Abstract: Development of reliable energy consumption models is the essential step toward sustainable machining. Due to the complex relationship between energy consumption and a large number of contributing factors, obtaining an accurate model is very difficult, if not impossible. This paper aims to review the literature in this field over the last two decades. The research work on energy models is grouped into three categories, i.e. theoretical, experimental and discrete event-based models. It is found that machine tool structure, set-up conditions, and machining parameters have major impacts on energy consumption, suggesting that energy consumption models need to be constructed not only at the process level but also at the system level. Hybrid models that combine more than one approach are considered an option.
Abstract: The yarn production is a complex industrial process, and the relation between the spinning variables and the yarn properties has not been established conclusively so far. The SVM regression algorithms are briefly introduced in this study, and then SVM models for predicting yarn properties have been presented. Model selection which amounts to search in hyper-parameter space is performed for study of suitable parameters with Genetic Algorithms. The yarn experimental results indicate that GA- SVM models are capable of remaining the stability of predictive accuracy, and more suitable for noisy and dynamic industrial process.
Abstract: In the present study, a specific and simple second law based exergoeconomic model with instant access to the production costs is introduced. The model is generalized for a case study of Shiraz solar thermal power plant with parabolic collectors for nominal power supply of 500 kW. Its applications include the evaluation of utility costs such as products or supplies of production plant, the energy costs between process operations of an energy converter such as production of an industry. Also attempt is made to minimize objective function including investment cost of the equipments and cost of exergy destruction for finding optimum operating condition for such plant.
Abstract: Computational fluid dynamics (CFD) is one of the computer-based solution methods which are more widely employed in aerospace engineering. The computational power and time required to carry out the analysis increases as the fidelity of the analysis increases. Aerodynamic shape optimization has become a vital part of aircraft design in the recent years. The Method of search algorithms or optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays various optimization methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc., are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterisation becomes an important factor to be considered during the aerodynamic shape optimization process. Generally if we want to optimize an airfoil we have to describe the airfoil and for that, we need to have at least hundred points of x and y co-ordinates. It is really difficult to optimize airfoils with this large number of co-ordinates. Nowadays many different schemes of parameter sets are used to describe general airfoil such as B-spline, Hicks- Henne Bump function, PARSEC etc. The main goal of these parameterization schemes is to reduce the number of needed parameters as few as possible while controlling the important aerodynamic features effectively. Here the work has been done on the PARSEC geometry representation method. The objective of this work is to introduce the knowledge of describing general airfoil using twelve parameters by representing its shape as a polynomial function. And also we have introduced the concept of Particle Swarm optimization Algorithm which is one kind of Non-Traditional Optimization technique to optimize the aerodynamic characteristics of a general airfoil for specific conditions. An aerodynamic shape optimization problem is formulated for NACA 2411 airfoil and solved using the method of Particle Swarm Optimization for 5.0 deg angle of attack. A MATLAB program has been developed to implement PARSEC, Panel Technique, and PSO Algorithm. This program has been tested for a standard NACA 2411 airfoil and optimized to improve its coefficient of lift. Pressure distribution and co-efficient of lift for airfoil geometries has been calculated using panel method. NACA 2411 airfoil has been generated using PARSEC and optimized for 5.0 deg angle of attack using PSO Algorithm. The results show that the particle swarm optimization scheme is more effective in finding the optimum solution among the various possible solutions.
Abstract: Toxic gases resulted from fire events in subways stations are more dangerous than high temperature radiation from it. So, a well designed smoke exhaust system must be installed in subway station to control the smoke’s propagation and discharging. Smoke extraction in subway station depends on the duct laid above the ceiling, so vents are situated in the same level of platform layer’s ceiling. If subway station catches fire, smoke will cumulate in smoke reservoirs at the beginning and mechanical fan cannot exhaust any smoke in this process. In This paper, FDS 5.0 is used to simulate smoke’s movement in a side platform of an actual subway station in case of a fire. Simulations are carried out at the same volume flux of mechanical fan to investigate the effects of height of vents and depth of smoke reservoirs.
Abstract: An improved chaos particle swarm optimization (CPSO) algorithm is proposed on path planning for unmanned aerial vehicle (UAV) to overcome the inadequacy of particle swarm optimization (PSO) algorithm, which falls into local optimum easily and converges slowly in process with poor precision. Through the in-depth analysis of PSO algorithm, the chaos optimization (CO) algorithm principle is introduced into it based on the traditional update operations on the particles’ velocity and position; as a result, the diversity of particles is increased, the suboptimal search on path planning is avoided and the quickness accompanied with accuracy of convergence is improved. Combined with digital map for modeling the UAV’s flight environment, the 3-D path planning is achieved. As the simulation results demonstrated, this hybrid algorithm is superior to the traditional PSO algorithm on path searching, especially in the 3-D environment.
Abstract: The methods to reduce phase noise of crystal oscillators based on Leeson model are presented in the paper. According to analysis of Leeson formula, phase noise has a direct relation with noise factor, corner frequency and loaded quality factor. It can be seen that optimization of phase noise can be realized form these three aspects. The feasibility of these methods to reduce phase noise is analyzed. Based on the method of reducing phase noise by improving loaded quality factor, calculation of loaded quality factor is carried out and phase noise of a Butler crystal oscillator is simulated by the Agilent Advanced Design System (ADS). The simulation results prove that this method to reduce phase noise based on improving loaded quality factor is feasible and effective.
Abstract: Granularity is the main parameter of evaluating materials, from the analysis of powder producing system that made of vibration mill, the material’s size can be controlled through controlling the speed of motor. Focus on the complex nonlinear in the processing of ground breaking, the two dimensional controller is designed. Due to the subjectivity and randomness in the designing method of classic fuzzy controller, so genetic algorithm is used to put fuzzy controller some learning function in order to obtain better control effect of the system.
Abstract: Thermally induced errors play a critical role in the control of machining accuracy. They can account for as much as 70% of dimensional errors in produced parts. Since thermal errors cannot totally be eliminated at the design phase, errors compensation appears to be the most economical solution. Accurate and efficient modeling of the thermally induced errors is an essential part of the error compensation process. This paper presents a comprehensive approach for thermal error modeling optimization. The proposed optimization method is based on multiple temperature measurements, Taguchi’s orthogonal arrays, various statistical tools and artificial neural networks to provide cost effective selection of appropriate temperature variables and modeling conditions as well as to achieve robust and accurate thermal error models. The proposed approach can be effectively and advantageously used for real-time thermal error compensation since it presents the benefit of straightforward application, reduced modeling time and uncertainty. The experimental results on a CNC turning center confirm the feasibility and efficiency of the proposed optimization method and show that the resultant model can accurately predict the time-variant thermal error components under various operating conditions.
Abstract: The applications of flexible manipulators are increasing and due to the high demand on fuel consumption there is a need to optimize the energy consumption for stable and durable operation of the flexible manipulators. In the present work the Genetic Algorithm (GA) is employed to optimize the total torque and the torque of the first link of a two-link flexible manipulator with a fourth order polynomial trajectory. The mathematical model of the manipulator is obtained using the extended Hamilton's Principle where the flexible links are treated as Euler- Bernoulli's beam theory. A fifth order polynomial trajectory undergoes a rest-to rest maneuvering is proposed as a bench mark for validation.