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Multidisciplinary Optimization of Unmanned Aircraft Considering Radar Signature, Sensors, and Trajectory Constraints
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8013-9787
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, Faculty of Science & Engineering.
2018 (English)In: Journal of Aircraft, ISSN 0021-8669, E-ISSN 1533-3868, Vol. 55, no 4, p. 1629-1640Article in journal (Refereed) Published
Abstract [en]

This paper presents a multidisciplinary design optimization framework applied to the development of unmanned aerial vehicles with a focus on radar signature and sensor performance requirements while simultaneously considering the flight trajectory. The primary emphasis herein is on the integration and development of analysis models for the calculation of the radar cross section and sensor detection probability, whereas traditional aeronautical disciplines such as aerodynamics and mission simulation are also taken into account in order to ensure a flyable concept. Furthermore, this work explores the effect of implementing trajectory constraints as a supplementary input to the multidisciplinary design optimization process and presents a method that enables the optimization of the aircraft under a three-dimensional flight scenario. To cope with the additional computational cost of the high-fidelity radar cross section and sensor calculations, the use of metamodels is also investigated and an efficient development methodology that can provide high-accuracy approximations for this particular problem is proposed. Overall, the operation and performance of the framework are evaluated against five surveillance scenarios, and the obtained results show that the implementation of trajectory constraints in the optimization has the potential to yield better designs by 12–25% when compared to the more “traditional” problem formulations.

Place, publisher, year, edition, pages
American Institute of Aeronautics and Astronautics, 2018. Vol. 55, no 4, p. 1629-1640
Keywords [en]
UAV, MDO, RCS, Trajectory, Sensors
National Category
Aerospace Engineering
Identifiers
URN: urn:nbn:se:liu:diva-150980DOI: 10.2514/1.C034314ISI: 000449304100025Scopus ID: 2-s2.0-85050865062OAI: oai:DiVA.org:liu-150980DiVA, id: diva2:1246366
Funder
VINNOVA, 2013-03758
Note

Funding agencies: Innovative Multidisciplinary Product Optimization (IMPOz) project of Swedens innovation agency VINNOVA [2013-03758]

Available from: 2018-09-07 Created: 2018-09-07 Last updated: 2019-11-13Bibliographically approved
In thesis
1. Design Optimization of Unmanned Aerial Vehicles: A System of Systems Approach
Open this publication in new window or tab >>Design Optimization of Unmanned Aerial Vehicles: A System of Systems Approach
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the last years, Unmanned Aerial Vehicles (UAVs) have gradually become a more efficient alternative to manned aircraft, and at present, they are being deployed in a broad spectrum of both military as well as civilian missions. This has led to an unprecedented market expansion with new challenges for the aeronautical industry, and as a result, it has created a need to implement the latest design tools in order to achieve faster idea-to-market times and higher product performance.

As a complex engineering product, UAVs are comprised of numerous sub-systems with intricate synergies and hidden dependencies. To this end, Multidisciplinary Design Optimization (MDO) is a method that can identify systems with better performance through the concurrent consideration of several engineering disciplines under a common framework. Nevertheless, there are still many limitations in MDO, and to this date, some of the most critical gaps can be found in the disciplinary modeling, in the analysis capabilities, and in the organizational integration of the method.

As an aeronautical product, UAVs are also expected to work together with other systems and to perform in various operating environments. In this respect, System of Systems (SoS) models enable the exploration of design interactions in various missions, and hence, they allow decision makers to identify capabilities that are beyond those of each individual system. As expected, this significantly more complex formulation raises new challenges regarding the decomposition of the problem, while at the same time, it sets further requirements in terms of analyses and mission simulation.

In this light, this thesis focuses on the design optimization of UAVs by enhancing the current MDO capabilities and by exploring the use of SoS models. Two literature reviews serve as the basis for identifying the gaps and trends in the field, and in turn, five case studies try to address them by proposing a set of expansions. On the whole, the problem is approached from a technical as well as an organizational point of view, and thus, this research aims to propose solutions that can lead to better performance and that are also meaningful to the Product Development Process (PDP).

Having established the above foundation, this work delves firstly into MDO, and more specifically, it presents a framework that has been enhanced with further system models and analysis capabilities, efficient computing solutions, and data visualization tools. At a secondary level, this work addresses the topic of SoS, and in particular, it presents a multi-level decomposition strategy, multi-fidelity disciplinary models, and a mission simulation module. Overall, this thesis presents quantitative data which aim to illustrate the benefits of design optimization on the performance of UAVs, and it concludes with a qualitative assessment of the effects that the proposed methods and tools can have on both the PDP and the organization. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 81
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2018
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-161915 (URN)10.3384/diss.diva-161915 (DOI)9789175190013 (ISBN)
Public defence
2019-12-06, Hörsal C3, C-huset, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
Vinnova
Available from: 2019-11-13 Created: 2019-11-13 Last updated: 2020-02-06Bibliographically approved

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Papageorgiou, AthanasiosTarkian, MehdiAmadori, KristianAndersson (Ölvander), Johan

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