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Results for “Parameter Dimensionality Reduction”

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Characterization and differentiation of aluminum powders used in improvised explosive devices – Part 1: Proof of concept of the utility of particle micromorphometry

Abstract Aluminum (Al) powders are commonly used in improvised explosive devices as metallic fuels, a component of explosive mixtures. These powders can be obtained readily from industrial‐scale and consumer products, and produced using unsophisticated “kitchen chemistry” techniques. This research demonstrates the potential of automated particle micromorphometry for comparisons between known source and questioned Al powders recovered from IEDs, as well as for insight into the method of Al powder manufacture. Al powder samples were obtained from legitimate manufacturers, and 56 samples were produced “in‐house” from Al‐containing spray paints and ball‐milled Al foils. Transmitted light microscope images of Al powder particles were acquired using an automated stage with automated z‐focus; 17 size and shape parameters were measured for all particles. Approximately 37,000–2,500,000 particles/sample were analyzed using an open‐source statistical package with customized code. Dimensionality reduction was required for processing the large datasets: eight of the 17 measured variables were selected based on inspection of the correlation matrix. Data from four subsamples from each of the 56 samples produced using “in‐house” methods were analyzed using ANOVA to assess the within‐ and between‐sample variation. High within‐sample variation was noted; however, ANOVA and post‐hoc Tukey's honestly significant difference (HSD) tests demonstrated that the between‐sample variation was substantially larger than the within‐sample variation. Each sample could be differentiated from all other samples in the test set. Future experiments will focus on ways to reduce the within‐sample variation, and additional statistical and microanalytical methods to classify sources and confidently constrain the method of Al powder manufacture.

Baldaino, JenaMarie↗
DOE OSTI2015

Systematic characterization of protein folding pathways using diffusion maps: Application to Trp-cage miniprotein

Understanding the mechanisms by which proteins fold from disordered amino-acid chains to spatially ordered structures remains an area of active inquiry. Molecular simulations can provide atomistic details of the folding dynamics which complement experimental findings. Conventional order parameters, such as root-mean-square deviation and radius of gyration, provide structural information but fail to capture the underlying dynamics of the protein folding process. It is therefore advantageous to adopt a method that can systematically analyze simulation data to extract relevant structural as well as dynamical information. The nonlinear dimensionality reduction technique known as diffusion maps automatically embeds the high-dimensional folding trajectories in a lower-dimensional space from which one can more easily visualize folding pathways, assuming the data lie approximately on a lower-dimensional manifold. The eigenvectors that parametrize the low-dimensional space, furthermore, are determined systematically, rather than chosen heuristically, as is done with phenomenological order parameters. We demonstrate that diffusion maps can effectively characterize the folding process of a Trp-cage miniprotein. By embedding molecular dynamics simulation trajectories of Trp-cage folding in diffusion maps space, we identify two folding pathways and intermediate structures that are consistent with the previous studies, demonstrating that this technique can be employed as an effective way of analyzing and constructing protein folding pathways from molecular simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesian inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Efficient Stochastic Inversion Using Adjoint Models and Kernel-PCA

Performing stochastic inversion on a computationally expensive forward simulation model with a high-dimensional uncertain parameter space (e.g. a spatial random field) is computationally prohibitive even with gradient information provided. Moreover, the ‘nonlinear’ mapping from parameters to observables generally gives rise to non-Gaussian posteriors even with Gaussian priors, thus hampering the use of efficient inversion algorithms designed for models with Gaussian assumptions. In this work, we propose a novel Bayesian stochastic inversion methodology, characterized by a tight coupling between a gradient-based Langevin Markov Chain Monte Carlo (LMCMC) method and a kernel principal component analysis (KPCA). This approach addresses the ‘curse-of-dimensionality’ via KPCA to identify a low-dimensional feature space within the high-dimensional and nonlinearly correlated spatial random field. Moreover, non-Gaussian full posterior probability distribution functions are estimated via an efficient LMCMC method on both the projected low-dimensional feature space and the recovered high-dimensional parameter space. We demonstrate this computational framework by integrating and adapting recent developments such as data-driven statistics-on-manifolds constructions and reduction-through-projection techniques to solve inverse problems in linear elasticity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Simulating quantum mechanics with a $\theta$-term and an ’t Hooft anomaly on a synthetic dimension

A topological θ-term in gauge theories, including quantum chromodynamics in 3 +1 dimensions, gives rise to a sign problem that makes classical Monte Carlo simulations impractical. Quantum simulations are not subject to such sign problems and are a promising approach to studying these theories in the future. In the near term, it is interesting to study simpler models that retain some of the physical phenomena of interest and their implementation on quantum hardware. For example, dimensionally reducing gauge theories on small spatial tori produces quantum mechanical models which, despite being relatively simple to solve, retain interesting vacuum and symmetry structures from the parent gauge theories. We consider quantum mechanical particle-on-a-circle models, related by dimensional reduction to the 1 + 1d Schwinger model, that possess a θ-term and realize an ’t Hooft anomaly or global inconsistency at θ = π. These models also exhibit the related phenomena of spontaneous symmetry breaking and instanton-anti-instanton interference in real time. We propose an experimental scheme for the real-time simulation of a particle on a circle with a θ-term and a $\mathbb{Z}_n$ potential using a synthetic dimension encoded in a Rydberg atom. Simulating the Rydberg atom with realistic experimental parameters, we demonstrate that the essential physics can be well captured by the experiment, with expected behavior in the tunneling rate as a function of θ. Similar phenomena and observables can also arise in more complex quantum mechanical models connected to higher-dimensional non-Abelian gauge theories by dimensional reduction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Large structural modulations in the relaxor ferroelectric and intermediate state of strontium rich members (x>0.6) of the Sr{sub x}Ba{sub 1−x}Nb{sub 2}O{sub 6} (sbn) solid solution series

The amplitudes of the positional, occupational and adp modulations of sbn mixed crystals are strongly enhanced for high strontium contents. The increase of structural modulations is accompanied by reduced spontaneous electric polarization largely due to smaller off-center shifts of the niobium atoms. Beyond the room temperature ferroelectric – intermediate transition near x=0.77, anomal large U{sub 33} atomic displacement parameters of the niobium atoms indicate static disorder caused by loss of orientational coupling between residual shifts of Nb atoms in neighboring NbO{sub 6} octahedra. Change of satellite intensities show a reduction from two-dimensional to one-dimensional modulation which is not consistent with tetragonal symmetry. The pseudo-tetragonally twinned crystal structure of sbn82 was refined in the orthorhombic super-space group A2mm(½0γ)000. The apparent tetragonal symmetry of the other investigated sbn samples also seems to be due to pseudo tetragonal twinning with equal twin volumes. The modulations mainly consist of cooperatively tilted NbO{sub 6} octahedra and wave-like ordered incomplete occupation of the largest cation sites (Me2a and b) by Ba{sup 2+} and Sr{sup 2+}. Furthermore, the atomic displacement parameters of the Me2 sites are strongly modulated. - Graphical abstract: Satellite reflections and modulation coefficients in the solid solution series Sr{sub x}Ba{sub 1−x}Nb{sub 2}O{sub 6}. - Highlights: • The modulationed structures are refined for the whole composition range of sbn32–sbn82 in tetragonal and orthorhombic setting. • The amplitudes of positional, occupational and adp modulations increase strongly with the strontium content. • Evidence is presented that the sbn crystals are pseudo tetragonally twinned. • The ferroelectric–intermediate paraelectric transition is not accompanied by a change of symmetry. • Anomal adp of intermediate (non-ferroelectric) sbn82 indicate loss of coupling between off-center shifts of neighboring niobium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Extracting galactic structure parameters from multivariated density estimation

Multivariate statistical analysis, including includes cluster analysis (unsupervised classification), discriminant analysis (supervised classification) and principle component analysis (dimensionlity reduction method), and nonparameter density estimation have been successfully used to search for meaningful associations in the 5-dimensional space of observables between observed points and the sets of simulated points generated from a synthetic approach of galaxy modelling. These methodologies can be applied as the new tools to obtain information about hidden structure otherwise unrecognizable, and place important constraints on the space distribution of various stellar populations in the Milky Way. In this paper, we concentrate on illustrating how to use nonparameter density estimation to substitute for the true densities in both of the simulating sample and real sample in the five-dimensional space. In order to fit model predicted densities to reality, we derive a set of equations which include n lines (where n is the total number of observed points) and m (where m: the numbers of predefined groups) unknown parameters. A least-square estimation will allow us to determine the density law of different groups and components in the Galaxy. The output from our software, which can be used in many research fields, will also give out the systematic error between the model and the observation by a Bayes rule.

Chen, B.↗
DOE OSTI2026

Reaction Diffusion Modelling of 3D Pillar Electrodes in Single-Catalyst CO 2 Reduction Cascades

Effective electrochemical CO 2 reduction to liquid fuels requires that the local catalytic environment facilitates the desired reactivity, yet a microscopic understanding of this environment is difficult to achieve from experiment alone. In this work, a 3D reaction-diffusion model was developed to explore the effects of electrode surface area and local geometry on the performance of a heterogeneous catalyst that performs a two-step CO 2 reduction cascade reaction to CO and then CH 3 OH under aqueous conditions. Kinetic parameters for the model were inspired by experimental results using a cobalt phthalocyanine (CoPc) catalyst. Three-dimensional architectures composed of arrays of square pillars with varying dimensions and either smooth or periodically modulated surfaces were tested, revealing the extent to which geometry modulates the performance of the cascade reactions. Although structural variations modulate local concentration gradients, we find that electrochemically active surface area predominantly governs the overall cascade reaction. Moreover, the results suggest that supersaturation of CO, with concentrations up to ten-fold higher than the equilibrium solubility limit, might be critical for more efficient conversion to CH 3 OH. For any given geometry, the spatially averaged ratio of [CO] to [CO 2 ] is dictated by the electrochemically active surface area and determines the yield of CH 3 OH. For a fixed surface area, geometries that spatially confine the electrolyte yield moderate local [CO] to [CO 2 ] ratios within small volumes. In contrast, less confining geometries result in a broader distribution of local ratios spread over larger volumes, with both configurations yielding the same spatially averaged [CO] to [CO 2 ] ratio. These insights provide valuable design principles—highlighting the critical importance of surface area and possibly CO supersaturation—for engineering advanced electrode architectures that leverage intermediate trapping and CO supersaturation to enhance overall performance in tandem CO 2 reduction systems.

COMSOL↗
DOE OSTI2014

CFD simulation of MSW combustion and SNCR in a commercial incinerator

Highlights: • Presented a CFD scheme for modeling MSW incinerator including SNCR process. • Performed a sensitivity analysis of SNCR operating conditions. • Non-uniform distributions of gas velocity, temperature and NO{sub x} in the incinerator. • The injection position of reagent was critical for a desirable performance of SNCR. • A NSR 1.5 was recommended as a compromise of NO{sub x} reduction rates and NH{sub 3} slip. - Abstract: A CFD scheme was presented for modeling municipal solid waste (MSW) combustion in a moving-grate incinerator, including the in-bed burning of solid wastes, the out-of-bed burnout of gaseous volatiles, and the selective non-catalytic reduction (SNCR) process between urea (CO(NH{sub 2}){sub 2}) and NO{sub x}. The in-bed calculations provided 2-D profiles of the gas–solid temperatures and the gas species concentrations along the bed length, which were then used as inlet conditions for the out-of-bed computations. The over-bed simulations provided the profiles of incident radiation heat flux on the top of bed. A 3-dimensional benchmark simulation was conducted with a 750 t/day commercial incinerator using the present coupling scheme incorporating with a reduced SNCR reduction mechanism. Numerical tests were performed to investigate the effects of operating parameters such as injection position, injection speed and the normalized stoichiometric ratio (NSR) on the SNCR performance. The simulation results showed that the distributions of gas velocity, temperature and NO{sub x} concentration were highly non-uniform, which made the injection position one of the most sensitive operating parameters influencing the SNCR performance of moving grate incinerators. The simulation results also showed that multi-layer injections were needed to meet the EU2000 standard, and a NSR 1.5 was suggested as a compromise of a satisfactory NO{sub x} reduction and reasonable NH{sub 3} slip rates. This work provided useful guides to the design and operation of SNCR process in moving-grate incinerators.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

On the small divergence of laser-driven ion beams from nanometer thick foils

We report on experimental studies of divergence of proton beams from nanometer thick diamond-like carbon foils irradiated by a linearly polarized intense laser with high contrast. Proton beams with extremely small divergence (half angle) of 2° are observed in addition with a remarkably well-collimated feature over the whole energy range, showing one order of magnitude reduction of the divergence angle in comparison to the results from μm thick targets. Similar features are reproduced in two-dimensional particle-in-cell simulations with parameters representing our experiments, indicating a strong influence from the electron density distribution on the divergence of protons. Our comprehensive experimental study reveals grand opportunities for using nm foils in experiments that require high ion flux and small divergence.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Active learning approach to simulations of strongly correlated matter with the ghost Gutzwiller approximation

Quantum embedding (QE) methods such as the ghost Gutzwiller approximation (gGA) offer a powerful approach to simulating strongly correlated systems, but come with the computational bottleneck of computing the ground state of an auxiliary embedding Hamiltonian (EH) iteratively. In this work, we introduce an active learning (AL) framework integrated within the gGA to address this challenge. The methodology is applied to the single-band Hubbard model and results in a significant reduction in the number of instances where the EH must be solved. Through a principal component analysis (PCA), we find that the EH parameters form a low-dimensional structure that is largely independent of the geometric specifics of the systems, especially in the strongly correlated regime. Our AL strategy enables us to discover this low-dimensionality structure on the fly, while leveraging it for reducing the computational cost of gGA, laying the groundwork for more efficient simulations of complex strongly correlated materials. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
NASA NTRS1976

Application of the aerodynamic energy concept to flutter suppression and gust alleviation by use of active controls

The effects of active controls on flutter suppression and gust alleviation of the Arava twin turboprop STOL transport and the Westwind twinjet business transport are investigated. The active control surfaces are introduced in pairs which include, in any chosen wing strip, a 20-percent chord leading-edge control and a 20-percent chord trailing-edge control. Each control surface is driven by a combined linear-rotational sensor system located on the activated strip. The control law is based on the concept of aerodynamic energy and utilizes previously optimized control law parameters based on two-dimensional aerodynamic theory. The best locations of the activated system along the span of the wing are determined for bending-moment alleviation, reduction in fuselage accelerations, and flutter suppression. The effectiveness of the activated system over a wide range of maximum control deflections is also determined. Two control laws are investigated. The first control law utilizes both rigid-body and elastic contributions of the motion. The second control law employs primarily the elastic contribution of the wing and leads to large increases in the activated control effectiveness as compared with the basic control law. The results indicate that flutter speed can be significantly increased (over 70 percent increase) and that the bending moment due to gust loading can be almost totally eliminated by a control system of about 10 to 20 percent span with reasonable control-surface rotations.

Nissim, E.↗
DOE OSTI2015

GIXSGUI : a MATLAB toolbox for grazing-incidence X-ray scattering data visualization and reduction, and indexing of buried three-dimensional periodic nanostructured films

GIXSGUI is a MATLAB toolbox that offers both a graphical user interface and script-based access to visualize and process grazing-incidence X-ray scattering data from nanostructures on surfaces and in thin films. It provides routine surface scattering data reduction methods such as geometric correction, one-dimensional intensity linecut, two-dimensional intensity reshaping etc . Three-dimensional indexing is also implemented to determine the space group and lattice parameters of buried organized nanoscopic structures in supported thin films.

Jiang, Zhang↗
DOE OSTI2018

Cylindrical Damped Solitary Propagation in Superthermal Plasmas

Wave properties of damped solitons in a collisional unmagnetized four-components dusty fluid plasma system contains superthermal distributed electrons, mobile ions, and negative-positive dusty grains have been examined. To study dissipative DIA mode properties, a reductive perturbation (RP) analysis is used under convenient geometrical coordinate transformation, three-dimensional damped Kadomtsev–Petviashvili (3D-CDKP) equation in cylindrical coordinates is obtained. Effects of collisional parameters on damped soliton pulse structures are studied. More specifically, the impact of axial, radial, and polar coordinates with the time on solitary propagation are examined. This investigation may be viable in plasma of the Earth’s mesosphere.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Extending Parsimonious Bayesian Inference

Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Reactive burn model calibration using high-throughput initiation experiments at sub-millimeter length scales

We report a first-of-its-kind model calibration was performed using Sandia National Laboratories’ high-throughput initiation (HTI) experiment for two types of vapor-deposited explosive films consisting of hexanitrostilbene (HNS) or pentaerythritol tetranitrate (PETN). These films exhibit prompt initiation, and they reach steady detonation at sub-millimeter length scales. Following prior work on HNS, we test the hypothesis of approximating these explosive films as fine-grained homogeneous solids with simple Arrhenius kinetics burn models. The model calibration process is described herein using a single-step as well as a two-step Arrhenius rate law, and it consists of systematic parameter sampling leading to a reduction in the model degrees of freedom. Multiple local minima are observed; results are given for seven different optimized parameter sets. Each model set is further evaluated in a two-dimensional simulation of the critical failure thickness for a sustained detonation. Overall, the two-step Arrhenius kinetics model captures the observed behavior for HNS; however, neither model produces a good fit to the PETN data. We hypothesize that the HTI results for PETN correspond to a heterogeneous response, owing to the smaller reaction zone of PETN compared to HNS (i.e., it does not homogenize the fine-grained hot spots as well). Future work should consider using the ignition and growth model for PETN, as well as other reactive burn models such as xHVRB, AWSD, PiSURF, and CREST.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Finite-dimensional approximation for optimal fixed-order compensation of distributed parameter systems

In controlling distributed parameter systems it is often desirable to obtain low-order, finite-dimensional controllers in order to minimize real-time computational requirements. Standard approaches to this problem employ model/controller reduction techniques in conjunction with LQG theory. In this paper we consider the finite-dimensional approximation of the infinite-dimensional Bernstein/Hyland optimal projection theory. This approach yields fixed-finite-order controllers which are optimal with respect to high-order, approximating, finite-dimensional plant models. The technique is illustrated by computing a sequence of first-order controllers for one-dimensional, single-input/single-output, parabolic (heat/diffusion) and hereditary systems using spline-based, Ritz-Galerkin, finite element approximation. Numerical studies indicate convergence of the feedback gains with less than 2 percent performance degradation over full-order LQG controllers for the parabolic system and 10 percent degradation for the hereditary system.

Bernstein, Dennis S.↗
NASA NTRS1991

A parametric study of transonic blade-vortex interaction noise

Several parameters of transonic blade-vortex interactions (BVI) are being studied and some ideas for noise reduction are introduced and tested using numerical simulation. The model used is the two-dimensional high frequency transonic small disturbance equation with regions of distributed vorticity (VTRAN2 code). The far-field noise signals are obtained by using the Kirchhoff method with extends the numerical 2-D near-field aerodynamic results to the linear acoustic 3-D far-field. The BVI noise mechanisms are explained and the effects of vortex type and strength, and angle of attack are studied. Particularly, airfoil shape modifications which lead to noise reduction are investigated. The results presented are expected to be helpful for better understanding of the nature of the BVI noise and better blade design.

Lyrintzis, A. S.↗