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Results for “volumetric strain”

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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114 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Analysis of time-dependent strain heterogeneity in shales using X-ray microscopy and digital volume correlation

In this paper, we report the results of a 4-dimensional (3 spatial dimensions plus time) in-situ creep experiment on a milli-core shale plug from Barnett formation in Texas. An outstanding challenge in shale mechanics is connecting observable characteristics to predictable mechanical and hydraulic behavior. We employ novel imaging and analysis techniques to statistically link sample mineralogy and structure to observed deformation. In particular, we focus on mechanisms of porosity reduction during transient response to step changes in stress, analogous to the aseismic deformation that occurs after an episode of fracturing. Our main goal in this study is to take the first steps to visualize and capture the small-scale deformation mechanisms involved in creep of shales, using imaging techniques. To do so, we conducted a uniaxial creep experiment inside an Xradia Versa 520 micro-CT system for 12 h at room temperature under a constant uniaxial stress of 25 MPa. We employed a Digital Volume Correlation (DVC) method to measure micro-scale deforspmation of the sample by comparing the resulting volumetric images with a high-resolution scan of the sample obtained prior to the creep experiment. The results indicate that the time-dependent deformation in the compliant clay plus kerogen matrix constituents facilitates rigid grain rotation and compaction of intergranular porosity at the boundary between rigid grains and matrix.

58 GEOSCIENCES↗
DOE OSTI2021

Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data

Automatic boundary detection of 4D ultrasound (4DUS) cardiac data is a promising yet challenging application at the intersection of machine learning and medicine. Using recently developed murine 4DUS cardiac imaging data, we demonstrate here a set of three machine learning models that predict left ventricular wall kinematics along both the endo- and epi-cardial boundaries. Each model is fundamentally built on three key features: (1) the projection of raw US data to a lower dimensional subspace, (2) a smoothing spline basis across time, and (3) a strategic parameterization of the left ventricular boundaries. Model 1 is constructed such that boundary predictions are based on individual short-axis images, regardless of their relative position in the ventricle. Model 2 simultaneously incorporates parallel short-axis image data into their predictions. Model 3 builds on the multi-slice approach of model 2, but assists predictions with a single ground-truth position at end-diastole. To assess the performance of each model, Monte Carlo cross validation was used to assess the performance of each model on unseen data. For predicting the radial distance of the endocardium, models 1, 2, and 3 yielded average R2 values of 0.41, 0.49, and 0.71, respectively. Monte Carlo simulations of the endocardial wall showed significantly closer predictions when using model 2 versus model 1 at a rate of 48.67%, and using model 3 versus model 2 at a rate of 83.50%. These finding suggest that a machine learning approach where multi-slice data are simultaneously used as input and predictions are aided by a single user input yields the most robust performance. Subsequently, we explore the how metrics of cardiac kinematics compare between ground-truth contours and predicted boundaries. We observed negligible deviations from ground-truth when using predicted boundaries alone, except in the case of early diastolic strain rate, providing confidence for the use of such machine learning models for rapid and reliable assessments of murine cardiac function. To our knowledge, this is the first application of machine learning to murine left ventricular 4DUS data. Future work will be needed to strengthen both model performance and applicability to different cardiac disease models.

4D ultrasound↗
NASA NTRS2008

Software Performs Complex Design Analysis

Designers use computational fluid dynamics (CFD) to gain greater understanding of the fluid flow phenomena involved in components being designed. They also use finite element analysis (FEA) as a tool to help gain greater understanding of the structural response of components to loads, stresses and strains, and the prediction of failure modes. Automated CFD and FEA engineering design has centered on shape optimization, which has been hindered by two major problems: 1) inadequate shape parameterization algorithms, and 2) inadequate algorithms for CFD and FEA grid modification. Working with software engineers at Stennis Space Center, a NASA commercial partner, Optimal Solutions Software LLC, was able to utilize its revolutionary, one-of-a-kind arbitrary shape deformation (ASD) capability-a major advancement in solving these two aforementioned problems-to optimize the shapes of complex pipe components that transport highly sensitive fluids. The ASD technology solves the problem of inadequate shape parameterization algorithms by allowing the CFD designers to freely create their own shape parameters, therefore eliminating the restriction of only being able to use the computer-aided design (CAD) parameters. The problem of inadequate algorithms for CFD grid modification is solved by the fact that the new software performs a smooth volumetric deformation. This eliminates the extremely costly process of having to remesh the grid for every shape change desired. The program can perform a design change in a markedly reduced amount of time, a process that would traditionally involve the designer returning to the CAD model to reshape and then remesh the shapes, something that has been known to take hours, days-even weeks or months-depending upon the size of the model.

Source record↗
DOE OSTI2020

Beyond Volume Variation: Anisotropic and Protrusive Lithiation in Bismuth Nanowire

Materials storing energy via an alloying reaction are promising anode candidates in rechargeable lithium-ion batteries (LIBs) due to their much higher energy density than the current graphite anode. Until now, the volumetric expansion of such electrode particles during lithiation has been considered as solely responsible for cycling-induced structural failure. Here, we report different structural failure mechanisms using single-crystalline bismuth nanowires as the alloying-based anode. The Li–Bi alloying process exhibits a two-step transition, that is, Bi–Li 1 Bi and Li 1 Bi–Li 3 Bi. Interestingly, the Bi–Li 1 Bi phase transition occurs not only in the bulk Bi nanowire but also on the particle surface showing its characteristic behavior. The bulk alloying kinetics favors a Bi-(012)-facilitated anisotropic lithiation, whose mechanism and energetics are further studied using the density functional theory calculations. More importantly, the protrusion of Li 1 Bi nanograins as a result of anisotropic Li–Bi alloying is found to dominate the surface morphology of Bi particles. The growth kinetics of Li 1 Bi protrusions is understood atomically with the identification of two different controlling mechanisms, that is, the dislocation-assisted strain relaxation at the Bi/Li 1 Bi interface and the short-range migration of Bi supporting the off-Bi growth of Li 1 Bi. As loosely rooted to the bulk substrate and easily peeled off and detached into the electrolyte, these nanoscale protrusions developed during battery cycling are believed to be an important factor responsible for the capacity decay of such alloying-based anodes at the electrode level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Characterization of Annealing-Induced Phase Segregation in Composite Silicon Anodes for Li-ion Batteries

Silicon (Si) anodes present a promising alternative to graphite anodes for lithium-ion batteries (LIBs), as Si has a greater specific capacity. However, one major constraint is the volumetric change of Si during lithiation that results in an unstable solid-electrolyte interphase (SEI), so Si-containing electrodes require the use of various strategies to mitigate these effects and improve performance. One such solution is the use of Si nanoparticles (NPs) that maximize the surface area to volume ratio. Here, we discuss electrodes made with Si NPs treated with polyethylene oxide (PEO) (for improving dispersion during processing), conductive carbon NPs, and P84 polyimide binder which shows significant impacts of annealing treatment on improvements of active material utilization, first cycle efficiency, and capacity retention with extensive cycling . We used air-free argon ion polishing to create electrode cross sections and imaged through the electrode thickness using atomic force microscopy (AFM)-based nano-electrical characterization of scanning spreading resistance microscopy (SSRM), nano-mechanical characterizations of contact resonance and force volume (CR-FV), and scanning electron microscopy-based energy dispersive x-ray spectroscopy (SEM-EDS). Results show that the Si and conductive carbon segregate into phases with a distinctive carbon-rich banded morphology that surrounds the Si-rich phase during annealing. In pristine electrodes, the carbon- and SEI-rich bands exhibit a higher electronic conductivity and a lower elastic modulus than the Si active material phase. These structures, as well as distinct electronic and mechanical properties, remain during cycling, suggesting an improvement of electrical conduction pathways and a mechanical strain buffer for active Si material expansion during cycling. This phase separation may be a major factor in the improvement seen in electrochemical performance due to annealing. Additionally, our nm-scale and multi-mode characterizations provide a novel route for understanding and improving energy storage devices, which is advantageous due to the highly inhomogeneous of composite electrodes in nm-mm scales.

ENERGY STORAGE↗
DOE OSTI2025

High tensile alloy of copper to mitigate current collector deformation in silicon electrodes for lithium-ion batteries

The volumetric changes of silicon electrodes, along with the strong adhesive properties of certain binders, can lead to plastic deformation of the current collector and create damage in the electrode coating. Here, in this study, we report a detailed study of silicon coatings on a high-tensile alloy (HTA) foil of copper with strength over twice that of conventional copper foils. The HTA current collectors with high mechanical strength can mitigate plastic deformation upon continuous cycling. At moderate areal capacities (2.5–3 mAh cm −2 ), conventional copper foils show significant wrinkling after only a few electrochemical cycles, whereas the HTA foils remain intact. We demonstrate viability of the HTA foils in large format xx6395 pouch cells, in which the HTA current collectors remain intact even at an areal capacity of 4.5 mAh cm −2 ; in contrast, wrinkles form in conventional copper current collectors increasing the likelihood of lithium plating. Computational studies show that stresses generated during cycling of silicon electrodes are very high in the current collector and at the current collector-coating interface, explaining the wrinkling of conventional Cu foils. Our studies highlight importance of current collector to solve the electrochemical and chemo-mechanical performance challenges associated with high-loading silicon electrodes.

Chemo-mechanical degradation↗