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Introduction
Wassila Grine has done her Ph.D. in Process Engineering from the University of 8 May 1945, Guelma, Algeria. She is a member of the laboratory of Industrial Analysis and Materials Engineering (LAIGM). Her research interests include Nanofluids, Heat transfer, Chemical Engineering, Artificial Neural networks, Ansys and Matlab simulation. She is currently working on the modeling and simulation of hybrid nanofluids' properties in different fields. She is open to scientific cooperation.
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Publications
Publications (19)
Nanofluids are nowadays the most widely used working heat transfer fluids. Therefore, a more accurate assessment of their thermophysical properties, as well as their performance, is required. Thermal conductivity is the most influential thermophysical property in the application of nanofluids. The increase in nanofluids' thermal conductivity cannot...
Currently, nanofluids are the most widely used heat transfer fluid. This is due to the nanofluid-enhanced thermal properties. Therefore, a more accurate evaluation of their thermophysical properties is required. In addition to its performance, viscosity is among the most influential thermophysical properties in the application of nanofluids. For th...
Abstract:
Nanofluids are widely used to increase heat transfer; and their thermal conductivity as well as their viscosity are the most influential thermophysical properties along with other physical factors of varying importance. Moreover, models of larger structures cannot mimic nanoscale thermal behavior, therefore, further study is needed to des...
The main aim of this paper is to develp and validate an artificial neural network (ANN) approch to predict the thermal conductivity and viscosity of Alumina water based nanofluids as function nanofluid concentration, temperature, naoparticles diameter and sape.
In this paper, a full factorial design analysis is proposed for predicting nanofluid thermal conductivity ratio (TCR) as well as determining the effects of critical factors and their interactions. A statistical design of experiment approach with three variables (volume fraction, temperature, and nanoparticle diameter) at two levels is carried out....
The viscosity of nanofluids can be affected by many factors. In pursuit of such improved accuracy, model-based viscosity prediction methods have become more complicated. Therefore, there is a need to find an alternative approach that is able to provide a quick solution to viscosity prediction for nanofluids. In this paper, an Artificial Neural Netw...
The aims of the present study are to develop and validate an artificial neural network (ANN) approach to estimate the thermal conductivity ratio (TCR) of Alumina/water and Alumina /EG nanofluids as a function of temperature, volume fraction and diameter of the nanoparticle. For the purpose of evaluating the accuracy of the proposed model by ANN for...
The nanofluids are solutions composed of particles of a nanometric size suspended in a liquid. Studies of these composite fluids show interesting aptitudes specifically: a better thermal conductivity and a coefficient of convective exchange significantly increased compared to traditional liquids, water in particular.
Nanofluids, containing nanometr...
The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure properties of nanofluids. Many researchers have developed empirical correlations to estimate nanofluids thermal conductivity. This paper focuses on designing a design of experiments (DOE) approach which can...
The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. Many researchers have developed empirical correlations of nanofluids' thermal conductivity as a function of temperature, volume fraction and nanoparticle size to estimate nanof...
Les nanoparticules sont des solutions composées de particules de taille nanométriques en suspension dans un liquide. Les études de ces fluides composites montrent des aptitudes particulièrement intéressantes du fait d'une meilleure conductivité thermique et d'un coefficient d'échange convectif significativement accru par rapport à des liquides clas...
