Analysis of Thermoelastohydrodynamic Performance of Journal Misaligned Engine Main Bearings

To understand the engine main bearings＇ working condition is important in order to improve the performance of engine. However, thermal effects and thermal effect deformations of engine main bearings are rarely considered simultaneously in most studies. A typical finite element model is selected and the effect of thermoelastohydrodynamic（TEHD） reaction on engine main bearings is investigated. The calculated method of main bearing＇s thermal hydrodynamic reaction and journal misalignment effect is finite difference method, and its deformation reaction is calculated by using finite element method. The oil film pressure is solved numerically with Reynolds boundary conditions when various bearing characteristics are calculated. The whole model considers a temperature-pressure-viscosity relationship for the lubricant, surface roughness effect, and also an angular misalignment between the journal and the bearing. Numerical simulations of operation of a typical I6 diesel engine main bearing is conducted and importance of several contributing factors in mixed lubrication is discussed. The performance characteristics of journal misaligned main bearings under elastohydrodynamic（EHD） and TEHD loads of an I6 diesel engine are received, and then the journal center orbit movement, minimum oil film thickness and maximum oil film pressure of main bearings are estimated over a wide range of engine operation. The model is verified through the comparison with other present models. The TEHD performance of engine main bearings with various effects under the influences of journal misalignment is revealed, this is helpful to understand EHD and TEHD effect of misaligned engine main bearings.

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.

Research on Vane End Face of Cam-Rotor Vane Servo Motor Based on Disturbing Torque

Cam-rotor vane motor(CRVM) is one of the new continuous hydraulic servo motors with the characteristics of no pulsation of instantaneous flow rate and output torque,small volume and rotating inertia.It is one of the appropriate actuators for hydraulic servo system which has good dynamic and steady-state performance requirements.The ideal output torque of CRVM is pulseless,but the actual output torque of CRVM is pulsating.This is caused by the disturbing torque of contact components,especially the friction between vane and cam-rotor.In order to get better performance of CRVM,which means more stable output torque and smaller disturbing torque,we discuss four kinds of vane end faces(VEFs).Analytic formulae of the normal contact force and the disturbing torque caused by the vane are derived from systematical force analysis.The normal contact force and the disturbing torque vary through a period under different VEF,and the reduced oil pressure is simulated in this paper.The simulation shows that the VEF with the proper round and reduced oil pressure can significantly decrease the disturbing torque and get better servo performance.The experiment results verify the correctness of the theoretical analysis and simulation.

Application of hybrid support vector regression artificial bee colony for prediction of MMP in CO2-EOR process

Minimum miscibility pressure(MMP)is a key parameter in the successful design of miscible gases injection such as CO2 flooding for enhanced oil recovery process(EOR).MMP is generally determined through experimental tests such as slim tube and rising bubble apparatus(RBA).As these tests are time-consuming and their cost is very expensive,several correlations have been developed.However,and although the simplicity of these correlations,they suffer from inaccuracies and bad generalization due to the limitation of their ranges of application.This paper aims to establish a global model to predict MMP in both pure and impure CO2-crude oil in EOR process by combining support vector regression(SVR)with artificial bee colony(ABC).ABC is used to find best SVR hyper-parameters.201 data collected from authenticated published literature and covering a wide range of variables are considered to develop SVR-ABC pure/impure CO2-crude oil MMP model with following inputs:reservoir temperature(TR),critical temperature of the injection gas(Tc),molecular weight of pentane plus fraction of crude oil(MWC5+)and the ratio of volatile components to intermediate components in crude oil(xvol/xint).Statistical indicators and graphical error analyses show that SVR-ABC MMP model yields excellent results with a low mean absolute percentage error(3.24%)and root mean square error(0.79)and a high coefficient of determination(0.9868).Furthermore,the results reveal that SVR-ABC outperforms either ordinary SVR with trial and error approach or all existing methods considered in this work in the prediction of pure and impure CO2-crude oil MMP.Finally,the Leverage approach(Williams plot)is done to investigate the realm of prediction capability of the new model and to detect any probable erroneous data points.