Multimission Aircraft Fuel-Burn Minimization via Multipoint Aerostructural Optimization

Abstract

Aerodynamic shape and aerostructural design optimizations that maximize the performance at a single flight condition may result in designs with unacceptable off-design performance. While considering multiple flight conditions in the optimization improves the robustness of the designs, there is a need to develop a way of choosing the flight conditions and their relative emphases such that multipoint optimizations reflect the true objective function. In addition, there is a need to consider uncertain missions and flight conditions. To address this, a new strategy to formulate multipoint design optimization problems is developed that can maximize the aircraft performance over a large number of different missions. This new strategy is applied to the high-fidelity aerostructural optimization of a long-range twin-aisle aircraft with the objective of minimizing the fuel burn over all the missions it performs in one year. This is accomplished by determining 25 flight conditions and their respective emphases on drag and structural weight that emulate the fuel-burn minimization for over 100,000 missions. The design optimization is based on the computational fluid dynamics of a full aircraft configuration coupled to a detailed finite element model of the wing structure, enabling the simultaneous optimization of wing aerodynamic shape and structural sizing leading to optimal static aeroelastic tailoring. A coupled adjoint method in conjunction with a gradient-based optimizer enable optimization with respect to 311 design variables subject to 152 constraints. Given the high computational cost of the aerostructural analysis, kriging models are used to evaluate the multiple missions. The results show that the multipoint optimized design reduced the total fuel burn by 6.6%, while the single-point optimization reduced it by only 1.7%. This capability to analyze large numbers of flight conditions and missions and to reduce the multimission problem to a multipoint problem could be used with a few modifications to minimize the expected value of any objective function given the probability density functions of the flight conditions.

Objective Function and Constraints for Robust Transonic Aerofoil Optimization

“…The first is that to apply it successfully requires careful selection of both the design points (although these are often known a priori ), and the weightings between the objectives at those design points. This issue can be eased by using automated weight selections, 24,25 or an integral approach. 26 The second common issue is the cost surrounding multipoint optimization.…”

Aerostructural Design Optimization of an Adaptive Morphing Trailing Edge Wing

“…This framework has been used previously to perform aerostructural optimization of the uCRM geometry [7], as well as other investigations [9,10,11].…”

Strategies for Solving High-Fidelity Aerodynamic Shape Optimization Problems

“…We solve the adjoint equations with preconditioned GMRES [26] using PETSc [27,28,29]. We have previously performed extensive Euler-based aerodynamic shape optimization [30,31] and aerostructural optimization [21,32]. However, we have observed serious issues with the resulting optimal Euler-based designs due to the missing viscous effects.…”