GPU-accelerated inverse identification of radiative properties of particle suspensions in liquid by the Monte Carlo method

- A GPU accelerated inverse model to obtain radiative properties of particle suspensions.
- Demonstrated over 100x speedup for the GPU accelerated inverse model than the CPU implementation.
- Presented a sensitivity analysis to understand uncertainties of retrieved radiative parameters.
- Tests using experimental data to retrieve radiative properties of microalgae.

Inverse identification of radiative properties of participating media is usually time consuming. In this paper, a GPU accelerated inverse identification model is presented to obtain the radiative properties of particle suspensions. The sample medium is placed in a cuvette and a narrow light beam is irradiated normally from the side. The forward three-dimensional radiative transfer problem is solved using a massive parallel Monte Carlo method implemented on graphics processing unit (GPU), and particle swarm optimization algorithm is applied to inversely identify the radiative properties of particle suspensions based on the measured bidirectional scattering distribution function (BSDF). The GPU-accelerated Monte Carlo simulation significantly reduces the solution time of the radiative transfer simulation and hence greatly accelerates the inverse identification process. Hundreds of speedup is achieved as compared to the CPU implementation. It is demonstrated using both simulated BSDF and experimentally measured BSDF of microalgae suspensions that the radiative properties of particle suspensions can be effectively identified based on the GPU-accelerated algorithm with three-dimensional radiative transfer modelling.