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Comparison of detached eddy simulation and standard k—ε RANS model for rack-level airflow analysis inside a data center

Building Simulation volume 15, pages 1595–1610 (2022)Cite this article

Abstract

High computing demands and data privacy regulations from many countries in the world have resulted in the expansion of data centers. With this, energy consumption by data centers has increased to an alarming level. Data centers are highly dynamic due to time-dependent server heat generation and cold-hot aisle arrangements, making it difficult to have real-time control for efficient thermal management. Traditional cooling strategies based on conservative set points affect energy consumption. Instead of expensive field measurements, CFD analysis of the flow field inside the data center can provide insightful data to assist the heat release from the racks. The detailed rack-level flow field inside the data center is missing in the literature as most of the studies are based on approximate results using the RANS-based k—ε model. However, DES-based models have shown the ability to resolve complex flow fields. With this finding, rack-level CFD analysis of the data center is performed using DES and standard k—ε techniques. At first, average rack inlet and outlet air temperatures in steady-state were validated with experiments within the accuracy of 1.4 µC. The distributions of turbulent kinetic energy and mean velocity inside the cold aisle were examined. The recirculation region in the cold aisle was well-captured by the DES and qualitatively validated with the experiments compared to the k—ε model. The k—ε model failed to predict SHI, RTI, and β metrics, whereas the DES model successfully captured recirculation and self-heating of the upper servers. An acceptable trade-off between computational cost and accuracy for the simulations would be a pivotal parameter for the selection of either DES or the k—ε model for the data center CFD analysis. The porous media assumption of the servers can bring uncertainties for turbulent quantities and hence further DES model for a data center can provide additional insights in this study.

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References

  • Alimohammadi S, McEvoy J, Delauré Y, et al. (2018). Benchmarking the application of detached eddy simulation techniques in data center server flow modelling using stereoscopic particle image velocimetry. In: Proceedings of the 24th International Workshop on Thermal Investigations of ICs and Systems, Stockholm, Sweden.

  • Alkharabsheh S, Fernandes J, Gebrehiwot B, et al. (2015). A brief overview of recent developments in thermal management in data centers. Journal of Electronic Packaging, 137: 040801.

    Article  Google Scholar 

  • Andrae A, Edler T (2015). On global electricity usage of communication technology: trends to 2030. Challenges, 6: 117–157.

    Article  Google Scholar 

  • Arghode VK, Kumar P, Joshi Y, et al. (2013). Rack level modeling of air flow through perforated tile in a data center. Journal of Electronic Packaging, 135: 030902.

    Article  Google Scholar 

  • Arghode VK, Joshi Y, (2016). Air Flow Management in Raised Floor Data Centers. Switzerland: Springer International Publishing.

    Book  Google Scholar 

  • Arghode VK, Kang T, Joshi Y, et al. (2017). Measurement of air flow rate through perforated floor tiles in a raised floor data center. Journal of Electronic Packaging, 139: 011007.

    Article  Google Scholar 

  • Argyropoulos CD, Markatos NC (2015). Recent advances on the numerical modelling of turbulent flows. Applied Mathematical Modelling, 39: 693–732.

    MathSciNet  MATH  Article  Google Scholar 

  • ASHRAE (2011). ASHRAE TC 9.9. 2011 Thermal Guidelines for Data Processing Environments—Expanded Data Center Classes and Usage Guidance. Atlanta, GA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  • Ashton N, West A, Lardeau S, et al. (2016). Assessment of RANS and DES methods for realistic automotive models. Computers & Fluids, 128: 1–15.

    Article  Google Scholar 

  • Athavale J, Yoda M, Joshi Y (2019). Comparison of data driven modeling approaches for temperature prediction in data centers. International Journal of Heat and Mass Transfer, 135: 1039–1052.

    Article  Google Scholar 

  • Berselli LC, Iliescu T, Layton WJ (2006). Introduction to eddy viscosity models. In: Berselli LC, Iliescu T, Layton WJ (eds), Mathematics of Large Eddy Simulation of Turbulent Flows. New York: Springer, pp 71–104.

    MATH  Google Scholar 

  • Capozzoli A, Serale G, Liuzzo L, et al. (2014). Thermal metrics for data centers: a critical review. Energy Procedia, 62: 391–400.

    Article  Google Scholar 

  • Cheng Y, Lien FS, Yee E, et al. (2003). A comparison of large Eddy simulations with a standard k-ε Reynolds-averaged Navier-Stokes model for the prediction of a fully developed turbulent flow over a matrix of cubes. Journal of Wind Engineering and Industrial Aerodynamics, 91: 1301–1328.

    Article  Google Scholar 

  • Data Center Map (2021). Colocation Data Centers. Available at https://www.datacentermap.com/datacenters.html. Accessed 29 July 2021.

  • Durbin PA (2018). Some recent developments in turbulence closure modeling. Annual Review of Fluid Mechanics, 50: 77–103.

    MathSciNet  MATH  Article  Google Scholar 

  • Erden HS, Khalifa HE, Schmidt RR (2013). Room-Level Transient CFD Modeling of Rack Shutdown. In: Proceedings of the ASME 2013 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems, Burlingame, California, USA.

  • Fadai-Ghotbi A, Friess C, Manceau R, et al. (2010). A seamless hybrid RANS-LES model based on transport equations for the subgrid stresses and elliptic blending. Physics of Fluids, 22: 055104.

    MATH  Article  Google Scholar 

  • Friess C, Manceau R, Gatski TB (2015). Toward an equivalence criterion for hybrid RANS/LES methods. Computers & Fluids, 122: 233–246.

    MathSciNet  MATH  Article  Google Scholar 

  • Fröhlich J, von Terzi D (2008). Hybrid LES/RANS methods for the simulation of turbulent flows. Progress in Aerospace Sciences, 44: 349–377.

    Article  Google Scholar 

  • Fulpagare Y, Bhargav A (2015). Advances in data center thermal management. Renewable and Sustainable Energy Reviews, 43: 981–996.

    Article  Google Scholar 

  • Fulpagare Y, Mahamuni G, Bhargav A (2015). Effect of plenum chamber obstructions on data center performance. Applied Thermal Engineering, 80: 187–195.

    Article  Google Scholar 

  • Fulpagare Y, Joshi Y, Bhargav A (2016a). Rack level transient CFD modeling of data center. In: Proceedings of the 4th International Conference on Computational Methods for Thermal Problems, Atlanta, USA.

  • Fulpagare Y, Joshi Y, Bhargav A (2016b). Transient characterization of data center racks. In: Proceedings of the ASME 2016 International Mechanical Engineering Congress and Exposition, Phoenix, AZ, USA.

  • Fulpagare Y, Joshi Y, Bhargav A (2017). Rack level forecasting model of data center. In: Proceedings of the 16th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), Orlando, FL, USA.

  • Fulpagare Y, Joshi Y, Bhargav A (2018). Rack level transient CFD modeling of data center. International Journal of Numerical Methods for Heat & Fluid Flow, 28: 381–394.

    Article  Google Scholar 

  • Fulpagare Y, Bhargav A, Joshi Y (2019). Dynamic thermal characterization of raised floor plenum data centers: Experiments and CFD. Journal of Building Engineering, 25: 100783.

    Article  Google Scholar 

  • Fulpagare Y, Bhargav A, Joshi Y (2020). Predictive model development and validation for raised floor plenum data center. Journal of Electronic Packaging, 142: 021009.

    Article  Google Scholar 

  • Hannemann R, Chu H (2007). Analysis of alternative data center cooling approaches. In: Proceedings of the ASME 2007 InterPACK Conference, British Columbia, Canada.

  • Haupt SE, Zajaczkowski FJ, Peltier LJ (2011). Detached eddy simulation of atmospheric flow about a surface mounted cube at high Reynolds number. Journal of Fluids Engineering, 133: 031002.

    Article  Google Scholar 

  • Herrlin MK (2007). Improved data center energy efficiency and thermal performance by advanced airflow analysis. In: Proceedings of the Digital Power Forum.

  • Iyengar M, Schmidt RR (2007). Analytical modeling of energy consumption and thermal performance of data center cooling systems—From the chip to the environment. In: Proceedings of the ASME 2007 InterPACK Conference, British Columbia, Canada.

  • Kumar P, Joshi Y (2010). Experimental investigations on the effect of perforated tile air jet velocity on server air distribution in a high density data center. In: Proceedings of the 12th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, Las Vegas, NV, USA.

  • Larsson IAS, Lindmark EM, Lundström TS, et al. (2011). Secondary flow in semi-circular ducts. Journal of Fluids Engineering, 133: 101206.

    Article  Google Scholar 

  • Launder BE, Spalding DB (1974). The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering, 3: 269–289.

    MATH  Article  Google Scholar 

  • Liang Z, Xue L (2014). Detached-eddy simulation of wing-tip vortex in the near field of NACA 0015 airfoil. Journal of Hydrodynamics, 26: 199–206.

    Article  Google Scholar 

  • Liebert (2007). Liebert Deluxe System/3™—Chilled Water System Design Manual.

  • Liu C, Liu C, Ma W (2015). Rans, detached Eddy simulation and large eddy simulation of internal Torque converters flows: A comparative study. Engineering Applications of Computational Fluid Mechanics, 9: 114–125.

    Article  Google Scholar 

  • Madabhushi RK, Vanka SP (1991). Large eddy simulation of turbulence-driven secondary flow in a square duct. Physics of Fluids A: Fluid Dynamics, 3: 2734–2745.

    MATH  Article  Google Scholar 

  • Manceau R, Hanjalić K (2002). Elliptic blending model: A new near-wall Reynolds-stress turbulence closure. Physics of Fluids, 14: 744–754.

    MATH  Article  Google Scholar 

  • Masanet E, Shehabi A, Lei N, et al. (2020). Recalibrating global data center energy-use estimates. Science, 367: 984–986.

    Article  Google Scholar 

  • Morton S, Forsythe J, Mitchell A, et al. (2002). Detached-eddy simulations and reynolds-averaged Navier-Stokes simulations of delta wing vortical flowfields. Journal of Fluids Engineering, 124: 924–932.

    Article  Google Scholar 

  • Nelson G (2007). Development of an experimentally-validated compact model of a server rack. PhD Thesis, Georgia Institute of Technology.

  • Nicoud F, Ducros F (1999). Subgrid-scale stress modelling based on the square of the velocity gradient tensor. Flow, Turbulence and Combustion, 62: 183–200.

    MATH  Article  Google Scholar 

  • Pope SB (2000). Turbulent Flows. Cambridge: Cambridge University Press.

    MATH  Book  Google Scholar 

  • Rambo JD, Joshi YK (2003). Multi-Scale Modeling of High Power Density Data Centers. In Proceedings of the International Electronic Packaging Technical Conference and Exhibition, Maui, Hawaii, USA.

  • Roache PJ (1994). Perspective: a method for uniform reporting of grid refinement studies. Journal of Fluids Engineering, 116: 405–413.

    Article  Google Scholar 

  • Saiyad A, Fulpagare Y, Bhargav A (2018). Data center rack analysis using detached eddy simulations. In: Proceedings of the International Conference on Computational Methods for Thermal Problems, Bangalore, India.

  • Saiyad A, Patel A, Fulpagare Y, et al. (2021). Predictive model for raised floor plenum data center using artificial neural networks. Journal of Building Engineering, 42: 102397.

    Article  Google Scholar 

  • Samadiani E, Joshi Y (2010). Proper orthogonal decomposition for reduced order thermal modeling of air cooled data centers. Journal of Heat Transfer, 132: 071402.

    Article  Google Scholar 

  • Schmidt RR, Cruz EE, Iyengar M (2005). Challenges of data center thermal management. IBM Journal of Research and Development, 49: 709–723.

    Article  Google Scholar 

  • Siemens (2018). STAR-CCM+ Users Manual. Siemens Product Lifecycle Management Software Inc.

  • Spalart PR (2009). Detached-eddy simulation. Annual Review of Fluid Mechanics, 41: 181–202.

    MATH  Article  Google Scholar 

  • Varsamopoulos G, Jonas M, Ferguson J, et al. (2013). Using transient thermal models to predict cyberphysical phenomena in data centers. Sustainable Computing: Informatics and Systems, 3: 132–147.

    Google Scholar 

  • Von Terzi DA, Fröhlich J (2010). Segregated coupling of large-eddy simulations with downstream Reynolds-Averaged Navier-Stokes calculations. Computers & Fluids, 39: 1314–1331.

    MATH  Article  Google Scholar 

  • Wibron E, Ljung AL, Lundström T (2018). Computational fluid dynamics modeling and validating experiments of airflow in a data center. Energies, 11: 644.

    Article  Google Scholar 

  • Zhai Z, Hermansen KA, Al-Saadi S (2012). The development of simplified rack boundary conditions for numerical data center models. ASHRAE Transactions, 118(2): 436–449.

    Google Scholar 

  • Zhang H, Li Y, Xiao J, et al. (2017). Large eddy simulation of turbulent flow using the parallel computational fluid dynamics code GASFLOW-MPI. Nuclear Engineering and Technology, 49: 1310–1317.

    Article  Google Scholar 

  • Zou Y, Zhao X, Chen Q (2018). Comparison of STAR-CCM+ and ANSYS Fluent for simulating indoor airflows. Building Simulation, 11: 165–174.

    Article  Google Scholar 

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Authors and Affiliations

  1. Mechanical Engineering, Energy Systems Research Laboratory, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar, GJ, 382355, India

    Anashusen Saiyad, Yogesh Fulpagare & Atul Bhargav

Authors
  1. Anashusen Saiyad
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  2. Yogesh Fulpagare
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  3. Atul Bhargav
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Correspondence to Atul Bhargav.

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Saiyad, A., Fulpagare, Y. & Bhargav, A. Comparison of detached eddy simulation and standard k—ε RANS model for rack-level airflow analysis inside a data center. Build. Simul. 15, 1595–1610 (2022). https://doi.org/10.1007/s12273-021-0879-3

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  • DOI: https://doi.org/10.1007/s12273-021-0879-3

Keywords

  • data center
  • CFD
  • DES
  • RANS
  • thermal management