Browsing by Author "Moreno, Miguel"
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Item Open Access Design and optimisation of a Mach 2.5 wind tunnel nozzle(AIAA, 2023-01-19) Moreno, Miguel; Migliorini, Matteo; Zachos, Pavlos K.; Haslam, Anthony; MacManus, DavidThe paper presents a methodology for the numerical design and optimization of a distortion-free two-dimensional Mach 2.5 nozzle based on a parametric model. The non-uniformities generated at the Mach wave reflections downstream of the nozzle throat that the Method of Characteristics only partially addresses are minimized. The spatial discretization of the domain is integrated with the boundary layer analysis for fast and robust data processing, especially in the final viscous sublayers in the transition regions within the bulk of the fluid. The flow patterns and corner flows of the supersonic nozzle are assessed via three-dimensional high-fidelity computational fluid dynamics. As a result, a fast workflow for nozzle design to meet prescribed flow quality requirements is herein illustrated.Item Open Access Summary of the 6th Propulsion Aerodynamics Workshop: NASA 1507 Inlet(AIAA, 2024-01-04) Moreno, Miguel; Zachos, Pavlos K.; Gantt, Erick J.; Tobaldini Neto, Luiz; Ferolla de Ambreu, Diego; Domel, Neal D.; Slater, John W.The 6th AIAA Propulsion Aerodynamics Workshop (PAW6) was held as part of AIAA’s Science and Technology Forum between January 21st -22nd 2023 in National Harbor, US. The goal of the workshop was to evaluate the current capability of computational fluid dynamics (CFD) on complex flows, pertinent to the high-speed propulsion community. PAW6 inlet test case was a mixed-compression supersonic inlet referred to here as the NASA 1507 inlet which featured a complex shock system attached at the entry along with a range of different flow control methods such as porous bleeds and vortex generators. Among several experimental test cases, four were selected for the workshop across a range of back-pressures, or inlet flow ratios, that yield different levels of pressure recovery, engine face distortion and bleed flows. Flow prediction data from 8 different participants was submitted using a total of 5 different computational domains for which 9 computational grids were developed and provided by Cadence. In general, flow predictions were better able to match the test data near the critical point of intake operation regardless of the flow solver, grid refinement level or turbulence model. Models with fully resolved rather than modelled bleed and/or vortex generators showed better results. Across the sub-critical range of operation, a notable under prediction of the flow ratio was seen across all flow solvers and models, indicating significant variations in the porous bleed modelling between the CFD datasets. The work indicates that more effort is needed by the relevant community toward the development of robust predictive capabilities, especially when complex flow control systems are in place for stability across the operating range.