فناوری در مهندسی هوافضا

فناوری در مهندسی هوافضا

طراحی و بهینه‌سازی فنس روی بال یک مدل هواپیمای لامبدا شکل جهت کاهش ضریب گشتاور غلتشی

نوع مقاله : علمی پژوهشی

نویسندگان
1 دانشجوی کارشناسی ارشد، گروه مکانیک، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران
2 استاد، گروه مکانیک، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران
چکیده
پژوهش حاضر، به بررسی تأثیر فنس بال بر گردابه‌‌های راس بال و سطوح کنترلی در هواپیمای بال پرنده با استفاده از روش دینامیک سیالات محاسباتی ‌می‌پردازد. در طراحی و قرار دادن فنس‌‌های بال، ابعاد متوسط استخراج شده از گردابه‌‌های راس بال در زوایای حمله مختلف به کار گرفته ‌شده‌است. همچنین، فنس‌‌ها با ابعاد مشخص شده در سه ارتفاع و سه موقعیت مختلف در طول بال نصب شده (طول فنس برابر با میانگین طول گردابه در آن قسمت و ارتفاع فنس 30 درصد قطر گردابه آن قسمت است) و در زوایای حمله 7 تا 16 درجه بررسی شده‌اند. گام بعدی، طراحی ابعاد فنس به روش بهینه سازی تک هدفه است. هدف دستیابی به بهترین طراحی است که با حداقل زمان و هزینه به یک راه حل بهینه همگرا شود. طراحی فنس‌‌ها در سه نقطه بر اساس ابعاد گردابه راس بال با روش دینامیک سیالات محاسباتی انجام ‌می‌شود. با این حال، تجزیه و تحلیل دینامیک سیالات محاسباتی (CFD) به زمان محاسباتی زیادی نیاز دارد، برای حل این مشکل، از تابع پایه شبکه عصبی (RBF) و مدل کریجینگ (Kriging) با قرار دادن شرایط اولیه مسئله و نتایج حل دینامیک سیالات محاسباتی در صورت وجود فنس، استفاده شده تا با تعیین مناسبترین مکان، ضریب گشتاور غلتشی را به حداقل رسانیم.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Design and Optimization of the Wing Fence of a Lambda-Shaped Aircraft Model to Reduce the Rolling Moment Coefficient

نویسندگان English

Mohammad Hossein Moghimi Esfandabadi 1
Mohammad Hassan javareshkian 2
1 M. Sc. Student, Professor, Department of Mechanics, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
2 Professor, Department of Mechanics, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده English

One of the main propositions of predictive maintenance is Prognostics and Health Management (PHM), which plays a special role in identifying, diagnosing, and predicting the health status of physical assets. To that end, one of the fundamental solutions is to assess the condition of the equipment in the aviation sector in order to provide maintenance plans by determining the trend of deterioration or destruction. In this study, a developed model of an artificial neural network was presented, focusing on the concept of deep learning and its comparison with other conventional methods in response to the limitations and uncertainties in conventional prediction methods in determining the deterioration process of the equipment. The comparative results revealed that the deep learning neural network method with a prediction accuracy of 94% had a high performance in determining the deterioration process in aircraft turbine engines compared to other conventional methods. The findings of this study can be used to predict the remaining useful life of aviation industry equipment as well as to provide appropriate maintenance programs.

کلیدواژه‌ها English

Wing Fence
Optimization
Numerical Simulation
Lambda Wing
UAV
Aerodynamic Coefficients
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  • تاریخ دریافت 25 خرداد 1402
  • تاریخ بازنگری 03 مرداد 1402
  • تاریخ پذیرش 08 مرداد 1402
  • تاریخ اولین انتشار 15 مرداد 1402