No abstract

No abstract

The suspensions used in heavy vehicles often consist of several oil and two gas chambers. In order to perform an analytical study of the mass flow transferred between two gas chambers separated by a nozzle, and when considering the gas as compressible and real, it is usually needed to determine the discharge coefficient of the nozzle. The nozzle configuration analyzed in the present study consists of a T shape, and it is used to separate two nitrogen chambers employed in heavy vehicle suspensions. In the present study, under compressible dynamic real flow conditions and at operating pressures, discharge coefficients were determined based on experimental data. A test rig was constructed for this purpose, and air was used as working fluid. The study clarifies that discharge coefficients for the T shape nozzle studied not only depend on the pressure gradient between chambers but also on the flow direction. Computational Fluid Dynamic (CFD) simulations, using air as working fluid and when flowing in both nozzle directions, were undertaken, as well, and the fluid was considered as compressible and ideal. The CFD results deeply helped in understanding why the dynamic discharge coefficients were dependent on both the pressure ratio and flow direction, clarifying at which nozzle location, and for how long, chocked flow was to be expected. Experimentally-based results were compared with the CFD ones, validating both the experimental procedure and numerical methodologies presented. The information gathered in the present study is aimed to be used to mathematically characterize the dynamic performance of a real suspension.

Genetic Algorithms (GA) are useful optimization methods for exploration of the search space, but they usually have slowness problems to exploit and converge to the minimum. On the other hand, gradient based methods converge faster to local minimums, although are not so robust (e.g., flat areas and discontinuities can cause problems) and they lack exploration capabilities. This article presents a hybrid optimization method trying to combine the virtues of genetic and gradient based algorithms, and to overcome their corresponding drawbacks. The performance of the Hybrid Method is compared against a gradient based method and a Genetic Algorithm, both used alone. The rate of convergence of the methods is used to compare their performance. To take into account the robustness of the methods, each one has been executed more than once, with different starting points for the gradient based method and different random seeds for the Genetic Algorithm and the Hybrid Method. The performance of the different methods is tested against an optimization Active Flow Control (AFC) problem over a 2D Selig–Donovan 7003 (SD7003) airfoil at Reynolds number 6×104 and a 14 degree angle of attack. Five design variables are considered: jet position, jet width, momentum coefficient, forcing frequency and jet inclination angle. The objective function is defined as minus the lift coefficient (−Cl), so it is defined as a minimization problem. The proposed Hybrid Method enables working with N optimization algorithms, multiple objective functions and design variables per optimization algorithm.