scholarly journals A comprehensive multi-objective, multi-parameter and multi-condition optimization of a spiral groove in dry gas seals

2020 ◽  
Author(s):  
Jinbo Jiang ◽  
Wenjing Zhao ◽  
Jie Jin ◽  
Jiyun Li ◽  
Xudong Peng
Keyword(s):  
Genetic Algorithm ◽  
Working Conditions ◽  
Optimization Problems ◽  
Geometric Parameters ◽  
The Other ◽  
Operating Efficiency ◽  
Spiral Groove ◽  
Gas Seals ◽  
Film Stiffness ◽  
Dimensional Optimization

Abstract Dry gas seals are widely used in rotating equipment for fluid leakage control and operating efficiency enhancement. Multi-dimensional optimization of geometric parameters of the spiral groove was conducted with considering the comprehensive effect of working conditions, objective functions and the other geometric parameters. Different optimization methods were proposed for solving the multi-dimensional optimization problems. The optimal values of groove width ratio, groove length ratio and spiral angle for the excellent steady performance of spiral grooves under different working conditions were obtained by employing a genetic algorithm and loop iteration optimization method. The performance comparison of two dry gas seals with different geometric parameters was conducted experimentally to verify the effectiveness of numerical results. The results showed that optimal geometric parameters of the spiral groove were significantly influenced by working conditions, objective function and the other geometric parameters. Maximum film stiffness and stiffness-leakage ratio of the spiral groove dry gas seal obtained by genetic algorithm enhanced up to 30% and 45% larger than those obtained by the conventional single factor optimization. The film stiffness and stiffness-leakage ratio were more sensitive to geometric parameters of a spiral groove than that of opening force. The optimization results obtained in this paper provide a theoretical and experimental reference for the design of dry gas seals in different working conditions to meet various sealing performance requirements.

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

2021 ◽  
pp. 2150020
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Traveling Salesman ◽  
The Other ◽  
Discrete Optimization Problem ◽  
Social Spider ◽  
Social Spider Algorithm

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

Hydrodynamics and Sonic Flow Transition in Dry Gas Seals

2014 ◽  
Author(s):  
Azam Thatte ◽  
Xiaoqing Zheng
Keyword(s):  
Linear Partial Differential Equation ◽  
Hydrodynamic Pressure ◽  
Secondary Flows ◽  
Operating Conditions ◽  
Operating Pressure ◽  
Spiral Groove ◽  
Local Pressure ◽  
Choked Flow ◽  
Gas Seals ◽  
Film Stiffness

Dry gas seals (DGS) are widely used in turbomachinery applications. They are recently being also recommended for sealing novel super critical CO2 turbomachinery space. However, these seals can render interesting behavior under certain operating conditions which needs to be carefully monitored so that intended level of dynamic characteristics can be achieved. The ability of these seals to maintain low leakage by riding at small clearances makes them an attractive solution where secondary flows need to be minimized. To understand the significance of some of the key design features of these seals, in this work an analysis on a gas lubricated spiral groove dry gas seal is presented. Equations in polar coordinates governing the compressible flow through the DGS gap and a numerical method to solve such non-linear partial differential equation is presented. The resulting sets of equations are solved for hydrodynamic pressure distribution and the axial separation force and the film stiffness at the rotor-stator interface is calculated. A detailed study on key spiral groove features is then performed to investigate the effect of spiral angle, groove depth, groove pitch and dam width ratio on the hydrodynamic pressure generation capacity, film stiffness and hence on overall performance of the DGS. Another important phenomenon that can occur in DGS under high operating pressure is the sonic transition. It is shown that choked flow under such conditions can take place over the dam section of the seal which manifests itself into large local pressure and temperature variations and can result into dynamic instabilities.

Forking Genetic Algorithms: GAs with Search Space Division Schemes

1997 ◽  
Vol 5 (1) ◽  
pp. 61-80 ◽  
Author(s):  
Shigeyoshi Tsutsui ◽  
Yoshiji Fujimoto ◽  
Ashish Ghosh
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Complex Function ◽  
Feature Space ◽  
Search Space ◽  
Function Optimization ◽  
The Other ◽  
Parent Population ◽  
New Type ◽  
Multimodal Problems

In this article, we propose a new type of genetic algorithm (GA), the forking GA (fGA), which divides the whole search space into subspaces, depending on the convergence status of the population and the solutions obtained so far. The fGA is intended to deal with multimodal problems that are difficult to solve using conventional GAs. We use a multi-population scheme that includes one parent population that explores one subspace and one or more child populations exploiting the other subspace. We consider two types of fGAs, depending on the method used to divide the search space. One is the genoqtypic fGA (g-fGA), which defines the search subspace for each subpopulation, depending on the salient schema within the genotypic search space. The other is the phenotypic fGA (p-fGA), which defines a search subspace by a neighborhood hypercube around the current best individual in the phenotypic feature space. Empirical results on complex function optimization problems show that both the g-fGA and the p-GA perform well compared to conventional GAs. Two additional utilities of the p-fGA are also studied briefly.

A Survey of Quantum Genetic Algorithm for Combinatorial Optimization Problems

2014 ◽  
Vol 568-570 ◽  
pp. 822-826 ◽  
Author(s):  
Feng Mei Wei ◽  
Jian Pei Zhang ◽  
Bing Li ◽  
Jing Yang
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Job Shop ◽  
Optimization Problems ◽  
The Other ◽  
Quantum Genetic Algorithm ◽  
Combinatorial Optimization Problems ◽  
Combinational Optimization ◽  
Rotation Strategy

Combined with quantum computing and genetic algorithm, quantum genetic algorithm (QGA) shows considerable ability of parallelism. Experiments have shown that QGA performs quite well on TSP, job shop problem and some other typical combinatorial optimization problems. The other problems like nutritional diet which can be transformed into specific combinational optimization problem also can be solved through QGA smoothly. This paper sums up the main points of QGA for general combinatorial optimization problems. These points such as modeling of the problem, qubit decoding and rotation strategy are useful to enhance the convergence speed of QGA and avoid premature at the same time.

Challenges of Media Education Standards in Georgia

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Marine Vekua
Keyword(s):  
Working Conditions ◽  
Teaching Methods ◽  
International Standards ◽  
The Other ◽  
Historical Background ◽  
Journalism Education ◽  
Education Standards ◽  
Modern Media ◽  
The One ◽  
The Right

The main goal of this research is to determine whether the journalism education of the leading media schools inGeorgia is adequate to modern media market’s demands and challenges. The right answer to this main questionwas found after analyzing Georgian media market’s demands, on the one hand, and, on the other hand, differentaspects of journalism education in Georgia: the historical background, development trends, evaluation ofeducational programs and curricula designs, reflection of international standards in teaching methods, studyingand working conditions.

Genetic algorithm application for optimization of geometry of synchronous electric motor with permanent magnets by criterion of electromagnetic torque

2019 ◽  
pp. 56-62
Author(s):  
I. N. Belezyakov ◽  
K. G. Arakancev
Keyword(s):  
Genetic Algorithm ◽  
Electric Motor ◽  
Permanent Magnets ◽  
Evolutionary Optimization ◽  
Electric Machines ◽  
Geometric Parameters ◽  
Geometrical Parameters ◽  
Electromagnetic Torque ◽  
Automatic Control Systems ◽  
Optimization Of Geometry

At present time there is a need to develop a methodology for electric motors design which will ensure the optimality of their geometrical parameters according to one or a set of criterias. With the growth of computer calculating power it becomes possible to develop methods based on numerical methods for electric machines computing. The article describes method of a singlecriterion evolutionary optimization of synchronous electric machines with permanent magnets taking into account the given restrictions on the overall dimensions and characteristics of structural materials. The described approach is based on applying of a genetic algorithm for carrying out evolutionary optimization of geometric parameters of a given configuration of electric motor. Optimization criteria may be different, but in automatic control systems high requirements are imposed to electromagnetic torque electric machine produces. During genetic algorithm work it optimizes given geometric parameters of the electric motor according to the criterion of its torque value, which is being calculated using finite element method.

Enterprise distribution routing optimization with soft time windows based on genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Kaixian Gao ◽  
Guohua Yang ◽  
Xiaobo Sun
Keyword(s):  
Genetic Algorithm ◽  
Customer Satisfaction ◽  
Optimization Problems ◽  
Rapid Development ◽  
Time Windows ◽  
Theory And Practice ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
A Company

With the rapid development of the logistics industry, the demand of customer become higher and higher. The timeliness of distribution becomes one of the important factors that directly affect the profit and customer satisfaction of the enterprise. If the distribution route is planned rationally, the cost can be greatly reduced and the customer satisfaction can be improved. Aiming at the routing problem of A company’s vehicle distribution link, we establish mathematical models based on theory and practice. According to the characteristics of the model, genetic algorithm is selected as the algorithm of path optimization. At the same time, we simulate the actual situation of a company, and use genetic algorithm to plan the calculus. By contrast, the genetic algorithm suitable for solving complex optimization problems, the practicability of genetic algorithm in this design is highlighted. It solves the problem of unreasonable transportation of A company, so as to get faster efficiency and lower cost.

The Numerical Analysis of the Velocity and Pressure Distribution of the Oil Film in Heavy Hydrostatic Thrust Bearing

2014 ◽  
Vol 541-542 ◽  
pp. 658-662
Author(s):  
Jian Li ◽  
Yuan Chen ◽  
Yang Chun Yu ◽  
Zhu Xin Tian ◽  
Yu Huang
Keyword(s):  
Mathematical Model ◽  
Numerical Analysis ◽  
Pressure Distribution ◽  
Working Conditions ◽  
Thrust Bearing ◽  
Rotation Speed ◽  
The Other ◽  
Surface Load ◽  
Oil Film ◽  
Volume Method

To study the velocity and pressure distribution of the oil film in a heavy hydrostatic thrust bearing, a mathematical model of the velocity is proposed and the finite volume method (FVM) has been used to simulate the flow field under different working conditions. Some pressure experiments were carried out and the results verified the correctness of the simulation. It is concluded that the pressure distribution varies small under different rotation speed when the surface load on the workbench is constant. But the velocity of the oil film is influenced greatly by the rotation speed. When the rotation speed of the workbench is as quick as enough, the velocity of the oil film on one radial side of the pad will be zero, that is to say the lubrication oil will be drained from the other three sides of the recess.

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