The dynamics of wind turbine has to be studied carefully to avoid unpredictable outputs and to make sure that consistent and efficient power is supplied according to the load requirements. There is a great and urgent necessity to increase the efforts in the development of the researches of the renewable energy to decrease the dependency on the conventional ones. The objective of this research is to make a contribution to the ongoing wind turbine research in the area of modeling, which is the first step required for the control and implementation of wind turbines. The wind turbine transfer function is derived and its performance has been established using the MATHLAB Software. This research provided a different approach to wind turbine modeling methodology. The results of this research may be used in another step for completing the control process of the wind turbine.
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%.
Prefabricated schools are educational structures of great significance and utility, especially given our country's current challenges. They expedite construction, reduce environmental impacts, and enhance educational activities with strong structural integrity and comfortable, flexible, and healthy design. A compelling comparison can be made with traditional concrete schools. With advancements in technology and information in engineering and the construction industry, Building Information Modeling (BIM) technology has emerged, playing a vital role in prefabricated concrete engineering through 3D modeling simulations.Numerous challenges are encountered, such as meeting client requirements, project delays, cost overruns, quality issues, stakeholder conflicts, labor shortages, safety concerns, increased change orders, material wastage, and project complexity. Developed countries utilize BIM to mitigate these challenges and profoundly improve the AEC industry's performance. BIM tools provide a comprehensive building visualization, empowering stakeholders to make informed decisions that ensure efficiency, sustainability, and cost savings. These features motivate engineers and contractors to rely on them as essential engineering applications.This research involves modeling a school building in the Revit program, studying work flow between Revit and ETABS program, and conducting a dynamic analysis of the model from Revit. It also emphasizes the benefits of prefabricated construction and BIM technology, facilitated by Revit. The study emphasizes how important it is to visualize the building's actual form before beginning the design and decision-making processes.in summery, this study provides the possibility of growth and application in the industrialization of the construction industry and raise the project's overall quality. The development of tools and plugins programmed to reduce interoperability problems between various software packages allows for integrating all design activities.
Modeling and simulation are indispensable when dealing with complex engineering systems. It makes it possible to do essential assessment before systems are built, Cantilever, which help alleviate the need for expensive experiments and it can provide support in all stages of a project from conceptual design, through commissioning and operation. This study deals with intelligent techniques modeling method for nonlinear response of uniformly loaded paddle. Two Intelligent techniques had been used (Redial Base Function Neural Network and Support Vector Machine). Firstly, the stress distributions and the vertical displacements of the designed cantilevers were simulated using (ANSYS v12.1) a nonlinear finite element program, incremental stages of the nonlinear finite element analysis were generated by using 25 schemes of built paddle Cantilevers with different thickness and uniform distributed loads. The Paddle Cantilever model has 2 NN; NN1 has 5 input nodes representing the uniform distributed load and paddle size, length, width and thickness, 8 nodes at hidden layer and one output node representing the maximum deflection response and NN2 has inputs nodes representing maximum deflection and paddle size, length, width and thickness and one output representing sensitivity (∆R/R). The result shows that of the nonlinear response based upon SVM modeling better than RBFNN on basis of time, accuracy and robustness, particularly when both has same input and output data.
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.
Productivity improvement in the manufacturing industry of piping is a key challenge facing manufacturers in today's competitive markets. Improving productivity in the pipe manufacturing companies by implementing manufacturing principles that utilize simulation modeling was the purpose of this study. To improve productivity, an approach that focuses on the workstations and workforces process was suggested. The suggested approach’s goal was to increase productivity by providing customer prerequisites and leaving some products for other customers in the store. Based on the data has been gathered from the company of steel pipes, Bansal Ispat Tubes Private Limited in India, a simulation model was utilized to enhance its performance of operational. The investigation methodology consists of a simulation model, acceptable distribution, and data investigation. By simulating individual workstations and evaluating all relevant processes according to the data collected, the simulation model was built. Actual employment data were gathered from the line of manufacturing and supervisory workers, with observations carried out throughout the process of manufacturing. The used method involves videotaping of the process and interviewing workers using a video-camera. The superior continuous distributions were picked to fulfill a convenient statistical model. The results could be helps to ameliorate the manufacturing industry productivity. Furthermore, the outcomes could assist to solve the problems of scheduling in pipe manufacturing "simulating and modeling" which reveals active ways in enhancing pipe manufacturing productivity. Consequently, the findings might support well competition among companies.
In the current article, an experimental investigation has been implemented of flow and heat transfer characteristics in a parabolic trough solar collector (PTSC) using both nano-fluids and artificial neural networks modeling. Water was used as a standard working fluid in order to compare with two different types of nano-fluid namely, nano-CuO /H2O and nano-TiO2/ H2O, both with a volume concentration of 0.02. The performance of the PTSC system was eval-uated using three main indicators: outlet water temperature, useful energy and thermal efficiency under the influence of mass flowrate ranging from 30 to 80 Lt/hr. In parallel, an artificial neural network (ANN) has been proposed to predict the thermal efficiency of PTSC depending on the experimental re-sults. An Artificial Neural Network (ANN) model consists of four inputs, one output parameter and two hidden layers, two neural network models (4-2-2-1) and (4-9-9-1) were built. The experimental results show that CuO/ H2O and TiO2/H2O have higher thermal performance than water. Overall, it was veri-fied that the maximum increase in thermal efficiency of TiO2/H2O and CuO/H2O compared to water was 7.12% and 19.2%, respectively. On the oth-er hand, the results of the model 4-9-9-1 of ANN provide a higher reliability and accuracy for predicting the Thermal efficiency than the model 4-2-2-1. The results revealed that the agreement in the thermal efficiency between the ANN analysis and the experimental results about of 91% and RMSE 3.951 for 4-9-9-1 and 86% and RMSE 5.278 for 4-2-21.
The universal motor, versatile and capable of running on both AC and DC sources, is utilized in various household appliances and power tools. This paper presents a featured methodology for analyzing a universal motor (UM) that does not have design data by extracting it via reverse engineering. These gained data were used to model the motor by Maxwell program and analyzing it by finite element method (FEM). Adopting the Maxwell program's drawing capability to design the square-shaped stator of a universal motor not part of the program library will also enable the Maxwell program to be widely used and unrestricted to use with particular motor designs. After modeling and solving the motor model, the performance characteristics of UM when operated with alternating current (AC) and direct current (DC) power supplies were investigated. The UM simulation results were compared with test results with good agreement. The success of a proposed methodology paved the way for the analysis of any electric motor included in the Maxwell program, even if this motor does not have design data.
The detection of faults in electronic circuits is crucial to ensure the proper performance and reliability of electronic applications that utilize these devices. This work discovers, for the first time, that a direct tester board for fault diagnosis can be used not only for the intended measurement of current and voltage but also for studying the potential development of these magnitudes in inaccessible locations, as it detects register transfer level signals through oscilloscopes with low acquisition speeds. The experimental analysis carried out combines the use of commercial software with spatial distribution tracking and the exploitation of the sizes of network links in their computer graphical representation. The proper detection of malfunctions in electronic systems is crucial for enhancing their performance and reliability. We intend to explore the troubleshooting of analog electronic systems, for which we use wide-band direct tester boards. To evaluate its performance in routine practice, we perform experimentation using two different analog circuits designed. They consist of conventional operational amplifiers and element modeling based on equivalent resistance-capacitance networks. Given the procedure followed, commercial programs were used. Special mention should be made of the conclusion matrix, which is interesting when selecting suitable diagnostic parameters. The effectiveness of direct measurement based on integrated probes in the two projects, which allowed for fault insertion, was also confirmed. The results and discussions were enriched by the summarized experimental test report. The work concludes with a reflection on the relationship between this work and the existing state of the art, as well as the new challenges posed by international researchers.
Several modal split models have been created around the world to forecast which mode of transportation will be selected by the trip - maker from among a variety of available modes of transportation. This modeling is essential from a planning standpoint, as transportation systems typically receive significant investment. In this study, the main purpose was to develop a mode choice model using multiple linear regressions for Ramadi city in Iraq. The study area was divided into traffic analysis zones (TAZ) to facilitate data collection. The data was collected through a home interview of the trip makers in their home units through a questionnaire designed for this purpose. The result showed that the most influential factors on the mode choice for the general trips model using multiple linear regressions are car ownership, age, and trip cost. This model gave a good correlation coefficient of 0.829 meaning that the independent variables explain 82.9 of variance in the dependent variable (type of mode), which will help transport planners in developing policies and solutions for future
The Artificial Neural Network (ANN) and numerical methods are used widely for modeling andpredict the performance of manufacturing technologies. In this paper, the influence of millingparameters (spindle speed (rpm), feed rate (mm/min) and tool diameter (mm)) on material removalrate were studied based on Taguchi design of experiments method using (L16) orthogonalarray with 3 factor and 4 levels and Neural Network technique with two hidden layers and neurons.The experimental data were tested with analysis of variance and artificial neural networkmodel has been proposed to predict the responses. Analysis of variance result shows that tooldiameters were the most significant factors that effect on material removal rate. The predictedresults show a good agreement between experimental and predicted values with mean squarederror equal to (0.000001), (0.00003025), (0.002601) and (0.006889) respectively, which produceflexibility to the manufacturing industries to select the best setting based on applications.
This research project focused on examining and (rehabilitation) redesigning water networks in a city using the GIS-EPANET program in hydraulic network analysis. Due to the availability of outline data about the study area from the municipality's water distribution system (WDS), this study dealt with four cases. From a statistical calculation, the last case was best optimized, which resulted in a high pressure and an acceptable velocity as a result of high mean pressure (13.58) m, logical mean velocity (0.43) m/s, and accurate standard deviations of 1.214 and 0.48 for pressure and velocity, respectively. The study found that the network had a shortfall in pressure, estimated at 40%, due to the lack of expansion to accommodate the growing population. However, after conducting the analysis and identifying the problem, it was found that all regions were receiving adequate amounts of water. Nevertheless, the water speed in the pipelines throughout the network was deficient, below the recommended rate, with a minimum velocity of 0.02 m/s in the pipe (p3) but a minimum pressure of 7.02 m at the junction (607), indicating that the network design was ineffective. Comparing the results obtained with the real-world situation, it was discovered that the network has many violations and disruptions, causing water loss and resulting in low pressure reaching the customers. While the study found that the pressure inside the network was within acceptable modeling limits of (7–12) m, there was a reduction in the pressure charge due to the frequent use of water pumps inside the houses, especially as the circulated area was pumped further away. The error between the model and the real problem may be attributed to water leaks and disruptions from trees, gardens, landscaping, and livestock grazing, as well as the absence of a counter to calculate the water discharge volume to consumers
The demand for strengthening structures becomes necessary when an increase in load is inevitable. For instance very little information is available on the time-dependent behaviour of strengthened concrete columns. Also, this is a primary factor hindering the widespread uses of FRP strengthening technologies in the construction implementations. This paper investigates the behaviour of strengthened concrete columns with FRP sheets subjected to long-term loading by non linear finite element analysis using ANSYS computer package. A three-dimensional finite element model has been used in this investigation. This study achieved a good agreement between numerical and experimental results, it was found that the percentage of error of specimens do not pass (5%) for creep strain. In addition, a parametric study was performed to study the effect of different factors on the behaviour of FRP strengthened concrete columns.
Recently, the use of sandwich panels has become increasingly important. This is due to its good mechanical properties and high strength-to-weight ratio. It is used in many fields, especially in aviation, construction and aerospace. It is necessary to know the behavior of the materials used, especially the free vibrations, to know the effect of external factors on the sandwich panels. The honeycomb core sandwich panel was studied. A model for analysis and modeling is proposed. A previous model was chosen for analysis and comparison. Hoff theory was applied to convert honeycomb sandwich panel into equivalent sandwich panel to facilitate the solution and save time. The limits were considered fixed on the one hand and moving on the other hand, and the ANSYS program was used to analyze and extract the results, and the results were compared and were promising and accurate, which proves to us the validity and accuracy of the proposed theoretical results
Designing large structures like dams requires carefully selecting various geometric, hydraulic, and structural characteristics. The required structural design and performance criteria are considered when selecting these characteristics. In order to find the best solution, a variety of restrictions must simultaneously be carefully taken into account. This study presents an effective method for determining the optimal shape design for concrete buttress dams. The research was divided into two crucial phases. The dam's initial design and subsequent modeling were mostly done using DIANA FEA and traditional design and stability analysis. After that, a genetic algorithm was used on the MATLAB platform to control optimizing the dam's shape. Three design factors were used in this phase to alter the goal function and to reduce the amount of Concrete used, which decreased project costs. These variables covered three areas of the buttress's cross-section. Two important limitations were scrutinized during this optimization process: establishing a safety margin against overtopping and preventing sliding. The analysis included a detailed assessment of Shear friction stability to complete a thorough stability study. The optimization efforts had a spectacular result, resulting in a significant 52.365% reduction in the total volume of Concrete used, dropping from 19147.5 cubic meters to 9122.55 cubic meters. This decrease was made possible by reducing three distinct components (X1, X2, X3), with respective proportions of 37.5%, 13.33%, and 30%, including two segments related to the buttress and the final segment linked (slab) to the strip footing.
An experimental investigation as well as nonlinear analysis is carried out in this paper to study the behavior of polymer members (Epoxy & Polyester) under direct tension. The ANSYS model accounts for nonlinear phenomenon, such as, Tension Softening Material (TSM) and Enhanced Multilinear Isotropic Softening (EMIS) models. The polymer specimens are modeled using PLANE82 element – eight node plane element – eight node plane element, which is capable of simulating the failure behavior of polymer material members. The intention of this paper is thereby to discuss the proposed softening models to validate the complete Stress-Strain and Load-Deflection response of prismatic specimens subjected to uniaxial tension. The outcomes from the verifications of both modeling techniques have shown good agreement with the experimental results obtained from literature.
Recently, the investigations studies of simulating flow over spillways have increased using numerical models. Due to its important structure in the dams to pass flood wave to the downstream safely. Researches finding have shown that CFD (Computational fluid dynamics) models as the numerical method are a perfect alternative for laboratory tests. Performance analysis of the CFD platforms Ansys Fluent-2D and Flow-3D are presented, focus on finding the variations between the numerical results of the two programs to simulate the flow over ogee spillway. The present study treats the turbulence using RNG k-ε of RANS approach, and also use the Volume of Fluid (VOF) algorithm to track the water-air interaction. The Fluent-2D and Flow-3D accuracy are assessed by comparing representative flows variables (velocity; free surface profiles; pressure; and the turbulent kinetic energy). The results of both codes have been also compared with experimental data. The results of the analysis show an excellent agreement between the two platforms data, which could assist in the future by using both programs to calibrate each other, rather than traditionally relying on laboratory calibration models.