Blade Arrangement Optimization for Mistuned Bladed Disk Based on Gaussian Process Regression and Genetic Algorithm

2020 ◽  
Vol 142 (2) ◽  
Author(s):  
Wangbai Pan ◽  
Meiyan Zhang ◽  
Guoan Tang
Keyword(s):  
Genetic Algorithm ◽  
Gaussian Process ◽  
High Efficiency ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Computational Cost ◽  
Combinatorial Problems ◽  
Optimal Arrangement ◽  
Bladed Disk ◽  
Gp Model

Abstract Mistuning phenomena exist in the bladed disk due to the inevitable deviations among blades' properties, e.g., stiffness, mass, geometry, etc., leading to localization and response amplification. The dynamic performance of mistuned bladed disk is sensitive to the arrangement of blades. The blade arrangement optimization aims to obtain the optimal arrangement that minimizes the influence of mistuning. In this paper, a framework of high efficiency is raised to deal with the challenge of high computational cost this optimization. It comprehensively utilizes mixed-dimensional finite element model (MDFEM), Gaussian process (GP) regression, and genetic algorithm (GA). The MDFEM can perform mistuned modal analysis efficiently and provides the training set of GP regression rapidly. The GP model, as a surrogate model, predicts the desired dynamic performance directly without calculating the numerical model and can function as fitness function in optimization. GA has the capability to deal with combinatorial problems and is a good option for problems with large search domains and several local maxima/minima. The techniques and processes of three methods are illustrated in detail. Case studies, based on a real turbine, are concretely presented in a gradually progressive manner to test and verify the effectiveness, accuracy, and efficiency of methods and entire framework step by step. The results show the satisfactory optimal arrangement for a randomly chosen set of mistuned blades, and the influence of mistuning is reduced indeed. The time cost of the optimization has been reduced several orders of magnitude. This framework can be a promising approach for the blade arrangement optimization problem.

scholarly journals Gaussian Process Methodology for Multi-Frequency Marine Controlled-Source Electromagnetic Profile Estimation in Isotropic Medium

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 661 ◽  
Author(s):  
Muhammad Naeim Mohd Aris ◽  
Hanita Daud ◽  
Sarat Chandra Dass ◽  
Khairul Arifin Mohd Noh
Keyword(s):  
Gaussian Process ◽  
Computational Cost ◽  
Computer Experiment ◽  
Controlled Source ◽  
Uncertainty Measurement ◽  
Process Methodology ◽  
Mathematical Equations ◽  
Marine Csem ◽  
Profile Estimation ◽  
Gp Model

The marine controlled-source electromagnetic (CSEM) technique is an application of electromagnetic (EM) waves to image the electrical resistivity of the subsurface underneath the seabed. The modeling of marine CSEM is a crucial and time-consuming task due to the complexity of its mathematical equations. Hence, high computational cost is incurred to solve the linear systems, especially for high-dimensional models. Addressing these problems, we propose Gaussian process (GP) calibrated with computer experiment outputs to estimate multi-frequency marine CSEM profiles at various hydrocarbon depths. This methodology utilizes prior information to provide beneficial EM profiles with uncertainty quantification in terms of variance (95% confidence interval). In this paper, prior marine CSEM information was generated through Computer Simulation Technology (CST) software at various observed hydrocarbon depths (250–2750 m with an increment of 250 m each) and different transmission frequencies (0.125, 0.25, and 0.5 Hz). A two-dimensional (2D) forward GP model was developed for every frequency by utilizing the marine CSEM information. From the results, the uncertainty measurement showed that the estimates were close to the mean. For model validation, the calculated root mean square error (RMSE) and coefficient of variation (CV) proved in good agreement between the computer output and the estimated EM profile at unobserved hydrocarbon depths.

Parameter Optimization of PID Controller for Boiler Combustion System by Applying Adaptive Immune Genetic Algorithm

2012 ◽  
Vol 546-547 ◽  
pp. 961-966
Author(s):  
Fei Xiang ◽  
Shan Li
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Adaptive Mutation ◽  
System Response ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Adaptive Immune ◽  
Design Scheme

For power plant boiler combustion control system has large inertia, nonlinear and other complex characteristics, a control algorithm of PID optimized by means of adaptive immune genetic algorithm is presented. A variety of improved schemes of GA were designed, include: initial population generating scheme, fitness function design scheme, immunization strategy, adaptive crossover probability and adaptive mutation probability design scheme. By taking the rise time, error integral and overshoot of system response as the performance index, and using genetic algorithm for real-coded of PID parameters, then a group of optimal values were obtained. Simulation results show that the method has a good dynamic performance, superior to the conventional PID controller.

scholarly journals Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A Distributed Genetic Algorithm Approach with Hierarchical Fitness Evaluation

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3526 ◽  
Author(s):  
Zi-Jia Wang ◽  
Zhi-Hui Zhan ◽  
Jun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Fitness Function ◽  
Computational Cost ◽  
Wireless Sensor ◽  
Fitness Evaluation ◽  
Disjoint Sets ◽  
Distributed Genetic Algorithm

This paper proposed a distributed genetic algorithm (DGA) to solve the energy efficient coverage (EEC) problem in the wireless sensor networks (WSN). Due to the fact that the EEC problem is Non-deterministic Polynomial-Complete (NPC) and time-consuming, it is wise to use a nature-inspired meta-heuristic DGA approach to tackle this problem. The novelties and advantages in designing our approach and in modeling the EEC problems are as the following two aspects. Firstly, in the algorithm design, we realized DGA in the multi-processor distributed environment, where a set of processors run distributed to evaluate the fitness values in parallel to reduce the computational cost. Secondly, when we evaluate a chromosome, different from the traditional model of EEC problem in WSN that only calculates the number of disjoint sets, we proposed a hierarchical fitness evaluation and constructed a two-level fitness function to count the number of disjoint sets and the coverage performance of all the disjoint sets. Therefore, not only do we have the innovations in algorithm, but also have the contributions on the model of EEC problem in WSN. The experimental results show that our proposed DGA performs better than other state-of-the-art approaches in maximizing the number of disjoin sets.

scholarly journals Parameters optimization of two-speed powertrain of electric vehicle based on genetic algorithm

2020 ◽  
Vol 12 (1) ◽  
pp. 168781402090165
Author(s):  
Yuhang Li ◽  
Bo Zhu ◽  
Nong Zhang ◽  
Hao Peng ◽  
Yongzhong Chen
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Optimization Model ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Parameters Optimization ◽  
Optimization Process ◽  
Performance Parameters ◽  
Improved Genetic Algorithm

Aiming at the shortcomings of only optimizing the gear ratios of two-speed transmission in the optimization process of two-speed powertrain parameters of electric vehicles, the optimization of two-speed powertrain parameters of electric vehicles based on genetic algorithm is proposed. The optimization process is to optimize the main performance parameters of the drive motor and the gear ratios of two-speed transmission. That is, taking the economy and dynamic of the electric vehicle as the fitness function, the gear ratios of two-speed transmission is optimized under the main performance parameters of different drive motors, so as to find the powertrain parameter with the best fitness function value. Among them, the AMESim software is used to build the vehicle optimization model, the genetic algorithm is improved by MATLAB, and the improved genetic algorithm is used to optimize the vehicle optimization model. The results show that the optimization of the vehicle’s economic and dynamic performance has been improved, indicating that this optimization method is effective.

scholarly journals An Efficient Genome Fragment Assembling Using GA with Neighborhood Aware Fitness Function

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Satoko Kikuchi ◽  
Goutam Chakraborty
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Fitness Function ◽  
Computational Cost ◽  
Search Space ◽  
Search Efficiency ◽  
Genome Sequences ◽  
Modified Genetic Algorithm ◽  
Correct Sequence ◽  
Np Problem

To decode a long genome sequence, shotgun sequencing is the state-of-the-art technique. It needs to properly sequence a very large number, sometimes as large as millions, of short partially readable strings (fragments). Arranging those fragments in correct sequence is known as fragment assembling, which is an NP-problem. Presently used methods require enormous computational cost. In this work, we have shown how our modified genetic algorithm (GA) could solve this problem efficiently. In the proposed GA, the length of the chromosome, which represents the volume of the search space, is reduced with advancing generations, and thereby improves search efficiency. We also introduced a greedy mutation, by swapping nearby fragments using some heuristics, to improve the fitness of chromosomes. We compared results with Parsons’ algorithm which is based on GA too. We used fragments with partial reads on both sides, mimicking fragments in real genome assembling process. In Parsons’ work base-pair array of the whole fragment is known. Even then, we could obtain much better results, and we succeeded in restructuring contigs covering 100% of the genome sequences.

scholarly journals Improved Genetic Algorithm with Two-Level Approximation Method for Laminate Stacking Sequence Optimization by Considering Engineering Requirements

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Haichao An ◽  
Shenyan Chen ◽  
Hai Huang
Keyword(s):  
Genetic Algorithm ◽  
High Efficiency ◽  
Fitness Function ◽  
Industrial Applications ◽  
Improved Genetic Algorithm ◽  
Computational Costs ◽  
Adaptive Penalty ◽  
Practical Engineering ◽  
Adaptive Crossover ◽  
Analysis Models

Laminated composites have been widely applied in aerospace structures; thus optimization of the corresponding stacking sequences is indispensable. Genetic algorithms have been popularly adopted to cope with the design of stacking sequences which is a combinatorial optimization problem with complicated manufacturing constraints, but they often exhibit high computational costs with many structural analyses. A genetic algorithm using a two-level approximation (GATLA) method was proposed previously by the authors to obtain the optimal stacking sequences, which requires significantly low computational costs. By considering practical engineering requirements, this method possesses low applicability in complicated structures with multiple laminates. What is more, it has relatively high dependence on some genetic algorithm control parameters. To address these problems, now we propose an improved GA with two-level approximation (IGATLA) method which includes improved random initial design, adaptive penalty fitness function, adaptive crossover probability, and variable mutation probability, as well as enhanced validity check criterion for multiple laminates. The efficiency and feasibility of these improvements are verified with numerical applications, including typical numerical examples and industrial applications. It is shown that this method is also able to handle large, real world, industrial analysis models with high efficiency.

Dynamic Performance Optimization of the Dual Clutch Transmission Based on Genetic Algorithm

2018 ◽  
Author(s):  
Dongwei He ◽  
Guangqiang Wu ◽  
Yimin Shi ◽  
Wenbin Yang
Keyword(s):  
Genetic Algorithm ◽  
Performance Optimization ◽  
Control Method ◽  
Fitness Function ◽  
Dynamic Performance ◽  
Simulation Models ◽  
Pressure Control ◽  
Control Logic ◽  
Engagement Process ◽  
Overall Performance

Gear shift law is determined, and shifting control logic, clutch pressure control strategy are developed, Simulation models of the continuous up-shift and down-shift process of DCT are established. Comparison of simulation results by the indicators of friction work, friction power and jerk, Influence on shifting performance about clutch pressure rate and circulates pressure are analyzed qualitatively. According to the fitness function of overall performance of shifting quality, the pressure optimal control objectives of DCT are proposed, and based on genetic algorithm for present control method in engagement process of DCT, the parameter optimization is studied.

scholarly journals Experimental Investigation and Comparison of the Thermal Performance of Additively and Conventionally Manufactured Heat Exchangers

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 574
Author(s):  
Ana Vafadar ◽  
Ferdinando Guzzomi ◽  
Kevin Hayward
Keyword(s):  
Surface Roughness ◽  
Thermal Performance ◽  
Heat Exchangers ◽  
High Efficiency ◽  
Cooling System ◽  
Fluid Dynamic ◽  
Dynamic Performance ◽  
Experimental Studies ◽  
Manufacturing Method ◽  
Industrial Sectors

Air heat exchangers (HXs) are applicable in many industrial sectors because they offer a simple, reliable, and cost-effective cooling system. Additive manufacturing (AM) systems have significant potential in the construction of high-efficiency, lightweight HXs; however, HXs still mainly rely on conventional manufacturing (CM) systems such as milling, and brazing. This is due to the fact that little is known regarding the effects of AM on the performance of AM fabricated HXs. In this research, three air HXs comprising of a single fin fabricated from stainless steel 316 L using AM and CM methods—i.e., the HXs were fabricated by both direct metal printing and milling. To evaluate the fabricated HXs, microstructure images of the HXs were investigated, and the surface roughness of the samples was measured. Furthermore, an experimental test rig was designed and manufactured to conduct the experimental studies, and the thermal performance was investigated using four characteristics: heat transfer coefficient, Nusselt number, thermal fluid dynamic performance, and friction factor. The results showed that the manufacturing method has a considerable effect on the HX thermal performance. Furthermore, the surface roughness and distribution, and quantity of internal voids, which might be created during and after the printing process, affect the performance of HXs.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

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