40-Ton Articulated Truck Cooling System Modelling Using STAR-CCM+

Transcription

1 40-Ton Articulated Truck Cooling System Modelling Using STAR-CCM+ Gary Yu, Martin Timmins and Mario Ciaffarafa DENSO Marston Ltd, Bradford, BD17 7JR, UK

2 DENSO Marston Founded in 1904 Acquired by DENSO in 1989 Located in Shipley, West Yorkshire Designs and Manufactures engine cooling modules for Heavy Duty Cooling applications Product Range includes radiators, oil coolers, inter-coolers and condensers

3 Project Background Research project partly funded by the UK government - Validation of Complex Systems (VOCS); DNMN was part of a consortium of UK based companies participating in the project which was led by a major off highway original equipment manufacturer (OEM); The project covered many aspects of vehicle simulation, with DNMN focussing on cooling system simulation and validation. Acknowledgement: This work was co-funded by the Technology Strategy Board (TSB) and One North East, UK under the Validation of Complex Systems (VOCS) grant programme. The Technology Strategy Board is an executive body established by the United Kingdom Government to drive innovation. It promotes and invests in research, development and the exploitation of science, technology and new ideas for the benefit of business - increasing sustainable economic growth in the UK and improving quality of life.

4 Outline 1. 1D KULI simulation of cooling system coupled with STAR-CCM+ single stream heat exchanger model ; 2. 3D CFD simulation of cooling system STAR-CCM+ dual stream heat exchanger model; 3. Comparison of 1D KULI and 3D CFD results. Tier 3 Off Highway Truck Charge air cooler (CAC) and cooling fan Radiator module including radiator, condenser, oil cooler and cooling fan

5 Computational Domain and Boundary Conditions Vehicle speed (m/s) 0 Wind tunnel size L X W X H (m^3) 22 X 13 X12 Top and Sides Pressure outlet Ground Wall Ambient temperature ( o C) 20 Radiator fan rpm 1500 Charge Air Cooler fan rpm 2100 Vehicle overall size: m (L) x 3.43 m (W) x m (H); Vehicle modelled at stationary and idle condition.

6 Truck Model Meshing Import CAD volume mesh refinement Surface Wrap Surface Remesh Volume Mesh Mesh size : 8mm (in dense area) Number of cells : ~21.6 million

7 Numerical Model 1. 3D, steady state flow, κ-ε model for turbulence; 2. Cooling air, charged air modelled as ideal gas, whilst radiator coolant, oil cooler oil as constant density; 3. Segregated flow temperature; 4. Heat exchangers modelled as porous media for fluid flow, dual stream model for heat rejection calculation; 5. Moving reference frame (MRF) fan model, momentum source model used but with convergence problem.

8 Coupling of KULI with CFD KULI is a 1D tool used for vehicle cooling system simulation Images courtesy of Magna Powertrain 1. Air flow distribution is uniform in traditional 1D KULI model across heat exchanger face; Velocity distribution 2. 3D distribution based on velocity profile from CFD simulation.

9 Coupling of KULI with CFD Typical procedure: CFD velocity field (isothermal) KULI resistance matrix KULI Model STOP CFD single stream heat exchanger model CFD velocity field (thermal) Q 1. Initial CFD isothermal simulation results; 2. KULI generation of resistance matrix; 3. KULI modelling for heat rejection rate, Q; 4. Update of CFD model with Q values for all heat exchangers; 5. Calculation of thermal velocity field by CFD; 6. Feedback to KULI for next iteration until converged. It is very expensive for CFD volume cells > 20M; Dual stream heat exchanger model in STAR-CCM+ can avoid this.

10 Thermal output (kw) DNMN Product Development Dual Stream Heat Exchanger CFD Model Test Data Required: Cold stream side: Q Table for heat rejection rate and mass flow rate; Hot stream side: Mass flow rate in the test should be the same as the condition modelled in CFD. hot stream mass flow actual working condition CAC cold stream flow rate (kg/min) Heat exchanger 3D heat transfer map from KULI Use KULI to obtain actual working condition

11 Determination of Porous Media Pressure Drop Coefficients Pressure drop over heat exchanger core is calculated by DP/DL=αv 2 +βv, α and β are determined based on test data, v is the inlet flow velocity; CFD under predicts the pressure drop at a fixed mass flow rate because it does not use the correct throughway area, so needs to be corrected using these equations. Radiator, Oil cooler α cfd = (A cfd /A test ) 2 α test β cfd = (A cfd /A test )β test Real Geometry CFD Charge Air Cooler α cfd = (ρ cfd /ρ in ) 2 (A cfd /A test ) 2 α test β cfd = (ρ cfd /ρ in ) (A cfd /A test )β test

12 Example Results from Dual Stream Model: Charge Air Cooler Charged Air Temperature Distribution Better cooled in the core center corresponding to the cooling air velocity profile on front face; Charged air temperature drop: ΔT 115 o C. Air flow distribution matches front grille

13 Example Results from Dual Stream Model: Radiator Coolant Temperature Distribution Better cooled on one side due to the cooling air velocity profile on front face; Coolant temperature drop: ΔT 6 o C. Air flow distribution matches layout (more complex than CAC)

14 Comparison of 1D KULI and 3D CFD results Cooling air velocity and temperature fields from CFD dual stream heat exchanger model used by KULI to compare the heat rejection and pressure drop predictions; Working conditions modeled in CFD and KULI: Mass flow rate and inlet temperature of internal fluid in each heat exchanger e.g. coolant, charge air and oil; Ambient temperature.

15 Comparison of CFD and Initial KULI Model Results DP cold stream DP hot stream Heat transfer rate (%) (%) (%) KULI CFD KULI CFD KULI CFD Radiator Oil Cooler Charge Air Cooler Big discrepancy in Oil Cooler predictions

16 KULI Model Set-up Based on CFD Results Uniform cooling air temperature T1 T3 T2 T4 Basic 1D KULI model requires Ambient temperature plus uniform warm up STAR-CCM+ Simulation 2D temperature map Improved 1D KULI model 4 blocks with targeted temperatures from CFD

17 KULI Model Set-up Based on CFD Results Dummy plane in front of heat exchangers to show cooling air inlet temperature distribution; From CFD results, 4 separate inlet temperature targets / zones are required in the KULI model.

18 KULI Model Set-up Based on CFD Results RAD Oil Cooler m 1 m 3 m 2 m cfd = m 1 = 0.24 kg/s m KULI = m 1 + m 2 = m 3 = kg/s In basic KULI model, cooling air mass flow through oil cooler is m 3 which is higher than real condition & CFD; From CFD results, a separate mass flow target is required for the oil cooler.

19 KULI Model Set-up Based on CFD Results 1. Two mass flow targets, one of which is for Oil Cooler; 2. Four blocks; 3. Four cooling air inlet temperature targets; 4. Three resistance matrixes; 5. No resistance matrix for oil cooler.

20 Comparison of CFD and Improved KULI Model Results DP cold stream DP hot stream Heat transfer rate (%) (%) (%) KULI CFD KULI CFD KULI CFD Radiator Oil Cooler Charge Air Cooler Good correlation between KULI and CFD models

21 Conclusions and Summary Standard procedure for coupling KULI and CFD requires multiple iterations and is not practical in this case; The dual stream heat exchanger model in STAR-CCM+ is efficient and was used successfully to simulate the cooling system of the 40-ton truck; STAR-CCM+ can be used to help generate an improved KULI model; STAR-CCM+ and KULI predictions agree well.

22 THANK YOU