Oil droplet movement and micro-flow characteristics during interaction process between gas bubble and oil droplet in flotation

Flotation is an efficient pre-treatment technology for oily water.In this work,the interaction process between the moving oil droplet and the gas bubble was studied by high-speed camera and Bassset-Boussinesq-Oseen(BBO)theoretical model,and the experimental and simulation results of the oil droplet trajectory were compared.Moreover,the micro-particle image velocimetry system was utilized to observe the flow inside and outside of the moving oil droplet.The results show that the BBO model with the mobile bubble’s surface can reflect the velocity change trend of the oil droplet during the interaction process between the moving oil droplet and the gas bubble,but there are some significant differences between the experimental and simulation results.While the oil droplet is moving on the bubble’s surface,the velocity of the area near the contact point of oil droplet–gas bubble is less than that of the other areas inside the oil droplet.Meanwhile,the flow of water above the oil drop is more biased towards the gas bubble.

STUDY ON THE INFLUENCES OF BUBBLY OIL ON THE CAVITATION EROSION IN JOURNAL BEARINGS OF ENGINES

A simulating experimental device for journal bearings of engines is established by use of the mechanism of ultrasonic vibration This device can make the pressure inside the oil film changed at ultrasonic frequency,which enable the specimen surface to be damaged by cavitation erosion in a comparatively short time Connecting with the bubbly oil producing device,this rig can investigate the influence of bubbly oil on the cavitation erosion Through detailed experimental research it is found that the bubbly oil decreases the cavitation erosion in journal bearings of engines This result is analyzed reasonably from mechanism of cavitation erosion.

A lubrication model for bubbly oil lubrication with interfacial effect and thermal effect using the theory of multiphase mixtures

Bubbly oil lubrication is a type of lubrication method.However,the lubrication model of the bubbly oil has not been thoroughly considered.This paper aims to investigate the modelling for bubbly oil lubrication considering the interfacial effect and thermal effect,and a theoretical model is established based on the theory of multiphase mixtures.The interfacial and thermal effects on the static characteristics of a thrust bearing are analyzed.A test rig for the thrust bearing is developed to measure the static characteristics of the bearing under bubbly oil lubrication.The results show that the bearing static characteristics,i.e.bearing temperature rise,film thickness,friction torque,and volume flow,increase with consideration of three interfacial effects;the bearing temperature rise increases but the film thickness,friction torque,and volume flow rate decrease with consideration of the three thermal effects;the thermal effect on the bearing static characteristics is greater than the interfacial effect.

Transparent open-box learning network and artificial neural network predictions of bubble-point pressure compared

The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships among the underlying input variables of the datasets to which it is applied.It also has the capability to achieve credible and auditable levels of prediction accuracy to complex,non-linear datasets,typical of those encountered in the oil and gas sector,highlighting the potential for underfitting and overfitting.The algorithm is applied here to predict bubble-point pressure from a published PVT dataset of 166 data records involving four easy-tomeasure variables(reservoir temperature,gas-oil ratio,oil gravity,gas density relative to air)with uneven,and in parts,sparse data coverage.The TOB network demonstrates high-prediction accuracy for this complex system,although it predictions applied to the full dataset are outperformed by an artificial neural network(ANN).However,the performance of the TOB algorithm reveals the risk of overfitting in the sparse areas of the dataset and achieves a prediction performance that matches the ANN algorithm where the underlying data population is adequate.The high levels of transparency and its inhibitions to overfitting enable the TOB learning network to provide complementary information about the underlying dataset to that provided by traditional machine learning algorithms.This makes them suitable for application in parallel with neural-network algorithms,to overcome their black-box tendencies,and for benchmarking the prediction performance of other machine learning algorithms.