An enhanced manta ray foraging optimization algorithm for shape optimization of complex CCG-Ball curves

2022 ◽  
pp. 108071
Author(s):  
Gang Hu ◽  
Min Li ◽  
Xiaofeng Wang ◽  
Guo Wei ◽  
Ching-Ter Chang
2021 ◽  
Vol 7 ◽  
pp. 1068-1078
Author(s):  
Jiaying Feng ◽  
Xiaoguang Luo ◽  
Mingzhe Gao ◽  
Adnan Abbas ◽  
Yi-Peng Xu ◽  
...  

2020 ◽  
Vol 6 ◽  
pp. 2887-2896
Author(s):  
Biqi Sheng ◽  
Tianhong Pan ◽  
Yun Luo ◽  
Kittisak Jermsittiparsert

Author(s):  
O. Valero ◽  
L. He ◽  
Y. S. Li

Given the ever increasing demands on turbomachinery performance, various advanced blade shape optimizations have been actively developed and applied in modern blading designs. Multidisciplinary and concurrent optimizations have attracted considerable attention, offering the advantage of disciplinary interactions being included more simultaneously in a design process. This paper presents the development of a multidisciplinary optimization algorithm for the concurrent blade aerodynamic and aeromechanic shape optimization of realistic 3D turbine stages. A non-gradient algorithm is enhanced by a new re-scaled response surface (RSM) model. This meta-model is able to rescale the design space and redefine the response surface during a blade shape optimization process, leading to a much enhanced convergence compared to a standard RSM approach. The optimization algorithm is developed in conjunction with an efficient nonlinear harmonic phase solution method solving the unsteady flow equations in the frequency domain, combined with a finite element analysis (FEA) to extract the structural dynamic characteristics of the blades. The effectiveness of the concurrent method is examined for an optimized design of a realistic LP turbine stage. The optimization goals are the maximization of the isentropic stage efficiency and aeroelastic flutter stability (aero-damping). Two sets of cases are considered. In the first set, the shaping is applied only to stator blades, while for the second set, both stator and rotor blades are shaped. The concurrent cases are compared with their single-disciplinary counterparts. For both sets of the cases, the advantages of the concurrent treatment are clearly demonstrated.


Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3847
Author(s):  
Mahmoud G. Hemeida ◽  
Salem Alkhalaf ◽  
Al-Attar A. Mohamed ◽  
Abdalla Ahmed Ibrahim ◽  
Tomonobu Senjyu

Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.


2020 ◽  
Vol 62 (4) ◽  
pp. 365-370 ◽  
Author(s):  
Betül Sultan Yıldız ◽  
Ali Rıza Yıldız ◽  
Emre İsa Albak ◽  
Hammoudi Abderazek ◽  
Sadiq M. Sait ◽  
...  

Abstract This article presents an implementation of one of the latest optimization methods of obtaining light vehicle designs. First, the problem of coupling with a bolted rim is optimized using the butterfly optimization algorithm (BOA). Finally, the BOA is used to solve the shape optimization of a vehicle suspension arm. It is utilized from the Kriging metamodeling method to obtain equations of objective and constraint functions in shape optimization. At the end of the research effort in this paper, the weight reduction of the suspension arm by using the BOA is 32.9 %. The results show the BOA’s ability to design better optimum components in the automotive industry.


Author(s):  
Laurent Michel ◽  
Marco Picasso ◽  
Daniel Farinotti ◽  
Andreas Bauder ◽  
Martin Funk ◽  
...  

AbstractWe present a shape optimization algorithm to estimate the ice thickness distribution within a two-dimensional, non-sliding mountain glacier, given a transient surface geometry and a mass-balance distribution. The approach is based on the minimization of the surface topography misfit at the end of the glacier's evolution in the shallow ice approximation of ice flow. Neither filtering of the surface topography where its gradient vanishes nor interpolation of the basal shear stress is involved. Novelty of the presented shape optimization algorithm is the use of surface topography and mass-balance only within a time-dependent Lagrangian approach for moving-boundary glaciers. On real-world inspired geometries, it is shown to produce estimations of even better quality in smaller time than the recently proposed steady and transient inverse methods. A sensitivity analysis completes the study and evinces the method's higher susceptibility to perturbations in the surface topography than in surface mass-balance or rate factor.


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