Nonadiabatic Flamelet Formulation for Predicting Wall Heat Transfer in Rocket Engines

Combustion chamber wall heat transfer is a major contributor to efficiency losses in diesel engines. In this context, thermal swing materials (adapting to the surrounding gas temperature) have been pinpointed as a promising mitigative solution. In this study, experiments are carried out in a high-pressure/high-temperature vessel to (a) characterise the wall heat transfer process ensuing from wall impingement of a combusting fuel spray, and (b) evaluate insulative improvements provided by a coating that promotes thermal swing. The baseline experimental condition resembles that of Spray A from the Engine Combustion Network, while additional variations are generated by modifying the ambient temperature as well as the injection pressure and duration. Wall heat transfer and wall temperature measurements are time-resolved and accompanied by concurrent high-speed imaging of natural luminosity. An investigation with an uncoated wall is carried out with several sensor locations around the stagnation point, elucidating sensor-to-sensor variability and setup symmetry. Surface heat flux follows three phases: (i) an initial peak, (ii) a slightly lower plateau dependent on the injection duration, and (iii) a slow decline. In addition to the uncoated reference case, the investigation involves a coating made of porous zirconia, an established thermal swing material. With a coated setup, the projection of surface quantities (heat flux and temperature) from the immersed measurement location requires additional numerical analysis of conjugate heat transfer. Starting from the traces measured beneath the coating, the surface quantities are obtained by solving a one-dimensional inverse heat transfer problem. The present measurements are complemented by CFD simulations supplemented with recent rough-wall models. The surface roughness of the coated specimen is indicated to have a significant impact on the wall heat flux, offsetting the expected benefit from the thermal swing material.

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Data-driven reduced order modeling based on tensor decompositions and its application to air-wall heat transfer in buildings

SeMA Journal ◽

10.1007/s40324-021-00252-3 ◽

2021 ◽

Author(s):

M. Azaïez ◽

T. Chacón Rebollo ◽

M. Gómez Mármol ◽

E. Perracchione ◽

A. Rincón Casado ◽

...

Keyword(s):

Heat Transfer ◽

Reduced Order Modeling ◽

Data Driven ◽

Wall Heat Transfer ◽

Reduced Order ◽

Tensor Decompositions

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Special Features of First-Wall Heat Transfer in Liquid-Metal Fusion Reactor Blankets

Fusion Technology ◽

10.13182/fst87-a25054 ◽

1987 ◽

Vol 12 (1) ◽

pp. 104-113 ◽

Cited By ~ 4

Author(s):

K. Taghavi ◽

M. S. Tillack ◽

H. Madarame

Keyword(s):

Heat Transfer ◽

Liquid Metal ◽

Fusion Reactor ◽

Wall Heat Transfer ◽

First Wall

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Numerical Study of Convective Heat Transfer From Expanding Annular Pipe Flows

Volume 8: Heat Transfer and Thermal Engineering ◽

10.1115/imece2016-67486 ◽

2016 ◽

Author(s):

Khaled J. Hammad

Keyword(s):

Heat Transfer ◽

Reynolds Number ◽

Prandtl Number ◽

Convective Heat Transfer ◽

Diameter Ratio ◽

Wall Heat Transfer ◽

Reattachment Point ◽

Transfer Rates ◽

Pipe Flows ◽

Annular Pipe

Convective heat transfer from suddenly expanding annular pipe flows are numerically investigated within the steady laminar flow regime. A parametric study is performed to reveal the influence of the annular diameter ratio, k, the Prandtl number, Pr, and the Reynolds number, Re, over the following range of parameters: k = {0, 0.5, 0.7}, Pr = {0.7, 1, 7, 100}, and Re = {25, 50, 100}. Heat transfer enhancement downstream of the expansion plane is only observed for Pr > 1. Peak wall-heat-transfer-rates always appear downstream of the flow reattachment point, in the case of suddenly expanding round pipe flows, i.e. k = 0. However, for suddenly expanding annular pipe flows, i.e., k = 0.5 and 0.7, peak wall-heat-transfer-rates always appear upstream of the flow reattachment point. The observed heat transfer augmentation is more dramatic for suddenly expanding annular flows, in comparison with the one observed for suddenly expanding pipe flows. For a given annular diameter ratio and Reynolds number, increasing the Prandtl number, always results in higher wall-heat-transfer-rates downstream the expansion plane.

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Application of RANS and LES to the Prediction of Flows in High Pressure Turbine Components

Volume 7: Turbomachinery, Parts A, B, and C ◽

10.1115/gt2011-46518 ◽

2011 ◽

Cited By ~ 2

Author(s):

Nicolas Gourdain ◽

Laurent Y. M. Gicquel ◽

Remy Fransen ◽

Elena Collado ◽

Tony Arts

Keyword(s):

Heat Transfer ◽

Vortex Shedding ◽

Guide Vane ◽

Unsteady Flows ◽

Separated Flow ◽

Heat Fluxes ◽

Boundary Layer Transition ◽

Wall Heat Transfer ◽

Layer Transition ◽

Turbine Guide Vane

This paper investigates the capability of numerical simulations to estimate unsteady flows and wall heat fluxes in turbine components with both structured and unstructured flow solvers. Different numerical approaches are assessed, from steady-state methods based on the Reynolds Averaged Navier-Stokes (RANS) equations to more sophisticated methods such as the Large Eddy Simulation (LES) technique. Three test cases are investigated: the vortex shedding induced by a turbine guide vane, the wall heat transfer in another turbine guide vane and a separated flow phenomenon in an internal turbine cooling channel. Steady flow simulations usually fail to predict the mean effects of unsteady flows (such as vortex shedding) and wall heat transfer, mainly because laminar-to turbulent transition and the inlet turbulent intensity are not correctly taken into account. Actually, only the LES (partially) succeeds to accurately estimate unsteady flows and wall heat fluxes in complex configurations. The results presented in this paper indicate that this method considerably improves the level of physical description (including boundary layer transition). However, the LES still requires developments and validations for such complex flows. This study also points out the dependency of results to parameters such as the freestream turbulence intensity. When feasible solutions obtained with both structured and unstructured flow solvers are compared to experimental data.

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