مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

یک روش تکامل یافته برای طراحی بر مبنای قابلیت اطمینان به کمک مدل‌های جایگزین و کاربرد در طراحی سیستم‌های پیچیده

نویسندگان
علی اصغر بظالبلو 1
جعفر روشنی یان 2
بنیامین ابراهیمی 3
1 دانشجو دکترا/ دانشگاه صنعتی خواجه نصیرالدین طوسی
2 دانشگاه صنعتی خواجه نصیر-دانشکده هوا فضا
3 دانشجو کارشناسی ارشد/ دانشگاه صنعتی خواجه نصیرالدین طوسی
چکیده
بهینه‌سازی طراحی بر مبنای قابلیت اطمینان (RBDO) برای بهینه‌سازی سیستم‌های مهندسی در حضور عدم‌قطعیت‌ها در متغیرهای طراحی، پارامترهای سیستم یا هر دو آنها مورد استفاده قرار گرفته است. RBDO دارای یک بخش تحلیل قابلیت‌اطمینان است که نیاز به مقدار زیادی تلاش محاسباتی، به خصوص در رویارویی با مسائل کاربردی دنیای واقعی دارد. برای تعدیل این موضوع، یک روش جدید و کارآمد به کمک مدل‌های جایگزین در این مقاله ارائه شده است. هوش محاسباتی و روش‌های مبتنی بر تجزیه RBDO با یکدیگر ترکیب شده‌اند تا یک روش سریع برای RBDO توسعه یابد. این روش جدید مبتنی بر شبکه‌های عصبی مصنوعی به عنوان مدل جایگزین و روش بهینه-سازی ترتیبی و ارزیابی قابلیت اطمینان (SORA) به عنوان روش RBDO است. در SORA، مسئله به یک حلقه بهینه‌سازی معین ترتیبی و یک حلقه ارزیابی قابلیت اطمینان تجزیه می‌شود. به منظور بهبود کارایی محاسباتی و گسترش محدوده کاربرد روش SORA، یک نسخه تکامل یافته از آن در این مقاله پیشنهاد شده است. در روش توسعه یافته، از یک معیار برای شناسایی قیود احتمالاتی غیرفعال و جدا کردن قیود برآورده شده از حلقه ارزیابی قابلیت‌اطمینان استفاده شده است تا هزینه‌های محاسباتی مربوطه را کاهش یابد. بعلاوه، تغییرات بردارهای جابجایی به دست آمده برای قیود برآورده شده کنترل شده است تا در تکرار بعدی حلقه قابلیت اطمینان مقدار آنها دقیقا صفر باشد. به منظور نشان دادن کارایی و دقت روش ارائه شده، چند مثال ریاضی با سطوح پیچیدگی متفاوت و یک مثال کاربردی مهندسی حل شده و نتایج مورد بحث و بررسی قرار گرفته است.
کلیدواژه‌ها
بهینه‌سازی
طراحی بر مبنای قابلیت اطمینان
هوش محاسباتی
مدل جایگزین
شبکه عصبی

عنوان مقاله English

An Augmented Surrogate-Assisted Reliability-based Design Approach and Application to Complex Systems Design

نویسندگان English

Ali Asghar Bataleblu 1
benyamin ebrahimi 3
1 PHD student/ Khaje Nasir Toosi University of Technology
3 college student/ Khaje Nasir Toosi University of Technology
چکیده English

Reliability-based design optimization (RBDO) has been used for optimizing engineering systems in presence of uncertainties in design variables, system parameters or both of them. RBDO involves reliability analysis, which requires a large amount of computational effort, especially in real-world application. To moderate this issue, a novel and efficient Surrogate-Assisted RBDO approach is proposed in this article. The computational intelligence and decomposition based RBDO procedures are combined to develop a fast RBDO method. This novel method is based on the artificial neural networks as a surrogate model and Sequential Optimization and Reliability Assessment (SORA) method as RBDO method. In SORA, the problem is decoupled into sequential deterministic optimization and reliability assessment. In order to improve the computational efficiency and extend the application of the original SORA method, an Augmented SORA (ASORA) method is proposed in this article. In developed method, A criterion is used for identification of inactive probabilistic constraints and refrain the satisfied constraints from reliability assessment to decrease computational costs associated with probabilistic constraints. Further, the variations of shifted vectors obtained for satisfied constraints are controlled to be exactly equal to zero for the next RBDO iteration. Several mathematical examples with different levels of complexity and a practical engineering example are solved and results are discussed to demonstrate efficiency and accuracy of the proposed methods.

کلیدواژه‌ها English

Optimization
Reliability-based Design
Computational Intelligence
Surrogate Model
Neural Networks
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مهندسی مکانیک مدرس
دوره 18، شماره 3
خرداد 1397
صفحه 247-258