Vol 5 - Issue 1

Uncertainties and Reliability of Multiphysical Systems

List of Articles

A comparative analysis between the capacities of the RANS and DES models to predict the distribution of flows and turbulence was carried out in order to develop guidelines for the transient simulation of NACA 0012 airfoil. The objective of this review is to highlight the fundamental differences between the turbulence models based on the Reynolds averaged Navier-Stokes (RANS) and DES approach around a 2D aircraft wing of the NACA 0012 type, in order to compare these two approaches and to provide future prospects. After the comparison of the relative computation time, the DES model proved to be a feasible method to efficiently and accurately simulate the unstable 2D turbulent flow of the NACA 0012 airfoil. The encouraging agreement obtained suggests that the widely-acknowledged status of DES as a near-future approach for aerodynamic application is justified.

The present study investigates the dynamic transmission efficiency of a two-stage wind turbine gearbox by considering the uncertainty. The work consists of considering the uncertainty in determining the dynamic behavior of input and output angular displacement velocity to achieve the efficiency. The uncertainties considered in this work are the gear modulus and torsional stiffness, applied independently to understand the effect of each on the system and finally in a coupled manner to reach results by interaction. In order to approach the real experimental values, the uncertainty is applied with the Monte Carlo method, whose results will be the verification reference for the probabilistic Polynomial Chaos approach.

The metaheuristic known as the optimization algorithm; a resolution of difficult problems of minimization or maximization of a function in order to find almost optimal solutions. There are a wide variety of metaheuristics, but in this research article we will only talk about three optimization algorithms that will help us optimize the cost of packaging an industry by using MATLAB software to program them. The first algorithm is the best-known particle swarm optimization in the optimization field, which is inspired by the simulation movement of a group of birds, the second is the simulated annealing inspired by annealing in metallurgy, a Heat treatment technique also involving controlled cooling of a material which affects both temperature and energy. And the last is the genetic algorithm which is commonly used to give high quality results to optimization problems by relying on bio-inspired operators such as mutation, crossing and selection. We will compare the performance of each of them using the test functions according to their uptime and convergence and will apply to our industrial optimization problem.

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In this research, two population-based, nature-inspired optimization paradigms were described, dubbed ‘’Harris Hawks Optimization’’ (HHO) and ‘’Ant Colony Optimization’’ (ACO). The real inspiration of HHO is the cooperative behavior and pursuing technique of Harris’ hawks in nature, termed "surprise pounce." At the same time, ACO is inspired by observing the behavior of actual ants. Those two natural movements were mathematically modeled to create optimization algorithms. The effectiveness of HHO and ACO optimizers is checked throughout comparisons that show that the HHO algorithm provides better results when the comparison is based on test functions, while for the case study treated, which is the planning of schedules and the minimization of the cost of staff assignment, ACO is better.

The main goal of this paper is to study the influence of geometrical parameters of the high electron mobility transistor (HEMT) structure. We will develop the electro-thermomechanical modeling by the finite element method, using Comsol multiphysics software. This model allowed us to simulate the thermomechanical behavior of the HEMT according to the operating conditions. It also allowed us to study the influence of the nucleation layer on this behavior. The results of the numerical simulations obtained showed that, although the thickness of the layer does not exceed 1 μm, it has a great influence on the thermal and mechanical behavior of the component. Therefore, this layer must be taken into consideration for any study that aims to develop or optimize this technology.