[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126193-en":3,"doc-seo-126193-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},126193,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Comparative Analysis of Machine Learning Approaches for Boiling ONB Prediction - read online","This study investigates Machine Learning models for predicting both wall temperature and heat flux at the Onset of Nucleate Boiling (ONB). Experimental data from a Joule-heated boiling test bench support the training of five supervised approaches: Artificial Neural Networks (ANN), XGBoost, Support Vector Regression, AdaBoost, and Random Forest. Findings show AdaBoost underperforms for both targets, while Random Forest indicates overfitting and potential limited generalization. ANN achieves the best wall-temperature prediction, whereas XGBoost leads for heat-flux prediction. Both models learn complex input–output relationships involving bulk temperature, pressure, channel inclination, and velocity.","Comparative Analysis of Machine Learning Approaches for Boiling  \nONB Prediction  \nAdrián Cabarcos1, Concepción Paz1, Miguel Concheiro1, Marcos Conde-Fontenla1, Eduardo Suárez1  \n1CINTECX  \nUniversidade de Vigo, 36310 Vigo, España  \n[acabarcos@uvigo.gal](acabarcos@uvigo.gal); [cpaz@uvigo.gal](cpaz@uvigo.gal) ; [mconcheiro@uvigo.gal](mconcheiro@uvigo.gal) ; [mfontenla@uvigo.gal](mfontenla@uvigo.gal) ; [suarez@uvigo.gal](suarez@uvigo.gal)  \nAbstract-This study investigates the use of Machine Learning models for predicting both wall temperature and heat flux at the Onset of Nucleate Boiling (ONB). The dataset used in this work was obtained from an experimental test bench using Joule heating for boiling generation. Furthermore, five models, including Artificial Neural Networks (ANN), XGBoost, Support Vector Regression, AdaBoost, and Random Forest, were trained and evaluated. Results reveal that AdaBoost performed the worst in both wall temperature and heat flux predictions, indicating limitations in its ability to accurately forecast the ONB parameters. Conversely, the Random Forest model showed signs of overfitting in both predictions, suggesting that it may struggle to generalize to unseen data. In contrast, ANN demonstrated superior performance in predicting wall temperature (with mean square errors of 3.79 °C² and 3.84 °C² for training and testing), while XGBoost outperformed other models in heat flux prediction. Both models successfully captured the complex relationships between inputs (bulk temperature, pressure, channel inclination and velocity) and ONB parameters, leading to accurate predictions.  \nKeywords: Boiling, ONB, Machine Learning, Decision Trees, Support Vector Regression, Artificial Neural Network  \n1. Introduction  \nThe prediction of wall temperature and heat flux during boiling phenomena is a critical aspect in numerous engineering applications, such as in the design and operation of heat transfer systems like heat exchangers or water-cooled nuclear reactors [1–3]. This prediction is particularly significant at the Onset of Nucleate Boiling (ONB), which occurs when vapor bubbles start to form and grow on a heated surface, resulting in a complex heat transfer phenomenon that has been extensively studied but is still not fully understood [4–6] . These bubbles, as they initiate and expand, greatly enhance the heat transfer capabilities, effectively extracting heat from the surface [7] . Consequently, nucleate boiling is widely used in various engineering fields due to its high heat transfer coefficients [8], [9] . However, the presence of bubbles can also lead to undesirable effects, such as the formation of vapor film, bubble coalescence, or a significant decrease in heat transfer efficiency once the Critical Heat Flux (CHF) is reached [10], [11] . Therefore, accurately estimating boiling processes becomes crucial for optimizing heat transfer and enabling the efficient design and performance of thermal systems.  \nTraditionally, researchers have proposed physics-based models and empirical correlations to predict the onset of nucleate boiling based on specific input conditions, often relying on experimental data. For instance, Hsu [12] conducted pioneering research on the conditions that enable nucleate boiling to occur, proposing a correlation that mainly considers the superheat and physical properties of the liquid. Qu and Mudawar [13] conducted experiments to measure the incipient boiling heat flux in micro-channel heat sinks and developed a model that considers both mechanical and thermal factors, including the force balance on the bubble, through a bubble departure criterion. Similarly, Liu et al. [14] formulated an analytical model based on experimental work to predict heat flux and bubble size at the ONB, incorporating various parameters such as fluid inlet conditions, subcooling, contact angle, microchannel dimensions, and fluid exit pressure. More recently, Lim et al. [15] explored the onset of nucleate bo","cbCainiA6RWOmXI8","https://ap.wps.com/l/cbCainiA6RWOmXI8","pdf",618722,1,"English","en",105,"# Abstract\n# Introduction\n## Engineering importance of ONB prediction\n## Traditional physics-based correlations\n## Motivation for Machine Learning approaches\n# Machine Learning study scope","[{\"question\":\"What boiling phenomenon does the study focus on?\",\"answer\":\"The study targets the Onset of Nucleate Boiling (ONB), predicting wall temperature and heat flux when vapor bubbles start forming and growing on a heated surface.\"},{\"question\":\"Which machine learning models are evaluated for ONB prediction?\",\"answer\":\"Five models are trained and compared: Artificial Neural Networks (ANN), XGBoost, Support Vector Regression (SVR), AdaBoost, and Random Forest.\"},{\"question\":\"How do the models compare in predictive performance?\",\"answer\":\"AdaBoost performs the worst for both outputs, Random Forest shows signs of overfitting, ANN gives superior wall-temperature results, and XGBoost performs best for heat-flux prediction.\"}]","Comparative Analysis of Machine Learning Approaches for Boiling ONB Prediction - 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