scholarly journals Large-Scale Path-Dependent Optimization of Supersonic Aircraft

Aerospace ◽  
2020 ◽  
Vol 7 (10) ◽  
pp. 152
Author(s):  
John P. Jasa ◽  
Benjamin J. Brelje ◽  
Justin S. Gray ◽  
Charles A. Mader ◽  
Joaquim R. R. A. Martins

Aircraft are multidisciplinary systems that are challenging to design due to interactions between the subsystems. The relevant disciplines, such as aerodynamic, thermal, and propulsion systems, must be considered simultaneously using a path-dependent formulation to assess aircraft performance accurately. In this paper, we construct a coupled aero-thermal-propulsive-mission multidisciplinary model to optimize supersonic aircraft considering their path-dependent performance. This large-scale optimization problem captures non-intuitive design trades that single disciplinary models and path-independent methods cannot resolve. We present optimal flight profiles for a supersonic aircraft with and without thermal constraints. We find that the optimal flight trajectory depends on thermal system performance, showing the need to optimize considering the path-dependent multidisciplinary interactions.

2020 ◽  
Vol 53 (2) ◽  
pp. 12572-12577
Author(s):  
Fernando Lezama ◽  
Ricardo Faia ◽  
Omid Abrishambaf ◽  
Pedro Faria ◽  
Zita Vale

Author(s):  
Jie Guo ◽  
Zhong Wan

A new spectral three-term conjugate gradient algorithm in virtue of the Quasi-Newton equation is developed for solving large-scale unconstrained optimization problems. It is proved that the search directions in this algorithm always satisfy a sufficiently descent condition independent of any line search. Global convergence is established for general objective functions if the strong Wolfe line search is used. Numerical experiments are employed to show its high numerical performance in solving large-scale optimization problems. Particularly, the developed algorithm is implemented to solve the 100 benchmark test problems from CUTE with different sizes from 1000 to 10,000, in comparison with some similar ones in the literature. The numerical results demonstrate that our algorithm outperforms the state-of-the-art ones in terms of less CPU time, less number of iteration or less number of function evaluation.


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