The study of complex multiscale flows (Groen et al., 2014), like for example the motion of small-scale turbulent eddies over large aerodynamic structures (Jofre & Doostan, 2022), microconfined high-pressure supercritical fluids for enhanced energy transfer (Bernades & Jofre, 2022), or hydrodynamic focusing of microorganisms in wall-bounded flows (Palacios et al., 2022), greatly benefits from the combination of interconnected theoretical, computational and experimental approaches. This manifold methodology provides a robust framework to corroborate the phenomena observed, validate the modeling assumptions utilized, and facilitates the exploration of wider parameter spaces and extraction of more sophisticated insights. These analyses are typically encompassed within the field of Predictive Science & Engineering (Njam, 2009), which has attracted attention in the Fluid Mechanics community and is expected to exponentially grow as computational studies transition from (mostly) physics simulations to active vectors for scientific discovery and technological innovation with the advent of Exascale computing (Alowayyed et al., 2017). In this regard, the computational flow solver presented aims at bridging the gap between studying complex multiscale flow problems and utilizing present and future state-of-the-art supercomputing systems in academic environments.