An Adaptive Surrogate Model Applied to the Design Optimizations of Waverider-Based Hypersonic Vehicle

2011 ◽  
Vol 110-116 ◽  
pp. 5118-5122
Author(s):  
Ping Li ◽  
Wan Chun Chen
Keyword(s):  
Global Optimization ◽  
Process Design ◽  
Surrogate Model ◽  
Computer Experiment ◽  
Optimization Method ◽  
Primary Objective ◽  
Optimization Scheme ◽  
Set Of Points ◽  
Drag Ratio ◽  
Lift To Drag Ratio

The primary objective of this work is to optimize the lift-to-drag ratio of a waverider-based configuration by a new global optimization method — the adaptive surrogate model (ASM), which is based on latin hypercube computer experiment and kriging surrogate model. Additional design points will be added in experiment set of points during iterative process. Design spaces of each variable are reduced by an adaptive reduction radius, which is improved gradually by the adaptive inconsistency of optimum solutions during the optimization process. Also the search efficiency and the accuracy of the optimization are compared with another global optimization scheme. At last, this paper gives maximum L/D optimization with restrictions of actual volumetric efficiency and the total mass, which shows that the adaptive surrogate model is quite suitable for the design optimization of waveriders.

scholarly journals A global optimization method, αBB, for process design

1996 ◽  
Vol 20 ◽  
pp. S419-S424 ◽  
Author(s):  
C.S. Adjiman ◽  
I.P. Androulakis ◽  
C.D. Maranas ◽  
C.A. Floudas
Keyword(s):  
Global Optimization ◽  
Process Design ◽  
Optimization Method ◽  
Global Optimization Method

scholarly journals SADEA-II: A generalized method for efficient global optimization of antenna design

2016 ◽  
Vol 4 (2) ◽  
pp. 86-97 ◽  
Author(s):  
Bo Liu ◽  
Slawomir Koziel ◽  
Nazar Ali
Keyword(s):  
Data Mining ◽  
Global Optimization ◽  
Design Optimization ◽  
Surrogate Model ◽  
Simulation Models ◽  
Optimization Methods ◽  
Optimization Method ◽  
Antenna Design ◽  
Efficient Global Optimization ◽  
Optimization Framework

Abstract Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality. Highlights An EFFICIENT antenna design global optimization method for problems requiring very expensive EM simulations. A new multi-fidelity surrogate-model-based optimization framework to perform RELIABLE efficient global optimization A data mining method to address distortions of EM models of different fidelities (bottleneck of multi-fidelity design).

scholarly journals A New Aerodynamic Optimization Method with the Consideration of Dynamic Stability

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mi Baigang ◽  
Wang Xiangyu
Keyword(s):  
Dynamic Stability ◽  
Optimization Design ◽  
Optimization Method ◽  
Aerodynamic Optimization ◽  
Conventional Optimization ◽  
And Control ◽  
Drag Ratio ◽  
Lift To Drag Ratio ◽  
Oscillation Method ◽  
Small Amplitude Oscillation

Dynamic stability is significantly important for flying quality evaluation and control system design of the advanced aircraft, and it should be considered in the initial aerodynamic design process. However, most of the conventional aerodynamic optimizations only focus on static performances and the dynamic motion has never been included. In this study, a new optimization method considering both dynamic stability and general lift-to-drag ratio performance has been developed. First, the longitudinal combined dynamic derivative based on the small amplitude oscillation method is calculated. Then, combined with the PSO (particle swarm optimization) algorithm, a dynamic stability derivative that must not be decreased is added to the constraints of optimization and the lift-drag ratio is chosen as the optimization objective. Finally, a new aerodynamic optimization method can be built. We take NACA0012 as an example to validate this method. The results show that the dynamic derivative calculation method is effective and conventional optimization design can significantly improve the lift-drag ratio. However, the dynamic stability is enormously changed at the same time. By contrast, the new optimization method can improve the lift-drag performance while maintaining the dynamic stability.

A New Global Optimization Scheme for Quadratic Programs with Low-Rank Nonconvexity

2021 ◽  
Author(s):  
Xiaoli Cen ◽  
Yong Xia
Keyword(s):  
Global Optimization ◽  
Operations Research ◽  
Hessian Matrix ◽  
Optimization Method ◽  
Low Rank ◽  
Quadratic Program ◽  
Nonconvex Quadratic Programming ◽  
Optimization Scheme ◽  
Negative Eigenvalues ◽  
Computational Comparison

We consider the classical convex constrained nonconvex quadratic programming problem where the Hessian matrix of the objective to be minimized has r negative eigenvalues, denoted by (QPr). Based on a biconvex programming reformulation in a slightly higher dimension, we propose a novel branch-and-bound algorithm to solve (QP1) and show that it returns an [Formula: see text]-approximate solution of (QP1) in at most [Formula: see text] iterations. We further extend the new algorithm to solve the general (QPr) with r > 1. Computational comparison shows the efficiency of our proposed global optimization method for small r. Finally, we extend the explicit relaxation approach for (QP1) to (QPr) with r > 1. Summary of Contribution: Nonconvex quadratic program (QP) is a classical optimization problem in operations research. This paper aims at globally solving the QP where the Hessian matrix of the objective to be minimized has r negative eigenvalues. It is known to be nondeterministic polynomial-time hard even when r = 1. This paper presents a novel algorithm to globally solve the QP for r = 1 and then extends to general r. Numerical results demonstrate the superiority of the proposed algorithm in comparison with state-of-the-art algorithms/software for small r.

Gurney flap scaling for optimum lift-to-drag ratio

AIAA Journal ◽  
1997 ◽  
Vol 35 ◽  
pp. 1888-1890 ◽  
Author(s):  
Philippe Giguere ◽  
Guy Dumas ◽  
Jean Lemay
Keyword(s):  
Gurney Flap ◽  
Drag Ratio ◽  
Lift To Drag Ratio
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