Verification¶
This section lists the published benchmarks of SIMPLE’s methods and what the automated tests check. Numerical verification report maps each numerical claim to its test, oracle and tolerance and explains the generated report.
Published benchmarks¶
Symplectic integrators. Albert, Kasilov and Kernbichler (2020, J. Comput. Phys.) compare the symplectic schemes with adaptive Runge–Kutta for guiding-centre orbits in an axisymmetric tokamak and a three-dimensional stellarator field. For alpha loss statistics over the slowing-down time, symplectic Euler is more than three times faster at equal statistical accuracy.
Loss fractions. Albert, Kasilov and Kernbichler (2020, J. Plasma Phys.) compute collisionless alpha losses of 1000 markers in quasi-isodynamic, quasi-helical and quasi-axisymmetric configurations; with early classification the cost is about one order of magnitude below adaptive Runge–Kutta tracing.
Classification metrics. Albert et al. (2023, J. Plasma Phys.) show that the \(J_\parallel\) and topological classifiers need at least ten times less tracing than the fractal classifier, and compare the metrics with collisional losses for two quasi-isodynamic, one quasi-helical and two quasi-axisymmetric configurations.
Wall heat loads. Albert et al. (2026, Nucl. Mater. Energy) compute alpha wall heat loads in stellarator reactors with the STL wall intersection and compare with BEAMS3D (references: Publications and citation).
Automated tests¶
make test runs all tests except the golden-record regression tests,
which make test-regression runs; labels and oracle rules are in
Testing. The suites cover
splines, coordinate maps, canonical constructions and the STL intersection
against analytic fixtures; per-scheme energy bounds, axis crossing and
invariant reconstruction on an analytic tokamak; collisions against
slowing-down, Spitzer-drag and Maxwellian oracles; fusion-birth sampling
against analytic sources; pysimple against simple.x; and whole-run
golden records (test/golden_record).