Numerical investigation of thermomechanical behavior of Yttrium barium zirconate-coated aluminum alloy piston in an internal combustion engine Show others and affiliations
2024 (English) In: Applied Thermal Engineering, ISSN 1359-4311, E-ISSN 1873-5606, Vol. 236, no part B, article id 121603Article in journal (Refereed) Published
Abstract [en]
Increasing engine power to volume density is under investigation and being analysed extensively. Turbocharger, which is used to boost volumetric efficiency, also raises cylinder temperature and pressure, thus resulting in thermal distortions and reducing clearances in tribo-contacts, thereby compromising engine life. Thermal barrier coatings (TBCs) have shown potential to provide remedies to reduce heat losses, hazardous emissions, and heat flow toward the piston skirt in an internal combustion engine. In this study, a detailed thermo-mechanical analysis was performed for a diesel engine piston with a novel yttrium barium zirconate (YBZ) coating and then compared with other TBCs with varying thicknesses. The results revealed a notable decrease in piston substrate surface temperature when coated with various TBCs, with YBZ coating demonstrating superior performance over others. The 0.2 mm coating of YBZ-coated piston exhibited significant reductions of 15% and 10.3% in temperature and thermal stress respectively, thus enhancing piston durability. The better performance of the novel YBZ coating could be attributed to its stable thermal and elastic properties and lower thermal conductivity than other TBC materials. YBZ coating provides a promising solution to improve engine efficiency while extending engine life, making it an attractive option for the automotive industry.
Place, publisher, year, edition, pages Elsevier, 2024. Vol. 236, no part B, article id 121603
Keywords [en]
Diesel engine, Piston, Thermal barrier coating, Substrate surface temperature, Thermal stress
National Category
Manufacturing, Surface and Joining Technology Energy Engineering
Research subject Machine Learning; Machine Elements
Identifiers URN: urn:nbn:se:ltu:diva-101384 DOI: 10.1016/j.applthermaleng.2023.121603 ISI: 001079552100001 Scopus ID: 2-s2.0-85173059224 OAI: oai:DiVA.org:ltu-101384 DiVA, id: diva2:1798477
Note Validerad;2023;Nivå 2;2023-09-19 (joosat);
Funder: Korean government (No. 002086731G0003118)
2023-09-192023-09-192024-03-11 Bibliographically approved