A survey of multidisciplinary design optimization methods in launch vehicle design

Balesdent, Mathieu; Bérend, Nicolas; Dépincé, Philippe; Chriette, Abdelhamid

doi:10.1007/s00158-011-0701-4

Cited by 107 publications

(79 citation statements)

References 82 publications

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“…As far as monolithic architectures are concerned, the MDF is the simplest to implement, while at the same time, it also ensures that there is always system consistency even if the optimization process is terminated early (Balesdent et al, 2011). In MDF, all the sub-systems are coupled together in an analysis module that receives the design variables x, then iterates with the discipline outputs yi and the state variables zi until convergence has been reached, and finally calculates the objective function f as well as the equality h and inequality g constraints (see Figure 9 left).…”

Section: Figure 8 the Derivation Of The Fundamental Mdo Decompositiomentioning

confidence: 99%

“…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). CO divides the problem in many different parts which are then controlled by a global optimizer, and in this respect, it has the main advantage of enabling a better problem decoupling and allowing disciplines to be analyzed in parallel (Balesdent et al, 2011). In CO, all the sub-systems receive the design as well as coupling variables x, y and then modify their local copies subject to the local constraints hi, gi as well as the coupling functions ci, Ji.…”

Section: Figure 8 the Derivation Of The Fundamental Mdo Decompositiomentioning

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: Figure 8 the Derivation Of The Fundamental Mdo Decompositiomentioning

confidence: 99%

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Παπαγεωργίου¹

2018

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“…The author [4], for example, class examples into two categories: methods with expression of preferences in advance (a priori preferences) and the Pareto methods (a posteriori preferences). The reader may refer to the studies on MOO methods in [6], [7] and [8] for further references.…”

Section: F I (X) X I * Is the Point Minimizing The Objective Functiomentioning

confidence: 99%

Pareto optimality and Nash equilibrium for building stable systems

Doufene¹,

Krob²

2015

2015 Annual IEEE Systems Conference (SysCon) Proceedings

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Abstract-This paper introduces a design approach based on system analysis and game theory for the identification of architectural equilibrium which guarantees the stability of the system being designed and its environment after the integration. We introduce multi-objective optimization and game theory, and their links with systems engineering through mathematical models. While Pareto optimality is used to select best architectures and to support independent decisions, Nash equilibrium is used to find out architectural equilibrium and to support interdependent decisions. This approach was illustrated previously in a case study.

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“…Many of the MDO methods intended for large-scale problems are developed in conjunction with the aerospace industry, and the question is then whether these methods are also suitable for automotive applications. There are studies that review MDO methods for aerospace applications [14] and launch vehicle applications [15]. However, there do not appear to be any studies which focus on suitable MDO methods for automotive applications.…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary design optimisation methods for automotive structures

Bäckryd¹,

Ryberg²,

Nilsson³

2017

Int. J. Automot. Mech. Eng.

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Multidisciplinary design optimisation (MDO) can be used as an effective tool to improve the design of automotive structures. Large-scale MDO problems typically involve several groups who must work concurrently and autonomously in order to make the solution process efficient. In this article, the formulations of existing MDO methods are compared and their suitability is assessed in relation to the characteristics of automotive structural applications. Both multi-level and single-level optimisation methods are considered. Multi-level optimisation methods distribute the design process but are complex. When optimising automotive structures, metamodels are often required to relieve the computational burden of detailed simulation models. The metamodels can be created by individual groups prior to the optimisation process, and thus offer a way of distributing work. Therefore, it is concluded that a single-level method in combination with metamodels is the most straightforward way of implementing MDO into the development of automotive structures. If the benefits of multi-level optimisation methods, in a special case, are considered to compensate for their drawbacks, analytical target cascading has a number of advantages over collaborative optimisation, but both methods are possible choices.

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A survey of multidisciplinary design optimization methods in launch vehicle design

Abstract: International audienc

Cited by 107 publications

References 82 publications

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Pareto optimality and Nash equilibrium for building stable systems

Multidisciplinary design optimisation methods for automotive structures

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