Allocation-mission-design optimization of next-generation aircraft using a parallel computational framework

Hwang, John T.; Martins, Joaquim R. R. A.

doi:10.2514/6.2016-1662

Cited by 23 publications

(19 citation statements)

References 31 publications

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“…The current application problem used FLOPS as the aircraft sizing tool and employed a simplistic representation of a typical airline operation. The effort to replace FLOPS with a high-fidelity aircraft design tool is underway [6,17], and we will then solve the DARM problem with high-fidelity aircraft design analyses using AMIEGO within NASA's OpenMDAO framework.…”

Section: Discussionmentioning

confidence: 99%

“…[1], thereby establishing the need for a better approach. Follow-up efforts [16,17] extended the three-route allocation-mission problem to consider a more realistic 128-route network and leverage a parallel computing framework. The work employs a high-fidelity aerostructural analysis tool for the large airline network problem, has over 6000 design variables, and uses the adjoint-based method to compute the analytic derivatives.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For this paper, we use FLOPS [23] as the sizing tool to model the next generation aircraft. Later, as a part of future work, we will switch to a more sophisticated physics-based high fidelity tool [17] as a part of the aircraft sizing process. For this application, the airline uses the following allocation and revenue management formulation.…”

Section: Fig 4 Eleven Route Airline Network With Hub At Bostonmentioning

confidence: 99%

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Next generation aircraft design considering airline operations and economics

Roy

Crossley

Moore

et al. 2018

2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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Traditional approaches to design and optimization of a new system often use a systemcentric objective and do not take into consideration how the operator will use this new system alongside other existing systems. When the new system design is incorporated into the broader group of systems, the performance of the operator-level objective can be sub-optimal due to the unmodeled interaction between the new system and the other systems. Among the few available references that describe attempts to address this disconnect, most follow an MDO-motivated sequential decomposition approach of first designing a very good system and then providing this system to the operator who, decides the best way to use this new system along with the existing systems. This paper addresses this issue by including aircraft design, airline operations, and revenue management "subspaces"; and presents an approach that could simultaneously solve these subspaces posed as a monolithic optimization problem rather than the traditional approach described above. The monolithic approach makes the problem an expensive Mixed Integer Non-Linear Programming problem, which are extremely difficult to solve. To address the problem, we use a recently developed optimization framework that simultaneously solves the subspaces to capture the "synergy" in the problem that the previous decomposition approaches did not exploit, addresses mixed-integer/discrete type design variables in an efficient manner, and accounts for computationally expensive analysis tools. This approach solves an 11-route airline network problem consisting of 94 decision variables including 33 integer and 61 continuous type variables. Simultaneously solving the subspaces leads to significant improvement in the fleet-level objective of the airline when compared to the previously developed sequential subspace decomposition approach. NomenclatureBH a, j = Block hours of aircraft type a on route j dem j = Daily passenger demand on route j E I = Expected Improvement f leet a = Number of aircraft type a k I = Number of integer type design variables of the problem M H a, j = Maintenance hour per block hour of aircraft type a on route j pax a, j = Number of passengers per flight on aircraft type a on route j

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Fig 4 Eleven Route Airline Network With Hub At Bostonmentioning

confidence: 99%

See 1 more Smart Citation

Next generation aircraft design considering airline operations and economics

Roy

Crossley

Moore

et al. 2018

2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

Self Cite

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“…OpenMDAO is open-source and is written in Python, which is a language known for its ease of interfacing to compiled languages such as C++ and Fortran. OpenMDAO has been used to solve MDO problems in satellite design [15], wind turbine design [14], aircraft design [16], and aircraft trajectory optimization [17].…”

Section: A Openmdaomentioning

confidence: 99%

Implementation of topology optimization using openMDAO

Chung

Hwang

Gray

et al. 2018

2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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Topology optimization is one of the widely known branches among the structural optimization, and it distinguishes itself being able to generate extremely lightweight structures.Recently it has drawn particular interest from both industry and academia because of its natural applicability to additive manufacturing. However, its implementation is often a daunting task for engineers in practice. In particular there can potentially be a large programming effort required to modify the method, even from subtle tweaks in the problem definition or solution algorithm. Changes to the code can leave to corresponding updates to relevant derivative calculations, further compounding the problem. Implementation, therefore, is not only time-consuming but also repetitive and susceptible to human-induced errors. In this regard, topology optimization implementations stand to benefit from changes that result in more code modularity, ease of restructuring, and more automated derivative calculations. In this work we propose using OpenMDAO, a computational framework for multidisciplinary design optimization, as a generic platform for to built topology optimization implementations with in order to achieve these implementation improvements. Two widely used topology optimization techniques-density-based and level-set-are implemented as to serve as reference code designs. These techniques are implemented in a decomposed manner, with the aid of the modular architecture of OpenMDAO as well as state-of-the-art numerical methods. To demonstrate the flexibility of the new topology optimization architecture, two variations on the density-based topology optimization approach are shown. Nomenclature f = objective function SI M P = Solid Isotropic Material with Penalization LSTO = Level Set Topology Optimization λ i = Lagrange multiplier for sensitivity of function i ρ = element-wise material density φ = signed distance function for level-set method CF L = Courant-Frederichs-Lowy condition X DSM = eXtended Design Structure Matrix

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“…One other hand, OpenMDAO has ability to subdivide a problem into groups or components and link their inputs and outputs, made it easier to decompose large and complex problems. OpenMDAO has been used to solve MDO problems in satellite design [8], wind turbine design [9], aircraft design [10] and aircraft trajectory optimization [11].…”

Section: A Open Mdao Frameworkmentioning

confidence: 99%

Aero structural optimization for sailplane wing in preliminary design

2018

J. adv. tec. eng. res.

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The purpose of this research is to obtain optimized sailplane wing design, which contributes to better performance in terms of range and endurance. This paper presents aerostructural design optimization using Multidisciplinary Design Optimization approach (MDO). This is carried out using Open Aero Struct (OAS), an open-source low-fidelity aerostructural analysis and optimization tool written in Python and developed in NASA's Open MDAO framework. The aerostructural model in OAS coupled a Vortex Lattice Method for aerodynamic with a 1-D FiniteElement Analysis for structure, to model the sailplane wing. An aerostructural optimization problem was formulated to minimize the Sink Speed, as the objective function, with varying twelve design variables, and subjected to two constraints. An initial simple wing design is used as baseline for the optimization. The indings of the aerostructural optimization indicated that the optimized wing exhibited improvements in both disciplines, resulting a better cross-country performance for the sailplane in terms of range and endurance. The optimized wing achieved increase of 37% in endurance and 27% in range.

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Allocation-mission-design optimization of next-generation aircraft using a parallel computational framework

Cited by 23 publications

References 31 publications

Next generation aircraft design considering airline operations and economics

Next generation aircraft design considering airline operations and economics

Implementation of topology optimization using openMDAO

Aero structural optimization for sailplane wing in preliminary design

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