首页|Studies from University of Vigo Have Provided New Data on Machine Learning (Appl ication of Supervised Learning Algorithms for Temperature Prediction In Nucleate Flow Boiling)
Studies from University of Vigo Have Provided New Data on Machine Learning (Appl ication of Supervised Learning Algorithms for Temperature Prediction In Nucleate Flow Boiling)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Machine Learning are presented in a new report. According to news reporting out of Vigo, Spain, by NewsRx editors, research stated, "This work investigates the use of supervised learning algorith ms to predict temperatures in an experimental test bench, which was initially de signed for studying nucleate boiling phenomena with ethylene glycol/water mixtur es. The proposed predictive model consists of three stages of machine learning." Financial support for this research came from Universidade de Vigo/CISUG. Our news journalists obtained a quote from the research from the University of V igo, "In the first one, a supervised algorithm block is employed to determine wh ether the critical heat flux (CHF) will be reached within the test bench limits. This classification relies on input parameters including bulk temperature, tilt angle, pressure, and inlet velocity. Once the CHF condition is established, ano ther machine learning algorithm predicts the specific heat flux at which CHF wil l occur. Subsequently, based on the classification generated by the first block, the evolution of temperature in response to increases in heat flux is predicted using either the previously estimated heat flux or the physical limits of the e xperimental facility as the stopping criterion. To accomplish all these predicti ons, the study compares the performance of various algorithms including artifici al neural networks, random forest, support vector machine, AdaBoost, and XGBoost . These algorithms were specifically trained using cross-validation and grid sea rch methods to optimize their effectiveness. Results for the CHF classification purpose demonstrate that the support vector machine algorithm performs the best, achieving an F1-score of 0.872 on the testing dataset, while the boosting metho ds (AdaBoost and XGBoost) exhibit signs of overfitting. In predicting the CHF va lue, the artificial neural network achieved the lower nMAE on the testing datase t (6.18%)."
VigoSpainEuropeAlgorithmsCyborgsEmerging TechnologiesMachine LearningSupervised LearningSupport Vector M achinesVector MachinesUniversity of Vigo
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