SEARCH · Engineering Papers
Results for “Elastic Shape Analysis”
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.
VEESA R package
SAND2024-04584O R package for applying the VEESA pipeline method is a technique used for explainable machine learning with functional data. The VEESA pipeline makes use of the elastic-shape analysis framework for functional data. It also implements functional principal component analysis and permutation feature importance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.
Regional Source-Type Discrimination Using Nonlinear Alignment Algorithms
The discrimination problem in seismology aims to accurately classify different underground source types based on local, regional, and/or teleseismic observations of ground motion. Typical discriminant approaches are rooted in fundamental, physics-based differences in radiation pattern or wave excitation, which can be frequency-dependent and may not make use of the full waveform. In this article, we explore whether phase and amplitude distances derived from dynamic time warping (DTW) and elastic shape analysis (ESA) can inform event discrimination. We demonstrate the ability to distinguish underground point sources using synthetic waveforms calculated for a 1D Earth model and various source mechanisms. We then apply the method to recorded data from events in the Korean Peninsula, which includes declared nuclear explosions, a collapse event, and naturally occurring earthquakes. Phase and amplitude distances derived from DTW and ESA are then used to classify the event types via dendrogram and k-nearest-neighbor clustering analyses. Using information from the full waveform, we show how different underground sources can be distinguished at regional distances. We highlight the potential of these nonlinear alignment algorithms for discrimination and comment on ways we can extend the framework presented here.
Semi-supervised Bayesian Low-shot Learning
Deep neural networks (NNs) typically outperform traditional machine learning (ML) approaches for complicated, non-linear tasks. It is expected that deep learning (DL) should offer superior performance for the important non-proliferation task of predicting explosive device configuration based upon observed optical signature, a task which human experts struggle with. However, supervised machine learning is difficult to apply in this mission space because most recorded signatures are not associated with the corresponding device description, or “truth labels.” This is challenging for NNs, which traditionally require many samples for strong performance. Semi-supervised learning (SSL), low-shot learning (LSL), and uncertainty quantification (UQ) for NNs are emerging approaches that could bridge the mission gaps of few labels and rare samples of importance. NN explainability techniques are important in gaining insight into the inferential feature importance of such a complex model. In this work, SSL, LSL, and UQ are merged into a single framework, a significant technical hurdle not previously demonstrated. Exponential Average Adversarial Training (EAAT) and Pairwise Neural Networks (PNNs) are chosen as the SSL and LSL methods of choice. Permutation feature importance (PFI) for functional data is used to provide explainability via the Variable importance Explainable Elastic Shape Analysis (VEESA) pipeline. A variety of uncertainty quantification approaches are explored: Bayesian Neural Networks (BNNs), ensemble methods, concrete dropout, and evidential deep learning. Two final approaches, one utilizing ensemble methods and one utilizing evidential learning, are constructed and compared using a well-quantified synthetic 2D dataset along with the DIRSIG Megascene.
Assessing Dynamic Time Warping Techniques for Discriminating Seismic Sources at Local and Regional Distances
Effective monitoring of seismic explosions and hazard assessment relies heavily on the accurate discrimination of underground seismic sources. This study investigates the application of novel nonlinear alignment techniques, specifically Dynamic Time Warping (DTW), for event-type discrimination at regional and local distances. Building on prior research that used DTW and Elastic Shape Analysis (ESA) in discrimination at regional distances, we evaluate the performance of recently developed variants of DTW, including a method that employs Pearson cross-correlation as a measure of warping distance and a time distortion coefficient that quantifies the type and degree of time distortion between signals. By analyzing observational datasets that include different source types, we assess the performance of these approaches for realistic monitoring scenarios. Specifically, we consider a dataset recorded at regional distances in the Korean Peninsula and a local-distance subset from the Unconstrained Utah Event Bulletin catalog to evaluate DTW-based discrimination across multiple distance scales. Additionally, we introduce the maximum cross-correlations of warped waveforms as a similarity metric for event classification. Through hierarchical cluster analysis and dendrogram interpretation, we present our findings, highlighting the strengths and limitations of these techniques in seismic event classification.
Explainable Machine Learning for Functional Data
Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).
Shape design sensitivity analysis and optimization of three dimensional elastic solids using geometric modeling and automatic regridding
An automatic regridding method and a three dimensional shape design parameterization technique were constructed and integrated into a unified theory of shape design sensitivity analysis. An algorithm was developed for general shape design sensitivity analysis of three dimensional eleastic solids. Numerical implementation of this shape design sensitivity analysis method was carried out using the finite element code ANSYS. The unified theory of shape design sensitivity analysis uses the material derivative of continuum mechanics with a design velocity field that represents shape change effects over the structural design. Automatic regridding methods were developed by generating a domain velocity field with boundary displacement method. Shape design parameterization for three dimensional surface design problems was illustrated using a Bezier surface with boundary perturbations that depend linearly on the perturbation of design parameters. A linearization method of optimization, LINRM, was used to obtain optimum shapes. Three examples from different engineering disciplines were investigated to demonstrate the accuracy and versatility of this shape design sensitivity analysis method.
Three-dimensional finite strip analysis of elastic solids
Finite element shape functions were combined with beam eigenfunctions to formulate three-dimensional finite strips. Because of the orthogonality of the beam functions, three-dimensional problems were reduced to a series of two-dimensional problems, often with stiffness matrices of very narrow bandwidth. Isoparametric and high order finite element shape functions were used in the formulation of the 3-D finite strips. Numerical examples such as the static and free vibration analyses of simply supported thick plates are presented. Results were compared with existing solutions and good agreements were obtained in all cases.
Sensitivity analysis of a wing aeroelastic response
The present shape-sensitivity analysis of wing aeroelastic response proceeds from aeroelastic response sensitivities obtained on the basis of the aerodynamic performance valid for high aspect ratio wings in subsonic, subcritical flow. Attention is given to the shape sensitivity of various static aeroelastic responses; the formulation is general, and assumes that, for a given shape and elastic deformation, the aerodynamic analysis will furnish the distribution of the pressure and the pressure sensitivity derivatives with respect to the shape parameters of interest. Wing displacements are obtained by means of an iterative scheme.
Investigating Some Technical Issues on Cohesive Zone Modeling of Fracture
This study investigates some technical issues related to the use of cohesive zone models (CZMs) in modeling fracture processes. These issues include: why cohesive laws of different shapes can produce similar fracture predictions; under what conditions CZM predictions have a high degree of agreement with linear elastic fracture mechanics (LEFM) analysis results; when the shape of cohesive laws becomes important in the fracture predictions; and why the opening profile along the cohesive zone length needs to be accurately predicted. Two cohesive models were used in this study to address these technical issues. They are the linear softening cohesive model and the Dugdale perfectly plastic cohesive model. Each cohesive model constitutes five cohesive laws of different maximum tractions. All cohesive laws have the same cohesive work rate (CWR) which is defined by the area under the traction-separation curve. The effects of the maximum traction on the cohesive zone length and the critical remote applied stress are investigated for both models. For a CZM to predict a fracture load similar to that obtained by an LEFM analysis, the cohesive zone length needs to be much smaller than the crack length, which reflects the small scale yielding condition requirement for LEFM analysis to be valid. For large-scale cohesive zone cases, the predicted critical remote applied stresses depend on the shape of cohesive models used and can significantly deviate from LEFM results. Furthermore, this study also reveals the importance of accurately predicting the cohesive zone profile in determining the critical remote applied load.
Atomistic Simulations of the Elastic Compression of Platinum Nanoparticles
Abstract The elastic behavior of nanoparticles depends strongly on particle shape, size, and crystallographic orientation. Many prior investigations have characterized the elastic modulus of nanoscale particles using experiments or simulations; however their reported values vary widely depending on the methods for measurement and calculation. To understand these discrepancies, we used classical molecular dynamics simulation to model the compression of platinum nanoparticles with two different polyhedral shapes and a range of sizes from 4 to 20 nm, loaded in two different crystal orientations. Multiple standard methods were used to calculate the elastic modulus from stress-vs-strain data for each nanoparticle. The magnitudes and particle-size dependence of the resulting moduli varied with calculation method and, even for larger nanoparticles where bulk-like behavior may be expected, the effective elastic modulus depended strongly on shape and orientation. Analysis of per-atom stress distributions indicated that the shape- and orientation-dependence arise due to stress triaxiality and inhomogeneity across the particle. When the effective elastic modulus was recalculated using a representative volume element in the center of a large nanoparticle, the elastic modulus had the expected value for each orientation and was shape independent. It is only for single-digit nanoparticles that meaningful differences emerged, where even the very center of the particle had a lower modulus due to the effect of the surface. These findings provide better understanding of the elastic properties of nanoparticles and disentangle geometric contributions (such as stress triaxiality and spatial inhomogeneity) from true changes in elastic properties of the nanoscale material.
An experimental-theoretical study of free vibrations of plates on elastic point supports
A theoretical and experimental study is made to investigate the effect on plate vibrations of varying the stiffness of corner elastic point supports. A theoretical model is developed using a Rayleigh-Ritz analysis which approximates the plate mode shapes as products of free-free beam modes. The elastic point supports are modelled both as massless translational springs, and springs with tip masses. The tip masses are included to better represent the experimental supports. An experiment is constructed using the bending stiffness of horizontal beams to support a square plate at its four corners. The stiffness of these supports can be varied over such a range that the plate fundamental frequency is lowered to 40% of the rigid support frequency. The variation with support stiffness of the frequencies of the first eight plate modes is measured, and compared with the theoretical results. The plate mode shapes for rigid supports are analyzed using holographic interferometry. There is excellent agreement between the theoretical and experimental results, except for high plate modes where the theoretical model is demonstrated to be inadequate.
A life comparison of tube and channel cooling passages for thrust chambers
The life analysis used to compare copper tubes and milled copper channels for rocket engine cooling passages is described. Copper tubes were chosen as a possible replacement for the existing milled copper channel configuration because (1) they offer increased surface area for additional enthalpy extraction; (2) they have ideal pressure vessel characteristics; and (3) the shape of the tube is believed to allow free expansion, thus accommodating the strain resulting from thermal expansion. The analysis was a two-dimensional elastic-plastic comparison, using a finite element method, to illustrate that, under the same thermal and pressure loading, the compliant shape of the tube increases the life of the chamber. The analysis indicates that for a hot-gas-side-wall temperature of 100 F the critical strain decreases from 1.25 percent in the channel to 0.94 percent in the tube. Since the life of rocket thrust chambers is most often limited by cyclic strain or strain range, this decrease corresponds to an expected tube life which is nearly twice the channel life.
Stress analysis of spacecraft parachutes using finite elements and large deformation theory
Spacecraft parachute stress analysis, using finite elements with nonlinear elastic properties to obtain shape and load distribution
BEST3D user's manual: Boundary Element Solution Technology, 3-Dimensional Version 3.0
The theoretical basis and programming strategy utilized in the construction of the computer program BEST3D (boundary element solution technology - three dimensional) and detailed input instructions are provided for the use of the program. An extensive set of test cases and sample problems is included in the manual and is also available for distribution with the program. The BEST3D program was developed under the 3-D Inelastic Analysis Methods for Hot Section Components contract (NAS3-23697). The overall objective of this program was the development of new computer programs allowing more accurate and efficient three-dimensional thermal and stress analysis of hot section components, i.e., combustor liners, turbine blades, and turbine vanes. The BEST3D program allows both linear and nonlinear analysis of static and quasi-static elastic problems and transient dynamic analysis for elastic problems. Calculation of elastic natural frequencies and mode shapes is also provided.
Improvements to the SADSAM computer program for aeroelasticity analysis
A computer program for calculating the elastic element forces and strain energy distribution for normal mode shapes is presented. The subjects discussed are: (1) revised aerodynamic force representation, (2) flap aerodynamics, (3) chordwise motions and aerodynamic forces, (4) wing-tail interaction, and (5) gust analysis. An aircraft configuration used in sample gust analysis is included.
Approximating the stress field within the unit cell of a fabric reinforced composite using replacement elements
This report concerns the prediction of the elastic moduli and the internal stresses within the unit cell of a fabric reinforced composite. In the proposed analysis no restrictions or assumptions are necessary concerning yarn or tow cross-sectional shapes or paths through the unit cell but the unit cell itself must be a right hexagonal parallelepiped. All the unit cell dimensions are assumed to be small with respect to the thickness of the composite structure that it models. The finite element analysis of a unit cell is usually complicated by the mesh generation problems and the non-standard, adjacent-cell boundary conditions. This analysis avoids these problems through the use of preprogrammed boundary conditions and replacement materials (or elements). With replacement elements it is not necessary to match all the constitutional material interfaces with finite element boundaries. Simple brick-shaped elements can be used to model the unit cell structure. The analysis predicts the elastic constants and the average stresses within each constituent material of each brick element. The application and results of this analysis are demonstrated through several example problems which include a number of composite microstructures.