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A three-dimensional Eulerian two-phase flow solver, SedFoam, has been developed for various sediment transport applications. The solver has demonstrated success in modeling sheet flow and bedforms driven by oscillatory flows using a Reynolds-averaged Navier–Stokes (RANS) formulation. However, the accuracy of the RANS formulation for more complex flows, such as scour around structures, requires further evaluation. SedFoam has recently been enhanced to incorporate two-phase large-eddy simulation (LES) capability. In this study, RANS and LES approaches are tested via a three-dimensional case of wave-induced local scour around a single vertical circular pile. Two laboratory experiments, one with an erodible bed and the other with a rigid bed, were chosen for simulation, with both experiments having a Keulegan-Carpenter (KC) number of 10. The k-ω turbulence closure was selected for the RANS simulation, and the dynamic Lagrangian subgrid closure was chosen for the LES simulation. Numerical results reveal that both RANS and LES simulations can resolve lee-wake vortices, although the vortices are significantly weaker in the RANS simulation. In comparison with the LES results, the RANS approach fails to predict horseshoe vortex with sufficient intensity, leading to an underestimation of scour hole depth development. Although the scour depths develop at a very similar rate in the early stage, the scour depth predicted by the RANS simulation quickly reaches equilibrium, while the LES simulation follows the measured trend. These findings indicate that a turbulence-resolving methodology, i.e. LES, is necessary for accurate scour simulations.
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This content will become publicly available on February 25, 2026
Multi-Fidelity Optimization of Turbulence in a Gas Turbine Combustor Simulator
Abstract Combustor turbulence in a gas turbine engine greatly influences the efficiency of the downstream high pressure turbine stage. Here we use a multi-fidelity computational optimization methodology to modify the geometry of a non-reacting combustor simulator such that turbulence properties are optimized at the combustor-turbine interface. We modify the size, orientation, and positioning of the primary and dilution jets to minimize turbulence intensity at the combustor exit while demonstrating negligible or favorable changes to the pressure loss and mixing characteristics of the combustor. The optimization is performed using a machine learning surrogate-assisted genetic algorithm coupled with large eddy simulations (LES) and Reynolds-averaged Navier Stokes (RANS) simulations. The optimization is performed in three phases: (i) we develop a continuously-learning artificial neural network surrogate model, (ii) we perform a stochastic optimization with RANS simulations to narrow the parameter space, and (iii) we perform a stochastic optimization with a coarse-grid LES to identify the optimal solution. Using this approach, we are able to achieve a 5.35% reduction in turbulence intensity and a 0.42% reduction in pressure loss while maintaining good mixing uniformity at the combustor exit. These changes are enabled primarily by changing the aspect ratio and diameter of the primary zone and dilution jets, as well as the chute height and spacing of the primary zone jets. This successful demonstration of multi-fidelity optimization in the combustor simulator can be extended in the future to the design of improved gas turbine combustors.
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- Award ID(s):
- 1847111
- PAR ID:
- 10575566
- Publisher / Repository:
- ASME
- Date Published:
- Journal Name:
- Journal of Engineering for Gas Turbines and Power
- ISSN:
- 0742-4795
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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