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Neural Concept

https://www.neuralconcept.com/post/conjugated-heat-transfer-best-practices-applications

Overall: Company-blog piece on CHT fundamentals (conduction+convection physics, meshing, BCs, solver/turbulence choices, convergence targets) — genuinely substantive (~70% technical) but wrapped in soft marketing (~30%) for Neural Concept's AI/CNN-based CAD-to-thermal-field prediction platform, with lead-gen forms. Introductory-level, not deep/advanced.

Video ideas:

  • Why turbulence model choice (k-ω SST vs. laminar) swings CHT results — laminar pipe Nu≈3.66 vs. turbulent Nu>100, 2–10x jumps in convective h
  • Meshing the solid-fluid interface: inflation layers, hex/prism elements in thin regions, why sharp edges/thin walls cause numerical instability
  • What convergence actually means in CHT: residual targets (1e-4 to 1e-6, energy to 1e-6) plus checking heat-flux balance across the interface, not just residual plots
  • When to bother with radiation in CHT: T⁴ nonlinear scaling vs. linear conduction/convection, DO/S2S model tradeoffs at high temperatures
  • Coupled vs. segregated solvers: how interface coupling strength should drive the choice, with a worked example
  • Thermal inertia in solids explained via the stone-wall/diurnal-swing analogy — a plain-language way to teach transient CHT to non-specialists

Feature gaps:

  • Radiation modeling (DO or S2S) for high-temperature CHT cases — worth checking whether/how this is exposed
  • Adaptive mesh refinement driven specifically by thermal gradients (vs. geometry/curvature-driven refinement)
  • Hybrid RANS/LES or other transient-turbulence options for cases where thermal transients matter, beyond the automatic laminar/kOmegaSST switch
  • Built-in heat-flux-balance-at-interface diagnostic as an explicit convergence/QA check surfaced to the user, not just residual tracking