PyCav

Objective

• Learn integration technique for computing required statistical moments of the evolving bubble dynamics

Todo

• Verify Monte Carlo gets moments right
• Implement Simpson's rule
• Turn pressure from constant to time-dependent function

• SSP-RK2
• SSP-RK3
• Adaptive Euler/RK2

• Adaptive RK23
• Linear bubble dynamics

• Keller Miksis
• Stage 1 complete
• Add $dpdt$ to Keller Miksis

Development plan

Stage 1:

• Uncoupled polydisperse bubble cavitation
  • Modular bubble model (e.g. RPE, KM, etc.)
  • Modular bubble distribution in $R_o$ coordinate (default to log-normal)
• Time dependence for bubble forcing (pressure $p(t)$
• Approximate exact moments via Monte Carlo

Stage 2:

• One-way coupled dynamics
  • Pressure affects bubble state (radii, void fraction, number density function, etc.)
  • Changing bubble state does not effect time-dependent pressure forcing
  • Use high-class-count to approximate exact solution

Stage 3:

• Fully-coupled dynamics
• Spatial dependencies