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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 163 records · Page 9

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DOE OSTI2022

Parameter estimation from spontaneous imbibition into volcanic tuff

Two-phase fluid flow properties underlie quantitative prediction of water and gas movement, but constraining these properties typically requires multiple time-consuming laboratory methods. The estimation of two-phase flow properties (van Genuchten parameters, porosity, and intrinsic permeability) is illustrated in cores of vitric nonwelded volcanic tuff using Bayesian parameter estimation that fits numerical models to observations from spontaneous imbibition experiments. The uniqueness and correlation of the estimated parameters is explored using different modeling assumptions and subsets of the observed data. The resulting estimation process is sensitive to both moisture retention and relative permeability functions, thereby offering a comprehensive method for constraining both functions. The data collected during this relatively simple laboratory experiment, used in conjunction with a numerical model and a global optimizer, result in a viable approach for augmenting more traditional capillary pressure data obtained from hanging water column, membrane plate extractor, or mercury intrusion methods. This method may be useful when imbibition rather than drainage parameters are sought, when larger samples (e.g., including heterogeneity or fractures) need to be tested that cannot be accommodated in more traditional methods, or when in educational laboratory settings.

58 GEOSCIENCES↗

DOE OSTI2017

Leaf Photosynthetic Parameters Vcmax and Jmax and Supporting Gas Exchange Data, Barrow, Alaska, 2012-2016

Photosynthetic parameters Vcmax and Jmax estimated from CO2 response curves and from light saturated photosynthesis using the one point method. Data were collected in Barrow 2012-2016 on 7 species - Arctophila fulva, Arctagrostis latifolia, Carex aquatilis, Dupontia fisheri, Eriophorum angustifolium, Petasites frigidus, Salix pulchra. To allow for future reanalysis of our data, and to maximize further use of it by the modelling community, all our data including our raw gas exchange data is available. This dataset contains 3 csv, 3 xlsx, and 1 pdf files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Online Static Load Model Estimation in Distribution Systems: Preprint

The paper focuses on parameter identification for time varying voltage dependent polynomial load models, namely the constant impedance (Z), constant current (I), and constant power (P), in the distribution systems. Typically, parameter identification has been done for aggregated loads at substations for the transmission planning studies. Given the higher voltage volatility faced by active distribution systems, this paper takes the load model estimation to the distribution networks. The paper assumes the presence of measurements at the distribution nodes from various sources, each at varying temporal resolutions. Least squares estimation (LSE) method with bounded variables is used to convert the measurements into ZIP parameter estimations for loads at various locations of the feeder. The results and discussions focus on some of the estimation issues faced in a distribution system due to varying voltage sensitivities of loads along a feeder, measurement resolution, impact of window size and sampling rate of measurement data, and the use of dynamic simulations to create synthetic measurement data in the absence of real ones.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Time Variations of Observed H α Line Profiles and Precipitation Depths of Nonthermal Electrons in a Solar Flare

We compare time variations of the H α and X-ray emissions observed during the pre-impulsive and impulsive phases of the C1.1-class solar flare on 2013 June 21 with those of plasma parameters and synthesized X-ray emission from a 1D hydrodynamic numerical model of the flare. The numerical model was calculated assuming that the external energy is delivered to the flaring loop by nonthermal electrons (NTEs). The H α spectra and images were obtained using the Multi-channel Subtractive Double Pass spectrograph with a time resolution of 50 ms. The X-ray fluxes and spectra were recorded by RHESSI . Pre-flare geometric and thermodynamic parameters of the model and the delivered energy were estimated using RHESSI data. The time variations of the X-ray light curves in various energy bands and those of the H α intensities and line profiles were well correlated. The timescales of the observed variations agree with the calculated variations of the plasma parameters in the flaring loop footpoints, reflecting the time variations of the vertical extent of the energy deposition layer. Our result shows that the fast time variations of the H α emission of the flaring kernels can be explained by momentary changes of the deposited energy flux and the variations of the penetration depths of the NTEs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗

DOE OSTI2014

Characterizing unknown systematics in large scale structure surveys

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data, we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

A rigorous physics-based enhanced parameter estimation (EPE) methodology for calibration of building energy simulations

Buildings rarely perform as designed/simulated and there are numerous tangible benefits if this gap is reconciled. A new scientifically rigorous yet pragmatic methodology for calibrating building energy simulations - called Enhanced Parameter Estimation (EPE) - is proposed that allows physically relevant parameter estimation rather than a blind force-fit to energy use data. Starting with a rapidly created simulation model, calibration is performed in two stages: (a) building shell calibration with the HVAC system replaced by an ideal system that meets the loads (b) HVAC system calibration with the building shell and all internal loads replaced by a box with only process loads. In the first stage, EPE identifies a small number of high-level heat flows in the energy balance, calculates them with specifically tailored individual driving functions, introduces physically significant parameters to best accomplish energy balance, and, estimates the parameters and their uncertainty bounds. Calibration is thus done with corrective heat flows without any arbitrary tuning of input parameters. Calibration accuracy is enhanced by machine learning of the residual errors. The EPE methodology is demonstrated by means of: a synthetic building and an actual 75,000 sq. ft. building in Pennsylvania. Future work needed for widespread application is discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2015

MODELING ATMOSPHERIC EMISSION FOR CMB GROUND-BASED OBSERVATIONS

Atmosphere is one of the most important noise sources for ground-based cosmic microwave background (CMB) experiments. By increasing optical loading on the detectors, it amplifies their effective noise, while its fluctuations introduce spatial and temporal correlations between detected signals. We present a physically motivated 3D-model of the atmosphere total intensity emission in the millimeter and sub-millimeter wavelengths. We derive a new analytical estimate for the correlation between detectors time-ordered data as a function of the instrument and survey design, as well as several atmospheric parameters such as wind, relative humidity, temperature and turbulence characteristics. Using an original numerical computation, we examine the effect of each physical parameter on the correlations in the time series of a given experiment. We then use a parametric-likelihood approach to validate the modeling and estimate atmosphere parameters from the polarbear-i project first season data set. We derive a new 1.0% upper limit on the linear polarization fraction of atmospheric emission. We also compare our results to previous studies and weather station measurements. The proposed model can be used for realistic simulations of future ground-based CMB observations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO 2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO 2 (ffCO 2 ) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO 2 emissions and synthetic observations of ffCO 2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Understanding the nature of the blazar CGRaBS J0211+1051

The source CGRaBS J0211+1051 (MG1 J021114+1051, z = 0.20) flared up on 2011 January 23 in high-energy γ-rays as reported by the Large Area Telescope (LAT) on board Fermi. This event was followed by increased activity at the UV, optical, and radio frequencies as detected by the observing facilities worldwide. The source also showed a high and variable optical polarization based on which it was proposed to be a low-energy peaked BL Lac object (LBL). The present work reports first comprehensive multi-wavelength study of this source using data in the radio, optical, UV, X- and γ-rays, and optical polarization. Using these multi-wavelength data on the source, we have estimated various parameters and verified its classification vis-a-vis blazar sequence. Multi-waveband light curves are used to discuss the flaring events of 2011 January in an attempt to address the nature of the source and pinpoint the possible physical processes responsible for the emission. The light curves show variations in the high-energy γ-rays to be correlated with X-ray, UV, and optical variations, perhaps indicating their co-spatial origin. Our optical data, quasi-simultaneous with UV (Swift-UVOT) and X-ray (Swift-XRT) data, enabled us to trace the low-energy (synchrotron) component of the spectral energy distribution (SED) for CGRaBS J0211+1051 for the first time. The SED shows the synchrotron peak to lie at ∼1.35 × 10{sup 14} Hz, confirming that CGRaBS J0211+1051 is an LBL. Some other parameters, such as the local magnetic field (∼5.93 G) and black hole mass (∼2.4 × 10{sup 8} M {sub ☉}), are also estimated which are in agreement with their typical values for the blazars. Based on the present study, identification of the Fermi/LAT source, 2FGL J0211.2+1050, with its BL Lac counterpart CGRaBS J0211+1051 is confirmed.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Automated Signal Timing Plan Reconstruction Using High-Resolution Event-Based Controller Data for Digital Twins

Transportation digital twins are essential tools for evaluating emerging technologies such as connected and automated vehicles, adaptive traffic signal control, and mobility optimization strategies. Realistic digital twins require accurate emulation of real-world signal controllers and detailed signal timing plans. However, signal timing plans are often unavailable or difficult to access, forcing researchers and modelers to rely on assumed fixed timings or halt their analysis. To overcome this challenge, we present a method that directly estimates signal timing plan parameters using high-resolution, event-based data from traffic signal controllers. The proposed method extracts key parameters, including cycle length, offset, phase sequence, coordinated phases, phase-specific minimum and maximum green durations, vehicle extensions, and splits under coordination. A rule-based deterministic signal timing reconstruction algorithm based on traffic signal operation rules, such as those outlined in the Signal Timing Manual, is developed and validated. We evaluate this method, which uses high-resolution controller event logs and verified signal timing plans, on 94 signalized intersections in Nashville, Tennessee, demonstrating their ability to generate accurate, simulation-ready signal timing plans for tools such as SUMO and Vissim.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗

DOE OSTI2018

Elastic functional principal component regression

We study regression using functional predictors in situations where these functions contains both phase and amplitude variability. In other words, the functions are misaligned due to errors in time measurements, and these errors can significantly degrade both model estimation and prediction performance. The current techniques either ignore the phase variability, or handle it via preprocessing, that is, use an off–the–shelf technique for functional alignment and phase removal. We develop a functional principal component regression model which has a comprehensive approach in handling phase and amplitude variability. The model utilizes a mathematical representation of the data known as the square–root slope function. These functions preserve the L 2 norm under warping and are ideally suited for simultaneous estimation of regression and warping parameters. Furthermore, using both simulated and real–world data sets, we demonstrate our approach and evaluate its prediction performance relative to current models. In addition, we propose an extension to functional logistic and multinomial logistic regression.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Convective Parameters Derived from Radiosonde Data (SONDEPARAM) Value-Added Product Report

Radiosondes provide fundamental observations of the vertical profile of atmospheric state (pressure, temperature, humidity, and winds), with important implications for subsequent studies on environmental controls on cloud conditions. Within convective cloud environments, there is an increasing demand for additional value-added products (VAPs) to facilitate the use of U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility radiosonde data sets. Such VAPs should provide quick and reliable estimates for several standard radiosonde parameters or quantities of interest using common assumptions, as well as open, flexible code for visualization and user interaction. The Convective Parameters Derived from Radiosonde Data (SONDEPARAM) VAP will apply several robust algorithms used in Wang et al. (2020) for the calculation of useful radiosonde convective cloud parameters, including the convective available potential energy (CAPE), convective inhibition (CIN), and other convective parameters, for several different assumptions regarding the initial parcel characteristics (i.e., surface-based, most unstable, mixed layer). These ARM VAP codes are developed in open, flexible Python formats, with the intention that these parameters/calculations will be incorporated into traditional ARM quick-look radiosonde plotting, yet associated with user-available codes for ease in user reproduction and assumption modification.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.

42 ENGINEERING↗

DOE OSTI2016

Parameter Estimator for Engineering Systems

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.

Source record↗

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 OSTI2016

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

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↗