Abdelmajid Jemni

Effect of the Heat Pipe Adiabatic Region.

The main motivation of conducting this work is to present a rigorous analysis and investigation of the potential effect of the heat pipe adiabatic region on the flow and heat transfer performance of a heat pipe under varying evaporator and condenser conditions. A two-dimensional steady-state model for a cylindrical heat pipe coupling, for both regions, is presented, where the flow of the fluid in the porous structure is described by Darcy-Brinkman-Forchheimer model which accounts for the boundary and inertial effects. The model is solved numerically by using the finite volumes method, and a fortran code was developed to solve the system of equations obtained. The results show that a phase change can occur in the adiabatic region due to temperature gradient created in the porous structure as the heat input increases and the heat pipe boundary conditions change. A recirculation zone may be created at the condenser end section. The effect of the heat transfer rate on the vapor radial velocities and the performance of the heat pipe are discussed.

An optimal artificial neural network system for designing knit stretch fabrics

In this paper, a computer-aided system for designing knit stretch materials is presented. It allows designers to optimize the structure of knit stretch materials according to the functional properties. This system aims at modeling the relation between functional properties (outputs) and structural parameters (inputs) of knitted fabrics. Thirteen features characterizing knit structures and operating parameters were taken as input parameters of the artificial neural networks (ANNs). These parameters were preselected according to their possible influence on the outputs which were the elongation, growth, and elastic recovery. In order to reduce the complexity of the models, an original fuzzy logic-based method was proposed to select the most relevant parameters which were taken as input variables of the ANNs. The selection procedure of structural parameters allows designers to focus on the most relevant parameters in order to conduct production experiments related to the new product. Then, two types of model are set up by utilizing multilayer feed forward neural networks, which take into account the generality and the specificity of the product families, respectively. The presented models have been validated with the use of experimental data concerning several families of knitted fabrics.

Study the effect of operating parameters and intrinsic features of yarn and fabric on thermal conductivity of stretch knitted fabrics using artificial intelligence system

An artificial intelligence-based system approach is presented in which the effects of the operating parameters and intrinsic features of yarn and fabric on Thermal Conductivity of Stretch Knitted Fabrics are investigated. These parameters were pre-selected according to their possible influence on the outputs which were the thermal conductivity. An original fuzzy logic based method was proposed to select the most relevant parameters. The results show that Knitted Structure’s is the most important input parameter, followed by Lycra Proportion (%), Loop length (cm), Yarn Count, Weight per Unit Area (g/m2), Thickness (m), Gauge, Lycra Yarn Count (dtex) and Yarn Composition. According to our previous works, two types of model have been set up by utilizing multilayer feed forward neural networks, which take into account the generality and the specificity of the product families respectively. The relative importance of the input variables was calculated using the connection weight approach. The results were found to agree with the fuzzy logic based sensitivity criterion. The trend analysis of the developed model revealed the influence of various input parameters on the thermal conductivity of knitted fabrics. Thus, it is believed that artificial intelligence System could efficiently be applied to the knit industry to understand, evaluate and predict thermal comfort parameters of stretch knitted fabrics.

Heat and Mass Transfer During Hydriding

The hydrogen economy is a proposed scheme and technique of delivering energy using hydrogen. The hydrogen economy is committed to eliminate all of the problems that the fossil fuel economy creates. The advantages of the hydrogen economy consist of (Marban and Valdes-Solis 2007): (i) the elimination of pollution caused by fossil fuels, since the conversion technologies of hydrogen into energy are completely clean; (ii) the elimination of greenhouse gases, if hydrogen is produced using clean energy sources; and (iii) distributed production as hydrogen can be produced almost anywhere worldwide. However, the hydrogen economy faces several technological barriers before implementation such as the storage issues. The hydrogen storage in gaseous or liquid form presents serious safety concerns and requires high-energy input. The hydrogen storage in solid form, namely, reversible metal hydrides, is much safer and requires low-pressure conditions. Research on the design and performance optimization of the metal hydride tanks (MHT for short) is essential for the efficient operation of corresponding systems, thus considerable efforts are made in that regard.

A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics

Today numerous consumers consider thermal comfort to be one of the most significant attributes when purchasing textile and apparel products, so there is a need to develop a model able to simulate objectively the consumers’ perception. The global thermal comfort of stretch knitted fabrics is a multi-criteria phenomenon that requires the satisfaction of several properties at the same time. In this paper, we used the desirability functions to evaluate the satisfaction degree of global thermal comfort. Statistical method was used to investigate the interrelationship among knit thermo-physical properties, and group them into factors. Two models of artificial neural network (general and special) have been set up to predict the global thermal comfort from structural parameters (inputs) of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. A virtual leave one out approach dealing with over fitting phenomenon and allowing the selection of the optimal neural network architecture was used. By combining the strengths of statistics and fuzzy logic (data reduction and information summation) also a neural network (self-learning ability), hybrid model was developed to simulate the consumer thermal comfort perception. After that, ANN model is inverted. With a required output value and some input parameters it is possible to calculate the unknown optimum input parameter. Finally, this forecasting can help industrials to anticipate the consumer’s taste. Thus, they can adjust the knitting production parameter to reach the desired global thermal comfort to satisfy this consumer.

A neural network system for designing new stretch fabrics

In this paper, an artificial neural network (ANN) aided system for designing knit stretch materials based on the virtual leave one out approach is presented. This system aims at modeling the relation between functional properties (outputs) and structural parameters (inputs) of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. Knitted fabric structure type, yarn count, yarn composition, gauge, elasthane fiber proportion (%), elasthane yarn linear density, fabric thickness and fabric areal density, were used as inputs to ANN model. These models have been validated by a testing data. The developed neural model allows designers to optimize the structure of knit stretch materials according to the functional properties.