Overview
NavCad and CFD tools are complementary rather than competing. NavCad's parametric resistance and propulsion predictions are well suited to benchmarking, verification of CFD setup, and system-level performance simulation. CFD provides high-spatial-resolution flow field data that NavCad's methods cannot produce. Used together, the two approaches increase overall fidelity.
Using NavCad to Benchmark CFD
Before committing to a full CFD run, NavCad's resistance prediction establishes expected resistance magnitudes and speed trends. Significant disagreement between NavCad's result and the CFD output is a useful diagnostic it prompts review of CFD mesh settings, boundary conditions, or hull form interpretation before the discrepancy is discovered after a full simulation campaign.
Propulsor Body Forces LPROP and TPROP
NavCad displays propulsor lift (LPROP) and shaft line thrust (TPROP) in resistance results. These forces represent the normal (lift) and axial (thrust) components of propulsor loading on the hull. For high-speed planing craft, LPROP can rival the influence of stern lift devices such as trim tabs and interceptors, particularly at hump speed. Both values can be as body forces within a CFD self-propulsion to improve accuracy.
KT-KQ Export for Actuator Disk Modeling
NavCad's Export KTKQ utility generates a KT-KQ dataset for the project's propeller in CSV format suitable for defining a CFD virtual propeller actuator disk. When oblique flow settings are enabled, the export includes additional coefficients:
- JH oblique horizontal inflow advance coefficient.
- KTH oblique horizontal thrust coefficient.
- KZP force coefficient (vertical propulsor force from oblique flow).
- KT* total thrust coefficient including the KZP normal force .
These coefficients are particularly valuable for CFD actuator disk models of planing craft where the propeller shaft angle generates significant normal force.
Feeding CFD Results Back into NavCad
CFD-derived resistance data can be imported into NavCad as aligned prediction reference data, anchoring the system simulation to the high-fidelity CFD result. This is particularly effective when CFD run is conducted and NavCad is used to predict performance across the full speed range.
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