Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “parameterization”

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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow

We report, in fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for Navier–Stokes equations to approximate spatiotemporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous neural ordinary differential equations for tracking the evolution of the governing differential equation. We demonstrate this technique on a three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization

To evaluate the high temperature sensing properties of metal oxide and perovskite materials suitable for use in combustion environments, it is necessary to understand the temperature dependence of their bandgaps. Although such temperature-driven changes can be calculated via the Allen–Heine–Cardona (AHC) theory, which assesses electron–phonon coupling for the bandgap correction at given temperatures, this approach is computationally demanding. Another approach to predict bandgap temperature-dependence is the O’Donnell model, which uses analytical expressions with multiple fitting parameters that require bandgap information at 0 K. This work employs data-driven Gaussian process regression (GPR) to predict the parameters employed in the O’Donnell model from a set of physical features. We use a sample of 54 metal oxides for which density functional theory has been performed to calculate the bandgap at 0 K, and the AHC calculations have been carried out to determine the shift in the bandgap at non-zero temperatures. As the AHC calculations are impractical for high-throughput screening of materials, the developed GPR model attempts to alleviate this issue by predicting the O'Donnell parameters purely from physical features. To mitigate the reliability issues arising from the very small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as quantify the uncertainty associated with the predictions. The method captures well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions of the O’Donnell parameters and, therefore, the bandgap as a function of temperature for any novel material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 to 0.10 Pa. Notably, our method signifies a paradigm shift as—to our knowledge—no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Parameterizing empirical interatomic potentials for predicting thermophysical properties via an irreducible derivative approach: the case of ThO 2 and UO 2

The accuracy of classical physical property predictions using molecular dynamics simulations is determined by the quality of the interatomic potentials. Here we introduce a training approach for empirical interatomic potentials (EIPs) which is well suited for capturing phonons and phonon-related properties. Our approach is based on direct comparisons of the second- and third-order irreducible derivatives (IDs) between an EIP and the Born–Oppenheimer potential within density functional theory (DFT) calculations. IDs fully exploit space group symmetry and allow for training without redundant information. We demonstrate the fidelity of our approach in the context of ThO 2 and UO 2 , where we optimize parameters of an embedded-atom method potential in addition to core–shell interactions. Our EIPs provide thermophysical properties in good agreement with DFT and outperform widely utilized EIPs for phonon dispersion and thermal conductivity predictions. Reasonable estimates of thermal expansion and formation energies of Frenkel pairs are also obtained.

empirical interatomic potential↗
DOE OSTI2019

An embedded-atom method potential parameterized for sulfur-induced embrittlement of nickel

The embrittling or strengthening effect of solute atoms at grain boundaries (GBs), commonly known as the embrittling potency, is a necessary thermodynamic property for characterizing the effects of solute segregation on GB fracture. One of the more technologically relevant material systems related to embrittlement is the Ni–S system where S has a deleterious effect on fracture behavior in polycrystalline Ni. Here, we develop a Ni–S embedded-atom method (EAM) interatomic potential that accounts for the embrittling behavior of S at Ni GBs. Results using this new interatomic potential are then compared to previous density functional theory studies and a reactive force-field potential via a layer-by-layer segregation analysis. Our potential shows strong agreement with existing literature and performs well in predicting properties that are not included in the fitting database. Finally, we calculate embrittling potencies and segregation energies for six [100] symmetric-tilt GBs using the new EAM potential. We observe that embrittling potency is dependent on GB structure, suggesting that specific GBs are more susceptible to sulfur-induced embrittlement.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Adaptive pruning-based optimization of parameterized quantum circuits

Abstract Variational hybrid quantum–classical algorithms are powerful tools to maximize the use of noisy intermediate-scale quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call ‘parameter-efficient circuit training (PECT)’. Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set. To update the parameter subset between iterations, we adapt the Dynamic Sparse Reparameterization scheme which was originally proposed for training deep convolutional neural networks. We demonstrate PECT for the Variational Quantum Eigensolver, in which we benchmark unitary coupled-cluster ansatze including UCCSD and k -UpCCGSD, as well as the Low-Depth Circuit Ansatz (LDCA), to estimate ground state energies of molecular systems. We additionally use a layerwise variant of PECT to optimize a hardware-efficient circuit for the Sycamore processor to estimate the ground state energy densities of the one-dimensional Fermi-Hubbard model. From our numerical data, we find that PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).

Physics↗
DOE OSTI2016

An Optimal Parameterization Framework for Infrasonic Tomography of the Stratospheric Winds Using Non-Local Sources

A method is developed to apply acoustic tomography methods to a localized network of infrasound arrays with intention of monitoring the atmosphere state in the region around the network using non-local sources without requiring knowledge of the precise source location or non-local atmosphere state. Closely spaced arrays provide a means to estimate phase velocities of signals that can provide limiting bounds on certain characteristics of the atmosphere. Larger spacing between such clusters provide a means to estimate celerity from propagation times along multiple unique stratospherically or thermospherically ducted propagation paths and compute more precise estimates of the atmosphere state. In order to avoid the commonly encountered complex, multimodal distributions for parametric atmosphere descriptions and to maximize the computational efficiency of the method, an optimal parametrization framework is constructed. This framework identifies the ideal combination of parameters for tomography studies in specific regions of the atmosphere and statistical model selection analysis shows that high quality corrections to the middle atmosphere winds can be obtained using as few as three parameters. Lastly, comparison of the resulting estimates for synthetic data sets shows qualitative agreement between the middle atmosphere winds and those estimated from infrasonic traveltime observations.

58 GEOSCIENCES↗
DOE OSTI2018

Impact of alternative transmission coeffcient parameterizations on Hauser-Feshbach theory

Here, we investigate different formulations of the transmission coefficient T c , including the form implied by Moldauer’s “sum rule for resonance reactions” and the Moldauer-Simonius form. Within these different formulations, we compute the neutron transmission coefficients in the resolved and unresolved resonance regions, allowing a direct comparison with the transmission coefficients computed using an optical model potential. For nuclei for which there are no measured resonances, these approaches allow one to predict the average neutron resonance parameters directly from the optical model and level densities. Some of the approaches are valid in both the strong and weak coupling limits (i.e., any value of the average width and mean level spacing). Finally, both the Moldauer-Simonius and Moldauer’s Sum Rule forms approaches suggest that superradiance, that is, the quantum chaotic enhancement of certain channels, may be a common phenomena in nuclear collisions. Our results suggest why superradiance has been previously overlooked. We apply our approach to neutron reactions on the closed shell 90 Zr nucleus and the mid-shell 197 Au nucleus.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Development of a scalar-based geometric parameterization approach for the crystal structure landscape of dithienylethene-based crystalline solids

Dithienylethenes (DTEs) are a promising class of organic photoswitches that can be used to create crystalline solids with properties controlled by light. However, the ability of DTEs to adopt multiple conformations, only one of which is photoactive, complicates the rational design of these materials. Herein, the synthesis and structural characterization of 19 crystalline solids containing a single DTE molecule are described. A novel D – D analysis of the molecular geometries obtained from rotational potential energy surface calculations and the ensemble of experimental structures were used to construct a crystal landscape for DTE. Of the 19 crystal structures, 17 contained photoinactive DTE rotamers and only 2 were photoactive. These results highlight the challenges associated with the design of these materials. Overall, the D – D analysis described herein provides rapid, effective and intuitive means of linking the molecular structure to photoactivity that could be applied more broadly to afford a general strategy for producing photoactive diarylethene-based crystalline solids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Calibration, modeling, parameterization, and verification of the instrument transfer function of an interferometric microscope

Interferometric microscopes are used to measure surface roughness, from which the computed power spectral density function can be used to extract bandwidth-limited values of the various surface properties, such as root-mean-square (rms) height and slope errors. Measurements with a microscope equipped with different objectives that have an overlapping spatial frequency range usually give different rms results over the common frequency bandwidth. This is a result of different instrument transfer functions (ITFs) that attenuate spatial frequencies by different amounts over the overlapping range. We report on the use of binary pseudo-random array (BPRA) standards to characterize the ITF of an interferometric microscope with the various objectives. We use a simple model of a 1D binary Results show that the spectrum for an undersampled array is a cosine function, rather than a straight line, constant white noise spectrum. We have an analytical model of the ITF that includes the effects of an obscured aperture and defocus, in addition to the usual parameters of numerical aperture, wavelength, and sampling period. In addition, the model includes the effect of aliasing of spatial frequency components beyond the Nyquist back into the sub-Nyquist region. We compare the model PSD predictions to the measurements performed with different objectives. Departures of the measured PSDs from the model predictions indicate that there are higher-order ITF corrections yet to be identified and included in the model.

Takacs, PZ↗
DOE OSTI2015

SU-F-BRD-04: Robustness Analysis of Proton Breast Treatments Using An Alpha-Stable Distribution Parameterization

Purpose: Currently, planning systems allow robustness calculations to be performed, but a generalized assessment methodology is not yet available. We introduce and evaluate a methodology to quantify the robustness of a plan on an individual patient basis. Methods: We introduce the notion of characterizing a treatment instance (i.e. one single fraction delivery) by describing the dose distribution within an organ as an alpha-stable distribution. The parameters of the distribution (shape(α), scale(γ), position(δ), and symmetry(β)), will vary continuously (in a mathematical sense) as the distributions change with the different positions. The rate of change of the parameters provides a measure of the robustness of the treatment. The methodology is tested in a planning study of 25 patients with known residual errors at each fraction. Each patient was planned using Eclipse with an IBA-proton beam model. The residual error space for every patient was sampled 30 times, yielding 31 treatment plans for each patient and dose distributions in 5 organs. The parameters’ change rate as a function of Euclidean distance from the original plan was analyzed. Results: More than 1,000 dose distributions were analyzed. For 4 of the 25 patients the change in scale rate (γ) was considerably higher than the lowest change rate, indicating a lack of robustness. The sign of the shape change rate (α) also seemed indicative but the experiment lacked the power to prove significance. Conclusion: There are indications that this robustness measure is a valuable tool to allow a more patient individualized approach to the determination of margins. In a further study we will also evaluate this robustness measure using photon treatments, and evaluate the impact of using breath hold techniques, and the a Monte Carlo based dose deposition calculation. A principle component analysis is also planned.

60 APPLIED LIFE SCIENCES↗