Rafic Younes

Study of a Hybrid Wind-Diesel System with Compressed Air Energy Storage

The electricity supply in remote areas around the world uses mostly diesel generators. This method, relatively inefficient and expensive, is responsible for the emission of 1.2 million tons of greenhouse gas (GHG) annually, only in Canada. Some low and high penetration wind-diesel hybrid systems (WDS) have been experimented in order to reduce the diesel consumption. The use of a high penetration system together with compressed air energy storage (CAES) it is a viable alternative to improve the overall percentage of renewable energy and reduce the cost of electricity. In this paper we compare different technical solutions for the CAES system and choose the one that optimize the performance and the cost of the overall system. While in this extended abstract only a superficial description of this system is introduced, detailed results of the simulation will be presented in the complete paper. This new design conducts to the increase of diesel power and efficiency, to the reduction of fuel consumption and GHG emissions, in addition to economies on the maintenance and replacement cost of the diesels.

Optimal Control of a Variable Geometry Turbocharged Diesel Engine Using Neural Networks: Applications on the ETC Test Cycle

Modern diesel engines are typically equipped with variable geometry turbo-compressor, exhaust gas recirculation (EGR) system, common rail injection system, and post-treatment devices in order to increase their power while respecting the emissions standards. Consequently, the control of diesel engines has become a difficult task involving five to ten control variables that interact with each other and that are highly nonlinear. Actually, the control schemes of the engines are all based on static lookup tables identified on test-benches; the values of the control variables are interpolated using these tables and then, they are corrected, online, by using the control techniques in order to obtain better engines response under dynamic conditions. In this paper, we are interested in developing a mathematical optimization process that search for the optimal control schemes of the diesel engines under static and dynamic conditions. First, we suggest modeling a turbocharged diesel engine and its opacity using the mean value model which requires limited experiments; the models simulations are in excellent agreement with the experimental data. Then the created model is integrated in a dynamic optimization process based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. The optimization results show the reduction of the opacity while enhancing the engines effective power. Finally, we proposed a practical control technique based on the neural networks in order to apply these control schemes online to the engine. The neural controller is integrated into the engines simulations and is used to control the engine in real time on the European transient cycle (ETC). The results confirm the validity of the neural controller.

Comparative Review Study on Elastic Properties Modeling for Unidirectional Composite Materials

© 2012 Younes et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Comparative Review Study on Elastic Properties Modeling for Unidirectional Composite Materials

Review of recent trends in gas sensing technologies and their miniaturization potential

Purpose – This paper aims to give an overview about the state of the art and novel technologies used in gas sensing. It also discusses the miniaturization potential of some of these technologies in a comparative way. Design/methodology/approach – In this article, the authors state the most of the methods used in gas sensing discuss their advantages and disadvantages and at last the authors discuss the ability of their miniaturization comparing between them in terms of their sensing parameters like sensitivity, selectivity and cost. Findings – In this article, the authors will try to cover most of the important methods used in gas sensing and their recent developments. The authors will also discuss their miniaturization potential trying to find the best candidate among the different types for the aim of miniaturization. Originality/value – In this article, the authors will review most of the methods used in gas sensing and discuss their miniaturization potential delimiting the research to a certain type of...

Numerical/analytical methods to evaluate the mechanical behavior of interlock composites

It is seen that 2.5D interlocks are particular reinforcements for high advanced applications (i.e., spatial and aeronautics fields) that are believed to have a high structural potential. This kind of reinforcement entails to consider the composite as a structure because interlocks are built by crossing the warp yarns with the weft (or fill) yarns in the three directions. In this article, a new numerical and analytical model is proposed. To evaluate the mechanical behavior, one may obtain numerically, the anisotropic elastic engineering constants from a finite element model (FEM). This technique of virtual testing consists of modeling the composite at the meso-scale to obtain a macro-scale response with a stress—strain analysis. At the moment, numerical simulations of such materials mainly involve geometrical models and automated tetrahedral meshes that make it difficult to cope with the orthotropic behavior of yarns materials. We thus propose a new meshing methodology to build an elementary volume made of tetrahedra for the isotropic matrix and of mapped hexaedra for the transversely isotropic yarns in order to achieve the FE discretization. A new analytical model is also been proposed, based on a geometrical modeling of the yarns using sinusoidal function and on homogenization at the macrolevel based on both iso-strain and iso-stress assumptions. This model allows the estimation of stiffness matrix of the composite in terms of the properties of its constituents and the geometry of the fabric. Two 2.5D interlock composites from the ‘layer—layer’ family are studied where the nine engineering constants are evaluated. The FEM and the analytical model show a good agreement with each other and with available in-plane Young’s modulus and Poisson’s ratio experimental results.

Dimensionality reduction on hyperspectral images: A comparative review based on artificial datas

In this research we address the problem of high-dimensional in hyperspectral images, which may contain rare /anomaly vectors introduced in the subspace observation that we wish to preserve. Linear techniques Principal Component Analysis(PCA), and non linear techniques Kernel PCA, Isomap, Multidimensional scaling (MDS), Local Tangent Space Alignment (LTSA), Diffusion maps, Sammon mapping, Symmetric Stochastic Neighbor Embedding (SymSNE), Stochastic Neighbor Embedding (SNE), Locally Linear Embedding(LLE), Locality Preserving Projection(LPP), Neighborhood Preserving embedding (NPE), Linear Local Tangent Space Alignment (LLTSA) was presented. Classical approaches criterion based on the norm ld, derivative spectral, nearest neighbors and quality criteria are used for obtaining a good preservation of these vectors in the reduction dimension. We have observed from the results obtained that Sammon and Isomap are less sensitive to these rare vectors compared to the other presented methods.

A corrective function for the estimation of the longitudinal Young’s modulus in a developed analytical model for 2.5D woven composites:

In this article, the problem that is faced when the developed analytical model is applied to 2.5D interlocks of the ‘yarn—yarn’ type is discussed. The difficulty encountered is taking into account the influence of the number of weft yarns covered by the warp yarn. Different architectures of these interlocks will have the same stiffness matrix when modeled with the developed analytical model that uses the volume proportion of the three phases: warp, weft, and matrix. The influence of the number of weft yarns on the longitudinal Young’s modulus is studied. A corrective function has been evaluated using a finite element numerical modeling of four fictive woven composites created with ‘ANSYS’ software. These woven composite have the same undulated warp and linear weft yarns, while they differ in the number of weft yarns covered by a warp yarn. The longitudinal Young’s modulus is evaluated for each composite using a numerical model which is also presented in this study. Moreover, a cross-ply laminate (0,90) is modeled by the classical laminate theory in the estimation procedure of the corrective function. The corrected longitudinal Young’s moduli show better agreement with numerical results compared to noncorrected ones.

Fuel consumption evaluation of an optimized new hybrid pneumatic–combustion vehicle engine on several driving cycles

In this paper, we describe an optimization followed by a fuel-saving evaluation of a new concept of a hybrid pneumatic–combustion engine that can be obtained by modifying a conventional internal combustion engine without developing a new cylinder head. Until now, most studies on the pneumatic hybridization of internal combustion engines have dealt with a two-stroke pure pneumatic mode. The few concept studies that have dealt with a hybrid pneumatic–combustion four-stroke mode required a supplementary valve to be added to charge compressed air in the combustion chamber. This heavy modification cannot be carried out by simply adjusting an existing internal combustion engine because a new cylinder head should be developed. It is therefore not logical to suggest this concept as an option in vehicle powertrains to reduce fuel consumption. Moreover, those studies focus on spark-ignition engines; there are reasons to think that their concepts might not work adequately for diesel engines. Our concept is capable of making a diesel engine operate under two-stroke pneumatic motor modes, two-stroke pneumatic pump modes and four-stroke hybrid modes, without requiring an additional valve in the combustion chamber. This fact constitutes our study’s strength and innovation. The evaluation of our concept is based on ideal thermodynamic cycle modeling. The optimized valve actuation timings for all modes lead to generic maps that are independent of the engine size. The fuel economy is calculated based on the new European driving cycle and on the assessment and reliability of transport emission models and inventory system urban and rural cycles.

Stability of Dimensionality Reduction Methods Applied on Artificial Hyperspectral Images

Dimensionality reduction is a big challenge in many areas. In this research we address the problem of high-dimensional hyperspectral images in which we are aiming to preserve its information quality. This paper introduces a study stability of the non parametric and unsupervised methods of projection and of bands selection used in dimensionality reduction of different noise levels determined with different numbers of data points. The quality criteria based on the norm and correlation are employed obtaining a good preservation of these artificial data in the reduced dimensions. The added value of these criteria can be illustrated in the evaluation of the reductions performance, when considering the stability of two categories of bands selection methods and projection methods. The performances of the method are verified on artificial data sets for validation. An hybridization for a better stability is proposed in this paper, Band Clustering (BandClust) with Multidimensional Scaling (MDS) for dimensionality reduction. Examples are given to demonstrate the hybridization originality and relevance(BandClust/MDS) of the analysis carried out in this paper.

A constraint-handling technique for genetic algorithms using a violation factor

Over the years, several meta-heuristic algorithms were proposed and are now emerging as common methods for constrained optimization problems. Among them, genetic algorithms (GA’s) shine as popular evolutionary algorithms (EA’s) in engineering optimization. Most engineering design problems are difficult to resolve with conventional optimization algorithms because they are highly nonlinear and contain constraints. In order to handle these constraints, the most common technique is to apply penalty functions. The major drawback is that they require tuning of parameters, which can be very challenging. In this paper, we present a constraint-handling technique for GA’s solely using the violation factor, called VCH (Violation Constraint-Handling) method. Several benchmark problems from the literature are examined. The VCH technique was able to provide a consistent performance and match results from other GA-based techniques.