Development of a Multilevel Multidisciplinary-Optimization Capability for an Industrial Environment

Piperni, Pat; Deblois, A.; Henderson, Ryan

doi:10.2514/1.j052180

Cited by 59 publications

(63 citation statements)

References 32 publications

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“…Depending on the type and scope of the application, even a large error can be sometimes acceptable, while in general, it can be said that the most influencing factors which can affect the final accuracy are the number of the input variables, the amount of noise in the function, and the quality/size of the training sample (Persson, 2015). To this end, there are many authors who have investigated various methods to increase the performance of the metamodels, and the most notable examples are to recalibrate the metamodels after each iteration (Lefebvre et al, 2012), to limit their predictions only at a narrow area of the design space (Choi et al, 2008), and lastly, to decompose the problem into smaller parts and then use multiple metamodels (Piperni et al, 2013).…”

Section: Efficient Computing Methodsmentioning

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%

“…The level of fidelity that the disciplinary models should deliver is primarily determined by the development stage which the MDO process aims to enhance, and therefore, it is always crucial to take into account the degree of design maturity that needs to be achieved (Piperni et al, 2013). According to the review of paper I, the majority of authors abstain from specifying the development stage that they are working on, while at the same time, there is general tendency where the choice of tools is based on availability rather than suitability.…”

Section: Level Of Fidelitymentioning

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: Efficient Computing Methodsmentioning

confidence: 99%

Section: Disciplinary Modelingmentioning

confidence: 99%

Section: Level Of Fidelitymentioning

confidence: 99%

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Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Παπαγεωργίου¹

2018

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“…As another example, in many settings a low-fidelity model can be derived by neglecting nonlinear terms. For example, lower-fidelity linearized models are common in aerodynamic and structural analyses [166]. Yet another example is when the highfidelity model relies on an iterative solver (e.g., Krylov subspace solvers or Newton's method), a low-fidelity model can be derived by loosening the residual tolerances of the iterative method-thus, to derive a low-fidelity approximation, the iterative solver is stopped earlier than if a high-fidelity output were computed.…”

Section: Types Of Low-fidelity Modelsmentioning

confidence: 99%

Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

Peherstorfer¹,

Willcox²,

Gunzburger³

2018

SIAM Rev.

765

454

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In many situations across computational science and engineering, multiple computational models are available that describe a system of interest. These different models have varying evaluation costs and varying fidelities. Typically, a computationally expensive high-fidelity model describes the system with the accuracy required by the current application at hand, while lower-fidelity models are less accurate but computationally cheaper than the high-fidelity model. Outer-loop applications, such as optimization, inference, and uncertainty quantification, require multiple model evaluations at many different inputs, which often leads to computational demands that exceed available resources if only the high-fidelity model is used. This work surveys multifidelity methods that accelerate the solution of outer-loop applications by combining high-fidelity and low-fidelity model evaluations, where the low-fidelity evaluations arise from an explicit low-fidelity model (e.g., a simplified physics approximation, a reduced model, a data-fit surrogate, etc.) that approximates the same output quantity as the high-fidelity model. The overall premise of these multifidelity methods is that low-fidelity models are leveraged for speedup while the high-fidelity model is kept in the loop to establish accuracy and/or convergence guarantees. We categorize multifidelity methods according to three classes of strategies: adaptation, fusion, and filtering. The paper reviews multifidelity methods in the outer-loop contexts of uncertainty propagation, inference, and optimization.

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“…Currently employed codes are based on the potential flow theory [11]. The theory is valid for irrotational, inviscid and incompressible flows, for which the velocity field can be represented as a gradient of a scalar function, called the velocity potential.…”

Section: Introductionmentioning

confidence: 99%

Accelerating low-fidelity aerodynamic codes on multi- and many-core architectures

Chrust

Laurendeau

Ostiguy³

2015

J Supercomput

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Vortex lattice and panel methods belong to a broad family of aerodynamic codes based on potential flow theory. They are used in preliminary aerodynamic studies in early stages of aircraft design where hundreds of thousands candidate configurations are analyzed. In this paper, we describe their efficient implementation on modern multiand many-core architectures. We show how to bridge the 'ninja gap', defined as the performance gap between an unoptimized C/C++ code and best optimized CPU code. We port the Vortex Lattice Method to a Graphics Processing Unit using the OpenACC standard. An elegant solution for implementation of data movements for C++ classes is also presented.

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Development of a Multilevel Multidisciplinary-Optimization Capability for an Industrial Environment

Cited by 59 publications

References 32 publications

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Optimization of Unmanned Aerial Vehicles: Expanding the Multidisciplinary Capabilities

Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

Accelerating low-fidelity aerodynamic codes on multi- and many-core architectures

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