Solution strategy for increasing the efficiency of turbochargers by reducing energy losses in the lubrication system

Solution strategy for increasing the efficiency of turbochargers by reducing energy losses in the lubrication system

článek v časopise ve Web of Science, Jimp

en

The mechanical efficiency of turbochargers is mainly influenced by the lubrication system, which includes bearings. Energy loss can be prevented by optimising the individual elements of the lubrication system under preferred operating conditions, but it is also necessary to consider verification of functionality under off-design operating conditions. Evaluating, optimising or verifying bearing performances requires different levels of computational models with different physical depths of descriptions of individual processes. The solution strategy combines three levels of bearing computational models and effectively solves the problem. Genetic algorithms and an efficient model of bearing lubrication are used to optimise the bearing. The off-design transient operating processes of the rotor-bearing system are solved by a virtual turbocharger, and the off-design steady-state operating conditions are solved by an advanced computational model using computational fluid dynamics. The strategy is applied to reduce friction losses in thrust bearing and represents a reduction in bearing mechanical losses under the preferred operating conditions by 35%. These savings lead to 20% energy savings in the lubrication system of the stationary internal combustion engine turbocharger without significant risk of the lubrication system failing. The strategy is generally applicable to any element of the lubrication system.

The mechanical efficiency of turbochargers is mainly influenced by the lubrication system, which includes bearings. Energy loss can be prevented by optimising the individual elements of the lubrication system under preferred operating conditions, but it is also necessary to consider verification of functionality under off-design operating conditions. Evaluating, optimising or verifying bearing performances requires different levels of computational models with different physical depths of descriptions of individual processes. The solution strategy combines three levels of bearing computational models and effectively solves the problem. Genetic algorithms and an efficient model of bearing lubrication are used to optimise the bearing. The off-design transient operating processes of the rotor-bearing system are solved by a virtual turbocharger, and the off-design steady-state operating conditions are solved by an advanced computational model using computational fluid dynamics. The strategy is applied to reduce friction losses in thrust bearing and represents a reduction in bearing mechanical losses under the preferred operating conditions by 35%. These savings lead to 20% energy savings in the lubrication system of the stationary internal combustion engine turbocharger without significant risk of the lubrication system failing. The strategy is generally applicable to any element of the lubrication system.

Mechanical efficiency; Lubrication system; Thrust bearing; Off-design operating conditions; Genetic algorithms; Turbocharger

01.12.2021

Elsevier

UK

0360-5442

236

121402

1–16

16