Add a fused relperm + capillary pressure entry point - #5287
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krw(), krn(), krg(), pcnw() and pcgn() each re-read the fluid state and re-derive their own two-phase saturation argument, and krn() calls two helpers that derive sw and sg a third time. A caller that needs relperms and capillary pressures together -- the intensive quantities do -- pays for that five times over. relativePermeabilitiesAndCapillaryPressures() forms the three distinct arguments once and calls the two-phase laws directly. Same arithmetic, same table lookups, same results; the new test checks the fused and separate paths agree exactly over a saturation sweep, float and double. Measured on Norne (44 927 cells, 3-derivative AD, standalone harness, identical states and checksums): 89.9 -> 59.8 ns/cell for relperm + pc. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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krw(),krn(),krg(),pcnw()andpcgn()each re-read the fluid state and re-derive their own two-phase saturation argument;krn()calls two helpers that deriveswandsgagain. A caller needing both sets — the intensive quantities do — pays for that five times per cell.relativePermeabilitiesAndCapillaryPressures()forms the three distinct arguments once and calls the two-phase laws directly. Same arithmetic, same lookups, same results; the new test checks both paths agree exactly over a saturation sweep, float and double.Norne, 44 927 cells, 3-derivative AD, identical states and checksums: 89.9 → 59.8 ns/cell (−33 %) for relperm + pc. The gain is larger for AD than for plain double (−13 %) because the redundant derivation runs in
Evaluationarithmetic.No representation change, no new storage, no accuracy question. Consumer-side hook: OPM/opm-simulators#7304.
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