Scholarlink

<p>This study compares the durability, thermal performance, and emission characteristics of internal combustion engines operating in dual-fuel mode, considering the effects of varying thermal and mechanical stresses. In order to study how the heat and mechanical stress cause cylinder liner failures in dual-fuel automobiles that meet BS6 standards, an integrated experimental/computational/AI approach was taken. The experimental studies were designed to measure tensile strength, hardness, microstructure, and material integrity. Mechanical testing, metallographic analyses, and non-destructive evaluations were used to evaluate the experimental data. To establish a software model for predicting the mechanical behaviour of the cylinder liner and develop testing and validation procedures, a computational thermo-mechanical model was created, which used analytical stress equations and validated against finite element analysis (FEA) to provide estimates of the total mechanical, thermal and combined loads placed on the cylinder liner during operation. The study included an evaluation of three types of materials—grey cast iron, nickel-chrome iron alloy, and aluminum alloy. The findings from this study indicate that grey cast iron has the highest total mechanical stress of 893.55&#xa0;N/mm<sup>2</sup> and therefore has the highest likelihood of thermal fatigue failure compared to the nickel-chrome iron alloy with a total mechanical stress of 232.63&#xa0;N/mm<sup>2</sup>, which has the best mechanical stability when subjected to high heat and mechanical stresses. Artificial Neural Network (ANN)–based optimization was employed using experimentally measured mechanical properties and validated stress data to refine hardness and tensile strength while preserving graphite morphology. Experimental observations and FEA predictions were used to validate both computational and ANN models. The proposed methodology provides a robust, data-driven approach for optimized cylinder liner material selection, enhancing engine reliability, durability, and sustainability.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Performance optimization of cylinder liners through failure analysis, thermomechanical evaluation, and advanced material selection

  • 期刊论文

  • 发表日期：

    2026年6月16日

查看全文
  • Shekhar T. Shinde,
  • Kishor R. Borole,
  • Mohan P. Khond,
  • Kedarnath Chaudhary,
  • Snehal S. Shinde

摘要

This study compares the durability, thermal performance, and emission characteristics of internal combustion engines operating in dual-fuel mode, considering the effects of varying thermal and mechanical stresses. In order to study how the heat and mechanical stress cause cylinder liner failures in dual-fuel automobiles that meet BS6 standards, an integrated experimental/computational/AI approach was taken. The experimental studies were designed to measure tensile strength, hardness, microstructure, and material integrity. Mechanical testing, metallographic analyses, and non-destructive evaluations were used to evaluate the experimental data. To establish a software model for predicting the mechanical behaviour of the cylinder liner and develop testing and validation procedures, a computational thermo-mechanical model was created, which used analytical stress equations and validated against finite element analysis (FEA) to provide estimates of the total mechanical, thermal and combined loads placed on the cylinder liner during operation. The study included an evaluation of three types of materials—grey cast iron, nickel-chrome iron alloy, and aluminum alloy. The findings from this study indicate that grey cast iron has the highest total mechanical stress of 893.55 N/mm2 and therefore has the highest likelihood of thermal fatigue failure compared to the nickel-chrome iron alloy with a total mechanical stress of 232.63 N/mm2, which has the best mechanical stability when subjected to high heat and mechanical stresses. Artificial Neural Network (ANN)–based optimization was employed using experimentally measured mechanical properties and validated stress data to refine hardness and tensile strength while preserving graphite morphology. Experimental observations and FEA predictions were used to validate both computational and ANN models. The proposed methodology provides a robust, data-driven approach for optimized cylinder liner material selection, enhancing engine reliability, durability, and sustainability.

Graphical Abstract