Publications

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Here, the contribution from loss of Li⁺ inventory to capacity fade is described for slow rates (C/10) and long-term cycling (up to 80 cycles). It was found through electrochemical testing and ex-situ Raman analysis that at these slow rates, the entirety of capacity loss up to 80 cycles can be explained by loss of Li⁺ inventory in the cell. The Raman spectrum of LiCoO₂ is sensitive to the state of lithiation and can therefore be leveraged to quantify the state of lithiation for individual particles. With these Raman derived estimates, the lithiation state of the cathode in the discharged state is compared to electrochemical data as a function of cycle number. High correlation is found between Raman quantifications of cycleable lithium and the capacity fade. Additionally, the linear relationship between discharge capacity and cell overpotential suggests that the loss of capacity stems from an impedance rise of the electrodes, which based on Li inventory losses, is caused by SEI formation and repair.

Experiments on the Sandia Z pulsed-power accelerator have demonstrated the ability to produce warm dense matter (WDM) states with unprecedented uniformity, duration, and size, which are ideal for investigations of fundamental WDM properties. For the first time, space-resolved x-ray Thomson scattering (XRTS) spectra from shocked carbon foams were recorded on Z. The large (>20 MA) electrical current produced by Z was used to launch Al flyer plates up to 25 km/s. The impact of the flyer plate on a CH2 foam target produced a shocked state with an estimated pressure of 0.75 Mbar, density of 0.52 g/cm3, and temperature of 4.3 eV. Both unshocked and shocked portions of the foam target were probed with 6.2 keV x-rays produced by focusing the Z-Beamlet laser onto a nearby Mn foil. The data are composed of three spatially distinct spectra that were simultaneously captured with a single spectrometer with high spectral (4.8 eV) and spatial (190 μm) resolutions. Detailed spectral information from three target locations is provided simultaneously: the incident x-ray source, the scattered signal from unshocked foam, and the scattered signal from shocked foam.

The second Sandia Fracture Challenge illustrates that predicting the ductile fracture of Ti-6Al-4V subjected to moderate and elevated rates of loading requires thermomechanical coupling, elasto-thermo-poro-viscoplastic constitutive models with the physics of anisotropy and regularized numerical methods for crack initiation and propagation. We detail our initial approach with an emphasis on iterative calibration and systematically increasing complexity to accommodate anisotropy in the context of an isotropic material model. Blind predictions illustrate strengths and weaknesses of our initial approach. We then revisit our findings to illustrate the importance of including anisotropy in the failure process. Mesh-independent solutions of continuum damage models having both isotropic and anisotropic yields surfaces are obtained through nonlocality and localization elements.

Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Rather than evaluate the predictions of a single simulation approach, the Sandia Fracture Challenge relies on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in (Formula presented.) 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. Additional shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.

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We discuss algorithm-based resilience to silent data corruption (SDC) in a task- based domain-decomposition preconditioner for partial differential equations (PDEs). The algorithm exploits a reformulation of the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to SDC. The implementation is based on a server-client model where all state information is held by the servers, while clients are designed solely as computational units. Scalability tests run up to ~ 51 K cores show a parallel efficiency greater than 90%. We use a 2D elliptic PDE and a fault model based on random single bit-flip to demonstrate the resilience of the application to synthetically injected SDC. We discuss two fault scenarios: one based on the corruption of all data of a target task, and the other involving the corruption of a single data point. We show that for our application, given the test problem considered, a four-fold increase in the number of faults only yields a 2% change in the overhead to overcome their presence, from 7% to 9%. We then discuss potential savings in energy consumption via dynamics voltage/frequency scaling, and its interplay with fault-rates, and application overhead.

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