A computer program for uncertainty analysis integrating regression and Bayesian methods
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Lu, Dan↗
SEARCH · Engineering Papers
Results for “Bayesian analysis”
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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A novel CMB component separation method: hierarchical generalized morphological component analysis
ABSTRACT We present a novel technique for cosmic microwave background (CMB) foreground subtraction based on the framework of blind source separation. Inspired by previous work incorporating local variation to generalized morphological component analysis (GMCA), we introduce hierarchical GMCA (HGMCA), a Bayesian hierarchical graphical model for source separation. We test our method on Nside = 256 simulated sky maps that include dust, synchrotron, free–free, and anomalous microwave emission, and show that HGMCA reduces foreground contamination by $25{{\ \rm per\ cent}}$ over GMCA in both the regions included and excluded by the Planck UT78 mask, decreases the error in the measurement of the CMB temperature power spectrum to the 0.02–0.03 per cent level at ℓ > 200 (and $\lt 0.26{{\ \rm per\ cent}}$ for all ℓ), and reduces correlation to all the foregrounds. We find equivalent or improved performance when compared to state-of-the-art internal linear combination type algorithms on these simulations, suggesting that HGMCA may be a competitive alternative to foreground separation techniques previously applied to observed CMB data. Additionally, we show that our performance does not suffer when we perturb model parameters or alter the CMB realization, which suggests that our algorithm generalizes well beyond our simplified simulations. Our results open a new avenue for constructing CMB maps through Bayesian hierarchical analysis.
79 ASTRONOMY AND ASTROPHYSICS↗
Demonstration of emulator-based Bayesian calibration of safety analysis codes: Theory and formulation
System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. This study uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Development of Integrated Safety and Security Models for Comprehensive Reliability and Resiliency Evaluation
The security of the electric grid and supporting energy systems is crucial to national security. One of the complexities in analyzing the security of energy systems is the safety consequences that may result from accidents. For energy systems, the goal is to ensure that they operate as intended and that any consequences are mitigated or prevented. The integration of safety and security is paramount to protecting these systems from attacks and ensuring that large consequences are prevented. This report describes an integrated safety and security methodology to evaluate cybersecurity events that can lead to large consequences. This novel approach first describes how Systems-Theoretic Process Analysis (STPA) provides a digital causal analysis for Bayesian Networks (BNs). The use of STPA causal analysis provides a systematic approach to constructing BNs that adequately model cyber scenarios that result in consequences. When combined with the technical principles described in Risk-Informed Management of Enterprise Systems (RIMES), a comprehensive risk-informed cybersecurity analysis results that allows decision-makers to prioritize systems that most impact risk.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A novel Bayesian approach to PDV analysis
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Allison, James↗
Quantitative Interpretation of Impedance Spectroscopy Data on Porous LSM Electrodes Using X-ray Computed Tomography and Bayesian Model-Based Analysis
Presented by NETL at 17th Annual Solid Oxide Fuel Cell (SOFC) Project Review Meeting, 07/16
Brunello, Giuseppe↗
Bayesian Fit for the NOvA Three Flavor Oscillation Analysis
NOvA is a long baseline neutrino oscillation experiment, using Fermilab's NuMI beam and a functionally identical near and far detector. NOvA measures muon neutrino disappearance and electron neutrino appearance to probe neutrino oscillation parameters, including the large neutrino mixing angle, the mass ordering, and the CP-violating phase. NOvA has developed a Bayesian analysis in addition to its Frequentist analysis, using Markov Chain Monte Carlo. This Bayesian framework allows for measurements previously difficult to make with the Frequentist framework, such as the Jarlskog invariant and the reactor mixing angle. The details and status of the Bayesian Framework will be presented, as well as latest NOvA results on measurements of three-flavor oscillation parameters.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Sizing up the milky way: a bayesian mixture model meta-analysis of photometric scale length measurements
The exponential scale length (L {sub d}) of the Milky Way’s (MW’s) disk is a critical parameter for describing the global physical size of our Galaxy, important both for interpreting other Galactic measurements and helping us to understand how our Galaxy fits into extragalactic contexts. Unfortunately, current estimates span a wide range of values and are often statistically incompatible with one another. Here, we perform a Bayesian meta-analysis to determine an improved, aggregate estimate for L {sub d}, utilizing a mixture-model approach to account for the possibility that any one measurement has not properly accounted for all statistical or systematic errors. Within this machinery, we explore a variety of ways of modeling the nature of problematic measurements, and then employ a Bayesian model averaging technique to derive net posterior distributions that incorporate any model-selection uncertainty. Our meta-analysis combines 29 different (15 visible and 14 infrared) photometric measurements of L {sub d} available in the literature; these involve a broad assortment of observational data sets, MW models and assumptions, and methodologies, all tabulated herein. Analyzing the visible and infrared measurements separately yields estimates for L {sub d} of 2.71{sub −0.20}{sup +0.22} kpc and 2.51{sub −0.13}{sup +0.15} kpc, respectively, whereas considering them all combined yields 2.64 ± 0.13 kpc. The ratio between the visible and infrared scale lengths determined here is very similar to that measured in external spiral galaxies. We use these results to update the model of the Galactic disk from our previous work, constraining its stellar mass to be 4.8{sub −1.1}{sup +1.5}×10{sup 10} M {sub ⊙}, and the MW’s total stellar mass to be 5.7{sub −1.1}{sup +1.5}×10{sup 10} M {sub ⊙}.
79 ASTRONOMY AND ASTROPHYSICS↗
A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem
Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.
McClay, Wilbert↗
A Measurement of νe Appearance and νµ Disappearance Using 10 Years of Data from the NOvA Experiment
NOvA is a long baseline neutrino experiment with an 810 km baseline, using the NuMIbeam at Fermilab, and a functionally identical near and far detector operating at an angle14 mrad off axis from the beam. NOvA jointly measures muon neutrino (and antineutrino)disappearance and electron neutrino (and antineutrino) appearance to make a measurementof sin2θ23, δCP, and ∆m232, including its sign, the mass ordering.This dissertation reports a new measurement from NOvA, using 10 years of data, with a totalexposure of 26.6 ×1020 POT of neutrino beam and 12.5 ×1020 POT of antineutrino beam.This represents 95.6% more neutrino beam exposure since the last NOvA analysis. Aspects ofthe analysis are discussed in detail, including neutrino energy estimation, analysis systematicuncertainties, including the implementation of systematics new to the NOvA analysis, andthe Bayesian fit infrastructure using Markov Chain Monte Carlo (MCMC).The analysis yields the following credible intervals for the oscillation parameters assumingthe normal ordering: δCP = 0.930+0.210−0.290 π ∪0.150+0.150−0.110 π,∆m232 = 2.424+0.035−0.040 × 10−3eV2,and sin2θ23 = 0.55+0.02−0.06, with a 76% preference for normal ordering using a prior for sin2(2θ13)using Daya Bay’s measurement. If the Daya Bay sin2(2θ13) vs ∆m232 constraint is used as aprior instead, an 87% preference for normal ordering emerges.
43 PARTICLE ACCELERATORS↗
An in situ USAXS–SAXS–WAXS study of precipitate size distribution evolution in a model Ni-based alloy
Intermetallic γ' precipitates typically strengthen nickel-based superalloys. The shape, size and spatial distribution of strengthening precipitates critically influence alloy strength, while their temporal evolution characteristics determine the high-temperature alloy stability. Combined ultra-small-, small- and wide-angle X-ray scattering (USAXS–SAXS–WAXS) analysis can be used to evaluate the temporal evolution of an alloy's precipitate size distribution (PSD) and phase structure during in situ heat treatment. Analysis of PSDs from USAXS–SAXS data employs either least-squares fitting of a preordained PSD model or a maximum entropy (MaxEnt) approach, the latter avoiding a priori definition of a functional form of the PSD. However, strong low- q scattering from grain boundaries and/or structure factor effects inhibit MaxEnt analysis of typical alloys. Lastly, this work describes the extension of Bayesian–MaxEnt analysis methods to data exhibiting structure factor effects and low- q power law slopes and demonstrates their use in an in situ study of precipitate size evolution during heat treatment of a model Ni–Al–Si alloy.
36 MATERIALS SCIENCE↗
Herschel-spire Fourier transform spectrometer observations of excited CO and [C I] in the antennae (NGC 4038/39): Warm and cold molecular gas
We present Herschel Spectral and Photometric Imaging Receiver (SPIRE) Fourier Transform Spectrometer (FTS) observations of the Antennae (NGC 4038/39), a well-studied, nearby (22 Mpc), ongoing merger between two gas-rich spiral galaxies. The SPIRE-FTS is a low spatial ( FWHM ∼ 19''-43'') and spectral (∼1.2 GHz) resolution mapping spectrometer covering a large spectral range (194-671 μm, 450-1545 GHz). We detect five CO transitions (J = 4-3 to J = 8-7), both [C I] transitions, and the [N II] 205 μm transition across the entire system, which we supplement with ground-based observations of the CO J = 1-0, J = 2-1, and J = 3-2 transitions and Herschel Photodetecting Array Camera and Spectrometer (PACS) observations of [C II] and [O I] 63 μm. Using the CO and [C I] transitions, we perform both a local thermodynamic equilibrium (LTE) analysis of [C I] and a non-LTE radiative transfer analysis of CO and [C I] using the radiative transfer code RADEX along with a Bayesian likelihood analysis. We find that there are two components to the molecular gas: a cold (T {sub kin} ∼ 10-30 K) and a warm (T {sub kin} ≳ 100 K) component. By comparing the warm gas mass to previously observed values, we determine a CO abundance in the warm gas of x {sub CO} ∼ 5 × 10{sup –5}. If the CO abundance is the same in the warm and cold gas phases, this abundance corresponds to a CO J = 1-0 luminosity-to-mass conversion factor of α{sub CO} ∼ 7 M {sub ☉} pc{sup –2} (K km s{sup –1}){sup –1} in the cold component, similar to the value for normal spiral galaxies. We estimate the cooling from H{sub 2}, [C II], CO, and [O I] 63 μm to be ∼0.01 L {sub ☉}/M {sub ☉}. We compare photon-dominated region models to the ratio of the flux of various CO transitions, along with the ratio of the CO flux to the far-infrared flux in NGC 4038, NGC 4039, and the overlap region. We find that the densities recovered from our non-LTE analysis are consistent with a background far-ultraviolet field of strength G {sub 0} ∼ 1000. Finally, we find that a combination of turbulent heating, due to the ongoing merger, and supernova and stellar winds are sufficient to heat the molecular gas.
79 ASTRONOMY AND ASTROPHYSICS↗
Deep Data Analysis of Conductive Phenomena on Complex Oxide Interfaces: Physics from Data Mining
Spatial variability of electronic transport in BiFeO3-CoFe2O4 (BFO-CFO) self-assembled heterostructures is explored using spatially resolved first order reversal curve (FORC) current voltage (IV) mapping. Multivariate statistical analysis of FORC-IV data classifies statistically significant behaviors and maps characteristic responses spatially. In particular, regions of grain, matrix, and grain boundary responses are clearly identified. K-means and Bayesian demixing analysis suggests the characteristic response be separated into four components, with hysteretic type behavior localized at the BFO-CFO tubular interfaces. The conditions under which Bayesian components allow direct physical interpretation are explored, and transport mechanisms at the grain boundaries and individual phases are analyzed. This approach conjoins multivariate statistical analysis with physics-based interpretation, actualizing a robust, universal, data driven approach to problem solving, which can be applied to exploration of local transport and other functional phenomena in other spatially inhomogeneous systems.
big data↗
Bayesian Fit to NOvA Data Subsamples for Three Flavor Oscillation Analysis
NOvA (NuMI Off-Axis $\nu_e$ Appearance) is a long baseline neutrino experiment designed to measure the oscillation of muon neutrinos to electron neutrinos over a distance of 810 km. NOvA uses a near and far detector to observe $\nu_\mu$ disappearance and $\nu_e$ appearance of neutrinos produced by the NuMI beam at Fermilab. NOvA uses a Bayesian analysis framework in addition to its Frequentist method to measure neutrino oscillation parameters such as the mixing angles, mass ordering, and CP-violating phase. We report preliminary results of Bayesian fits to representative NOvA datasets. Comparison of fits to $\nu_\mu$ disappearance and $\nu_e$ appearance enables a cross-check of NOvA results with reactor $\bar{\nu_e}$ disappearance measurements. NOvA also searches for violation of Lorentz invariance by analyzing fits of forward horn current (FHC) versus reverse horn current (RHC) samples. The results validate and advance NOvA's contributions to precision measurements of neutrino properties.
Zhao, Larry [Fermilab]↗
Data from: Comparing traditional and Bayesian approaches to ecological meta-analysis
Despite the wide application of meta-analysis in ecology, some of the traditional methods used for meta-analysis may not perform well given the type of data characteristic of ecological meta-analyses. We reviewed published meta-analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (ni) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta-analysis methods for estimating a mean effect size and its uncertainty interval. Our literature review showed that ni and k were highly variable, distributions were right-skewed, and were generally small (median ni =5, median k=44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). When k was low (<40), 95% coverage was achieved by a confidence interval based on the t-distribution that uses an adjusted standard error (the Hartung-Knapp-Sidik-Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap or z-distribution confidence intervals had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta-analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults. In general, for a simple random-effects meta-analysis, the performance of the best frequentist and Bayesian methods were similar for the same combinations of factors (k and mean replication), though the Bayesian approaches had higher than nominal (>95%) coverage for the mean effect when k was very low (k<15). Our literature review suggests that many meta-analyses that used z-distribution or bootstrapping confidence intervals may have over-estimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta-analyses.
54 ENVIRONMENTAL SCIENCES↗
Confronting lattice parton distributions with global QCD analysis
We present the first Monte Carlo based global QCD analysis of spin-averaged and spin-dependent parton distribution functions (PDFs) that includes nucleon isovector matrix elements in coordinate space from lattice QCD. We investigate the degree of universality of the extracted PDFs when the lattice and experimental data are treated under the same conditions within the Bayesian likelihood analysis. For the unpolarized sector, we find rather weak constraints from the current lattice data on the phenomenological PDFs, and difficulties in describing the lattice matrix elements at large spatial distances. In contrast, for the polarized PDFs we find good agreement between experiment and lattice data, with the latter providing significant constraints on the spin-dependent isovector quark and antiquark distributions.
Sato, Nobuo↗
Modeling Effects of Annealing on Coal Char Reactivity to O 2 and CO 2 , Based on Preparation Conditions
Oxy-fired coal combustion is a promising potential carbon capture technology. Predictive computational fluid dynamics (CFD) simulations are valuable tools in evaluating and deploying oxyfuel and other carbon capture technologies, either as retrofit technologies or for new construction. However, accurate predictive combustor simulations require physically realistic submodels with low computational requirements. A recent sensitivity analysis of a detailed char conversion model (Char Conversion Kinetics (CCK)) found thermal annealing to be an extremely sensitive submodel. In the present work, further analysis of the previous annealing model revealed significant disagreement with numerous datasets from experiments performed after that annealing model was developed. The annealing model was accordingly extended to reflect experimentally observed reactivity loss, because of the thermal annealing of a variety of coals under diverse char preparation conditions. The model extension was informed by a Bayesian calibration analysis. In addition, since oxyfuel conditions include extraordinarily high levels of CO 2 , the development of a first-ever CO 2 reactivity loss model due to annealing is presented.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta-analysis
The leaf economic spectrum is a widely-studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of PFTs. We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (SLA, N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (R_d, V_{c,max}, J_max) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, these covariance were an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.
biodiversity, ecological modeling, ecology, leaf b↗