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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part is to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i) the Elementary Effect for screening the parameters, and ii) the variance decomposition method for calculating the Sobol indices that quantify both the main and interaction effects. The application of the proposed approach is illustrated with an industrial application with the goal of optimizing a drilling process using a Gaussian laser beam.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Exploring Replica-Exchange Wang-Landau sampling in higher-dimensional parameter space

We considered a higher-dimensional extension for the replica-exchange Wang-Landau algorithm to perform a random walk in the energy and magnetization space of the two-dimensional Ising model. This hybrid scheme combines the advantages of Wang-Landau and Replica-Exchange algorithms, and the one-dimensional version of this approach has been shown to be very efficient and to scale well, up to several thousands of computing cores. This approach allows us to split the parameter space of the system to be simulated into several pieces and still perform a random walk over the entire parameter range, ensuring the ergodicity of the simulation. Previous work, in which a similar scheme of parallel simulation was implemented without using replica exchange and with a different way to combine the result from the pieces, led to discontinuities in the final density of states over the entire range of parameters. From our simulations, it appears that the replica-exchange Wang-Landau algorithm is able to overcome this diculty, allowing exploration of higher parameter phase space by keeping track of the joint density of states.

Valentim, Alexandra↗
DOE OSTI2023

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

Abstract Simultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.

14 SOLAR ENERGY↗
DOE OSTI2020

Deuterium retention in Be-D co-deposits formed over an ITER relevant parameter space

Be-D co-deposit samples are produced over an extended range of plasma-material interaction parameters reflective of the conditions expected in ITER deposition dominant locations. Deposition temperature, D impact energy, atomic Be deposition rate, and D2 neutral pressure were varied in the ranges 373–623 K, ~2–100 eV, 1–10 × 10 19 m 2 s –1 , and 0.27–13.3 Pa respectively. Thermal desorption spectrometry was carried out on samples, subsequently, to measure the D release profile and retention. In this work, it was found that the D retention in samples agrees well with the existing GDT D/Be scaling over the extended parameter space, and that increased D impact energy and D2 pressure can lead to the observation of a sharp low-temperature release peak at ~440 K in desorption data. Desorption data without the presence of the sharp release mechanism are modeled with TMAP-7 utilizing three traps of de-trapping energies, 0.8, 0.9 and 1.0 eV.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Efficiently enclosing the compact binary parameter space by singular-value decomposition

Gravitational-wave searches for the merger of compact binaries use matched filtering as the method of detecting signals and estimating parameters. Such searches construct a fine mesh of filters covering a signal parameter space at high density. Previously it has been shown that singular-value decomposition can reduce the effective number of filters required to search the data. Here we study how the basis provided by the singular-value decomposition changes dimension as a function of template-bank density. We will demonstrate that it is sufficient to use the basis provided by the singular-value decomposition of a low-density bank to accurately reconstruct arbitrary points within the boundaries of the template bank. Since this technique is purely numerical, it may have applications to interpolating the space of numerical relativity waveforms.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Multimodal parameter spaces of a complex multi-channel neuron model

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A new parameter space study of the fermionic cold dark matter model

We consider the standard model (SM) extended by a gauge singlet fermion as cold dark matter (SFCDM) and a gauge singlet scalar (singlet Higgs) as a mediator. The parameter space of the SM is enlarged by seven new ones. We obtain the total annihilation cross section of singlet fermions to the SM particles and singlet Higgs at tree level. Regarding the relic abundance constraint obtained by WMAP observations, we study the dependency on each parameter separately, for dark matter masses up to 1 TeV. In particular, the coupling of SFCDM to singlet Higgs g{sub s}, the SFCDM mass m{sub ψ}, the second Higgs mass m{sub h{sub 2}}, and the Higgs bosons mixing angel θ are investigated accurately. Three other parameters play no significant role. For a maximal mixing of Higgs bosons or at resonances, g{sub s} is applicable for the perturbation theory at tree level. We also obtain the scattering cross section of SFCDM off nucleons and compare our results with experiments which have already reported data in this mass range; XENON100, LUX, COUPP and PICASSO collaborations. Our results show that the SFCDM is excluded by these experiments for choosing parameters which are consistent with perturbation theory and relic abundance constraints.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

GMC COLLISIONS AS TRIGGERS OF STAR FORMATION. I. PARAMETER SPACE EXPLORATION WITH 2D SIMULATIONS

We utilize magnetohydrodynamic (MHD) simulations to develop a numerical model for giant molecular cloud (GMC)–GMC collisions between nearly magnetically critical clouds. The goal is to determine if, and under what circumstances, cloud collisions can cause pre-existing magnetically subcritical clumps to become supercritical and undergo gravitational collapse. We first develop and implement new photodissociation region based heating and cooling functions that span the atomic to molecular transition, creating a multiphase ISM and allowing modeling of non-equilibrium temperature structures. Then in 2D and with ideal MHD, we explore a wide parameter space of magnetic field strength, magnetic field geometry, collision velocity, and impact parameter and compare isolated versus colliding clouds. We find factors of ∼2–3 increase in mean clump density from typical collisions, with strong dependence on collision velocity and magnetic field strength, but ultimately limited by flux-freezing in 2D geometries. For geometries enabling flow along magnetic field lines, greater degrees of collapse are seen. We discuss observational diagnostics of cloud collisions, focussing on {sup 13}CO(J = 2–1), {sup 13}CO(J = 3–2), and {sup 12}CO(J = 8–7) integrated intensity maps and spectra, which we synthesize from our simulation outputs. We find that the ratio of J = 8–7 to lower-J emission is a powerful diagnostic probe of GMC collisions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A variational approach for dissipative quantum transport in a wide parameter space

Recent development of theoretical method for dissipative quantum transport has achieved notable progresses in the weak or strong electron-phonon coupling regime. However, a generalized theory for dissipative quantum transport in a wide parameter space had not been established. In this work, a variational polaron theory for dissipative quantum transport in a wide range of electron-phonon coupling is developed. The optimal polaron transformation is determined by the optimization of the Feynman-Bogoliubov upper bound of free energy. The free energy minimization ends up with an optimal mean-field Hamiltonian and a minimal interaction Hamiltonian. Hence, second-order perturbation can be applied to the transformed system, resulting in an accurate and efficient method for the treatment of dissipative quantum transport with different electron-phonon coupling strength. Numerical benchmark calculation on a single site model coupled to one phonon mode is presented.

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

A Grand Scan of the pMSSM Parameter Space for Snowmass 2021

We present a flexible framework for interpretation of SUSY sensitivity studies for future colliders in terms of the phenomenological minimal supersymmetric standard model (pMSSM). We perform a grand scan of the pMSSM 19-dimensional parameter space that covers the accessible ranges of many collider scenarios, including electron, muon, and hadron colliders at a variety of center of mass energies. This enables comparisons of sensitivity and complementarity across different future experiments, including both colliders and precision measurements in the Cosmological and Rare Frontiers. The details of the scan framework are discussed, and the impact of future precision measurements on Higgs couplings, the anomalous muon magnetic moment, and dark matter quantities is presented. The next steps for this ongoing effort include performing studies with simulated events in order to quantitatively assess the sensitivity of selected future colliders in the context of the pMSSM.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Exploration of the parameter space of quasisymmetric stellarator vacuum fields through adjoint optimisation

Optimising stellarators for quasisymmetry leads to strongly reduced collisional transport and energetic particle losses compared with unoptimised configurations. Although stellarators with precise quasisymmetry have been obtained in the past, it remains unclear how broad the parameter space is where good quasisymmetry may be achieved. We study the range of aspect ratios and rotational transform values for which stellarators with excellent quasisymmetry on the boundary can be obtained. A large number of Fourier harmonics is included in the boundary representation, which is made computationally tractable by the use of adjoint methods to enable fast gradient-based optimisation and by the direct optimisation of vacuum magnetic fields, which converge more robustly compared with solutions from magnetohydrostatics. Several novel configurations are presented, including stellarators with record levels of quasisymmetry on a surface, three field period quasiaxisymmetric stellarators with substantial magnetic shear, and compact quasisymmetric stellarators at low aspect ratios similar to tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Extending the limits of powder diffraction analysis: diffraction parameter space, occupancy defects, and atomic form factors

Although the determination of site occupancies is often a major goal in Rietveld refinement studies, the accurate refinement of site occupancies is exceptionally challenging due to the many correlations and systematic errors that have a hidden impact on the final refined occupancy parameters. Through the comparison of results independently obtained from neutron and synchrotron powder diffraction, improved approaches capable of detecting occupancy defects with an exceptional sensitivity of 0.1% (absolute) in the class of layered NMC Li-ion battery cathode materials have been developed. A new method of visualizing the diffraction parameter space associated with crystallographic site scattering power through the use of f* diagrams is described and this method is broadly applicable to ternary compounds. The f* diagrams allow the global minimum fit to be easily identified and also permit a robust determination of the number and type of occupancy defects within a structure. Through a comparison of neutron and X-ray diffraction results, a systematic error in the synchrotron results was identified using f* diagrams for a series of NMC compounds. Using neutron diffraction data as a reference, this error was shown to specifically result from problems associated with the neutral oxygen X-ray atomic form factor and could be eliminated by using the ionic O 2- form factor for this anion while retaining the neutral form factors for cationic species. The f* diagram method offers a new opportunity to experimentally assess the quality of atomic form factors through powder diffraction studies on chemically related multi-component compounds.

25 ENERGY STORAGE↗
DOE OSTI2021

Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

Abstract The density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest U correction. PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning

Abstract Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information known about the beam response is scarce. Here in this work, we describe an adaptation of the popular Bayesian optimization algorithm, which enables a turn-key exploration of input parameter spaces. Our algorithm replaces the need for parameter scans while minimizing prior information needed about the measurement’s behavior and associated measurement constraints. We experimentally demonstrate that our algorithm autonomously conducts an adaptive, multi-parameter exploration of input parameter space, potentially orders of magnitude faster than conventional grid-like parameter scans, while making highly constrained, single-shot beam phase-space measurements and accounts for costs associated with changing input parameters. In addition to applications in accelerator-based scientific experiments, this algorithm addresses challenges shared by many scientific disciplines, and is thus applicable to autonomously conducting experiments over a broad range of research topics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian

This repository contains both simulated and experimental structure factor data for the data challenge involving the inverse scattering problem. The simulated data were generated during a machine-learning-assisted optimization routine described in ref[1]. The experimental structure factor was measured on a rare-earth oxide, Dy2Ti2O7 using diffuse neutron scattering from time-of-flight techniques on the CORELLI instrument at the Spallation Neutron Source, Oak Ridge National Laboratory. A Metropolis Monte Carlo code implemented to run in a High-performance computing setting was used to calculated simulated structure factors for the spin-ice Hamiltonian at 680 mK, which is the same temperature as for the experimental data. The total size of all the files in this repository is 5.12 GB. A detailed description of the files is given below. ExperimentalData_630mK.dat – A linearized version of 3-dimensional experimental data of size 61×81×21. This data was processed to remove an estimation of non-magnetic background, including nuclear scattering signal and instrumentation background. Parameters.dat – 6700 samples were evaluated over the 4-dimensional parameter space (J_1, J_2, J_3 and J_(3^' )). There is an additional parameter, D in the spin Hamiltonian to mimic the dipolar interaction between magnetic ions. However, this parameter, D was fixed to a value determined by prior work. This file contains five columns for the parameters J_1, J_2, J_3, J_(3^' ) and D respectively. 3D_Simulation_Data.dat – The simulated structure factor, S(Q) data are included in this file. Each raw contains a linearized array of 3D volumes of S(Q) calculated for the parameter set given in the corresponding row of the file Parameters.dat. The size of the volume data was matched to the experimental data. Qx(h,-h,0).dat, Qy(k,k,-2k).dat, Qz(l,l,l).dat – These files contain the h, k, and l values along with the reciprocal vectors [h,-h,0], [k,k,-2k] and [l,l,l] respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exploring Changes in Quasar Spectral Energy Distributions across C IV Parameter Space

We examine the UV/X-ray properties of 1378 quasars in order to link empirical correlations to theoretical models of the physical mechanisms dominating quasars as a function of mass and accretion rate. The clarity of these correlations is improved when (1) using C IV broad emission line equivalent width (EQW) and blueshift (relative to systemic) values calculated from high signal-to-noise ratio reconstructions of optical/UV spectra and (2) removing quasars expected to be absorbed based on their UV/X-ray spectral slopes. In addition to using the traditional C IV parameter space measures of C IV EQW and blueshift, we define a "C IV ∥ distance" along a best-fit polynomial curve that incorporates information from both C IV parameters. We find that the C IV ∥ distance is linearly correlated with both the optical-to-X-ray slope, α ox , and broad-line He II EQW, which are known spectral energy distribution indicators, but does not require X-ray or high spectral resolution UV observations to compute. The C IV ∥ distance may be a better indicator of the mass-weighted accretion rate, parameterized by L/L Edd , than the C iv EQW or blueshift alone, as those relationships are known to break down at the extrema. Conversely, there is only a weak correlation with the X-ray energy index (Γ), an alternate L/L Edd indicator. We find no X-ray or optical trends in the direction perpendicular to the C IV distance that could be used to reveal differences in accretion disk, wind, or corona structure that could be widening the C IV EQW–blueshift distribution. A different parameter (such as metallicity) not traced by these data must come into play.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Design Parameter Space for a High Pressure Optimized Dense Plasma Focus Operating with Deuterium

The potential of the Dense Plasma Focus (DPF) for industrial applications in many fields is well recognized, although yet to be realized in practice. Particularly attractive is the possibility of its use as inexpensive industrial source of nuclear reactions for diverse high value applications such as fast pulsed neutron radiography of hydrogenous materials, non-intrusive neutron interrogation of concealed organic contraband and rapid production of short lived radioisotopes for medical diagnostics and therapy. Recently, it has been suggested that it may be possible to operate the DPF efficiently in a High-Pressure-Optimized (HPO) mode. This paper explores the design parameter space for such HPO-DPF based on the revised Resistive Gratton-Vargas (RGV) model with a view to identify a practicable set of system parameters and their scaling. The current waveform predicted by the revised RGV model for the chosen set of parameters is fitted to the Lee model to estimate the likely neutron yield.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2017

The carbon nanotube formation parameter space: data mining and mechanistic understanding for efficient resource use

Carbon nanotube (CNT) synthesis via catalytic chemical vapor deposition is relatively energy consumptive and among the least efficient reactions with respect to carbon conversion efficiency. Interestingly, these processes can be fed using a diverse set of hydrocarbon starting materials, including methane, ethylene, and acetylene, over a wide range of operating temperatures and carbon-to-hydrogen feedstock ratios. Mapping the parameter space for successful CNT growth through data extraction from published literature illuminated the most energy- and material- efficient synthetic pathways in practice to date and provided insights on thermodynamic limitation of CNT growth (i.e., the fundamental mechanisms of CNT formation). Further experimental investigations confirmed that emergent trends in the literature were the result of physicochemical constraints on the process rather than behavioral inertia in the community. The initiation temperatures for CNT growth from acetylene, ethylene, and methane feedstocks via direct experimentation were 550, 700, and 950 °C, respectively, consistent with the trend in literature-extracted mean optima (642 ± 128, 739 ± 82, and 858 ± 125 °C, respectively). These relative temperatures are consistent with a universal CNT growth mechanism, wherein all carbon feedstocks are converted to alkyne-containing species that serve as direct precursors for CNT growth. Mitigating this step with rational carbon precursor delivery, rather than relying on heat to generate the most reactive precursors in situ, could largely reduce the environmental burdens in CNT manufacturing. Indeed, manipulating the starting gas-phase composition and minimizing the thermal treatment through the use of C 2 H 2 increased carbon conversion yield by a factor of more than 10 compared to C 2 H 4 , and, consequently, should minimize hazardous volatile organic compound and polycyclic aromatic hydrocarbon emissions. Finally, the methodology utilized in this study is transferrable to guide the green synthesis of other materials and should be automated in the future for high-throughput screening of the vast process chemistry literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗