Simultaneous aircraft allocation and mission optimization using a modular adjoint approach

Abstract

The aircraft design optimization problem is typically formulated to maximize performance at a small number of representative operating conditions. This approach makes simplifying assumptions such as ignoring the climb and descent phases, but they can be avoided by performing simultaneous designmission-allocation optimization with surrogate models for the aircraft design disciplines. As a first step towards this goal, this paper presents a method for simultaneous allocation-mission optimization. We integrate aerodynamic and propulsion surrogates, a mission analysis tool, and allocation models within a computational framework that automates solving the coupled simulation and computing derivatives using the adjoint method for gradient-based optimization. We solve the mixed-integer allocation-mission optimization problem by using the linear allocation-only optimization to generate a good starting point and applying the branch-and-bound method to find an optimum for the mixedinteger nonlinear allocation-mission problem. The results show that this approach efficiently finds good local optima with, on average, roughly 10 node evaluations in the branch-and-bound method and most of the continuous optimizations converging almost immediately. The results show that adding next-generation aircraft to a fleet yields a 200-400 % profit increase for a 3-route test problem.

Monolithic Approach for Next-Generation Aircraft Design Considering Airline Operations and Economics

“…The first attempt to demonstrate the combined problem uses a form of branch-and-bound (BB) algorithm that enforces the integer constraints and solves only the mission-allocation problem (i.e., it ignores the aircraft design) for a simplistic three-route airline network problem [17]. For this three-route example problem, the simultaneous approach showed an improvement in the airline profit compared to the sequential approach.…”

Next generation aircraft design considering airline operations and economics

“…The first attempt to demonstrate the combined problem uses a form of Branch and Bound (BB) algorithm that enforces the integer constraints and solves only the mission-allocation problem (i.e., it ignores the aircraft design) for a simplistic three-route airline network problem [15]. For this three-route example problem, the simultaneous approach showed an improvement in the airline profit compared to the sequential approach.…”

Parallel allocation-mission optimization of a 128-route network

“…Compared to design-only optimization, this problem requires tens of thousands more evaluations of the aircraft performance model because of the number of mission points, the number of iterations required to converge the coupled mission analysis, and the number of routes in the airline network. A previous study outlined a method for making this problem tractable and focused on allocation-mission optimization as a starting point [1]. The proposed method would use aerostructural and propulsion surrogate models that are retrained every optimization iteration, and these surrogate models would be used for the mission analysis at much lower cost.…”

“…This framework, which uses the modular analysis and unified derivatives (MAUD) architecture [2], greatly simplified the code implementation and solution of the optimization problem. MAUD is now implemented in OpenMDAO [3], and it has been applied to the solution of allocation [1], mission [4], satellite [5], and wind turbine [6] design optimization problems.…”

“…While the solutions to these 3 challenges were successful, the major limitation in the results from Hwang et al [1] is that the problem considered a network with only 3 routes because the algorithm scaled poorly for larger networks. Realistic networks representative of airlines contain O(100) routes.…”