Uncertainty Quantification: A Stochastic Method for Heat Transfer Prediction Using LES

2012 ◽  
Author(s):  
M. Carnevale ◽  
F. Montomoli ◽  
A. D’Ammaro ◽  
S. Salvadori
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Large Eddy Simulation ◽  
Uncertainty Quantification ◽  
Epistemic Uncertainty ◽  
Stochastic Approach ◽  
Operating Conditions ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Turbulence Closures

In Computational Fluid Dynamics (CFD) is possible to identify namely two uncertainties: epistemic, related to the turbulence model, and aleatoric, representing the random-unknown conditions such as the boundary values and or geometrical variations. In the field of epistemic uncertainty, Large Eddy Simulation (LES and DES) is the state of the art in terms of turbulence closures to predict the heat transfer in internal channels. The problem concerning the stochastic variations and how to include these effects in the LES studies is still open. In this paper, for the first time in literature, a stochastic approach is proposed to include these variations in LES. By using a classical Uncertainty Quantification approach, the Probabilistic Collocation Method is coupled to Numerical Large Eddy Simulation (NLES) in a duct with pin fins. The Reynolds number has been chosen as a stochastic variable with a normal distribution. It is representative of the uncertainties associated to the operating conditions, i.e. velocity and density, and geometrical variations such as the pin fin diameter. This work shows that by assuming a Gaussian distribution for the value of Reynolds number of +/−25%, is possible to define the probability to achieve a specified heat loading under stochastic conditions, which can affect the component life by more than 100%. The same method, applied to a steady RANS, generates a different level of uncertainty. This procedure proves that the uncertainties related to the unknown conditions, aleatoric, and those related to the physical model, epistemic, are strongly interconnected. This result has directed consequences in the Uncertainty Quantification science and not only in the gas turbine world.

Uncertainty Quantification: A Stochastic Method for Heat Transfer Prediction Using LES

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
M. Carnevale ◽  
F. Montomoli ◽  
A. D’Ammaro ◽  
S. Salvadori ◽  
F. Martelli
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Large Eddy Simulation ◽  
Uncertainty Quantification ◽  
Epistemic Uncertainty ◽  
Stochastic Approach ◽  
Operating Conditions ◽  
Eddy Simulation ◽  
Large Eddy ◽  
First Time

In computational fluid dynamics (CFD), it is possible to identify namely two uncertainties: epistemic, related to the turbulence model, and aleatoric, representing the random-unknown conditions such as the boundary values and or geometrical variations. In the field of epistemic uncertainty, large eddy simulation (LES and DES) is the state of the art in terms of turbulence closures to predict the heat transfer in internal channels. The problem is still unresolved for the stochastic variations and how to include these effects in the LES studies. In this paper, for the first time in literature, a stochastic approach is proposed to include these variations in LES. By using a classical uncertainty quantification approach, the probabilistic collocation method is coupled to numerical large eddy simulation (NLES) in a duct with pin fins. The Reynolds number has been chosen as a stochastic variable with a normal distribution. The Reynolds number is representative of the uncertainties associated with the operating conditions, i.e., velocity and density, and geometrical variations such as the pin fin diameter. This work shows that assuming a Gaussian distribution for the Reynolds number of ±25%, it is possible to define the probability to achieve a specified heat loading under stochastic conditions, which can affect the component life by more than 100%. The same method, applied to a steady RANS, generates a different level of uncertainty. New methods have been proposed based on the different level of aleatoric uncertainties which provides information on the epistemic uncertainty. This proves, for the first time, that the uncertainties related to the unknown conditions, aleatoric, and those related to the physical model, epistemic, are strongly interconnected. This result, which is idealized for this specific issue, can be extrapolated, and has direct consequences in uncertainty quantification science and not only in the gas turbine world.

Large Eddy Simulation of Turbulent Heat Transfer Around a Circular Cylinder in Crossflow

2007 ◽  
Author(s):  
Sung-Eun Kim ◽  
Hajime Nakamura
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Circular Cylinder ◽  
Large Eddy Simulation ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Heat Transfer Characteristics ◽  
Eddy Simulation ◽  
Flow And Heat Transfer ◽  
Large Eddy

Large eddy simulation has been carried out of turbulent flow and heat transfer around a circular cylinder in crossflow at three subcritical Reynolds numbers (Re = 3,900, 10,000, 18,900) where the flow and heat transfer characteristics change rapidly with the Reynolds number. The computations were carried out using a second-order-accurate finite-volume Navier-Stokes solver that permits use of arbitrary unstructured meshes. A fully implicit, non-iterative fractional-step method was employed to advance the solution in time. The subgrid-scale (SGS) turbulent stresses and heat fluxes were modeled using the dynamic Smagorinsky model. The LES predictions were found to be in good agreement with the experimental data of Hajime and Igarashi (2004). The salient features of turbulent heat transfer in subcritical regime such as the laminar thermal boundary layer and the rapid increase with Reynolds number both in the mean and the r.m.s. Nusselt number in the separated region are closely reproduced by the predictions. The numerical results confirmed that the heat transfer characteristics are closely correlated with the structural change in the underlying flow with the Reynolds number.

scholarly journals Effects of Centrifugal Buoyancy and Reynolds Number on Turbulent Heat Transfer in a Two-Pass Angled-RIB-Roughened Channel with Sharp 180° Turns Investigated by Using Large Eddy Simulation

2008 ◽  
Vol 2008 ◽  
pp. 1-14 ◽  
Author(s):  
Akira Murata ◽  
Sadanari Mochizuki
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Large Eddy Simulation ◽  
Rotation Speed ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Eddy Simulation ◽  
Sharp Turn ◽  
Large Eddy ◽  
Rib Roughened

The effects of the centrifugal buoyancy and the Reynolds number on heat transfer in a rotating two-pass rib-roughened channel with 180° sharp turns were numerically investigated by using the large eddy simulation. The effect of the Reynolds number was seen in the finer flow structure. The effect of the aiding/opposing buoyancy contributions was seen more vigorously on the pressure surface than that on the suction surface, though the details depended on the Reynolds number, the rotation number, and the existence of the ribs. As the buoyancy increased, the friction factor dominated by the pressure loss of the sharp turn decreased, and the decreasing rate is smaller for the higher rotation speed case. The Colburn'sjfactor stayed almost constant irrespective of the rotation speed. As a result, the heat transfer efficiency index slightly increased by the buoyancy, and it became smaller for the higher rotation speed and higher Reynolds number cases.

A CFD Study of Water Condensation Inside the Tubes of an Automotive Compact Charge Air Cooler Using Large Eddy Simulation Approach

2016 ◽  
Author(s):  
Robin Cash ◽  
Apoorv Talekar ◽  
Bashar AbdulNour
Keyword(s):  
Heat Transfer ◽  
Large Eddy Simulation ◽  
Engine Performance ◽  
Local Heat Transfer ◽  
Operating Conditions ◽  
Local Data ◽  
Compression Process ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Air Cooler

The usage of compressed air generated by supercharger or turbocharger by automotive Original Equipment Manufacturers (OEM) is growing with the aim to increase engine performance by increasing the density of the air charge being drawn into the cylinder. Denser air coupled with more fuel pulled into the combustion chamber results in increased engine performance. The inlet air is heated during compression which can cause pre-ignition, which leads to reduced engine functionality. The charge air cooler (CAC) is a heat exchanger introduced to extract heat created during the compression process. Previous research developed a 3-D Computational Fluid Dynamics (CFD) model using the k-epsilon turbulent model with near wall treatment to resolve turbulence in the small channels of the CAC. [1] The present research uses a refined computational scheme with a Large Eddy Simulation (LES) model to solve local data as a function of time and location and correlates the result to the experimental measurements, as well as compare to the k-epsilon approach. Using LES resulted in the ability to correlate any portion of the experimental data and take a closer look at local heat transfer between the outside surface of the tube and the cooling air. Large Eddy Simulation for heat transfer gave more information required for design of CACs which is difficult to collect for various operating conditions by experiment. The overall benefit presented is a validated simulation methodology that predicts condensation, which is then used to evaluate and design CACs that function outside the condensate formation zone during various vehicle operation modes.

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