JOURNAL ARTICLE

Natural gas jet evolution and structural characterization of shockwaves downstream of an injector in a simulated engine environment.

  • Published In: Physics of Fluids, 2025, v. 37, n. 3. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kalwar, Ankur; Agarwal, Avinash Kumar; Lakshminarayanan, P. A.; Pham, Quangkhai; Park, Suhan; Park, Sungwook 3 of 3

Abstract

This article focuses on the computational investigation of methane jet characteristics during fuel injection in compressed natural gas (CNG)-fueled internal combustion (IC) engines, analyzing the effects of varying fuel injection pressures (8, 16, and 24 bar) and ambient pressures (1, 2, and 4 bar) relevant to port and direct injection applications. Using Reynolds-averaged Navier–Stokes (RANS) simulations validated against experimental data, the study characterizes jet development, shockwave formation, turbulence, and energy conversion efficiencies across subsonic, moderately under-expanded, and highly under-expanded jet regimes defined by pressure ratios. Key findings include the identification of secondary instabilities at higher pressure ratios, limited jet penetration gains due to choking effects, consistent jet cone angles (~20°–25°) regardless of conditions, and energy transfer efficiencies around 80%–82% for pressure ratios above 4. The research provides detailed insights into jet structure, shock parameters such as Mach disk geometry, turbulence intensity localized mainly in shear layers, and vorticity patterns, contributing to optimized injector design and improved fuel-air mixing in CNG engines.

Additional Information

  • Source:Physics of Fluids. 2025/03, Vol. 37, Issue 3, p1
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2025
  • ISSN:1070-6631
  • DOI:10.1063/5.0248546
  • Accession Number:184176530
  • Copyright Statement:Copyright of Physics of Fluids is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.