Engineering Papers⌕ Search

SEARCH · Engineering Papers

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.

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

At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Determining the bubble nucleation efficiency of low-energy nuclear recoils in superheated C$_3$F$_8$ dark matter detectors

The bubble nucleation efficiency of low-energy nuclear recoils in superheated liquids plays a crucial role in interpreting results from direct searches for weakly interacting massive particle (WIMP) dark matter. The PICO Collaboration presents the results of the efficiencies for bubble nucleation from carbon and fluorine recoils in superheated C$_3$F$_8$ from calibration data taken with 5 distinct neutron spectra at various thermodynamic thresholds ranging from 2.1 keV to 3.9 keV. Instead of assuming any particular functional forms for the nuclear recoil efficiency, a generalized piecewise linear model is proposed with systematic errors included as nuisance parameters to minimize model-introduced uncertainties. A Markov-Chain Monte-Carlo (MCMC) routine is applied to sample the nuclear recoil efficiency for fluorine and carbon at 2.45 keV and 3.29 keV thermodynamic thresholds simultaneously. The nucleation efficiency for fluorine was found to be $\geq 50\, \%$ for nuclear recoils of 3.3 keV (3.7 keV) at a thermodynamic Seitz threshold of 2.45 keV (3.29 keV), and for carbon the efficiency was found to be $\geq 50\, \%$ for recoils of 10.6 keV (11.1 keV) at a threshold of 2.45 keV (3.29 keV). Simulated data sets are used to calculate a p-value for the fit, confirming that the model used is compatible with the data. The fit paradigm is also assessed for potential systematic biases, which although small, are corrected for. Additional steps are performed to calculate the expected interaction rates of WIMPs in the PICO-60 detector, a requirement for calculating WIMP exclusion limits.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

System and method for measuring sun-induced chlorophyll fluorescence

A chlorophyll fluorescence measuring system having at least one spectrometer coupled to a data logger. The data logger provides direct control of the spectrometer and includes on-board memory for storage of target and reference spectrum data obtained by the spectrometer. The data logger may be coupled to an external computer that receives and analyzes target and reference spectrum data to determine SIF using a spectral fitting algorithm. The system may include a spectrometer aiming system coupled to and controlled by the data logger. The system may also include one or more environmental sensors configured to measure environment variables. The environmental sensors may be coupled to the data logger for control and data storage. The environmental data may be communicated to the external computer for use in the spectral fitting algorithm. The data logger may be connected to a network for remote monitoring and control.

Gu, Lianhong↗
DOE OSTI2022

Practical guide to understanding goodness-of-fit metrics used in chemical state modeling of x-ray photoelectron spectroscopy data by synthetic line shapes using nylon as an example

Chemical state analysis of a sample surface through fitting bell-shaped curves to x-ray photoelectron spectroscopic polymer data is reviewed using nylon to introduce and discuss aspects of data analysis. Different strategies for modeling chemistry in nylon spectra are presented and in so doing, a case is made to include in published science the design logic and implementation in terms of line shapes and optimization parameter constraints between components in a peak model. Imperfections in line shape relative to the true shape for photoemission lines, when compensated for using constraints to optimization parameters, are shown to provide chemical state information about a sample that justify, for peak models constructed with these limitations, metrics for goodness-of-fit different from those expected for pulse-counted data.

Materials Science↗
DOE OSTI2022

LArQL: A phenomenological model for treating light and charge generation in liquid argon

Experimental data shows that both ionization charge and scintillation light in LAr depend on the deposited energy density ($dE/dx$) and electric field ($\mathcal{E}$). Moreover, free ionization charge and scintillation light are anticorrelated, complementary at a given ($dE/dx$, $\mathcal{E}$) pair. We present LArQL, a phenomenological model that provides the anticorrelation between light and charge and its dependence on the deposited energy as well as on the electric field applied. It modifies the Birks' charge model considering the contribution from the escape electrons at null and low electric fields, and reconciles with Birks' model prediction at higher fields. Deviations from current Birks' model are observed for LArTPCs operating at low $\mathcal{E}$ and for heavily ionizing particles. The LArQL model presents a satisfactory description at $dE/dx$ and field ranges for interacting particles in LArTPCs and fits well the available data. Improvements via data sets compilation and global fits are also interesting features of the model.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Deuterium scattering experiments in CTEQ global QCD analyses: a comparative investigation

Abstract Experimental measurements in deep-inelastic scattering and lepton-pair production on deuterium targets play an important role in the flavor separation of u and d (anti)quarks in global QCD analyses of the parton distribution functions (PDFs) of the nucleon. We investigate the impact of theoretical corrections accounting for the light-nuclear structure of the deuteron upon the fitted u , d -quark, gluon, and other PDFs in the CJ15 and CT18 families of next-to-leading order CTEQ global analyses. The investigation is done using the $$L_2$$ L 2 sensitivity statistical method, which provides a common metric to quantify the strength of experimental constraints on various PDFs and ratios of PDFs in the two distinct fitting frameworks. Using the $$L_2$$ L 2 sensitivity and other approaches, we examine the compatibility of deuteron data sets with other fitted experiments under varied implementations of the deuteron corrections. We find that freely-fitted deuteron corrections modify the PDF uncertainty at large momentum fractions and will be relevant for future PDFs affecting electroweak precision measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Re-calibration of PBX9501 SURF model

PBX 9501 is a plastic bonded explosive composed of 95 wt % HMX and a binder; see [Gibbs and Popolato, 1980, pp. 109–119]. SURF is a reactive burn model for shock initiation and propagation of detonation waves. It has previously been calibrated for PBX 9501. Here the SURF model is recalibrated for PBX 9501; specifically, lot 730-010 at ρ = 1.837 g/cc 3 . The new calibration uses the Davis reactants and products EOS calibrated for the AWSD model [Aslam et al., 2020]. The burn rate in the shock initiation regime is fit to the Pop plot from 5 embedded gauge shock-to-detonation transition (SDT) experiments from [Gustavsen et al., 1999, see fig 12 and table 5]. In the propagation regime, the burn rate is fit to curvature effect data (detonation speed as function of front curvature); see [Aslam, 2007]. Also the burn parameters are adjusted to fit the gap-stick experiment [Hill et al., 2018]. Simulating the detonation wave speed in this experiment requires a model that is accurate for initiation with complex shock loading; in particular, a pressure decreasing gradient behind a curved lead shock. This is more difficult than calibrating to the standard SDT experiments which are 1-D and driven by a sustained shock. Simulations of the gap-stick experiment for PBX 9501 with the SURF model will be discussed in a subsequent report.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Implementation of a self-consistent slab model of bilayer structure in the SasView suite

Slab models are simple and useful structural descriptions which have long been used to describe lyotropic lamellar phases, such as lipid bilayers. Typically, slab models assume a midline symmetry and break a bilayer structure into three pieces, a central solvent-free core and two symmetric outer layers composed of the soluble portion of the amphiphile and associated solvent. This breakdown matches reasonably well to the distribution of neutron scattering length density and therefore is a convenient and common approach for the treatment of small-angle scattering data. Here, an implementation of this model within the SasView software suite is reported. The implementation is intended to provide physical consistency through the area per amphiphile molecule and number of solvent molecules included within the solvent-exposed outer layer. The proper use of this model requires knowledge of (or good estimates for) the amphiphile and solvent molecule volume and atomic composition, ultimately providing a self-consistent data treatment with only two free parameters: the lateral area per amphiphile molecule and the number of solvent molecules included in the outer region per amphiphile molecule. Here, the use of this code is demonstrated in the fitting of standard lipid bilayer data sets, obtaining structural parameters consistent with prior literature and illustrating the typical and ideal cases of fitting for neutron scattering data obtained using single or multiple contrast conditions. While demonstrated here for lipid bilayers, this model is intended for general application to block copolymers, surfactants, and other lyotropic lamellar phase structures for which a slab model is able to reasonably estimate the neutron scattering length density/electron-density profile of inner and outer layers of the lamellae.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Using SPT polarization, Planck 2015, and non-CMB data to constrain tilted spatially-flat and untilted nonflat ΛCDM, XCDM, and ΦCDM dark energy inflation cosmologies

We use six tilted spatially-flat and untilted non-flat dark energy cosmological models in analyses of South Pole Telescope polarization (SPTpol) cosmic microwave background (CMB) data, alone and in combination with Planck 2015 CMB data and non-CMB data, namely, the Pantheon Type Ia supernovae apparent magnitudes, a collection of baryon acoustic oscillation data points, Hubble parameter measurements, and growth rates. Although the cosmological models that best-fit the Planck CMB and non-CMB data do not provide good fits to the SPTpol data, with the χ2's exceeding the expected value, given the uncertainties, in each model the cosmological parameter constraints from the SPTpol data and from the Planck CMB and non-CMB data are largely mutually consistent. Furthermore, when the smaller angular scale SPTpol data are used jointly with either the Planck data alone or with the Planck CMB and the non-CMB data to constrain untilted non-flat models, spatially-closed models remain favored over their corresponding flat limits. When used in conjunction with Planck data, non-CMB data (baryon acoustic oscillation measurements in particular, from six experiments) have significantly more constraining power than the SPTpol data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Covariant spectator theory of n p scattering: Deuteron form factors

The deuteron form factors are calculated using two model wave functions obtained from the 2007 CST high precision fits to $np$ scattering data. Included in the calculation are a new class of isoscalar $np$ interaction currents which are automatically generated by the nuclear force model used in these fits. If the nuclear model WJC2 is used, a precision fit ($\chi^2$/datum $\eqsim1$) to the Sick Global Analysis (GA) of all $ed$ elastic scattering data can be obtained by adjusting the unknown off-shell nucleon form factors $F_3(Q^2)$ (discussed before) and $F_4(Q^2)$ (introduced in this paper), and predicting the high $Q^2$ behavior of the neutron charge form factor $G_{En}(Q^2)$ well beyond the region where it has been measured directly. Relativistic corrections, isoscalar interaction currents, and off-shell effects are defined, discussed, and their size displayed. A rationale for extending $ed$ elastic scattering measurements to higher $Q^2$ is presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A Bayesian approach to evaluation of soil biogeochemical models

Abstract. To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The separate effect of halo mass and stellar mass on the evolution of massive disc galaxies

ABSTRACT We analyse a sample of massive disc galaxies selected from the fourth-generation Sloan Digital Sky Survey/Mapping Nearby Galaxies at Apache Point Observatory survey to investigate how the evolution of these galaxies depends on their stellar and halo masses. We applied a semi-analytic spectral fitting approach to the data from different regions in the galaxies to derive several of their key physical properties. From the best-fitting model results, together with direct observables such as morphology, colour, and the Mgb/〈Fe〉 index ratio measured within 1Re, we find that for central galaxies both their stellar and halo masses have a significant influence in their evolution. For a given halo mass, galaxies with higher stellar mass accumulate their stellar mass and become chemically enriched earlier than those with smaller stellar mass. Furthermore, at a given stellar mass, galaxies living in more massive haloes have longer star formation time-scales and are delayed in becoming chemically enriched. In contrast, the evolution of massive satellite galaxies is mostly determined by their stellar mass. The results indicate that both the assembled halo mass and the halo assembly history impact the evolution of central galaxies. Our spatially resolved analysis indicates that only the galaxy properties in the central region (0.0–0.5Re) show the dependencies described above. This fact supports a halo-driven formation scenario since the galaxies’ central regions are more likely to contain old stars formed along with the halo itself, keeping a memory of the halo formation process.

Zhou, Shuang (ORCID:0000000289996814)↗
DOE OSTI2022

Hubbard-corrected oxide formation enthalpies without adjustable parameters

A density functional theory (DFT) approach to computing transition metal oxide heat of formation without adjustable parameters is presented. Different degrees of d-electron localization in oxides are treated within the DFT+U approach with site-dependent, first-principles Hubbard U-parameters obtained from linear response theory, and delocalized states in the metallic phases are treated without Hubbard corrections. Comparison of relative stabilities of these differently treated phases is enabled by a local d-electron density matrix-dependent model, which was found by genetic programming against experimental reference formation enthalpies. This mathematically simple model does not explicitly depend on the Hubbard-corrected ionic species and is shown to reproduce the heats of formation of the Mott insulators Ca 2 RuO 4 and Y 2 Ru 2 O 7 within ~3% of experimental results, where the experimental training data did not contain Ru oxides. This newly developed method thus absolves from the need for element-specific corrections fitted to experiments in existing Hubbard-corrected approaches to the prediction of reaction energies of transition metal oxides and metals. The absence of fitting parameters opens up here the possibility to predict relative thermodynamic stabilities and reaction energies involving d-states of varying degree of localization at transition metal oxide interfaces and defects, where site-dependent U-parameters will be particularly important and devising a fitting scheme against experimental data with predictive power would be exceedingly difficult.

transition metal oxides↗
DOE OSTI2021

pyDRESCALk

Modern data scientists are tasked to analyze ever-growing data sets with increasingly complex relationships. Tensor decompositions have come to play a central role in identifying underlying latent structures in higher-order data. The problem of fitting tensor models to different distributions is complicated by the combinations of size, dimensionality, and sparsity present in real world data. The situation demands efficient algorithms designed for shared-memory and distributed systems. This work will present new research that tackles these challenges on several different fronts, leveraging optimizations in numerical algorithms and sparse tensor representations in heterogeneous high performance computing environments.

Bhattarai, Manish↗
DOE OSTI2020

Validation of Recent Changes in Actinide Evaluations U-235, U-233, Pu-239 [Slides]

This presentation discusses U-235 updates, including fluctuations in the fission cross section in the URR range that were refined to represent measured data following the Paradela et al. evaluation. There were small changes to the low-energy resonances to improve the fit to measured capture data from RPI (capture reduced by 5% from 0.06-7.8 eV, by 7.7% from 7.8-11 eV) and there was a very small change to the thermal nu-bar within uncertainties. Discrete level data are stored in MF=6 (format requirement, no impact on calculations) and issues leading to negative eigenvalues in cross-covariances in the RR have been sorted out. In conclusion, many improvements were made to evaluated nuclear data of actinides in the resonance region and above, such as minor changes to U-235 including assessment of changes that depend on the finalization of the O-16 evaluation. U-233 improvements show significant progress in performance and some further improvement might be needed at intermediate energies. Pu-239 ORNL/IAEA evaluation is promising; however, more work is needed.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.

battery↗
DOE OSTI2021

Application of spectroscopic evidence to diffuse layer model (DLM) parameter estimation for cation adsorption onto ferrihydrite in single- and bi-solute systems

The diffuse layer model (DLM) is one of the most frequently used surface complexation models (SCM) for predicting cation adsorption onto ferrihydrite. However, the DLM with its existing database sometimes fails to predict cation adsorption, especially in multi-solute systems. Notably, the failure is often attributed to inaccurate selection of the surface complexes and/or underestimation of the site density for DLM formulation. DLM simulations of batch adsorption data conducted with cadmium, copper, lead, and zinc on ferrihydrite in CO2 free systems demonstrates the ability of the DLM to fit single-solute data reasonably well but highlights its limitations with respect to predicting bi-solute data. Modifications to the DLM were made in two steps. First, spectroscopic evidence was used to guide cation complexation reactions at the ferrihydrite surface. Second, surface characteristics (reactive site density, specific surface area, and acidity constants) of ferrihydrite were updated according to recent literature. The results from this study suggest that when spectroscopic verification is employed and surface site density is increased, a simple two-site DLM can predict cation adsorption on ferrihydrite in single-solute systems for pH data above 3.5 and in most of the bi-solute systems tested.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Zavitsas’ hydration model for electrolytes only stable in the presence of another: NaAl(OH) 4 in aqueous NaOH solution

Many aqueous electrolytes are only stable in the presence of another electrolyte, such as electrolytes that are only soluble in strong acid or base. An example is sodium aluminate [NaAl(OH) 4 ], which is only stable in aqueous NaOH. This complicates developing thermodynamic parameters because it is difficult to separate the contributions of individual electrolytes in multicomponent solutions to measured bulk thermodynamic properties. The present study develops a method to determine the liquid phase parameters from solubility data for Zavitsas’ Hydration model, a model that incorporates hydration parameters into the activities of dissolved species. Furthermore, this study uses gibbsite [Al(OH) 3 ] solubility data in aqueous NaOH solution to develop Zavitsas' model parameters for NaAl(OH) 4 simultaneously with the equilibrium constants. A good fit of the solubility data was found, showing that Zavitsas’ model is effective for this system.

Bayer process↗
DOE OSTI2026

Gearbox bearing crack growth prognostics and uncertainty quantification with physics-informed machine learning

This paper introduces the extreme theory of functional connections (X-TFC), a physics-informed machine learning algorithm, and tailors it to estimate the remaining useful life (RUL) of wind turbine gearbox bearings experiencing fatigue crack growth. Unlike purely data-driven methods, X-TFC embeds a physics model, based on Head's theory in this work, into its training objective. The core of X-TFC is a random-projection single-layer neural network trained via an extreme learning machine, which requires only limited damage progression data and solves for output weights with a least-squares optimization algorithm. A composite loss function balances the network's fit to observed degradation data against the residuals of the governing crack growth differential equation, ensuring the learned damage trajectory remains physically plausible. When applied to a vibration-based health-index (HI) dataset measured during the growth of a crack on the inner ring of a high-speed bearing in a wind turbine gearbox (Bechhoefer and Dubé, 2020), X-TFC achieves near-zero prediction bias. Even when trained on only the first 10 %–20 % of the damage progression data, with sufficient physics weighting its predictions remain monotonic and smooth, delivering high prognosability and trendability. To quantify the epistemic uncertainty, we employ a Monte Carlo ensemble of independently initialized X-TFC models trained on noise-perturbed data, which yields confidence intervals around each RUL estimate and captures both model-parameter and epistemic uncertainty. In addition to a vibration-based HI, we demonstrate that the proposed framework can be directly applied to a supervisory control and data acquisition (SCADA) data-based HI (Eftekhari Milani et al., 2026) measured during similar wind turbine gearbox bearing crack faults, preserving its accuracy and interpretability. This extension shows the versatility of our approach, which is applicable to bearings of multiple gearbox manufacturers, models, and ratings using only SCADA data. By integrating domain knowledge with machine learning, X-TFC offers a rapid, reliable tool for crack prognostics. Its adaptability to other bearing failure modes, such as pitch bearing ring cracks, positions X-TFC as a powerful enabler of data-driven, physics-informed asset management in the wind energy sector and beyond.

17 WIND ENERGY↗