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Multi-Response optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

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MATERIALS 2022年第6期15卷 2018页

作者： Chaudhari, Rakesh Prajapati, Parth Khanna, Sakshum Vora, Jay Patel, Vivek K. Pimenov, Danil Yurievich Giasin, Khaled Pandit Deendayal Energy Univ Sch Technol Dept Mech Engn Raisan 382007 Gandhinagar India Pandit Deendayal Energy Univ Sch Technol Dept Solar Engn Raisan 382007 Gandhinagar India South Ural State Univ Dept Automated Mech Engn Lenin Prosp 76 Chelyabinsk 454080 Russia Univ Portsmouth Sch Mech & Design Engn Portsmouth PO1 3DJ Hants England

Shape memory alloy (SMA), particularly those having a nickel-titanium combination, can memorize and regain original shape after heating. The superior properties of these alloys, such as better corrosion resistance, inherent shape memory effect, better wear resistance, and adequate superelasticity, as well as biocompatibility, make them a preferable alloy to be used in automotive, aerospace, actuators, robotics, medical, and many other engineering fields. Precise machining of such materials requires inputs of intellectual machining approaches, such as wire electrical discharge machining (WEDM). Machining capabilities of the process can further be enhanced by the addition of Al2O3 nanopowder in the dielectric fluid. Selected input machining process parameters include the following: pulse-on time (T-on), pulse-off time (T-off), and Al2O3 nanopowder concentration. Surface roughness (SR), material removal rate (MRR), and recast layer thickness (RLT) were identified as the response variables. In this study, Taguchi's three levels L-9 approach was used to conduct experimental trials. The analysis of variance (ANOVA) technique was implemented to reaffirm the significance and adequacy of the regression model. Al2O3 nanopowder was found to have the highest contributing effect of 76.13% contribution, T-on was found to be the highest contributing factor for SR and RLT having 91.88% and 88.3% contribution, respectively. Single-objective optimization analysis generated the lowest MRR value of 0.3228 g/min (at T-on of 90 mu s, T-off of 5 mu s, and powder concentration of 2 g/L), the lowest SR value of 3.13 mu m, and the lowest RLT value of 10.24 (both responses at T-on of 30 mu s, T-off of 25 mu s, and powder concentration of 2 g/L). A specific multi-objective teaching-learning-based optimization (TLBO) algorithm was implemented to generate optimal points which highlight the non-dominant feasible solutions. The least error between predicted and actual values suggests the effective

关键词： shape memory alloy Al2O3 nanopowder Nitinol surface morphology wire electrical discharge machining teaching-learning-based optimization algorithm

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Set Theoretical Variants of optimization algorithms for System Reliability-based Design of Truss Structures

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2021年第3期65卷 717-729页

作者： Kaveh, Ali Hamedani, Kiarash Biabani Kamalinejad, Mohammad Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran

In this paper, recently developed set theoretical variants of the teaching-learning-based optimization (TLBO) algorithm and the shuffled shepherd optimization algorithm (SSOA) are employed for system reliability-based design optimization (SRBDO) of truss structures. The set theoretical variants are designed based on a simple framework in which the population of candidate solutions is divided into some number of smaller well-arranged sub-populations. In addition, the framework is applied to the Jaya algorithm, leading to a set-theoretical variant of the Jaya algorithm. So far, most of the reliability-based design optimization studies have focused on the reliability of single structural members. This is due to the fact that the optimization problems with system reliability-based constraints are computationally expensive to solve. This is especially the case of statically redundant structures, where the number of failure modes is so high that it is impractical to identify all of them. System-level reliability analysis of truss structures is carried out by the branch and bound method by which the stochastically dominant failure paths are identified within a reasonable time. At last, three numerical examples, including size optimization of truss structures, are presented to illustrate the effectiveness of the proposed SRBDO approach. The results indicate the efficiency and applicability of the set theoretical optimization algorithms to solve the SRBDO problems of truss structures.

关键词： teaching-learning-based optimization algorithm shuffled shepherd optimization algorithm Jaya algorithm truss structure structural optimization structural reliability theory

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A novel hybrid method based on teaching-learning algorithm and leader progression model for evaluating the lightning performance of launch sites and experimental tests

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ELECTRICAL ENGINEERING 2019年第2期101卷 619-633页

作者： Yahyaabadi, M. Aslani, F. Vahidi, B. Dept Power Generat Co Esfahan Iran Islamic Azad Univ Dept Elect Engn Khomeinishahr Branch Esfahan Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

In this article, a numerical optimization method has been used to study the lightning effect on satellite launch sites. In the conventional direct method, the leader progression model along with charge simulation method is employed for all the lightning leader tip positions and all the possible lightning current values on the equipment. This time-consuming calculation set is reduced in the developed method by using an optimization algorithm. For this purpose, the specifications of a real air termination system have been used for simulation and a system in a laboratory scales has been used for both purposes of simulation and practical tests. teaching-learning-based optimization (TLBO) algorithm has been employed as an intelligent method to find the number of lightning strikes to the equipment, to detect critical areas on the structure surface and to reduce the execution time. Also, some experimental tests conducted in a high-voltage laboratory are referred on the small-scale system to investigate the conditions of leaders inception and propagation. An appropriate adaptation between the simulation results of TLBO and conventional direct method verifies the application of this method to investigate the performance of other air termination systems.

关键词： Lightning Air terminals Leader progression model Number of direct lightning strokes teaching-learning-based optimization algorithm

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A new-intelligent method for evaluating the lightning protection system performance of complex and asymmetric structures

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 190卷 106843-106843页

作者： Aslani, Farshad Yahyaabadi, Mostafa Vahidi, Behrooz Islamic Azad Univ Dept Elect Engn Khomeinishahr Branch Esfahan Iran Power Generat Co Esfahan Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

In order to model and visualize the performance of lightning and its protection system, several analytical methods such as the rolling sphere method (RSM) have been developed in which protected and unprotected areas are separated. However, in none of these methods, the scale and severity of the damage can be quantified numerically. In this paper, by using the leader progression model (LPM) in 3D space along with an intelligent algorithm named teaching-learning-based optimization (TLBO), a new method is proposed to identify the vulnerable areas with the highest probability of lightning strikes. In using the TLBO algorithm, a new method for determining the critical range of current is defined and the most vulnerable points on the structure are determined, so that, based on the simulation results, the best positions of the lightning rods can be specified respectively. A sample asymmetric structure of height less than 60 meters is analyzed as a case study, and the optimal locations and heights of lightning rods are determined using the rolling sphere method and the proposed method, respectively. The simulation results validate the effectiveness of the proposed method which makes it suitable for other complex structures.

关键词： Leader progression model teaching-learning-based optimization algorithm Lightning protection system

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An improved explicit double-diode model of solar cells: Fitness verification and parameter extraction

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ENERGY CONVERSION AND MANAGEMENT 2018年 169卷 345-358页

作者： Chen, Yujie Sun, Yize Meng, Zhuo Donghua Univ Coll Mech Engn 2999 North Renmin Rd Shanghai 201620 Peoples R China

Accurate simulation of photovoltaic characteristics is now a mandatory obligation before validating an experiment;hence, accurate model and parameters of solar cells are indispensable. This paper presents an improved explicit double-diode model based on the Lambert W function (EDDM-LW), and then compares the fitness and parameter extraction performance. By defining two new parameters (K and r) to separate the exponential function in double-diode model (DDM) and using the Lambert W function, the explicit expression for I-V characteristics is proposed. In contrast to exiting works, the new parameters can readily be computed by the electrical characteristics of the standard test condition without an implicit characteristic. To verify the accuracy of the proposed model, the fitness difference is first investigated with a solar cell and three different types of solar modules. The results indicate that under the same parameter values, EDDM-LW achieves the lowest root mean square error value and exhibits better fitness in representing the I-V characteristics. In addition, the optimal parameters are extracted by an improved teaching-learning-based optimization algorithm. The experimental results show that the optimal parameter values extracted from EDDM-LW are more accurate than those extracted from DDM. based on these observations, EDDM-LW can be deemed a useful and practical model for the simulation, evaluation, and optimization of the photovoltaic system.

关键词： Solar cells Double-diode model Explicit model Parameter extraction teaching-learning-based optimization algorithm

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Crow search algorithm based optimal dynamic performance control of SVC assisted SMIB system 20

Crow search algorithm based optimal dynamic performance cont...

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20th National Power Systems Conference (NPSC)

作者： Kumar, Sudeep Kumar, Akshay Shankar, Gauri Indian Inst Technol ISM Elect Engn Dept Dhanbad Jharkhand India

ISBN: (纸本)9781538661598

A study on improvement of dynamic as well as steady state performance of power system model using static VAR compensator (SVC) is carried out in this paper. SVC refers to a shunt device within the family of flexible alternating current transmission system and is widely used for enhancing voltage profile and power transmission capability. SVC along with power system stabilizer (PSS) is employed to damp out the oscillation and take back the system to a stable state following disturbance. Crow search algorithm (CSA) is implemented to optimize conventional parameters of PSS and SVC. Simulation results found by it are compared with others optimization algorithms such as particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO). Also, power system model under the effect of CSA based tuned PSS and SVC is verified using eigenvalue analysis under steady state condition for investigating steady state stability. A comparative time domain simulation under MATLAB/SIMULINK platform employing CSA, PSO and TLBO tuned PSS and SVC with studied single machine infinite bus system is carried for dynamic stability assessment. Results obtained confirm the efficacy of the CSA in better tuning of PSS and SVC than other counterparts.

关键词： Crow search algorithm particle swarm optimization algorithm power system stabilizer static VAR compensator teaching-learning-based optimization algorithm

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Optimal design of Stirling heat engine using an advanced optimization algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2016年第11期41卷 1321-1331页

作者： Rao, R. V. More, K. C. Taler, J. Oclon, P. SV Natl Inst Technol Dept Mech Engn Surat 395007 India Politech Krakowska Inst Maszyn & Urzadzen Energetycznych Al Jana Pawla II 37 PL-31864 Krakow Poland

The Stirling engine presents an excellent theoretical output equivalent to the output of Carnot engine and it is less pollutant and requires little maintenance. In this paper, Stirling heat engine is considered for optimization with multiple criteria. A recently developed advanced optimization algorithm namely "teaching-learning-based optimization (TLBO) algorithm" is used for maximization of output power, minimization of pressure losses and maximization of the thermal efficiency of the entire solar Stirling system. The comparisons of the proposed algorithm are made with those obtained by using the decision-making methods like linear programming technique for multi-dimensional analysis of preference (LINMAP), technique for order of preference by similarity to ideal solution (TOPSIS) and fuzzy Bellman-Zadeh method that have used the Pareto frontier gained through non-dominated sorting genetic algorithm-II (NSGA-II). The comparisons were also made with those obtained by the experimental results. It is found that the TLBO algorithm has produced comparatively better results than those given by the decision-making methods and the experimental results presented by the previous researchers.

关键词： Stirling heat engine multi-objective optimization thermal efficiency pressure loss teaching-learning-based optimization algorithm

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Structure optimization of Stand-Alone Renewable Power Systems based on Multi Object Function

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ENERGIES 2016年第8期9卷 649-649页

作者： Cho, Jae-Hoon Chun, Myung-Geun Hong, Won-Pyo Hankyong Natl Univ Smart Logist Technol Inst 327 Chungang Ro Anseong 17579 Kyonggi Do South Korea Chungbuk Natl Univ Dept Elect Engn Cheongju 28644 Chungbuk South Korea Hanbat Natl Univ Dept Bldg Serv Engn 125 Dongseodae Ro Daejeon 34158 South Korea

This paper presents a methodology for the size optimization of a stand-alone hybrid PV/wind/diesel/battery system while considering the following factors: total annual cost (TAC), loss of power supply probability (LPSP), and the fuel cost of the diesel generator required by the user. A new optimization algorithm and an object function (including a penalty method) are also proposed;these assist with designing the best structure for a hybrid system satisfying the constraints. In hybrid energy system sources such as photovoltaic (PV), wind, diesel, and energy storage devices are connected as an electrical load supply. Because the power produced by PV and wind turbine sources is dependent on the variation of the resources (sun and wind) and the load demand fluctuates, such a hybrid system must be able to satisfy the load requirements at any time and store the excess energy for use in deficit conditions. Therefore, reliability and cost are the two main criteria when designing a stand-alone hybrid system. Moreover, the operation of a diesel generator is important to achieve greater reliability. In this paper, TAC, LPSP, and the fuel cost of the diesel generator are considered as the objective variables and a hybrid teaching-learning-based optimization algorithm is proposed and used to choose the best structure of a stand-alone hybrid PV/wind/diesel/battery system. Simulation results from MATLAB support the effectiveness of the proposed method and confirm that it is more efficient than conventional methods.

关键词： stand-alone hybrid PV/wind/diesel/battery system teaching-learning-based optimization algorithm total annual cost loss of power supply probability

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An optimized model of electricity price forecasting in the electricity market based on fuzzy time series

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2014年第1期2卷 677-683页

作者： Safarinejadian, Behrooz Gharibzadeh, Masihollah Rakhshan, Mohsen Shiraz Univ Technol Control Engn Dept Modarres BlvdPOB 71555-313 Shiraz Iran

Electricity price forecasting in the electricity market is one of the important purposes for improving the performance of market players and increasing their profits in a competitive electricity market. Since the system load is one of the important factors affecting electricity price changes, a two-factorial model based on fuzzy time series is presented in this paper for electricity price forecasting using the electricity prices of the previous days and the system load. In the proposed method, price and system load time series are fuzzified by fuzzy sets created based on the fuzzy C-means clustering algorithm. After determining proposed model coefficients by the teaching-learning-based optimization algorithm, this model is used for forecasting the next day electricity price. The promising performance of the proposed model is examined using Australia and Singapore electricity markets data.

关键词： electricity market price forecasting fuzzy time series teaching-learning-based optimization algorithm

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Experimental investigation on submerged arc welding of Cr-Mo-V steel

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第1-4期69卷 93-106页

作者： Rao, R. Venkata Kalyankar, V. D. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

Welding aspects of a high-quality Cr-Mo-V steel are investigated in the present work. Cr-Mo-V steel can be suggested as a best choice for fabrication of pressure vessels to be operated in high-temperature operating conditions. Welding of this group of steel demands very critical attention on the parameters setting of chosen welding process. Only a few researchers had carried out research on the optimization aspects of the submerged arc welding of Cr-Mo-V steel. In the present work, complete experimental analysis is carried out on the submerged arc welding of Cr-Mo-V steel. The important input process parameters considered are welding current, voltage, welding speed, and wire feed. The effect of these input parameters is studied on various responses related to weld bead geometry and few mechanical properties. Taguchi's L-9 orthogonal array is used for design of experiment and the mathematical models are developed for the responses using MINITAB 15 software. The models developed are validated by conducting more experiments. Optimised parameter setting is also obtained by using a recently developed teaching-learning-based optimization algorithm.

关键词： Cr-Mo-V steel Submerged arc welding Process parameters teaching-learning-based optimization algorithm

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