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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Bayesian-based response expansion and uncertainty quantification using sparse measurement sets

Systems subjected to dynamic loads often require monitoring of their vibrational response, but limitations on the total number and placement of the measurement sensors can hinder the data-collection process. Here, we present an indirect approach to estimate a system’s full-field dynamic response, including all uninstrumented locations, using response measurements from sensors sparsely located on the system. This approach relies on Bayesian inference that utilizes a system model to estimate the full-field response and quantify the uncertainty in these estimates. By casting the estimation problem in the frequency domain, this approach utilizes the modal frequency response functions as a natural, frequency-dependent weighting scheme for the system mode shapes to perform the expansion. This frequency-dependent weighting scheme enables an accurate expansion, even with highly correlated mode shapes that may arise from spatial aliasing due to the limited number of sensors, provided these correlated modes do not have natural frequencies that are closely spaced. Furthermore, the inherent regularization mechanism that arises in this Bayesian-based procedure enables the utilization of the full set of system mode shapes for the expansion, rather than any reduced subset. This approach can produce estimates when considering a single realization of the measured responses, and with some modification, it can also produce estimates for power spectral density matrices measured from many realizations of the responses from statistically stationary random processes. A simply supported beam provides an initial numerical validation, and a cylindrical test article excited by acoustic loads in a reverberation chamber provides experimental validation.

42 ENGINEERING↗
DOE OSTI2014

Breaking Computational Barriers: Real-time Analysis and Optimization with Large-scale Nonlinear Models via Model Reduction

Model reduction for dynamical systems is a promising approach for reducing the computational cost of large-scale physics-based simulations to enable high-fidelity models to be used in many- query (e.g., Bayesian inference) and near-real-time (e.g., fast-turnaround simulation) contexts. While model reduction works well for specialized problems such as linear time-invariant systems, it is much more difficult to obtain accurate, stable, and efficient reduced-order models (ROMs) for systems with general nonlinearities. This report describes several advances that enable nonlinear reduced-order models (ROMs) to be deployed in a variety of time-critical settings. First, we present an error bound for the Gauss-Newton with Approximated Tensors (GNAT) nonlinear model reduction technique. This bound allows the state-space error for the GNAT method to be quantified when applied with the backward Euler time-integration scheme. Second, we present a methodology for preserving classical Lagrangian structure in nonlinear model reduction. This technique guarantees that important properties--such as energy conservation and symplectic time-evolution maps--are preserved when performing model reduction for models described by a Lagrangian formalism (e.g., molecular dynamics, structural dynamics). Third, we present a novel technique for decreasing the temporal complexity --defined as the number of Newton-like iterations performed over the course of the simulation--by exploiting time-domain data. Fourth, we describe a novel method for refining projection-based reduced-order models a posteriori using a goal-oriented framework similar to mesh-adaptive h -refinement in finite elements. The technique allows the ROM to generate arbitrarily accurate solutions, thereby providing the ROM with a 'failsafe' mechanism in the event of insufficient training data. Finally, we present the reduced-order model error surrogate (ROMES) method for statistically quantifying reduced- order-model errors. This enables ROMs to be rigorously incorporated in uncertainty-quantification settings, as the error model can be treated as a source of epistemic uncertainty. This work was completed as part of a Truman Fellowship appointment. We note that much additional work was performed as part of the Fellowship. One salient project is the development of the Trilinos-based model-reduction software module Razor , which is currently bundled with the Albany PDE code and currently allows nonlinear reduced-order models to be constructed for any application supported in Albany. Other important projects include the following: 1. ROMES-equipped ROMs for Bayesian inference: K. Carlberg, M. Drohmann, F. Lu (Lawrence Berkeley National Laboratory), M. Morzfeld (Lawrence Berkeley National Laboratory). 2. ROM-enabled Krylov-subspace recycling: K. Carlberg, V. Forstall (University of Maryland), P. Tsuji, R. Tuminaro. 3. A pseudo balanced POD method using only dual snapshots: K. Carlberg, M. Sarovar. 4. An analysis of discrete v. continuous optimality in nonlinear model reduction: K. Carlberg, M. Barone, H. Antil (George Mason University). Journal articles for these projects are in progress at the time of this writing.

97 MATHEMATICS AND COMPUTING↗
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↗
NASA NTRS2016

The Impact of Truth Surrogate Variance on Quality Assessment/Assurance in Wind Tunnel Testing

Minimum data volume requirements for wind tunnel testing are reviewed and shown to depend on error tolerance, response model complexity, random error variance in the measurement environment, and maximum acceptable levels of inference error risk. Distinctions are made between such related concepts as quality assurance and quality assessment in response surface modeling, as well as between precision and accuracy. Earlier research on the scaling of wind tunnel tests is extended to account for variance in the truth surrogates used at confirmation sites in the design space to validate proposed response models. A model adequacy metric is presented that represents the fraction of the design space within which model predictions can be expected to satisfy prescribed quality specifications. The impact of inference error on the assessment of response model residuals is reviewed. The number of sites where reasonably well-fitted response models actually predict inadequately is shown to be considerably less than the number of sites where residuals are out of tolerance. The significance of such inference error effects on common response model assessment strategies is examined.

DeLoach, Richard↗
DOE OSTI2020

Exploring an ‘ideal hill': how lithology and transport mechanisms affect the possibility of a steady state during weathering and erosion

We introduce a model of chemical reaction within hills to explore how evolving porosity (and by inference, permeability) affects flow pathways and weathering. The model consists of hydrologic and reactive-transport equations that describe alteration of ferrous minerals and feldspar. These reactions were chosen because previous work emphasized that oxygen- and acid-driven weathering affects porosity differently in felsic and mafic rocks. A parameter controlling the order of the fronts is presented. In the absence of erosion, the two reaction fronts move at different velocities and the relative depths depend on geochemical conditions and starting composition. In turn, the fronts and associated changes in porosity drastically affect both the vertical and lateral velocities of water flow. For these instances, estimates of weathering advance rates based on simple models that posit unidirectional constant-velocity advection do not apply. In the model hills, weathering advance rates diminish with time as the Darcy velocities decrease with depth. The system can thus attain a dynamical steady state at any erosion rate where the regolith thickness is constant in time and velocities of both fronts become equal to one another and to the erosion rate. The slower the advection velocities in a system, the faster it attains a steady state. For example, a massive rock with relatively fast-dissolving minerals such as diabase -- where solute transport across the reaction front mainly occurs by diffusion -- can reach a steady state more quickly than granitoid rocks in which advection contributes to solute transport. The attainment of a steady state is controlled by coupling between weathering and hydrologic processes that force water to flow horizontally above reaction fronts where permeability changes rapidly with depth and acts as a partial barrier to fluid flow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Diagnostics of the solar corona from comparison between Faraday rotation measurements and magnetohydrodynamic simulations

Polarized natural radio sources passing behind the Sun experience Faraday rotation as a consequence of the electron density and magnetic field strength in coronal plasma. Since Faraday rotation is proportional to the product of the density and the component of the magnetic field along the line of sight of the observer, a model is required to interpret the observations and infer coronal structures. Faraday rotation observations have been compared with relatively ad hoc models of the corona. Here for the first time we compare these observations with magnetohydrodynamic (MHD) models of the solar corona driven by measurements of the photospheric magnetic field. We use observations made with the NRAO Very Large Array of 34 polarized radio sources occulted by the solar corona between 5 and 14 solar radii. The measurements were made during 1997 May, and 2005 March and April. We compare the observed Faraday rotation values with values extracted from MHD steady-state simulations of the solar corona. We find that (1) using a synoptic map of the solar magnetic field just one Carrington rotation off produces poorer agreements, meaning that the outer corona changes in the course of one month, even in solar minimum; (2) global MHD models of the solar corona driven by photospheric magnetic field measurements are generally able to reproduce Faraday rotation observations; and (3) some sources show significant disagreement between the model and the observations, which appears to be a function of the proximity of the line of sight to the large-scale heliospheric current sheet.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951–2005 using a regional climate model

Daily moderate rainfall events, which constitute a major portion of seasonal summer monsoon rainfall over central India, have decreased significantly during the period 1951 through 2005. On the other hand, mean and extreme near-surface daily temperature during the monsoon season have increased by a maximum of 1–1.5 °C. Using simulations made with a high-resolution regional climate model (RegCM4) and prescribed land cover of years 1950 and 2005, it is demonstrated that part of the changes in moderate rainfall events and temperature have been caused by land-use/land-cover change (LULCC), which is mostly anthropogenic. Model simulations show that the increase in seasonal mean and extreme temperature over central India coincides with the region of decrease in forest and increase in crop cover. Our results also show that LULCC alone causes warming in the extremes of daily mean and maximum temperatures by a maximum of 1–1.2 °C, which is comparable with the observed increasing trend in the extremes. Decrease in forest cover and simultaneous increase in crops not only reduces the evapotranspiration over land and large-scale convective instability, but also contributes toward decrease in moisture convergence through reduced surface roughness. These factors act together in reducing significantly the moderate rainfall events and the amount of rainfall in that category over central India. Additionally, the model simulations are repeated by removing the warming trend in sea surface temperatures over the Indian Ocean. As a result, enhanced warming at the surface and greater decrease in moderate rainfall events over central India compared to the earlier set of simulations are noticed. Results from these additional experiments corroborate our initial findings and confirm the contribution of LULCC in the decrease in moderate rainfall events and increase in daily mean and extreme temperature over India. Therefore, this study demonstrates the important implications of LULCC over India during the monsoon season. Although, the regional climate model helps in better resolving land–atmosphere feedbacks over the Indian region, the inferences do depend on the fidelity of the model in capturing the features of Indian monsoon realistically. Lastly, it is proposed that similar studies using a suite of climate models will further enrich our understanding about the role of LULCC in the Indian monsoon climate.

58 GEOSCIENCES↗
DOE OSTI2020

Operational, gauge-free quantum tomography

As increasingly impressive quantum information processors are realized in laboratories around the world, robust and reliable characterization of these devices is now more urgent than ever. These diagnostics can take many forms, but one of the most popular categories is tomography, where an underlying parameterized model is proposed for a device and inferred by experiments. Here, we introduce and implement efficient operational tomography, which uses experimental observables as these model parameters. This addresses a problem of ambiguity in representation that arises in current tomographic approaches (the gauge problem). Solving the gauge problem enables us to efficiently implement operational tomography in a Bayesian framework computationally, and hence gives us a natural way to include prior information and discuss uncertainty in fit parameters. We demonstrate this new tomography in a variety of different experimentally-relevant scenarios, including standard process tomography, Ramsey interferometry, randomized benchmarking, and gate set tomography.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1985

The albedo field and cloud radiative forcing produced by a general circulation model with internally generated cloud optics

A general circulation model (GCM) study is presented in which cloud radiative properties are computed from cloud liquid water content inferred from the GCM hydrological cycle. Model-generated and satellite albedos are in rough agreement. Analysis of the cloud radiative forcing indicates that cloud albedo effects overcome cloud infrared opacity effects in most regions. Both computed and observed albedo of clouds decrease from low to high altitudes. The model with variable cloud optics produces significantly different regional albedos from the same one with fixed cloud optics, especially over the tropics. The cloud droplet size distribution also has a significant impact on the model albedos. The temperature of the tropical upper troposphere is somewhat sensitive to the microphysical characteristics of the model cirrus clouds.

Charlock, T. P.↗
NASA NTRS2011

FTEs: Theory, Simulation, and Observations

Flux transfer events (FTEs) are ropes of interconnected magnetosheath and magnetospheric magnetic field lines generated by bursty reconnection at the dayside magnetopause. Theory predicts that the combined pressure gradient and magnetic curvature forces should determine the speed at which the events move and the locations where they can be observed. We present results indicating that events form on the dayside magnetopause for both northward and southward IMF orientations, but that the events for northward IMF orientations exhibit far weaker signatures until they reach the magnetospheric flanks and argue that this is consistent with observations indicating that events on the dayside tend to occur for southward IMF orientations, but those on the flank do not. We show that the component and antiparallel reconnection models predict events in strikingly different quadrants outside the flanks of the magnetotail.Observations of events in all four quadrants indicate that both models are required. The motion of events inferred from multispacecraft timing during periods of northward IMF orientation is generally consistent with the component reconnection model.

Siebeck, David G.↗
DOE OSTI2023

A forward modeling approach to analyzing galaxy clustering with S IM BIG

We present cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the SimBIG forward modeling framework. SimBIG leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small nonlinear scales. In this work, we apply SimBIG to the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample and analyze the power spectrum, P ℓ (k), to k max = 0.5 h/Mpc. We construct 20,000 simulated galaxy samples using our forward model, which is based on 2,000 high-resolution Quijote N -body simulations and includes detailed survey realism for a more complete treatment of observational systematics. We then conduct SBI by training normalizing flows using the simulated samples and infer the posterior distribution of ΛCDM cosmological parameters: Ω m , Ω b , h, n s , σ 8 . We derive significant constraints on Ω m and σ 8 , which are consistent with previous works. Our constraint on σ 8 is 27% more precise than standard P ℓ analyses because we exploit additional cosmological information on nonlinear scales beyond the limit of current analytic models, k > 0.25 h/Mpc. This improvement is equivalent to the statistical gain expected from a standard P ℓ analysis of galaxy sample ~ 60% larger than CMASS. While we focus on P ℓ in this work for validation and comparison to the literature, SimBIG provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent SimBIG analyses of summary statistics beyond P ℓ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

An Assessment of the Laminar Hypersonic Double-Cone Experiments in the LENS-XX Tunnel

This is an investigation on two experimental datasets of laminar hypersonic flows, over a double-cone geometry, acquired in Calspan—University at Buffalo Research Center’s Large Energy National Shock (LENS)-XX expansion tunnel. These datasets have yet to be modeled accurately. A previous paper suggested that this could partly be due to mis-specified inlet conditions. The authors of this paper solved a Bayesian inverse problem to infer the inlet conditions of the LENS-XX test section and found that in one case they lay outside the uncertainty bounds specified in the experimental dataset. However, the inference was performed using approximate surrogate models. Here in this paper, the experimental datasets are revisited and inversions for the tunnel test-section inlet conditions are performed with a Navier–Stokes simulator. The inversion is deterministic and can provide uncertainty bounds on the inlet conditions under a Gaussian assumption. It was found that deterministic inversion yields inlet conditions that do not agree with what was stated in the experiments. An a posteriori method is also presented to check the validity of the Gaussian assumption for the posterior distribution. This paper contributes to ongoing work on the assessment of datasets from challenging experiments conducted in extreme environments, where the experimental apparatus is pushed to the margins of its design and performance envelopes.

42 ENGINEERING↗
NASA NTRS1991

Three-dimensional kinematic reconnection of plasmoids

The kinematic reconnection model of Lau and Finn (1990) is applied in a theoretical investigation of three-dimensional plasmoid morphology, as seen in the solar corona and earth magnetotail. The derivation of the governing equations is outlined; long, short, and periodic plasmoid models are developed; and the evolution of the stable and unstable manifolds in these models is presented graphically. It is inferred that sheet currents and tangential discontinuities can form on surfaces topologically identical to those where Delta(phi) about equal to Delta(z) singularities occur in the kinematic reconnection model, and can be broadened in a similar way by nonideal effects. Two such surfaces exist in long plasmoids, and also in short plasmoids in the presence of finite resistivity; the intertwined surfaces characteristic of the periodic plasmoid form a fractal set but merge in the presence of finite resistivity, producing structures similar to those proposed by the sheet-current theory of Parker (1983).

Lau, Yun-Tung↗
NASA NTRS1992

Global electric field determination in the Earth's outer magnetosphere using charged particles

Although many properties of the Earth's magnetosphere have been measured and quantified in the past 30 years since it was discovered, one fundamental (for a zeroeth order magnetohydrodynamic (MHD) equilibrium) measurement was made infrequently and with poor spatial coverage: the global electric field. This oversight is in part due to the difficulty of measuring a plasma electric field, and in part due to the difficulty of measuring a plasma electric field, and in part due to the neglect of theorists. However, there is renewed interest in the convection electric field, since it has been realized that it is vital for understanding many aspects of the magnetosphere: the global MHD equilibrium, reconnection rates, Region 2 Birkeland currents, magnetosphere-ionosphere coupling, ring current and radiation belt transport, substorm injections, acceleration mechanisms, etc. Unfortunately the standard experimental methods have not been able to synthesize a global field (excepting the pioneering work of McIlwain's geostationary models), and we are left with an overly simplistic theoretical field, the Volland-Stern electric field mode. Again, single point measurements of the plasma pause were used to infer the appropriate amplitudes of the model, parameterized by Kp (Maynard & Chen, JGR 1975). Although this result was never intended to be the definitive electric field model, it has gone nearly unchanged for 15 years. However, the data sets being taken today require a great deal more accuracy than can be provided by the Volland-Stern model. Nor has the variability of the electric field shielding been properly addressed, although effects of penetrating magnetospheric electric fields has been seen in mid- and low-latitude ionospheric data sets. The growing interests in substorm dynamics also requires a much better assessment of the electric fields responsible for particle injections. Thus, we proposed and developed algorithms for extracting electric fields from particle data taken in the earth's magnetosphere. As a test of the effectiveness of these techniques, we analyzed data taken by the AMPTE/CCE spacecraft in equatorial orbit between 1984-1988. Some analytic tools had to be developed before construction of computer algorithms, and they are discussed.

Eastman, T.↗
DOE OSTI2019

Non-stationary Geostatistical Modeling: A Case Study Comparing LVA Estimation Frameworks

Incorporating locally varying anisotropy (LVA) in geostatistical modeling improves estimates for structurally complex domains where a single set of anisotropic parameters modeled globally do not account for all geological features. In this work, the properties of two LVA-geostatistical modeling frameworks are explored through application to a complexly folded gold deposit in Ghana. The inference of necessary parameters is a significant requirement of geostatistical modeling with LVA; this work focuses on the case where LVA orientations, derived from expert geological interpretation, are used to improve the grade estimates. The different methodologies for inferring the required parameters in this context are explored. The results of considering different estimation frameworks and alternate methods of parameterization are evaluated with a cross-validation study, as well as visual inspection of grade continuity along select cross sections. Results show that stationary methodologies are outperformed by all LVA techniques, even when the LVA framework has minimal guidance on parameterization. Findings also show that additional improvements are gained by considering parameter inference where the LVA orientations and point data are used to infer the local range of anisotropy. Considering LVA for geostatistical modeling of the deposit considered in this work results in better reproduction of curvilinear geological features.

58 GEOSCIENCES↗
DOE OSTI2015

Aerosol variability, synoptic‐scale processes, and their link to the cloud microphysics over the northeast Pacific during MAGIC

Abstract Shipborne aerosol measurements collected from October 2012 to September 2013 along 36 transects between the port of Los Angeles, California (33.7°N, 118.2°), and Honolulu, Hawaii (21.3°N, 157.8°W), during the Marine ARM GPCI (Global Energy and Water Cycle Experiment (GEWEX)‐Cloud System Study (GCSS)‐Pacific Cross‐section Intercomparison) Investigation of Clouds campaign are analyzed to determine the circulation patterns that modulate the synoptic and monthly variability of cloud condensation nuclei (CCN) in the boundary layer. Seasonal changes in CCN are evident, with low magnitudes during autumn/winter, and high CCN during spring/summer accompanied with a characteristic westward decrease. CCN monthly evolution is consistent with satellite‐derived cloud droplet number concentration N d from the Moderate Resolution Imaging Spectroradiometer. One‐point correlation ( r ) analysis between the 1000 hPa zonal wind time series over a region between 125°W and 135°W, 35°N and 45°N, and the N d field yields a negative r (up to −0.55) over a domain that covers a zonal extent of at least 20° from the California shoreline, indicating that N d decreases when the zonal wind intensifies. The negative r expands southwestward as the zonal wind precedes N d by up to 3 days, suggesting a transport mechanism from the coast of North America mediated by the California low‐coastal jet, which intensifies in summer when the aerosol concentration and N d reach a maximum. A first assessment of aerosol‐cloud interaction (ACI) is performed by combining CCN and satellite N d values from the Fifteenth Geostationary Operational Environmental Satellite. The CCN‐ N d correlation is 0.66–0.69, and the ACI metric defined as ACI = ∂ln( N d )/∂ln(CCN) is high at 0.9, similar to other aircraft‐based studies and substantially greater than those inferred from satellites and climate models.

Painemal, David↗
DOE OSTI2019

Methane Production by Co-Digesting Vinasse and Whey in an AnSBBR: Effect of Mixture Ratio and Feed Strategy

The most common approach to deal with vinasse (sugarcane stillage) is fertigation, but this technique compromises soil structure and surrounding water bodies. A possible solution is to transport vinasse to local cheese whey producers and perform the co-digestion of these wastewaters together, reducing their organic load and generating bioenergy. Therefore, this study investigated the application of an AnSBBR (anaerobic sequencing batch biofilm reactor) operated in batch and fed-batch mode, co-digesting vinasse and whey at 30 °C. The effect of influent composition and feeding strategy was assessed. In all conditions, the system achieved high organic matter removal (approximately 83%). Increasing the percentage of vinasse from 0 to 100% in the influent resulted in a decrease in methane productivity (76.3 to 51.1 molCH{sub 4} m{sup −3} day{sup −1}) and yield (12.7 to 9.1 molCH{sub 4} kgCOD{sup −1}), but fed-batch mode operation improved reactor performance (73.0 molCH{sub 4} m{sup −3} day{sup −1} and 11.5 molCH{sub 4} kgCOD{sup −1}). From the kinetic metabolic model, it was possible to infer that, at the best condition, methane is produced in a similar way from the acetoclastic and hydrogenotrophic routes. A scheme of four parallel reactors with a volume of 16,950 m{sup 3} each was proposed in the scale-up estimation, with an energy recovery estimated in 28,745 MWh per month.

60 APPLIED LIFE SCIENCES↗
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↗