Abstract: Inspired by topology of humpback whale flippers, a meta-model is designed for wing planform design. The net is trained based on experimental data using cascade-forward artificial neural network (ANN) to investigate effects of the amplitude and wavelength of sinusoidal leading edge configurations on the wing performance. Afterwards, the trained ANN is coupled with a genetic algorithm method towards an optimum design strategy. Finally, flow physics of the problem for an optimized rectangular planform and also a real flipper geometry planform is simulated using Lam-Bremhorst low Reynolds number turbulence model with damping wall-functions resolving to the wall. Lift and drag coefficients and also details of flow are presented along with comparisons to available experimental data. Results show that the proposed strategy can be adopted with success as a fast-estimation tool for performance prediction of wing planforms with wavy leading edge at preliminary design phase.
Abstract: Application of neural networks in execution of
programmed pulse width modulation (PPWM) of a voltage source
inverter (VSI) is studied in this paper. Using the proposed method it is
possible to cancel out the desired harmonics in output of VSI in
addition to control the magnitude of fundamental harmonic,
contineously. By checking the non-trained values and a performance
index, the most appropriate neural network is proposed. It is shown
that neural networks may solve the custom difficulties of practical
utilization of PPWM such as large size of memory, complex digital
circuits and controlling the magnitude of output voltage in a discrete
manner.
Abstract: During the last few years, several sheet hydroforming
processes have been introduced. Despite the advantages of these
methods, they have some limitations. Of the processes, the two main
ones are the standard hydroforming and hydromechanical deep
drawing. A new sheet hydroforming die set was proposed that has the
advantages of both processes and eliminates their limitations. In this
method, a polyurethane plate was used as a part of the die-set to
control the blank holder force. This paper outlines the Taguchi
optimization methodology, which is applied to optimize the effective
parameters in forming cylindrical cups by the new die set of sheet
hydroforming process. The process parameters evaluated in this
research are polyurethane hardness, polyurethane thickness, forming
pressure path and polyurethane hole diameter. The design of
experiments based upon L9 orthogonal arrays by Taguchi was used
and analysis of variance (ANOVA) was employed to analyze the
effect of these parameters on the forming pressure. The analysis of
the results showed that the optimal combination for low forming
pressure is harder polyurethane, bigger diameter of polyurethane hole
and thinner polyurethane. Finally, the confirmation test was derived
based on the optimal combination of parameters and it was shown
that the Taguchi method is suitable to examine the optimization
process.