Aerodynamic Modeling Techniques for Efficient Supersonic Air Vehicle Multidisciplinary Design Optimization

2014 ◽  
Author(s):  
Chris M. Meckstroth ◽  
Edward J. Alyanak ◽  
Ned J. Lindsley ◽  
Mark Gabbard ◽  
Alexander Hunton
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Multidisciplinary Design ◽  
Aerodynamic Modeling ◽  
Air Vehicle ◽  
Modeling Techniques

Development of a multidisciplinary design optimization framework for an efficient supersonic air vehicle

2015 ◽  
Vol 2 (1) ◽  
pp. 17-44 ◽  
Author(s):  
Darcy L. Allison ◽  
Craig C. Morris ◽  
Joseph A. Schetz ◽  
Rakesh K. Kapania ◽  
Layne T. Watson ◽  
...  
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Multidisciplinary Design ◽  
Optimization Framework ◽  
Air Vehicle

scholarly journals Multidisciplinary design optimization of the flapping wing system for forward flight

2017 ◽  
Vol 9 (2) ◽  
pp. 93-110
Author(s):  
Jung-Sun Choi ◽  
Gyung-Jin Park
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Multidisciplinary Design Optimization ◽  
Flapping Wing ◽  
Multidisciplinary Design ◽  
Vehicle Model ◽  
Thrust Coefficient ◽  
Analysis And Design ◽  
Aeroelastic Analysis ◽  
Air Vehicle

The success of a flapping wing air vehicle flight is strongly related to the flapping motion and wing structure. Various disciplines should be considered for analysis and design of the flapping wing system. A design process for a flapping wing system is defined by using multidisciplinary design optimization. Unsteady aeroelastic analysis is employed as the system analysis. From the results of the aeroelastic analysis, the deformation of the wing is transmitted to the fluid discipline and the dynamic pressure is conveyed to the structural discipline. In the fluid discipline, a kinematic optimization problem is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency simultaneously. In the structural discipline, nonlinear dynamic topology optimization is performed to find the distribution of reinforcement by using the equivalent static loads method for nonlinear static response structural optimization. The defined design process is applied to a flapping wing air vehicle model and the flapping wing air vehicle model is fabricated based on the optimization results.

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.

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