Reliable predictions of bubble departure frequency in subcooled flow boiling: A machine learning-based approach

Universal Model for Predicting Maximum Spreading of Drop Impact on a Smooth Surface Developed Using Boosting Machine Learning Models

“…CatBoost is trained by minimizing the expected loss function through gradient descent h t = arg nobreak0em.25em⁡ min nobreak0em.25em⁡ double-struckE h ∈ H ( prefix− ∂ L ( y , s ) ∂ s | s = F t − 1 false( x false) − ∑ j = 1 J b j double-struckl { x ∈ R j } ) where L is a smooth loss function, h is a gradient step function selected from H , R j denotes the disjoint regions ns corresponding the leaves of the tree, b j is the predictive value of the region, and double-struckE and double-struckl are the expectation and indicator functions. Further details on these ML methods can be found in previous references. − ,− …”

“…CatBoost is trained by minimizing the expected loss function through gradient descent where L is a smooth loss function, h is a gradient step function selected from H , R j denotes the disjoint regions ns corresponding the leaves of the tree, b j is the predictive value of the region, and and are the expectation and indicator functions. Further details on these ML methods can be found in previous references. − ,− …”

“…Despite numerous attempts, developing a universal model that can predict the maximum spreading of droplets of various liquids impacting on a smooth surface from low to high impact velocity accurately remains challenging due to the different and complex nonlinear characteristics exhibited by drop impact in viscous and capillary regimes. Recently, data-driven machine learning (ML) has been demonstrated to be a powerful technique for investigating fluid mechanics. − ML methods have been successful to predict various fluid phenomena, such as boiling, condensation, and multiphase flow. In recent studies by Yancheshme et al, the random forest method was utilized to predict β max by training their ML model with a database of 198 data points.…”

Experimental investigation on flow boiling bubble motion under ultrasonic field in vertical minichannel by using bubble tracking algorithm

“…Xu et al [23] found that the fully developed turbulent gas–liquid slug flow and slug translation velocity depended on the local maximum velocity adjacent to the trailing edge of the Taylor bubbles in a long horizontal pipe with an inner diameter of 51 mm. Many methods have been employed to investigate the bubble velocity during flow boiling in minichannels [24] , [25] , [26] , [27] , [28] . Pavlov et al [24] used a high-speed camera to trace the motion of a single bubble in a rectangular minichannel.…”

A general neural network model co-driven by mechanism and data for the reliable design of gas–liquid T-junction microdevices

“…There is much research on gas hold-up prediction in a bubble column, and some of it is based on machine learning, Based on an ANN model, a useful prediction model had been developed by Hazare et al 28 He et al also achieved a reliable prediction of bubble departure frequency in subcooled flow boiling by X-G boost. 29 In the region of microflows, Su et al constructed a neural network (NN) to predict the inertial lift in microchannels. 30 Zhou et al established a way to predict the flow condensation heat transfer coefficient in microchannels.…”