English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a novel unsupervised machine-learning sock sensor based onGaussian Mixture Models (GMMs). The proposed GMM sensor demonstrates remarkableaccuracy in detecting shocks and is robust across diverse test cases withsignificantly less parameter tuning than other options. We compare theGMM-based sensor with state-of-the-art alternatives. All methods are integratedinto a high-order compressible discontinuous Galerkin solver, where twostabilization approaches are coupled to the sensor to provide examples ofpossible applications. The Sedov blast and double Mach reflection casesdemonstrate that our proposed sensor can enhance hybrid sub-cellflux-differencing formulations by providing accurate information of the nodesthat require low-order blending. Besides, supersonic test cases including highReynolds numbers showcase the sensor performance when used to introduceentropy-stable artificial viscosity to capture shocks, demonstrating the sameeffectiveness as fine-tuned state-of-the-art sensors. The adaptive nature andability to function without extensive training datasets make this GMM-basedsensor suitable for complex geometries and varied flow configurations. Ourstudy reveals the potential of unsupervised machine-learning methods,exemplified by this GMM sensor, to improve the robustness and efficiency ofadvanced CFD codes.

An unsupervised machine-learning-based shock sensor for high-order supersonic flow solvers