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Results for “Model fitting”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Using Bayesian Methodology to Estimate Liquefied Natural Gas Leak Frequencies
This analysis provides estimates on the leak frequencies of nine components found in liquefied natural gas (LNG) facilities. Data was taken from a variety of sources, with 25 different data sets included in the analysis. A hierarchical Bayesian model was used that assumes that the log leak frequency follows a normal distribution and the logarithm of the mean of this normal distribution is a linear function of the logarithm of the fractional leak area. This type of model uses uninformed prior distributions that are updated with applicable data. Separate models are fit for each component listed. Five order-of-magnitude fractional leak areas are considered, based on the flow area of the component. Three types of supporting analyses were performed: sensitivity of the model to the data set used, sensitivity of the leak frequency estimates to differences in the model structure or prior distributions, and sufficiency of sample sized used for convergence. Recommended leak frequency distributions for all component types and leak sizes are given. These leak frequency predictions can be used for quantitative risk assessments in the future.
Multi-wavelength Spectral Analysis of Ellerman Bombs Observed by FISS and IRIS
Ellerman bombs (EBs) are a kind of solar activity that is suggested to occur in the lower solar atmosphere. Recent observations using the Interface Region Imaging Spectrograph (IRIS) show connections between EBs and IRIS bombs (IBs), which imply that EBs might be heated to a much higher temperature (8 × 10{sup 4} K) than previous results. Here we perform a spectral analysis of EBs simultaneously observed by the Fast Imaging Solar Spectrograph and IRIS. The observational results show clear evidence of heating in the lower atmosphere, indicated by the wing enhancement in H α , Ca ii 8542 Å, and Mg ii triplet lines and also by brightenings in images of the 1700 Å and 2832 Å ultraviolet continuum channels. Additionally, the intensity of the Mg ii triplet line is correlated with that of H α when an EB occurs, suggesting the possibility of using the triplet as an alternative way to identify EBs. However, we do not find any signal in IRIS hotter lines (C ii and Si iv). For further analysis, we employ a two-cloud model to fit the two chromospheric lines (H α and Ca ii 8542 Å) simultaneously, and obtain a temperature enhancement of 2300 K for a strong EB. This temperature is among the highest of previous modeling results, albeit still insufficient to produce IB signatures at ultraviolet wavelengths.
New generalized rheological model for lubrication of a ball spinning in a nonconforming groove
The elastohydrodynamic theory for predicting the spinning friction of a ball in a nonconforming groove was modified to incorporate a rheological model. The rheological model is based on the exponential pressure viscosity relation for low shear stresses, but at high shear rates and pressures, the relation is altered to one in which the shear stress is porportional to the normal stress. The model was fitted to experimental spinning torques for four different lubricants: a synthetic paraffinic lubricant, di-2-ethylhexyl sebacate, a super-refined naphthenic mineral oil, and a polyphenyl ether (5P4E). Good agreement between the model and experiment was found.
Observations of A0535 + 26 with the SMM satellite
An examination of archival data from the hard X-ray instruments on the Solar Maximum Mission (SMM) satellite has revealed a previously undetected outburst from the recurrent X-ray transient, A0535 + 26. The outburst occurred in June 1983 and reached a peak intensity of about 2 crab units in the energy range 32-91 keV. The outburst was detected over a span of 18 days, and the pulse period was observed to spin-up with an average rate of about -6 x 10 to the -8th s/s. A recently proposed model for A0535 + 26 has a pulsar powered by a short-lived accretion disk. A thin accretion disk model is fitted to the present data, assuming an orbital period of 111 days. Two solutions to the magnetic moment of the neutron star are derived. The slow rotator solution is more consistent with the model than the fast rotator, on the grounds that the conditions for the formation of an accretion disk are more favorable for a lower magnetic field strength.
The X-ray Halo of GX5-1
Using Chandra observations we have measured the energy-resolved dust-scattered X-ray halo around the low-mass X-ray binary GX5-1, detecting for the first time multiply scattered X-rays from interstellar dust. % e compared the observed X-ray halo at various energies to predictions from a range of dust models. These fits used both smoothly-distributed dust as well as dust in clumped clouds, with CO and 21 cm observations helping to determine the position of the clouds along the line of sight. We found that the BARE-GR-B model of Zubko, Dwek & Arendt (2004) generally led to the best results, although inadequacies in both the overall model and the data limit our conclusions. We did find that the composite dust models of Zubko, Dwek & Arendt (2004), especially the "no carbon" models, gave uniformly poor results. Although models using cloud positions and densities derived naively from CO and 21 cm data gave generally poor results, plausible adjustments to the distance of the largest cloud and the mass of a cloud in the expanding 3 kpc Arm lead to significantly improved fits. We suggest that combining X-ray halo, CO, and 21 cm observations will be a fruitful method to improve our understanding of both the gas and dust phases of the interstellar medium.
The diffuse galactic far-ultraviolet sky
We present an all-sky map of the diffuse Galactic far ultraviolet (1344-1786 Å) background using Galaxy Evolution Explorer data, covering 65% of the sky with 11.79 arcmin{sup 2} pixels. We investigate the dependence of the background on Galactic coordinates, finding that a standard cosecant model of intensity is not a valid fit. Furthermore, we compare our map to Galactic all-sky maps of 100 μm emission, N {sub H} {sub I} column, and Hα intensity. We measure a consistent low level far-UV (FUV) intensity at zero points for other Galactic quantities, indicating a 300 photons cm{sup –2} s{sup –1} sr{sup –1} Å{sup –1} non-scattered isotropic component to the diffuse FUV. There is also a linear relationship between FUV and 100 μm emission below 100 μm values of 8 MJy sr{sup –1}. We find a similar linear relationship between FUV and N {sub H} {sub I} below 10{sup 21} cm{sup –2}. The relationship between FUV and Hα intensity has no such constant cutoff. For all Galactic quantities, the slope of the linear portion of the relationship decreases with Galactic latitude. A modified cosecant model, taking into account dust scattering asymmetry and albedo, is able to accurately fit the diffuse FUV at latitudes above 20°. The best fit model indicates an albedo, a, of 0.62 ± 0.04 and a scattering asymmetry function, g, of 0.78 ± 0.05. Deviations from the model fit may indicate regions of excess FUV emission from fluorescence or shock fronts, while low latitude regions with depressed FUV emission are likely the result of self-shielding dusty clouds.
Kinetic isotope effects significantly influence intracellular metabolite 13 C labeling patterns and flux determination
Rigorous mathematical modeling of carbon-labeling experiments allows estimation of fluxes through the pathways of central carbon metabolism, yielding powerful information for basic scientific studies as well as for a wide range of applications. However, the mathematical models that have been developed for flux determination from 13 C labeling data have commonly neglected the influence of kinetic isotope effects on the distribution of 13 C label in intracellular metabolites, as these effects have often been assumed to be inconsequential. We have used measurements of the 13 C isotope effects on the pyruvate dehydrogenase enzyme from the literature to model isotopic fractionation at the pyruvate node and quantify the modeling errors expected to result from the assumption that isotope effects are negligible. We show that under some conditions kinetic isotope effects have a significant impact on the 13 C labeling patterns of intracellular metabolites, and the errors associated with neglecting isotope effects in 13 C-metabolic flux analysis models can be comparable in size to measurement errors associated with GC–MS. Thus, kinetic isotope effects must be considered in any rigorous assessment of errors in 13 C labeling data, goodness-of-fit between model and data, confidence intervals of estimated metabolic fluxes, and statistical significance of differences between estimated metabolic flux distributions.
A measurement of the high-mass $\tau \overline{\tau}$ production cross-section at $\sqrt{s}=13$ TeV with the ATLAS detector and constraints on new particles and couplings
The production cross-section of high-mass τ-lepton pairs is measured as a function of the dilepton visible invariant mass, using 140 fb −1 of $\sqrt{s}=13$ TeV proton-proton collision data recorded with the ATLAS detector at the Large Hadron Collider. The measurement agrees with the predictions of the Standard Model. A fit to the invariant mass distribution is performed as a function of b-jet multiplicity, to constrain the non-resonant production of new particles described by an effective field theory or in models containing leptoquarks or Z′ bosons that couple preferentially to third-generation fermions. The constraints on new particles improve on previous results, and the constraints on effective operators include those affecting the anomalous magnetic moment of the τ-lepton.
Simultaneous parameter optimization of x-ray and neutron reflectivity data using genetic algorithms
X-ray and neutron reflectivity are two non destructive techniques which provide a wealth of information on thickness, structure and interracial properties in nanometer length scale. Combination of X-ray and neutron reflectivity is well suited for obtaining physical parameters of nanostructured thin films and superlattices. Neutrons provide a different contrast between the elements than X-rays and are also sensitive to the magnetization depth profile in thin films and superlattices. The real space information is extracted by fitting a model for the structure of the thin film sample in reflectometry experiments. We have applied a Genetic Algorithms technique to extract depth dependent structure and magnetic in thin film and multilayer systems by simultaneously fitting X-ray and neutron reflectivity data.
SU-F-J-199: Predictive Models for Cone Beam CT-Based Online Verification of Pencil Beam Scanning Proton Therapy
Purpose: To utilize online CBCT scans to develop models for predicting DVH metrics in proton therapy of head and neck tumors. Methods: Nine patients with locally advanced oropharyngeal cancer were retrospectively selected in this study. Deformable image registration was applied to the simulation CT, target volumes, and organs at risk (OARs) contours onto each weekly CBCT scan. Intensity modulated proton therapy (IMPT) treatment plans were created on the simulation CT and forward calculated onto each corrected CBCT scan. Thirty six potentially predictive metrics were extracted from each corrected CBCT. These features include minimum/maximum/mean over and under-ranges at the proximal and distal surface of PTV volumes, and geometrical and water equivalent distance between PTV and each OARs. Principal component analysis (PCA) was used to reduce the dimension of the extracted features. Three principal components were found to account for over 90% of variances in those features. Datasets from eight patients were used to train a machine learning model to fit these principal components with DVH metrics (dose to 95% and 5% of PTV, mean dose or max dose to OARs) from the forward calculated dose on each corrected CBCT. The accuracy of this model was verified on the datasets from the 9th patient. Results: The predicted changes of DVH metrics from the model were in good agreement with actual values calculated on corrected CBCT images. Median differences were within 1 Gy for most DVH metrics except for larynx and constrictor mean dose. However, a large spread of the differences was observed, indicating additional training datasets and predictive features are needed to improve the model. Conclusion: Intensity corrected CBCT scans hold the potential to be used for online verification of proton therapy and prediction of delivered dose distributions.
A consensus mathematical model of vaccine-induced antibody dynamics for multiple vaccine platforms and pathogens
Introduction: Vaccine platforms used in successful, licensed vaccines have varied among pathogens. However, antibody level is still the main clinical correlate of protection in most approved vaccines. Decisions as to the best vaccine platform to pursue for a given pathogen may be informed through improved understanding of the process of antibody generation and its temporal dynamics, as well as the relationship between these processes and the type of vaccine. Methods: We have analyzed the dynamics of antibody generation for different vaccine platforms against diverse pathogens, and developed a consensus mathematical model that captures antibody dynamics across these diverse systems. Initially, the model was fitted to a rich dataset of antibody and immune cell concentrations in a SARS-CoV-2 vaccine experiment. We then used concepts from machine learning, such as transfer learning, to apply the same model to a variety of systems, involving different pathogens, vaccine platforms, and booster dose use/timing, fixing most parameter values relating to the dynamics of the immune system. Results: The model includes B cell proliferation and differentiation, as well as the generation of plasma cells, which secrete large amounts of antibody, and memory B cells. Overall, the model describes antibody generation in all systems tested well and shows that the main differences across platforms are related to the dynamics of antigen presentation. Discussion: This model can be used to predict antibody generation in pairs of vaccine platform/pathogen, allowing for the use of in silico results to narrow down experimental burden in vaccine development.
Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)
Land surface models (LSMs), which form the land component of earth system models, rely on numerous processes for describing carbon, water and energy budgets, often associated with highly uncertain parameters. Data assimilation (DA) is a useful approach for optimising the most critical parameters in order to improve model accuracy and refine future climate predictions. In this study, we compare two different DA methods for optimising the parameters of seven plant functional types (PFTs) of the ORCHIDEE LSM using daily averaged eddy-covariance observations of net ecosystem exchange and latent heat flux at 78 sites across the globe. We perform a technical investigation of two classes of minimisation methods – local gradient-based (the L-BFGS-B algorithm, limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints) and global random search (the genetic algorithm) – by evaluating their relative performance in terms of the model–data fit and the difference in retrieved parameter values. We examine the performance of each method for two cases: when optimising parameters at each site independently (“single-site” approach) and when simultaneously optimising the model at all sites for a given PFT using a common set of parameters (“multi-site” approach). We find that for the single site case the random search algorithm results in lower values of the cost function (i.e. lower model–data root mean square differences) than the gradient-based method; the difference between the two methods is smaller for the multi-site optimisation due to a smoothing of the cost function shape with a greater number of observations. The spread of the cost function, when performing the same tests with 16 random first-guess parameters, is much larger with the gradient-based method, due to the higher likelihood of being trapped in local minima. When using pseudo-observation tests, the genetic algorithm results in a closer approximation of the true posterior parameter value in the L-BFGS-B algorithm. We demonstrate the advantages and challenges of different DA techniques and provide some advice on using it for the LSM parameter optimisation.
Aerodynamic Measurements of a Gulfstream Aircraft Model With and Without Noise Reduction Concepts
Steady and unsteady aerodynamic measurements of a high-fidelity, semi-span 18% scale Gulfstream aircraft model are presented. The aerodynamic data were collected concurrently with acoustic measurements as part of a larger aeroacoustic study targeting airframe noise associated with main landing gear/flap components, gear-flap interaction noise, and the viability of related noise mitigation technologies. The aeroacoustic tests were conducted in the NASA Langley Research Center 14- by 22-Foot Subsonic Wind Tunnel with the facility in the acoustically treated open-wall (jet) mode. Most of the measurements were obtained with the model in landing configuration with the flap deflected at 39º and the main landing gear on and off. Data were acquired at Mach numbers of 0.16, 0.20, and 0.24. Global forces (lift and drag) and extensive steady and unsteady surface pressure measurements were obtained. Comparison of the present results with those acquired during a previous test shows a significant reduction in the lift experienced by the model. The underlying cause was traced to the likely presence of a much thicker boundary layer on the tunnel floor, which was acoustically treated for the present test. The steady and unsteady pressure fields on the flap, particularly in the regions of predominant noise sources such as the inboard and outboard tips, remained unaffected. It is shown that the changes in lift and drag coefficients for model configurations fitted with gear/flap noise abatement technologies fall within the repeatability of the baseline configuration. Therefore, the noise abatement technologies evaluated in this experiment have no detrimental impact on the aerodynamic performance of the aircraft model.
Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques
Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature. A very efficient approach for optimizing systems without analytical expressions is through fitting surrogate models. Due to their increased flexibility, nonlinear interpolating functions, such as radial basis functions and Kriging, have been predominantly used as surrogates for data-driven optimization; however, these methods lead to complex nonconvex formulations. Alternatively, commonly used regression-based surrogates lead to simpler formulations, but they are less flexible and inaccurate if the form is not known a priori. In this work, we investigate the efficiency of subset selection regression techniques for developing surrogate functions that balance both accuracy and complexity. Subset selection creates sparse regression models by selecting only a subset of original features, which are linearly combined to generate a diverse set of surrogate models. Five different subset selection techniques are compared with commonly used nonlinear interpolating surrogate functions with respect to optimization solution accuracy, computation time, sampling requirements, and model sparsity. Furthermore, our results indicate that subset selection-based regression functions exhibit promising performance when the dimensionality is low, while interpolation performs better for higher dimensional problems.
A statistical mechanics framework for polymer chain scission, based on the concepts of distorted bond potential and asymptotic matching
To design increasingly tough, resilient, and fatigue-resistant elastomers and hydrogels, the relationship between controllable network parameters at the molecular level (bond type, non-uniform chain length, entanglement density, etc.) to macroscopic quantities that govern damage and failure must be established. Many of the most successful constitutive models for elastomers have been rooted in statistical mechanical treatments of polymer chains. Typically, such constitutive models have used variants of the freely jointed chain model with rigid links. However, since the free energy state of a polymer chain is dominated by enthalpic bond distortion effects as the chain approaches its rupture point, bond extensibility ought to be accounted for if the model is intended to capture chain rupture. To that end, a new bond potential is supplemented to the freely jointed chain model (as derived in the u FJC framework of Buche and Silberstein (2021) and Buche et al. (2022)), which we have extended to yield a tractable, closed-form model of single chain behavior that should be amenable to continuum-level constitutive model development. Inspired by the asymptotically matched u FJC model response in both the low/intermediate chain force and high chain force regimes, a simple, quasi-polynomial bond potential energy function is derived. This bond potential exhibits harmonic behavior near the equilibrium state and anharmonic behavior for large bond stretches tending to a characteristic energy plateau (akin to the Lennard-Jones and Morse bond potentials). Using this bond potential, approximate yet highly-accurate analytical functions for bond stretch and chain force dependent upon chain stretch are established. Then, using this polymer chain model, a stochastic thermal fluctuation-driven chain rupture framework is developed. This framework is based upon a force-modified tilted bond potential that accounts for distortional bond potential energy, allowing for the derivation and subsequent calculation of the dissipated chain scission energy. Here, the cases of rate-dependent and rate-independent scission are accounted for throughout the rupture framework. The impact of Kuhn segment number on chain rupture behavior is also investigated. The model is fit to single chain mechanical response data collected from atomic force microscopy tensile tests for validation and to glean deeper insight into the molecular physics taking place. Due to their analytical nature, this polymer chain model and the associated rupture framework can in the future be implemented in finite element models accounting for fracture and fatigue in polydisperse elastomer networks.
High-yield hydrogen production from biomass by in vitro metabolic engineering: Mixed sugars coutilization and kinetic modeling
The use of hydrogen (H 2 ) as a fuel offers enhanced energy conversion efficiency and tremendous potential to decrease greenhouse gas emissions, but producing it in a distributed, carbon-neutral, low-cost manner requires new technologies. Herein we demonstrate the complete conversion of glucose and xylose from plant biomass to H 2 and CO 2 based on an in vitro synthetic enzymatic pathway. Glucose and xylose were simultaneously converted to H 2 with a yield of two H 2 per carbon, the maximum possible yield. Parameters of a nonlinear kinetic model were fitted with experimental data using a genetic algorithm, and a global sensitivity analysis was used to identify the enzymes that have the greatest impact on reaction rate and yield. After optimizing enzyme loadings using this model, volumetric H 2 productivity was increased 3-fold to 32 mmol H 2 ∙L ₋1 ∙h ₋1 . The productivity was further enhanced to 54 mmol H 2 ∙L ₋1 ∙h ₋1 by increasing reaction temperature, substrate, and enzyme concentrations—an increase of 67-fold compared with the initial studies using this method. The production of hydrogen from locally produced biomass is a promising means to achieve global green energy production.
PHASE-RESOLVED X-RAY SPECTRA OF MAGNETARS AND THE CORONAL OUTFLOW MODEL
We test a model recently proposed for the persistent hard X-ray emission from magnetars. In the model, hard X-rays are produced by a decelerating electron-positron flow in the closed magnetosphere. The flow decelerates as it radiates its energy away via resonant scattering of soft X-rays, then it reaches the top of the magnetic loop and annihilates there. We test the model against observations of three magnetars: 4U 0142+61, 1RXS J1708-4009, and 1E 1841-045. We find that the model successfully fits the observed phase-resolved spectra. We derive constraints on the angle between the rotational and magnetic axes of the neutron star, the object inclination to the line of sight, and the size of the active twisted region filled with the plasma flow. Using the fit of the hard X-ray component of the magnetar spectrum, we revisit the remaining soft X-ray component. We find that it can be explained by a modified two-temperature blackbody model. The hotter blackbody is consistent with a hot spot covering 1%-10% of the neutron star surface. Such a hot spot is expected at the base of the magnetospheric e {sup ±} outflow, as some particles created in the e {sup ±} discharge flow back and bombard the stellar surface.