Concurrent Aircraft Design and Trip Assignment under Uncertainty: Fractional Operations

Mane, Muharrem; Crossley, William A.

doi:10.2514/6.2009-7007

Cited by 11 publications

(8 citation statements)

References 4 publications

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“…They extended their research further by incorporating uncertainty in passenger demand for on-demand and fractional operations. 2,6 Similarly, Davendralingam et al incorporated a dynamic passenger demand model and economic trends in the coupled design and allocation problem. 7,8,9 Research to date has focused on low fidelity conceptual design of a single new aircraft using mainly simple analytical models.…”

Section: Introductionmentioning

confidence: 97%

Coupled Optimization of Aircraft Family Design and Fleet Assignment for Minimum Cost and Fuel Burn

Jansen

Perez

2012

12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis And

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Increasing air traffic demand and the resulting climate impact of aircraft are of growing public concern. Reductions in future climate impact from air transportation require not only the design of efficient new individual aircraft, but also consideration of the operations of these aircraft during the design stage. This paper describes a multidisciplinary design optimization approach for the coupled optimization of aircraft in a family and the simultaneous assignment of these aircraft to operational routes. Including operational assignment of aircraft in the design stage can reduce operational inefficiencies, while the design of an aircraft family aims at reducing cost through the use of common components. A multi-objective optimization test case with respect to total cost and fuel burn involving a three-member aircraft family combined with a 24 non-stop route network is introduced. The coupled optimization is decomposed following a hierarchical approach, where the fleet assignment optimization is performed at the objective function level of the overall aircraft family design optimization. The concurrent aircraft family design optimization itself is performed using an approach analogous to the individual design feasible architecture. The optimizer used is a multi-objective parallel particle swarm optimization algorithm. Results obtained show no significant tradeoff between optimizing for total network cost and total network fuel burn. Compared to individually optimized aircraft, with fixed design missions, the use of common components results in a weight penalty, but this penalty is offset by the significant reduction in development and production cost. At the same time, coupling the operational use of the aircraft in the family leads to overall reduced total cost and fuel burn for the given network.

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Section: Introductionmentioning

confidence: 97%

Coupled Optimization of Aircraft Family Design and Fleet Assignment for Minimum Cost and Fuel Burn

Jansen

Perez

2012

12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis And

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“…Monte Carlo method, stochastic probability function and probability distribution function are commonly used in uncertainty modeling, and they have been applied in some research. 7,56,68 This also shows the value of introducing multidisciplinary theories and methods for SoSed design. (4) System dynamics methodSystem dynamics is a highly visual method for modeling complex SoS.…”

Section: Sosed Effectiveness Evaluationmentioning

confidence: 93%

System of systems oriented flight vehicle conceptual design: Perspectives and progresses

Liu

Tian

Gao

et al. 2015

Chinese Journal of Aeronautics

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In order to obtain optimized flight vehicle concepts which meet system of systems (SoS) operation requirements, designers have to pay high attention to the impact of SoS at conceptual design stage since operation environment goes increasingly complex. Based on this tendency, perspectives and progresses of SoS oriented flight vehicle conceptual design, which is abbreviate as SoSed design, are reviewed in this paper. Such basic concepts of SoS as definition, characteristics, differences between systems engineering and SoS engineering, as well as SoSed design process are introduced, then SoS engineering process model for research and development of flight vehicles and SoSed design wheel model for conceptual design are proposed. Related literature is classified and analyzed in accordance with four major elements including requirements, design concept, design analysis, and trade studies and optimization: typical SoS architectures, description and quantization of indexes are introduced; Application of inverse design in designing concept is analyzed; Modeling and simulation (M&S)-based methods and their applications in SoSed effectiveness evaluation are highlighted; According to SoSed trade studies and optimization related research, the importance of such points as decision-making and using multidisciplinary design optimization for reference are emphasized. Finally, the value of SoSed design is concluded, and five directions which are worthy of attention in this field are presented.

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“…2 His approach for this problem used a similar decomposition scheme to his previous work, 2 and Taylor and de Weck, only applying a Monte Carlo sampling to vary demand between city pairs, and address uncertainty in trip assignment.…”

Section: A Aircraft Design With a System-of-systems Perspectivementioning

confidence: 99%

A UAV Design / Sizing Study from a System of Systems Perspective

Bociaga

Crossley

2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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Traditional approaches to aircraft sizing focus on maximizing the performance of the individual airframe as a standalone system. Customers, such as the Department of Defense, have looked recently more towards all-encompassing solutions to large problems by using a System-of-Systems approach instead of maximizing capability from a single aircraft or similar monolithic system. Shrinking budgets call for making the most efficient use of existing assets and addressing capability gaps in broad frameworks. The result is that optimizing the aircraft alone may not provide the customer with needed capabilities or optimizing the aircraft alone may not make best use of the entire collection of aircraft to provide the capabilities in the most efficient manner. A fleet or collection of Unmanned Aerial Vehicles (UAVs) performing Intelligence, Surveillance, and Reconnaissance (ISR) missions provide an example of systems designed to operate with other independent systems as part of a system-of-systems. In this research, a fleet of UAVs will conduct surveillance over high terrain. The research investigates how to allocate existing systems along with a number of yet-to-be-defined UAVs. In this manner, the new UAVs may not be optimal for individual aircraft-based performance metrics, but they will enhance the capabilities of the fleet. Such a problem requires decomposition into a resource allocation problem , and an aircraft sizing sub-problem. The allocation problem uses a Design of Experiments (DoE) to select UAV fleet combinations to test by simulation using STK's Aircraft Mission Modeler software package. The aircraft sizing problem uses an SQP algorithm and a Breguetendurance equation-based sizing code to predict the dimensions, weight and performance of the new UAV. Both a main effects approach and a response surface approximation approach show the tradeoff between fleet cost and area coverage, enabling the customer to select the optimal allocation of existing and new UAVs while also determining the key characteristics of the new UAVs that fit into this fleet. The new UAV, sized for this specific mission but from a System-of-Systems perspective, complements the UAV fleet, enhancing the capabilities of the UAV fleet while only occasionally replacing the existing UAV assets.Nomenclature AR = wing aspect ratio f i = objective function h loiter = design loiter altitude of the new UAV n global_hawk = number of Global Hawk-like UAVs deployed n ISR-X = number of new ISR UAVs deployed n predators = number of Predator-like UAVs deployed P/W = power-to-weight ratio W 0 = aircraft takeoff weight W e = aircraft empty weight W f = aircraft fuel weight W/S = wing loading x = aircraft level problem design variables y = fleet level decision variables 1 Graduate Student, School of Aeronautics and Astronautics, 701 W. Stadium Ave., AIAA Student Member; currently Engineer, Lockheed

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Concurrent Aircraft Design and Trip Assignment under Uncertainty: Fractional Operations

Cited by 11 publications

References 4 publications

Coupled Optimization of Aircraft Family Design and Fleet Assignment for Minimum Cost and Fuel Burn

Coupled Optimization of Aircraft Family Design and Fleet Assignment for Minimum Cost and Fuel Burn

System of systems oriented flight vehicle conceptual design: Perspectives and progresses

A UAV Design / Sizing Study from a System of Systems Perspective

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