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The Lattice Boltzmann (LB) method offers an alternative to conventional computational fluid dynamics (CFD) methods. However, its practical use for complex turbulent flows of engineering interest is still at an early stage. This paper aims to outline an LB wall-modeled large-eddy simulation (WMLES) solver.

The solver is dedicated to complex high-Reynolds flows in the context of WMLES. It relies on an improved LB scheme and can handle complex geometries on multi-resolution block structured grids.

Dynamic and acoustic characteristics of a turbulent airflow past a rod-airfoil tandem are examined to test the capabilities of this solver. Detailed direct comparisons are made with both experimental and numerical reference data.

This study allows assessing the potential of an LB approach for industrial CFD applications.

Bioenergy from agriculture is considered to be a way to reduce GHG emissions and thus global warming and climate change. Bioenergy also presents other environmental externalities as impacts on air, soil and water quality, biodiversity, etc. In addition, bioenergy presents socio‐economic externalities as impacts on human health, social wellbeing, local prosperity, etc. These externalities must be assessed in order to enhance responsible politics' choice of the best bioenergy routes to support through incentives as subsidies or quotas. The aim of this research project is to enhance the political choice of bioenergy routes to support through incentives as subsidies or quotas.

From the literature review and assessment of certification initiatives, the paper has derived a list of environmental externalities, i.e. environmental sustainability criteria, and a list of socio‐economic externalities, i.e. socio‐economic sustainability criteria, to be taken into account in bioenergy routes evaluation. Environmental and socio‐economic externalities selected are interlinked and cannot be assessed in isolation. They are thus articulated into a qualitative model, which defines links between externalities and characterizes them into positive or negative correlations, and indeterminate relations.

From this model, it appears that many interactions between environmental externalities or between socio‐economic externalities from bioenergy are not straightforward. Many of them are time or space‐dependent. Agricultural practices vary from one region to another; indirect effects are far from being understood and assessed correctly, long‐term effects of climate change are still unknown, etc. Moreover, environmental externalities should be articulated together with socio‐economic externalities.

On the basis of the consolidated qualitative model, a quantitative model will be built. It will enable the monetization of externalities and their introduction in a political decision‐making tool. This tool will help politics to compare different bioenergy routes and choose the best according to their sustainability.

The quantitative model should allow the monetization of externalities and their introduction in a political decision‐making tool. This instrument will help politics to take into account sustainability in their comparison of different bioenergy routes when they want to promote: employment, GHG emissions reduction, biodiversity conservation, etc.