Vol. 33 Issue 4
A. Horimek, N. Ait Messaoudene, W. Aich, M. Aichouni, L. Kolsi and J. Ghernaout
In this work, the problem of thermal development for laminar forced convection of a pseudoplastic thermo dependent fluid in a concentric annular horizontal duct is numerically addressed. Three thermal conditions are assumed: inner cylinder subjected to an imposed heat flux while the outer is insulated; then the inverse; and finally both cylinders are heated with the same heat flux densities. All computations are carried out from the entrance up to the thermal fully developed stage for a flow already developed dynamically. The study objective is to illustrate the effects of the rheological index (n), aspect ratio (r1), thermodependency (Pn) and heating mode on velocity profiles and Nusselt number evolution in addition to thermal entrance length. The results show an improvement in heat exchange with the shear-thinning of the fluid. The outer Nusselt decreases as r1 decreases while the inner one improves. Overall, the thermodependency has a similar effect to that of the shear-thinning. A very important result shows the reduction in thermal length with increasing n and/or Pn. Simple, accurate and widely valid correlations are provided for Nusselt number and thermal entrance length under different conditions.
Afnan Mahmood Freije, Hussain Abdulla Ali, Maryam Mohamed Al Ansari, Mustafa Ebrahim Ali.
The Kingdom of Bahrain is facing escalating concerns about the current municipal solid waste (MSW) management. These concerns are aggravated by the increased MSW generated per capita as well as the current recycling rate in Bahrain which does not exceed 1%. The residential areas produce substantial amount of MSW and therefore can be considered as good places to start any recycling program. A total of 300 randomly selected household representatives were selected
to answer a questionnaire that was designed to identify their socio-economic status, measure their
awareness, their recycling practice, to determine their willingness, and recognize any challenges
and obstacles that hinder the recycling practice. The results have revealed that the correspondents
have shown high awareness (75%) concerning recycling, however their willingness to participate
in recycling was limited to 54.3%. In addition 46.3% of the respondents have never recycled any
of the most recyclable materials such as paper, glass, plastic, aluminum and tin cans, food waste,
garden waste, batteries, and medicine. Nevertheless, the majority of the respondents (87.3%)
were willing to engage in recycling or composting scheme mainly if obstacles such as lack
of proper recycling infrastructure and spaces to store different bins in their living spaces were
resolved. In view of the results obtained from the current study, several recommendations were
suggested including the implementation of effective legislations regarding waste recycling as
well as public engagement through awareness campaigns.
Mukesh Mann, Pradeep Tomar and Om Prakash Sangwan
In this paper, Artificial Particle Swarm Optimization (PSO) inspired by real Swarm social–psychological tendency is used to solve time constraint prioritization problem-the techniques to prioritize the test cases that finds faults as early as possible, or maximize the rate of fault detection in the suite. The proposed technique is compared with three searches based metaheuristic approaches–(1) an ant-colony optimization approach, (2) Cuscuta search algorithm and (3) Hybrid Particle Swarm Optimization algorithm and two evolutionary metaheuristic- (1) Multi-Criteria Genetic algorithm technique which the fitness is APFD and (2) Multi-Criteria Genetic algorithm technique which the fitness is the proposed fitness multiplied by APFD and with five other non-search based prioritization techniques- (1) optimal, (2) random, (3) reverse, (4) untreated and (5) average faults found per minute algorithm based ordering. We investigate whether the proposed PSO metaheuristic outperforms existing prioritizing techniques in terms of APFD Score.
Ammar Mahjoubi, Noureddine Elboughdiri, Djamel Ghernaout, Mohamed Boujelbene, Lioua Kolsi and Ammar Ben Brahim
In order to obtain a high electrical efficiency for photovoltaic (PV) system, it is necessary to cooling it; a Photovoltaic Thermal (PV/T) solar system is one of the most important methods for cooling photovoltaic modules. In this study, a thermal model of a PV/T air solar system was developed, validated from experimental data and then used to study the effects of various parameters on the performance of the system. The thermal model is based on the energy balance of the PV/T air module in which all essential heat
transfer mechanisms between the module to the environment and related electrical output are modeled
to observe the net change in PV/T air module temperature. The thermal model of PV/T module,
developed for the present study, has been numerically solved using finite element method (FEM) with
The main objective of the thermal model is to investigate the dependence of PV/T air module
temperature on the global solar irradiation and on air flow velocity. The results obtained from the
proposed thermal model are validated experimentally. The results indicate that increasing the air mass
flow rate when the design parameters are optimum will result into a significant increase in the overall
performance of the system.
Naif K. Al Shammari, Lioua Kolsi, Ghassan Al-hassan and Mohammed AL-Meshaal
An investigation of the air contained water extraction using a solar vapor absorption chiller working under the climatic condition of Riyadh city is carried out. Three typical days from different seasons were chosen for the study to evaluate the best operating conditions. Despite that the maximum cooling production is in July, the maximum water production is in January due
to the high relative humidity, in fact the maximum of daily production 10.22
L/day. The performances of the absorption chiller and the solar collector are
also evaluated and are found to be much influenced by the operating climatic
Electric load forecasting is considered nowadays as one of the key issues for electricity utilities to ensure both good planning and design in long-term and efficient operation and management in short and medium terms. In fact, predicting as accurately as possible the electric load and peak-load can contribute to avoiding electricity feeding disturbances and allow thus a high quality of service provided to consumers.
This paper reviews a selected set of papers on electricity demand forecasting techniques,
published from 2008 to 2016. This review is intended to classify the proposed models and
methods. The results presented in the surveyed papers show that a wide range of models
and methods have been implemented and tested. Depending on the forecasting horizon,
the study area and the used tools, the accuracy of the obtained forecasts are case-sensitive.
Although classical time-series continue to be used, combined approaches involving
more than two tools (models and methods) have started to attract more attention. More
particularly, artificial neural networks (ANN) and swarm intelligence techniques are being
increasingly used for their universal character and for the fact they do not need particular
regularity of the load records.