Composite pressure vessels (i.e. types III and IV) are widely used for compressed natural gas (CNG) vehicles, as storage cylinders to reduce the weight while maintaining high mechanical properties. These vessels can achieve 70-80% of weight saving, as compared to steel vessels (type I). So, prediction of first ply failure and burst pressure of these vessels is of great concern. Thus, this paper involved a review of literature regarding the first ply failure and burst pressure of composite pressure vessels (types III and IV). The review included the researches related to the simulation, mathematical modeling, and experimental analysis. The study focused on simulation-related research more than others due to the complexities of mathematical modeling of such problems in addition to the high cost of experimental tests. The results indicated that the stacking sequence of layers, vessel thickness and the type of selected composites were the main factors that mainly affect the vessel burst pressure performance. Accordingly, the optimization in the vessel structure (composite fabric architecture) parameters plays an important role in the performance of burst pressure. This in turn will lead to a high vessel durability, longer life-time and better prediction of burst pressure. Furthermore, the study showed that the prediction of first ply failure is more important than burst pressure knowledge of pressure vessels because it gives an initial prediction of vessel failure before the final failure occurrence. This in turn, may prevent the catastrophic damage of vessel.
TMF (thermomechanical fatigue) damage model devoted to prediction of the high temperature fatigue lifetime of navel copper alloy (C46400) , was proposed .This model was built on the basis of the stress –number of cycles curve responsible for damage due to interaction of high temperature and fatigue. This obtained prediction compared was very favorably with the cumulative experimental TMF results.
The research studies the prediction of thermal characteristics for open designer shape of solar collector of flat plate of area 2.34m2, connected to water tank of 85 liter capacity . Mathematical model was represented and made the system of private accounts, transactions and through the creation of mathematical equations and solved numerically using the method of Finite Difference Method (FDM).The results of research is to obtain hot water at average temperatures up to 520C at mid-day during February month, as the water temperature is at its lowest value in this month in Baghdad city, with an average efficiency of the system up to 53.6% .This predictive study is compared with a previous measurement work and confirmed that the results match well.
Estimation of the reliability for repairable system after maintenance actions is usually based on mathematical models, which can be classified as parametric and non-parametric models where the parametric model is required a prior specified life time distribution while Non-parametric model is that relaxes of the assumption of the life time distribution. Nonparametric life time models are including proportional hazard model and proportional odd model. In this paper we develop repairable reliability model concentrate on generalized repairable model that indicate the mixture of proportional hazard model and proportional odd model. A proportional hazard-proportional odds (PH-PO) model for the purpose of to improve the repairable reliability to obtain accurate estimates of reliability for repairable industrial boiler system at normal operating conditions depending on transformation parameter for reliability prediction for repairable system that represent Beji industrial boiler in power plant. The results show the odd model better than hazard model for repairable system after preventive maintenance depends on time to repair where transformation parameter (c) equal 0.0525094 it is closer to odds model than hazard model. In addition, reliability industrial boiler in case without temperature effect is better than reliability with temperature effect by using exponential model where we note that the reliability at 500 it is worse state where degrade more than (400,450) .
Turning is the most popular machining operation. The quality of the product may be determined using a variety of metrics, such as the surface generation method and the surface roughness of the product. This work uses cutting variables to obtain the best surface quality through a mathematical model. The suggested surface generation in this work results from deriving it using the Bezier technique, with degree (5th) having six chosen control points. One of the critical indicators of the quality of machined components is the surface roughness created during the machining process. Surface roughness improvement via machining process parameter optimization has been extensively researched. The Taguchi Method and actual tests were employed for evaluating the surface quality of complicated forms; regression models with three different variables for the cutting process, such as cutting speed, depth of cut, and feed rate, were also used. According to the experimental findings, the most significant effect of feed rate on the surface roughness is approximately (40.9%), and the more minor effect of depth of cut on the surface roughness is almost (16.23%). In addition, the average percentage error is 4.93%, the maximum error is 0.14 mm, and the minimum error is -0.143 mm for the prediction using the regression equation.
One way of obtaining information about reliability of units is to accelerate their life by testing at higher levels of stress (such as increasing elevated temperatures or voltages). Predicting the lifetime of a unit at normal operating conditions based on data collected at accelerated conditions is a common objective of these tests. Different models of accelerated life testing are used for such extrapolations. Two statistical based models are widely used: parametric models which require a prior specified lifetime distribution, and nonparametric models that relax of the assumption of the life time distribution. The proportional odds model is a nonparametric model in accelerated life testing based on the odds function and show that it gives a more accurate reliability estimates than proportional hazard model. This paper will concentrate on the models of proportional odds nonparametric accelerated life test for reliability prediction.
This research focuses on studying the speed flow density relationships which are considered the fundamental traffic flow relationships. The objective of the present study is to predict statistical models represent these relationships depending on a field survey data collected from Al-Thirthar road in Falluja city.Data were collected by using video-recording technique. The required data were abstracted, analyzed, grouped, and processed using computer programs developed for this purpose. Standard statistical analysis techniques were used to examine and analyze the observed data.FWASIM simulation traffic software program was used to verify the predicted traffic stream models, while the obtained results were presented in this research. To test the validity and reliability of the model, the output results of the predicated model were compared with the output data obtained from FWASIM model using similar input data and segment geometry. The comparison leads to consider that the developed regression model may be used to evaluate the performance of urban streets in Falluja city.
In satellite communications with frequencies above 10GHz the major problems in radio-wave propagation is signal level attenuation caused by tropospheric scintillation, together with signal level attenuation by rain. There are several methods to measure the magnitude of scintillation. The equations of most of these methods do not include meteorological element. In meantime we can not measure the magnitude of scintillation with elevation angle 5¢ھ-10¢ھ. A prediction method is suggested to measure tropospheric scintillation on earth-space path. It would apply this method to standard atmosphere and we studied the effect of meteorological conditions, frequency, antenna diameter and elevation angle on the magnitude of scintillation.
The purpose of this paper is to developing a mathematical relationship between the Ultrasonic Pulse Velocity (UPV) and the compressive strength for concrete specimens subjected to different amounts of exposure of sulfate attack. The experimental data were collected from a research work by the author using concrete subjected to sulfate exposure and form a literature used an extensive concrete work without sulfate exposure. The sulfate exposures studied were 0%, 3%, and 6% of fine aggregate. It is found that with the same amount of sulfate exposure a clear relationship curve can be drawn to describe the UPV and compressive strength. This paper proposes the UPV-strength mathematical expression suitable for prediction of the concrete strengths when subjected to sulfate attack.
The main objective of this paper is to create a method for designing and studying the performance of a multistage axial flow compressor. A mathematical methodology based on aerothermodynamics is used to study the on /off design performance of the compressor. Performance curves are obtained by changing the performance parameters in terms of design parameters (diffusion factor, solidity, Mach number, and inlet flow angle). Results show the great effect of diffusion factor on increasing efficiency than that of solidity, also the effect of both (diffusion factor and solidity) in increasing the amount of compression and efficiency of the compressor. Higher efficiency was found at the mean line between the root and tip of the blade. Best lift to drag ratio is found at inlet flow angle of (55o).
Pile foundations are typically employed when top-soil layers are unstable and incapable of bearing super-structural pressures. Accurately modeling pile behavior is crucial for ensuring optimal structural and serviceability performance. However, traditional methods such as pregnancy testing, while highly accurate, are expensive and time-consuming. Consequently, various approaches have been developed to predict load settlement behavior, including using artificial neural networks (ANNs). ANNs offer the advantage of accurately replicating substrate behavior's nonlinear and intricate relationship without requiring prior formulation.This research aims to employ artificial neural network (ANN) modeling techniques to simulate the load-settlement relationship of drilled piles. The primary aims of this study are threefold: firstly, to assess the effectiveness of the generated ANN model by comparing its results with experimental pile load test data; secondly, to establish a validation method for ANN models; and thirdly, to conduct a sensitivity analysis to identify the significant input factors that influence the model outputs. In addition, this study undertakes a comprehensive review of prior research on using artificial neural networks for predicting pile behavior. Evaluating efficiency measurement indicators demonstrates exceptional performance, particularly concerning the agreement between the predicted and measured pile settlement. The correlation coefficient (R) and coefficient of determination (R^2) indicate a strong correlation between the predicted and measured values, with values of 0.965 and 0.938, respectively. The root mean squared error (RMSE) is 0.051, indicating a small deviation between the predicted and actual values. The mean percentage error (MPE) is 11%, and the mean absolute percentage error (MAPE) is 21.83%.
Milling includes a variety of different tasks and tools, ranging from small individual pieces to large, powerful group processes. It is one of the most commonly used techniques for producing custom parts with exact tolerances. Surface roughness of machined parts has a significant impact on the finished item's quality, which may have an impact on its tolerance and performance. This paper studies the prediction of the values of surface roughness of low-carbon steel AISI 1015 in milling operations. Three different machining parameters with nine variable samples are selected to investigate the resultant surface roughness of the AISI 1015 low-carbon steel samples, including different spindle speeds, feed rates, and depths of cut. The results revealed that the feed rate of 100 mm/min at a spindle speed of 930 rpm and a depth of 1.5 mm produced the lowest surface roughness (Ra) value of 1.170 µm, while the feed rate of 300 mm/min at a spindle speed of 1100 rpm produced the greatest surface roughness value of 2.605.
In this article, an experimental study of the single-pass hybrid (PV/T) collector is conducted in the climatic conditions of Fallujah city, where the experimental results are compared with a previous research to validate the results. The effect of changing the angle of inclination of the hybrid collector (PV/T) and its effect on the electrical power in the range (20°-50°) is studied. The optimum angle of the collector is found to be 30°, which gives a maximum electrical power of 58.8 W at average solar radiation of 734.35 W/m2. In another experimental study with different air flow rates ranged from 0.04 kg/s to 0163 kg/s, where it is found that the maximum electrical power of 57.66 W at an air flow rate of 0.135 kg/s, while the maximum thermal efficiency reaches 33.53% at an air flow of 0.163 kg/s at average solar radiation of 786 W/m2.