پیش‌بینی ضریب انتقال حرارت در جریان آشفته نانوسیالات مختلف درون لوله‌های دایره‌ای، با استفاده از شبکه عصبی مصنوعی

نویسندگان

1 گروه مهندسی مکانیک، دانشکده فنی دانشگاه امام حسین (ع)

2 باشگاه پژوهشگران جوان و نخبگان، واحد نجف آباد، دانشگاه آزاد اسلامی، واحد نجف آباد، ایران

3 دانشکده فنی مهندسی مکانیک دانشگاه کاشان

چکیده

هدف این پژوهش مدل‌سازی انتقال حرارت جابجایی نانوسیالات در جریان آشفته داخل یک لوله دایره‌ای با شرایط مرزی دما ثابت و شار حرارتی ثابت است. این مدل‌سازی با روش شبکه عصبی مصنوعی انجام شده است. تعداد 610 داده از نتایج مطالعات محققان مختلف جمع-آوری شده و برای آموزش شبکه‌ عصبی مورد استفاده قرار گرفته است. نانوذراتی که در این پژوهش مورد بررسی قرار گرفته اند عبارتند از Al2O3، TiO2، Graphene، SiC، CuO، SiO2، Fe3O4 و Cu که سیال پایه در تمام این موارد آب است. این شبکه دارای شش ورودی است که عبارتند از چگالی نانوذره، اندازه نانوذره، غلظت نانوذره، عدد رینولدز جریان، نوع شرایط مرزی شار- ثابت یا دما- ثابت و با توجه به نوع مسئله مقدار شار ثابت دیواره یا دمای ثابت آن است. همچنین، خروجی شبکه عصبی طراحی شده عدد ناسلت جریان نانوسیال است. از مقایسه نتایج این مدل شبکه‌ عصبی با نتایج پژوهش‌های گذشته مشاهده می‌شود که مدل شبکه عصبی پیشنهاد شده تطابق بسیار خوبی با نتایج حاصل از پژوهش‌های آن‌ها دارد. در این پژوهش، برای انتخاب پیکربندی مناسب شبکه عصبی، 400 پیکربندی مختلف مورد بررسی قرار گرفت که از میان آن‌ها شبکه‌ عصبی با بالاترین میزان دقت تخمین و با 9998/0=R2 انتخاب شد.

کلیدواژه‌ها

عنوان مقاله [English]

Forecasting of Convective Heat Transfer Coefficient in Turbulent Flow of Different Nanofluids in Circular Tubes, Using Artificial Neural Network

نویسندگان [English]

  • mohammad hemmat asfe 1
  • saeed esfande 2
  • mohammad akhoound zade 3
1 emam hosein
2 azad , najaf abad
3 kashan
چکیده [English]

Modeling of turbulent convective heat transfer of nanofluids in circular tubes with constant temperature and constant heat flux boundary condition have been performed using artificial neural network. 610 sets of data have been gathered using previous investigations and have been used to train neural network (ANN). The investigated nanoparticles are: TiO2, Graphene, SiC, CuO, SiO2, Fe3O4, and Cu. The base fluid for all these nanofluids is water. The neural network used has 6 inputs, which includes: nanoparticle density, nanoparticle size, nanoparticle volume fraction, flow Re number, type of boundary condition (constant heat flux or constant temperature) and the amount of heat flux or temperature related to these boundary conditions. Also, the output of neural network is Nusselt number. Comparing our results with previous investigation, showed that the proposed ANN topology are in good agreement. In this study, the proposed topology of R2=0.9998 have been choosen between 400 examined ones.

کلیدواژه‌ها [English]

  • Nanofluid
  • Turbulent Flow
  • Nusselt Number
  • Artificial Neural Network

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سابقه مقاله

  • تاریخ دریافت: 13 شهریور 1397
  • تاریخ بازنگری: 30 بهمن 1397
  • تاریخ پذیرش: 28 شهریور 1397
  • تاریخ انتشار: 01 تیر 1397

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