Multidisciplinary Design Optimization for Performance Improvement of an Axial Flow Fan Using Free-Form Deformation

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Richard Amankwa Adjei ◽  
Chengwei Fan ◽  
WeiZhe Wang ◽  
YingZheng Liu
Keyword(s):  
Data Mining ◽  
Design Optimization ◽  
Performance Improvement ◽  
Multidisciplinary Design Optimization ◽  
Static Stress ◽  
Chord Length ◽  
Multidisciplinary Design ◽  
Free Form ◽  
Free Form Deformation ◽  
Operating Points

Abstract This paper describes a multidisciplinary design optimization for performance improvement of an electric-ducted fan rotor using free-form deformation (FFD) and data mining techniques. A practical partitioning approach for FFD parameterization was applied in combination with engineering design parameters to optimize the fan rotor. Regression analysis was used to initially determine an approximation function for the blade static stress and subsequently integrated into a fully coupled iterative loop to optimize the blade considering two operating points. Two optimization solutions for 10 and 12 blades were performed. Percentage improvements in the efficiency of 1.05% and 1.32% were realized for 10 and 12 blades, respectively, at near peak efficiency flowrate. Also, blade static stress was reduced by percentages of 5.49% and 12.37% for 10 and 12 blades compared with the baseline. Data mining results revealed key design variable sensitivities where blade twist, sweep, chord, and hub thickness distribution were found to be the most influential for 12 blades while for 10 blades, blade lean, sweep and chord at the midspan and tip. The optimized blades were found to have a significant increase in chord from midspan to tip mimicking a wide chord fan blade particularly for 10 blades. Analysis of the flow field revealed that the axial velocity from 0.4 to 0.8 spanwise length increased significantly for the optimum blades due to the increase in blade twist and chord length at all stable operating points. However, the leakage trajectory relative to the blade chord was observed to be larger and interacted with the trailing edge wake flow downstream for the optimum blades at near-stall conditions. Furthermore, the increase in chord length and the thinning of the blade close to the trailing edge from 0.4 to 0.8 span reduced the suction-side blade loading and static stress.

Multidisciplinary Design Optimization Based on Parameterized Free-Form Deformation for Single Turbine

AIAA Journal ◽  
2019 ◽  
Vol 57 (5) ◽  
pp. 2075-2087
Author(s):  
Lei Li ◽  
Tianyu Yuan ◽  
Yuan Li ◽  
Weizhu Yang ◽  
Jialei Kang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Multidisciplinary Design ◽  
Free Form ◽  
Free Form Deformation

scholarly journals Metamodel-Assisted Multidisciplinary Design Optimization of a Radial Compressor

2019 ◽  
Vol 4 (4) ◽  
pp. 35
Author(s):  
Mohamed H. Aissa ◽  
Tom Verstraete
Keyword(s):  
Design Optimization ◽  
Ordinary Kriging ◽  
Multidisciplinary Design Optimization ◽  
Optimization Problems ◽  
Pressure Ratio ◽  
Multidisciplinary Design ◽  
Feasible Region ◽  
Radial Compressor ◽  
Baseline Design ◽  
Operating Points

Kriging is increasingly used in metamodel-assisted design optimization. For expensive simulations; however, one can afford only a few samples to build the Kriging model, which consequently lacks prediction accuracy. We propose a bounded Kriging able to handle optimization problems with a small initial database. During the optimization, the proposed Kriging suggests designs close to database samples and finds optimal designs while staying in a feasible region (with respect to mesh and CFD convergence). The bounded Kriging is applied along with the ordinary Kriging to a multidisciplinary design optimization of a radial compressor. The shape of the compressor blades is optimized by considering the aero performance at different operating points and the mechanical stresses. The objective of the optimization is to maximize the efficiency at two operating points, while constraints are imposed on the maximum stress level in the material, the choke mass flow, the pressure ratio and the momentum of inertia of the impeller. While ordinary Kriging stopped prematurely because of many failing design evaluations, the bounded Kriging satisfied all constraints and reached an improvement of 2.59% in efficiency over the baseline design that does not comply with any constraints. The bounded Kriging covers a special need for robust methods in optimization able to deal with challenging geometries and a small database, which is the case for most industrial design optimizations.

Multidisciplinary Design Optimization and Data Mining for Transonic Regional-Jet Wing

2007 ◽  
Vol 44 (4) ◽  
pp. 1100-1112 ◽  
Author(s):  
Kazuhisa Chiba ◽  
Akira Oyama ◽  
Shigeru Obayashi ◽  
Kazuhiro Nakahashi ◽  
Hiroyuki Morino
Keyword(s):  
Data Mining ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Multidisciplinary Design

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Support Vector Regression-Based Multidisciplinary Design Optimization for Ship Design

2012 ◽  
Author(s):  
Dongqin Li ◽  
Yifeng Guan ◽  
Qingfeng Wang ◽  
Zhitong Chen
Keyword(s):  
Design Optimization ◽  
Support Vector Regression ◽  
Design Process ◽  
Multidisciplinary Design Optimization ◽  
Initial Point ◽  
Ship Design ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Support Vector ◽  
Independent Design

The design of ship is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional design process of ship only involves independent design optimization within each discipline. With such an approach, there is no guarantee to achieve the optimum design. And at the same time improving the efficiency of ship optimization is also crucial for modem ship design. In this paper, an introduction of both the traditional ship design process and the fundamentals of Multidisciplinary Design Optimization (MDO) theory are presented and a comparison between the two methods is carried out. As one of the most frequently applied MDO methods, Collaborative Optimization (CO) promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, Design Of Experiment (DOE) and a new support vector regression algorithm are applied to CO to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method. Then this new Collaborative Optimization (CO) method using approximate technology is discussed in detail and applied in ship design which considers hydrostatic, propulsion, weight and volume, performance and cost. It indicates that CO method combined with approximate technology can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.

Sign in / Sign up

Export Citation Format

Share Document