Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering
Volume:15 Issue: 2, Jun 2023

The Barrel finishing process is a finishing method applied for cleaning, polishing, improving surface quality, Deburring, and rounding corners of both metallic and non-metallic parts. There are Several factors affect the final surface integrity of the barrel finished samples such as initial surface roughness, piece length, operation time, and different abrasive materials (i.e. aluminum oxide, steel balls, and ceramic). On the other hand, each factor has different levels, and handling this amount of data to reach desired results is approximately impossible due to the “curse of dimensionality”. Machine learning is a promising method to pave this avenue for computing huge amounts of data and predicting the future state of the system. Accordingly, in this study, it is attempted to apply a supervised machine learning algorithm, an artificial neural network- to improve surface quality in the barrel finishing process. Python is used to code the program and extract several simulations and related graphs. Results show that time has the greatest effect on surface roughness, moreover, among the different abrasive media, steel balls have the best performance to improve surface roughness and the combination of 75% steel balls and 25% aluminum oxide has the effective effect. The simulation results have an acceptable compatibility with experimental ones.

The most widespread use of solar energy as an alternative to fossil fuels is for heating. Considering the location of Iran and its location on the global Sunbelt, the aim of the present work is to provide part of the heat needed for the treatment space of Seyed Al-Shohda hospital in Yazd by using a flat plate solar collectors system. A one-year dynamic simulation has been performed using TSOL Pro 5.5 software, and energy-environmental-economic (3E) analyzes have been performed on the proposed system. The difference between the present work and the past works is that the provision of space heating and sanitary spa needed for a treatment space has not been done yet. The results of the investigations showed that about 2MWh of solar heat is produced, which prevents the annual release of about 630kg of CO2 pollutants. Examining the system loss diagram also shows that the bottleneck of energy loss in the system under review is the hot water storage tanks, which have an annual energy loss of about 800 kWh. Financial analysis showed that the price of each kWh of solar heat production and the internal rate of return (IRR) are 0.0021 euros and 2.23%, respectively. The results of the present work indicate the excellent potential of Yazd in the field of using solar water heaters (SWH) for heating treatment spaces. The results of the present work can be used as a roadmap to help energy decision-makers and investors in this sector and accelerate the development of SWHs.

Nowadays, the world needs safe and smart machines that can prevent human errors in different situations. Stress is an important factor in accidents which causes the human error. Many accidents can be prevented by identifying the stress of the driver and warning them. Due to its complexity, identifying stress in drivers is only possible by intelligent algorithms. In this paper, the Electrocardiogram (ECG) signal from drivedb dataset is used to detect stress in drivers, which has useful information that can be recorded more easily while driving. Afterwards, with a set of statistical, entropy, morphology, and chaos features, useful information is extracted from the signal. Then, in order to optimize the features, the Relief feature selector is used. Optimal features information is evaluated using Artificial Neural Networks (ANNs). Using the proposed method, the stress in drivers is detected with an accuracy of 92.6%, which has increased classification accuracy compared to recent researches.

In this paper, the numerical and exact analytical calculation of elastic strains and stresses in gas turbine engine rotating disk with variable thickness, subjected to temperature gradient are presented. Galerkin method is applied to solve any kind of profiles with arbitrary thickness, temperature and density functions while the other numerical and analytical methods used in previous works, are applied to profiles with certain thickness functions. Therefore, a comprehensive approach that takes all the circumstances into account was used in an attempt to fill this gap. To verify the numerical method, a few examples of rotating disks with non-linear variable thicknesses were solved using the analytical method as a reference method and their results were compared with numerical solution. A good agreement between numerical and analytical solutions was observed. In the analytical part, a new method to convert equilibrium equation of rotating disks to hyper-geometric differential equation was provided and then it was solved. Using hyper-geometric method is the main novelty of this research. The distributions of radial displacement and stresses were obtained and an appropriate comparisons and discussions were made at the same environmental conditions.

Due to demands for the economical operations of power plants, performance control of a boiler-turbine unit has great importance. Besides, the multi-input multi-output (MIMO) structure of boiler systems has some challenges that make their control some problems. In this research, the control of boiler systems is performed based on the neural network algorithm. In the boiler system, the liquid (usually water) reaches its desired temperature and its output steam is employed for making electricity, locomotive movement, environmental heating, health domain and etc. Since the water level in the tank has a great effect on the stability of the boiler, controlling dram water level significantly affects the system's performance. In this paper, controlling the water level of the boiler system is applied utilizing LQG and a neural network algorithm. For the system variables estimation, neural networks are employed instead of system conditions, due to their high ability in system identification in various conditions. The simulation results of the proposed method are compared with the Kalman-filter-based LQG controller.

Climatic conditions have a great impact on the erosion of the coverage and the materials destruction of the dome gradually. Therefore, studying the shape and form of the dome in historic mosques can greatly assist to identify the affected points of the different types of the domes and provide solutions to prevent early destruction of the domes. In the present work, the turbulent flow of wind around the four samples of different domes is investigated, using ANSYS CFX software, to determine which parts of the dome geometry are most affected by wind and erosion. In the present work, for the first time, it will be tried to study different types of domes used in different climates and their geometric shapes, besides conducting research to prevent early erosion. The results demonstrated that a large vortex has shaped on the opposite side of the wind, which affects the area behind the dome and causes a negative pressure through velocity reduction. Also, the highest wind velocity is formed a little higher and hinder of the dome. The results of the shear stress on the crown of the dome for the four cases illustrated that for the dome type W4, the highest shear stress is about 15Pa on the face against the wind and it is about 12 Pa for that of W1 on the face opposite the wind. It should be noted that the position of the most stresses on the dome crown corresponding to the most damage to the building is estimated.