Thermal conductivity of Water-based nanofluids: Prediction and comparison of models using machine learning
Author(s):
Abstract:
Statistical methods، and especially machine learning، have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learning models were proposed to represent the thermal conductivity as a function based on the temperature، nanoparticles volume fraction and the thermal conductivity of the nanoparticles. The results of models were in appropriate agreement with the experimental data. This work represents 8 machine learning models for the predicting the thermal conductivity of water-based nanofluids. The models have been trained and tested on two separate sets of data. Three metrics have been employed to evaluate the performance of the models. The best method for each system is selected using results.
Keywords:
Language:
Persian
Published:
International Journal of Nano Dimension, Volume:5 Issue: 1, Winter 2014
Pages:
47 to 55
magiran.com/p1190335
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یکساله به مبلغ 1,390,000ريال میتوانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
توجه!
- حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران میشود.
- پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانههای چاپی و دیجیتال را به کاربر نمیدهد.
In order to view content subscription is required
Personal subscription
Subscribe magiran.com for 70 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!