Full vehicle CFD investigations on the influence of front-end configuration on radiator performance and cooling drag

Zhang, Chunhui; Uddin, Mesbah; Robinson, Austin Clay; Foster, Lee

doi:10.1016/j.applthermaleng.2017.11.086

Cited by 49 publications

(35 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These minor effects can also be explained in the same manner as b Ã but opposite since these coefficients only affect the diffusion term in the k equation (5). Analysis of s v1 and s v2 show that they affect both the diffusion and production terms in the v equation (6) and (12). The cross-diffusion term is also affected by s v2 in the v equation (6).…”

Section: Summary Observationsmentioning

confidence: 91%

“…The existing literature shows that the interactions between this pair of vortices and the flow over the slant have been studied in great detail using both experimental (e.g. in previous works [33][34][35][36][37][38][39][40][41] to name but a few) and computational (see, e.g., in previous works, 6,34,[41][42][43][44][45][46] again to name but a few studies) approaches. The original publication of Ahmed et al 24 suggests that at the 30°slant angle, the body incurs the maximum drag penalty.…”

Section: Ahmed Bodymentioning

confidence: 99%

“…This is partly because, per equations (4) and (10), the value of b Ã directly influences the value of turbulence viscosity, n t , which then implicates computations of the Reynolds stress tensor, Àu i u j , see equation (3). In addition, b Ã also affects the dynamics of the v-transport equation (see equations (6), (9), and (11)- (14)). However, because of this complexity and interdependence, it is rather difficult to narrow down the root cause further about this dependence on b Ã of the SST flow predictions.…”

Section: Naca 4412 Airfoilmentioning

confidence: 99%

“…Changes in the b Ã coefficient proved to be the greatest influence in the lift and drag predictions as well as showed some nonphysical results in the Ahmed body study. The strong influence of b Ã 's may be due to the fact that it appears in both transport equations, the dissipation term in the k equation 5, the production term in the v equations (6) and (12), and the blending function F2 (10). The coefficients s k1 and s k2 , on the other hand, were seen to have minor effects on the lift and drag predictions.…”

Section: Summary Observationsmentioning

confidence: 99%

“…In general, variants of the k − ϵ and k − ω models are among the most commonly used RANS turbulence modeling approaches for automotive applications, see Ashton et al 4 and Zhang et al 6 to name but a few. The standard k − ϵ model, SKE hereinafter, was originally proposed by Jones and Launder 7 ; variants of this model are arguably the most widely used turbulence modeling approach in various engineering applications.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improved CFD prediction of flows past simplified and real-life automotive bodies using modified turbulence model closure coefficients

Bounds

Zhang

Uddin

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

View full text Add to dashboard Cite

In spite of its shortcomings, faster turnaround time and cost-effectiveness make the Reynolds-averaged Navier–Stokes modeling approach still a popular and widely used methodology in many industrial applications, including the automotive industries in general, but the motorsports sector in particular. Existing literature suggests that all Reynolds-averaged Navier–Stokes models generally fail to predict pressure and velocity flow fields with a reasonable accuracy, especially in the vehicle wake region. Recent numerical works suggest that, when using two-equation eddy viscosity turbulence models, improved correlation between the experiment and computational fluid dynamics is not achievable through only mesh refinements or adding additional corrective terms in the turbulence transport equations, and additional efforts are necessary for better predictions. In this backdrop, the prediction improvement strategy adopted in this paper is based on the realization that the turbulence model closure coefficients are normally specified as constants and their values are determined from either a single observation or a simple functional form is assumed and that these coefficients are constructed and/or constrained to behave correctly in extremely limiting circumstances. Subsequently, this study investigated the influence of a few selected turbulence model closure coefficients on the veracity of computational fluid dynamics predictions by analyzing simulations run with turbulence model closure coefficient values that were different from the commonly used default ones. This was done by first investigating the individual effect of each model parameters on the prediction veracity, and then a combination of model closure coefficient values was formulated in order to obtain a prediction with the best experimental correlation. This procedure was applied to four different test objects which include NACA 4412 airfoil at 12° angle of attack, the 25 and 35° slant angle Ahmed body, and a full-scale sedan type passenger vehicle. The shear-stress transport [Formula: see text] was chosen as the turbulence model because of its popularity in automotive applications. It was observed that, irrespective of the test model, the computational fluid dynamics predictions are somewhat independent of some closure coefficients, while the prediction showed strong dependencies on certain model constant values, especially those in the dissipation equation. The present study demonstrated that, by using a modified set of closure coefficient values, very well correlated computational fluid dynamics predictions were possible. The findings from this study simply reiterate a 20-year-old conclusion by Stephen Pope that the closure coefficients for a model are not static and universal but instead are dynamic and must be calibrated for specific flow situations.

show abstract

Section: Summary Observationsmentioning

confidence: 91%

Section: Ahmed Bodymentioning

confidence: 99%

Section: Naca 4412 Airfoilmentioning

confidence: 99%

Section: Summary Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved CFD prediction of flows past simplified and real-life automotive bodies using modified turbulence model closure coefficients

Bounds

Zhang

Uddin

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

View full text Add to dashboard Cite

show abstract

Numerical investigation on the temperature distribution inside the engine compartment of a fuel cell vehicle with nanofluids as coolant

Tao

Chen

et al. 2021

Int J Energy Res

View full text Add to dashboard Cite

Proton exchange membrane fuel cells have a development prospect to replace the internal combustion engines due to their sustainability and environmental friendliness. However, one of the challenges is the requirement of heat removal from fuel cell engines and keep operation at relatively low temperatures (ie,~60 C-80 C). In the current research, the thermal performance of zinc oxide (ZnO) and boron nitride (BN) nanofluids is investigated by numerical simulation inside the engine compartment for a fuel cell vehicle. The maximum heat transfer rate was obtained by theoretical calculation and onedimensional simulation. The results revealed that the maximum heat transfer rate for ZnO nanofluids and BN nanofluids had increased by 7.6% and 2.0% than 50% water and ethylene glycol (WEG), respectively. Compared to 50% WEG, the energy consumption of the pump and fan could be further reduced with the nanofluids as a coolant. The influence on the temperature field and flow field inside the engine compartment of fuel cell vehicle is revealed with nanofluids by computational fluid dynamic. The results could provide data for the manufacturers to use nanofluids as a coolant to optimize the structure arrangement inside the engine compartment of fuel cell vehicle.

show abstract

Overall Heat Transfer Coefficient of Different Coolants and Frontal Air Velocity in Automotive Radiators

Manan

Anuar

Khalid

et al. 2022

Progress in Engineering Technology IV

View full text Add to dashboard Cite

Full vehicle CFD investigations on the influence of front-end configuration on radiator performance and cooling drag

Cited by 49 publications

References 28 publications

Improved CFD prediction of flows past simplified and real-life automotive bodies using modified turbulence model closure coefficients

Improved CFD prediction of flows past simplified and real-life automotive bodies using modified turbulence model closure coefficients

Numerical investigation on the temperature distribution inside the engine compartment of a fuel cell vehicle with nanofluids as coolant

Overall Heat Transfer Coefficient of Different Coolants and Frontal Air Velocity in Automotive Radiators

Contact Info

Product

Resources

About