به روز رسانی مدل عنصر محدود
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به روز رسانی مدل عنصر محدود

عنوان فارسی مقاله: به روز رسانی مدل عنصر محدود با استفاده از بهینه سازی قطعی: یک رویکرد جستجوی الگوی جهانی
عنوان انگلیسی مقاله: Finite element model updating using deterministic optimisation: A global pattern search approach
مجله/کنفرانس: سازه های مهندسی – Engineering Structures
رشته های تحصیلی مرتبط: ریاضی
گرایش های تحصیلی مرتبط: آنالیز عددی
کلمات کلیدی فارسی: بهینه سازی قطعی، روش های عاری از مشتق، روش عنصر محدود، به روز رسانی مدل، توربین بادی، تیغه های روتور
کلمات کلیدی انگلیسی: Deterministic optimisation، Derivative-free methods، Finite element method، Model updating، Wind turbine، Rotor blades
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.engstruct.2019.05.047
دانشگاه: Leibniz University Hannover/ForWind, Institute of Structural Analysis, Appelstraße 9A, 30167 Hannover, Germany
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 3.604 در سال 2018
شاخص H_index: 114 در سال 2019
شاخص SJR: 1.628 در سال 2018
شناسه ISSN: 0141-0296
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 9
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E12435
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1. Introduction


2. Global pattern search algorithm


3. Finite element model updating


4. Results


5. Summary and outlook


Acknowledgments


Supplementary material


References

نمونه متن انگلیسی مقاله

Abstract


With this work, we present a novel derivative-free global optimisation approach for finite element model updating. The aim is to localise structural damage in a wind turbine rotor blade. For this purpose, we create a reference finite element model of the blade as well as a model with a fictitious damage. To validate the approach, we use a model updating scheme to locate the artificially induced damage. This scheme employs numerical optimisation using the parameterised finite element model and an objective function based on modal parameters. Metaheuristic algorithms are the predominant class of optimisers for global optimisation problems. With this work, we show that deterministic approaches are competitive for engineering problems such as model updating. The proposed optimisation algorithm is deterministic and a generalisation of the pattern search algorithm. It picks up features known from local deterministic algorithms and transfers them to a global algorithm. We demonstrate the convergence, discuss the numerical performance of the proposed optimiser with respect to several analytical test problems and propose a possible trade-off between parallelisation and convergence rate. Additionally, we compare the numerical performance of the proposed deterministic algorithm concerning the model updating problem to the performance of well-established metaheuristic and local optimisation algorithms. The introduced algorithm converges quickly on test functions as well as on the model updating problem. In some cases, the deterministic algorithm outperforms metaheuristic algorithms. We conclude that deterministic optimisation algorithms should receive more attention in the field of engineering optimisation.


Introduction


For optimisation tasks considering non-linear problems, derivativefree global algorithms are particularly suited. Objective functions of such problems often involve transient numerical simulations or discrete and non-linear evaluations. This is why it is usually not possible to find a direct solution for the derivative of such objective functions. We concentrate on derivative-free algorithms, since obtaining derivatives in a numerically complex design variable space is challenging. Indeed, derivatives can easily be obtained numerically by using singlesided or symmetric sampling around a base point. The Hessian matrix needed for sequential quadratic programming [1] is commonly obtained by this method. However, numerical noise and the difficulty to receive an appropriate value for the step size necessitate some numerical experiments to yield a stable optimisation. Derivative-free methods are thus desirable due to the numerical robustness they provide. Most commonly used derivative-free algorithms are metaheuristic. This means that they rely on pseudo-random numbers in order to stochastically explore the design variable space of the underlying problem. Examples of this class of algorithms are genetic algorithms [2], particle swarm optimisation [3] or harmony search [4]. More recent contributions also include algorithms inspired by biological phenomena and swarm intelligence like whale optimisation [5], bacterial foraging optimisation [6], anarchic society optimisation [7] or social-spider optimisation [8].

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