Numerical and Neural Network Analysis of Natural Convection from a Cold Horizontal Cylinder above an Adiabatic Wall

Free convection around cold circular cylinder above an adiabatic plate at steady-state condition has been investigated both numerically and by artificial neural networks. There is a growing demand for a better understanding of free convection from a horizontal cylinder in the areas like air cooling, refrigeration and air conditioning system, etc. Governing equations are solved in some specified cases by finite volume method to generate the database for training the neural network in the range of Rayleigh numbers of 105 to 108 and a range of cylinder distance from adiabatic plate (L/D) of 1/4, 1/2, 1/1, 3/2 and 4/2, thereafter a Multi-Layer Perceptron network is used to capture the behavior of flow and temperature fields and then generalized this behavior to predict the flow and temperature fields for other Rayleigh numbers. Different training algorithms are used and it is found that the back-propagation method with Levenberg-Marquardt learning rule is the best algorithm regarding the faster training procedure. It is observed that ANN can be used more efficiently to determine cold plume and thermal field in less computational time and with an excellent agreement. From obtained results, average Nusselt number of the cylinder investigated to study the effect of adiabatic wall on the isothermal cylinder. It also observed that in spaces farther than L/D = 3/2, average Nusselt number is almost constant, so the affect is renouncement and it works like a cylinder in an infinite environment

Numerical Simulation of the Early Stages of Glaucoma in Human Eye

Background: The eye is one of the most vital organs of human body, and glaucoma is the second-leading cause of blindness after cataracts in the world. However, glaucoma is the leading cause of preventable blindness. The main objective of this study is to investigate intraocular pressure (IOP), stress, strain, and deformation in the retina in early stages of glaucoma. Methods: In this study, a model of the human eye is numerically investigated. The aqueous humor pressure is considered as 30, 35, and 40 mmHg and compared with normal eye pressure. The problem is considered as transient 3D and accurate. Comparison between obtained results shows that the model has been applied. Eye components are also considered with their real properties. Due to the inappreciable effects of turbulence and temperature variation, these effects have been neglected. To determine the pressure field, a two-way fluid-structure interaction is applied, and then, the results are used in a one-way fluid-structure interaction to determine the amount of stress, strain, and deformation of the retina. Results: The maximum deformation in the retina of a glaucoma patient is about 0.33 mm higher than a normal eye, the maximum stress is about 1,300 Pa higher than a normal eye, and the maximum strain is about 0.06 higher than a normal eye. Conclusion: In patients with increased IOP, the amount of deformation in the retina has increased, and the maximum deformation occurs near the optic disc in all cases. Furthermore, maximum stress and maximum strain occur at the place of maximum deformation

Numerical Simulation of Turbulent Airflow and Micro-Particle Deposition in Upper Human Respiratory System

The nasal cavity and sinuses are a component of the upper respiratory system and study the air passage into the upper component of human airway is consequential to amend or remedy deficiency in human respiration cycle. The nose performs many paramount physiological functions, including heating, humidifying and filtering inspired air, as well as sampling air to smell. Aforetime, numerical modeling of turbulent flow in authentic model of nasal cavity, sinus, pharynx and larynx has infrequently been employed. This research has tried to study details of turbulent airflow and particle deposition through all spaces in three-dimensional authentic model of human head which is obtained from computed tomography scan images of a 26-years old female head, neck and chest without any problem in her respiratory system that air can flow them. The particle size in this study was opted to be in the range of 5-30 µm. The particles are tracked through the continuum fluid discretely utilizing the Lagrangian approach

Modeling of the height control system using artificial neural networks

Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN) was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of action. The mechanical parts were computer-generated by engineering software in assembled, exploded and standard two-dimensional drawing required for the manufacturing process. Carrier and framework of control unit and actuator mainly designed to have the capability to support and hold the hardware and sensor assembly in an easy mountable fashion. This arrangement performed feasibility of the movement and allocating of control unit along the travel length of belt above the conveyor unit. In this work a multilayer perceptron network with different training algorithm was used and it is found that the backpropagation algorithm with Levenberge-Marquardt learning rule was the best choice for this analysis because of the accurate and faster training procedure. The Levenberg-Marquardt algorithm was an iterative technique that locates the minimum of a multivariate function that was expressed as the sum of squares of nonlinear real-valued functions. It has become a standard technique for non-linear least-squares problems, widely adopted in a broad spectrum of disciplines. LM can be thought of as a combination of steepest descent and the Gauss-Newton method. When the current solution was far from the correct one, the algorithm behaves like a steepest descent method: slow, but guaranteed to converge. When the current solution is close to the correct solution, it becomes a Gauss-Newton method. The Levenberg algorithm is: 1. Do an update as directed by the rule above. 2. Evaluate the error at the new parameter vector. 3. If the error has increased as a result the update, then retract the step (i.e. reset the weights to their previous values) and increase l by a factor of 10 or some such significant factor, then goes to (1) and try an update again. 4. If the error has decreased as a result of the update, then accept the step (i.e. keep the weights at their new values) and decrease l by a factor of 10 or so. Results and Discussion The study of multi artificial neural network learning algorithm by using base Levenberg–Marquardt was the best choice to estimate function experimental data convergence. Artificial neural networks databases were generated by experimental measurement data condition scales. It has been observed that the artificial neural networks could be used in height control. The function estimation problem with parameters in Levenberg–Marquardt algorithm showed a high performance and has a high speed, the error in the most cases were decrease and show a high convergence. Sum square error between ANN predictions and experimental measurements was less than 0.001 and correlation coefficient is above 0.99. Conclusions ANN method was capable to predict and capture the behavior of experimental measurements. ANN method can easily be used to determine new results with considerably less computational cost and time. Results show that the back-propagation method with Levenberg-Marquardt learning rule was suitable for training the networks. The Sum square error between ANN predictions and experimental measurements was less than 0.001 and the correlation coefficient is above 0.99. Replacement of the identity matrix with the diagonal of the Hessian in Levenberge-Marquardt update equation has great advantages in convergence and computation time