M. Zaouche, A. Beloula, R. Louali¹, S. Bouaziz², M. Hamerlain³

CMES-Computer Modeling in Engineering & Sciences, Vol.104, No.3, pp. 159-184, 2015, DOI:10.3970/cmes.2015.104.159

Abstract Safety automation of complex mobile systems is a current topic issue in industry and research laboratories, especially in aeronautics. The dynamic models of these systems are nonlinear, Multi-Input Multi-Output (MIMO) and tightly coupled. The nonlinearity resides in the dynamic equations and also in the aerodynamic coefficients’ variability.
This paper is devoted to developing the piloting law based on the combination of the robust differentiator with a dynamic adaptation of the gains and the robust controller via second order sliding mode, by using an aircraft in virtual simulated environments.
To deal with the design of an autopilot controller, we propose… More >

Sung-Eun Kim¹, Shin Hyung Rhee²

CMES-Computer Modeling in Engineering & Sciences, Vol.62, No.3, pp. 291-310, 2010, DOI:10.3970/cmes.2010.062.291

Abstract Turbulent flow past a finite wing has been computed to assess the fidelity of modern computational fluid dynamics in predicting tip vortex flows. The efficacy of a feature-adaptive local mesh refinement to resolve the steep gradients in the flow field near the tip vortex is demonstrated. The impact of turbulence modeling is evaluated using several popular eddy viscosity models and a Reynolds stress transport model. The results indicate that the combination of a computational mesh with an adequate resolution, high-order spatial discretization scheme along with the use of advanced turbulence models can predict tip vortex flows with acceptable accuracy. More >

Peter Lucas¹, Alexander H. van Zuijlen¹, Hester Bijl¹

CMES-Computer Modeling in Engineering & Sciences, Vol.41, No.2, pp. 147-176, 2009, DOI:10.3970/cmes.2009.041.147

Abstract Mesh adaptation is a fairly established tool to obtain numerically accurate solutions for flow problems. Computational efficiency is, however, not always guaranteed for the adaptation strategies found in literature. Typically excessive mesh growth diminishes the potential efficiency gain. This paper, therefore, extends the strategy proposed by [Aftosmis and Berger (2002)] to compute the refinement threshold. The extended strategy computes the refinement threshold based on a user desired number of grid cells and adaptations, thereby ensuring high computational efficiency. Because our main interest is flow around wind turbines, the adaptation strategy has been optimized for flow around wind turbine airfoils. The… More >

Jacek Narski^1,2, Marco Picasso¹

FDMP-Fluid Dynamics & Materials Processing, Vol.3, No.1, pp. 49-64, 2007, DOI:10.3970/fdmp.2007.003.049

Abstract An adaptive phase field model for the solidification of binary alloys in three space dimensions is presented. The fluid flow in the liquid due to different liquid/solid densities is taken into account. The unknowns are the phase field, the alloy concentration and the velocity/pressure in the liquid. Continuous, piecewise linear finite elements are used for the space discretization, a semi-implicit scheme is used for time discretization. An adaptive method allows the number of degrees of freedom to be reduced, the mesh tetrahedrons having high aspect ratio whenever needed. Numerical results show that our method is effective and allows to perform… More >

Huiyu Sun^1,*, Suzanne McIntosh¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 1-9, 2018, DOI:10.32604/cmc.2018.03684

Abstract The majority of big data analytics applied to transportation datasets suffer from being too domain-specific, that is, they draw conclusions for a dataset based on analytics on the same dataset. This makes models trained from one domain (e.g. taxi data) applies badly to a different domain (e.g. Uber data). To achieve accurate analyses on a new domain, substantial amounts of data must be available, which limits practical applications. To remedy this, we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task: Selectively choosing a small amount of datapoints from a new domain while… More >