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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).
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.
RUScal : Software for the analysis of resonant ultrasound spectroscopy measurements
Resonant ultrasound spectroscopy is used to nondestructively measure the elastic resonances of small solids to elucidate the material's elastic properties or other qualities like size, shape, or composition. In this work, we introduce the software RUScal for the purpose of determining elastic properties by analyzing the eigenfrequencies of solid specimens with common shapes, such as rectangular parallelepipeds, cylinders (solid and hollow tube), ellipsoids, and octahedrons, as well as irregularly shaped ellipsoids that can be described analytically. All symmetry classes are supported, from isotropic to triclinic, along with the option to add or remove up to three orthogonal mirror planes as well as the ability to reorient the crystal axes with respect the sample edges via Euler angles. Additional features include tools to help find initial sets of elastic constants, including grid exploration and Monte Carlo methods, a tool to analyze frequencies as a function of sample length or crystal orientation, an error analysis tool to assess fit quality, and formatting of the input and output files for batch fitting, e.g., as a function of temperature. This software was validated with published resonant ultrasound spectroscopy data for various materials, shapes, and symmetries with noted improvements in calculation time compared to finite element methods.
Support of the Development of EPP Plus SMT Design Method (FY2020 Test Results)
Experiments in support of the development of the integrated Elastic–Perfectly Plastic (EPP) analysis and Simplified Model Test (SMT) design methodology continued in FY 2020. The previously developed single-bar SMT (SBSMT) technique was successfully extended to tubular shaped Alloy 617 specimens for evaluation of the effect of primary pressure load on SMT creep-fatigue (CF) properties at 950°C. A series of pressurized SMT tests were performed with various elastic follow-up factors and strain ranges. The results show that the primary pressure load reduces the SMT CF cycles. The reduction of SMT CF life due to primary load was found to be dependent on strain ranges, elastic follow up and test temperatures. These results are crucial for verification of the EPP strain range analysis in the EPP+SMT evaluation procedure. Additionally, hold time effect on the SMT CF life cycles is a determining factor for generating the EPP+SMT CF design curves. Lack of test data in the high cycle and low strain range region results in major uncertainty in developing the design curves. In FY2020, experiments were designed, and testing started on Alloy 617 at 950°C to fill the gap in experimental data. The on-going CF and additional SMT CF with elastic follow-up at low strain ranges will generate the information needed to verify the methods for extrapolation to long hold time and finalize the EPP+SMT design method.
The effect of elastic strains on the adsorption energy of H, O, and OH in transition metals
The influence of elastic strains on the adsorption of H, O, and OH on the (111) surfaces of 8 fcc (Ni, Cu, Pd, Ag, Pt, Au, Rh, Ir) and on the (0001) surfaces of 3 hcp (Co, Zn, Cd) transition metals was analyzed by means of density functional theory calculations. To this end, surface slabs were subjected to different strain states (uniaxial, biaxial, shear, and a combination of them) up to strains dictated by the mechanical stability limits indicated by phonon calculations. It was found that the adsorption energy followed the predictions of the d-band theory but – surprisingly – the variations in the adsorption energy only depended on the area of the adsorption hole and not on the particular elastic strain tensor applied to achieve this area. The analysis of the electronic structure showed that the applied strains did not modify the shape of the Projected Density of States (PDOS) of the d-orbitals of the transition metals but only led to a shift in the energy levels. Moreover, the presence of the adsorbates on the surfaces led to negligible changes in the PDOS. Thus, the adsorption energies were a function of the Fermi energy which in turn was associated with the change of the area of the adsorption through a general linear law that was valid for all metals. Furthermore, the information in this paper allows the immediate and accurate estimation of the effect of any elastic strain on the adsorption energies of H, O, and OH in 11 transition metals with more than half-filled d-orbitals.
Bifurcation Buckling Analysis and Non-Linear Collapse Analysis of Teardrop Shaped Vacuum Chamber
The Spallation Neutron Source (SNS) accelerator is being upgraded to increase the beam power from 1.4MW at 1GeV to 2.8MW at 1.3GeV. The currents in the middle two injection chicane magnets cannot simply be scaled up to accommodate the increased injection energy of 1.3GeV due to potential excessive H- stripping; the magnets must be replaced with longer, lower-field magnets and the associated vacuum chambers need to be redesigned. A new teardrop-shaped vacuum chamber was initially designed to accommodate the new magnets and the updated beam paths and instrumentation. This paper focuses on the structural stability study of the teardrop shape vacuum chamber based on buckling analysis. Protection against collapse from buckling according to the ASME BPVC requirement has been evaluated in depth. First, a Type-1 bifurcation buckling analysis using a linear eigenvalue solution to determine the critical load factor was performed. Subsequently, a Type-3 nonlinear collapse analysis was conducted using the static Riks method with elastic-plastic material properties and imperfections explicitly considered in the model geometry. The critical buckling load for the teardrop shape vacuum chamber was confidently estimated based upon this two-stage approach.
Shape memory alloy engine for high efficiency low-temperature gradient thermal to electrical conversion
More than half of the energy generated worldwide is lost as unused thermal energy because of the lack of efficient methodology for harnessing the low-grade heat. In this paper, we demonstrate that shape-memory alloy can be an effective mechanism for recovering low-grade heat. Shape memory alloys exhibit thermally induced martensite to austenite phase transformation and super-elasticity (stress-induced martensitic transformation). Employing these two characteristics, we demonstrate a thermal engine for harnessing waste energy through all modes of heat transfer: convection, conduction, and radiation. In this work, we performed material and heat transfer analysis for achieving high frequency, sustainable and efficient operation of our engine. An optimized shape memory alloy engine generated 36 W per kilogram or 234 kW of electricity per cubic meter of active material. A continuous three-day operation of several SMA engines could generate 7.2 kWh of electricity when installed on a 500 m long hot pipe network. This generated power can potentially reduce the carbon footprint by 5.1 kg of CO 2 illustrating the promise of this technology for addressing climate change.
Assessment of mechanical, thermal properties and crystal shapes of monoclinic tricalcium silicate from atomistic simulations
The two most common polymorphs in industrial alite, M1 and M3, were characterized at the molecular scale. Different methods were employed and discussed to assess mechanical properties and specific heat of both polymorphs. The calculated homogenized elastic moduli and specific heat were found in good agreement with experimental measurements. A comparative analysis of spacial Youngs modulus reveal isotropic and anisotropic spacial distribution for M{sub 1} and M{sub 3} respectively. A more isotropic compressive strength is also reported for M{sub 1} when compared to M{sub 3} polymorph. Cleavage energies computation allowed to proposed equilibrium shapes for both polymorph, with significant differences. While the lowest cleavage energies were found along (100) and (001) for both polymorphs, the constructed M1 crystal possesses 3 independent facets, against seven for the M3 polymorph.
Experimental Verification of Pulse Shaping in Elastic Metamaterials Under Impact Excitation
Here, we present experimental verification of pulse shaping in elastic metamaterials together with a procedure to design, fabricate, and verify metamaterial pulse shapers under impact excitation. The split Hopkinson pressure bar (SHPB) test, a fundamental dynamic test introduced more than 70 years ago, often incorporates pulse shaping as a means to alter a stress wave, providing the primary motivation for the presented study. Elastic metamaterials hold promise for enhancing conventional pulse shaping abilities and improving capabilities of the SHPB test. We first design the pulse shaper by numerically optimizing its response using finite element analysis. The pulse shaper consists of repeated unit cells based on a combination of a phononic crystal and a local resonator. Then, we fabricate and test pulse shaper candidates to validate the procedural efficacy. An iterative element corrects inaccuracies in input force and material properties and allows convergence on an appropriate pulse shaper. We carry out this procedure by designing pulse shapers fabricated from 3D-printed polylactic acid (PLA) to achieve an extended dwell acceleration pulse shape. In experimental impact tests, the procedure results in rise, dwell, and fall behaviors comparable to that predicted, effectively confirming the efficacy of the presented procedure and verifying the performance of metamaterial-based pulse shapers.
Coarse-grained methods for heterogeneous vesicles with phase-separated domains: Elastic mechanics of shape fluctuations, plate compression, and channel insertion
We develop coarse-grained particle approaches for studying the elastic mechanics of vesicles with heterogeneous membranes having phase-separated domains. We perform simulations both of passive shape fluctuations and of active systems where vesicles are subjected to compression between two plates or subjected to insertion into narrow channels. Analysis methods are developed for mapping particle configurations to continuum fields with spherical harmonics representations. Heterogeneous vesicles are found to exhibit rich behaviors where the heterogeneity can amplify surface two-point correlations, reduce resistance during compression, and augment vesicle transport times in channels. The developed methods provide general approaches for characterizing the mechanics of coarse-grained heterogeneous systems taking into account the roles of thermal fluctuations, geometry, and phase separation.
Estimation of elastic properties of an additively manufactured lattice using resonant ultrasound spectroscopy
Development of improved approaches in the characterization of additively manufactured structures continues to be a topic of interest for the advanced manufacturing community. This article will investigate an approach using resonant ultrasound spectroscopy (RUS) to determine the effective elastic constants of an orthotropic lattice structure. The evaluation is performed on a cube shaped 316 L stainless steel test specimen, constructed using selective laser melting techniques. The approach uses RUS techniques in conjunction with the assumption that in the frequency regime of interest, the wavelength of the diagnostic ultrasound is greater than the discrete structural features of the unit cell of the lattice; thus, the AM structure can be treated as an anisotropic continuum with effective material properties and symmetry inherited from the unit cell. The RUS analysis estimates the nine elastic coefficients associated with orthotropic sample symmetry, which, in turn, are used to determine the elastic moduli and Poisson ratios. Current results show good agreement between experiments and modeled data. Comparisons to published results are also in good agreement, indicating the potential applicability of this characterization technique for estimating the linear elastic properties of innovative additive manufactured metal lattice structures.
Uncovering the role of nanoscale precipitates on martensitic transformation and superelasticity
Here we characterize the role of coherent nanoscale B2 Ni 50 Al 50 precipitates on the temperature- and stress-induced martensitic phase transformation in nanocrystalline Ni 63 Al 37 shape memory alloys using multi-million-atoms molecular dynamics (MD) simulations. We studied two types of precipitates: one with single crystal precipitates (SXP) and a second where grain boundaries cut through precipitates (PXP). Simulations reveal that the presence of B2 precipitates stabilizes the cyclic flag-shaped stress-strain response, characteristic of superelasticity, and reduces remnant strain. In contrast, single-phase nanocrystalline Ni 63 Al 37 exhibits degradation of the reverse transformation during cyclic loading and, eventually, incomplete reversible transformation within a few cycles. This is consistent with previous experimental findings of ultra-low fatigue in Ni-Ti-Cu alloys with Ti 2 Cu precipitates. The simulations reveal that the presence of precipitates significantly improves the reversibility of the transformation by acting as elastic zones that partially shield the martensitic transformation and drive the reverse transformation. A detailed analysis of the MD trajectories reveals that the martensitic transformation of the matrix induces ultra-large elastic deformation in some of the B2 precipitates (approximately 12%) to the point of resulting in a martensite-like atomic structure.