دانشگاه سمنان

 آمار

تعداد نشریات21
تعداد شماره‌ها691
تعداد مقالات10,035
تعداد مشاهده مقاله71,039,750
تعداد دریافت فایل اصل مقاله62,489,340
Das, Dipak, Singh, Aman, Verma, Prakhar, Roy, Debleena, Bhattacharya, Dhruba, Saha, Bidyut. (1404). Regression-Prediction Model for Low-subcooled Film Boiling on a Vertical Flat Plate. هنرهای کاربردی, (), -. doi: 10.22075/jhmtr.2025.38906.1823
Dipak Chandra Das; Aman Kumar Singh; Prakhar Verma; Debleena Roy; Dhruba Bhattacharya; Bidyut Baran Saha. "Regression-Prediction Model for Low-subcooled Film Boiling on a Vertical Flat Plate". هنرهای کاربردی, , , 1404, -. doi: 10.22075/jhmtr.2025.38906.1823
Das, Dipak, Singh, Aman, Verma, Prakhar, Roy, Debleena, Bhattacharya, Dhruba, Saha, Bidyut. (1404). 'Regression-Prediction Model for Low-subcooled Film Boiling on a Vertical Flat Plate', هنرهای کاربردی, (), pp. -. doi: 10.22075/jhmtr.2025.38906.1823
Das, Dipak, Singh, Aman, Verma, Prakhar, Roy, Debleena, Bhattacharya, Dhruba, Saha, Bidyut. Regression-Prediction Model for Low-subcooled Film Boiling on a Vertical Flat Plate. هنرهای کاربردی, 1404; (): -. doi: 10.22075/jhmtr.2025.38906.1823

Regression-Prediction Model for Low-subcooled Film Boiling on a Vertical Flat Plate

Journal of Heat and Mass Transfer Research
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 08 آذر 1404
نوع مقاله: Full Length Research Article
شناسه دیجیتال (DOI): 10.22075/jhmtr.2025.38906.1823
نویسندگان
Dipak Chandra Das* 1؛ Aman Kumar Singh2؛ Prakhar Verma2؛ Debleena Roy2؛ Dhruba Bhattacharya2؛ Bidyut Baran Saha3
1Mechanical Engineering Department, National Institute of Technology Agartala
2National Institute of Technology Agartala
32International Institute for Carbon-Neutral Energy Research (WPI-I2CNER), 744 Motooka, Nishi-ku, Fukuoka-shi, Fukuoka 819-0395, Japan
تاریخ دریافت: 11 شهریور 1404،  تاریخ بازنگری: 05 آبان 1404،  تاریخ پذیرش: 08 آذر 1404 
چکیده
This study presents a prediction model for a vertical flat plate under conditions of high wall superheat and low water subcooling in mixed-convection film boiling, utilizing Linear, AdaBoost, Random Forest, and Gradient Boosting regression models integrated with machine learning approaches. The analysis of saturated and low subcooled film boiling has been conducted from heat and mass transmission perspectives and relevant vaporization criteria using heat ratio. Predictions for the Nusselt number have been formulated for several flow configurations, encompassing wall superheat ranging from 260 to 1200°C, liquid subcooling from 0 to 10°C, and flow velocities from 0 to 2.65 m/s. The Gradient-boosting regression model precisely predicted Nu across diverse flow conditions with an error margin of less than ±1%, indicating as an effective instrument for predicting the thermal performance of high wall superheat, low water subcooling mixed-convection film boiling, outperforming the predictive abilities of Linear, AdaBoost, and Random Forest regression models when compared with experimental data.
کلیدواژه‌ها
gradient-boosting regression؛ high wall superheat؛ low water subcooling؛ mixed convection film boiling
آمار
تعداد مشاهده مقاله: 112


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب