2008-01-0735l

2008-01-0735

CFD Simulations of an Automotive HVAC Blower: Operating under Stable and Unstable Flow Conditions

Moulay Bel-Hassan and Asad SardarAir International Thermal Systems

Reza GhiasANSYS Inc.

Copyright © 2007 SAE International

ABSTRACT

Computational Fluid Dynamics (CFD) is heavily used in automotive HVAC industry in order to reduce the time and cost in design, optimization, and development of different components [2]. Correct prediction of the aerodynamic characteristics of an HVAC blower is crucial in development of the accurate CFD models for the whole HVAC system. CFD models are extensively used in the optimization of both thermal and airflow characteristics of automotive HVAC [3-5].

In this study we have performed CFD simulations for different blower operating conditions in order to assess the CFD results in prediction of the aerodynamic performance in an automotive HVAC forward curved (FC) centrifugal blower. The realizable k-ε turbulence model was used on the Reynolds Averaged Navier-Stokes approach to model complex flow field properly.

Steady state analysis showed good correlation for the stable flow conditions (high airflow and low pressure), whereas this approach showed large discrepancies for unsteady flow conditions (low airflow and high pressure). By a transient simulation and realizable K- model, the CFD analysis showed good correlation compared to experimental test results.

INTRODUCTION

A typical HVAC system includes an air conditioning module comprised primarily of a blower, evaporator, heater core, doors, and actuators; an electronic control head; a compressor; a condenser; a thermostatic expansion valve or orifice tube and associated plumbing. The primary function of the HVAC system is to provide heated and cooled air to the passenger compartment for passenger comfort and to maintain clear vision (both defogging and deicing) from the windshield and side windows. Air is supplied by the blower and delivered to the passenger through the duct via the HVAC casing and heat exchangers [1].

In the last decade, the automotive air-conditioning (AC) system market has increased rapidly. Due to this widespread use, large-scale production and cost-effectiveness have been the primary drivers influencing the automotive system design. Although these drivers remain, focus on the improvement of the overall system performance increases rapidly. Therefore CFD simulations of the internal flow in AC Units, as well as experimental investigations have received more attention in recent years. Some of the key performance issues (e.g., evaporator performance, evaporator freeze control, total airflow, airflow, and temperature distribution) are related to the blower. Better understating of the flow field characteristics and turbulent structure in such components can lead to the development of AC systems that provides higher thermal and flow performances and significant noise level reduction.

HVAC system resistance typically varies from low in panel full cold mode (maximum torque load; high airflow) to high in floor full hot (low torque load; low airflow). Characterizing the flow in the maximum torque conditions is essential for blower motor selection, and thereby determines the maximum power consumption. Toward the other end of the spectrum, high pressure and low airflow operating conditions also need to be properly characterized since it determines the maximum amount of airflow available for restrictive heater modes (further restrictions when adding filters electric heaters). Additionally, for enhanced end users comfort, OEMs require multiple airflow and temperature detent settings. In order to satisfy the various blower operating conditions of the automotive HVAC, complete blower performance curve needs to be characterized (i.e., simulated). Table 1 presents the customer specifications in terms of the blower speed and the corresponding airflow for different HVAC operating modes (i.e. various flow system resistance characteristics). HVAC is required to perform under a wide range of blower speeds and airflow rates.

Table 1: Airflow (l/s) quantities for a typical blower setting. Mi is the blower speed ranging from high to low. OAS is for outside air.

Air Delivery

Inlet Air

Temp Door

M5 M4 M3 M2 M1 Low

Vent OSA FC 107 93 76 58 38 17Bi-level OSA 50 % 124 107 94 71 46 21Floor OSA FH 78 71 53 38 25 10Defog OSA FH 79 70 56 40 25 9Defrost OSA FH 71 54 42 32 21 9

NUMERICAL SIMULATIONS

FLUENT V6.3 - Realizable K- model was used as a solver in this study. Flow field was assumed steady state incompressible (except for one case) and a second order implicit finite volume scheme was used to solve three dimensional Reynolds Averaged Navier-Stokes (RANS) equations for all cases. In the following sections some details about turbulence models and numerical set up are described.

TURBULENCE MODEL

The simplest "complete models'' of turbulence are two-equation models in which the solution of two separate transport equations allows the turbulent velocity and length scales to be independently determined. The standard K- model falls within this class and has become the workhorse of practical engineering flow calculations in the time since it was proposed by Launder and Spalding [10]. Robustness, economy, and reasonable accuracy for a wide range of turbulent flows explain its popularity in industrial flow and heat transfer simulations. It is a semi-empirical model, and the derivation of the model equations relies on phenomenological considerations and empiricism.

As the strengths and weaknesses of the standard model K- have become known, improvements have been made to the model to improve its performance. Two of these variants are available in FLUENT: the RNG K- model [11] and the realizable K- model [9].

The standard K- model [10] is a semi-empirical model based on model transport equations for the turbulence kinetic energy (K) and its dissipation rate (). The model transport equation for K is derived from the exact equation, while the model transport equation for was obtained using physical reasoning and bears little resemblance to its mathematically exact counterpart.

In the derivation of the K- model, the assumption is that the flow is fully turbulent, and the effects of molecular viscosity are negligible. The standard K- model is therefore valid only for fully turbulent flows.

The realizable K- model [9] is a relatively recent developed model and differs from the standard K- model in two important ways:

The realizable K- model contains a new formulation for the turbulent viscosity.

A new transport equation for the dissipation rate, , has been derived from an exact equation for the transport of the mean-square vorticity fluctuation.

One of the weaknesses of the standard K- model or other traditional K- models lies within the equation that is used to model the dissipation rate (). The well-known round-jet anomaly (named based on the finding that the spreading rate in planar jets is predicted reasonably well, but prediction of the spreading rate for axisymmetric jets is unexpectedly poor) is considered to be mainly due to the modeled dissipation equation.

The realizable K- model proposed by Shih [7] was intended to address these deficiencies of traditional K- models by adopting the following:

A new eddy-viscosity formula involving a variable C originally proposed by Reynolds [8].

A new model equation for dissipation () based on the dynamic equation of the mean-square vorticity fluctuation

The term "realizable'' means that the model satisfies certain mathematical constraints on the Reynolds stresses, consistent with the physics of turbulent flows. Neither the standard K- model nor the RNG K- model is realizable [2].

An immediate benefit of the realizable K- model is relatively accurate prediction of the spreading rates in both planar and round jets. It is also likely to provide superior performance for flows involving rotation, boundary layers under strong adverse pressure gradients, separation, and recirculation.

Both the realizable and RNG K- models have shown substantial improvements over the standard K- model where the flow features include strong streamline curvature, vortices, and rotation. Since the model is still relatively new, it is not clear in exactly which instances the realizable K- model consistently outperforms the RNG model. However, initial studies have shown that the realizable model provides the best performance of all the K- model versions for several validations of separated flows and flows with complex secondary flow features [9,12].

One limitation of the realizable K- model is production of the non-physical turbulent viscosities in situations when the computational domain contains both rotating and stationary fluid zones (e.g. multiple reference frames, rotating sliding meshes). This is due to the fact that the realizable K- model includes the effects of mean rotation in the definition of the turbulent viscosity. This extra rotation effect has been tested on single

rotating reference frame systems and showed superior behavior over the standard K- model. However, due to the nature of this modification, its application to multiple reference frame systems should be taken with some caution.

The realizable K- model has been extensively validated for a wide range of flows [9-12], including rotating homogeneous shear flows, free flows including jets and mixing layers, channel and boundary layer flows, and separated flows. For all these cases, the performance of the model has been found to be substantially better than that of the standard K- model. It is worth to note that the realizable K- model resolves the round-jet anomaly i.e. it predicts the spreading rate for axisymmetric jets as well as that for planar jets.

MULTIPLE ZONES APPROACH

Numerical simulations of multiple moving parts or stationary surfaces which are not surfaces of revolution have encounter challenges. For these problems, one must break up the model into multiple fluid/solid cell zones, with interface boundaries which separate the zones. Zones with moving components can then be solved using the moving reference frame equations, whereas stationary zones can be solved with the stationary frame equations. The manner in which the equations are treated at the interface lead to two approaches which are supported in FLUENT [2]:

Multiple Rotating Reference Frames (MRF). Sliding Mesh Model (SMM).

MRF is a steady-state approximation in which individual cell zones move at different rotational and/or translational speeds. The flow in each moving cell zone is solved using the moving reference frame equations. If the zone is stationary, the stationary equations are used. At the interfaces between cell zones, a local reference frame transformation is performed to enable flow variables in one zone to be used to calculate fluxes at the boundary of the adjacent zone. MRF is relatively simple and more efficient than SMM approach. It should be noted that the MRF approach does not account for the relative motion of a moving zone with respect to adjacent zones (which may be moving or stationary); the grid remains fixed for the computation. This is analogous to freezing the motion of the moving part in a specific position and observing the instantaneous flow-field with the rotor in that position. Hence, the MRF is often referred to as the "frozen rotor approach."

While the MRF approach is clearly an approximation, it can provide a reasonable model of the flow for many applications. For example, the MRF model can be used for turbomachinery applications in which rotor-stator interaction is relatively weak, and the flow is relatively uncomplicated at the interface between the moving and stationary zones. In mixing tanks, for example, since the impeller-baffle interactions are relatively weak, large-

scale transient effects are not present and the MRF model can be used.

Another potential use of the MRF model is to compute a flow field that can be used as an initial condition for a transient sliding mesh calculation. This eliminates the need for a startup calculation. The multiple reference frame model should not be used, however, if it is necessary to actually simulate the transients that may occur in strong rotor-stator interactions, the sliding mesh model alone should be used.

The sliding mesh model approach is, on the other hand, inherently unsteady due to the motion of the mesh with time. In this approach, motion of stationary and rotating components in a rotating machine will give rise to unsteady interactions. These interactions are generally classified as follows:

Potential interactions: flow unsteadiness due to pressure waves which propagate both upstream and downstream.

Wake interactions: flow unsteadiness due to wakes from upstream blade rows, convecting downstream.

Shock interactions: for transonic/supersonic flow unsteadiness due to shock waves striking the downstream blade row.

The sliding mesh model accounts for the relative motion of stationary and rotating components and make it possible to model the unsteady interaction effects which are neglected in MRF approach.

MESH GENERATION

The surface mesh was generated in Unigraphics (UG).The surface mesh quality was improved using in ICEM-CFD and volume mesh was created in Tgrid. Arbitrary interface was created between moving and stationary grids. The mesh consisted of a total of 3,800,000 tetrahedral cells including 2,650,000 cells in rotating zone. Figure 1 shows the created mesh that was used for this study.

BOUNDARY CONDITIONS

The Steady state calculation using the K- realizable and multiple rotating reference frame model were used for high airflow regimes (3023, 3290, 3612 rpm). For higher blower speeds and low airflow (3923 rpm, 50 cfm), the MRF model failed to deliver good results due to high turbulence and airflow instabilities. The sliding Mesh model was employed to simulate this kink of flow. A zero pressure inlet was used at the inlet boundary. The outlet pressure was derived from the experimental data. A logarithmic law of the wall relative to frame is used in moving rotor surfaces.

Figure 1: Shows the mesh in different parts of the blower.

It is worth to mention that the outlet boundary was extended in order to reduce the effect of the numerical reflections from outlet on the flow field. All steady state

simulations were begun using a first order upwind scheme for pressure, momentum, turbulent equations and after converging, they were switched to the second order upwind.

This time step in unsteady calculations was related to the rotational speed of the impeller and was small enough to get the necessary time resolution and to capture the phenomena due to the blades passage and their interactions with the volute casing wall [13].

BLOWER TESTING SETUP

The selected blower for this study was a single inlet forward curved (FC) centrifugal fan having a squirrel cage wheel. Table 2 represents the geometric characteristics of this blower.

Table 2: The geometric characteristics of the blower.

WheelFan inlet diameter 127 mmFan outlet diameter 160 mmFan width 80 mmNumber of blades 43Inlet blade angle 23 degOutlet blade angle 60 degBlade chord length 17.6 mmPitch to chord ratio 66 %ScrollCasing width 85 mmCut-off edge distance 13 mmExpansion angle at s=210deg 5.8 degDiameter of inlet opening 137.5 mm

In order to determine the fan performance, the blower and scroll are attached to an FM600 airflow test bench which is dual chamber AMCA certified design (see Figures 2 and 3). The test set up and measurements of blower aerodynamic performance were made according to ANSI/AMCA Standard 210-85 [6].

The blower inlet configuration was a “free type”; that is open to atmosphere. The fan total pressure was measured at the FM600 airflow chamber (averaged over 4 piezometric static pressure tabs). The volumetric airflow was measured in the dual chamber using ASME certified flow nozzles. The test blower was powered using the DC Power supply. The rotational speed of the blower wheel was measured using a fiber-optic sensor connected to a digital display on the FM600 airflow bench. Signal pick was based on reflective tape attached to the rotating wheel.

The blower performance curves were obtained using fixed voltages (i.e., motor voltages selected were: 12V, 13V & 14V). Using the auxiliary FM600 airflow bench fan and damper-valve combination, the airflow was set for each data point (ranging from 50 cfm to 350 cfm), while the fan static/total pressure and blower rpm were

measured at each data point. Then current draw at each data point was obtained through a shunt. This was repeated for each data point until full airflow range was tested (from 50 to 350 cfm). The range of airflow testing at the various voltages tested allowed enough spread in blower rpm for matching different rpm and pressure conditions used in CFD analysis. The blower performance curves based on pressure versus airflow, current versus blower speed, and current versus airflow are shown in Figures 4, 5, and 6 respectively.

Figure 2: The blower and scroll attached to an FM600 airflow test bench.

Figure 3: Shows the locations for measurement of flow variables.

RESULTS AND ANALYSIS

Table 3 shows a comparison of the volume rates between test and numerical simulation in four blower speeds and outlet pressure. Last row shows the results for unsteady transient simulation.

The same tetrahedral mesh has been used for all cases. Also The MRF approach was employed for the first four

configurations, while SMM approach was used for configuration 5.

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Figure 4: Blower Performance: Pressure vs. airflow.

Ve Blower Performance Curves: free Inlet, no LPM 06-AIUS-TR-00558

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Figure 5: Blower Performance: Current vs. blower speed

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Figure 6: Blower Performance: Current vs. airflow

Simulations were carried out on 8-CPU, 2.4GHz Dual Core AMD Opteron cluster with a 64-bit operating system. It took about 8 hrs to perform 3500 iterations, in order to reach a constant mass flow rate at the outlet for

Airflow directionBlower Exit

12.5 in 29.5 in

4 Static Pressure Taps (piezometric)

Blower attaches here

Airflow Nozzles location


12.5 in 29.5 in





12.5 in 29.5 in




configurations 1-4 (MRF approach). Configuration 4 was used as an initial condition for configuration 5 with SMM approach. Using 50 time steps for each wheel rotation, it took about 25hrs to simulate five rotations of the wheel. This shows that SSM approach is a costly method for these kinds of applications and is only recommended for cases with unsteadiness.

For configurations 1, 2, and 3, CFD results show very good correlation with test results. The maximum difference of the total air flow from test and CFD is within 5%.

CFD result shows an overestimate in total airflow compared to the test results in configuration 4 (high pressure, high rpm), Generally forward curved centrifugal fans show an instability region (very high pressure, low airflow) within their blower performance curves [5,14,15]. This instability region is characterized by bi-stable pressure/airflow states, which lead to unsteady flow behavior. The steady state CFD simulations are not well suited to simulate these types of flows (i.e. flow rates that are much lower than at the best efficiency point). By switching to a transient simulation (configuration 5) and using the sliding mesh model, the CFD results were considerably improved (4% difference between test and CFD results). Figure 7 shows the total air flow results form test and CFD as a function of pressure. It shows clearly that results form CFD are match with corresponding one in test.

Table 3: Blower air flow rate Comparison.

Figure 8 shows the cross sections that have been used in post processing.

Figure 7: Blower performance of the blower based on test and CFD results.

Figure 9 shows static pressure, velocity vector, total pressure, turbulent kinetic energy, and turbulence intensity distributions at the mid section of the wheel for configuration 1. In the vicinity of the cut-off edge, overpressure is not that high compared to the other flow regions, but the circumferential pressure gradient is maximal in the front portion of the casing. Although the results represent the flow-field at the higher flow rate, the flow behavior appears much like the case at a low flow rate; the gradients along the casing channel are steep enough to cause complete flow reversal in the blade passage while they approach the cut-off. Except in this region, the static and total pressures inside the wheel are slightly uniform. This figure also shows that local regions of negative total pressure are apparent on the suction side near the leading edge. This is indicative of entry losses.

Typically a positive total pressure at the interior region of the wheel (blade inlet) is indicative of reverse flow. Comparing Figures 9 and 11 show that the region of positive total pressure on the blade inlet in low pressure flow (configuration 5), is much higher than in the high flow rate (configuration 1).

In addition to the reverse flow that comes from high to low flow rate, the turbulent kinetic energy (tke) CFD contours (see figures 9 and 11, sections 1 and 2) show that:

tke spreads over a large region circumferentially and axially in low flow rate configuration, whereas it is highly concentrated in the lower portion of the blade exit region and close to the scroll cut-off region in high flow rate configuration.

On the blade inlet region, in the low flow rate configuration, there is significant amount of tke, whereas there is negligible tke in the high flow rate configuration.

Figure 10 and Figure 12 show a larger flow blockage region in the lower half of the wheel in configuration 5 compared to configuration 1.

Test Results vs. CFD

Pressue increase (Pa)

Blower Speed (rpm)

Airflow (cfm) Test

Airflow (cfm) CFD

Test/CFD difference (%)

(1) Steady state - K- Realizable - second order

581 3023 350 363 4


756 3289 275 263 5


936 3612 176 173 2


1138 3923 50 284 468

(5)Transient sliding mesh - K- Realizable - second order

1138 3923 50 52 4

Blower Performance: CFD and Test Results Comparison

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Section 1: Circumferential Section

Section 2: Mid section

Section 3: Cut-off exit section

Figure 8: Sections of Post-processing

Based on the reverse flow and tke results, we can conclude that there is a higher level of unsteadiness in the low flow rate configuration. This conclusion is

consistent with previous experimental studies conducted on forward curved centrifugal fans [14].

The results indicate that an increase in flow blockage across the blades leads to higher recirculation and flow reversal, which contributes to higher turbulence levels and flow unsteadiness. This gives credence to why transient CFD model need to be used for such complex flows.

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Figure 9: Shows (a) Pressure, (b) Velocity vector, (c) Total pressure, (d) Turbulent kinetic energy, and (e) Turbulent intensity distributions at section 2 in configuration 1.


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CONCLUTIONS

CFD analysis and blower performance measurements were conducted on a forward curved centrifugal blower (for automotive HVAC applications).

Comparison of predicted total airflow rates, using a steady state K- realizable model, showed a good correlation with three test configurations. For the fourth

configuration (high pressure, low airflow), the predicted total airflow was highly overestimated.

For the fourth configuration, the CFD results analysis indicate that an increase in flow blockage across the blades leads to higher recirculation and flow reversal, which contributes to higher turbulence levels and flow unsteadiness. As a result, using a transient approach for this configuration was necessary.

By switching to a transient simulation, the predicted total airflow was in reasonable agreement with measured total airflow. The general flow structure appeared to be similar for all configurations, although with differences with respect to the location and intensity of the vortices.

This CFD study shows that numerical results for a forward curved blower are in good agreement with test results and can be used for accurate prediction of the airflow characteristics of automotive HVAC systems.

In order to have an accurate analysis of the unsteadiness in low flow rate configurations, it is recommended to perform the transient simulations of the whole blower including wheel, inlet and scroll and compare the results to corresponding pressure, velocity, and turbulence results measured in experimental test. Specially, the effect of the back-flow through the blade passages close the cut-off scroll should be evaluated by experiments. In addition, the position of the stagnation point of the cut-off scroll area and the degree of the flow blockage both in the inlet and outlet of the blower should be confirmed by test results.

REFERENCES

1. “Automotive Air Conditioning”, DELMAR, Thomson Learning, 8th Edition.

2. Fluent Users Manual3. Eck B., " Fans: Design and Operation of Centrifugal,

Axial-Flow and Cross-Flow Fans", Translated and edited by Azad R.S. and Scott D.R.), Pergamon Press, 1973.

4. D. Fischer, "Airflow Simulation Through Automotive Blowers Using Computational Fluid Dynamics ", SAE 950438

5. E. Kwon, K. Baek, N. Cho, "Some Aerodynamic Aspects of Centrifugal Fan Characteristics of an Automotive HVAC Blower ", SAE 2001-01-0291

6. AMCA 210-85, “Laboratory Methods of Testing Fans for Fating”

7. T.-H. Shih, W. W. Liou, A. Shabbir, Z. Yang, and J. Zhu., "A New K- Eddy-Viscosity Model for High Reynolds Number Turbulent Flows - Model Development and Validation", Computers Fluids, 24(3):227-238, 1995.

8. W. C. Reynolds, "Fundamentals of turbulence for turbulence modeling and simulation", Lecture Notes for Von Karman Institute Agard Report No. 755, 1987.

c

d

e

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9. T.-H. Shih, W. W. Liou, A. Shabbir, Z. Yang, and J. Zhu, "A New K- Eddy-Viscosity Model for High Reynolds Number Turbulent Flows - Model Development and Validation", Computers Fluids, 24(3):227-238, 1995.

10. B. E. Launder and D. B. Spalding. “Lectures in Mathematical Models of Turbulence” , Academic Press, London, England, 1972.

11. V. Yakhot and S. A. Orszag. “Renormalization Group Analysis of Turbulence: I. Basic Theory” Journal of Scientific Computing, 1(1):1-51, 1986

12. S.-E. Kim, D. Choudhury, and B. Patel. “Computations of Complex Turbulent Flows Using the Commercial Code FLUENT” In Proceedings of the ICASE/LaRC/AFOSR Symposium on Modeling Complex Turbulent Flows, Hampton, Virginia, 1997.

13. M. Younsi, F. Bakir, S. Kouidri, R. Rey, “Influence of Impeller Geometry on the Unsteady Flow in a Centrifugal Fan: Numerical and Experimental Analysis”, Internal paper of Ecole Superieure d’Arts et Metiers – France.

14. R.J. Kind, M.G. Tobin, “Flow in a centrifugal fan of the squirrel-cage type”, Transactions of the ASME, 84/Vol. 112, January 1990.

15. Raj, D.and Swim, W. B., 1981,”Measurements of the mean flow velocity and velocity fluctuations at the exit of an FC centrifugal fan rotor”, ASME Journal of Engineering for Power, Vol. 103, pp. 393-399.

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2008-01-0735l