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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

De-biased populations of Kuiper belt objects from the deep ecliptic survey

The Deep Ecliptic Survey (DES) was a survey project that discovered hundreds of Kuiper Belt objects from 1998 to 2005. Extensive follow-up observations of these bodies has yielded 304 objects with well-determined orbits and dynamical classifications into one of several categories: Classical, Scattered, Centaur, or 16 mean-motion resonances with Neptune. The DES search fields are well documented, enabling us to calculate the probability on each frame of detecting an object with its particular orbital parameters and absolute magnitude at a randomized point in its orbit. The detection probabilities range from a maximum of 0.32 for the 3:2 resonant object 2002 GF {sub 32} to a minimum of 1.5 × 10{sup –7} for the faint Scattered object 2001 FU {sub 185}. By grouping individual objects together by dynamical classes, we can estimate the distributions of four parameters that define each class: semimajor axis, eccentricity, inclination, and object size. The orbital element distributions (a, e, and i) were fit to the largest three classes (Classical, 3:2, and Scattered) using a maximum likelihood fit. Using the absolute magnitude (H magnitude) as a proxy for the object size, we fit a power law to the number of objects versus H magnitude for eight classes with at least five detected members (246 objects). The Classical objects are best fit with a power-law slope of α = 1.02 ± 0.01 (observed from 5 ≤ H ≤ 7.2). Six other dynamical classes (Scattered plus five resonances) have consistent magnitude distribution slopes with the Classicals, provided that the absolute number of objects is scaled. Scattered objects are somewhat more numerous than Classical objects, while there are only a quarter as many 3:2 objects as Classicals. The exception to the power law relation is the Centaurs, which are non-resonant objects with perihelia closer than Neptune and therefore brighter and detectable at smaller sizes. Centaurs were observed from 7.5 < H < 11, and that population is best fit by a power law with α = 0.42 ± 0.02. This is consistent with a knee in the H-distribution around H = 7.2 as reported elsewhere. Based on the Classical-derived magnitude distribution, the total number of objects (H ≤ 7) in each class is: Classical (2100 ± 300 objects), Scattered (2800 ± 400), 3:2 (570 ± 80), 2:1 (400 ± 50), 5:2 (270 ± 40), 7:4 (69 ± 9), 5:3 (60 ± 8). The independent estimate for the number of Centaurs in the same H range is 13 ± 5. If instead all objects are divided by inclination into 'Hot' and 'Cold' populations, following Fraser et al., we find that α{sub Hot} = 0.90 ± 0.02, while α{sub Cold} = 1.32 ± 0.02, in good agreement with that work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

TU-CD-207-11: Patient-Driven Automatic Exposure Control for Dedicated Breast CT

Purpose: To implement automatic exposure control (AEC) in dedicated breast CT (bCT) on a patient-specific basis using only the pre-scan scout views. Methods: Using a large cohort (N=153) of bCT data sets, the breast effective diameter (D) and width in orthogonal planes (Wa,Wb) were calculated from the reconstructed bCT image and pre-scan scout views, respectively. D, Wa, and Wb were measured at the breast center-of-mass (COM), making use of the known geometry of our bCT system. These data were then fit to a second-order polynomial “D=F(Wa,Wb)” in a least squares sense in order to provide a functional form for determining the breast diameter. The coefficient of determination (R{sup 2}) and mean percent error between the measured breast diameter and fit breast diameter were used to evaluate the overall robustness of the polynomial fit. Lastly, previously-reported bCT technique factors derived from Monte Carlo simulations were used to determine the tube current required for each breast diameter in order to match two-view mammographic dose levels. Results: F(Wa,Wb) provided fitted breast diameters in agreement with the measured breast diameters resulting in R{sup 2} values ranging from 0.908 to 0.929 and mean percent errors ranging from 3.2% to 3.7%. For all 153 bCT data sets used in this study, the fitted breast diameters ranged from 7.9 cm to 15.7 cm corresponding to tube current values ranging from 0.6 mA to 4.9 mA in order to deliver the same dose as two-view mammography in a 50% glandular breast with a 80 kV x-ray beam and 16.6 second scan time. Conclusion: The present work provides a robust framework for AEC in dedicated bCT using only the width measurements derived from the two orthogonal pre-scan scout views. Future work will investigate how these automatically chosen exposure levels affect the quality of the reconstructed image.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-TeP2-14: Universal Function Form for Photon Open Field In-Water Output Ratio

Purpose: In-Water-Output-Ratio (IWOR) plays a significant role in linac-based radiotherapy treatment planning, linking MUs to delivered radiation dose. For an open rectangular field, IWOR depends on both its width and length, and changes rapidly when one of them becomes small. In this study, a universal functional form is proposed to fit the open field IWOR tables in Varian TrueBeam representative datasets for all photon energies. Methods: A novel Generalized Mean formula is first used to estimate the Equivalent Square (ES) for a rectangular field. The formula’s weighting factor and power index are determined by collapsing all data points as much as possible onto a single curve in IWOR vs. ES plot. The result is then fitted with a novel universal function IWOR=1+b*Log(ES/10cm)/(ES/10cm)^c via a least-square procedure to determine the optimal values for parameters b and c. The maximum relative residual error in IWOR over the entire two-dimensional measurement table with field sizes between 3cm and 40cm is used to evaluate the quality of fit for the function. Results: The two-step fitting strategy works very well in determining the optimal parameter values for open field IWOR of each photon energies in the Varian data-set. Relative residual error ≤0.71% is achieved for all photon energies (including Flattening-Filter-Free modes) with field sizes between 3cm and 40cm. The optimal parameter values change smoothly with regular photon beam quality. Conclusion: The universal functional form fits the Varian TrueBeam open field IWOR measurement tables accurately with small relative residual errors for all photon energies. Therefore, it can be an excellent choice to represent IWOR in absolute dose and MU calculations. The functional form can also be used as a QA/commissioning tool to verify the measured data quality and consistency by checking the IWOR data behavior against the function for new photon energies with arbitrary beam quality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

SU-G-IeP3-11: On the Utility of Pixel Variance to Characterize Noise for Image Receptors of Digital Radiography Systems

Purpose: To characterize noise for image receptors of digital radiography systems based on pixel variance. Methods: Nine calibrated digital image receptors associated with nine new portable digital radiography systems (Carestream Health, Inc., Rochester, NY) were used in this study. For each image receptor, thirteen images were acquired with RQA5 beam conditions for input detector air kerma ranging from 0 to 110 µGy, and linearized ‘For Processing’ images were extracted. Mean pixel value (MPV), standard deviation (SD) and relative noise (SD/MPV) were obtained from each image using ROI sizes varying from 2.5×2.5 to 20×20 mm{sup 2}. Variance (SD{sup 2}) was plotted as a function of input detector air kerma and the coefficients of the quadratic fit were used to derive structured, quantum and electronic noise coefficients. Relative noise was also fitted as a function of input detector air kerma to identify noise sources. The fitting functions used least-squares approach. Results: The coefficient of variation values obtained using different ROI sizes was less than 1% for all the images. The structured, quantum and electronic coefficients obtained from the quadratic fit of variance (r>0.97) were 0.43±0.10, 3.95±0.27 and 2.89±0.74 (mean ± standard deviation), respectively, indicating that overall the quantum noise was the dominant noise source. However, for one system electronic noise coefficient (3.91) was greater than quantum noise coefficient (3.56) indicating electronic noise to be dominant. Using relative noise values, the power parameter of the fitting equation (|r|>0.93) showed a mean and standard deviation of 0.46±0.02. A 0.50 value for this power parameter indicates quantum noise to be the dominant noise source whereas values around 0.50 indicate presence of other noise sources. Conclusion: Characterizing noise from pixel variance assists in identifying contributions from various noise sources that, eventually, may affect image quality. This approach may be integrated during periodic quality assessments of digital image receptors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

COVID-19 global pandemic planning: Performance and electret charge of N95 respirators after recommended decontamination methods

Shortages of N95 respirators for use by medical personnel have driven consideration of novel conservation strategies, including decontamination for reuse and extended use. Decontamination methods listed as promising by the Centers for Disease Control and Prevention (CDC) (vaporous hydrogen peroxide (VHP), wet heat, ultraviolet irradiation (UVI)) and several methods considered for low resource environments (bleach, isopropyl alcohol and detergent/soap) were studied for two commonly used surgical N95 respirators (3M™ 1860 and 1870+ Aura™). Although N95 filtration performance depends on the electrostatically charged electret filtration layer, the impact of decontamination on this layer is largely unexplored. As such, respirator performance following decontamination was assessed based on the fit, filtration efficiency, and pressure drop, along with the relationship between (1) surface charge of the electret layer, and (2) elastic properties of the straps. Decontamination with VHP, wet heat, UVI, and bleach did not degrade fit and filtration performance or electret charge. Isopropyl alcohol and soap significantly degraded fit, filtration performance, and electret charge. Pressure drop across the respirators was unchanged. Modest degradation of N95 strap elasticity was observed in mechanical fatigue testing, a model for repeated donnings and doffings. CDC recommended decontamination methods including VHP, wet heat, and UV light did not degrade N95 respirator fit or filtration performance in these tests. Extended use of N95 respirators may degrade strap elasticity, but a loss of face seal integrity should be apparent during user seal checks. NIOSH recommends performing user seal checks after every donning to detect loss of appropriate fit. Decontamination methods which degrade electret charge such as alcohols or detergents should not be used on N95 respirators. The loss of N95 performance due to electret degradation would not be apparent to a respirator user or evident during a negative pressure user seal check.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Simulations of Turbulent Flows with Strong Shocks and Density Variations

In this report, we present the research efforts made by our group at UCLA in the SciDAC project Simulations of turbulent flows with strong shocks and density variations. We use shock-fitting methodologies as an alternative to shock-capturing schemes for the problems where a well defined shock is present. In past five years, we have focused on development of high-order shock-fitting Navier-Stokes solvers for perfect gas flow and thermochemical non-equilibrium flow and simulation of shock-turbulence interaction physics for very strong shocks. Such simulation has not been possible before because the limitation of conventional shock capturing methods. The limitation of shock Mach number is removed by using our high-order shock-fitting scheme. With the help of DOE and TeraGrid/XSEDE super computing resources, we have obtained new results which show new trends of turbulence statistics behind the shock which were not known before. Moreover, we are also developing tools to consider multi-species non-equilibrium flows. The main results are in three areas: (1) development of high-order shock-fitting scheme for perfect gas flow, (2) Direct Numerical Simulation (DNS) of interaction of realistic turbulence with moderate to very strong shocks using super computing resources, and (3) development and implementation of models for computation of mutli-species non-quilibrium flows with shock-fitting codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Angular analysis of the B o → K *o μ + μ – decay using 3 fb –1 of integrated luminosity

An angular analysis of the B o → K *o (→ K + π – )μ + μ – decay is presented. The dataset corresponds to an integrated luminosity of 3.0 fb –1 of pp collision data collected at the LHCb experiment. The complete angular information from the decay is used to determine CP-averaged observables and CP asymmetries, taking account of possible contamination from decays with the K + π – system in an S-wave configuration. The angular observables and their correlations are reported in bins of q 2 , the invariant mass squared of the dimuon system. The observables are determined both from an unbinned maximum likelihood fit and by using the principal moments of the angular distribution. In addition, by fitting for q 2 -dependent decay amplitudes in the region 1.1 < q 2 < 6.0 GeV 2 /c 4 , the zero-crossing points of several angular observables are computed. A global fit is performed to the complete set of CP-averaged observables obtained from the maximum likelihood fit. This fit indicates differences with predictions based on the Standard Model at the level of 3.4 standard deviations. These differences could be explained by contributions from physics beyond the Standard Model, or by an unexpectedly large hadronic effect that is not accounted for in the Standard Model predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Parameter inference from event ensembles and the top-quark mass

One of the key tasks of any particle collider is measurement. In practice, this is often done by fitting data to a simulation, which depends on many parameters. Sometimes, when the effects of varying different parameters are highly correlated, a large ensemble of data may be needed to resolve parameter-space degeneracies. An important example is measuring the top-quark mass, where other physical and unphysical parameters in the simulation must be profiled when fitting the top-quark mass parameter. We compare four different methodologies for top-quark mass measurement: a classical histogram fit similar to one commonly used in experiment augmented by soft-drop jet grooming; a 2D profile likelihood fit with a nuisance parameter; a machine-learning method called DCTR; and a linear regression approach, either using a least-squares fit or with a dense linearly-activated neural network. Despite the fact that individual events are totally uncorrelated, we find that the linear regression methods work most effectively when we input an ensemble of events sorted by mass, rather than training them on individual events. Although all methods provide robust extraction of the top-quark mass parameter, the linear network does marginally best and is remarkably simple. For the top study, we conclude that the Monte-Carlo-based uncertainty on current extractions of the top-quark mass from LHC data can be reduced significantly (by perhaps a factor of 2) using networks trained on sorted event ensembles. More generally, machine learning from ensembles for parameter estimation has broad potential for collider physics measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Interactions of Alumina-Based and Magnesia-Based Refractories with Iron Melts and Slags: A Review

A novel flash ironmaking technology (FIT) based on the direct reduction of iron ore concentrate with a reductant gas (such as hydrogen, natural gas, coal gas, or a combination thereof) in a flash furnace is being developed at the University of Utah. This technology which is undergoing large-scale laboratory testing aims at overcoming the limitations of blast furnace ironmaking by bypassing the problematic pelletization/sintering and cokemaking steps.[1–5] Refractory selection is expected to play an important step in the development of FIT and its proposed scale-up. For nominating an appropriate refractory for the FIT, understanding the interactions of candidate refractories with iron/iron oxide and slags under H{sub 2}/CO/CO{sub 2}/H{sub 2}O environments is necessary. This work is undertaken to review the existing literature on the interactions of important refractories with iron melts and relevant slags with an emphasis on two of the most commonly used refractories in ironmaking and steelmaking applications: the alumina-based refractories (used widely in blast furnace operations) and the magnesia-based refractories (used extensively in primary as well as secondary steelmaking). First, a comprehensive review on the interactions of alumina-based refractories with iron melts and slags has been done. Next the existing literature on the interactions of magnesia-based refractories with iron melts and relevant slags has been reviewed. Summaries have been included after each section and sub-section along with comments and critical insights from the authors. Finally, in the concluding remarks the differences in operating conditions between existing iron and steelmaking practices and the novel FIT have been highlighted. On the basis of these differences, it has been argued that the results and conclusions available from previous studies on refractory–metal–slag interactions are of little significance to flash ironmaking. Thus, there exists a need to carry out laboratory experiments for evaluating refractory performance in flash ironmaking under conditions relevant to the current process (FIT).

36 MATERIALS SCIENCE↗
DOE OSTI2025

SymbolFit: Automatic Parametric Modeling with Symbolic Regression

We introduce SymbolFit (API: https://github.com/hftsoi/symbolfit), a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data while simultaneously providing uncertainty estimates in a single run. Traditionally, constructing a parametric model to accurately describe binned data has been a manual and iterative process, requiring an adequate functional form to be determined before the fit can be performed. The main challenge arises when the appropriate functional forms cannot be derived from first principles, especially when there is no underlying true closed-form function for the distribution. In this work, we develop a framework that automates and streamlines the process by utilizing symbolic regression, a machine learning technique that explores a vast space of candidate functions without requiring a predefined functional form because the functional form itself is treated as a trainable parameter, making the process far more efficient and effortless than traditional regression methods. We demonstrate the framework in high-energy physics experiments at the CERN Large Hadron Collider (LHC) using five real proton-proton collision datasets from new physics searches, including background modeling in resonance searches for high-mass dijet, trijet, paired-dijet, diphoton, and dimuon events. We show that our framework can flexibly and efficiently generate a wide range of candidate functions that fit a nontrivial distribution well using a simple fit configuration that varies only by random seed, and that the same fit configuration, which defines a vast function space, can also be applied to distributions of different shapes, whereas achieving a comparable result with traditional methods would have required extensive manual effort.

Tsoi, Ho Fung [Univ. of Pennsylvania, Philadelphia↗
DOE OSTI2017

A near-stationary subspace for ridge approximation

Response surfaces are common surrogates for expensive computer simulations in engineering analysis. However, the cost of fitting an accurate response surface increases exponentially as the number of model inputs increases, which leaves response surface construction intractable for high-dimensional, nonlinear models. Here we describe ridge approximation for fitting response surfaces in several variables. A ridge function is constant along several directions in its domain, so fitting occurs on the coordinates of a low-dimensional subspace of the input space. We review essential theory for ridge approximation – e.g., the best mean-squared approximation and an optimal low-dimensional subspace – and we prove that the gradient-based active subspace is near-stationary for the least-squares problem that defines an optimal subspace. Motivated by the theory, we propose a computational heuristic that uses an estimated active subspace as an initial guess for a ridge approximation fitting problem. We show a simple example where the heuristic fails, which reveals a type of function for which the proposed approach is inappropriate. We then propose a simple alternating heuristic for fitting a ridge function, and we demonstrate the effectiveness of the active subspace initial guess applied to an airfoil model of drag as a function of its 18 shape parameters.

42 ENGINEERING↗
DOE OSTI2013

Using Generalized Equivalent Uniform Dose Atlases to Combine and Analyze Prospective Dosimetric and Radiation Pneumonitis Data From 2 Non-Small Cell Lung Cancer Dose Escalation Protocols

Purpose: To demonstrate the use of generalized equivalent uniform dose (gEUD) atlas for data pooling in radiation pneumonitis (RP) modeling, to determine the dependence of RP on gEUD, to study the consistency between data sets, and to verify the increased statistical power of the combination. Methods and Materials: Patients enrolled in prospective phase I/II dose escalation studies of radiation therapy of non-small cell lung cancer at Memorial Sloan-Kettering Cancer Center (MSKCC) (78 pts) and the Netherlands Cancer Institute (NKI) (86 pts) were included; 10 (13%) and 14 (17%) experienced RP requiring steroids (RPS) within 6 months after treatment. gEUD was calculated from dose-volume histograms. Atlases for each data set were created using 1-Gy steps from exact gEUDs and RPS data. The Lyman-Kutcher-Burman model was fit to the atlas and exact gEUD data. Heterogeneity and inconsistency statistics for the fitted parameters were computed. gEUD maps of the probability of RPS rate {>=}20% were plotted. Results: The 2 data sets were homogeneous and consistent. The best fit values of the volume effect parameter a were small, with upper 95% confidence limit around 1.0 in the joint data. The likelihood profiles around the best fit a values were flat in all cases, making determination of the best fit a weak. All confidence intervals (CIs) were narrower in the joint than in the individual data sets. The minimum P value for correlations of gEUD with RPS in the joint data was .002, compared with P=.01 and .05 for MSKCC and NKI data sets, respectively. gEUD maps showed that at small a, RPS risk increases with gEUD. Conclusions: The atlas can be used to combine gEUD and RPS information from different institutions and model gEUD dependence of RPS. RPS has a large volume effect with the mean dose model barely included in the 95% CI. Data pooling increased statistical power.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Lung Size and the Risk of Radiation Pneumonitis

Purpose: The purpose of this study was to identify patient populations treated for non-small cell lung cancer (NSCLC) who may be more at risk of radiation pneumonitis. Methods and Materials: A total of 579 patients receiving fractionated 3D conformal or intensity modulated radiation therapy (IMRT) for NSCLC were included in the study. Statistical analysis was performed to search for cohorts of patients with higher incidences of radiation pneumonitis. In addition to conventional risk factors, total and spared lung volumes were analyzed. The Lyman-Kutcher-Burman (LKB) and cure models were then used to fit the incidence of radiation pneumonitis as a function of lung dose and other factors. Results: Total lung volumes with a sparing of less than 1854 cc at 40 Gy were associated with a significantly higher incidence of radiation pneumonitis at 6 months (38% vs 12% for patients with larger volumes, P<.001). This patient cohort was overwhelmingly female and represented 22% of the total female population of patients and nearly 30% of the cases of radiation pneumonitis. An LKB fit to normal tissue complication probability (NTCP) including volume as a dose modifying factor resulted in a dose that results in a 50% probability of complication for the smaller spared volume cohort that was 9 Gy lower than the fit to all mean lung dose data and improved the ability to predict radiation pneumonitis (P<.001). Using an effective dose parameter of n=0.42 instead of mean lung dose further improved the LKB fit. Fits to the data using the cure model produced similar results. Conclusions: Spared lung volume should be considered when treating NSCLC patients. Separate dose constraints based on smaller spared lung volume should be considered. Smaller spared lung volume patients should be followed closely for signs of radiation pneumonitis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Predicting the 5-Year Risk of Biochemical Relapse After Postprostatectomy Radiation Therapy in ≥PT2, pN0 Patients With a Comprehensive Tumor Control Probability Model

Purpose: To fit the individual biochemical recurrence-free survival (bRFS) data from patients treated with postprostatectomy radiation therapy (RT) with a comprehensive tumor control probability (TCP) model. Methods and Materials: Considering pre-RT prostate-specific antigen (PSA) as a surrogate of the number of clonogens, bRFS may be expressed as a function of dose-per-fraction–dependent radiosensitivity (α{sub eff}), the number of clonogens for pre-RT PSA = 1 ng/mL (C), and the fraction of patients who relapse because of clonogens outside the treated volume (K), assumed to depend (linearly or exponentially) on pre-RT PSA and Gleason score (GS). Data from 894 node-negative, ≥pT2, pN0 hormone-naive patients treated with adjuvant (n=331) or salvage (n=563) intent were available: 5-year bRFS data were fitted grouping patients according to GS (<7:392, =7:383, >7:119). Results: The median follow-up time, pre-RT PSA, and dose were 72 months, 0.25 ng/mL, and 66.6 Gy (range 59.4-77.4 Gy), respectively. The best-fit values were 0.23 to 0.26 Gy{sup −1} and 10{sup 7} for α{sub eff} and C for the model considering a linear dependence between K and PSA. Calibration plots showed good agreement between expected and observed incidences (slope: 0.90-0.93) and moderately high discriminative power (area under the curve [AUC]: 0.68-0.69). Cross-validation showed satisfactory results (average AUCs in the training/validation groups: 0.66-0.70). The resulting dose-effect curves strongly depend on pre-RT PSA and GS. bRFS rapidly decreases with PSA: the maximum obtainable bRFS (defined as 95% of the maximum) declined by about 2.7% and 4.5% for each increment of 0.1 ng/mL for GS <7 and ≥7, respectively. Conclusions: Individual data were fitted by a TCP model, and the resulting best-fit parameters were radiobiologically consistent. The model suggests that relapses frequently result from clonogens outside the irradiated volume, supporting the choice of lymph-node irradiation, systemic therapy, or both for specific subgroups (GS <7: PSA >0.8-1.0 ng/mL; GS ≥7: PSA >0.3 ng/mL). Early RT should be preferred over delayed RT; the detrimental effect of PSA increase can never be fully compensated by increasing the dose, especially for patients with GS ≥7.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

A kinetic study of the fast micro-pyrolysis of hybrid poplar

Here, hybrid poplar from the clone DN34 was studied to determine the rate of production of bio-oil species with respect to time during the fast pyrolysis process. 300–660 ug samples were pyrolyzed using a micropyrolzer at 500 °C at very high heating rates and very short vapor residence times. Individual poplar samples were run in triplicate at discrete time points ranging from 1 to 20 s in the micropyrolzer. Several bio-oil compounds from each individual sample were analyzed using GC/MS. Select bio-oil compounds, derived from the hemicellulose, cellulose, and lignin fractions of the wood, were used to determine their rates of production from thermal degradation of the biomass. These rates were quantified using standards to determine the weight percent relative to the original raw biomass with respect to time. Pyrolysis kinetic reaction models were fit to this experimental data in order to determine suitability of each model. A first order exponential decay model for degradation of the solid biomass was fit against the data along with a six-step consecutive degradation model previously developed by our group. The experimental data suggests that compounds derived from the hemicellulose are produced at faster rates than that of the lignin or cellulose fractions of the wood, consistent with prior data from thermogravimetric analysis (TGA). When applying the first order exponential decay model, the reaction rates calculated did prove that holocellulose compounds reaction rates were approximately twice that of the lignin compounds reaction rate but did not provide the best fit. The six-step degradation model provided a better fit to all of the biooil compounds and char data, and the stoichiometric parameters derived from the model fit showed that the first three reactions involved mostly the hemicellulose derived compounds while reaction steps 4–6 released cellulose- and lignin-derived compounds.

09 BIOMASS FUELS↗
DOE OSTI2021

Combined selection of the dynamic model and modeling error in nonlinear aeroelastic systems using Bayesian Inference

Here, we report a Bayesian framework for concurrent selection of physics-based models and (modeling) error models. We investigate the use of colored noise to capture the mismatch between the predictions of calibrated models and observational data that cannot be explained by measurement error alone within the context of Bayesian estimation for stochastic ordinary differential equations. Proposed models are characterized by the average data-fit, a measure of how well a model fits the measurements, and the model complexity measured using the Kullback–Leibler divergence. The use of a more complex error models increases the average data-fit but also increases the complexity of the combined model, possibly over-fitting the data. Bayesian model selection is used to find the optimal physical model as well as the optimal error model. The optimal model is defined using the evidence, where the average data-fit is balanced by the complexity of the model. The effect of colored noise process is illustrated using a nonlinear aeroelastic oscillator representing a rigid NACA0012 airfoil undergoing limit cycle oscillations due to complex fluid–structure interactions. Several quasi-steady and unsteady aerodynamic models are proposed with colored noise or white noise for the model error. The use of colored noise improves the predictive capabilities of simpler models.

42 ENGINEERING↗
DOE OSTI2024

Measurement of (anti)alpha production in central Pb–Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV

In this letter, measurements of (anti)alpha production in central (0–10%) Pb–Pb collisions at a center-of-mass energy per nucleon–nucleon pair of $\sqrt{s_{NN}} = 5.02$ TeV are presented, including the first measurement of an antialpha transverse-momentum spectrum. Owing to its large mass, the production of (anti)alpha is expected to be sensitive to different particle production models. The production yields and transverse-momentum spectra of nuclei are of particular interest because they provide a stringent test of these models. The averaged antialpha and alpha spectrum is compared to the spectra of lighter particles, by including it into a common blast-wave fit capturing the hydrodynamic-like flow of all particles. This fit is indicating that the (anti)alpha also participates in the collective expansion of the medium created in the collision. A blast-wave fit including only protons, (anti)alpha, and other light nuclei results in a similar flow velocity as the fit that includes all particles. A similar flow velocity, but a significantly larger kinetic freeze-out temperature is obtained when only protons and light nuclei are included in the fit. The coalescence parameter B 4 is well described by calculations from a statistical hadronization model but significantly underestimated by calculations assuming nucleus formation via coalescence of nucleons. Similarly, the (anti)alpha-to-proton ratio is well described by the statistical hadronization model. On the other hand, coalescence calculations including approaches with different implementations of the (anti)alpha substructure tend to underestimate the data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗