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The need to devise a low-speed balancing method for balancing high-speed rotors was recognized and addressed. In this paper, a scheme that combines both the influence coefficients and modal balancing techniques is presented. The scheme is developed for low-speed balancing of high-speed rotors, and relies on knowledge of the modal characteristics of the rotor. The conditions for applicability of the method were stated in the light of the experientially estimated rotor deflection mode shapes. An experimental test rig of a flexible rotor was constructed to verify the applicability and reliability of the low-speed balancing scheme.

Wall-thinning due to chemical reactions, heat, erosion, or a combination of such influences is the most dominant type of internal surface damage in piping systems. In order to examine the effect of wall-thinning on the natural frequencies, the elastodynamic model of the fiber-reinforced polymer pipe is formulated using a wavelet-based finite element method. In this context, a new set of Hermite shape functions is developed. The generalized eigen value problem is solved and the natural frequencies are obtained for an fiber-reinforced polymer pipe with different depths and locations of the wall-thinning. Moreover, the effect of wall-thinning on the modal frequencies of the pipe was verified experimentally. Both the analytical and experimental results demonstrate the potential of using vibration signature to detect internal surface damage in fiber-reinforced polymer pipes.

Zinc selenide (ZnSe) nanomaterial is a binary semiconducting material with unique features, such as high chemical stability, high photosensitivity, low cost, great excitation binding energy, non-toxicity, and a tunable direct wide band gap. These characteristics contribute significantly to its wide usage as sensors, optical filters, photo-catalysts, optical recording materials, and photovoltaics, among others. The light energy harvesting capacity of this material can be enhanced and tailored to meet the required application demand through band gap tuning with compositional modulation, which influences the nano-structural size, as well as the crystal distortion of the semiconductor. This present work provides novel ways whereby the wide energy band gap of zinc selenide can be effectively modulated and tuned for light energy harvesting capacity enhancement by hybridizing a support vector regression algorithm (SVR) with a genetic algorithm (GA) for parameter combinatory optimization. The effectiveness of the SVR-GA model is compared with the stepwise regression (SPR)-based model using several performance evaluation metrics. The developed SVR-GA model outperforms the SPR model using the root mean square error metric, with a performance improvement of 33.68%, while a similar performance superiority is demonstrated by the SVR-GA model over the SPR using other performance metrics. The intelligent zinc selenide energy band gap modulation proposed in this work will facilitate the fabrication of zinc selenide-based sensors with enhanced light energy harvesting capacity at a reduced cost, with the circumvention of experimental stress.

An electro-mechanical analysis of a microcantilever beam considering the effect of size dependence and flexible supports is presented. Both static and dynamic analyses are performed to show the coupled effect of flexible support and electrical voltage on the static and dynamic performance of the microcantilever beam. The wavelet-based finite element method (FEM) is used to derive the elastodynamic model of the microcantilever beam. The energy expressions are derived using couple stress theory, while additional energy terms are introduced to represent the flexible boundaries and fringing fields. Numerical simulations and comparisons with experimental data show the validity of the developed waveletbased finite element model and its potential in tackling practical problems in the design and evaluation of micro-devices.