This INCITE project seeks to create a direct numerical simulation (DNS) dataset capturing all the relevant processes involved in hypersonic boundary layer transition (including free-stream noise, receptivity, modal growth, and turbulent breakdown) in support of a recent experimental test campaign carried out at the Air Force Research Labs demonstrating passive transition control with the use of porous walls. Simulations are carried out using a high-scalable block-spectral code at Reynolds numbers of up to 22.9 x 106, which is prohibitively expensive for typical high-performance computing systems. The resulting dataset will lead to a deeper understanding of the onset of second-mode waves on a flat plate (as opposed to conical geometries, which are more commonly studied) and their acoustic suppression via wall porosity under realistic flow conditions. This has strong implications for hypersonic vehicle design and thermal management specifically. Lower-order models, including wall-resolved large eddy simulations (LES), are not reliable at these extreme flow conditions, warranting the use of large-scale DNS approaches. The work relies on years of experience in developing techniques for acoustic-based passive control of external and internal flows for hypersonic applications. The dataset created by this project will support the development of data-driven turbulence models, such as graph-neural-network-based machine learning algorithms to extract subfilter- scale (LES) or total (Reynolds-averaged Navier-Stokes) turbulent stresses.