2007 Under-hood Thermal Simulation

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SAE TECHNICALPAPER SERIES 2007-01-4280

Under-hood Thermal Simulation of a Class 8 Truck

Clinton L. Lafferty Volvo Group North America

Ales Alajbegovic and Kevin HorriganExa Corporation

Commercial Vehicle EngineeringCongress and Exhibition

Rosemont, IllinoisOctober 30-November 1, 2007

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ABSTRACT

A validation study was performed comparing the simulation results of the Lattice-Boltzmann Equation (LBE) based flow solver, PowerFLOW, to cooling cell measurements conducted at Volvo Trucks North America (VTNA). The experimental conditions were reproduced in the simulations including dynamometer cell geometry, fully detailed under-hood, and external tractor geometry. Interactions between the air flow and heat exchangers were modeled through a coupled simulation with the 1D-tool, PowerCOOL, to solve for engine coolant and charge air temperatures. Predicted temperatures at the entry and exit plane of the radiator and charge-air-cooler were compared to thermocouple measurements. In addition, a detailed flow analysis was performed to highlight regions of fan shroud loss and cooling airflow recirculation. This information was then used to improve cooling performance in a knowledge-based incremental design process.

INTRODUCTION

Computational fluid dynamics (CFD) along with other virtual design methods can significantly increase development process efficiency. The benefits of these tools for design processes in the mainstream automotive industry are extensively documented through a wide range of popular and academic literature. Even with such a proven record for providing a positive impact to product development, adding these virtual design methods to an internal process can be difficult. This is due to the established internal development practices as well as engineering culture. A key to gaining acceptance of new methods is continued demonstration of their capability, accuracy, and associated cost savings.

Volvos entry into the owner/operator segment of the Class 8 on-highway truck market in 2004 with VT 880 provided a number of product development challenges, especially in the area of engine cooling. During the original 2004 development of the VT 880, flow simulations gave engineers direction on sizing and

positioning the cooling fan as well as the influence of downstream obstructions. EPA US07 emission regulations contributed to additional challenges related to vehicle cooling.

Meeting these challenges required utilizing well validated virtual tools; this meant comparing multiple points of data from a physical prototype test with corresponding numerical simulations. Correlation provided that the design method improved final vehicle performance and reduced the design cycle length.

Previous studies validating computational fluid dynamics simulations against test data for the heavy-duty on-highway (Class 8) trucks/tractors were limited. Nobel and Jain [1] compared CFD simulations using a commercial software package with test data for radiator heat rejection, which was within 4%, Charge-Air-Cooler (CAC) outlet within 9C, and cooling fan air flow performance with a maximum 5% deviation. Siqueira etal. [2] discussed results related to the second objective regarding utilizing qualitative CFD results to enhance the development process, such as airflow distribution to the passenger side of the vehicle and observed recirculation zones within the chassis.

Similar correlation studies in the automotive industry can be found much earlier. Andra et al. [3] used CFD to correlate predictions with wind tunnel data for cooling system mass-flow rates. They determined the influence of geometry and boundary simplifications on the resulting air mass-flow rates. Knaus et al. [4] compared two finite-volume based CFD codes finding that one of them had a maximum cooling air mass flow deviation of 13% across a wide range of vehicle operating conditions. Even though the other code was less accurate; simulations could be completed with 28% less simulation effort while providing correct trends for the development process. Fortunato et al. [5] took a novel approach of linking a LBE and finite-volume CFD codes utilizing the advantages of each code to shorten time required to provide results to an underhood thermal development process. The Lattice-Boltzmann code possessed strengths in handling multiple CAD surfaces while the finite-volume code encompassed several

2007-01-4280

Under-hood Thermal Simulation of a Class 8 Truck Clinton L. Lafferty

Volvo Group North America

Ales Alajbegovic and Kevin Horrigan Exa Corporation



advanced physics models. Calculated versus measured temperatures were within a maximum deviation of 10C, which was considered satisfactory.

Utilizing a common finite-volume code, Ding et al. [6] compared the flow results for simplified and full vehicle models and their influence on vehicle drag. Relative results were presented for changes in the front grille meshing processes, effect of ducting on the cooling system aerodynamic drag contribution, and the influence of thermal recirculation on vehicle climate performance. Data comparison showed that the cooling system drag coefficient was 0.020 compared to wind tunnel measurements ranging between 0.025 0.030 and that the air conditioner condenser inlet temperature was 5C lower than measured in the wind tunnel.

Alajbegovic et al. [7] conducted a validation study of the Lattice-Boltzmann equation solver coupled with a one-dimensional (1D) tool, using a similar simulation approach as in the present study, on a sport utility vehicle. The predicted radiator top tank temperature was 1C higher than the measured value. Also, Alajbegovic et al. [8] showed good correlation between the measured and predicted aerodynamic parameters and radiator inlet face temperatures.

Previous studies involved one or two key cooling system performance parameters for model validation. This work attempts to use multiple parameters for the validation of the simulation approach for cooling system performance predictions.

OBJECTIVES

This present work has two major focus areas, both utilizing CFD methods in the development of a Class 8 truck/tractor cooling system.

1. Perform a model validation to build confidence into the product development environment by predicting key cooling performance temperatures within a 1 to 3C target window allowing for both measurement and computational errors.

2. Demonstrate examples of utilizing in-depth quantitative and qualitative flow analysis techniques to improve design concepts during early periods of vehicle development.

METHODOLOGY

Validation of the simulation model for the Volvo VT 880 centered on three major areas:

1. CFD modeling with LBE CFD solver coupled to a 1D heat exchanger tool.

2. Collection of operating data at the Volvo Trucks North America (VTNA) chassis dynamometer cell in Greensboro, North Carolina.

3. Comparison and discussion of the CFD results and dynamometer test data.

CFD PROCEDURE

The virtual representation of the geometry included a detailed vehicle model (Figure 1) as well as a simplified geometric test cell representation. An LBE CFD solver, PowerFLOW was utilized to simulate the airflow and temperature distribution in the entire test cell domain including the engine compartment and vehicle exterior. PowerCOOL coupled with the flow field simulation performed heat exchanger performance calculations of radiator top and bottom tank temperatures as well as the charge-air-cooler heat rejection and charge air outlet temperature.

Figure 1. Overview of the detailed Class 8 tractor model geometry.



Mathematical Model

Lattice-Boltzmann Equation (LBE) solvers are numerically very efficient and robust. The increased numerical efficiency allows handling of lattices with very large voxel (or element) counts. Properties of the Boltzmann equation allow for an improved treatment of fluid interaction with the wall surface. Surfels, surface elements, are designed as active elements that interact with the neighboring lattice elements. The combination of both large lattices and dynamic surface treatment allow accurate representation of surfaces without the need for geometry simplification.

The use of Lattice-Boltzmann equation in fluid flow simulations was demonstrated by Frisch, Hasslacher, and Pomeneau [9]. Since then, considerable effort was invested into the development of a Lattice-Boltzmann flow solver and several reviews were presented in the recent past [10, 11]. Turbulence effects are modeled using a modified k-H model based on the original RNG formulation [12, 13]. This LBE based description of turbulent fluctuation carries flow history and upstream information, and contains high order terms to account for the nonlinearity of the Reynolds stress [13]. This is contrasted with typical Navier-Stokes solvers, which tend to use the conventional linear eddy viscosity based on the Reynolds stress closure models.

Turbulence and temperature equations are solved on the same lattice using a modified Lax-Wendroff-like explicit time marching finite difference scheme. Simulations presented in this work were performed using the solver described in the following references [14, 15, 16, 17, and 18].

By using detailed fan blade geometry in combination with the multiple-reference-frame (MRF) fan model approach, an accurate prediction of the fan operating point was made. MRF approaches remove limitations with momentum source approaches which depend on the fan performance curves to be taken from similar vehicle installation and operating conditions (vehicle restriction points and forward travel speeds). Internal validation has shown the MRF formulation to predict fan performance within 5% of measured data.

Input data for the heat exchanger model was obtained from the cooling package heat exchanger manufacturer, Figure 2 and Figure 3. Measured thermal characteristics of the heat exchangers are an important input to the cooling airflow simulations. The cooling air pressure drop across heat exchangers such as radiators, charge-air-coolers or condensers are modeled as porous media by Darcys Law [19]. Heat transfer between the air and heat exchangers is governed by the heat transfer coefficient, a measured parameter. Heat transfer coefficients are measured as a function of the air and coolant mass flow rates. The measured values can be interpolated using the sandwich formula which relates the heat transfer coefficient, Htc, to the coolant and air mass flow rates:

1

1 1a h c

a c

Htc

K D Km m

D E

(1)

where am is air mass flow rate, cm is coolant mass flow rate, and aK , cK , hD , D , E are the interpolation coefficients that are calculated from the experimental data using Monte-Carlo interpolation.

The heat transfer between cooling airflow and heat exchangers was modeled using the 1D-tool, PowerCOOL [18, 20]. The input parameters for the radiator and charge-air-cooler operation are shown in Table 1, Figure 2, and Figure 3. For both components the internal mass flow rates were provided. A fixed heat rejection value was given to the radiator and the coolant inlet temperature and temperature drop were calculated. CAC heat rejection and charge air outlet temperature were predicted given a fix inlet temperature.

Figure 2. Normalized heat exchanger cooling air static pressure drop curves.

Overview of CFD methodology

The CFD methodology can be divided into seven general steps which are covered in the following sections:

1) Export of native CAD data to IGES format for multiple sub-assemblies.

2) Import of IGES files into a commercial surface meshing and repair software.

a. Manual cleaning and repair of critical surfaces.

b. Initial surface triangulation for simplification.

3) Surface wrapping with a commercial tool to create a manifold surface for non-critical sub-assemblies.

4) Case or model file setup. a. Application of boundary and initial

conditions for both engine performance and test cell environment.

Cooling Air Static Pressure Drop

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Component Massflow / Max. System Massflow

Sta

tic

Pre

ssu

re D

rop

/ M

ax.

Sys

tem

Pre

ssu

re D

rop

RADCACAC COND



b. Definition of voxel size regions (VRs). c. Definition of heat exchangers and other

porous medias.5) Fully automated discretization (or volume

meshing).6) Simulation with LBE solver. 7) Data analysis with graphical and text-based

tools.

Geometry Preparation

The IGES files from Pro/E were converted to tessellated surfaces using a commercially available surface meshing tool [21]. Surfaces, critical to the accuracy of the analysis, such as the vehicles hood and cab as well as the fan blade, fan ring, and fan shroud were prepared by manual surface meshing techniques. Less critical surfaces such as the engine and chassis suspension elements were triangulated, then surface wrapped (enclosure in a manifold set of surface triangles) using a commercial software package [22]. Geometry preparation from raw CAD data to surfaces ready for CFD simulations was less than 5 man-days. PowerFLOW only requires that each part/assembly is closed; the domain can be composed of multiple water tight surfaces instead of a single water-tight surface that is required for many finite-volume codes. As a result, the case file setup time is limited to a single man-day. Generation of the lattice (or volume mesh) was fully automated, and required no user intervention after which the model was directly submitted to a multiple node Linux cluster for simulation.

a.)

b.)Figure 3. Normalized a.) radiator and b.) CAC heat

exchanger performance.

Boundary and Operating Conditions

As mentioned earlier the increased level of detail in both the geometric and mathematical models has reduced the need for all but the most basic test cell ambient and vehicle engine operating data which is shown in Table 1. This data was taken from the chassis dynamometer tests, since the upfront simulations were completed with significantly higher engine operating parameters.

Operating Conditions Engine Speed (RPM) 1200 1600 Ambient Temp (C) Per Test Code Atm. Pressure (kPa) 99.2

Boundary Conditions Ram Air Speed (kph) 55 55 Fan Speed (RPM) 1480 1962 Radiator Heat Rej (% of RAD+CAC) 77% 73% Coolant Flowrate (kg/s) 5.4 7.3 CAC Inlet Temp - Amb (C) 183.8 203.9 CAC Flowrate (kg/s) 0.33 0.48 AC Condenser Heat Rejection (kW) 12 12

Table 1. Operating and boundary conditions for PowerFLOW CFD model.

Solving Procedure

After preparation of the simulation case, the model was submitted to a remote computing cluster for automatic generation of the lattice (discretization) and subsequent simulation. The discretization occurred on a 4 processor AIX computer with 50 gigabytes of RAM and resulted in a lattice with the volume and surface element counts shown in Table 2.

Lattice Statistics Number of volume elements (voxels) (M) 81 Number of surface elements (surfels) (M) 17 Memory required for simulation (Gb) 37

Table 2. Lattice size statistics.

The simulation occurred on a 124 processor Linux cluster with dual core 2.4GHz AMD Opteron processors. The simulation used initial conditions from the results of a previous simulation with coarser resolution and isothermal heat exchangers. This has been shown to significantly reduce overall simulation time in that it provides a better starting point for the momentum field of the fine simulation. The stopping point for the simulation was determined by monitoring air flow rates through each heat exchanger, inlet and outlet air temperatures, as well as coolant temperatures. All key quantities settled after 120,000 time steps or about 7000 CPU-hours. Simulations with similar operating conditions on other trucks generally have a computational expense in the range of 3000 to 8000 CPU-hours.



VTNA CHASSIS DYNAMOMETER PROCEDURE

Vehicle cooling system performance testing was conducted according to a proprietary test procedure [23]. The following quantities were measured:

x Ambient temperature (C) x Ambient atmospheric pressure (kPa) x Average front grille temperature (C) x Average Charge-Air-Cooler (CAC) cooling air inlet

temperature (C) x Average radiator cooling air inlet temperature (C) x Average radiator cooling air exit temperature (C) x Radiator top tank coolant temperature (C) x Radiator bottom tank coolant temperature (C) x CAC inlet tank charge air temperature (C) x CAC outlet tank charge air temperature (C) x Coolant flow rate (LPM) x Engine intake (charge air) air mass flow rate (kg/s)

Test Instrumentation and Calibration

Temperature values were measured with Type K thermocouples, and generic thermocouple coefficients are utilized. Thermocouples measuring critical cooling system performance values such as the radiator and CAC inlet and outlet temperatures are calibrated using an oil bath.

Coolant flow rates were measured with a turbine style flow meter, and engine intake air measurements were gathered with a Venturi-type flow meter. Pressures were measured using diaphragm-type pressure transducers. Flow rate and pressure sensors are calibrated at a standard calibration laboratory.

The average of two thermocouples measurements (usually within 0.1C) positioned near the tank/coolant pipe connection point provided radiator top and bottom tank temperatures. Only single thermocouples were utilized on the CAC inlet and outlet tanks. To measure the cooling package inlet temperature, the average of four thermocouples that were mounted to the back side of the inlet grille screen was taken. Cooling package core cooling air inlet and exit temperatures were measured using the mean of 9 uniformly spaced thermocouples. The instrumented cooling package is shown in Figure 4.

Test Procedures

The chassis dynamometer test cell resembles a semi-closed loop wind tunnel with a single roller configuration for applying load to the vehicles rear wheels (Figure 5) and a ram air tunnel capable of simulating varying forward travel speeds to the front grille of the vehicle. The facility utilizes an industry standard signal conditioning, conversion, and data collection system.

Vehicles are generally subjected to multiple operating conditions; however, only two points were selected for

the analytical model validation: 1200 and 1600 engine speeds (ERPM) both at full engine load. The 1200 ERPM point represents a worse case scenario; whereas 1600 ERPM represent a more common operating condition. At each operating point, the vehicle systems achieved steady state operation and then a series of measurements were taken over a set period of time. The average of these measurement periods were reported as the final test results. Temperature measurement stability was specified in the internal test procedure [23].

Figure 4. Thermocouple placement on the instrumented cooling package module.

Figure 5. Layout of VTNA chassis dynamometer test cell.

Test Vehicle

The vehicle chosen for the measurements was the Volvo VT 880, an on-highway Class 8 tractor (Figure 6). The vehicle was equipped with a 447 kW brake power (600 Hp) Volvo 16-liter engine, maximized frontal area cooling package, as well as a vendor fan drive and blade.



Figure 6. Volvo VT 880.

INITIAL CORRELATION RESULTS

Due to the LBE CFD solvers numerical stability and available best practices the first time yield (quality of the results from the first solving attempt) is generally very high. It usually reaches 100% with exceptions in cases of input data errors. Results shown in Table 3 represent the comparison between the measurement data and first simulation results. Differences between the data and predictions are calculated as the CFD prediction minus measured data value (i.e. a negative difference indicates that the CFD code under-predicted the measured value). Again, a key goal of this work was to establish correlation among several variables representing the entire cooling system instead of focusing just on one or two parameters.

At first inspection, the model validation results indicate good agreement with the measured data. However, those involved in cooling system performance prediction might be less impressed especially with the top tank temperature and average radiator cooling air exit temperatures. Regardless of the debate about sensor limitations or data dispersion, cooling system component responsible engineers and project management make decisions over differences less than 2C.

Initial differences in CFD results and test data Engine Speed (RPM) 1200 1600 Front Grille Exit Average (C) 0.4 1.0 Avg AC Condenser Temp Rise (C) -3.5 -4.8 Charge Air Out (C) -2.0 -1.4 CAC Heat Rejection (%) 2.6% 2.4% CAC Cooling Air Inlet Temp Avg (C) -2.0 -2.4 TopTank Temp (C) 4.3 2.9 Radiator Coolant Temp Drop (C) 0.0 0.0 RAD Cooling Air Inlet Temp Avg (C) -0.8 -1.0 RAD Cooling Air Exit Temp Avg (C) 7.9 5.0

Table 3. Differences between CFD predictions and chassis dynamometer test data.

For the accurate prediction of cooling system performance parameters, it is essential to determine the

correct cooling air mass flow rate. Extensive validation of the MRF model showed that it provides accurate predictions. The remaining issue affecting the mass flow rate is the resistance of the heat exchangers. The cooling package module contributes significantly to the total system resistance [24], and the mathematical model for the heat exchanger core uses measured data from the cooling package vendor. A common practice is to provide data from component wind tunnels, and this data may not be corrected for empty wind tunnel losses. Figure 7 shows the difference in corrected and uncorrected pressure loss curves.

Figure 7. Corrected heat exchanger cooling air static pressure drop curves.

After correcting the porous media coefficients for both the radiator and charge-air-cooler, the air flow through the cooling package increased over four percent. Radiator top tank prediction improved significantly; however, charge-air-cooler agreement decreased slightly. Results are shown in Table 4 for 1200 ERPM and Table 5 for 1600 ERPM.

Differences w/ Org

PM Coeff.

Differences w/ Corr. PM Coeff.

Engine Speed (RPM) 1200 1200 Front Grille Exit Avg (C) 0.4 0.4 Avg AC Condenser Temp Rise (C) -3.5 -3.6 Charge Air Out (C) -2.0 -2.3 CAC Heat Rejection (%) 2.6% 2.8% CAC Cooling Air Inlet Temp Avg (C) -2.0 -2.1 Top Tank Temp (C) 4.3 2.9 Radiator Coolant Temp Drop (C) 0.0 0.1 Radiator Cooling Air Inlet Temp Avg (C) -0.8 -1.1 Radiator Cooling Air Exit Temp Avg (C) 7.9 6.1

Table 4. Differences between CFD predictions and chassis dynamometer test data at 1200 ERPM, before

and after correcting porous media coefficients.



Differencew/ Org PM

Coeff.

Differencew/ Corr.

PM Coeff. Engine Speed (RPM) 1600 1600 Front Grille Exit Average (C) 1.0 1.0 Avg AC Condenser Temp Rise (C) -4.8 -4.9 Charge Air Out (C) -1.4 -1.9 CAC Heat Rejection (%) 2.4% 2.7% CAC Cooling Air Inlet Temp Avg (C) -2.4 -2.5 Top Tank Temp (C) 2.9 1.2 Radiator Coolant Temp Drop (C) 0.0 0.0 Radiator Cooling Air Inlet Temp Avg (C) -1.0 -1.6 Radiator Cooling Air Exit Temp Avg (C) 5.0 3.1

Table 5. Differences between CFD predictions and chassis dynamometer test data at 1600 ERPM, before

and after correcting porous media coefficients

DISCUSSION AND FOLLOW-UP ANALYSIS

From a numerical analysts perspective, excellent agreement exists between the test data and PowerFLOW results, except for the AC condenser temperature rise, CAC cooling air inlet temperature, and radiator cooling air inlet and exit temperatures. Example sources of error include the thermocouple calibration as well as error in matching the position of the thermocouple positions between physical test and simulation. Figure 8 shows the location of the thermocouples on the front of the CAC and radiator, marked with the plus (+) signs.

Measurements were taken utilizing thermocouple grids; however, each thermocouple wire extended approximately 10 mm from the grid structure. The final position during the test could have been slightly different due final test setup, instrumentation debugging, and for movement due to vehicle operation. Table 6 and Table 7 show the sensitivity of CFD measurement points varied 10mm around the original position coordinates and the impact on the difference between the mean for the nine grid measurement points and mean of CFD measurement points at 1200 and 1600 ERPM, respectively.

a.)

b.)Figure 8. Temperature measurement locations for a.)

radiator and b.) CAC from the CFD model.



OriginalPosition Y -10 Y +10 Z +10 Z -10

EngineSpeed 1200 1200 1200 1200 1200 Avg AC Condenser Temp Rise (C)

-3.6 -3.6 -3.6 -5.3 -1.2

CACCoolingAir Inlet Temp Avg (C)

-2.1 -2.1 -2.1 -3.2 -0.5

RadiatorCoolingAir Inlet Temp Avg (C)

-1.1 -1.6 -0.6 -0.9 -1.5

RadiatorCoolingAir Exit Temp Avg (C)

6.1 6.2 6.4 5.9 6.3

Table 6. 1200 ERPM thermocouple position sensitivity.

OriginalPosition Y -10 Y +10 Z +10 Z -10

EngineSpeed 1600 1600 1600 1600 1600 Avg AC CondenserTemp Rise (C)

-4.9 -4.9 -4.9 -6.2 -3.0

CACCooling Air Inlet Temp Avg (C)

-2.5 -2.5 -2.5 -3.4 -1.2

RadiatorCooling Air Inlet Temp Avg (C)

-1.6 -2.1 -1.0 -1.3 -1.7

RadiatorCooling Air Exit Temp Avg (C)

3.1 3.2 3.5 3.0 3.2

Table 7. 1600 ERPM thermocouple position sensitivity.

Based on the agreement obtained for other temperature comparisons, one may argue that the temperature grid on the CAC inlet face shifted downward 10mm during the actual test. Agreement between measured and CFD values for the CAC inlet face improved from -2.1 to -0.5C at 1200ERPM and from -2.5C to -1.2C at 1600ERPM. Also, note that the CAC inlet face measurements were sensitive to the temperature gradient created by the AC condenser. Varying the temperature measurement grid within the CFD results showed that the average radiator inlet or outlet temperatures had little sensitivity to measurement grid position.

PRODUCT DEVELOPMENT EVOLUTION USING SIMULATION RESULTS

Final model correlation is a last step in an analysis cycle. Just as vehicle programs begin with a few sketches and end with the market introduction and on-going product support, virtual simulations have a similar life cycle which today begins earlier in the vehicle development process. CFD simulations produce a significant amount of data and require significantly more resources for their creation. Utilizing both in-depth quantitative and qualitative CFD data analysis can increase engineering efficiency as well as the value of simulations to the development process. The time spent to do a detailed analysis of the flow field in the area of interest often leads to a logical choice for the optimized design. Examples of in-depth analysis during the initial development process were a modification to the fan-out- of-shroud (FOOS) depth as well as recirculation shield improvements.

A key metric for cooling system performance is a change in the radiator top tank temperature (assuming fixed engine heat rejection). Modification 1 to the fan out of shroud distance increased the top tank temperature by 0.5C, indicating degradation in performance. Analysis showed that the radiator cooling air flow had decreased by 1.2% due to changes in the fan operating point. Utilizing qualitative flow field analysis revealed that Modification 1 had changed the blade tip flow structure as shown in Figure 9 in an unfavorable way resulting in less efficient air handling.

a.)

b.)Figure 9. a.) Original FOOS position compared to increase

in blade tip flow due to b.) Modification 1 to fan out of shroud distance.



While a target for this phenomenon has not been established, it can now be compared between other design revisions or other vehicle platforms.

Finally, Figure 10 shows the qualitative particle traces from an earlier prototype concept and production vehicle showing cooling air recirculation paths that were reduced in the final production parts. The red particle traces on the lower right side of cooling package in Figure 10a have been reduced in Figure 10b. Previous testing has shown that reducing recirculation improves cooling performance.

a.)

b.)Figure 10. Improvements in recirculation shield from a.) early prototype concepts to b.) final production release.

CONCLUSIONS

A general methodology was defined utilizing a LBE CFD solver coupled with a 1D heat exchanger tool. This approach resulted in radiator top tank temperature predictions within 5C of measured values while requiring less than 10 man-days to complete the initial baseline case and 1 - 2 days for subsequent design revisions or CFD model improvements.

Initial model validation showed with basic heat exchanger input data top tank temperature were over-predicted by 4.3C at 1200 ERPM and 2.9C at 1600ERPM. Charge air outlet temperature was under-predicted by approximately 2C at both 1200 and 1600 ERPM. Adding corrections to the input data for the component wind tunnel losses improved the correlation results. Top tank temperature prediction improved by 1.4C and 1.7C at 1200 and 1600 ERPM, respectively; however, CAC charge air outlet temperature predictions were made worse by less than 1C. Agreement was achieved between simulation and test within the 1 to 3C target window for the two major factors: radiator top tank and CAC charge air outlet temperatures. Cooling air temperature grid sensitivity studies indicated that additional improvements could be made on the AC condenser temperature rise (from an under-prediction of 3.6C to 1.2C at 1200ERPM) and CAC cooling air inlet temperature (from 2.1C to 0.5C CFD under-prediction at 1200ERPM) by shifting the CFD measurement points downward (-Z) 10mm. Similar trends were observed at 1600ERPM reaffirming the challenges associated with measuring individual temperature locations in high gradient regions.

Finally, the accurate prediction of absolute, quantitative results allows engineers to make more definitive decisions; moreover, the use of relative quantitative as well as qualitative data and 3D results visualization also led to design improvements during the early phases of vehicle development while many sub-systems and engine parameters were not fully defined.

ACKNOWLEDGMENTS

The authors thank the VTNA Complete Vehicle (CV) department for providing the test results and practical background on the chassis dynamometer testing procedure.

Also, the authors are thankful for help from the physics and software groups at Exa Corporation responsible for the development of thermal functionality in the PowerFLOW code. Development of the hybrid code for thermal management in PowerFLOW was supported by the National Science Foundation under the Grant DMI-0239176.



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2007 Under-hood Thermal Simulation

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