Fluid Flow: Characteristics of PDEs and Discretization methods in CFD

A: That's the most common mistake — confusing steady-state intent with explicit stability. The difference matters because CFL is velocity times dt over cell size, so 30 × dt / 0.005 ≈ 1 gives dt around 1e-4 before anything blows up.

A: That's the most common mistake — confusing a mesh metric with a turbulence closure assumption. The difference matters because wall functions expect y+ in the log layer, and forcing y+ <1 there corrupts shear stress prediction.

A: That's the most common mistake — equating formal order with physical fidelity. The difference matters because hyperbolic systems propagate information along characteristics, and ignoring that near shocks gives you Gibbs-style ringing.

A: That's the most common mistake — confusing turbulence modeling with LES requirements. The difference matters because resolving the viscous sublayer and log region takes on the order of a dozen cells, not hundreds, if y+ is controlled.

A: That's the most common mistake — assuming pressure artifacts are cosmetic. The difference matters because without Rhie–Chow on collocated grids, pressure modes decouple and you get alternating high–low cells with zero net force error masking it.

A: That's the most common mistake — thinking functional safety ignores physics models. The difference matters because untracked assumptions can shift loads and margins, feeding DFMEA with wrong boundary conditions.

A: That's the most common mistake — focusing on iteration count instead of storage. The difference matters because each Krylov vector is millions of DOFs and ILU adds fill, so RAM limits hit first.

A: That's the most common mistake — treating residuals as physics. The difference matters because standards push you to monitor outputs, and drifting forces mean grid dependence or modeling error.

A: That's the most common mistake — thinking classification is academic. The difference matters because as Mach climbs, compressibility alters pressure work and density, and forcing incompressible math bakes bias into results you can't calibrate out.