مدلسازی با استفاده از شبکه عصبی مصنوعی جهت پیش بینی هدایت حرارتی نانوسیال نانولوله کربنی چند جداره عامل دار – آب و ارائه رابطه تجربی جدید

نوع مقاله: مقاله مکانیک

نویسندگان

1 استادیار، گروه مهندسی مکانیک، واحد نجف آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران

2 دانشگاه آزاد اسلامی، واحد خمینی شهر، باشگاه پژوهشگران جوان و نخبگان، اصفهان، ایران

چکیده

در این مقاله ، بر اساس نتایج آزمایشگاهی، و با استفاده از روش برازش منحنی و شبکه عصبی مصنوعی اثر دما و کسر حجمی نانولوله‌ها بر ضریب هدایت حرارتی نانوسیال نانولوله کربنی چند جداره عامل دار-آب بررسی شد. یک رابطه دقیق به صورت تابعی از کسر حجمی و دما برای پیش بینی ضریب هدایت حرارتی نانوسیال ارائه شد. همچنین شبکه های عصبی مختلفی به منظور مدلسازی ضریب هدایت حرارتی نانوسیال طراحی شد. در این شبکه‌ها دما و کسر حجمی به عنوان متغیرهای ورودی و ضریب هدایت حرارتی به عنوان متغیر خروجی در نظر گرفته شد. شبکه عصبی بهینه با در نظر گرفتن حداقل خطا در پیش بینی ضریب هدایت حرارتی نانوسیال به دست آمد. مقایسه‌ها نشان داد که شبکه عصبی مصنوعی می‌تواند پیش بینی دقیق‌تری نسبت به روش برازش منحنی در تخمین ضریب هدایت حرارتی این نانوسیال ارائه کند. همچنین نتایج نشان داد که رابطه تجربی ارائه شده به وسیله روش برازش منحنی دارای دقت قابل قبولی است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Artificial neural network modeling for prediction of thermal conductivity of functionalized MWCNTs/water nanofluids and a new empirical correlation

نویسندگان [English]

  • Masoud Afrand 1
  • Mohammad Hemmat Esfe 2
1 iran
2 iran
چکیده [English]

In this paper, based on experimental data, by employing regression method and artificial neural network, the effects of temperature and nanotubes concentration on thermal conductivity of COOH functionalized Multi Walled Carbon Nano Tubes / water was investigated . A very accurate correlation for thermal conductivity ratio was suggested as a function of temperature and solid volume fraction . Artificial neural network modeling was performed. Temperature and solid volume fraction were employed as input variables and thermal conductivity ratio was used as outputs variable. Optimized ANN by considering minimum prediction error was obtained. Comparisons showed that the ANN can more precisely predict the thermal conductivity ratio of COOH functionalized Multi Walled Carbon Nano Tubes/water nanofluids. The results also revealed that the empirical correlation has an acceptable accuracy.Experimental results showed that the thermal conductivity has a direct and reverse relationshipThe existing correlations in literature were unable to predict viscosity data, hence, a new correlation has been proposed.

کلیدواژه‌ها [English]

  • modeling
  • Neural Network
  • Thermal conductivity
  • Empirical correlation
  • Nanofluid
  • MWCNT
 
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