a b s t r a c tA novel optimization algorithm is presented, inspired by group hunting of animals such as lions, wolves, and dolphins. Although these hunters have differences in the way of hunting, they are common in that all of them look for a prey in a group. The hunters encircle the prey and gradually tighten the ring of siege until they catch the prey. In addition, each member of the group corrects its position based on its own position and the position of other members. If the prey escapes from the ring, hunters reorganize the group to siege the prey again. Several benchmark numerical optimization problems, constrained and unconstrained, are presented here to demonstrate the effectiveness and robustness of the proposed Hunting Search (HuS) algorithm. The results indicate that the proposed method is a powerful search and optimization technique. It yields better solutions compared to those obtained by some current algorithms when applied to continuous problems.

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The rheological behavior of field-dependent smart fluids in both the pre-yield and post-yield regimes is investigated. Typical viscoelastic and viscoplastic models are employed to model the fluids behavior. Viscoelastic models are used widely in the pre-yield regime. Viscoplastic models are also used extensively in both the pre-yield and post-yield regimes. Two smart fluids including a ferromagnetic nanoparticle fluid and an MR fluid are examined here. Using an MCR300 rheometer, the rheological properties of the fluids in both oscillation and rotational mode are measured. In the oscillation mode, the storage and loss moduli versus frequency are measured. In the rotational mode, shear stress, shear rate, viscosity and torque are measured. In the frequency domain, the pre-yield behavior of the ferromagnetic nano-particle fluid is modeled by Kelvin-Voigt solid model. Also, the three-parameter fluid model is used to model the pre-yield behavior of the MR fluid. Two viscoplastic models including Bingham-plastic and Herschel-Bulkley models are selected to model the rheological behavior of fluids in the time domain. Which model is more appropriate depends on the external magnetic field and the shear rate. Both models are used here to model the fluids' behavior. The models properly predict the results observed in the experiments.

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The complex shear modulus of an electrorheological (ER) adaptive sandwich beam is optimally estimated to model the system for vibration control. In the composition of a three layered beam, the ER fluid layer is embedded between two constraining layers. Using finite element (FE) method, the governing equations of the composite viscoelastic beam are derived. The developed model is compared with the results found in the literature. In addition, for a fabricated ER sandwich beam, the ASTM E756 standard is employed to estimate the complex shear modulus of the viscoelastic layer in different electric fields. An optimization procedure is conducted based on particle swarm optimization (PSO). In this process, the rough estimation of complex shear modulus extracted by ASTM E756 is modified to correlate the results of the FE model and the experimental tests. The updated FE model is mapped into an appropriate form that can be used for control objectives. Finally, a semi-active sliding mode control is utilized to attenuate the vibration of the adaptive sandwich beam by tuning its electric field dependent characteristics.

There are many techniques to characterize the hydrodynamics of fluidized beds, but new techniques are still needed for more reliable measurement. Bed vibrations were measured by an accelerometer in a gas-solid fluidized bed to characterize the hydrodynamics of the fluidized bed in a nonintrusive manner. Measurements were carried out at different superficial gas velocities and particle sizes. Pressure fluctuations were measured simultaneously. Vibration signals were processed using statistical analysis. For the sake of the evaluation, the vibration technique was used to calculate minimum fluidization velocity. It was shown that minimum fluidization velocity can be determined from the variation of standard deviation, skewness, and kurtosis of vibration signals against superficial gas velocity of the bed. Kurtosis was proved to be a new method of analyzing vibration signals. Results indicate that analyzing the vibration signals can be an effective nonintrusive technique to characterize the hydrodynamics of fluidized beds. V

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