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Results for “Experimental mechanics”
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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Dynamics of Quantum Phase Transitions
This presentation covers a study of the formation of topological defects and excitations in dynamical (quenched) quantum phase transitions (theory, numerics, & BEC experiment). The FY20 main accomplishment has been the establishment of the role of the “sonic horizon” in the Kibble-Zurek mechanism (KZM). Experimental aspects of the study, such as the numerical simulation of the expected dynamics) have been “on hold” due to COVID. Plans for the future entail studying the dynamics of quantum phase transition in 2D quantum Ising, as well as a BEC miscibility/immiscibility phase transition experiment (COVID permitting). The mission impact of this study is a contribution to LANL's “Science Discovery and Innovation” mission, e.g. to “Information Science and Technology”. (D-Wave is a dynamical phase transition.)
The Measurement of Shear Strain in Impact Ice Using a Modified Lap Joint Test and Digital Image Correlation
The results of ice adhesion testing efforts are typically quantified using the maximum shear stress sustained at the interface before the ice detaches from the underlying substrate. While this singular value can be useful for understanding the role icing wind tunnel conditions and substrate surface properties have on the strength of the ice-substrate interface, it does not capture the mechanical behavior of the ice during an experiment. To better understand the deformation of the ice sample and ice-substrate interface during testing, digital image correlation was applied to the lap-joint shear test methodology utilized at NASA Glenn Research Center. Preparation of the sample surface for digital image correlation, initial parsing of the correlation data, and the time synchronization of data from multiple sources are discussed. Using the directional components of deformation measured via digital image correlation, a novel approach to calculating the interfacial shear strain is presented. Results from analyzing several impact ice samples consistently reveals a non-linear response in the ice during mechanical loading which occurs prior to sample delamination, a behavior not observable in the force and displacement data gathered from the testing apparatus. Both the full and small angle forms of the engineering shear strain are evaluated, with the full shear strain form providing considerably lower noise levels. The transition of sample response from near the interface to the bulk behavior of the ice under shear loading is discussed with results from several samples being presented.
The Measurement of Shear Strain in Impact Ice Using a Modified Lap Joint Test and Digital Image Correlation
The results of ice adhesion testing efforts are typically quantified using the maximum shear stress sustained at the interface before the ice detaches from the underlying substrate. While this singular value can be useful for understanding the role icing wind tunnel conditions and substrate surface properties have on the strength of the ice-substrate interface, it does not capture the mechanical behavior of the ice during an experiment. To better understand the deformation of the ice sample and ice-substrate interface during testing, digital image correlation was applied to the lap-joint shear test methodology utilized at NASA Glenn Research Center. Preparation of the sample surface for digital image correlation, initial parsing of the correlation data, and the time synchronization of data from multiple sources are discussed. Using the directional components of deformation measured via digital image correlation, a novel approach to calculating the interfacial shear strain is presented. Results from analyzing several impact ice samples consistently reveals a non-linear response in the ice during mechanical loading which occurs prior to sample delamination, a behavior not observable in the force and displacement data gathered from the testing apparatus. Both the full and small angle forms of the engineering shear strain are evaluated, with the full shear strain form providing considerably lower noise levels. The transition of sample response from near the interface to the bulk behavior of the ice under shear loading is discussed with results from several samples being presented.
A macro-scale ruck and tuck mechanism for deformation in ion-irradiated polycrystalline graphite
A vein structure, which becomes more pronounced with increasing ion dose, was found on the surface of polycrystalline HOPG (highly oriented pyrolytic graphite) implanted by ex situ C + (up to 1.8 × 10 17 ions/cm 2 ), and in situ Ar + in a transmission electron microscope (TEM). These veins are found to be independent of the crystallographic orientations and are associated with the formation of pores. Underneath the veins, a triangular-shaped core was formed with the graphite platelet inside the core displaced up towards the surface. A macro-scale ‘ruck&tuck’ geometry was thus generated at these triangle structure boundaries. Progressive movement of dislocations along basal planes during irradiation was observed, and a mechanistic model was proposed on this basis to explain the vein formation. A small increase of c-spacing was observed with irradiation but it is believed that macro-scale vein formation plays a more vital role in the dimensional and property changes in polycrystalline graphite, especially when a stress gradient is present. Here, the model proposed also explains the change of thermal expansion in HOPG with irradiation. Together with Heggie’s ‘ruck&tuck’ and Barsoum’s ‘ripplocations’ models, the present model is considered to have provided an additional experimentally proven mechanism responsible for irradiation behaviour in graphite materials.
Mesoscale Linear Elastic Modeling and Homogenization of Marine Energy Composites
The design of fiber-reinforced composite (FRC)-based components for marine energy applications necessitates a fundamental understanding of material properties and the resulting geometry to predict long-term performance. In this work, we present a modeling workflow to predict linear elastic and diffusive bulk properties at the mesoscale for an idealized geometry based on knowledge of fiber and resin properties. A parametric study was performed to identify the key model input parameters that influence bulk properties. Furthermore, we demonstrate how bulk properties can be leveraged in high-fidelity image-based simulations, where imperfections in tow geometry and voids captured during X-ray computed tomography imaging are explicitly represented within the simulation. Bulk properties of interest include moduli, Poisson’s ratios, hygroscopic swelling, diffusivity, and moisture uptake, which are key parameters for characterizing FRC performance within marine environments. Modeling predictions agreed well with experimental data, except for estimating swelling coefficients, likely due to crack accumulation as a function of moisture uptake. The mesoscale modeling workflow ultimately highlights a versatile framework for understanding the influence of material and geometric properties, which can be leveraged to rapidly assess new FRC-based components.
Creep-induced elemental redistribution at grain boundaries of 304L stainless steel – An experimental evidence for diffusional creep mechanisms
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Mechanical behavior of frozen soils: Experimental investigation and numerical modeling
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Experimental and numerical investigation into mechanical degradation of polymers
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Experimental investigation of three distinct mechanisms for the transition from sheet to cloud cavitation
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Imposing equilibrium on experimental 3-D stress fields using Hodge decomposition and FFT-based optimization
Here, we present a methodology to impose micromechanical constraints, i.e. stress equilibrium at grain and sub-grain scale, to an arbitrary (non-equilibrated) voxelized stress field obtained, for example, by means of synchrotron X-ray diffraction techniques. The method consists in finding the equilibrated stress field closest (in L 2 -norm sense) to the measured non-equilibrated stress field, via the solution of an optimization problem. The extraction of the divergence-free (equilibrated) part of a general (non-equilibrated) field is performed using the Hodge decomposition of a symmetric matrix field, which is the generalization of the Helmholtz decomposition of a vector field into the sum of an irrotational field and a solenoidal field. The combination of: a) the Euler–Lagrange equations that solve the optimization problem, and b) the Hodge decomposition, gives a differential expression that contains the bi-harmonic operator and two times the curl operator acting on the experimental stress field. These high-order derivatives can be efficiently performed in Fourier space. The method is applied to filter the non-equilibrated parts of a synthetic piecewise constant stress fields with a known ground truth, and stress fields in Gum Metal, a beta-Ti-based alloy measured in-situ using Diffraction Contrast Tomography (DCT). In both cases, the largest corrections were obtained near grain boundaries.
High-Resolution Three-Dimensional Sculpting of Two-Dimensional Graphene Oxide by E-Beam Direct Write
On demand switchable additive/subtractive patterning of 2D nanomaterials is an essential capability for developing new concepts of functional nanomaterials and their device realizations. Traditionally, this is performed via a multi-step process using photoresist coating and patterning by conventional photo or electron beam lithography, which is followed by bulk dry/wet etch or deposition. This limits the range of functionalities and structural topologies that can be achieved, as well as increases the complexity, cost, and possibility of contamination which are significant barriers to device fabrication from highly sensitive 2D materials. Focused electron beam induced processing (FEBIP) enables a material chemistry/site-specific, high-resolution multi-mode atomic scale processing and provides unprecedented opportunities for direct-write, single-step surface patterning of 2D nanomaterials with an in-situ imaging capability. It allows for realizing a rapid multi-scale/multi-mode approach, ranging from an atomic scale manipulation (e.g., via targeted defect introduction as an active site) to a large-area surface modification on nano and micro scales, including patterned doping and material removal/deposition with 2D (in-plane)/3D (out-of-plane) control. In this work, we report on a new capability of FEBIP for nanoscale patterning of graphene oxide via removal of oxygenated carbon moieties with no use of reactive gas required for etching complemented by carbon atom deposition using focused electron beam. The mechanism of experimentally observed phenomena is explored using the density functional theory (DFT) calculations, revealing that interactions of e-beam liberated reactive oxygen radicals with carbon atoms on graphene basal plane lead to creation of atomic vacancies in the material. The reaction by-products are volatile carbon di-oxides, which are dissociated and volatilized from the graphene oxide surface functional groups by interactions with an energetic focused electron beam. Along with selective “subtractive” patterning of graphene oxide, the same electron beam with increased irradiation doses can deposit out-of-plane 3D carbon nanostructures on top of or around the 2D etched pattern, thus forming a hybrid 2D/3D nanocomposite with feature control down to a few nanometers. Furthermore, this in-operando dual nanofabrication capability of FEBIP is unmatched by any other nanopatterning techniques and opens a new design window for forming 2D/3D complex nanostructures and functional nanodevices.
Confinement Effects on Proton Transfer in TiO 2 Nanopores from Machine Learning Potential Molecular Dynamics Simulations
Improved understanding of proton transfer in nanopores is critical for a wide range of emerging applications, yet experimentally probing mechanisms and energetics of this process remains a significant challenge. To help reveal details of this process, we developed and applied a machine learning potential derived from first-principles calculations to examine water reactivity and proton transfer in TiO 2 slit-pores. Here, we find that confinement of water within pores smaller than 0.5 nm imposes strong and complex effects on water reactivity and proton transfer. Although the proton transfer mechanism is similar to that at a TiO 2 interface with bulk water, confinement reduces the activation energy of this process, leading to more frequent proton transfer events. This enhanced proton transfer stems from the contraction of oxygen–oxygen distances dictated by the interplay between confinement and hydrophilic interactions. Our simulations also highlight the importance of the surface topology, where faster proton transport is found in the direction where a unique arrangement of surface oxygens enables the formation of an ordered water chain. In a broader context, our study demonstrates that proton transfer in hydrophilic nanopores can be enhanced by controlling pore size, surface chemistry, and topology.
A Review of Computational Models for the Flow of Milled Biomass Part I: Discrete-Particle Models
Biomass is a renewable and sustainable energy resource. Current design of biomass handling and feeding equipment leverage both experiments and numerical modeling. This paper reviews the state-of-the-art discrete element methods (DEM) for the flow of milled biomass (Part I), accompanied by a comprehensive review on continuum-based computational models (Part II). The present review on DEM is primarily focused on the features and suitability of various particle shape models for different types of milled biomass because particle shape is the predominant attribute controlling the flow behavior of complex-shaped granular material. The general strengths and weaknesses in the applicability of those models for the milled biomass modeling are summarized. In particular, comments are provided to balance the numerical model capabilities and the computational cost for the development of DEM models. To our best knowledge, this is the first-of-its-kind review on DEM specifically for biomass. Our study indicates that the current DEM models require further development, calibration, and validation based on a deep understanding of biomass particle contact mechanics and experimental data support before they can be reliably used for predictive simulations in handling and feeding systems.
Experimental evolution reveals nitrate tolerance mechanisms in Desulfovibrio vulgaris
Elevated nitrate in the environment inhibits sulfate reduction by important microorganisms of sulfate-reducing bacteria (SRB). Several SRB may respire nitrate to survive under elevated nitrate, but how SRB that lack nitrate reductase survive to elevated nitrate remains elusive. To understand nitrate adaptation mechanisms, we evolved 12 populations of a model SRB (i.e., Desulfovibrio vulgaris Hildenborough, DvH) under elevated NaNO 3 for 1000 generations, analyzed growth and acquired mutations, and linked their genotypes with phenotypes. Nitrate-evolved (EN) populations significantly (p < 0.05) increased nitrate tolerance, and whole-genome resequencing identified 119 new mutations in 44 genes of 12 EN populations, among which six functional gene groups were discovered with high mutation frequencies at the population level. In this work, we observed a high frequency of nonsense or frameshift mutations in nitrosative stress response genes (NSR: DVU2543, DVU2547, and DVU2548), nitrogen regulatory protein C family genes (NRC: DVU2394-2396, DVU2402, and DVU2405), and nitrate cluster (DVU0246-0249 and DVU0251). Mutagenesis analysis confirmed that loss-of-functions of NRC and NSR increased nitrate tolerance. Also, functional gene groups involved in fatty acid synthesis, iron regulation, and two-component system (LytR/LytS) known to be responsive to multiple stresses, had a high frequency of missense mutations. Additionally, mutations in those gene groups could increase nitrate tolerance through regulating energy metabolism, barring entry of nitrate into cells, altering cell membrane characteristics, or conferring growth advantages at the stationary phase. This study advances our understanding of nitrate tolerance mechanisms and has important implications for linking genotypes with phenotypes in DvH.
Inverse Lieb materials: altermagnetism and more
The Lieb lattice, originally proposed for cuprate superconductors, has gained new attention in the emerging field of altermagnetism as a minimal analytical model for the latter. While the inverse Lieb lattice (ILL) was once considered purely theoretical, several materials with this motif have recently been discovered. Its unique geometry supports complex magnetic orders driven by geometric frustration, offering high tunability. In this work, we provide comprehensive insights into ILL magnetic phases and establish guidelines for identifying altermagnets. Using a Heisenberg model, we first construct phase diagrams to elucidate the mechanisms underlying experimental magnetic phases. We then bridge theory and experiment via density functional theory (DFT) calculations on existing ILL compounds, finding results consistent with experimental data. Notably, we identified a trend linking d-shell filling to magnetic order, where d x (x≤5) configurations show a propensity for altermagnetism. We also highlight Sr 2 CrO 2 Cr 2 OAs 2 as a promising metallic altermagnet with highly anisotropic J 2 exchange and a high Néel temperature (~600 K). Finally, our magnon spectra calculations confirm that chiral splittings correlate directly with anisotropies between inequivalent J 2 interactions.
Experimental evidence of kinetically driven mechanisms for soot inception
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