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DOE OSTI2019

Galaxies in X-ray selected clusters and groups in Dark Energy Survey data – II. Hierarchical Bayesian modelling of the red-sequence galaxy luminosity function

Using ~100 X-ray selected clusters in the Dark Energy Survey Science Verification data, in this work we constrain the luminosity function (LF) of cluster red-sequence galaxies as a function of redshift. This is the first homogeneous optical/X-ray sample large enough to constrain the evolution of the LF simultaneously in redshift (0.1 < z < 1.05) and cluster mass ($13.5 \le \rm {log_{10}}(M_{200crit}) \sim \lt 15.0$). We pay particular attention to completeness issues and the detection limit of the galaxy sample. We then apply a hierarchical Bayesian model to fit the cluster galaxy LFs via a Schechter function, including its characteristic break (m*) to a faint end power-law slope (α). Our approach allows us to avoid known issues in similar analyses based on stacking or binning the clusters. We find weak and statistically insignificant (~1.9σ) evolution in the faint end slope α versus redshift. We also find no dependence in α or m* with the X-ray inferred cluster masses. Yet, the amplitude of the LF as a function of cluster mass is constrained to ${\sim } 20{{\ \rm per\ cent}}$ precision. As a by-product of our algorithm, we utilize the correlation between the LF and cluster mass to provide an improved estimate of the individual cluster masses as well as the scatter in true mass given the X-ray inferred masses. This technique can be applied to a larger sample of X-ray or optically selected clusters from the Dark Energy Survey, greatly improving the sensitivity of the analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Accessing the gluon momentum fraction of nucleons through the gradient flow

We calculate the gluon momentum fraction of the nucleon using lattice QCD, with a nonperturbative renormalization technique based on the gradient flow. The gluon momentum fraction is determined on a single Wilson-clover ensemble using 𝑁 𝑓 =2 +1 flavors with pion mass 358 MeV and lattice spacing 0.094 fm. We employ the variational method to reduce excited-state contamination and apply the distillation framework to ensure a large operator basis. To reduce systematic uncertainties, we apply Bayesian model averaging to all fit procedures. We apply matching coefficients to the flow-time dependent lattice results to recover the gluon momentum fraction in the $\overline{MS}$-scheme at 2 GeV. Our final result is ⟨𝑥⟩ 𝑔 ⁢(𝜇 =2 GeV) =0.482⁢(35), where we quote only statistical uncertainties.

Lattice QCD↗

DOE OSTI2022

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security

Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hampered by the limited available parameter space of the underlying mechanism that is transducing the anomaly. As a result, the formulation of anomaly detection for such systems often leads to inverse or ill-posed problems, requiring statistical treatments. Here, we present Bayesian inference of unknown parameters associated with a generic actuator considered as a representative vital element of a cyber-physical system. Via a series of experimental input-output measurements, a transfer function for the actuator is obtained numerically, which serves as our model for the proposed method. Linear, nonlinear, and delayed dynamics may be assumed for the actuator response. By devising a code-based malicious signal, we study the efficacy of Bayesian inference for its potential to produce a detection, including uncertainty quantification, with a remarkably small number of input data points. Our approach should be adaptable to a variety of real-time cyber-physical anomaly detection scenarios.

47 OTHER INSTRUMENTATION↗

DOE OSTI2011

A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model

Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's weather and climate, and other complex systems. It entails much more than attaching defensible error bars to predictions: in particular it includes assessing low-probability but high-consequence events. To achieve these goals with models containing a large number of uncertain input parameters, structural uncertainties, etc., raw computational power is needed. An automated, self-adapting search of the possible model configurations is also useful. Our UQ initiative at the Lawrence Livermore National Laboratory has produced the most extensive set to date of simulations from the US Community Atmosphere Model. We are examining output from about 3,000 twelve-year climate simulations generated with a specialized UQ software framework, and assessing the model's accuracy as a function of 21 to 28 uncertain input parameter values. Most of the input parameters we vary are related to the boundary layer, clouds, and other sub-grid scale processes. Our simulations prescribe surface boundary conditions (sea surface temperatures and sea ice amounts) to match recent observations. Fully searching this 21+ dimensional space is impossible, but sensitivity and ranking algorithms can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination. Bayesian statistical constraints, employing a variety of climate observations as metrics, also seem promising. Observational constraints will be important in the next step of our project, which will compute sea surface temperatures and sea ice interactively, and will study climate change due to increasing atmospheric carbon dioxide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The utility of Bayesian data reconciliation for separations

Data reconciliation methods for separation processes typically rely on classical statistical approaches to generate estimates of true mass flow rates from measurements. Knowledge regarding the uncertainty of these estimates has value in decision making, but is often not acquired. Bayesian approaches intrinsically quantify uncertainty; however, literature for Bayesian data reconciliation of separation processes is scarce. This publication outlines two Bayesian data reconciliation models and provides details for how the models were implemented for the BayesMassBal (V 1.0.0) software package written in R. To demonstrate the advantages of this approach for data reconciliation, the models were first applied to simulated data and then compared to a classical model through a Monte Carlo experiment. In this example, the Bayesian models were found to provide more accurate estimates of the simulated data, while also providing quantitative information on the estimate uncertainty. To demonstrate the use of the technique in a practical problem, the models were also applied to real data collected from a pilot-scale rare earth solvent extraction process. Here, this publication provides a small window into how Bayesian methods can be used for data reconciliation, but findings suggest Bayesian data reconciliation models for separation processes have distinct advantages over classical alternatives.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2012

THE DEMOGRAPHICS OF BROAD-LINE QUASARS IN THE MASS-LUMINOSITY PLANE. I. TESTING FWHM-BASED VIRIAL BLACK HOLE MASSES

We jointly constrain the luminosity function (LF) and black hole mass function (BHMF) of broad-line quasars with forward Bayesian modeling in the quasar mass-luminosity plane, based on a homogeneous sample of {approx}58, 000 Sloan Digital Sky Survey (SDSS) Data Release 7 quasars at z {approx} 0.3-5. We take into account the selection effect of the sample flux limit; more importantly, we deal with the statistical scatter between true BH masses and FWHM-based single-epoch virial mass estimates, as well as potential luminosity-dependent biases of these mass estimates. The LF is tightly constrained in the regime sampled by SDSS and makes reasonable predictions when extrapolated to {approx}3 mag fainter. Downsizing is seen in the model LF. On the other hand, we find it difficult to constrain the BHMF to within a factor of a few at z {approx}> 0.7 (with Mg II and C IV-based virial BH masses). This is mainly driven by the unknown luminosity-dependent bias of these mass estimators and its degeneracy with other model parameters, and secondly driven by the fact that SDSS quasars only sample the tip of the active BH population at high redshift. Nevertheless, the most likely models favor a positive luminosity-dependent bias for Mg II and possibly for C IV, such that at fixed true BH mass, objects with higher-than-average luminosities have overestimated FWHM-based virial masses. There is tentative evidence that downsizing also manifests itself in the active BHMF, and the BH mass density in broad-line quasars contributes an insignificant amount to the total BH mass density at all times. Within our model uncertainties, we do not find a strong BH mass dependence of the mean Eddington ratio, but there is evidence that the mean Eddington ratio (at fixed BH mass) increases with redshift.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

QLBT: a linear Boltzmann transport model for heavy quarks in a quark-gluon plasma of quasi-particles

Abstract We develop a new heavy quark transport model, QLBT, to simulate the dynamical propagation of heavy quarks inside the quark-gluon plasma (QGP) created in relativistic heavy-ion collisions. Our QLBT model is based on the linear Boltzmann transport (LBT) model with the ideal QGP replaced by a collection of quasi-particles to account for the non-perturbative interactions among quarks and gluons of the hot QGP. The thermal masses of quasi-particles are fitted to the equation of state from lattice QCD simulations using the Bayesian statistical analysis method. Combining QLBT with our advanced hybrid fragmentation-coalescence hadronization approach, we calculate the nuclear modification factor $$R_\mathrm {AA}$$ R AA and the elliptic flow $$v_2$$ v 2 of D mesons at the Relativistic Heavy-Ion Collider and the Large Hadron Collider. By comparing our QLBT calculation to the experimental data on the D meson $$R_\mathrm {AA}$$ R AA and $$v_2$$ v 2 , we extract the heavy quark transport parameter $$\hat{q}$$ q ^ and diffusion coefficient $$D_\mathrm {s}$$ D s in the temperature range of $$1-4~T_\mathrm {c}$$ 1 - 4 T c , and compare them with the lattice QCD results and other phenomenological studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2013

Experimental Studies of Elementary Particle Interactions at High Energies

This is the final report of a program of research on "Experimental Studies of Elementary Particle Interactions at High Energies'' of the High Energy Physics (HEP) group of The Rockefeller University. The research was carried out using the Collider Detector at Fermilab (CDF) and the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) at CERN. Three faculty members, two research associates, and two postdoctoral associates participated in this project. At CDF, we studied proton-antiproton collisions at an energy of 1.96 TeV. We focused on diffractive interactions, in which the colliding antiproton loses a small fraction of its momentum, typically less than 1%, while the proton is excited into a high mass state retaining its quantum numbers. The study of such collisions provides insight into the nature of the diffractive exchange, conventionally referred to as Pomeron exchange. In studies of W and Z production, we found results that point to a QCD-based interpretation of the diffractive exchange, as predicted in a data-driven phenomenology developed within the Rockefeller HEP group. At CMS, we worked on diffraction, supersymmetry (SUSY), dark matter, large extra dimensions, and statistical applications to data analysis projects. In diffraction, we extended our CDF studies to higher energies working on two fronts: measurement of the single/double diffraction and of the rapidity gap cross sections at 7 TeV, and development of a simulation of diffractive processes along the lines of our successful model used at CDF. Working with the PYTHIA8 Monte Carlo simulation authors, we implemented our model as a PYTHIA8-MBR option in PYTHIA8 and used it in our data analysis. Preliminary results indicate good agreement. We searched for SUSY by measuring parameters in the Constrained Minimal Supersymmetric extension of the Standard Model (CMSSM) and found results which, combined with other experimental constraints and theoretical considerations, indicate that the CMSSM is not a viable model. Expressing our results in terms of simple topologies, we exclude squark masses below 0.75 TeV and gluino masses below 1.1 TeV. Astrophysical measurements suggest that about 80% of the matter density of the Universe is non-luminous. One of the theories on dark matter attributes it to Weakly Interacting Massive Particles (WIMPs). We searched for WIMPs in 7 TeV and 8 TeV collisions at CMS and set limits on WIMP production rates, which are competitive and complementary to those of direct detection experiments. Searching for monojets (events with only one jet), which in a popular model could be produced by a jet paired by a gravitino that escapes into extra dimensions, we significantly improved the previously set limit. Our results have been used to set limits on Higgs decay to invisible particles and on production of top squarks in compressed SUSY scenarios. Statistics. We computed Bayesian reference priors for several types of measurement and used them in the analysis of CMS data; investigated the applicability of bootstrap methods to HEP measurements; studied several issues associated with simple-versus-simple hypothesis testing and applied the resulting methods to the measurement of some properties of the top quark and Higgs boson.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Estimating a Service-Life Distribution Based on Production Counts and a Failure Database

A manufacturer wanted to compare the service-life distributions of two similar products. These concern product lifetimes after installation (not manufacture). For each product, there were available production counts and an imperfect database providing information on failing units. In the real case, these units were expensive repairable units warrantied against repairs. Failure (of interest here) was relatively rare and driven by a different mode/mechanism than ordinary repair events (not of interest here). Approach: Data models for the service life based on a standard parametric lifetime distribution and a related limited failure population were developed. These models were used to develop expressions for the likelihood of the available data that properly accounts for information missing in the failure database. Results: A Bayesian approach was employed to obtain estimates of model parameters (with associated uncertainty) in order to investigate characteristics of the service-life distribution. Custom software was developed and is included as Supplemental Material to this case study. One part of a responsible approach to the original case was a simulation experiment used to validate the correctness of the software and the behavior of the statistical methodology before using its results in the application, and an example of such an experiment is included here. Because of confidentiality issues that prevent use of the original data, simulated data with characteristics like the manufacturer’s proprietary data are used to illustrate some aspects of our real analyses. Lastly, we also note that, although this case focuses on rare and complete product failure, the statistical methodology provided is directly applicable to more standard warranty data problems involving typically much larger warranty databases where entries are warranty claims (often for repairs) rather than reports of complete failures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Retrospective Analysis and Bayesian Model Averaging of CMIP6 Precipitation in the Nile River Basin

The Nile River basin is one of the global hotspots vulnerable to climate change impacts because of a fast-growing population and geopolitical tensions. Previous studies demonstrated that general circulation models (GCMs) frequently show disagreement in the sign of change in annual precipitation projections. Here, we first evaluate the performance of 20 GCMs from phase six of the Coupled Model Intercomparison Project (CMIP6) benchmarked against a high-spatial-resolution precipitation dataset dating back to 1983 from Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR). Next, a Bayesian model averaging (BMA) approach is adopted to derive probability distributions of precipitation projections in the Nile basin. Retrospective analysis reveals that most GCMs exhibit considerable (up to 64% of mean annual precipitation) and spatially heterogenous bias in simulating annual precipitation. Moreover, it is shown that all GCMs underestimate interannual variability; thus, the ensemble range is underdispersive and is a poor indicator of uncertainty. The projected changes from the BMA model show that the value and sign of change vary considerably across the Nile basin. Specifically, it is found that projected changes in the two headwaters basins, namely, the Blue Nile and Upper White Nile, are 0.03% and -1.65%, respectively; both are statistically insignificant at α = 0.05. The uncertainty range estimated from the BMA model shows that the probability of a precipitation decrease is much higher in the Upper White Nile basin whereas projected change in the Blue Nile is highly uncertain both in magnitude and sign of change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2013

STARS DO NOT EAT THEIR YOUNG MIGRATING PLANETS: EMPIRICAL CONSTRAINTS ON PLANET MIGRATION HALTING MECHANISMS

The discovery of ''hot Jupiters'' very close to their parent stars confirmed that Jovian planets migrate inward via several potential mechanisms. We present empirical constraints on planet migration halting mechanisms. We compute model density functions of close-in exoplanets in the orbital semi-major axis-stellar mass plane to represent planet migration that is halted via several mechanisms, including the interior 1:2 resonance with the magnetospheric disk truncation radius, the interior 1:2 resonance with the dust sublimation radius, and several scenarios for tidal halting. The models differ in the predicted power-law dependence of the exoplanet orbital semi-major axis as a function of stellar mass, and thus we also include a power-law model with the exponent as a free parameter. We use a Bayesian analysis to assess the model success in reproducing empirical distributions of confirmed exoplanets and Kepler candidates that orbit interior to 0.1 AU. Our results confirm a correlation of the halting distance with stellar mass. Tidal halting provides the best fit to the empirical distribution of confirmed Jovian exoplanets at a statistically robust level, consistent with the Kozai mechanism and the spin-orbit misalignment of a substantial fraction of hot Jupiters. We can rule out migration halting at the interior 1:2 resonances with the magnetospheric disk truncation radius and the interior 1:2 resonance with the dust disk sublimation radius, a uniform random distribution, and a distribution with no dependence on stellar mass. Note that our results do not rule out Type-II migration, but rather eliminate the role of a circumstellar disk in stopping exoplanet migration. For Kepler candidates, which have a more restricted range in stellar mass compared to confirmed planets, we are unable to discern between the tidal dissipation and magnetospheric disk truncation braking mechanisms at a statistically significant level. The power-law model favors exponents in the range of 0.38-0.9. This is larger than that predicted for tidal halting (0.23-0.33), which suggests that additional physics may be missing in the tidal halting theory.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

THE ALBEDOS OF KEPLER'S CLOSE-IN SUPER-EARTHS

Exoplanet research focusing on the characterization of super-Earths is currently limited to the handful of targets orbiting bright stars that are amenable to detailed study. This Letter proposes to look at alternative avenues to probe the surface and atmospheric properties of this category of planets, known to be ubiquitous in our galaxy. I conduct Markov Chain Monte Carlo light-curves analyses for 97 Kepler close-in R{sub P} ≲ 2.0 R {sub ⊕} super-Earth candidates with the aim of detecting their occultations at visible wavelengths. Brightness temperatures and geometric albedos in the Kepler bandpass are constrained for 27 super-Earth candidates. A hierarchical Bayesian modeling approach is then employed to characterize the population-level reflective properties of these close-in super-Earths. I find median geometric albedos A{sub g} in the Kepler bandpass ranging between 0.16 and 0.30, once decontaminated from thermal emission. These super-Earth geometric albedos are statistically larger than for hot Jupiters, which have medians A{sub g} ranging between 0.06 and 0.11. A subset of objects, including Kepler-10b, exhibit significantly larger albedos (A{sub g} ≳ 0.4). I argue that a better understanding of the incidence of stellar irradation on planetary surface and atmospheric processes is key to explain the diversity in albedos observed for close-in super-Earths.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Bayesian inference of the incompressibility, skewness and kurtosis of nuclear matter from empirical pressures in relativistic heavy-ion collisions

Within the Bayesian statistical framework we infer the incompressibility K 0 , skewness J 0 and kurtosis Z 0 parameters of symmetric nuclear matter (SNM) at its saturation density ρ 0 using the constraining bands on the pressure in cold SNM in the density range of 1.3ρ 0 to 4.5ρ 0 from transport model analyses of kaon production and nuclear collective flow in relativistic heavy-ion collisions. As the default option assuming the K 0 , J 0 and Z 0 have Gaussian prior probability distribution functions (PDFs) with the means and variances of 235±30, -200±200 and -146±1728 MeV, their posterior most probable values are narrowed down to 192$^{+12}_{-16}$ MeV, -180$^{+100}_{-110}$ MeV and ${\mathrm{200}}_{-250}^{+250}$ at 68% confidence level, respectively. The results are largely independent of the prior PDFs of J 0 and Z 0 used. However, if one adopts the strong belief that the incompressibility K 0 has a uniform prior PDF within its absolute boundary of 220–260 MeV as one can find easily in the literature, the posterior most probable values of K 0 , J 0 and Z 0 shift to ${K}_{0}=22{0}_{-0}^{+6}$ MeV, ${J}_{0}=-39{0}_{-70}^{+60}$ MeV and ${Z}_{0}=60{0}_{-200}^{+200}$ MeV, respectively. While the posterior PDFs of the SNM EOS parameters depend somewhat on the prior PDF of K 0 used, the results from using different prior PDFs are qualitatively consistent. The uncertainties of all three parameters are significantly reduced especially for the J 0 and Z 0 parameters compared to their current values.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2014

Implementation of a Bayesian Engine for Uncertainty Analysis

In probabilistic risk assessment, it is important to have an environment where analysts have access to a shared and secured high performance computing and a statistical analysis tool package. As part of the advanced small modular reactor probabilistic risk analysis framework implementation, we have identified the need for advanced Bayesian computations. However, in order to make this technology available to non-specialists, there is also a need of a simplified tool that allows users to author models and evaluate them within this framework. As a proof-of-concept, we have implemented an advanced open source Bayesian inference tool, OpenBUGS, within the browser-based cloud risk analysis framework that is under development at the Idaho National Laboratory. This development, the “OpenBUGS Scripter” has been implemented as a client side, visual web-based and integrated development environment for creating OpenBUGS language scripts. It depends on the shared server environment to execute the generated scripts and to transmit results back to the user. The visual models are in the form of linked diagrams, from which we automatically create the applicable OpenBUGS script that matches the diagram. These diagrams can be saved locally or stored on the server environment to be shared with other users.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2018

Data-Driver Interpretation of Ultradeep Azimuthal Propagation

We report the interpretation of geological features around the borehole stresses on the search of true earth model parameters. Different from the conventional logging-while-drilling (LWD) technologies, such as wireline induction and propagation resistivity tools, the azimuthal resistivity measurement acquires the ability of directional sensitivity. Within this decade, this type of tools has been greatly enhanced with over hundred feet depth-of-detection (DoD). To resolve the interpretation of geological features within 100 feet from the borehole, the multiple-layer structure must be considered. However, the traditional methods can never handle the highly nonlinear inverse problems especially facing on the complicated earth structure. Meanwhile, no justifiable uncertainty of inverse parameters can be provided. The statistical methods have been adopted to search for the global statistical distribution and uncertainties of earth model parameters. However, the most attempts are still stuck in non-uniqueness of the solutions since the structure of earth model has been predefined, where the number of layers is assumed, which introduces another ambiguity into the model interpretation. This paper develops a general trans-dimensional Bayesian methodology to solve non-linear inverse problem of earth layer resistivity and depth to the layer boundaries, from the interpretation of ultra-deep azimuthal resistivity measurements. The presented work is a data-driven approach to search out the multi-dimensional parameter spaces. Finally, the simulations tell that the algorithm can efficiently solve the earth model interpretation within a hundred feet from borehole, and examine the uncertainty of earth model parameters.

58 GEOSCIENCES↗

DOE OSTI2022

Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders

Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational efficiency of data assimilation. The standard MFEnKF uses linear couplings between models, and is statistically optimal in case of Gaussian probability densities. This work extends the MFEnKF into to make use of a broader class of surrogate model such as those based on machine learning methods such as autoencoders non-linear couplings in between the model hierarchies. We identify the right-invertibility property for autoencoders as being a key predictor of success in the forecasting power of autoencoder-based reduced order models. We propose a methodology that allows us to construct reduced order surrogate models that are more accurate than the ones obtained via conventional linear methods. Numerical experiments with the canonical Lorenz'96 model illustrate that nonlinear surrogates perform better than linear projection-based ones in the context of multifidelity ensemble Kalman filtering. We additionality show a large-scale proof-of-concept result with the quasi-geostrophic equations, showing the competitiveness of the method with a traditional reduced order model-based MFEnKF.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Geometric Scale-up Experiments on Fluidization of Geldart B Glass Beads

The objective of this work is to provide a valuable database from controlled experiments for validating computational models. Recently, coarse-grained techniques such as particle-in-cell (PIC) or coarse-grained discrete element modeling (DEM) have gained popularity due to their computational efficiency while modeling large-scale systems; however, the influence of model parameters and their sensitivities at different geometric scales and flow conditions remain to be analyzed. These datasets are critical for the multiphase flow research community to assess predictive capability of modeling techniques as well as elucidate the hydrodynamic behavior in these systems. This study performed fluidization experiments using three different test sections with internal diameters of 2.5, 4, and 6 in. The operating conditions, bed material, and range of flow velocities at the inlet were constant in all the units, which were not hydrodynamically scaled. Glass beads having a Sauter Mean Diameter of 332 μm were used. Superficial velocity was varied from 2.97 to 5.35 times the minimum fluidization velocity and the initial static bed height was 0.1524 m. The order in which the experiments were performed was randomized and replicates were included to provide uncertainty in measurements. Statistics of differential pressure and bed height from these tests were reported. Future plans include validating PIC methodology in the open-source software, MFiX (Multiphase Flow with Interphase Exchanges) using results from this study. This could further be extended to determine optimal model parameters using inverse techniques such as deterministic calibration or Bayesian inference.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Geometric Scale-up Experiments on Fluidization of Geldart B Glass Beads

42 ENGINEERING↗