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This paper provides a concise review of riblet-based strategies for skin-friction drag reduction, with a particular focus on emerging and unconventional designs. We first summarize the performance of conventional straight riblets, aligned with the mean flow direction, which are known to achieve drag reductions of up to about 10% under optimal conditions. We then explore alternative riblet geometries that have been proposed to address key limitations of straight configurations, notably their sensitivity to the design point and the rapid degradation of performance under off-design conditions. Concepts such as wavy, converging–diverging, and herringbone riblets suggest new pathways for broadening the operational envelope and potentially achieving enhanced drag-reduction levels. In this context, the integration of advanced optimization frameworks and machine-learning techniques offers a promising avenue for systematically exploring high-dimensional design spaces and uncovering novel riblet configurations with improved robustness and performance.
Amico et al. (Mon,) studied this question.