upper and lower)wedge and hartree)from generate import FalknerSkan
generator = FalknerSkan()
print(generator.value({'beta': '0', 'branch': 'upper', 'normalisation': 'wedge'}, digits=100))
Every listed entry is given to 100 significant decimal digits. The attached generate.py uses arbitrary-precision Taylor continuation and shooting for the upper-branch velocity profile, with integrated Taylor series for momentum thickness and thermal quadrature. The thermal integral includes a Gaussian asymptotic tail beyond the finite endpoint.
Every value is compared between two runs: 160 working decimal digits, Taylor order 140, step 1/8 and endpoint 32; and 210 digits, order 180, step 1/10 and endpoint 36. Across the three related tables, the largest observed relative difference is 4.209e-148. The momentum-integral identity and boundary residuals are checked at every pressure gradient. The Blasius case is also checked by Toepfer scaling, and the thermal value at Pr=1 is checked against the wall shear at beta=0. Uniform-flow and exact rational-solution controls test the Taylor recurrence. A separately implemented SciPy collocation solution and adaptive quadrature check all entries at ordinary floating-point precision.
Taylor truncation, finite-domain shooting and the asymptotic thermal tail are convergence-tested, not rigorously enclosed. The high-precision scaling control reuses the Taylor integrator; the separate collocation solver checks only ordinary floating-point accuracy. Arb is used internally, but step midpoints are retained. These checks support the stated heuristic rigour, not proven 100-digit error bounds. No mathematical definitions, parameter selections or entry annotations were changed.