Aeroservoelastic Optimization of Wing Structure Using Curvilinear Spars and Ribs (SpaRibs)

Robinson, Joe H.; Doyle, Steven; Ogawa, Grant; Baker, Myles L.; De, Shuvodeep; Jrad, Mohamed; Kapania, Rakesh K.

doi:10.2514/6.2016-3994

Cited by 15 publications

(8 citation statements)

References 5 publications

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“…Verification and Validation is a very important and essential field where the computational models [1][2][3][4][5][6][7][8][9][10][11][12][13][14] are tested for their credibility. Computer simulations are used to predict real-life outcomes, control vehicles precisely, design light-weight structures etc.…”

Section: Assumptions In Computational Modelsmentioning

confidence: 99%

Verification and Validation in Computational Mechanics

De¹

2022

Preprint

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Decades ago, when computational was expensive and limited, the structural design was mostly performed by hand calculations using simple mathematical models. For example, it was a common practice to design a structure as complex as the wing of an aircraft by simple beam analysis. However, ever since the classic paper by Turner et al., due to a rapid increase in computational, more complex mathematical models are being used to simulate the physical behavior of complex structural components. To solve intractable problems unsolvable by hand calculations, numerical techniques like Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Finite Difference Method etc. are being employed. In fact, the availability of these methods has led to the development of an entirely new area of research known as Multidisciplinary Design Optimization (MDO) where various disciplines are considered in an optimization problem. The most important question while using a mathematical model to represent practical industrial problems is to what extent these models represent the real-life situation. Computational models are always built on upon assumptions. Simply at looking at the simulation outcomes i.e. the graphical and numerical results, it is often very difficult to ensure if the underlying assumption holds and that the results are reliable. This has led to the development of another field of research known as Verification and Validation (called V&V in short).

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Section: Assumptions In Computational Modelsmentioning

confidence: 99%

Verification and Validation in Computational Mechanics

De¹

2022

Preprint

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“…The control parameter adaption is enlivened from the success history-based hyperparameter adaptation [25], [34], [35]. It keeps a memory archive of the effective DE hyperparameters, to be specific, scaling factor F in Eq (12) and crossover rate Cr in Eq (13). Success history based memory archives are allotted as in standard SHADE [35] to both the scaling factor F i as S F,i = F i , i = 1, .…”

Section: A Imode Algorithmmentioning

confidence: 99%

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

Saha¹,

De²

2022

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Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

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“…Other examples of a global-local approach, in which the local optimisation is implemented as a nested procedure, can be found in the work of Kapania and his group [44][45][46][47][48]. Their work is related to the design and optimisation of metallic wings with curvilinear spars and ribs.…”

Section: Nested Execution Of Global and Local Optimisationmentioning

confidence: 99%

“…References: Noevere and Wilhite [43], Kapania et al [44][45][46][47][48][49][50] Advantages: accurate and always applicable Disadvantages: computationally expensive…”

Section: Nested Localmentioning

confidence: 99%

Multidisciplinary Optimisation of Aircraft Structures with Critical Non-Regular Areas: Current Practice and Challenges

et al. 2021

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The design optimisation of aerostructures is largely based on Multidisciplinary Design Optimisation (MDO), which is a set of tools used by the aircraft industry to size primary structures: wings, large portions of the fuselage or even an entire aircraft. The procedure is computationally expensive, as it must account for several thousands of loadcases, multiple analyses with hundreds of thousands of degrees of freedom, thousands of design variables and millions of constraints. Because of this, the coarse Global Finite Element Model (GFEM), on which the procedure is based, cannot be further refined. The structures represented in the GFEM contain many components and non-regular areas, which require a detailed modelling to capture their complex mechanical behaviour. Instead, in the GFEM, these components are represented by simplified models with approximated stiffness, whose main role is to contribute to the identification of the load paths over the whole structure. Therefore, these parts are kept fixed and are not constrained during the optimisation, as the description of their internal deformation is not sufficiently accurate. In this paper, we show that it would nevertheless be desirable to size the non-regular areas and the overall structures at once. Firstly, we introduce the concept of non-regular areas in the context of a structural airframe MDO. Secondly, we present a literature survey on MDO with a critical review of several architectures and their current applications to aircraft design optimisation. Then, we analyse and demonstrate with examples the possible consequences of neglecting non-regular areas when MDO is applied. In the conclusion, we analyse the requirements for alternative approaches and why the current ones are not viable solutions. Lastly, we discuss which characteristics of the problem could be exploited to contain the computational cost.

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Aeroservoelastic Optimization of Wing Structure Using Curvilinear Spars and Ribs (SpaRibs)

Cited by 15 publications

References 5 publications

Verification and Validation in Computational Mechanics

Verification and Validation in Computational Mechanics

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

Multidisciplinary Optimisation of Aircraft Structures with Critical Non-Regular Areas: Current Practice and Challenges

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