Jiang Jin

This study demonstrates the feasibility of a data-driven approach to construct a quantitative relationship between nonlinear acoustic parameters and microstructural features of contact interfaces. The near-surface nonlinearity is measured using dynamic acousto-elastic testing (DAET) with a surface wave probe, while the microstructural features are extracted from scanning electron microscopy (SEM) images of fatigue cracks. Four aluminum alloy samples, each having a fatigue crack are prepared. Six local nonlinearity parameters are measured at different locations along the crack propagation direction. A total of 40 local measurements are acquired. A principal component analysis (PCA) reveals that all six nonlinearity parameters are correlated and hence can be replaced by one principal component (PC). Fifteen crack micro-geometrical features at each measurement point were extracted from the SEM images. Regression analysis is used to relate the PC of the nonlinearity parameters to the microstructural features at the crack interface. We compare three regression models that take variable selection into account: stepwise multiple linear regression (MLR), stepwise principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO). Despite having different principles, the three predictive models identify two features as the most significant in predicting the interface nonlinearity: the crack aperture (opening) distribution and the distance to the crack tip. The differences between the three models and the physical interpretation of the data-driven predictions are discussed.

As the largest consumer of petroleum and second highest producer of greenhouse gas emissions, the United States currently is a leading country in bioenergy production driven by a series of policies. One such public program that directly subsidizes biomass feedstock growers is the Biomass Crop Assistance Program (BCAP), which recently received attention for stimulating the development of bioenergy. However, critiques were also raised, concerning the possibility of intensifying the feedstock competition between bioenergy and forest products. This study, therefore, aimed at assessing the effects of BCAP on the forest product markets with the Global Forest Products Model (GFPM). Three alternative scenarios were designed to simulate three payments in BCAP. In the first scenario, the matching payment was simulated by adjusting the manufacturing cost of fuelwood and particleboard. In the second scenario, establishment payment was simulated by adjusting the supply rate of industrial roundwood. In the third scenario, the annual payment was simulated by linking the supply change rate with the soil rental rate for industrial roundwood. We found that, under the matching payment scenario, industrial roundwood and particleboard will experience a sharp decrease in production and an increase in price. For establishment payments, industrial roundwood will experience a continuous increase in production and a decrease in price. For annual payments, the industrial roundwood will experience a V-shape pattern production.

Nonlinearity of damaged materials is often assumed to be correlated with the cumulative length of micro-cracks discounting the influence of crack width and roughness. To investigate the influence of microstructural features of realistic cracks on the nonlinear elastic properties, Dynamic Acousto-Elastic Testing (DAET) is performed on two aluminum alloy samples (30 × 40 × 170 mm³) with a single fatigue-crack. The cracks on the two samples are of similar length but have dissimilar microstructural features due to the different stress intensity factors used during the fatigue tests. DAET is carried out with a surface wave probe to measure the near-surface nonlinearity at a number of locations along each crack. The geometric features of the cracks, including crack width and roughness, are extracted from a series of scanning electron microscope images. DAET results demonstrate that nonlinearity varies along the crack; both samples exhibit greater nonlinearity near the notch, where the crack is wider than near the crack tip. However, the nonlinearity parameters obtained from the sample with a smoother crack are one order of magnitude larger than those of the sample with a relatively rough crack. In addition, the nonlinearity signatures of the two cracks exhibit very different tension/compression asymmetry. This study provides valuable data that not only directly correlates the nonlinear ultrasonic signatures and crack interface features but also illuminates the micro-mechanisms and origins of the measured nonlinearities.

Volumetric microcracking is one of the early symptoms of distress in cementitious materials caused by excessive mechanical stress, chemical attacks, and environmental influences. The microcracks widen, coalesce, and develop into larger cracks with the progress of damage. Visible macro-cracks indicate severe damage that often cannot be mitigated. As such, detection of damage at the early stages of development is essential for designing optimal preventive maintenance programs for concrete structures. Nonlinear acoustics-based nondestructive testing techniques have shown great promise in identification of microscopic cracks in diverse materials including concrete. Impact-based alternatives of conventional techniques are gaining popularity for concrete testing mainly due to their field transportability. In this study, we focus on impact-based nonlinear resonance acoustic spectroscopy (INRAS). We compare the results from multi-impact INRAS, where several impacts of increasing intensities are applied, to those from single impact ring-down spectroscopy where only one impact of large intensity is used. Hilbert Huang Transform (HHT) is used to obtain the time-dependent frequency content of the single-impact ring-down. We propose several models for fitting the entire ring-down in order to extract nonlinear elastic material parameters. We demonstrate that our proposed approach for analyzing single-impact INRAS data yields material parameters compatible with those obtained from conventional testing and analysis.