Efficient dynamic mixed subgrid-scale model

Dynamic mixed scale-similarity/Smagorinsky type models (DMM) are promising as they typically have a high $a$ $p\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i$ correlation with subgrid stresses, and provide sufficient subgrid dissipation to be robust for practical applications. However, past DMMs require two or more levels of test filtering, making them unattractive for production codes. We propose an efficient DMM with a single level of test filtering, and through $a$ $p\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}s\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}i$ LES tests of turbulent channel flow and wall-modeled LES of turbulent smooth-body separation (see figure, for a Gaussian bump), demonstrate their robustness and improved accuracy with under 5% additional cost compared to the standard Dynamic Smagorinsky Model.