Skip to main content
SpringerLink
Log in

Reduced Order Modeling Based Energy Efficient and Adaptable Design

  • Chapter
  • First Online:
Energy Efficient Thermal Management of Data Centers

  • 2339 Accesses

Abstract

In this chapter, the sustainable and reliable operations of the electronic equipment in data centers are shown to be possible through a reduced order modeling based design. First, the literature on simulation-based design of data centers using computational fluid dynamics/heat transfer (CFD/HT) and low-dimensional modeling are reviewed. Then, two recent proper orthogonal decomposition (POD) based reduced order thermal modeling methods are explained to simulate multiparameter-dependent temperature field in multiscale thermal/fluid systems such as data centers. The methods result in average error norm of ~6% for different sets of design parameters, while they can be up to ~250 times faster than CFD/HT simulation in an iterative optimization technique for a sample data center cell. The POD-based modeling approach is applied along with multiobjective design principles to systematically achieve an energy efficient, adaptable, and robust thermal management system for data centers. The framework allows for intelligent dynamic changes in the rack heat loads, required cooling airflow rates, and supply air temperature based on the actual momentary center heat loads, rather than planned occupancy, to extend the limits of air cooling and/or increase energy efficiency. This optimization has shown energy consumption reduction by 12–46% in a data center cell.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Samadiani E, Joshi Y (2010) Reduced order thermal modeling of data centers via proper orthogonal decomposition – a review. Int J Numer Methods Heat Fluid Flow 20(5):529–550

    Article  Google Scholar 

  2. Report to Congress on Server and Data Center Energy Efficiency Public Law 109–431 (2007, August 2) U.S. Environmental Protection Agency ENERGY STARProgram

    Google Scholar 

  3. Greenberg S et al (2006) Best practices for data centers: lessons learned from benchmarking 22 data centers. In: ACEEE summer study on energy efficiency in buildings in asilomar, CA. pp 76–87

    Google Scholar 

  4. ASHRAE (2005) Datacom equipment power trends and cooling applications. American Society of Heating, Refrigeration and Air-Conditioning Engineers Atlanta, Atlanta

    Google Scholar 

  5. Belady CL (2007) In the data center, power and cooling costs more than the it equipment it supports. Electronics Cooling 13(1):24–27

    Google Scholar 

  6. Rambo JD (2006) Reduced-order modeling of multiscale turbulent convection: application to data center thermal management. In: Mechanical engineering. Georgia Institute of Technology, Atlanta

    Google Scholar 

  7. Patel CD et al (2002) Thermal considerations in cooling of large scale high compute density data centers. In: ITHERM 2002 – Eight intersociety conference on thermal and thermomechanical phenomena in electronic systems, San Diego, CA, pp 767–776

    Google Scholar 

  8. Rambo J, Joshi Y (2003) Multi-scale modeling of high power density data centers. In: ASME InterPACK03. ASME, Kauai, Hawaii

    Google Scholar 

  9. Rambo J, Joshi Y (2003) Physical models in data center airflow simulations. In: IMECE-03 – ASME international mechanical engineering congress and R&D exposition. IMECE03-41381, Washington, DC

    Google Scholar 

  10. Shrivastava S, et al (2005) Comparative analysis of different data center airflow management configurations. In: ASME InterPACK05. ASME, San Francisco, CA

    Google Scholar 

  11. Iyengar M et al (2005) Thermal characterization of non-raised floor air cooled data centers using numerical modeling. In: ASME InterPACK05. ASME, San Francisco, CA

    Google Scholar 

  12. Schmidt R, Karki KC, Patankar SV (2004) Raised-floor data center: perforated tile flow rates for various tile layouts. In: ITHERM 2004 – Ninth intersociety conference on thermal and thermomechanical phenomena in electronic systems, Las Vegas, NV

    Google Scholar 

  13. VanGilder JW, Schmidt R (2005) Airflow uniformity through perforated tiles in a raised-floor data center. In: ASME InterPACK. ASME, San Francisco, CA, IPACK2005-73375

    Google Scholar 

  14. Lawrence Berkeley National Laboratory and Rumsey Engineers. Data center energy benchmarking case study. http://datacenters.lbl.gov/

  15. Samadiani E, Joshi Y, Mistree F (2007) The thermal design of a next generation data center: a conceptual exposition. In: Thermal issues in emerging technologies, ThETA 1, Cairo, Egypt

    Google Scholar 

  16. Shah A et al (2005) Exergy-based optimization strategies for multi-component data center thermal management: Part I, analysis. In: ASME InterPACK05. ASME, San Francisco, CA

    Google Scholar 

  17. Shah A et al (2005) Exergy-based optimization strategies for multi-component data center thermal management: Part II, application and validation. In: ASME InterPACK05. ASME, San Francisco, CA

    Google Scholar 

  18. Bhopte S et al (2005) Optimization of data center room layout to minimize rack inlet air temperature. In: ASME InterPACK. ASME, San Francisco, CA, IPACK2005-73027

    Google Scholar 

  19. Schmit R, Iyengar M (2005) Effect of data center layout on rack inlet air temperatures. In: ASME InterPACK 05. ASME, San Francisco, CA

    Google Scholar 

  20. Sharma RK, Bash CE, Patel CD (2002) Dimensionless parameters for the evaluation of thermal design and performance of large-scale data centers. In: The eighth AIAA/ASME joint thermophysics and heat transfer conference, St. Louis

    Google Scholar 

  21. Kang S et al (2000) A methodology for the design of perforated tiles in a raised floor data center using computational flow analysis. In: ITHERM 2000 – Intersociety conference on thermal and thermomechanical phenomena in electronic systems, Las Vegas, NV

    Google Scholar 

  22. Boucher TD et al (2004) Viability of dynamic cooling control in a data center environment. In: International society conference on thermal phenomena, Las Vegas, NV

    Google Scholar 

  23. Rolander N (2005) An approach for the robust design of air cooled data center server cabinets. MS thesis, G.W. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta

    Google Scholar 

  24. Patel C et al (2001) Computational fluid dynamics modeling of high compute density data centers to assure system inlet air specifications. In: ASME IPACK’01. ASME, Kauai, Hawaii

    Google Scholar 

  25. Schmidt R et al (2001) Measurements and predictions of the flow distribution through perforated tiles in raised floor data centers. In: ASME InterPACK01, Kauai, Hawaii

    Google Scholar 

  26. Radmehr A et al (2005) Distributed leakage flow in raised-floor data centers. In: ASME InterPACK. ASME, San Francisco, CA, IPACK2005-73273

    Google Scholar 

  27. Karki KC, Radmehr A, Patankar SV (2003) Use of computational fluid dynamics for calculating flow rates through perforated tiles in raised-floor data centers. HVAC and R Res 9(2):153–166

    Article  Google Scholar 

  28. Samadiani E, Rambo J, Joshi Y (2007) Numerical modeling of perforated tile flow distribution in a raised-floor data center. In: ASME InterPACK ’07, Vancouver, British Columbia, Canada

    Google Scholar 

  29. Schmidt R, Cruz E (2003) Cluster of high powered racks within a raised floor computer data center: effects of perforated tiles flow distribution on rack inlet air temperature. In: IMECE-03 – ASME international mechanical engineering congress and R&D exposition, Washington, DC

    Google Scholar 

  30. Rambo J, Joshi Y (2006) Thermal modeling of technology infrastructure facilities: a case study of data centers. In: Minkowycz WJ, Sparrow EM, Murthy JY (eds) The handbook of numerical heat transfer. Taylor and Francis, New York, pp 821–850

    Google Scholar 

  31. Rambo J, Joshi Y (2007) Modeling of data center airflow and heat transfer: State of the art and future trends. Distributed and Parallel Databases 21:193–225

    Article  Google Scholar 

  32. Moore J et al (2005) Making scheduling cool: temperature-aware workload placement in data centers. In: Usenix technical conference, Anaheim, CA

    Google Scholar 

  33. Karlsson M, Karamanolis C, Zhu X (2004) Triage: performance isolation and differentiation for storage systems. In: The twelfth international workshop on quality of service, Montreal, Canada

    Google Scholar 

  34. Sharma RK et al (2003) Balance of power: dynamic thermal management for internet data centers. Whitepaper issued by Hewlett Packard Laboratories

    Google Scholar 

  35. Moore J et al (2005) Data center workload monitoring, analysis, and emulation. In: Eighth workshop on computer architecture evaluation using commercial workloads, San Francisco, CA

    Google Scholar 

  36. Tang Q et al (2006) Sensor-based fast thermal evaluation model for energy-efficient high-performance datacenters. In: Fourth international conference on intelligent sensing and information processing (ICISIP), Bangalore, India

    Google Scholar 

  37. Nathuji R et al (2008) CoolIT: coordinating facility and IT management for efficient datacenters. In: HotPower ’08; workshop on power aware computing and systems, San Diego, CA

    Google Scholar 

  38. Somani A, Joshi Y (2008) Ambient intelligence based load management, USPTO Provisional Patent, GTRC ID No. 4524

    Google Scholar 

  39. Qian Z (2006) Computer experiments: design, modeling, and integration. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta

    Google Scholar 

  40. Samadiani E, Joshi Y (2010) Multi-parameter model reduction in multi-scale convective systems. Int J Heat Mass Transfer 53:2193–2205

    Article  MATH  Google Scholar 

  41. Samadiani E, Joshi Y (2010) Proper orthogonal decomposition for reduced order thermal modeling of air cooled data centers. J Heat Trans 132(7):0714021–07140214

    Article  Google Scholar 

  42. Rambo J, Joshi Y (2007) Reduced-order modeling of turbulent forced convection with parametric conditions. Int J Heat Mass Trans 50(3–4):539–551

    Article  MATH  Google Scholar 

  43. Holmes P, Lumley JL, Berkooz G (1996) Turbulence, coherent structures, dynamical systems and symmetry. Cambridge University Press, Great Britain

    Book  MATH  Google Scholar 

  44. Ravindran SS (2002) Adaptive reduced-order controllers for a thermal flow using proper orthogonal decomposition. SIAM J Sci Comput 23(6):1924–1942

    Article  MathSciNet  MATH  Google Scholar 

  45. Park HM, Cho DH (1996) The use of the Karhunen-Loeve decomposition for the modeling of distributed parameter systems. Chem Eng Sci 51(1):81–98

    Article  MathSciNet  Google Scholar 

  46. Park HM, Cho DH (1996) Low dimensional modeling of flow reactors. Int J Heat Mass Trans 39(16):3311–3323

    Article  Google Scholar 

  47. Sirovich L, Park HM (1990) Turbulent thermal convection in a finite domain: Part I. Theory. Phys Fluids 2(9):1649–1658

    Article  MathSciNet  MATH  Google Scholar 

  48. Sirovich L, Park HM (1990) Turbulent thermal convection in a finite domain: Part II. Numerical results. Phys Fluids 2(9):1659–1668

    Article  MathSciNet  MATH  Google Scholar 

  49. Tarman IH, Sirovich L (1998) Extensions of Karhunen-Loeve based approximations of complicated phenomena. Comput Methods Appl Mech Eng 155:359–368

    Article  MathSciNet  MATH  Google Scholar 

  50. Park HM, Li WJ (2002) Boundary optimal control of natural convection by means of mode reduction. J Dyn Syst Meas Cont 124:47–54

    Article  Google Scholar 

  51. Ding P et al (2008) A fast and efficient method for predicting fluid flow and heat transfer problems. ASME J Heat Trans 130(3):032502

    Article  Google Scholar 

  52. Ly HV, Tran HT (2001) Modeling and control of physical processes using proper orthogonal decomposition. Math Comput Model 33:223–236

    Article  MATH  Google Scholar 

  53. Strang G (1988) Linear algebra and its applications. Thomson Learning, Singapore

    Google Scholar 

  54. Rolander N et al (2006) Robust design of turbulent convective systems using the proper orthogonal decomposition. J Mech Des 128(Special Issue Robust and Risk Based Design):844–855

    Google Scholar 

  55. Nie Q, Joshi Y (2008) Multiscale thermal modeling methodology for thermoelectrically cooled electronic cabinets. Numer Heat Transf A Appl 53(3):225–248

    Article  Google Scholar 

  56. Nie Q, Joshi Y (2008) Reduced order modeling and experimental validation of steady turbulent convection in connected domains. Int J Heat Mass Trans 51(25–26):6063–6076

    Article  MATH  Google Scholar 

  57. Samadiani E et al (2010) Adaptable robust design of multi-scale convective systems applied to energy efficient data centers. Numer Heat Trans A Appl 57(2):69–100

    Article  Google Scholar 

  58. Samadiani E, Joshi Y, Mistree F (2008) The thermal design of a next generation DataCenter: a conceptual exposition. J Electron Packag 130(4):0411041–0411048

    Article  Google Scholar 

  59. ASHRAE (2004) Thermal guidelines for data processing environments. American Society of Heating, Refrigeration, and Air-Conditioning Engineers, New York

    Google Scholar 

  60. Mistree F, Hughes OF, Bras B (1993) The compromise decision support problem and the adaptive linear programming algorithm. In: Kamat MP (ed) Structural optimization: status and promise. AIAA, Washington, DC, pp 247–286

    Google Scholar 

  61. Moore J et al (2005) Making scheduling cool: temperature-aware workload placement in data centers. In: USENIX annual technical conference, Anaheim, CA, pp 61–75

    Google Scholar 

  62. Lewis RM, Torczon V (2000) Pattern search methods for linearly constrained minimization. SIAM J Optim 10(3):917–941

    Article  MathSciNet  MATH  Google Scholar 

  63. Steuer RE (1986) Multiple criteria optimization, theory computations and applications. Wiley, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Emad Samadiani

Authors
  1. Emad Samadiani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Emad Samadiani .

Editor information

Editors and Affiliations

  1. George W. Woodruff School of, Mechanical Engineering, Georgia Institute of Technology, Ferst Dr. 771, Atlanta, 30332, Georgia, USA

    Yogendra Joshi

  2. George W. Woodruff School of, Mechanical Engineering, Georgia Institute of Technology, Ferst Dr. 771, Atlanta, 30332, Georgia, USA

    Pramod Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Samadiani, E. (2012). Reduced Order Modeling Based Energy Efficient and Adaptable Design. In: Joshi, Y., Kumar, P. (eds) Energy Efficient Thermal Management of Data Centers. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-7124-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-7124-1_10

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-7123-4

  • Online ISBN: 978-1-4419-7124-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation