arrangements and it was felt that performing sensitivity calculations with an equivalent R-Z reactor model would capture most of the feedback while the heterogeneity effects were believed to be of second order. Recently a new code, BuildZPRModel, was developed at ANL that automatically generates the inputs for cross section processing and core calculation of heterogeneous ZPR and ZPPR assemblies for 3D transport calculations. Additionally, the PERSENT code (recently developed at ANL as well) is able to carry out sensitivity calculations in transport theory on 3D geometries. With the benefit of the newly developed codes, a sensitivity study was performed for the heterogeneous model of ZPPR-15B assemblies 96 and 102 generated by BuildZPRModel. The results were compared with the sensitivities computed with the use of fully homogenized R-Z reactor models, in order to quantify the impact of heterogeneity effects neglected in the analysis performed in past years.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Investigation of Heterogeneity Effects on Calculated Sensitivities of the ZPPR-15B Assemblies

In past years, sensitivity studies of ZPR and ZPPR assemblies were carried out with deterministic codes for several purposes, such as uncertainty estimation of calculated parameter values, cross section adjustment, representativity analysis. etc.. However, these sensitivity studies used simplified R-Z models of ZPR/ZPPR assemblies where the multi-group cross section data was obtained from the homogenized material compositions of each reactor zone. The main reason for this simplification was the lack of a deterministic path able to compute sensitivity coefficients for complicated 3D geometries in transport theory. Also, each ZPR and ZPPR assembly consists of several different drawers each containing different plate types and plate arrangements and it was felt that performing sensitivity calculations with an equivalent R-Z reactor model would capture most of the feedback while the heterogeneity effects were believed to be of second order. Recently a new code, BuildZPRModel, was developed at ANL that automatically generates the inputs for cross section processing and core calculation of heterogeneous ZPR and ZPPR assemblies for 3D transport calculations. Additionally, the PERSENT code (recently developed at ANL as well) is able to carry out sensitivity calculations in transport theory on 3D geometries. With the benefit of the newly developed codes, a sensitivity study was performed for the heterogeneous model of ZPPR-15B assemblies 96 and 102 generated by BuildZPRModel. The results were compared with the sensitivities computed with the use of fully homogenized R-Z reactor models, in order to quantify the impact of heterogeneity effects neglected in the analysis performed in past years. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2004

Rapid Airplane Parametric Input Design(RAPID)

An efficient methodology is presented for defining a class of airplane configurations. Inclusive in this definition are surface grids, volume grids, and grid sensitivity. A small set of design parameters and grid control parameters govern the process. The general airplane configuration has wing, fuselage, vertical tail, horizontal tail, and canard components. The wing, tail, and canard components are manifested by solving a fourth-order partial differential equation subject to Dirichlet and Neumann boundary conditions. The design variables are incorporated into the boundary conditions, and the solution is expressed as a Fourier series. The fuselage has circular cross section, and the radius is an algebraic function of four design parameters and an independent computational variable. Volume grids are obtained through an application of the Control Point Form method. Grid sensitivity is obtained by applying the automatic differentiation precompiler ADIFOR to software for the grid generation. The computed surface grids, volume grids, and sensitivity derivatives are suitable for a wide range of Computational Fluid Dynamics simulation and configuration optimizations.

Smith, Robert E.↗
DOE OSTI2022

A functional global sensitivity measure and efficient reliability sensitivity analysis with respect to statistical parameters

Sensitivity analysis and reliability assessment are two important aspects of structural and system safety. Epistemic uncertainty with respect to probabilistic model of input parameters due to lack of knowledge is present in many scarce-data applications and complicates the characterization of uncertainty in model response. In this article, we present two importance measures to evaluate the impact of distribution parameters on the probability distribution function (PDF) of the output and the failure probability. The epistemic uncertainty associated with the distribution parameters is modeled as random variables. Additionally, a modified extended polynomial chaos expansion (MEPCE) approach is introduced in which aleatory and epistemic random variables are modeled and propagated simultaneously while allowing the separate assessment for any single epistemic variable. A MEPCE-based kernel density estimation (KDE) construction provides a composite map from each epistemic variable to the response PDF. The functional global sensitivity index of the PDF with respect to the distribution parameters is thus derived, as a function of output, which is both more informative and more efficient than standard scalar sensitivity measures. Reliability sensitivity indices can be readily evaluated by integrating the global sensitivity index function over the failure zone. Three illustrative examples are used to demonstrate the proposed methodology.

42 ENGINEERING↗
NASA NTRS2005

Sensitivity of Stratospheric Retrievals from Radio Occultations on Upper Boundary Conditions

The main uncertainty in the stratospheric retrievals from GPS radio occultation (RO) measurements comes from the lack of reliable measurements in the upper stratosphere and above where the bending due to the neutral atmosphere is weak and residual ionospheric effects are strong. In this work, we quantify the bias and uncertainty of the refractivity and temperature retrievals due to different upper boundary strategies using a simulation study. We use lidar refractivity and temperature profiles as the input states in generating the synthetic occultations. Random noise levels commensurate with the CHAMP RO measurements are then added to the simulated data. Through this study, the sensitivity of stratospheric retrievals to upper boundary methods and parameters are examined. Such error characterizations are important prerequisites towards the effective use of GPS RO data in climate monitoring.

GPS↗
NASA NTRS1974

Design of state-feedback controllers including sensitivity reduction, with applications to precision pointing

The design procedure of feedback controllers was described and the considerations for the selection of the design parameters were given. The frequency domain properties of single-input single-output systems using state feedback controllers are analyzed, and desirable phase and gain margin properties are demonstrated. Special consideration is given to the design of controllers for tracking systems, especially those designed to track polynomial commands. As an example, a controller was designed for a tracking telescope with a polynomial tracking requirement and some special features such as actuator saturation and multiple measurements, one of which is sampled. The resulting system has a tracking performance comparing favorably with a much more complicated digital aided tracker. The parameter sensitivity reduction was treated by considering the variable parameters as random variables. A performance index is defined as a weighted sum of the state and control convariances that sum from both the random system disturbances and the parameter uncertainties, and is minimized numerically by adjusting a set of free parameters.

Hadass, Z.↗
NASA NTRS2008

Uncertainty and Sensitivity Analyses of Duct Propagation Models

This paper presents results of uncertainty and sensitivity analyses conducted to assess the relative merits of three duct propagation codes. Results from this study are intended to support identification of a "working envelope" within which to use the various approaches underlying these propagation codes. This investigation considers a segmented liner configuration that models the NASA Langley Grazing Incidence Tube, for which a large set of measured data was available. For the uncertainty analysis, the selected input parameters (source sound pressure level, average Mach number, liner impedance, exit impedance, static pressure and static temperature) are randomly varied over a range of values. Uncertainty limits (95% confidence levels) are computed for the predicted values from each code, and are compared with the corresponding 95% confidence intervals in the measured data. Generally, the mean values of the predicted attenuation are observed to track the mean values of the measured attenuation quite well and predicted confidence intervals tend to be larger in the presence of mean flow. A two-level, six factor sensitivity study is also conducted in which the six inputs are varied one at a time to assess their effect on the predicted attenuation. As expected, the results demonstrate the liner resistance and reactance to be the most important input parameters. They also indicate the exit impedance is a significant contributor to uncertainty in the predicted attenuation.

Nark, Douglas M.↗
DOE OSTI2024

Jacobian-scaled K-means clustering for physics-informed segmentation of reacting flows

This work introduces Jacobian-scaled K-means (JSK-means) clustering, which is a physicsinformed clustering strategy centered on the K-means framework. The method allows for the injection of underlying physical knowledge into the clustering procedure through a distance function modification: instead of leveraging conventional Euclidean distance vectors, the JSKmeans procedure operates on distance vectors scaled by matrices obtained from dynamical system Jacobians evaluated at the cluster centroids. The goal of this work is to show how the JSKmeans algorithm - without modifying the input dataset - produces clusters that capture regions of dynamical similarity, in that the clusters are redistributed towards high-sensitivity regions in phase space and are described by similarity in the source terms of samples instead of the samples themselves. The algorithm is demonstrated on a complex reacting flow simulation dataset (a channel detonation configuration), where the dynamics in the thermochemical composition space are known through the highly nonlinear and stiff Arrhenius-based chemical source terms. Interpretations of cluster partitions in both physical space and composition space reveal how JSK-means shifts clusters produced by standard K-means towards regions of high chemical sensitivity (e.g., towards regions of peak heat release rate near the detonation reaction zone). Furthermore, the findings presented here illustrate the benefits of utilizing Jacobian-scaled distances in clustering techniques, and the JSK-means method in particular displays promising potential for improving former partition-based modeling strategies in reacting flow (and other multi-physics) applications.

Clustering↗
DOE OSTI2014

A low-noise transimpedance amplifier for the detection of “Violin-Mode” resonances in advanced Laser Interferometer Gravitational wave Observatory suspensions

This paper describes the design and performance of an extremely low-noise differential transimpedance amplifier, which takes its two inputs from separate photodiodes. The amplifier was planned to serve as the front-end electronics for a highly sensitive shadow-displacement sensing system, aimed at detecting very low-level “Violin-Mode” (VM) oscillations in 0.4 mm diameter by 600 mm long fused-silica suspension fibres. Four such highly tensioned fibres support the 40 kg test-masses/mirrors of the Advanced Laser Interferometer Gravitational wave Observatory interferometers. This novel design of amplifier incorporates features which prevent “noise-gain peaking” arising from large area photodiode (and cable) capacitances, and which also usefully separate the DC and AC photocurrents coming from the photodiodes. In consequence, the differential amplifier was able to generate straightforwardly two DC outputs, one per photodiode, as well as a single high-gain output for monitoring the VM oscillations—this output being derived from the difference of the photodiodes’ two, naturally anti-phase, AC photocurrents. Following a displacement calibration, the amplifier's final VM signal output was found to have an AC displacement responsivity at 500 Hz of (9.43 ± 1.20) MV(rms) m{sup −1}(rms), and, therefore, a shot-noise limited sensitivity to such AC shadow- (i.e., fibre-) displacements of (69 ± 13) picometres/√Hz at this frequency, over a measuring span of ±0.1 mm.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

TRISO SiC Failure Probability for Reactivity Initiated Accidents in High-Temperature Gas-Cooled Reactors

This work analyzes the failure process of the silicon carbide (SiC) layer in tristructural isotropic (TRISO) during reactivity-initiated accident scenarios for a high-temperature gas-cooled reactor (HTGR) with BISON. Two cases are considered—a group control rod withdrawal (CRW) and a control rod ejection (CRE)—reproduced from a previous study. Failure probability is modeled using Weibull statistics, and worst-case scenario Weibull parameters are adopted to simulate the envelopes in BISON with a one-dimensional TRISO model. CRW scenario results are characterized by higher values of maximum energy deposition and final temperature and volumetric strain with respect to the CRE ones, but the latter have remarkably higher SiC failure probability, mainly due to the offset in strain rates between the two cases. This work also confirms the validity and conservatism of the performance envelopes produced in a previous work by replicating the envelope formulation using RELAP5-3D and RAVEN with a different sampling technique and obtaining consistent results. A sensitivity analysis using the Sobol variance decomposition method on SiC failure probability is then performed involving a set of inputs on both CRW and CRE. The two most important parameters are Weibull modulus and characteristic stress, and their relative importance depends on the specific case. The proposed interpretation of the results is that both energy deposition and strain rate influence the relative degree of importance of the failure parameters. Computation of 95% confidence intervals around worst-case scenario SiC failure probability values is also carried out for four different sets of Weibull parameters. Heren a new criterion for SiC TRISO quality classification built upon safety-based ranges of Weibull parameters is proposed to be integrated in future Fuel-Production Quality Assurance Plans.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Modeling, Simulation, and Experimental Verification of a 20-kW Series-Series Wireless Power Transfer System for a Toyota RAV4 Electric Vehicle

Wireless power transfer is going to play a major role in transportation electrification due the convenience, flexibility, safety, and high-efficiency. Achieving high power levels is important in order to reduce the charge times and provide more convenience to electric vehicle (EV) owners while keeping the efficiency high and electric and electromagnetic field emissions lower than the limits set by the international guidelines. This study presents a 20-kW wireless charging system designed for a Toyota RAV4 electric vehicle for stationary charging with a dc-to-dc (high-frequency inverter input to the vehicle battery terminals) efficiency exceeding 95% over four power conversion stages. Additionally, the modeling, analysis, and sensitivity of the wireless charging system are presented for series-series resonant tuning configuration.

Onar, Omer↗
DOE OSTI2022

Continental United States may lose 1.8 petagrams of soil organic carbon under climate change by 2100

Abstract Aims High‐resolution information on soils’ vulnerability to climate‐induced soil organic carbon (SOC) loss can enable environmental scientists, land managers, and policy makers to develop targeted mitigation strategies. This study aims to estimate baseline and decadal changes in continental US surface SOC stocks under future emission scenarios. Location Continental United States. Time period 2014–2100. Methods We used recent SOC field observations ( n = 6,213 sites), environmental factors ( n = 32), and an ensemble machine learning (ML) approach to estimate baseline SOC stocks in surface soils across the continental United States at 100‐m spatial resolution, and decadal changes under the projected climate scenarios of Coupled Model Intercomparison Project Phase Six (CMIP6) earth system models (ESMs). Results Baseline SOC projections from ML approaches captured more than 50% of variability in SOC observations, whereas ESMs represented only 6–16% of observed SOC variability. ML estimates showed a mean total loss of 1.8 Pg C from US surface soils under the high‐emission scenario by 2100, whereas ESMs showed no significant change in SOC stocks with wide variation among ESMs. Both ML and ESM predictions agree on the direction of SOC change (net emissions or sequestration) across 46–51% of continental US land area. These differences are attributable to the high‐resolution site‐specific data used in the ML models compared to the relatively coarse grid represented in CMIP6 ESMs. Main conclusions Our high‐resolution estimates of baseline SOC stocks, identification of key environmental controllers, and projection of SOC changes from US land cover types under future climate scenarios suggest the need for high‐resolution simulations of SOC in ESMs to represent the heterogeneity of SOC. We found that the SOC change is sensitive to key soil related factors (e.g. soil drainage and soil order) that have not been historically considered as input parameters in ESMs, because currently more than 95% variability in the SOC of CMIP6 ESMs is controlled by net primary productivity, temperature, and precipitation. Using additional environmental factors to estimate the baseline SOC stocks and predict the future trajectory of SOC change can provide more accurate results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

SU-F-I-53: Coded Aperture Coherent Scatter Spectral Imaging of the Breast: A Monte Carlo Evaluation of Absorbed Dose

Purpose: Coherent scatter based imaging has shown improved contrast and molecular specificity over conventional digital mammography however the biological risks have not been quantified due to a lack of accurate information on absorbed dose. This study intends to characterize the dose distribution and average glandular dose from coded aperture coherent scatter spectral imaging of the breast. The dose deposited in the breast from this new diagnostic imaging modality has not yet been quantitatively evaluated. Here, various digitized anthropomorphic phantoms are tested in a Monte Carlo simulation to evaluate the absorbed dose distribution and average glandular dose using clinically feasible scan protocols. Methods: Geant4 Monte Carlo radiation transport simulation software is used to replicate the coded aperture coherent scatter spectral imaging system. Energy sensitive, photon counting detectors are used to characterize the x-ray beam spectra for various imaging protocols. This input spectra is cross-validated with the results from XSPECT, a commercially available application that yields x-ray tube specific spectra for the operating parameters employed. XSPECT is also used to determine the appropriate number of photons emitted per mAs of tube current at a given kVp tube potential. With the implementation of the XCAT digital anthropomorphic breast phantom library, a variety of breast sizes with differing anatomical structure are evaluated. Simulations were performed with and without compression of the breast for dose comparison. Results: Through the Monte Carlo evaluation of a diverse population of breast types imaged under real-world scan conditions, a clinically relevant average glandular dose for this new imaging modality is extrapolated. Conclusion: With access to the physical coherent scatter imaging system used in the simulation, the results of this Monte Carlo study may be used to directly influence the future development of the modality to keep breast dose to a minimum while still maintaining clinically viable image quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Building a Business Case for Compressed Natural Gas in Fleet Applications

VICE 2.0 is the second generation of the VICE financial model developed by the National Renewable Energy Laboratory for fleet managers to assess the financial soundness of converting their fleets to run on CNG. VICE 2.0 uses a number of variables for infrastructure and vehicles to estimate the business case for decision-makers when considering CNG as a vehicle fuel. Enhancements in version 2.0 include the ability to select the project type (vehicles and infrastructure or vehicle acquisitions only), and to decouple vehicle acquisition from the infrastructure investment, so the two investments may be made independently. Outputs now include graphical presentations of investment cash flow, payback period (simple and discounted), petroleum displacement (annual and cumulative), and annual greenhouse gas reductions. Also, the Vehicle Data are now built around several common conventionally fueled (gasoline and diesel) fleet vehicles. Descriptions of the various model sections and available inputs follow. Each description includes default values for the base-case business model, which was created so economic sensitivities can be investigated by altering various project parameters one at a time.

33 ADVANCED PROPULSION SYSTEMS↗