Exhaust Port Challenge

JORGE LOZANO

Advanced CFD using ANSYS Fluent

Exhaust Port Challenge

Objective

1. Give a brief description of why and where a CHT analysis is used.
2. Maintain the y+ value according to the turbulence model and justify the results.
3. Calculate the wall/surface heat transfer coefficient on the internal solid surface & show the velocity & temperature contours in appropriate areas.
4. How would you verify if the HTC predictions from the simulations are right? On what factors does the accuracy of the prediction depend on?

CHT: Conjugate heat transfer

Modes of heat transfer

• Conduction: diffusion of heat due to temperature gradients. A measure of the amount of conduction for a given gradient is the heat conductivity.
• Convection: when heat is carried away by moving fluid. The flow can either be caused by external influences, forced convection; or by buoyancy forces, natural convection. Convective heat transfer is tightly coupled to the fluid flow solution.
• Radiation: transfer of energy by electromagnetic waves between surfaces with different temperatures, separated by a medium that is at least partially transparent to the (infrared) radiation. Radiation is especially important at high temperatures, e.g. during combustion processes, but can also have a measurable effect at room temperatures.

Conjugate heat transfer

• “Conjugate heat transfer” refers to the ability to compute conduction of heat through solids, coupled with convective heat transfer in a fluid.
• Coupled boundary conditions are available for wall zones that separate two cell zones.
• Either the solid zone or the fluid zone, or both, may contain heat sources.
• The example here shows the temperature profile for coolant flowing over fuel rods that generate heat

F.1 Cooling flow over fuel rods

Typical design problems involve the determination of:

• Overall heat transfer coefficient, e.g. for a car radiator.
• Highest (or lowest) temperature in a system, e.g. in a gas turbine, chemical reaction vessels, food ovens.
• Temperature distribution (related to thermal stress), e.g. in the walls of a spacecraft.
• Temperature response in time dependent heating/cooling problems, e.g. engine cooling, or how fast does a car heat up in the sun and how is it affected by the shape of the windshield?

CHT can be performed to improve cooling performance of the water jacket and increase engine life. Advancements in cooling for applications such as gas turbines components require improved understanding of the complex heat transfer mechanisms and the interactions between those mechanisms, which our engineers can perform without hassle. Critical cooling applications often rely on multiple thermal protection techniques, including internal cooling, external film cooling, etc. which are efficiently used by our analysis to cool components and limit the use of coolant. We do extensive support for Motor and Battery CHT Analysis to get optimized design for High voltage systems. Conjugate Heat Transfer analysis provides the temperature distribution in solid and coolant of the engine and clear insight on velocity distribution and mechanism of heat transfer of coolant. Results of CHT analysis become input to structural simulations as thermal loads.

We have a wide experience in Engine CHT Analysis for various type of engines like Bikes, Passenger Cars, Racing cars, Commercial Vehicles, Marine, Agricultural equipment and Earth-moving equipment etc.

Maintain the y+ value according to the turbulence model and justify the results.

F.2 Hierarchy of Turbulence models

Although such a calculation have shown great promises, it is computationally prohibited to be considered as engineer in tool. There are other references to turbulent modeling such as order of closure. According to this terminology, a first order closure evaluates the Reynolds stresses through functions of the mean velocity and geometry alone. A second order closure employs a solution to a modeled from transport partial differential equation for one or more of the characteristics of turbulence. Since this straighter forward, we try to adapt this category as depicted in F. 2. These are some of models mentioned here and by no means is it exclusive. An ideal model should be with minimum amount of complexity while capturing the essence of the relevant physics.

Shortcomings of the k-ε Turbulence Model

• The model is essentially a high Reynolds model, meaning the law of the wall must be employed and provide velocity "boundary conditions" away from solid boundaries (what is termed "wall-functions"). From a mathematical standpoint, even if one could impose Dirichlet conditions for ε on solid boundary, after meshing it would still be difficult to numerically approach the problem due to what is termed in numerical analysis as stiffness of the numerical problem, partially related to the high gradients.
• In order to integrate the equations through the viscous/laminar sublayer a "Low Reynolds" approach must be employed. This is achieved as additional highly non-linear damping functions are needed to be added to low Reynolds formulations (low as in entering the viscous/laminar sublayer) to be able to integrate through the laminar sublayer (y+<5). This again produces numerical stiffness and in case is problematic to handle in view of linear numerical algorithms.
• The model suffers from lack of sensitivity to adverse pressure-gradient. It was observed that under such conditions it overestimates the shear stress and by that delays separation.

F.3 shows the minimum conditions to obtain a good convergence and precision of results according to several most used turbulence models.F.4 shows the advantages and disadvantages of using the models in CFD simulations.

F.3 Y+ turbulence condition

F.4 Turbulence models advantages and its disadvantages

Calculate the wall/surface heat transfer coefficient on the internal solid surface & show the velocity & temperature contours in appropriate area

First to perform the surface and volume mesh, the boundary total layer must be calculated, as seen in the project before, “Ahmed body simulation”, for this simulation the boundary layers are mainly for the fluid wall layer, although, due to the calculation of the Heat coefficient around the surface, an inflation layer must be perform offset to the solid involved.

F.5 General mesh and close image to the boundary layers.

By performing the simulation with F.6 conditions, the plot results are F.7-F.10, having the convergence of the temperature, heat transfer coefficient and y+ value.

F.6 K-epsilon, K-omega turbulence models

F.7 Residuals 500 iterations

F.8Area weighted heat transfer coef., fluid wall

F.9Area weighted outlet temperature

F.10Area weighted y-plus, fluid wall

Contour values of velocity and temperature can be obtained as F.11 and F.12 or F.13 and F.14 in a transverse plane or as the original geometry, as well as to be able to the particles traveling though the manifold.

F 11Plane Velocity contour

F12Plane Temperature contour

F13Contour temperature

F14Particles Velocity magnitude

F. 15-F. 17 show the Contour and result of the Heat transfer coefficient at the wall in a plane and the original geometry.

F15Contour Surface Heat transfer Coef.

F16Contour, Wall Fun. Heat transfer Coef.

F17Area-weighted average: Surface heat transfer coef.

How would you verify if the HTC predictions from the simulations are right? On what factors does the accuracy of the prediction depend on?

As there are many variables involved in this simulation for these simulations K-epsilon and K-omega, the key results to obtain a good simulation are the standard deviation temperature at the outlet, and the y+ value over the surface of the fluid, having in F. 18 -F. 20 the plot, contour, and histogram obtained in order to obtain the convergence and select the faster and more precise model to use in similar cases, depending on the velocity, model used, and iterations given to the simulation.

F 18Standard deviation temperature: Outlet

F 19Contour y+

F 20Histogram Wall Y

Conclusion

Two simulations K-epsilon and K-omega were performed with 500 iterations each to test both models over a manifold system, which obtaining the decisions from the plots, and contours obtained at the results, even though the y+ value had a better convergence and precision at the K-omega case, the K-epsilon performs better at converging the temperature and heat transfer values over the walls of both volumes.

Due to the size of the simulation workbench file, the following link will contain the simulation results:https://drive.google.com/file/d/1f0YgEfSrV_2y_qpsFZ_W47orOX2R6nN9/view?usp=sharing

References & Links

• https://www.depusa.com/solutions/services/cae-services/conjugate-heat-transfer/
• http://www.bakker.org/dartmouth06/engs150/13-heat.pdf
• “Turbulence Modeling” A review, Ideen Sadrehaghighi, PhD., ANNAPOLIS, MD. Nov.2019
• https://gpps.global/documents/events/shanghai17/papers/heat-transfer-cooling-technology/GPPS-AME17-145.pdf
• https://skill-lync.freshdesk.com/support/solutions/articles/43000574931-boundary-layer-y-plus-wall-functions-in-turbulent-flows-
• https://skill-lync.freshdesk.com/support/solutions/articles/43000548212-choosing-a-right-turbulence-model-