a b s t r a c tAt the kinetic level, the meaning of null-collisions is straightforward: they correspond to pure-forward scattering events. We here discuss their technical significance in integral terms. We first consider a most standard null-collision Monte Carlo algorithm and show how it can be rigorously justified starting from a Fredholm equivalent to the radiative transfer equation. Doing so, we also prove that null-collision algorithms can be slightly modified so that they deal with unexpected occurrences of negative values of the nullcollision coefficient (when the upper bound of the heterogeneous extinction coefficient is nonstrict). We then describe technically, in full details, the resulting algorithm, when applied to the evaluation of the local net-power density within a bounded, heterogeneous, multiple scattering and emitting/absorbing medium. The corresponding integral formulation is then explored theoretically in order to distinguish the statistical significance of introducing null-collisions from that of the integral-structure underlying modification.

The Monte-Carlo method is often presented as a reference method for radiative transfer simulation when dealing with participating, inhomogeneous media. The reason is that numerical uncertainties are only of a statistical nature and are accurately evaluated by measuring the standard deviation of the Monte Carlo weight. But classical Monte-Carlo algorithms first sample optical thicknesses and then determine absorption or scattering locations by inverting the formal integral definition of optical thickness as an increasing function of path length. This function is only seldom analytically invertible and numerical inversion procedures are required. Most commonly, a volumic grid is introduced and optical properties within each cell are replaced by approximate homogeneous or linear fields. Simulation results are then sensitive to the grid and can no longer be considered as references. We propose a new algorithmic formulation based on the use of null-collisions that eliminate the need for numerical inversion: no volumic grid is required. Benchmark configurations are first considered in order to evaluate the effect of two free parameters: the amount of null-collisions, and the criterion used to decide at which stage a Russian Roulette is used to exit the path tracking process. Then the corresponding algorithm is implemented using a development environment allowing to deal with complex geometries (thanks to computer graphics techniques), leading to a Monte Carlo code that can be easily used for validation of fast radiative transfer solvers embedded in combustion simulators. "Easily" means here that the way the Monte Carlo algorithm deals with both the geometry and the temperature/pressure/concentration fields is independent of the choices made inside the combustion solver: there is no need for the design of a new pathtracking procedure adapted to each new CFD grid. The Monte Carlo simulator is ready for use as soon as combustion specialists provide a localization/interpolation tool defining what they consider as the continuous input fields best suiting their numerical assumptions. The radiation validation tool introduces no grid in itself.

Monte Carlo is famous for accepting model extensions and model refinements up to infinite dimension. However, this powerful incremental design is based on a premise which has severely limited its application so far: a state-variable can only be recursively defined as a function of underlying state-variables if this function is linear. Here we show that this premise can be alleviated by projecting nonlinearities onto a polynomial basis and increasing the configuration space dimension. Considering phytoplankton growth in light-limited environments, radiative transfer in planetary atmospheres, electromagnetic scattering by particles, and concentrated solar power plant production, we prove the real-world usability of this advance in four test cases which were previously regarded as impracticable using Monte Carlo approaches. We also illustrate an outstanding feature of our method when applied to acute problems with interacting particles: handling rare events is now straightforward. Overall, our extension preserves the features that made the method popular: addressing nonlinearities does not compromise on model refinement or system complexity, and convergence rates remain independent of dimension.