The Aim of this paper is to investigate numerically the simulation of ice melting in one and two dimension using the cell-centered finite volume method. The mathematical model is based on the heat conduction equation associated with a fixed grid, latent heat source approach. The fully implicit time scheme is selected to represent the time discretization. The ice conductivity is chosen
to be the value of the approximated conductivity at the interface between adjacent ice and water control volumes. The predicted temperature distribution, percentage melt fraction, interface location and its velocity is compared with those obtained from the exact analytical solution. A good agreement is obtained when comparing the numerical results of one

RM Abbas, AA Abdulhameed, AI Salahaldin, International Conference on Geotechnical Engineering, 2010

            In this paper the definition of fuzzy anti-normed linear spaces and its basic properties are used to prove some properties of a finite dimensional fuzzy anti-normed linear space.    

Abstract
             In this research will be treated with a healthy phenomenon has a significant impact on different age groups in the community, but a phenomenon tonsillitis where they will be first Tawfiq model slope self moving averages seasonal ARMA Seasonal through systematic Xbox Cengnzla counter with rheumatoid tonsils in the city of Mosul, and for the period 2004-2009 with prediction of these numbers coming twelve months, has found that the specimen is the best representation of the data model is the phenomenon SARMA (1,1) * (2,1) 12  from the other side and explanatory variables using a maximum temperature and minimum temperature, sol

This paper introduces experimental results of eighteen simply supported reinforced concrete beams of cross sections ( ) and length 3000 mm to study the effect of lacing reinforcement on the performance of such beams under static and fatigue loads. Twelve reinforced concrete beams (two of them are casted with vertical shear reinforcement used as control beams) are tested under four points bending loading with displacement control technique and six laced reinforced concrete beams were exposed to high frequency (10 Hz) by fixing the fatigue load in each cycle. Three parameters are used in the designed beams, which are: lacing bar diameter (4mm, 6mm, and 8mm), lacing bar inclination angle to horizontal , and lacing steel rat

Six proposed simply supported high strength-steel fiber reinforced concrete (HS-SFRC) beams reinforced with FRP (fiber reinforced polymer) rebars were numerically tested by finite element method using ABAQUS software to investigate their behavior under the flexural failure. The beams were divided into two groups depending on their cross sectional shape. Group A consisted of four trapezoidal beams with dimensions of (height 200 mm, top width 250 mm, and bottom width 125 mm), while group B consisted of two rectangular beams with dimensions of (125 ×200) mm. All specimens have same total length of 1500 mm, and they were also considered to be made of same high strength concrete designed material with 1% volume fraction of steel fiber.

This paper describes flexural behavior of two spans continuous rectangular concrete beams reinforced with mild steel and partially prestressing strands, to evaluate using different prestressing level and prestressing area in continuous prestressed beams at serviceability and ultimate stages. Six continuous concrete beams with 4550 mm length reinforced with mild steel reinforcement and partially prestressed with two prestressing levels of (0.7fpy  or 0.55fpy.) of and different amount of 12.7 mm diameter seven wire steel strand were used. Test results showed that the partially prestressed reinforced beams with higher prestressing level exhibited the narrowest crack width, smallest deflection and strain in both steel and concrete at ul

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback