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Go to Editorial ManagerVan Kármán vortex street is considered an important phenomenon that accompanies fluid flow, especially when exposed to a certain barrier, as periodic vortexes occur on both sides of the body that rotate in two opposite directions. This phenomenon occurs in the atmosphere around mountains, oceans, seas, and islands. Also, this phenomenon makes it possible to induce a fluid flow around a specific body present in the flow path. In this study, a model for fluid flow around a cylinder of a certain diameter was taken, where the flow near the boundary layers of the cylinder surface moves slower than near the free stream. In addition, the pressure distribution was studied, and it was observed that there is a pressure gradient due to the difference in momentum at the surface of the cylinder in distant areas due to friction. The study area was divided into fine meshes with Fluent software, especially in the irregular areas. The simulation was implemented for Reynolds numbers Re = 100 and Re = 1500 for incompressible flows. Consequently, the equations that do not depend on pressure are difficult to solve. Therefore, methods linking pressure and velocity were adopted, where the pressure-velocity coupling simple method was used. The first-order forward difference scheme was adopted in representing the differential equations as a function of time when performing the simulation. From the steady state and upwards to Reynolds number Re = 100, it was observed that a twain of vortices appeared on the body at a certain speed range. When the state was changed from the stable state to the transitional state, the results changed, as the flow became asymmetric and unsteady due to vortex shedding phenomena, which led to the generation of vortexes in different ways. The U-Velocity curve was studied for two different cases, and the results showed a large discrepancy between the first order and the second order, where the second order had better behavior but required great effort to reach accurate results. Also, pressure-velocity was studied to satisfy mass conservation, and numerical techniques were used to c ompute the equations of Navier-Stokes in CFD, such as SIMPLEC, PISO, and SIMPLE. An acceptable convergence was not reached with the PISO; therefore, the SIMPLE method was adopted. The pressure gradient was drawn around the cylinder, where it was observed that the pressure was greatest at the front of the cylinder and its lowest value at the end.
This paper is concerned with the design of a new controller for active suspension system. The model is considered as a quarter-car. The presented controller depends on the fuzzy technique and NARMA-L2 linearization algorithm. The compensation system that added by the fuzzy rules improves the performance of the controller, while the neural network produces the required control signal. The new controller can achieve an improvement of the ride comfort with a reasonable value of power consumption. The mathematical analysis of the mechanical power used by the model is focused on the average and the RMS of the power supplied to the system, regardless of the frequency content of the vibration signal. The simulation results which are verified by a practical examples of road profiles, demonstrate the efficacy of the proposed controller.
An intelligent and anticipatory speed controller for internal combustion engines was designed theoretically and examined experimentally. This design was based on the addition of a torque loop to the main speed loop. The model can sense the external load with the help of a load cell and send this signal to a soft computing unit for analysis and processing. This scheme will improve the ability of anticipation of controller since it treats the factors that affect the speed, not the speed itself. The experimental design was implemented using two types of actuating techniques; an intelligent throttling actuator and an intelligent injection actuator. The signal was analyzed by using intelligent techniques such as fuzzy logic, neural network and genetic algorithm. The experimental data were used to train the neural and the Adaptive Neuro–Fuzzy Inference System. The comparison of the results obtained in this work with other available models proved the efficiency and the robustness of the present model.