Cryogenic jets atomisation

High-fidelity simulation of flashing, cavitating cryogenic jets and spray formation, enabling accurate prediction of dispersion, phase change, and safety-critical behaviour in energy systems

Technical capabilities in MPflow

MPflow provides advanced capabilities for modelling cryogenic jet atomisation under extreme thermodynamic conditions:

  • Dedicated solvers for compressible, multiphase, and phase-change flows
  • Modelling of flash boiling and cavitating jets under rapid depressurisation
  • Captures metastable thermodynamic states and non-equilibrium phase change
  • Eulerian–Lagrangian framework for dense-to-dilute spray transition
  • Advanced treatment of:bubble nucleation and growth, internal and external flashing and evaporation and heat transfer in cryogenic conditions
  • Fully coupled RANS and LES turbulence modelling
  • Validated across cryogenic fluids, including LNG and hydrogen

Our CFD approach

  • Simulation of pressurised cryogenic jets expanding into low-pressure environment
  • Resolution of internal nozzle flow and onset of flashing
  • Modelling of bubble nucleation within the liquid core and its impact on breakup
  • Captures multiple flow regimes: bubbly, slug, and annular structures
  • LES-based prediction of: Transient jet instability and breakup, spray dispersion and vapour cloud formation
  • Accounts for:

    • Superheat and subcooling effect
    • Ambient conditions and thermodynamic paths (isothermal / isobaric)
    • Geometry and nozzle L/D ratio

Physics-based ML acceleration

  • ML models trained on high-fidelity CFD and experimental cryogenic dataset
  • Surrogate prediction of: flashing onset and intensity, spray structure and dispersion, vapour cloud evolution
  • Embedded ML acceleration for:

    • Phase-change source terms
    • Turbulence modelling (RANS/LES)
  • Enables rapid evaluation across: Different cryogenic fluids (LNG, LH₂, etc.), Pressure and temperature conditions, Leak sizes and geometries
  • Preserves thermodynamic consistency and physical constraints

Why ML-CFD matters for Cryogenic Jets Atomisation

  • Simulation time reduced from ~8 hours to seconds with deep neural network deployment for flashing cryogenic releases
  • 3–5× acceleration with on-the-fly ML-enhanced RANS & LES turbulence modelling
  • High accuracy (<5–8% deviation) in spray structure, vapour cloud evolution, and dispersion behaviour
  • Instant evaluation of safety-critical scenarios, including accidental releases and leak conditions
  • Scalable across fluids and operating conditions, including LNG and hydrogen systems
  • Enables rapid risk assessment and integration into digital twins