SEARCH · Search NASA

Results for “Data 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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Automated Resonance Fitting for Nuclear Data Evaluation

Global and national efforts to deliver high-quality nuclear data to users have a wide-ranging impact, affecting applications in national security, reactor operations, basic science, medicine, and more. Cross section evaluation is a major part of this effort, combining theory and experimentation to produce recommended values and uncertainties for reaction probabilities. Resonance region evaluation is a specialized type of nuclear data evaluation that can require significant manual effort and months of time from expert scientists. In this article, non-convex non-linear optimization methods are combined with concepts of inferential statistics to infer a resonance model from experimental data in an automated manner that is not dependent on prior evaluation(s). This methodology aims to enhance the workflow of a resonance evaluator by minimizing time, effort, and the potential for bias from prior assumptions, while enhancing reproducibility and documentation, thereby addressing well-known challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1980

Wallops waveform analysis of SEASAT-1 radar altimeter data

Fitting a six parameter model waveform to over ocean experimental data from the waveform samplers in the SEASAT-1 radar altimeter is described. The fitted parameters include a waveform risetime, skewness, and track point; from these can be obtained estimates of the ocean surface significant waveheight, the surface skewness, and a correction to the altimeter's on board altitude measurement, respectively. Among the difficulties encountered are waveform sampler gains differing from calibration mode data, and incorporating the actual SEASAT-1 sampled point target response in the fitted wave form. There are problems in using the spacecraft derived attitude angle estimates, and a different attitude estimator is developed. Points raised in this report have consequences for the SEASAT-1 radar altimeter's ocean surface measurements are for the design and calibration of radar altimeters in future oceanographic satellites.

Hayne, G. S.↗

DOE OSTI2018

Gaining insight into the $T\,_2^*$– T 2 relationship in surface NMR free-induction decay measurements

One of the primary shortcomings of the surface nuclear magnetic resonance (NMR) freeinduction decay (FID) measurement is the uncertainty surrounding which mechanism controls the signal’s time dependence. Ideally, the FID-estimated relaxation time T$*\atop{2}$ that describes the signal’s decay carries an intimate link to the geometry of the pore space. In this limit the parameter T$*\atop{2}$ is closely linked to a related parameter T 2 , which is more closely linked to pore-geometry. If T$*\atop{2}$ T 2 the FID can provide valuable insight into relative pore-size and can be used to make quantitative permeability estimates. However, given only FID measurements it is difficult to determine whether T$*\atop{2}$ is linked to pore geometry or whether it has been strongly influenced by background magnetic field inhomogeneity. If the link between an observed T$*\atop{2}$ and the underlying T 2 could be further constrained the utility of the standard surface NMR FID measurement would be greatly improved. We hypothesize that an approach employing an updated surface NMR forward model that solves the full Bloch equations with appropriately weighted relaxation terms can be used to help constrain the T$*\atop{2}$ –T 2 relationship. Weighting the relaxation terms requires estimating the poorly constrained parameters T 2 and T 1 ; to deal with this uncertainty we propose to conduct a parameter search involving multiple inversions that employ a suite of forward models each describing a distinct but plausible T$*\atop{2}$ –T 2 relationship. We hypothesize that forward models given poor T 2 estimates will produce poor data fits when using the complex-inversion, while forward models given reliable T 2 estimates will produce satisfactory data fits. By examining the data fits produced by the suite of plausible forward models, the likely T$*\atop{2}$ –T 2 can be constrained by identifying the range of T 2 estimates that produce reliable data fits. Synthetic and field results are presented to investigate the feasibility of the proposed technique.

58 GEOSCIENCES↗

DOE OSTI2022

A modified Susceptible-Infected-Recovered model for observed under-reported incidence data

Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that the fraction of reported cases varies across all countries. For example, the reported incidence fraction for the United States of America varies from 0.3 to 0.6, while for Brazil it varies from 0.2 to 0.4.

60 APPLIED LIFE SCIENCES↗

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

How robust are estimates of key parameters in standard viral dynamic models?

Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Simple analytical model for fitting QENS data from liquids

The advent of new generation of neutron spectrometers for quasielastic scattering measurements has demonstrated the need for multi-component (at least two-component) fitting of data collected from liquids. The narrowest component is associated with long-range translational diffusion, whereas the broader component originates from localized center-of-mass motions in the transient cage of the nearest neighbors. Intra-particle dynamics (e.g., of molecular side groups) may give rise to additional components besides the center-of-mass motions. We present a simple analytical model for two-component fits of quasielastic neutron scattering data collected from liquids. Here, the use of such models is optional (albeit helpful) in the traditional approach, where fits are performed sequentially and independently for the data collected at separate Q values, but imperative in the global fitting, where the data are fitted simultaneously at all Q values, which may be necessary to rationalize the data from increasingly complex systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Generation and Validation of the iKp1289 Metabolic Model for Klebsiella pneumoniae KPPR1

Klebsiella pneumoniae has a reputation for causing a wide range of infectious conditions, with numerous highly virulent and antibiotic-resistant strains. Metabolic models have the potential to provide insights into the growth behavior, nutrient requirements, essential genes, and candidate drug targets in these strains. Here we develop a metabolic model for KPPR1, a highly virulent strain of K. pneumoniae. We apply a combination of Biolog phenotype data and fitness data to validate and refine our KPPR1 model. The final model displays a predictive accuracy of 75% in identifying potential carbon and nitrogen sources for K. pneumoniae and of 99% in predicting nonessential genes in rich media. We demonstrate how this model is useful in studying the differences in the metabolic capabilities of the low-virulence MGH 78578 strain and the highly virulent KPPR1 strain. For example, we demonstrate that these strains differ in carbohydrate metabolism, including the ability to metabolize dulcitol as a primary carbon source. Our model makes numerous other predictions for follow-up verification and analysis.

bacteria↗

NASA NTRS1998

Estimation of Aircraft Nonlinear Unsteady Parameters From Wind Tunnel Data

Aerodynamic equations were formulated for an aircraft in one-degree-of-freedom large amplitude motion about each of its body axes. The model formulation based on indicial functions separated the resulting aerodynamic forces and moments into static terms, purely rotary terms and unsteady terms. Model identification from experimental data combined stepwise regression and maximum likelihood estimation in a two-stage optimization algorithm that can identify the unsteady term and rotary term if necessary. The identification scheme was applied to oscillatory data in two examples. The model identified from experimental data fit the data well, however, some parameters were estimated with limited accuracy. The resulting model was a good predictor for oscillatory and ramp input data.

Klein, Vladislav↗

NASA NTRS2017

A Survey of Xenon Ion Sputter Yield Data and Fits Relevant to Electric Propulsion Spacecraft Integration

A survey of low energy xenon ion impact sputter yields was conducted to provide a more coherent baseline set of sputter yield data and accompanying fits for electric propulsion integration. Data uncertainties are discussed and different available curve fit formulas are assessed for their general suitability. A Bayesian parameter fitting approach is used with a Markov chain Monte Carlo method to provide estimates for the fitting parameters while characterizing the uncertainties for the resulting yield curves.

xenon↗

DOE OSTI2014

A HYBRID MODE MODEL OF THE BLAZHKO EFFECT, SHOWN TO ACCURATELY FIT KEPLER DATA FOR RR Lyr

The waveform for Blazhko stars can be substantially different during the ascending and descending parts of the Blazhko cycle. A hybrid model, consisting of two component oscillators of the same frequency, is proposed as a means to fit the data over the entire cycle. One component exhibits a sawtooth-like velocity waveform while the other is nearly sinusoidal. One method of generating such a hybrid is presented: a nonlinear model is developed for the first overtone mode, which, if excited to large amplitude, is found to drop strongly in frequency and become highly non-sinusoidal. If the frequency drops sufficiently to become equal to the fundamental frequency, the two can become phase locked and form the desired hybrid. A relationship is assumed between the hybrid mode velocity and the observed light curve, which is approximated as a power series. An accurate fit of the hybrid model is made to actual Kepler data for RR Lyr. The sinusoidal component may tend to stabilize the period of the hybrid which is found in real Blazhko data to be extremely stable. It is proposed that the variations in amplitude and phase might result from a nonlinear interaction with a third mode, possibly a nonradial mode at 3/2 the fundamental frequency. The hybrid model also applies to non-Blazhko RRab stars and provides an explanation for the light curve bump. A method to estimate the surface gravity is also proposed.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

MO-FG-303-02: BEST IN PHYSICS (THERAPY): Cherenkov Emission Dosimetry: Feasibility for Electron Radiotherapy

Purpose: To investigate from first principles, corroborated by Monte Carlo simulations and experimental measurements, the feasibility of developing a relative Cherenkov emission (CE) dosimetry protocol for electron beam radiotherapy. Methods: Monte Carlo (MC) simulations of mono-energetic electrons incident on water were carried out in Geant4. Percent depth Cherenkov emission (PDCE) and dose (PDD) distributions were scored for incidence energies of 4, 6, 9, 12, 15, and 18 MeV. PDCE-to-PDD analytical conversion models were developed from least-squares data fits generated for PDD as a function of PDCE at the same depth and at different depths. Experimental techniques for validation of these models are examined. Results: Same-depth PDD versus PDCE data fits indicate that although the relationship is linear to first order (correlation r > 0.9 for all energies), it is much more accurately approximated by separate linear and quadratic models for the build-up and drop-off regions, respectively (r > 0.999), which is theoretically underpinned. To understand the source of this relationship and its basis for developing robust conversion models, an approximate quadratic first-principles model was derived and found in agreement with MC/measured data (20% deviation at worst). Conversely, data fits of PDD versus different-depth PDCE unveiled a depth-invariant effective point of measurement of 1.5–2.1 mm downstream with 4–18 MeV incidence, respectively (r > 0.999 in the drop-off region). We present an analytical first-principles justification for this shift. This method led to errors of <1% in drop-off region PDD (<2% for PDD<20% with 4 MeV incidence) and <0.2 mm in practical range prediction. Conclusion: We present robust quantitative prediction models, derived from first-principles and supported by simulation and measurement, for relative dose from Cherenkov emission by high-energy electrons. This constitutes a major step towards development of protocols for routine clinical quality assurance as well as real-time in vivo Cherenkov dosimetry in radiotherapy. The authors acknowledge partial support by Fonds de recherche du Quebec - Nature et technologies (FRQNT), CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (NSERC), CREATE Integrated Sensor Systems grant of NSERC, the Canadian Institutes of Health Research (CIHR), and NSERC.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-T-685: Evaluation of Tumor Hypoxic Fraction Using Serial Volumetric Imaging During Radiation Therapy

Purpose: To develop a tumor response model which could be uses to compute tumor hypoxic fraction using serial volumetric tumor imaging. This algorithm may be used for treatment response assessment and also for guidance of more expensive PET imaging of hypoxia. Methods: Previously developed two-level cell population tumor response model was modified to include a third cell level describing hypoxic and necrotic cells. This third level was considered constant value during radiotherapy treatment; therefore, inclusion additional parameter did not compromise stability of model fitting to imaging data. Fitting the model to serial volumetric imaging data was performed using a least squares objective function and simulated annealing algorithm. The problem of reconstruction of radiobiological parameters from serial imaging data was considered as inverse ill-posed problem described by the Fredholm integral equation of the first kind. Variational regularization was used to stabilize solutions. Results: To evaluate performance of the algorithm, we used a set of serial CT imaging data on tumor-volume for 14 head and neck cancer patients. The hypoxic fractions were reconstructed for each patient and the distribution of hypoxic fractions was compared to the distribution of initial hypoxic fractions previously measured using histograph. The measured and reconstructed from imaging data distributions of hypoxic fractions are in good agreement. The reconstructed distribution of cell surviving fraction was also in better agreement with in vitro data than previously obtained using the two-level cell population model. Conclusion: Our results indicate that it is possible to evaluate the initial hypoxic tumor fraction using serial volumetric imaging and a tumor response model. This algorithm can be used for treatment response assessment and guidance of more expensive PET imaging.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

A Parallelised ROOT for Future HEP Data Processing

In the coming years, HEP data processing will need to exploit parallelism on present and future hardware resources to sustain the bandwidth requirements. As one of the cornerstones of the HEP software ecosystem, ROOT embraced an ambitious parallelisation plan which delivered compelling results. In this contribution the strategy is characterised as well as its evolution in the medium term. The units of the ROOT framework are discussed where task and data parallelism have been introduced, with runtime and scaling measurements. We will give an overview of concurrent operations in ROOT, for instance in the areas of I/O (reading and writing of data), fitting / minimization, and data analysis. This paper introduces the programming model and use cases for explicit and implicit parallelism, where the former is explicit in user code and the latter is implicitly managed by ROOT internally.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Dynamics of neutrals and ions in an ultrafast laser produced Zn plasma

Optical time of flight dynamics of neutrals and ions in an ultrafast laser produced zinc plasma generated by irradiating a solid zinc target using 100 fs laser pulses is investigated. An acceleration of ions is observed which arises from internal Coulomb forces acting between charged species in the plasma. Some of the fast ions recombine with electrons in the plasma and generate fast neutrals. Plasma plume imaging performed at various ambient pressures indicates adiabatic expansion at lower pressures and plume front deceleration at higher pressures: at lower pressures the plume front-time (R-t) plot displays a linear expansion, shock wave model fits to the data at 5 Torr and at higher pressures the data fits better to the drag model. Furthermore, around an intermediate pressure of 10 Torr, the R-t plot fits to the shock wave model at earlier stages of plasma expansion, while it fits to the drag model at the later stages. These investigations provide relevant information on the acceleration of ions and neutrals in an expanding zinc plasma plume produced by ultrafast laser pulses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗

NASA NTRS1974

An empirical analysis of the quantitative effect of data when fitting quadratic and cubic polynomials

A study is made of the extent to which the size of the sample affects the accuracy of a quadratic or a cubic polynomial approximation of an experimentally observed quantity, and the trend with regard to improvement in the accuracy of the approximation as a function of sample size is established. The task is made possible through a simulated analysis carried out by the Monte Carlo method in which data are simulated by using several transcendental or algebraic functions as models. Contaminated data of varying amounts are fitted to either quadratic or cubic polynomials, and the behavior of the mean-squared error of the residual variance is determined as a function of sample size. Results indicate that the effect of the size of the sample is significant only for relatively small sizes and diminishes drastically for moderate and large amounts of experimental data.

Canavos, G. C.↗

NASA NTRS1989

Optical evidence for the metallization of Xenon at 132(5) GPa

Xe has been compressed in a diamond-anvil cell to approximately 200 GPa. The metallization of Xe by band-gap closure was investigated by obtaining optical data in both the metallic and insulating states. In the metallic state, the pressure dependence of the plasma frequency was determined from absorption data fitted with a free-electron model. In the insulating state, the pressure dependence of the band gap was determined from absorption data fitted with an indirect-band-gap model. The optical data indicate that the insulator-to-metal transition in Xe occurs at 132(5) GPa.

Goettel, Kenneth A.↗