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<p>Fluid sloshing negatively impacts the stability and safety of liquid hydrogen during transportation. Computational fluid dynamics (CFD) analysis is employed to investigate the sloshing behavior in cargo and fuel storage tanks. Key influencing factors include liquid hydrogen fill level, wave amplitude and frequency, and tank design parameters. Rib structures are incorporated into transportation tanks to mitigate sloshing, although their installation may compromise structural integrity. To address this, CFD methods are integrated with metamodels and artificial intelligence (AI) models to optimize rib design, balancing sloshing suppression and structural stability. In this study, a random forest algorithm predicts optimal rib configurations with a error margin of 3.3 %. The application of AI enables rapid and efficient design optimization in fluid dynamics.</p>

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AI-based fast design optimization of rib structure in the sloshing tank to suppress sloshing dynamics

  • 期刊论文

  • 发表日期：

    2026年2月10日

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  • Ujjwal Shrestha,
  • Seok-Heum Baek,
  • Hyun-Seok Kim,
  • Hyunkyoo Cho,
  • Young-Do Choi

摘要

Fluid sloshing negatively impacts the stability and safety of liquid hydrogen during transportation. Computational fluid dynamics (CFD) analysis is employed to investigate the sloshing behavior in cargo and fuel storage tanks. Key influencing factors include liquid hydrogen fill level, wave amplitude and frequency, and tank design parameters. Rib structures are incorporated into transportation tanks to mitigate sloshing, although their installation may compromise structural integrity. To address this, CFD methods are integrated with metamodels and artificial intelligence (AI) models to optimize rib design, balancing sloshing suppression and structural stability. In this study, a random forest algorithm predicts optimal rib configurations with a error margin of 3.3 %. The application of AI enables rapid and efficient design optimization in fluid dynamics.