In film cooling flows, it is important to know the temperature distribution resulting from the interaction between a hot main flow and a cooler jet. However, current Reynolds-averaged Navier-Stokes (RANS) models yield poor temperature predictions. A novel approach for RANS modeling of the turbulent heat flux is proposed, in which the simple gradient diffusion hypothesis (GDH) is assumed and a machine learning algorithm is used to infer an improved turbulent diffusivity field. This approach is implemented using three distinct data sets: two are used to train the model and the third is used for validation. The results show that the proposed method produces significant improvement compared to the common RANS closure, especially in the prediction of film cooling effectiveness.
The development of a single-fluid solver supporting phase-change and able to capture the evolution of three fluids, two of which are miscible, into the sharp interface capturing Volume of Fluid (VOF) approximation, is presented. The transport of each phase-fraction is solved independently by a flux-corrected transport method to ensure the boundedness of the void fraction over the domain. The closure of the system of equations is achieved by a cavitation model, that handles the phase change between the liquid and the fuel vapor and it also accounts for the interaction with the non-condensable gases. Boundedness and conservativeness of the solver in the transport of the volume fraction are verified on two numerical benchmarks: a two-dimensional bubble rising in a liquid column and a cavitating/condensing liquid column. Finally, numerical predictions from largeeddy simulations are compared against experimental results available from literature; in particular, validation against high-speed camera visualizations and Laser Doppler Velocimetry (LDV) measurements of cavitating microscopic in-nozzle flows in a fuel injector is reported.
In film cooling flows, it is important to know the temperature distribution resulting from the interaction between a hot main flow and a cooler jet. However, current Reynoldsaveraged Navier-Stokes (RANS) models yield poor temperature predictions. A novel approach for RANS modeling of the turbulent heat flux is proposed, in which the simple gradient diffusion hypothesis (GDH) is assumed and a machine learning (ML) algorithm is used to infer an improved turbulent diffusivity field. This approach is implemented using three distinct data sets: two are used to train the model and the third is used for validation. The results show that the proposed method produces significant improvement compared to the common RANS closure, especially in the prediction of film cooling effectiveness. [DOI: 10.1115/1.4038275] Recently, the rapid increase in computational capability has led to 1 Corresponding author.
A preliminary analysis of turbine design, fit for pulsed flow, is proposed in this paper. It focuses on an academic 2D configuration using inviscid flows, since pressure loads due to wave propagation are several orders of magnitude higher than friction and viscous effects do not significantly impinge on the inviscid part, as previously shown by Hermet, 2021. As such, a large parametric study was carried out using the design of experiments methodology. A performance indicator adapted to unsteady environment is carefully defined before detailing the factors chosen for the design of experiments. Since the number of factors is substantial, a screening design to identify the factors influence on the output is first established. The non-influential factors are then omitted in a more quantitative study of the output law. The surface response calculation allows determining the factor level favouring the best output. Consequently, the main trends in the turbine design driven by a pulsed flow can be stated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.