The predominant part of the Indian population generally depends on a two-wheeler for transportation needs. However, very poor road conditions and poor vehicle designs have led to development of pains in the body. The percentage of such incidents involving musculoskeletal pains is alarmingly gaining impetus in the region. Hence, an attempt has been made to analyze and obtain ideal operating conditions of the vehicle for varying terrain amplitudes of 5, 10 and 15 mm, respectively. In this work, a coupled human body and twowheeler is modeled as a lumped parameter system. The composite model is analyzed by computer program (MAT lab) for vertical vibration responses of the different body parts to vertical vibrations inputs that are sinusoidal in nature and applied to wheels of the twowheeler. The numerical analysis is carried out for a Hero Honda splendor vehicle and an average male human body weighing around 80 kilograms. The analysis successfully concludes the torso as the part of the human body that experiences maximum displacement followed by head and thorax for all the terrain amplitudes involved in the study. The study also concludes that the ideal speed of the vehicle to be maintained for the body to experience minimum vibrations is 8 Hz i.e. 49.60 km/hr.
The current research focuses on the implementation of the fuzzy logic approach for the prediction of base pressure as a function of the input parameters. The relationship of base pressure (β ) with input parameters, namely, Mach number (M), nozzle pressure ratio (η), area ratio (α), length to diameter ratio (ξ ), and jet control (ϑ ) is analyzed. The precise fuzzy modeling approach based on Takagi and Sugeno's fuzzy system has been used along with linear and non-linear type membership functions (MFs), to evaluate the effectiveness of the developed model. Additionally, the generated models were tested with 20 test cases that were different from the training data. The proposed fuzzy logic method removes the requirement for several trials to determine the most critical input parameters. This will expedite and minimize the expense of experiments. The findings indicate that the developed model can generate accurate predictions
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