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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

THE IMPACT OF METALLICITY ON THE RATE OF TYPE Ia SUPERNOVAE

The metallicity of a star strongly affects both its evolution and the properties of the stellar remnant that results from its demise. It is generally accepted that stars with initial masses below {approx}8 M{sub Sun} leave behind white dwarfs and that some sub-population of these lead to Type Ia supernovae (SNe Ia). However, it is often tacitly assumed that metallicity has no effect on the rate of SNe Ia. We propose that a consequence of the effects of metallicity is to significantly increase the SN Ia rate in lower-metallicity galaxies, in contrast to previous expectations. This is because lower-metallicity stars leave behind higher-mass white dwarfs, which should be easier to bring to explosion. We first model SN Ia rates in relation to galaxy masses and ages alone, finding that the elevation in the rate of SNe Ia in lower-mass galaxies measured by Lick Observatory SN Search is readily explained. However, we then see that models incorporating this effect of metallicity agree just as well. Using the same parameters to estimate the cosmic SN Ia rate, we again find good agreement with data up to z Almost-Equal-To 2. We suggest that this degeneracy warrants more detailed examination of host galaxy metallicities. We discuss additional implications, including for hosts of high-z SNe Ia, the SN Ia delay time distribution, super-Chandrasekhar SNe, and cosmology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Novel Sensor for the In Situ Measurement of Uranium Fluxes

The goal of this project was to develop a sensor that incorporates the field-tested concepts of the passive flux meter to provide direct in situ measures of flux for uranium and groundwater in porous media. Measurable contaminant fluxes [J] are essentially the product of concentration [C] and groundwater flux or specific discharge [q ]. The sensor measures [J] and [q] by changes in contaminant and tracer amounts respectively on a sorbent. By using measurement rather than inference from static parameters, the sensor can directly advance conceptual and computational models for field scale simulations. The sensor was deployed in conjunction with DOE in obtaining field-scale quantification of subsurface processes affecting uranium transport (e.g., advection) and transformation (e.g., uranium attenuation) at the Rifle IFRC Site in Rifle, Colorado. Project results have expanded our current understanding of how field-scale spatial variations in fluxes of uranium, groundwater and salient electron donor/acceptors are coupled to spatial variations in measured microbial biomass/community composition, effective field-scale uranium mass balances, attenuation, and stability. The coupling between uranium, various nutrients and micro flora can be used to estimate field-scale rates of uranium attenuation and field-scale transitions in microbial communities. This research focuses on uranium (VI), but the sensor principles and design are applicable to field-scale fate and transport of other radionuclides. Laboratory studies focused on sorbent selection and calibration, along with sensor development and validation under controlled conditions. Field studies were conducted at the Rifle IFRC Site in Rifle, Colorado. These studies were closely coordinated with existing SBR (formerly ERSP) projects to complement data collection. Small field tests were conducted during the first two years that focused on evaluating field-scale deployment procedures and validating sensor performance under controlled field conditions. In the third and fourth year a suite of larger field studies were conducted. For these studies, the uranium flux sensor was used with uranium speciation measurements and molecular-biological tools to characterize microbial community and active biomass at synonymous wells distributed in a large grid. These field efforts quantified spatial changes in uranium flux and field-scale rates of uranium attenuation (ambient and stimulated), uranium stability, and quantitatively assessed how fluxes and effective reaction rates were coupled to spatial variations in microbial community and active biomass. Analyses of data from these field experiments were used to generate estimates of Monod kinetic parameters that are ‘effective’ in nature and optimal for modeling uranium fate and transport at the field-scale. This project provided the opportunity to develop the first sensor that provides direct measures of both uranium (VI) and groundwater flux. A multidisciplinary team was assembled to include two geochemists, a microbiologist, and two quantitative contaminant hydrologists. Now that the project is complete, the sensor can be deployed at DOE sites to evaluate field-scale uranium attenuation, source behavior, the efficacy of remediation, and off-site risk. Because the sensor requires no power, it can be deployed at remote sites for periods of days to months. The fundamental science derived from this project can be used to advance the development of predictive models for various transport and attenuation processes in aquifers. Proper development of these models is critical for long-term stewardship of contaminated sites in the context of predicting uranium source behavior, remediation performance, and off-site risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Allometric, Growth, and Biomass Estimation Models for Acacia dealbata Link.: A Case Study in Serra da Estrela Natural Park (Portugal)

Allometric, growth, and biomass estimation models for the quantification of missing parameters in trees are widely used to estimate the productivity of a stand. However, regarding species with lower economic interest, or exotic invasive species, the creation of these tools did not occur in the same way as for other species, lacking the development of relationships to allow a deeper study of these species. Thus, data were collected in a settlement of Acacia dealbata Link. in Portugal, in an area known for the current infestation of this species. After a bibliographic review, some of the identified models were tested to select those who best fit the characteristics of this stand, which were used to develop relationships that, based on the data collected, would be able to estimate parameters, such as height, volume, or mass. It was found that the models established, at least for the initial 20 years of the life of the trees, followed close to a linear model. However, the model still presents some weaknesses. For being considered an invasive species in Portugal, A. dealbata is frequently controlled, and for this reason it is very difficult to find trees older than 20 years.

Nunes, Leonel J. R. (ORCID:0000000154048163)↗
DOE OSTI2020

Mobile Sensing for Wind Field Estimation in Wind Farms: Preprint

This paper introduces a novel approach for estimating the wind field over an entire wind farm using a mobile sensor to collect limited amounts of data. The proposed method estimates the boundary conditions of a simplified turbine wake model by computing the model sensitivity matrix and using a recursive least-squares algorithm to recover the model parameters from the wind field measurements. To address the fact that it is not practical to take measurements across the entire wind farm, the proposed method classifies each area on the map based on its sensitivity to parameter variations. This classification is then used to generate a suitable path for a mobile sensor, which is charged with collecting data for the recursive least-squares algorithm. The proposed framework can successfully estimate the model boundary conditions using just the measurements collected along the path of the mobile sensor. This preliminary result paves the way for using real-time wind field estimates for the coordinated control of all the turbines within a wind farm.

estimation↗
DOE OSTI2016

Quantifying shallow subsurface water and heat dynamics using coupled hydrological-thermal-geophysical inversion

Improving our ability to estimate the parameters that control water and heat fluxes in the shallow subsurface is particularly important due to their strong control on recharge, evaporation and biogeochemical processes. The objectives of this study are to develop and test a new inversion scheme to simultaneously estimate subsurface hydrological, thermal and petrophysical parameters using hydrological, thermal and electrical resistivity tomography (ERT) data. The inversion scheme – which is based on a nonisothermal, multiphase hydrological model – provides the desired subsurface property estimates in high spatiotemporal resolution. A particularly novel aspect of the inversion scheme is the explicit incorporation of the dependence of the subsurface electrical resistivity on both moisture and temperature. The scheme was applied to synthetic case studies, as well as to real datasets that were autonomously collected at a biogeochemical field study site in Rifle, Colorado. At the Rifle site, the coupled hydrological-thermal-geophysical inversion approach well predicted the matric potential, temperature and apparent resistivity with the Nash–Sutcliffe efficiency criterion greater than 0.92. Synthetic studies found that neglecting the subsurface temperature variability, and its effect on the electrical resistivity in the hydrogeophysical inversion, may lead to an incorrect estimation of the hydrological parameters. The approach is expected to be especially useful for the increasing number of studies that are taking advantage of autonomously collected ERT and soil measurements to explore complex terrestrial system dynamics.

58 GEOSCIENCES↗
DOE OSTI2017

Bayesian Parameter Estimation for Heavy-Duty Vehicles

Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses Monte Carlo to generate parameter sets which is fed to a variant of the road load equation. Modeled road load is then compared to measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the current state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters. Results confirm the method's ability to estimate reasonable parameter sets, and indicates an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Estimating Parameters for the PVsyst Version 6 Photovoltaic Module Performance Model

We present an algorithm to determine parameters for the photovoltaic module performance model encoded in the software package PVsyst™ version 6. Our method operates on current-voltage (I-V) measured over a range of irradiance and temperature conditions. We describe the method and illustrate its steps using data for a 36 cell crystalline silicon module. We qualitatively compare our method with one other technique for estimating parameters for the PVsyst™ version 6 model.

14 SOLAR ENERGY↗
DOE OSTI2023

Meteorological, reference evapotranspiration and estimated transpiration data, July 2020-Dec 2021, Slate River Floodplain, Crested Butte, Colorado

This data package includes a time series of meteorological data (air temperature, relative humidity, barometric pressure, precipitation, wind speed, soil heat flux, incoming shortwave radiation, incoming longwave radiation, outgoing shortwave radiation and outgoing longwave radiation) from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package also includes reference evapotranspiration calculated via the Penman-Monteith equation and an estimate of willow (Salix spp.) evapotranspiration using the method of Irmak et al., J. Hydrology, 2013. Due to two malfunctioning sensors (incoming shortwave radiation from Oct 2020-May 2021 and incoming longwave radiation from May 2021-March 2022), observed net radiation and calculated evapotranspiration are only included from July-October 2020. However, users may be able to extend calculated evapotranspiration using estimates of these two parameters from other sources. Both sensors have been replaced and data beyond 2021 be uploaded to this repository when it is available.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 3 "*data.csv" files, two for meteorological data for 2020 and 2021 and one for reference evapotranspiration and transpiration data from 2020 ("SLT_OBJ2_PenmanMonteith_2020_data.csv"). Each "*data.csv" file of meteorological data has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding to each measured parameter. We also include a Jupyter notebook ("PenmanMonteithET_Calculations.ipynb") that details the assumptions and equations used to calculate daily and hourly reference evapotranspiration and daily willow transpiration from raw meteorological data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2022

IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2017

Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America

As South and Central American countries prepare for increased birth defects from Zika virus outbreaks and plan for mitigation strategies to minimize ongoing and future outbreaks, understanding important characteristics of Zika outbreaks and how they vary across regions is a challenging and important problem. We developed a mathematical model for the 2015/2016 Zika virus outbreak dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly available data provided by the Pan American Health Organization, using Approximate Bayesian Computation to estimate parameter distributions and provide uncertainty quantification. The model indicated that a country-level analysis was not appropriate for Colombia. We then estimated the basic reproduction number to range between 4 and 6 for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We estimated the reporting rate to be around 16% in El Salvador and 18% in Suriname with estimated total outbreak sizes of 73,395 and 21,647 people, respectively. The uncertainty in parameter estimates highlights a need for research and data collection that will better constrain parameter ranges.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

PEES (Parameter Estimator for Engineering Systems) [SWR-16-38]

This software model generates vehicle parameter sets for downstream modeling applications using real-world data. Value to users is provided through improvements to modeling activities as a result of decreases in time taken during model development and validation. Currently configured for transportation modeling activities, the software provides initial estimates for multiple fundamental vehicle parameters including mass, coefficient of drag, and tire rolling resistance using real-world drive and duty cycle information as inputs.

Miller, Eric↗
DOE OSTI2014

EXONEST: Bayesian model selection applied to the detection and characterization of exoplanets via photometric variations

EXONEST is an algorithm dedicated to detecting and characterizing the photometric signatures of exoplanets, which include reflection and thermal emission, Doppler boosting, and ellipsoidal variations. Using Bayesian inference, we can test between competing models that describe the data as well as estimate model parameters. We demonstrate this approach by testing circular versus eccentric planetary orbital models, as well as testing for the presence or absence of four photometric effects. In addition to using Bayesian model selection, a unique aspect of EXONEST is the potential capability to distinguish between reflective and thermal contributions to the light curve. A case study is presented using Kepler data recorded from the transiting planet KOI-13b. By considering only the nontransiting portions of the light curve, we demonstrate that it is possible to estimate the photometrically relevant model parameters of KOI-13b. Furthermore, Bayesian model testing confirms that the orbit of KOI-13b has a detectable eccentricity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Tightly-coupled camera/LiDAR integration for point cloud generation from GNSS/INS-assisted UAV mapping systems

Unmanned aerial vehicles (UAVs) equipped with integrated global navigation satellite systems/inertial navigation systems (GNSS/INS) together with cameras and/or LiDAR sensors are being widely used for topographic mapping in a variety of applications such as precision agriculture, coastal monitoring, and archaeological documentation. Integration of image-based and LiDAR point clouds can provide a comprehensive 3D model of the area of interest. For such integration, ensuring a good alignment between data from the different sources is critical. Although many works have been conducted on this topic, there is still a need for a rigorous integration approach that minimizes the discrepancy between camera and LiDAR data caused by inaccurate system calibration parameters and/or trajectory artifacts. This study proposes an automated tightly-coupled camera/LiDAR integration workflow for GNSS/INS-assisted UAV systems. The proposed strategy is conducted in three main steps. First, an image-based point cloud is generated using a LiDAR/GNSS/INS-assisted structure from motion (SfM) strategy. Then, feature correspondences between image-based and LiDAR point clouds are automatically identified. Finally, an integrated-bundle adjustment procedure including image points, LiDAR raw measurements, and GNSS/INS information is conducted to minimize the discrepancy between point clouds from different sensors while estimating system calibration parameters and refining the trajectory information. The proposed SfM strategy and integration framework are evaluated using five datasets. The SfM results show that using LiDAR data can facilitate feature matching and further increase the number of reconstructed 3D points. The experimental results also illustrate that the developed automated camera/LiDAR integration strategy is capable of accurately estimating system calibration parameters to achieve good alignment among camera/LiDAR data from single/multiple systems. Finally, an absolute accuracy in the range of 3–5 cm is achieved for the image/LiDAR point clouds after the integration process.

42 ENGINEERING↗
DOE OSTI2022

Synthesis of observed and simulated rain microphysics to inform a new Bayesian statistical framework for microphysical parameterization in climate models (Final Scientific Report)

This project aimed to investigate a new approach to bulk microphysics schemes. We identified the need to move beyond the fixed structural assumptions and approximation of existing schemes. For example, most schemes assume some functional form for the rain drop size distribution (e.g. a gamma or exponential distribution). Most schemes then evolve some number of statistical moments of that distribution via various processes, such as evaporation, sedimentation, collision-coalescence, and collisional breakup. These microphysical processes, in turn, are typically some fixed functional form derived from either some other (more detailed) model, or using some single-particle rates that are then integrated over the size distribution. While these process rate functions may have some free parameters to adjust, in other cases doing so is impossible (e.g. when the process rates are analytical or piecewise solutions to some target function). Our goal was to build and test a scheme that assumed no size distribution form, and used a series of power laws as the basis for the process rates. Moments of the size distribution would be predicted, but no underlying size distribution would be specified. Any number or choice of size distribution moments could be used, and any number of power laws could be employed to model the process rates. Thus, our approach is flexible, and can seamlessly scale across levels of complexity, as demanded by the data. Bayesian inference then would provide the formalism to estimate the model parameters and structure, allowing for robust uncertainty quantification. We proposed testing this framework in idealized simulations using bin microphysical schemes as a data source. We also proposed using real data to inform our microphysics scheme, and also that we would integrate our scheme into WRF.

58 GEOSCIENCES↗
DOE OSTI2024

Convergence in simulating global soil organic carbon by structurally different models after data assimilation

Abstract Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first‐order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high‐quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics‐function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-I-95: Personalized Radiography Technical Parameters for Each Patient and Exam

Purpose: To determine exact electrical parameters (kV, mAs) a radiological technologist shall use taking account the exam and patient's structure, with guarantee of minimum dose and adequate quality image. Methods: A patient's absorbed dose equation was developed by means of Entrance Skin Dose (ESD), irradiated area and patient width for specific anatomy. ESD is calculated from a developed equation, where entrance surface air-KERMA and backscatter factor are included, with air-to-skin coefficient conversion. We developed specific Lambert-Beer attenuation equations derived from mass energy-absorption coefficients data for skin, fat, and muscle and bone as one tissue. Anatomy tissue thickness distribution at central X-ray location in anteroposterior incidence for hand and chest, was estimate by discounting constant skin and bone thickness from patient measured width, assuming the result as muscle and fat. A clinical research at a big hospital were executed when real parameters (kV, mAs, filtration, ripple) used by technologists were combined with the image quality and patient's data: anatomy width, height and weight. A correlation among the best images acquired and electrical parameters used were confronted with patient's data and dose estimation. The best combinations were used as gold standards. Results: For each anatomy, two equations were developed to calculate voltage (kV) and exposure (mAs) to reproduce and interpolate the gold standards. Patient is measured and data are input into equations, giving radiological technologists the right set of electrical parameters for that specific exam. Conclusion: This work indicates that radiological technologist can personalize the exact electrical parameters for each patient exam, instead of using standard values. It also guarantee that patients under or over-sized measures will receive the right dose for the best image. It will stop wrong empiric adjusts technologists do when examining a non-standard patient and reduce probability of radiography retaken because of over or under exposition.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Modeling Substrate Utilization, Metabolite Production, and Uranium Immobilization in Shewanella oneidensis Biofilms

In this study, we developed a two-dimensional mathematical model to predict substrate utilization and metabolite production rates in Shewanella oneidensis MR-1 biofilm in the presence and absence of uranium (U). In our model, lactate and fumarate are used as the electron donor and the electron acceptor, respectively. The model includes the production of extracellular polymeric substances (EPS). The EPS bound to the cell surface and the EPS distributed in the biofilm were considered bound EPS (bEPS) and loosely associated EPS (laEPS), respectively. COMSOL ® Multiphysics finite element analysis software was used to solve the model numerically (model file provided in the Supplementary Material). The input variables of the model were the lactate and fumarate concentrations, cell density, EPS density, half saturation constant for fumarate, and diffusion coefficients of the substrates and metabolites. To estimate unknown parameters and calibrate the model, we used a custom designed biofilm reactor placed inside a nuclear magnetic resonance (NMR) microimaging and spectroscopy system and measured substrate utilization and metabolite production rates. From these data we estimated the yield coefficients, maximum substrate utilization rate, half saturation constant for lactate, stoichiometric ratio of fumarate and acetate to lactate and stoichiometric ratio of succinate to fumarate. These parameters are critical to predicting the activity of biofilms and are not available in the literature. Lastly, the model was used to predict uranium immobilization in S. oneidensis MR-1 biofilms by considering reduction and adsorption processes in the cells and in the EPS. We found that the majority of immobilization was due to cells, and that EPS were less efficient at immobilizing U. Furthermore, most of the immobilization occurred within the top 10 µm of the biofilm. To the best of our knowledge, this research is one of the first biofilm immobilization mathematical models based on experimental observation. It has the ability to predict the relative contributions to U immobilization of laEPS, bEPS and cells.

59 BASIC BIOLOGICAL SCIENCES↗