Numerical Study to Improve the Flow Uniformity of Blow-Down HVAC Duct System for a Train

Title & Authors
Numerical Study to Improve the Flow Uniformity of Blow-Down HVAC Duct System for a Train
Kim, Joon-Hyung; Rho, Joo-Hyun;

Abstract
A HVAC(Heating Ventilation and Air Conditioning) is adapted to increase the comfort of the cabin environment for train. The train HVAC duct system has very long duct and many outlets due to the shape of a train set. the duct cross section shape is limited by a roof structure and equipments. Therefore, the pressure distribution and flow uniformity is an important performance indicator for the duct system. In this study, the existing blow down type HVAC duct system for a train was supplemented to improve the flow uniformity by applying a design method combining design of experiment (DOE) with numerical analysis. The design variables and the test sets were selected and the performance for each test set was evaluated using CFD(Computational Fluid Dynamics). The influence of each design variable on the system performance was analysed based on the results of the performance evaluation on the test sets. Furthermore, the optimized model, whose the flow uniformity was improved was produced using the direct optimization(gradient-based method). Finally, the performance of the optimized model was evaluated using numerical analysis, and it was confirmed that its flow uniformity has indeed improved.
Keywords
CFD;DOE;Duct;Flow Uniformity;HVAC;Optimized design;
Language
Korean
Cited by
References
1.
Keating, M. K. and Staudt, F. J., 1996, "Hvac control system and method," U.S. Patent, No. 5,544,809.

2.
Lemke, N. C., Lemke, J. L., and Koehler, J., 2012, "Secondary Loop System for Automotiv HVAC Units Under Different Climatic Conditions," International Refrigeration and Air Conditioning Conference, pp. 2393-2404.

3.
Kintner-Meyer, M. and Emery, A. F., 1995, "Optimal control of an HVAC system using cold storage and building thermal capacitance," Energy and Buildings, Vol. 23, No. 1, pp. 19-31.

4.
Nassif, N., Kajl, S., and Sabourin, R., 2005, "Optimization of HVAC control system strategy using two-objective genetic algorithm," HVAC&R Research, Vol. 11, No. 3, pp. 459-486.

5.
Teeter, J. and Chow, M. Y., 1998, "Application of functional link neural network to HVAC thermal dynamic system identification," Industrial Electronics, IEEE Transactions on, Vol. 45, No. 1, pp. 170-176.

6.
Lu, L., Cai, W., Soh, Y. C., Xie, L., and Li, S., 2004, "HVAC system optimization condenser water loop," Energy Conversion and Management, Vol. 45, No. 4, pp. 613-630.

7.
Trcka, M. and Hensen, J. L., 2010, "Overview of HVAC system simulation," Automation in Construction, Vol. 19, No. 2, pp. 93-99.

8.
Hyundai-rotem, 2015, "The noise reduction technology development of the EMU," 2nd annual report for Railroad Technology Research Program, Infrastructure and Transport of Korean government.

9.
Chen, H. C., Patel, V. C., and Ju, S., 1990, "Solutions of Reynolds-averaged Navier-Stokes equations for threedimensional incompressible flows," Journal of Computational Physics, Vol. 88, No. 2, pp. 305-336.

10.
Shih, T. H., Liou, W. W., Shabbir, A., Yang, Z., and Zhu, J., 1995, "A new k-${\epsilon}$ eddy viscosity model for high reynolds number turbulent flows," Computers & Fluids, Vol. 24, No. 3, pp. 227-238.

11.
Vaz, G., Waals, O. J., Ottens, H., Fathi, F., Le Souef, T., and Kiu, K., 2009, "Current Affairs : Model Tests, Semi-Empirical Predictions and CFD Computations for Current Coefficients of Semi-Submersibles," ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering, USA, pp. 877-887.

12.
Idelchik, I. E., 1988, "Flow Resistance," Hemisphere Publishing Corp., New York.

13.
Guo, B. Y., Hou, Q. F., Yu, A. B., Li, L. F., and Guo, J., 2013, "Numerical modelling of the gas flow through perforated plates," Chemical Engineering Research and Design, Vol. 91, No. 3, pp. 403-408.

14.
Giunta, A. A., Wojtkiewicz, S. F., and Eldred, M. S., 2003, "Overview of modern design of experiments methods for computational simulations," In Proceedings of the 41st AIAA aerospace sciences meeting and exhibit, AIAA-2003-0649.

15.
Bengio, Y., 2000, "Gradient-based optimization of hyperparameters," Neural computation, Vol. 12, No. 8, pp. 1889-1900.

16.
Conn, A. R., Elfadel, I. M., Molzen Jr, W. W., O'Brien, P. R., Strenski, P. N., Visweswariah, C., and Whan, C. B., 1999, "Gradient-based optimization of custom circuits using a static-timing formulation," In Proceedings of the 36th annual ACM/IEEE Design Automation Conference, pp. 452-459.

17.
Beck, A. and Teboulle, M., 2009, "Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems," Image Processing, IEEE Transactions on, Vol. 18, No. 11, pp. 2419-2434.

18.
Wetter, M. and Wright, J., 2004, "A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization," Building and Environment, Vol. 39, No. 8, pp. 989-999.