A Multidisciplinary Design Optimization Framework for Design Studies of an Efficient Supersonic Air Vehicle

Allison, Darcy L.; Morris, Craig C.; Schetz, Joseph A.; Kapania, Rakesh K.; Sultan, Cornel; Deaton, Joshua D.; Grandhi, Ramana V.

doi:10.2514/6.2012-5492

Cited by 27 publications

(44 citation statements)

References 17 publications

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“…The convergence loops of MDF are based on fixed-point iterations which require multiple disciplinary analyses for each one of the global algorithm evaluations, and for that reason, a significant amount of computational time is often spent in this process. Thus, the MDF is more suitable for smaller problems where fast analysis times are expected, while to this date, in aircraft MDO it has been typically restricted to the decoupling of a small number of disciplines like for example propulsion and mission performance (Allison et al, 2012) or structures and aerodynamics (Brezillon et al, 2012). A distributed architecture that is based on the IDF formulation and has been frequently applied in aircraft MDO for achieving mission-based (Perez et al, 2006) or discipline-based (Iwaniuk et al, 2016) decomposition is Collaborative Optimization (CO).…”

Section: Figure 8 the Derivation Of The Fundamental Mdo Decompositiomentioning

confidence: 99%

“…Conceptual design is by nature a very promising field of application for MDO, and it has been shown in many instances (Amadori et al, 2006;Jeon et al, 2007) that it can be a quick tool for exploring the underlying tradeoffs between competing objectives in UAV design. Accordingly, MDO can have many uses in preliminary design where it can lead to a more holistic view of the total system behavior by taking into account the effects of the on-board systems like the propulsion (Allison et al, 2012) or the flight controls (Perez et al, 2006). Lastly, there are traditionally further uses of MDO in the detailed design stage, and more specifically, it can be an effective method to either refine the design with greater accuracy or provide more data earlier in the process through the use of increased problem sizes and high-fidelity simulations (Choi et al, 2010).…”

Section: Enhancing the Development Processmentioning

confidence: 99%

“…Thus, for UAVs it is generally important to include noise propagation models that show the sound impact of the design towards the ground observers (Choi et al, 2008), flight mechanics models that simulate the interactions between the control system and the behavior of the aircraft (Haghighat et al, 2012), and cost models that can estimate the economic implications and life-cycle evolution of the product (Sadraey, 2008). Finally, further additions which are typically neglected but are especially critical for surveillance UAVs are the simulation of the on-board sub-systems which can provide more data on the system interactions (Piperni et al, 2013), and the modeling of electromagnetics which can capture aspects such as the communications (Neidhoefer et al, 2009) and the stealth performance (Allison et al, 2012).…”

Section: Disciplinary Modelingmentioning

confidence: 99%

“…In total, low-fidelity tools are the most frequently used solution in MDO for conceptual design of UAVs (see Figure 16), and the main reason for this is that they can deliver a sufficient level of fidelity at very fast computational times (Iemma and Diez, 2006). Nevertheless, low fidelity has many disadvantages, and more specifically, it can be seen that it is often impossible for elementary solutions like empirical equations to capture the complexity of detailed design or unconventional configurations (Allison et al, 2012). Thus, in such cases it is imperative to employ higher-fidelity models or their metamodels which can be an efficient way of increasing the confidence on the design at a reasonable loss of accuracy.…”

Section: Level Of Fidelitymentioning

confidence: 99%

“…The stealth performance is usually expressed by the Radar Cross Section (RCS) of the aircraft which is measured in square meters and corresponds to the size of the aircraft area that the ground radars will eventually "detect" (Raymer, 2012). The RCS cannot be measured adequately with empirical equations because it depends highly on a detailed representation of the design geometry, and for that reason, complex optical codes that require a significant amount of computational time are often necessary in order to accurately predict it (Allison et al, 2012). Hence, for this case study the Physical Optics (PO) tool GRECO (Rius Casals et al, 1993) is used as a basis for all the simulations to better capture the physics of the problem, and then it is integrated into the conceptual design framework by using the undeniably faster, but yet less accurate, alternative of metamodels.…”

Section: Basic and Case-specific Modelsmentioning

confidence: 99%

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Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Παπαγεωργίου¹

2018

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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. vi vii

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Section: Figure 8 the Derivation Of The Fundamental Mdo Decompositiomentioning

confidence: 99%

Section: Enhancing the Development Processmentioning

confidence: 99%

Section: Disciplinary Modelingmentioning

confidence: 99%

Section: Level Of Fidelitymentioning

confidence: 99%

Section: Basic and Case-specific Modelsmentioning

confidence: 99%

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Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Παπαγεωργίου¹

2018

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Mixed Compression Air-Intake Design for High-Speed Transportation

Citak

Aksu

Harputlu

et al. 2021

Springer Proceedings in Mathematics &Amp; Statistics

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Advanced Aerostructural Optimization Techniques for Aircraft Design

Zuo

Chen²,

et al. 2015

Mathematical Problems in Engineering

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Traditional coupled aerostructural design optimization (ASDO) of aircraft based on high-fidelity models is computationally expensive and inefficient. To improve the efficiency, the key is to predict aerostructural performance of the aircraft efficiently. The cruise shape of the aircraft is parameterized and optimized in this paper, and a methodology named reverse iteration of structural model (RISM) is adopted to get the aerostructural performance of cruise shape efficiently. A new mathematical explanation of RISM is presented in this paper. The efficiency of RISM can be improved by four times compared with traditional static aeroelastic analysis. General purpose computing on graphical processing units (GPGPU) is adopted to accelerate the RISM further, and GPU-accelerated RISM is constructed. The efficiency of GPU-accelerated RISM can be raised by about 239 times compared with that of the loosely coupled aeroelastic analysis. Test shows that the fidelity of GPU-accelerated RISM is high enough for optimization. Optimization framework based on Kriging model is constructed. The efficiency of the proposed optimization system can be improved greatly with the aid of GPU-accelerated RISM. An unmanned aerial vehicle (UAV) is optimized using this framework and the range is improved by 4.67% after optimization, which shows effectiveness and efficiency of this framework.

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A Multidisciplinary Design Optimization Framework for Design Studies of an Efficient Supersonic Air Vehicle

Cited by 27 publications

References 17 publications

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Mixed Compression Air-Intake Design for High-Speed Transportation

Advanced Aerostructural Optimization Techniques for Aircraft Design

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