A real method of predication brake pad wear ,could lead to substantiol economies of time and money. This paper describes how such a procedure has been used and gives the results to establish is reliability by comparing the predicted wear with that which actually occurs in an existing service. The experimental work was carried out on three different commercial samples ,tested under different operation conditions (speed,load,time...etc)using a test ring especially modified for this purpose. Abrasive wear is mainly studied , since it is the type of wear that takes place in such arrangements. Samples wear tested in presences of sand or mud between the mating surfaces under different operational conditions of speed, load and braking time .Mechanical properties of the pad material samples (hardness, young,s modulus and collapse load under pure bending condition )wear established . The thermal conductivity and surface roughness of the pad material wear also found in order to enable comparison between the surface condition before and after testing. Sliding velocity had a small effect on the wear rate but it had great effect on friction coefficient. Wear rate was affected mainly by the surface temperature which causing a reduction friction coefficient and increasing the wear rate. Surface roughness had almost no effect on the wear rate since it was proved experimentally ,that the surface becomes softer during operation .mechanical properties of the pad material had fluctuating effect on wear rate. The existence of solid particles between pad and disc increasing wear rate and friction coefficient while the mud caused a reduction in wear rate of the pad surface since it acts as a lubricant absorbing the surface heat generated during sliding the area of contact between pad and disc. wear rate obtained experimentally agreed fairly well that found from empirically obtained equations.
Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F
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Phytochemical Screening and Antibacterial Effect of Stevia Rebaudiana (Bertoni) Alcoholic Leaves Extract on Streptococcus Oralis (Dental Plaque’s Primary Colonizer), Manar Ibrahim
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Background: Herbs are being widely explored to discover alternatives to synthetic antibacterial agents.Small Cardamom often referred to as queen of spices because of its very pleasant aroma and taste, have a history as old as human race. Most people use cardamom as a spice and are largely unaware of its numerous health benefits. The purpose of this study was to evaluate the effect of different concentrations of water and alcoholic cardamom extracts on sensitivities, growth, and adherence of Mutans streptococci in vitro. Materials and Methods: In this study, saliva was collected from ten volunteers (College students 18-22 years). Agar well technique was used to study the sensitivities of Mutans streptococci to different concentrations of s
... Show MoreObjective: To assess the Impact of Socio-economic status on age at menarche among secondary school students at
AL-Dora city in Baghdad, Iraq.
Methodology: This is a cross sectional study with multi-stage sampling was carried out during the period from the
3
th of December2013 to 12th of March 2014. The Sample comprised of 1760 girls, 1510 girls from urban area and
250 from rural area was included in the study. In first stage, selection of schools was done, and one class was
selected randomly from each level of Education, The data collection through a special questionnaire which Contain
the age of girl by year, class level, birth order, number of household, number of rooms, residency (urban/rural),
education level