An experimental study of thermally thick biomass (beech wood spheres) pyrolysis under high radiative heat flux was performed. The influence of sample diameter (5-20 mm), incident heat flux (60-180 kW/m 2 ) and initial moisture content (1-50 wt%) was studied. Char yields and temperature histories were monitored. Initial moisture content impact was highlighted. Indeed, steam coming from the sample core drying can gasify the external char layer, reducing therefore the char yield and increasing syngas production. This study was supported by a 2D unsteady numerical model of biomass degradation (mass, momentum and heat conservation coupled with Broido-Shafizadeh reaction scheme). This model gave more insight about phenomena occurring inside the degrading sample. It revealed that a pyrolysis front follows up a drying one. Therefore, steam is forced out of the sample through a high temperature char layer, making char steam gasification chemically possible.

We address the interest of using Symbolic Monte Carlo to obtain a reduced model for conduction-radiation coupling in complex geometries. Symbolic Monte Carlo was successfully used for radiative transfer in a decoupled manner, but no attempt has yet been reported to extend its use to radiation coupled with other modes. Here we show that from a unique Monte Carlo simulation of radiation coupled with conduction in a semi-transparent solid surrounded by a convective flow, it is possible to build a formulation of the local temperature as function of the convective heat transfer coefficient, for instance, including the evaluation of uncertainty. This reduced model (a transfer function) enables to decrease the computation time when the function needs to be evaluated plenty of times for different values of the parameters as in optimization or control algorithms.

The optimization of thermal transfers in engineering systems such as heat exchangers requires the analysis of the influence of heat sources upon the temperature at various positions of interest in the studied system. In order to achieve the resolution of the combined modes of heat transfer through these systems, their couplings and the complex 3D geometries involved need to be integrated with full accuracy. Recent developments in probabilistic formulations in the context of transient combined heat transfer (linearized conduction-radiation-convection) have opened a new route to solving such problems with Monte Carlo (MC) algorithms, using state-of-the-art computer graphics digital libraries to handle complex geometries. To estimate the temperature at a probe point of interest, random paths are generated from its position and propagated through the geometry until a known temperature is reached. From a single MC calculation to sample the path statistics, the Symbolic Monte Carlo (SMC) method is used to express the probe temperature as a linear function of the sources. This function can then be used to estimate the probe temperature for any source values, alleviating the need to repeat Monte-Carlo simulations for each source condition, resulting in greatly reduced computation time. This approach is applied to the case of an open-cavity porous medium and computation time insensitivity to the complexity and fineness of the geometry is demonstrated.

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