Publications

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It is experimentally observed that multilayer fibre–resin composites can soften and swell significantly when heated above their designed operating temperatures. This swelling is expected to further accelerate the pyrolysis, releasing volatile components which can ignite in an oxygenated environment if exposed to a spark, flame or sufficiently elevated temperature. Here the intumescent behaviour of resin-infused carbon-fibre is investigated. Preliminary experiments and simulations are compared for a carbon-fibre sample radiatively heated on the top side and insulated on the bottom. Simulations consider coupled thermal and porous media flow.

Understanding the dynamic behavior of geomaterials is critical for refining modeling and simulation of applications that involve impacts or explosions. Obtaining material properties of geomaterials is challenging, particularly in tension, due to the brittle and low-strength nature of such materials. Dynamic split tension technique (also called dynamic Brazilian test) has been employed in recent decades to determine the dynamic tensile strength of geomaterials. This is primarily because the split tension method is relatively straightforward to implement in a Kolsky compression bar. Typically, investigators use the peak load reached by the specimen to calculate the tensile strength of the specimen material, which is valid when the specimen is compressed at quasi-static strain rate. However, the same assumption cannot be safely made at dynamic strain rates due to wave propagation effects. In this study, the dynamic split tension (or Brazilian) test technique is revisited. High-speed cameras and digital image correlation (DIC) were used to image the failure of the Brazilian-disk specimen to discover when the first crack occurred relative to the measured peak load during the experiment. Differences of first crack location and time on either side of the sample were compared. The strain rate when the first crack is initiated was also compared to the traditional estimation method of strain rate using the specimen stress history.

A major and often unrecognized error source in digital image correlation (DIC) is the influence of the intervening air between the cameras and sample. Minute differences in air temperature, composition, or both can cause index of refraction changes that act as a lens and cause distortions in the DIC displacement and strain results (Jones and Reu, Exp Mech, 2017). There are limited options to correct this problem as it is both spatial and temporal in nature. One method is to use X-rays for imaging that are not affected by air refraction, but this requires costly equipment. A second method uses a vacuum chamber to minimize the intervening air to remove the distortions, but unfortunately this requires inconvenient setups.

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“Heat waves” is a colloquial term used to describe convective currents in air formed when different objects in an area are at different temperatures. In the context of Digital Image Correlation (DIC) and other optical-based image processing techniques, imaging an object of interest through heat waves can significantly distort the apparent location and shape of the object. There are many potential heat sources in DIC experiments, including but not limited to lights, cameras, hot ovens, and sunlight, yet error caused by heat waves is often overlooked. This paper first briefly presents three practical situations in which heat waves contributed significant error to DIC measurements to motivate the investigation of heat waves in more detail. Then the theoretical background of how light is refracted through heat waves is presented, and the effects of heat waves on displacements and strains computed from DIC are characterized in detail. Finally, different filtering methods are investigated to reduce the displacement and strain errors caused by imaging through heat waves. The overarching conclusions from this work are that errors caused by heat waves are significantly higher than typical noise floors for DIC measurements, and that the errors are difficult to filter because the temporal and spatial frequencies of the errors are in the same range as those of typical signals of interest. Therefore, eliminating or mitigating the effects of heat sources in a DIC experiment is the best solution to minimizing errors caused by heat waves.

This project targeted a full-field understanding of the conversion of plastic work into heat us- ing advanced diagnostics (digital image correlation, DIC, combined with infrared, IR, imaging). This understanding will act as a catalyst for reformulating the prevalent simplistic model, which will ultimately transform Sandia's ability to design for and predict thermomechanical behavior, impacting national security applications including nuclear weapon assessments of accident scenar- ios. Tensile 304L stainless steel dogbones are pulled in tension at quasi-static rates until failure and full-field deformation and temperature data are captured, while accounting for thermal losses. The IR temperature fields are mapped onto the DIC coordinate system (Lagrangian formulation). The resultant fields are used to calculate the Taylor-Quinney coefficient, p, at two strain rates rates (0.002 s -1 and 0.08 s -1 ) and two temperatures (room temperature, RT, and 250degC).

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Traditionally, material identification is performed using global load and displacement data from simple boundary-value problems such as uni-axial tensile and simple shear tests. More recently, however, inverse techniques such as the Virtual Fields Method (VFM) that capitalize on heterogeneous, full-field deformation data have gained popularity. In this work, we have written a VFM code in a finite-deformation framework for calibration of a viscoplastic (i.e. strain-rate dependent) material model for 304L stainless steel. Using simulated experimental data generated via finite-element analysis (FEA), we verified our VFM code and compared the identified parameters with the reference parameters input into the FEA. The identified material model parameters had surprisingly large error compared to the reference parameters, which was traced to parameter covariance and the existence of many essentially equivalent parameter sets. This parameter non-uniqueness and its implications for FEA predictions is discussed in detail. Finally, we present two strategies to reduce parameter covariance – reduced parametrization of the material model and increased richness of the calibration data – which allow for the recovery of a unique solution.

Modeling material and component behavior using finite element analysis (FEA) is critical for modern engineering. One key to a credible model is having an accurate material model, with calibrated model parameters, which describes the constitutive relationship between the deformation and the resulting stress in the material. As such, identifying material model parameters is critical to accurate and predictive FEA. Traditional calibration approaches use only global data (e.g. extensometers and resultant force) and simplified geometries to find the parameters. However, the utilization of rapidly maturing full-field characterization tech- niques (e.g. Digital Image Correlation (DIC)) with inverse techniques (e.g. the Virtual Feilds Method (VFM)) provide a new, novel and improved method for parameter identification. This LDRD tested that idea: in particular, whether more parameters could be identified per test when using full-field data. The research described in this report successfully proves this hypothesis by comparing the VFM results with traditional calibration methods. Important products of the research include: verified VFM codes for identifying model parameters, a new look at parameter covariance in material model parameter estimation, new validation tech- niques to better utilize full-field measurements, and an exploration of optimized specimen design for improved data richness.

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