998 resultados para mechanical robustness
Resumo:
Mechanical damages such as bruising, collision and impact during food processing stages diminish quality and quantity of productions as well as efficiency of operations. Studying mechanical characteristics of food materials will help to enhance current industrial practices. Mechanical properties of fruits and vegetables describe how these materials behave under loading in real industrial operations. Optimizing and designing more efficient equipments require accurate and precise information of tissue behaviours. FE modelling of food industrial processes is an effective method of studying interrelation of variables during mechanical operation. In this study, empirical investigation has been done on mechanical properties of pumpkin peel. The test was a part of FE modelling and simulation of mechanical peeling stage of tough skinned vegetables. The compression test has been conducted on Jap variety of pumpkin. Additionally, stress strain curve, bio-yield and toughness of pumpkin skin have been calculated. The required energy for reaching bio-yield point was 493.75, 507.71 and 451.71 N.mm for 1.25, 10 and 20 mm/min loading speed respectively. Average value of force in bio-yield point for pumpkin peel was 310 N.
Resumo:
Molecular dynamics (MD) simulations have been carried out to investigate the defect’s effect on the mechanical properties of single-crystal copper nanowire with different surface defects, under torsion deformation. The torsional rigidity is found insensitive to the surface defects and the critical angle appears an obvious decrease due to the surface defects, the largest decrease is found for the nanowire with surface horizon defect. The deformation mechanism appears different degrees of influence due to surface defects. The surface defects play a role of dislocation sources. Comparing with single intrinsic stacking faults formation for the perfect nanowire, much affluent deformation processes have been activated because of surface defects, for instance, we find the twins formation for the nanowire with a surface 45o defect.
Resumo:
The phase of an analytic signal constructed from the autocorrelation function of a signal contains significant information about the shape of the signal. Using Bedrosian's (1963) theorem for the Hilbert transform it is proved that this phase is robust to multiplicative noise if the signal is baseband and the spectra of the signal and the noise do not overlap. Higher-order spectral features are interpreted in this context and shown to extract nonlinear phase information while retaining robustness. The significance of the result is that prior knowledge of the spectra is not required.