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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 OSTI2012

Fourth level MSSM inflation from new flat directions

We propose a model of inflation driven by minimal extension of SUSY, commonly known as MSSM. Starting from gauge invariant flat directions in the n = 4 level comprising of QQQL,QuQd,QuLe and uude, we construct the inflaton potential and employ it to investigate for its consequences around the saddle point arising from the non-vanishing fourth derivative of the original potential. To this end, we derive the expressions for the important parameters in MSSM inflation using the loop corrected potential. We further estimate the observable parameters and find them to fit well with recent observational data from WMAP7 by using the code CAMB. We also explore the possibility of primordial black hole formation from our model. Finally, we analyze one loop RGE and compute different phenomenological parameters which could be precisely determined in LHC or future Linear Colliders.

79 ASTRONOMY AND ASTROPHYSICS↗

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 OSTI2025

Preliminary Results on Bayesian Inverse UQ for OECD/NEA WPNCS Subgroup 14 Benchmark Exercise for Error Recovery and Experimental Coverage

The Organization for Economic Cooperation and Development (OECD) Working Party on Nucelar Criticality Safety (WPNCS) has proposed a benchmark exercise representative of neutronic behavior in criticality experiments. Here, the goal is to develop confidence in data assimilation techniques used to adjust nuclear data. Participants are given synthetic experimental models with associated measured data and asked to estimate the model parameters given the model and measurements as well as provide predictions for separate application models. In this work, we performed data assimilation using Bayesian inverse Uncertainty Quantification (UQ) with machine learning surrogate models to produce posterior parameter distributions for the requested parameters and posterior predictive distributions for the requested responses. Several experimental models are shown to insufficiently inform the posterior parameter distributions for the applications involved. However, given sufficient experimental data, posterior parameter estimates yielded reduced uncertainty in the response predictions of interest while covering the experimental data.

Bayesian Inference↗

DOE OSTI2016

SU-F-P-19: Fetal Dose Estimate for a High-Dose Fluoroscopy Guided Intervention Using Modern Data Tools

Purpose: An accurate dose estimate is necessary for effective patient management after a fetal exposure. In the case of a high-dose exposure, it is critical to use all resources available in order to make the most accurate assessment of the fetal dose. This work will demonstrate a methodology for accurate fetal dose estimation using tools that have recently become available in many clinics, and show examples of best practices for collecting data and performing the fetal dose calculation. Methods: A fetal dose estimate calculation was performed using modern data collection tools to determine parameters for the calculation. The reference point air kerma as displayed by the fluoroscopic system was checked for accuracy. A cumulative dose incidence map and DICOM header mining were used to determine the displayed reference point air kerma. Corrections for attenuation caused by the patient table and pad were measured and applied in order to determine the peak skin dose. The position and depth of the fetus was determined by ultrasound imaging and consultation with a radiologist. The data collected was used to determine a normalized uterus dose from Monte Carlo simulation data. Fetal dose values from this process were compared to other accepted calculation methods. Results: An accurate high-dose fetal dose estimate was made. Comparison to accepted legacy methods were were within 35% of estimated values. Conclusion: Modern data collection and reporting methods ease the process for estimation of fetal dose from interventional fluoroscopy exposures. Many aspects of the calculation can now be quantified rather than estimated, which should allow for a more accurate estimation of fetal dose.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

A consistent approach for the development of a comprehensive data base of time-dependent parameters for concrete engineered barriers

This paper presents a consistent approach for the development of a comprehensive data base of time-dependent hydraulic and transport parameters for concrete engineered barriers of the future Dessel near surface repository for low level waste. The parameter derivation is based on integration of selected data obtained through an extensive literature review, data from experimental studies on cementitious materials specific for the Dessel repository and numerical modelling using physically-based models of water and mass transport. Best estimate parameter values for assessment calculations are derived, together with source and expert range and their probability density function wherever the data was sufficient. We further discuss a numerical method for up-scaling laboratory derived parameter values to the repository scale; the resulting large-scale effective parameters are commensurate with numerical grids used in models for radionuclide migration. To accommodate different levels of conservatism in the various assessment calculations defined by ONDRAF/NIRAS, several sets of parameter values have been derived based on assumptions that introduce different degrees of conservatism. For pertinent parameters, the time evolution of such properties due to the long-term concrete degradation is also addressed. The implementation of the consistent approach is demonstrated by considering the pore water diffusion coefficient as an example. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

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 OSTI2011

Testing Different Methods for Estimating Uncertainties on Rietveld Refined Parameters Using SrRietveld

The advent of fast computing allows more computationally expensive, though possibly more accurate, approaches for estimating uncertainties on Rietveld refined parameters. Here we compare three such methods, using two different refinement programs, FullProf and GSAS. This is facilitated by the use of a new Rietveld refinement package, SrRietveld, that provides Python wrappers for FullProf and GSAS. The refined values on the parameters from two different refinement engines match each other very well. The uncertainty estimates determined using the different methods are also consistent, though FullProf and GSAS estimate the parameter uncertainties slightly differently from each other, which is discussed. More importantly, we find that the refined results are very sensitive to the statistical weights used in the least squares equation. Different weights, for example from uncertain or incorrectly propagated random errors on the data, lead to significantly different refined values. This means that uncertainty estimates on refined parameters should be increased when the random errors on the data are not well known, as for example in many cases where area detectors are used, and care should be taken to propagate errors correctly in any data preprocessing such as normalization by a smoothed spectrum. The computationally expensive bootstrap error estimation methods are facilitated by the use of SrRietveld, but are not required in most cases though may be useful in some cases, for example if the random errors on the data are not well known.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

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 OSTI2012

MODELING EXTRAGALACTIC FOREGROUNDS AND SECONDARIES FOR UNBIASED ESTIMATION OF COSMOLOGICAL PARAMETERS FROM PRIMARY COSMIC MICROWAVE BACKGROUND ANISOTROPY

Using the latest physical modeling and constrained by the most recent data, we develop a phenomenological parameterized model of the contributions to intensity and polarization maps at millimeter wavelengths from external galaxies and Sunyaev-Zeldovich effects. We find such modeling to be necessary for estimation of cosmological parameters from Planck data. For example, ignoring the clustering of the infrared background would result in a bias in n{sub s} of 7{sigma} in the context of an eight-parameter cosmological model. We show that the simultaneous marginalization over a full foreground model can eliminate such biases, while increasing the statistical uncertainty in cosmological parameters by less than 20%. The small increases in uncertainty can be significantly reduced with the inclusion of higher-resolution ground-based data. The multi-frequency analysis we employ involves modeling 46 total power spectra and marginalization over 17 foreground parameters. We show that we can also reduce the data to a best estimate of the cosmic microwave background power spectra, with just two principal components (with constrained amplitudes) describing residual foreground contamination.

79 ASTRONOMY AND ASTROPHYSICS↗

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↗