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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Estimating the Likelihood of GHG Concentration Scenarios From Probabilistic Integrated Assessment Model Simulations

The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO 2 ) concentration scenarios used in key climate change modeling experiments. It computes the CO 2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO 2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO 2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5-8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO 2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Shaping the terahertz sound propagation in water under highly directional confinement

Here, we consider the possibility of shaping the high-frequency acoustic propagation in a liquid upon directional confinement. This hypothesis is investigated by inelastic x-ray scattering measurements on water confined in aligned multiwalled carbon nanotubes, in which the momentum transfer was either parallel or orthogonal to the confinement axis. The comparison between the spectra measured in these two scattering geometries highlights the anisotropic nature of the dynamic response of directionally confined water, thus potentially inspiring pathways to shape high-frequency sound propagation in isotropic systems. Finally, a Bayesian analysis of measured spectra unravels close similarities in the phonon dispersions of multiwalled nanotubes and graphite.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Quantifying heavy quark transport coefficients with an improved transport model

We report that the heavy-flavor transport coefficients contain important information on the strong interaction at finite temperatures. The extraction of these numbers from experimental data requires dynamical modeling of heavy-flavor transport that is coupled to realistic medium evolution. Furthermore, meaningful extractions necessitate both a faithful implementation of the physical inputs to be tested and the quantification of model uncertainty. For these purposes, we have developed a partonic transport model LIDO. It has an improved treatment of in-medium parton bremsstrahlung, which has been calibrated to theoretical calculations in a simple medium to reduce modeling uncertainty. Regarding the interaction between heavy quark and the medium, few-body perturbative scatterings are applied to large-momentum transfer (q) processes, while a diffusion equation models the dynamics of small-q processes. Such a separation restricts the explicit use of medium quasi-particles to large-q processes only. Another advantage is that deviations from the leading-order probe-medium coupling can be parametrized as an additional contribution to the diffusion constant. The heavy quark transport coefficients are then extracted with uncertainty estimation from a Bayesian analysis including both the RHIC and the LHC data. The results are found to be consistent with earlier extraction of the light-quark transport coefficients at high momentum and be comparable with lattice calculations of the heavy-flavor diffusion constant in the static limit at low momentum.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Interplaying mechanisms behind inclusive jet R and extraction of jet energy loss distributions

The observed inclusive jet suppression in heavy-ion collisions at LHC has a very weak p T dependence over a large range of p T =50–1000 GeV and is almost independent of the colliding energy, though the initial energy density of the bulk medium has increased from √s=2.76 to 5.02 TeV by about 20%. This interesting phenomenon is investigated in the linear Boltzmann transport (LBT) model for jet propagation in an event-by-event 3+1D hydro background. We show that the p T dependence of jet R AA is determined by the initial spectrum in p+p collisions and p T dependence of jet energy loss. Furthermore, jet energy loss distributions for inclusive jet and γ-jet at both LHC energies are extracted directly from experimental data through the state-of-art Bayesian analysis. Additionally, the averaged jet energy loss has a weak p T dependence and the scaled jet energy loss distributions have a large width, both of which are consistent with the LBT simulations and indicate that jet quenching is caused by only a few out-of-cone jet medium scatterings.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2017

A Survey of Eight Hot Jupiters in Secondary Eclipse Using WIRCam at CFHT

We present near-infrared high-precision photometry for eight transiting hot Jupiters observed during their predicted secondary eclipses. Our observations were carried out using the staring mode of the WIRCam instrument on the Canada-France-Hawaii Telescope (CFHT).We present the observing strategies and data reduction methods which delivered time series photometry with statistical photometric precision as low as 0.11 percent. We performed a Bayesian analysis to model the eclipse parameters and systematics simultaneously. The measured planet-to-star flux ratios allowed us to constrain the thermal emission from the day side of these hot Jupiters, as we derived the planet brightness temperatures. Our results combined with previously observed eclipses reveal an excess in the brightness temperatures relative to the blackbody prediction for the equilibrium temperatures of the planets for a wide range of heat redistribution factors. We find a trend that this excess appears to be larger for planets with lower equilibrium temperatures. This may imply some additional sources of radiation, such as reflected light from the host star and/or thermal emission from residual internal heat from the formation of the planet.

Bayesian Analysis↗
DOE OSTI2015

Past and present cosmic structure in the SDSS DR7 main sample

We present a chrono-cosmography project, aiming at the inference of the four dimensional formation history of the observed large scale structure from its origin to the present epoch. To do so, we perform a full-scale Bayesian analysis of the northern galactic cap of the Sloan Digital Sky Survey (SDSS) Data Release 7 main galaxy sample, relying on a fully probabilistic, physical model of the non-linearly evolved density field. Besides inferring initial conditions from observations, our methodology naturally and accurately reconstructs non-linear features at the present epoch, such as walls and filaments, corresponding to high-order correlation functions generated by late-time structure formation. Our inference framework self-consistently accounts for typical observational systematic and statistical uncertainties such as noise, survey geometry and selection effects. We further account for luminosity dependent galaxy biases and automatic noise calibration within a fully Bayesian approach. As a result, this analysis provides highly-detailed and accurate reconstructions of the present density field on scales larger than ∼ 3 Mpc/h, constrained by SDSS observations. This approach also leads to the first quantitative inference of plausible formation histories of the dynamic large scale structure underlying the observed galaxy distribution. The results described in this work constitute the first full Bayesian non-linear analysis of the cosmic large scale structure with the demonstrated capability of uncertainty quantification. Some of these results will be made publicly available along with this work. The level of detail of inferred results and the high degree of control on observational uncertainties pave the path towards high precision chrono-cosmography, the subject of simultaneously studying the dynamics and the morphology of the inhomogeneous Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

PyApprox: Enabling efficient model analysis

PyApprox is a Python-based one-stop-shop for probabilistic analysis of scientific numerical models. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent the most popular methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. Simple interfaces are provided for the most commonly-used algorithms to limit a user’s need to tune the various hyper-parameters of each algorithm. However, more advanced work flows that require customization of hyper-parameters is also supported. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of different algorithms for a wide range of model analyses. This paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in ground water.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Atikokan Digital Twin: Machine learning in a biomass energy system

The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. Here we then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.

09 BIOMASS FUELS↗
NASA NTRS1983

A Bayesian modification to the Jelinski-Moranda software reliability growth model

The Jelinski-Moranda (JM) model for software reliability was examined. It is suggested that a major reason for the poor results given by this model is the poor performance of the maximum likelihood method (ML) of parameter estimation. A reparameterization and Bayesian analysis, involving a slight modelling change, are proposed. It is shown that this new Bayesian-Jelinski-Moranda model (BJM) is mathematically quite tractable, and several metrics of interest to practitioners are obtained. The BJM and JM models are compared by using several sets of real software failure data collected and in all cases the BJM model gives superior reliability predictions. A change in the assumption which underlay both models to present the debugging process more accurately is discussed.

Littlewood, B.↗
DOE OSTI2017

Inflation model selection meets dark radiation

We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard ΛCDM model and an extension including dark radiation parametrised by its effective number of relativistic species N {sub eff}. Using a minimal dataset (Planck low-ℓ polarisation, temperature power spectrum and lensing reconstruction), we find that the observational status of most inflationary models is unchanged. The exceptions are potentials such as power-law inflation that predict large values for the scalar spectral index that can only be realised when N {sub eff} is allowed to vary. Adding baryon acoustic oscillations data and the B-mode data from BICEP2/Keck makes power-law inflation disfavoured, while adding local measurements of the Hubble constant H {sub 0} makes power-law inflation slightly favoured compared to the best single-field plateau potentials. This illustrates how the dark radiation solution to the H {sub 0} tension would have deep consequences for inflation model selection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Impact of multiplicity fluctuations on entropy scaling across system size

The initial state is one of the greatest uncertainties in heavy-ion collisions. A model-agnostic approach is taken in the phenomenological Trento framework which constrains parameters using Bayesian analysis. However, the color-glass condensate (CGC) effective theory predicts initial energy densities that lie outside the recent Bayesian analyses due, in part, to the assumption in Trento of event-by-event multiplicity fluctuations following a Γ distribution. We compare the Trento-preferred √T A T B scaling to CGC-like T A T B scaling coupled with log-normal fluctuations in AuAu and dAu collisions and find there is a significant impact on the multiplicity distributions and on the eccentricities, which may affect the extraction of viscosity in small systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

The application of Plant Reliability Data Information System (PRINS) to CANDU reactor

As risk-informed applications (RIAs) are actively implanted in the nuclear industry, an issue associated with technical adequacy of Probabilistic Safety Assessment (PSA) arises in its modeling and data sourcing. In Korea, PSA for all Korean NPPs has been completed and KHNP(Korea Hydro and Nuclear Power Plant Company) developed the database called the Plant Reliability Data Information System (PRinS). It has several characteristics that distinguish it from other database system such as NPRDs (INPO,1994), PRIS (IAEA), and SRDF (EdF). This database has the function of systematic data management such as automatic data-gathering, periodic data deposition and updating, statistical analysis including Bayesian method, and trend analysis of failure rate or unavailability. In recent PSA for CANDU reactor, the component failure data of EPRI ALWR URD and Component Reliability Database were preferentially used as generic data set. The error factor for most component failure data was estimated by using the information NUREG/CR-4550 and NUREG/CR-4639. Also, annual trend analysis was performed for the functional losses of components using the statistical analysis and chart module of PRinS. Furthermore, the database has been updated regularly and maintained as a living program to reflect the current status. This paper presents the failure data analysis using PRinS which provides Bayesian analysis on main components in the CANDU reactor. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

New approaches to Bayesian uncertainty quantification for Nuclear Science (Final Technical Report)

Inverse problems play a central role in experimentation and theory/data comparisons for many areas of modern Nuclear Physics (NP) and High-Energy Physics (HEP). Bayes’s Theorem is a powerful tool for solving Inverse Problems, providing conceptually transparent and unbiased constraints on theoretical parameters and their uncertainties (“Bayesian Inference”) and enabling the quantification of agreement or tension between models and data. However, analyses based on Bayesian Inference are often challenging for NP and HEP applications, either because of the large number of parameters in the problem, the high computational cost, or both. We propose a multi-institutional collaboration to develop and deploy novel Bayesian analysis tools that advance the scientific scope of a broad range of current and future NP experiments. This project brings together NP domain scientists working on several high-profile NP projects for which new, high-performance Bayesian Uncertainty Quantification (“Bayesian UQ”) methods are essential to carry out the science, and data scientists who are developing state-of-the-art methods applicable to these problems. The NP projects in this proposal comprise measurements of the mass and fundamental nature of the neutrino; study of the Quark-Gluon Plasma that filled the early universe; and mapping of natural and anthropogenic radiation environments. While these NP projects have very different scientific goals, with datasets and analysis approaches that differ significantly, they share common requirements for improving computationally intensive Bayesian analyses using advanced Machine Learning algorithms and will benefit strongly from a coherent effort to develop general solutions. This proposal brings together these projects and forefront ML-based data science algorithms to develop such general solutions. The methods developed in this project will also be more widely applicable, thereby advancing science in the larger Nuclear Physics portfolio.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

DECODING SPECTRAL ENERGY DISTRIBUTIONS OF DUST-OBSCURED STARBURST-ACTIVE GALACTIC NUCLEUS

We present BayeSED, a general purpose tool for Bayesian analysis of spectral energy distributions (SEDs) using pre-existing model SED libraries or their linear combinations. The artificial neural networks, principal component analysis, and multimodal-nested sampling (MultiNest) techniques are employed to allow the highly efficient sampling of posterior distribution and the calculation of Bayesian evidence. As a demonstration, we apply this tool to a sample of hyperluminous infrared galaxies (HLIRGs). The Bayesian evidence obtained for a pure starburst, a pure active galactic nucleus (AGN), and a linear combination of starburst+AGN models show that the starburst+AGN model has the highest evidence for all galaxies in this sample. The Bayesian evidence for the three models and the estimated contributions of starbursts and AGNs to infrared luminosity show that HLIRGs can be classified into two groups: one dominated by starbursts and the other dominated by AGNs. Other parameters and corresponding uncertainties about starbursts and AGNs are also estimated using the model with the highest Bayesian evidence. We find that the starburst region of the HLIRGs dominated by starbursts tends to be more compact and has a higher fraction of OB stars than that of HLIRGs dominated by AGNs. Meanwhile, the AGN torus of the HLIRGs dominated by AGNs tends to be more dusty than that of HLIRGs dominated by starbursts. These results are consistent with previous researches, but need to be tested further with larger samples. Overall, we believe that BayeSED could be a reliable and efficient tool for exploring the nature of complex systems such as dust-obscured starburst-AGN composite systems by decoding their SEDs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Bayesian Linear Regression for Hugoniot Data

This repository provides the code and datasets used in the paper Bayesian Analysis of Linear Shock Compression Data. This paper analyzes publicly available shock compression datasets on copper, argon, and nickel from Marsh (1980) using Bayesian linear regression, and compares the results with those obtained using bootstrapping methods. References: - Marsh, S. P. (1980). LASL shock Hugoniot data (Vol. 5). Univ of California Press.

Bernstein, JasonA [Lawrence Livermore National Lab↗
DOE OSTI2022

The public health exposome and pregnancy-related mortality in the United States: a high-dimensional computational analysis

Racial inequities in maternal mortality in the U.S. continue to be stark. The 2015–2018, 4-year total population, county-level, pregnancy-related mortality ratio (PRM; deaths per 100,000 live births; National Center for Health Statistics (NCHS), restricted use mortality file) was linked with the Public Health Exposome (PHE). Using data reduction techniques, 1591 variables were extracted from over 62,000 variables for use in this analysis, providing information on the relationships between PRM and the social, health and health care, natural, and built environments. Graph theoretical algorithms and Bayesian analysis were applied to PHE/PRM linked data to identify latent networks. PHE variables most strongly correlated with total population PRM were years of potential life lost and overall life expectancy. Population-level indicators of PRM were overall poverty, smoking, lack of exercise, heat, and lack of adequate access to food. In this high-dimensional analysis, overall life expectancy, poverty indicators, and health behaviors were found to be the strongest predictors of pregnancy-related mortality. This provides strong evidence that maternal death is part of a broader constellation of both similar and unique health behaviors, social determinants and environmental exposures as other causes of death.

60 APPLIED LIFE SCIENCES↗