Engineering Papers⌕ Search

SEARCH · Engineering Papers

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.

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

At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Framework for optimization of long-term, multi-period investment planning of integrated urban energy systems

In order to achieve stringent greenhouse gas emission reductions, a transition of our entire energy system from fossil to renewable resources needs to be designed. Such an energy transition brings two main challenges: most renewables generate variable electric energy, yet most demand is currently not electric (carrier mismatch) and does not always manifest at the same time as supply (temporal mismatch). Integrating multiple energy infrastructures can address both challenges by using the synergy between different energy carriers; building on existing infrastructure, while allowing a robust and flexible integration of the new. This paper proposes an optimization framework for long-term, multi-period investment planning of urban energy systems in an integrated manner. We formulate it as a mixed-integer linear program, combining a capacitated facility location with a multi-dimensional, capacitated network design problem. It includes generation and network expansion planning as well as interconnections between networks and storage infrastructure for each energy system. It can incorporate pathway effects like techno-economic developments, policy measures, and weather variations. The intended use is to support urban decision makers with long-term investment planning, though it can be tailored to fit other geographical or temporal scales. We demonstrate the model using two cases based on an average city in The Netherlands, which wants to reduce its CO 2 -emissions with 95% by 2050. In the first case, we include explicit carbon-emission constraints to study the effects of the carrier mismatch. In the second case, we implement interannual weather variations to analyze the temporal mismatch. The results give valuable insights into the energy transition design strategy for urban decision makers. They also show the future potential, as well as the computational challenges of the optimization framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

An Experimental Study of the Solubility of Rare Earth Chloride Salts (La, Nd, Er) in HCl Bearing Water Vapor from 350 – 425 °C

In this work, the solubilities of the rare earth chlorides REECl 3 , where REE = (La, Nd, Er), were measured in HCl bearing water vapor from 350 – 425°C with water partial pressures ranging from 8 – 170 bar. Solubility data were fit to the Pitzer-Pabalan quasi-chemical model in order to extract thermodynamic parameters for the formation of the gaseous REE-chloride-water clusters REECl 3 (H 2 O) n . The data show that the solubility of the REE chlorides are orders of magnitude higher than salts such as NaCl or CuCl at low water fugacities, despite their sublimation energies being substantially higher. This enhanced solubility is likely due to the high enthalpy associated with binding a single water molecule to form the species REECl 3 (H 2 O), with derived enthalpies ranging from –378 to –465 kJ/mol. Addition of further water molecules to form higher order clusters (n > 1) involves enthalpy changes of ~ -20 kJ/mol, and are in effect thermodynamically suppressed over the temperature range 350 – 425°C. Despite the enhanced solubility of small REECl 3 water clusters, simulations of boiling processes demonstrate that the REE show highly conservative behavior, partitioning strongly into the dense aqueous phase. Not surprisingly, the presence of phosphates in the system makes this effect even more pronounced, completely immobilizing the REE. This would reduce transport in both the vapor and aqueous phase to negligible levels. However, we suggest that vapor phase transport of the REE may play a significant role in systems having a relatively low partial pressure of water (below the saturation point), where the relatively high stability of the first hydrated REE chloride clusters (REECl 3 (H 2 O) and REECl 3 (H 2 O) 2 ) will give a preference for gas transport of the REE relative to other elements. This can likely happen in systems involving a gas/melt exchange in fumarolic exhalations, where water vapor discharges at relatively low (close to atmospheric) pressures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Dispersion of high oxygen permeability ionomers in water-propanol solutions

There is a growing interest in high oxygen permeability ionomers (HOPI) for better-performing proton exchange membrane fuel cell (PEMFC) catalyst layers. In this study, we used small-angle x-ray scattering (SAXS) to analyze dispersions of two types of perfluorinated sulfonic acid (PFSA) ionomers and a HOPI in water and propanol solvents, at concentrations between 1 wt% and 15 wt%. HOPI dispersions exhibited the interference peak typical of ionomers; by separately fitting the peak and higher- q regions with several models, we demonstrated that HOPI aggregates have a closer average spacing than conventional PFSA ionomers, and smaller aggregate size when assuming a cylindrical geometry. By measuring conventional PFSA ionomers with two side chain lengths and several equivalent weights, we showed that these side chain parameters alone did not explain the differences in aggregate spacing or size. In addition, the pattern was present whether samples were prepared in a 50/50 water/propanol or a more propanol-rich mixture. As such, the structural differences of HOPI could be due to its different backbone monomer. A better understanding of the influence of the HOPI chemistry on dispersion properties could help explain the ink structure and drying characteristics of HOPI observed in prior work. Future study of ionomer-catalyst mixtures, such as measuring ionomer adsorption to carbon, would help elucidate the role of ionomer from ink formulation to the cast electrode.

Aquivion↗
DOE OSTI2021

Parameterization of the Stoner-Wohlfarth model of magnetic hysteresis

The Stoner-Wohlfarth (SW) is the most used model of magnetic hysteresis, but its computation is time-consuming. We approximate piecewise this model by easy-to-compute analytic functions. Overall, our parametrization is suitable for fast quantitative evaluations and fitting experimental data, which we exemplify. Applicability of this parametrization is as broad as that of the SW model, which can be extended to include more parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Arsenic stabilization performance of a novel starch-modified Fe-Mn binary oxide colloid

Arsenic (As) is an environmentally hazardous contaminant and can be a serious threat to human health. In China, remediation needs for a large number of As-contaminated sites renders a strong demand for efficient remedial reagents and cost effective approaches. In this study, a novel starch-modified Fe-Mn binary oxide (SFM), an amorphous colloidal material, has been synthesized as a remedial reagent and its As stabilization performance has been evaluated. A set of laboratory batch experiments were carried out by directly adding SFM of different dosages into three contaminated soils. Results demonstrated that SFM could transform As in soil from non-specifically and specifically sorbed fractions to the more stable form bounded to amorphous iron hydrous oxides, thus reducing the As concentration in TCLP leachates by 42.0-93.2%. Results from adsorption tests and microscopic analysis indicated that the interactions between SFM and As are mainly controlled by adsorption, oxidation, and precipitation processes. SFM has abundant surface hydroxyl groups, with excellent adsorption properties for both As(V) and As(III), with the maximum adsorption capacities of 160.63 and 284.64 mg/g respectively at pH 7.0. The adsorption process closely fitted pseudo second-order kinetics and Freundlich isotherm model. SFM could increase soil Eh and oxidize As(III) to As(V), which facilitated the As stabilization in soilColloidal iron-based material directly used for stabilization in As contaminated soils is reported here for the first time. Starch modification improves both the reactivity and mobility of SFM in soil. Our findings propose an efficient and convenient reagent for As remediation in soil.

Yan, Xiulan↗
DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

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

Deciphering supramolecular and polymer-like behavior in metallogels: real-time insights into temperature-modulated gelation and rapid self-assembly dynamics

Bis(pyridyl) urea-based gelators, namely L2 and its isomeric mixture ( L1 + L2 ), are known to self-assemble into 1D architectures capable of inducing supramolecular gelation. Coordination with metal ions such as Ag( I ), Cu( II ), and Fe( III ) introduces structural reinforcement, enabling the formation of distinct 3D networks governed by metal-specific coordination geometries. Here, we present a comprehensive investigation into the temperature-responsive behavior (20–60 °C) of L2 and L1 + L2 , both in the absence and presence of Ag( I ), Dy( III ), Fe( III ), Cu( II ), and Ho( III ), using real-time small-angle neutron scattering (SANS). To probe long-term structural evolution/kinetics of self-assembly, real-time small-angle X-ray scattering (SAXS) was employed on L2 + Ag gels, complemented by differential scanning calorimetry (DSC) to evaluate thermal transitions. Our results reveal strikingly divergent gelation behaviors: L2 forms a highly rigid, covalent polymer-like network, while L1 + L2 exhibits remarkable thermal adaptability. Upon metal coordination, the assemblies exhibit pronounced crystallinity and exceptional thermal stability, as evidenced by persistent Bragg reflections and invariant d-spacings. Intriguingly, L2 : Fe (2 : 1) and L1 : L2 : Fe (0.5 : 0.5 : 1) in acetonitrile-d 3 (ACN-d 3 ) deviate from this trend, forming thermally labile amorphous gels. These systems show a complete loss of crystalline order, reduced Porod exponents—indicative of collapsed or branched fiber morphologies—and prominent melting and glass transition events in DSC. Fitting SANS and SAXS data to the correlation length model unveiled insightful nanostructural features. While most systems displayed minimal temperature-induced variation in mesh size or surface morphology, L2 : Ag in dimethyl sulfoxide-d 6 (DMSO-d 6 )/D 2 O and L2 : Fe (1 : 1) in ACN-d 3 exhibited a rare combination of thermally stable correlation lengths and increasing high- q exponents—strongly suggesting progressive fiber densification or surface smoothing within a robust gel framework. These findings highlight the tunability and structural resilience of supramolecular gels through precise control of ligand architecture, metal coordination, and temperature, offering valuable design principles for functional soft materials.

Pajoubpong, Jinnipha [Univ. of Cincinnati, OH (Uni↗
DOE OSTI2020

High electron mobility of Al x Ga1− x N evaluated by unfolding the DFT band structure

We calculate the alloy-disorder-limited electron mobility of AlxGa1−xN from first principles. AlxGa1−xN is a technologically important ultra-wide-bandgap alloy with promise in light emitting diodes and high-power transistors. Alloying introduces statistical disorder, which causes electrons to scatter between different crystal-momentum states, leading to a reduction in mobility for intermediate alloy compositions. The corresponding lifetime, which appears as an energy broadening in the band structure, can be evaluated by unfolding the band structure from the supercell basis to the primitive-cell basis. We fit the first-principles band broadening with a model scattering potential and evaluate the low-field electron mobility using the semiclassical Boltzmann transport equation in the relaxation-time approximation. Our calculated mobility is in agreement with experimental values. We also find the lowest alloy-scattering electron mobility (total electron mobility) across the entire composition range to be 186 cm2/V s (136 cm2/V s), which is comparable to the highest electron mobility predicted in the competitor system, β-(AlxGa1−x)2O3. Our results elucidate the intrinsic limits imposed by alloy disorder on electron transport in AlxGa1−xN.

Pant, Nick (ORCID:0000000254506045)↗
DOE OSTI2021

Effects of strain, disorder, and Coulomb screening on free-carrier mobility in doped cadmium oxide

Here, the interplay of stress, disorder, and Coulomb screening dictating the mobility of doped cadmium oxide (CdO) is examined using Raman spectroscopy to identify the mechanisms driving dopant incorporation and scattering within this emerging infrared optical material. Specifically, multi-wavelength Raman and UV-vis spectroscopies are combined with electrical Hall measurements on a series of yttrium (X = Y) and indium (X = In) doped X:CdO thin-films. Hall measurements confirm n-type doping and establish carrier concentrations and mobilities. Spectral fitting along the low-frequency Raman combination bands, especially the TA+TO(X) mode, reveals that the evolution of strain and disorder within the lattice as a function of dopant concentration is strongly correlated with mobility. Coupling between the electronic and lattice environments was examined through analysis of first- and second-order longitudinal–optical phonon–plasmon coupled modes that monotonically decrease in energy and asymmetrically broaden with increasing dopant concentration. By fitting these trends to an impurity-induced Fröhlich model for the Raman scattering intensity, exciton–phonon and exciton–impurity coupling factors are quantified. These coupling factors indicate a continual decrease in the amount of ionized impurity scattering with increasing dopant concentration and are not as well correlated with mobility. This shows that lattice strain and disorder are the primary determining factors for mobility in donor-doped CdO. In aggregate, the study confirms previously postulated defect equilibrium arguments for dopant incorporation in CdO while at the same time identifying paths for its further refinement.

42 ENGINEERING↗
DOE OSTI2022

A study of electroactive polyvinyl chloride (PVC) gel actuators through the use of the electric modulus formalism and cyclic linear voltage sweeps

The conductivity and dielectric properties are integral to the function of polyvinyl chloride (PVC) gel actuators. The frequency-dependent properties of PVC gel actuators are investigated here in terms of their impedance, permittivity, and for the first time, electric modulus. The data shows that PVC gels’ conductive properties are just as, if not more, important as their dielectric properties in electromechanical transduction applications. The electrode polarization (EP) and its impact on the impedance and dielectric spectra of PVC gels, as well as the developed asymmetric space charge at the anode, are discussed. The electric modulus and tan δ spectra are used for the fitting of Cole-Cole (CC) and Debye relaxation models for gels of varying plasticizer content. The electrostatic adhesive force for PVC gels of varying plasticizer is also measured, indicating large electrostatic adhesion (>2 N cm -2 ). A cyclic linear voltage sweep is used to clarify the dynamics of space charge within the gels. The peak current (associated with space charge development) is seen to be concurrent with the onset of mechanical deformation, showing the asymmetric charge as the origin of electromechanical transduction. Additionally, the maximum charge transferred (as measured by the integration of current over time) before space charge development is found to correlate with the electrostatic adhesive force measured for the gels, pointing to a new method of characterizing PVC gels for actuation.

Instruments & Instrumentation↗
DOE OSTI2025

Updated parameters of the LArQL model

The need for a microscopic description of scintillation light generation in liquid argon becomes increasingly desirable with the upcoming operation of large scale LArTPCs in the next decade. While a detailed mathematical account of the process is still to be achieved, a phenomenological model for simultaneously treating ionization and scintillation, LArQL, has been successfully employed to describe the range of electric fields from 0 to 0.75 kV/cm and dE/dx from 2 to 40 MeV/cm providing the anti-correlation between the free ionization charge and scintillation light. A reanalysis of the original model parameter values has been performed within a global fit procedure and is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Search for new physics using effective field theory in 13 TeV pp collision events that contain a top quark pair and a boosted Z or Higgs boson

A data sample containing top quark pairs ($\mathrm{t\bar{t}}$) produced in association with a Lorentz-boosted Z or Higgs boson is used to search for signs of new physics using effective field theory. The data correspond to an integrated luminosity of 138 fb$^{-1}$ of proton-proton collisions produced at a center-of-mass energy of 13 TeV at the LHC and collected by the CMS experiment. Selected events contain a single lepton and hadronic jets, including two identified with the decay of bottom quarks, plus an additional large-radius jet with high transverse momentum identified as a Z or Higgs boson decaying to a bottom quark pair. Machine learning techniques are employed to discriminate between $\mathrm{t\bar{t}}$Z or $\mathrm{t\bar{t}}$H events and events from background processes, which are dominated by $\mathrm{t\bar{t}}$ + jets production. No indications of new physics are observed. The signal strengths of boosted $\mathrm{t\bar{t}}$Z and $\mathrm{t\bar{t}}$H production are measured, and upper limits are placed on the $\mathrm{t\bar{t}}$Z and $\mathrm{t\bar{t}}$H differential cross sections as functions of the Z or Higgs boson transverse momentum. The effects of new physics are probed using a framework in which the standard model is considered to be the low-energy effective field theory of a higher energy scale theory. Eight possible dimension-six operators are added to the standard model Lagrangian and their corresponding coefficients are constrained via fits to the data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Differentiable vertex fitting for jet flavor tagging

This work explores the use of differentiable programming to integrate domain knowledge, in the form of domain specific software, into neural networks to develop scientific machine learning systems. We propose a differentiable vertex fitting algorithm that estimates the crossing point of multiple curves. In the high energy physics setting, these curves are defined by particle equations of motion and the crossing point represents the origin of particle production. This differentiable vertex fitting algorithm can be seamlessly integrated into neural networks, and we show its utility and efficacy in the high energy physics application of the classification of jets, i.e., collimated streams of particles in particle detectors whose originating parent particle we aim to classify. We demonstrate how differentiable vertex fitting can be integrated into larger transformer-based models for jet flavor tagging and show improvements in heavy flavor jet classification when compared to baseline models. Published by the American Physical Society 2024

Smith, Rachel E. C. (ORCID:0000000335851262)↗
DOE OSTI2024

Learning new physics from data: A symmetrized approach

Thousands of person years have been invested in searches for new physics (NP), the majority of them motivated by theoretical considerations. Yet, no evidence of beyond the Standard Model physics has been found. This suggests that model-agnostic searches might be an important key to explore NP, and help discover unexpected phenomena which can inspire future theoretical developments. A possible strategy for such searches is identifying asymmetries between data samples that are expected to be symmetric within the Standard Model. We propose exploiting neural networks (NNs) to quickly fit and statistically test the differences between two samples. Our method is based on an earlier work, originally designed for inferring the deviations of an observed dataset from that of a much larger reference dataset. We present a symmetric formalism, generalizing the original one, avoiding fine-tuning of the NN parameters and any constraints on the relative sizes of the samples. Our formalism could be used to detect small symmetry violations, extending the discovery potential of current and future particle physics experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models

Here, we propose a new method for inferring the governing stochastic ordinary differential equations (SODEs) by observing particle ensembles at discrete and sparse time instants, i.e., multiple “snapshots.” Particle coordinates at a single time instant, possibly noisy or truncated, are recorded in each snapshot but are unpaired across the snapshots. By training a physics-informed generative model that generates “fake” sample paths, we aim to fit the observed particle ensemble distributions with a curve in the probability measure space, which is induced from the inferred particle dynamics. We employ different metrics to quantify the differences between distributions, e.g., the sliced Wasserstein distances and the adversarial losses in generative adversarial networks. We refer to this method as generative “ensemble-regression” (GER), in analogy to the classic “point-regression,” where we infer the dynamics by performing regression in the Euclidean space. We illustrate the GER by learning the drift and diffusion terms of particle ensembles governed by SODEs with Brownian motions and Lévy processes up to 100 dimensions. We also discuss how to treat cases with noisy or truncated observations. Apart from systems consisting of independent particles, we also tackle nonlocal interacting particle systems with unknown interaction potential parameters by constructing a physics-informed loss function. Finally, we investigate scenarios of paired observations and discuss how to reduce the dimensionality in such cases by proving a convergence theorem that provides theoretical support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Analyzing Black Hole X-Rays with Simulations

We generate synthetic X-ray spectra of black holes from large-scale 3D GRMHD simulations performed on HPC machines. Here are a sampling of model spectra, characterized by the parameters indicated in the diagram below. The dashed red lines show power law fits to the parts of the spectrum observed by the X-ray telescope NuSTAR. How well these model spectra compare to real data helps us to characterize the physical realism of the underlying simulation techniques.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Constraints on Neutrino Oscillation Parameters from Neutrinos and Antineutrinos with Machine Learning

NOvA is a two detector, long baseline neutrino oscillation experiment measuring the oscillations of muon neutrinos from the \numi neutrino beam over a baseline of \SI{810}{km}. The experiment uses four oscillation channels, $\numu \rightarrow \numu$, $\numubar \rightarrow \numubar$, $\numu \rightarrow \nue$, and $\numubar \rightarrow \nuebar$, with a peak neutrino energy of \SI{1.8}{GeV}. This dissertation describes the analysis of these channels using a dataset of $13.6\times10^{20}$ protons on target neutrino beam mode and $12.5\times10^{20}$ protons on target antineutrino beam mode. The analysis makes use of improvements in the treatment of systematic uncertainties and machine learning techniques to reconstruct neutrino interactions. A technique for decorrelating systematic errors using principle component analysis was utilized to reduce and optimize neutrino cross section and beam related uncertainties. The improved machine learning algorithms make use of convolutional ne ural net works for neutrino event classification, particle classification, and instance segmentation. The selection of neutrino signal events utilizing the neutrino event classifier shows an efficiency of 63\% for the selection of electron neutrinos in neutrino beam mode and 75\% for electron antineutrinos in antineutrino beam mode. Using this algorithm, 82 appearing electron neutrino candidates and 33 appearing electron antineutrino candidates were observed with expected backgrounds of 26.8 and 14.0 respectively. In addition, 211 surviving muon neutrino candidates and 105 muon antineutrino candidates were identified with a purity of more than 96\% using the same neutrino event classifier. Fitting these data to the three flavor neutrino oscillation model, using constraints on \thetaonetwo, \thetaonethree, and \dmsqonetwo from solar and reactor neutrino experiments, the oscillation parameters are measured to be $\sintwothree = 0.57^{+0.04}_{-0.03}$, $\dmsqthreetwo = \SI[parse-numbers= false]{+ 2.41\pm0.07 \times 10^{-3}}{eV^2}$, and $\dcp=0.82^{+0.27}_{-0.87}\pi$ with a preference for the normal neutrino mass hierarchy. Leading systematic uncertainties for these measurements come from detector calibration and neutrino interaction models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗