Scholarlink

<p>The present investigation explores the application of proper orthogonal decomposition (POD) in a small-bore port fuel injection engine to study in-cylinder turbulence and flow cycle-to-cycle variations (CCVs). The study utilizes snapshot POD to systematically decompose high-dimensional particle image velocimetry flow datasets into orthogonal modes, thereby isolating coherent and turbulent flow structures. The findings reveal that dominant modes encapsulate preponderance of the total energy, underscoring their critical role in representing large-scale, stable flow structures. In contrast, higher-order modes, characterized by diminished energy contributions, delineate chaotic, small-scale turbulence. Also, the properties of POD modes were studied by application to in-cylinder flow fields. Through a comparative analysis of mode-wise energy distributions at different crank angle degrees, the research elucidates the relationship between steep energy concentrations in primary modes and reduced CCVs, indicative of enhanced flow stability. Conversely, a more evenly distributed energy spectrum across modes signifies amplified variability in flow behaviour. The study further highlights the traceability of predominant flow structures within high-energy modes. Therefore, this methodological framework provides a useful tool for understanding complex in-cylinder flow processes which can then provide valuable insights for optimizing engine performance and refining predictive modelling paradigms.</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

Proper orthogonal decomposition and its application for the analysis of in-cylinder flow in engines

  • 期刊论文

  • 发表日期：

    2025年9月20日

查看全文
  • Anuj Sharma,
  • K. Manjunath,
  • Soumyanil Nayek,
  • Mayank Mittal

摘要

The present investigation explores the application of proper orthogonal decomposition (POD) in a small-bore port fuel injection engine to study in-cylinder turbulence and flow cycle-to-cycle variations (CCVs). The study utilizes snapshot POD to systematically decompose high-dimensional particle image velocimetry flow datasets into orthogonal modes, thereby isolating coherent and turbulent flow structures. The findings reveal that dominant modes encapsulate preponderance of the total energy, underscoring their critical role in representing large-scale, stable flow structures. In contrast, higher-order modes, characterized by diminished energy contributions, delineate chaotic, small-scale turbulence. Also, the properties of POD modes were studied by application to in-cylinder flow fields. Through a comparative analysis of mode-wise energy distributions at different crank angle degrees, the research elucidates the relationship between steep energy concentrations in primary modes and reduced CCVs, indicative of enhanced flow stability. Conversely, a more evenly distributed energy spectrum across modes signifies amplified variability in flow behaviour. The study further highlights the traceability of predominant flow structures within high-energy modes. Therefore, this methodological framework provides a useful tool for understanding complex in-cylinder flow processes which can then provide valuable insights for optimizing engine performance and refining predictive modelling paradigms.