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

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  • Novotný, Pavel
  • Vacula, Jiří
  • Hrabovský, Jozef

Abstract

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.

Suggested Citation

  • Novotný, Pavel & Vacula, Jiří & Hrabovský, Jozef, 2021. "Solution strategy for increasing the efficiency of turbochargers by reducing energy losses in the lubrication system," Energy, Elsevier, vol. 236(C).
  • Handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221016509
    DOI: 10.1016/j.energy.2021.121402
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    References listed on IDEAS

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    1. Sandoval, Oscar R. & Fonda, Marina Vianna & Roso, Vinicius Rückert & da Costa, Roberto Berlini Rodrigues & Valle, Ramón Molina & Baêta, José G. Coelho, 2019. "Computational technique for turbocharger transient characterization using real driving conditions data," Energy, Elsevier, vol. 186(C).
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    Cited by:

    1. Wróblewski, Piotr, 2023. "Reduction of friction energy in a piston combustion engine for hydrophobic and hydrophilic multilayer nanocoatings surrounded by soot," Energy, Elsevier, vol. 271(C).
    2. Varbanov, Petar Sabev & Wang, Bohong & Ocłoń, Paweł & Radziszewska-Zielina, Elżbieta & Ma, Ting & Klemeš, Jiří Jaromír & Jia, Xuexiu, 2023. "Efficiency measures for energy supply and use aiming for a clean circular economy," Energy, Elsevier, vol. 283(C).
    3. Xin Bai & Liqun Lu & Tiezhu Zhang & Xiaoping Ouyang & Yi Wang, 2023. "Analysis of the Thermal–Mechanical–Hydraulic Coordination Mechanism of a Constrained Piston Hydraulic Engine," Sustainability, MDPI, vol. 15(12), pages 1-21, June.

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