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Development of a Multidisciplinary Design Optimization Framework Applied on UAV Design by Considering Models for Mission, Surveillance, and Stealth Performance
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.
Saab Aeronautics, Sweden.
2017 (English)In: 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2017Conference paper, Published paper (Other academic)
Abstract [en]

This paper presents a Multidisciplinary Design Optimization (MDO) framework that is intended to be employed in the early design stages of Unmanned Aerial Vehicles (UAVs) when the primary focus is on the tradeoffs between the mission, stealth, and surveillance performance requirements. The proposed MDO framework takes into account the aircraft’s geometry, the aerodynamics, the trim, the stability, and the simulation of the mission, but it also includes two additional models for computing the Radar Cross Section (RCS) and the sensor performance. A multi-level solution architecture is implemented in order to tackle the increased complexity of the problem, and it is shown that this type of decomposition can be a more efficient optimization approach compared to the traditional single-level formulation. The operation of the framework is evaluated through single objective optimizations by using the weighted sum method, while it is also investigated whether or not metamodels can be a viable alternative to the computationally expensive RCS and sensor analysis models. Overall, the results show that the mission, stealth, and surveillance performance are conflicting objectives, and therefore, their concurrent consideration in an optimization framework can help increase the available knowledge early on in the design of UAV applications.

Place, publisher, year, edition, pages
2017.
Series
AIAA AVIATION Forum ; 2017-4151
National Category
Aerospace Engineering
Identifiers
URN: urn:nbn:se:liu:diva-142277DOI: 10.2514/6.2017-4151OAI: oai:DiVA.org:liu-142277DiVA: diva2:1152307
Conference
18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Denver, Colorado, USA, 2017, AIAA AVIATION Forum, (AIAA 2017-4151)
Available from: 2017-10-24 Created: 2017-10-24 Last updated: 2017-12-13
In thesis
1. Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities
Open this publication in new window or tab >>Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Over the last decade, Unmanned Aerial Vehicles (UAVs) have experienced an accelerated growth, and nowadays they are being deployed in a variety of missions that have traditionally been covered by manned aircraft. This unprecedented market expansion has created new and unforeseen challenges for the manufacturing industry which is now called to further reduce the idea-to-market times while simultaneously delivering designs of even higher performance. In this environment of uncertainty and risk, it is without a doubt crucial for the involved actors to find ways to secure their strategic advantage, and hence, implementing the latest design tools has become a critical consideration in every Product Development Process (PDP).

To this end, a method that has been frequently applied in the PDP and has shown many successful results in the development of complex engineering products is Multidisciplinary Design Optimization (MDO). In general, MDO can bring additional knowledge regarding the best-suited designs much earlier in the process, and in this respect, it can lead to significant cost and time savings by reducing the total number of refinement iterations. Nevertheless, the organizational and cultural integration of MDO has been often overlooked, while at the same time, several technical aspects of the method for UAV design are still at an elementary level. On the whole, research on MDO is showing a slow progress, and to this date, there are many limitations in both the disciplinary models and the available analysis capabilities.

In light of the above, this thesis focuses on the particulars of the MDO methodology, and more specifically, on how it can be best adapted and evolved in order to enhance the development process of UAVs. The primary objective is to study the current trends and gaps of the MDO practices in UAV applications, and subsequently to build upon that and explore how these can be included in a roadmap that will be able to serve a guide for newcomers in the field. Compared to other studies, the problem is herein approached from both a technical as well as organizational perspective, and thus, this research not only aims to propose techniques that can lead to better designs but also solutions that will be meaningful to the PDP. Having established the above foundation, this work shows that the traditional MDO frameworks for UAV design have been neglecting several important features, and it elaborates on how those novel elements can be modeled in order to enable a better integration of MDO into the organizational functions. Overall, this thesis presents quantitative and qualitative data which illustrate the effectiveness of the new framework enhancements in the development process of UAVs, and concludes with discussions on the possible improvement directions towards achieving more and better MDO capabilities.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. p. 52
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1796
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:liu:diva-143672 (URN)10.3384/lic.diva-143672 (DOI)9789176853917 (ISBN)
Presentation
2017-12-14, A25, A-huset, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2018-01-08 Created: 2017-12-13 Last updated: 2018-01-12Bibliographically approved

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