Characteristic Tearing Energy and Fatigue Crack Propagation of Filled Natural Rubber

Below the incipient characteristic tearing energy (T0), cracks will not grow in rubber under fatigue loading. Hence, determination of the characteristic tearing energy T0 is very important in the rubber industry. A rubber cutting experiment was conducted to determine the T0, using the cutting method proposed originally by Lake and Yeoh. Then, a fatigue crack propagation experiment on a edge-notched pure shear specimen under variable amplitude loading was studied. A method to obtain the crack propagation rate da/dN from the relationship of the crack propagation length (Δa) with the number of cycles (N) is proposed. Finally, the T0 obtained from the cutting method is compared with the value decided by the fatigue crack propagation experiment. The values of T0 obtained from the two different methods are a little different.

Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence

Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.

Parameter Conversion between Controlled Pass-By Method and Alternative Close Proximity Method

To shorten the measurement period and reduce experiment costs, we investigated the parameter conversion between the experiment results of the controlled pass-by (CPB) method and alternative close proximity (A-CPX) method for automotive applications. The CPB and A-CPX methods were used to experiment with tire noise. The correlation between the tire noises of the two experimental methods was analyzed. Then, the quantitative transformation relationship between the tire noises of the two methods was obtained using an acoustic radiation propagation experiment in the semi-free field. The results indicate a good linear correlation between the experimental results of the two experimental methods. In the case of ignoring the shielding effect of the car body, the average difference between the measured value of the CPB method and the predicted value of the experimental tire is about 1.1 dB. When considering the shielding effect of the car body, the average difference between the measured value of the CPB method and the predicted value of the experimental tire is about 2.7 dB.

Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links

A novel methodology for estimating rainfall rate from satellite signals is presented. The proposed inversion algorithm yields rain rate estimates by making opportunistic use of the downlink signal and exploiting local ancillary meteorological information (0 °C isotherm height and monthly convectivity index), which can be extracted on a Global basis from Numerical Weather Prediction (NWP) products. The methodology includes different expressions to take the different impact of stratiform and convective rain events on the link into due account. The model accuracy in predicting the rain rate is assessed (and compared to the one of other models), both on a statistical and on an instantaneous basis, by exploiting a full year of data collected in Milan, in the framework of the Alphasat Aldo Paraboni propagation experiment.