Simultaneous Geometry Optimization Research Articles

Numerical investigation and simultaneous optimization of geometry and zeta-potential in electroosmotic mixing flows

Effects of zeta potential distribution and geometrical specifications on flow rate and mixing performance of electroosmotic flows in straight, converging, diverging, and converging-diverging microchannels are investigated by lattice Boltzmann method. Modified Navier-Stokes equations along with the Nernst–Planck equation for ionic distributions are solved and validated against the available electroosmotic flow solutions. The geometrical effects on flow patterns and mixing efficiencies in the presence of patched zeta potential distributions are examined in search for the simultaneously enhanced mixing performance and mass flow rate. Numerical results indicate that converging channel leads to a sizable increase in mixing efficiencies, while at the same time decreases the flow rate. In contrast, diverging channels increase the flow rate, where the mixing performance deteriorates. Thus, it is expected to achieve a balance between the mixing efficiency and mass flow rate using converging-diverging geometries. In this regard, the response surface methodology (RSM) is employed to maximize the mixing efficiency and flow rate of the converging-diverging microchannel. According to numerical results, the optimum parameters predicted by RSM are in excellent agreement with full numerical simulations.

Simultaneous Optimization of Geometry and Firing Angles for In-Wheel Switched Reluctance Motor Drive

Interests in using rare-earth free motors such as switched reluctance motors (SRMs) for electric and hybrid electric vehicles continue to gain popularity, owing to their low cost and robustness. Optimal design of an SRM, to meet specific characteristics for an application, should involve simultaneous optimization of the motor geometry and control in order to achieve the highest performance with the lowest cost. This paper presents a constrained multiobjective optimization framework for design and control of an SRM based on a nondominated sorting genetic algorithm II. The proposed methodology optimizes SRM operation for high volume traction applications by considering multiple criteria including efficiency, average torque, and torque ripple. Several constraints are defined by the considered application, such as the motor stack length, outer diameter, minimum operating power, minimum desired efficiency, rated speed, rated current, and supply voltage. The outcome of this optimization includes an optimal geometry, outlining variables such as air gap length, rotor inner diameter, stator pole arc angle, rotor pole arc angle, rotor back iron, stator pole height, and stator inner diameter as well as optimal turn-on and turn-off firing angles. Then the machine is manufactured according to the obtained optimal specifications. Comprehensive finite-element analysis and experimental results are provided to validate the theoretical findings.

Molecular geometries from spin Hamiltonian calculations through simultaneous optimization of geometry and wave function

The recently developed spin Hamiltonian approach to conjugatedπ-electron molecules is reexamined. A simultaneous optimization of the geometry and wave functions, achieved by the use of the conjugate gradient method, facilitates calculations of the molecular geometries in the ground and excited electronic states. The computation time increases approximately linearly with the number of basis functions, making calculations for molecules having up to 18 carbon atoms (48 620 basis functions) readily available. Geometries of several benzenoid hydrocarbons are optimized and the results are discussed.