مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

مقایسه روش‌های متامدل ساختار تابع پایه تطبیق‌پذیر و شبکه عصبی مصنوعی در طراحی گرین فینوسیل

نوع مقاله : پژوهشی اصیل

نویسندگان
گروه هوافضا، مجتمع دانشگاهی مهندسی مکانیک، دانشگاه مالک اشتر، شاهین‌شهر، ایران
چکیده
طراحی گرین مهمترین بخش طراحی موتور سوخت جامد می باشد، در این مقاله هدف طراحی گرین فینوسیل بر اساس توابع هدف از پیش تعیین شده با توجه به نمودارهای بالستیکی می باشد تا انواع الزامات تراست عملکردی را از طریق یک روش طراحی نوآورانه با بهره گیری از الگوریتم بهینه سازی ژنتیک، ارضا نماید . به منظور نمونه برداری در فضای طراحی از روش نمونه برداری کلاسیک استفاده شده است. برای شبیه سازی پسروی سطح سوزش گرین سوخت روش سطوح همتراز انتخاب شده است، در کنار کد سطوح همتراز الگوریتمی توسعه داده شده است که شکل اولیه گرین را با استفاده از کد نگارش شده در محیط نرم افزار پرواینجینیر به عنوان مدلهای تولیدی به کد سطوح همتراز ارسال نماید. به منظور تحلیل بالستیک داخلی، از روش صفر بعدی استفاده شده است. دو روش متامدل، اولی بر اساس روش ساختار تابع تطبیق پذیر و دومی بر اساس یک روش شبکه عصبی مصنوعی تحت عنوان پرسپترون چند لایه به عنوان جایگزین روش سطوح همتراز در حلقه طراحی بهینه استفاده می گردد. در انتهای این کار به منظور اعتبارسنجی الگوریتم ارائه شده یک نمونه گرین فینوسیل مورد بررسی قرار گرفته است و نتایج حاصله نشان می دهد که این روش طراحی گرین، زمان طراحی را به طور قابل توجهی کاهش می دهد و این الگوریتم می تواند در طراحی هر نوع گرینی مورد استفاده قرار گیرد.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Comparing Metamodel Methods of Adaptive Basis Function Construction and Artificial Neural Network in Finocyl Grain Design

نویسندگان English

S. Mesgary
M. Bazazzadeh
A.R. Mostofizadeh
Aerospace Department, University Complex of Mechanical Engineering, Malek-Ashtar University of Technology, Shahin-shahr, Iran
چکیده English

Grain design is the most important part of a solid rocket motor. The aim of this study is finocyl grain design based on predetermined objective function with respect to ballistic curves in order to satisfy various thrust performance requirements through an innovative design approach using a genetic algorithm optimization method. The classical sampling method has been used for design space-filling. The level set method has been used for simulating the evolution of the burning surface in the propellant grain. An algorithm has been developed beside the level set code that prepares the initial grain configuration using Pro/Engineer software to export generated models to level set code. The lumped method has been used to perform internal ballistic analysis. Two meta-models are used to surrogate the level set method in the optimization design loop. The first method is based on adaptive basis function construction and the second method is based on the artificial neural network. In order to validate the proposed algorithm, a grain finosyl sample has been investigated. The results show that both grain design method reduced the design time significantly and this algorithm can be used in designing of any grain configuration. In addition, data have more accuracy in grain design based on the artificial neural network, so this method is the more effective and practical method to grain burn-back training.

کلیدواژه‌ها English

Grain Design
Solid Rocket Motor
Meta-Model
Surrogate Model
Level set
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