پیش بینی عدد نوسلت استوانه گرم شده قرار گرفته در معرض جریان آشفته توسط شبکه حافظه طولانی کوتاه مدت عمیق بهینه شده توسط الگوریتم ازدحام ذرات

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی مکانیک، دانشگاه صنعتی اراک، اراک، ایران

2 گروه مهندسی مکانیک، دانشکده فنی و مهندسی، دانشگاه خوارزمی، تهران، ایران

چکیده

بهره‌گیری از هوش مصنوعی برای پیش‌بینی مشخصات انتقال حرارت در صنایع مختلف، توانایی بهبود طراحی تجهیزات حرارتی، افزایش کارایی انتقال حرارت، بهینه‌سازی سیستم‌های خنک‌کننده، و کاهش مصرف انرژی را فراهم می‌کند. نوآوری و هدف مقاله حاضر، پیش‌بینی رفتار زمانی عدد نوسلت در مسئله ارتعاشات ناشی از جریان آشفته بر روی استوانه گرم شده که آزادانه در جهات طولی و عرضی نوسان می‌کند، می‌باشد. پیش‌بینی عدد نوسلت بر مبنای جابجای‌های عرضی و طولی استوانه در سه سناریوی مختلف شامل ورودی جابجایی در جهت ، ورودی جابجایی در جهت ، و در نهایت ورودی ترکیبی  و  انجام شده است. پیش‌بینی توسط شبکه حافظه طولانی کوتاه مدت عمیق که معماری و ابرپارامترهای آن توسط الگوریتم ازدحام ذرات بهینه شده است، صورت گرفته است. نتایج نشان دهنده‌ی موفقیت شبکه‌های بهینه شده با ورودی‌های متفاوت می‌باشد؛ اگرچه دقت بالاتری در پیش‌بینی با ورودی‌های ترکیبی  و  بدست آمده است. ضریب همبستگی در بخش تست، برای شبکه‌های با ورودی ؛ با ورودی ؛ و با ورودی‌های  و  به ترتیب برابر با 0/967، 0/961، و 0/975 محاسبه شده‌اند. با بهره گیری از روش بکارگرفته شده در مطالعه حاضر، می‌توان مشخصات انتقال حرارت سازه‌های قرار گرفته در معرض جریان را پیش‌بینی نمود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prediction of Nusselt number of heated cylinder exposed to turbulent flow by deep long short-term memory network optimized by particle swarm algorithm

نویسندگان [English]

  • Amir Hossein Rabiee 1
  • mostafa esmaeili 2
1 mechanical engineering, arak university of technology
2 Department of Mechanical Engineering, Faculty of Engineering, Kharazmi University , Tehran, Iran
چکیده [English]

Leveraging artificial intelligence to forecast heat transfer characteristics across diverse industries holds significant potential for improving thermal equipment design, increasing heat transfer efficiency, optimizing cooling systems, and reducing energy consumption. The main contribution and purpose of the current study is predicting the Nusselt number in the context of turbulent flow-induced vibration around a heated cylinder experiencing unconfined oscillations along both streamwise and transverse axes. The anticipation of the Nusselt number relies on transverse and streamwise displacements of the oscillating cylinder and encompasses three distinct scenarios: displacement input in the x-direction, displacement input in the y-direction, and comprehensive amalgamation of both x and y inputs. This prediction is achieved through a sophisticated deep long short-term memory network, meticulously crafted and fine-tuned using a particle swarm optimization algorithm. The results highlight the effectiveness of the optimized networks across various inputs, with the highest predictive precision observed when employing combined x and y inputs. The correlation coefficients within the test segment are as follows: 0.967 for x input, 0.961 for y input, and 0.975 for combined x and y inputs. By applying the methodology elucidated in this study, the forecasting of heat transfer characteristics for structures subjected to fluid flow emerges as a feasible possibility.

کلیدواژه‌ها [English]

  • Nusselt number
  • fluid-solid interaction
  • vortex-induced vibration
  • long short-term memory network
  • Particle swarm optimization algorithm

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نشریه مهندسی مکانیک امیرکبیر
دوره 55، شماره 11
بهمن 1402
صفحه 1353-1372

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