Sponsored by U.S. Department of Energy (DOE)
We developed an open-source, free software package (see the following links) which provides practical, end-to-end (from the IT equipment to heat rejection to the ambient) modeling and optimization for data center cooling. It can be used as a stand-alone tool by data center designers, service consultants and facility managers, or be integrated into existing data center management software for autonomous optimal operation. It will be the first practical tool that couples the modeling of airflow-management and cooling systems to enable a global data center cooling optimization. Its self-learning regression model enabled by an in situ adaptive tabulation algorithm and a fast fluid dynamics model can predict the critical airflow information under various operational conditions within a few milliseconds. The equation-based modeling language allows a fast and flexible modeling of various cooling system configurations. We will demonstrate the usage of our tool at two data centers located in two different climate zones.
Open Source Models for Data Center Cooling
This project has resulted in open source Modelica models for the data center cooling system. The models haven been released as a part of the DOE's open source Modelica Buildings library: http://simulationresearch.lbl.gov/modelica/releases/latest/help/Buildings_Applications.html#Buildings.Applications.
The model development branch is at https://github.com/lbl-srg/modelica-buildings/tree/master/Buildings/Applications.
This project has also developed a reduced order model “ISAT-FFD” trained by simulations in Fast Fluid Dynamics to predict airflow in data centers: https://github.com/doetools/isat_ffd.
The development site of the ISAT module in Modelica Buildings library is at: https://bitbucket.org/sbslab-zuo/datacenter-mblisat.
The ISAT data center case can be found at: https://bitbucket.org/sbslab-zuo/datacenter-isat-examples/src/master/.
Please see this page for more information on tools related to this project.
Technical Advisory Group For The Project
|Amistadi, Henry R.||MITRE Corporation|
|Cleaver, Donald||Keystone Critical Systems & Advisors|
|Doppelhammer, F. James||University of Miami|
|Geraghty, Edward P.||CEC Group, LLC|
|Groenewold, John||JPMorgan Chase & Co.|
|Herrlin, Magnus||Lawrence Berkeley National Laboratory|
|Kaiser, Raymond||Amzur Technologies|
|Meneghan, Brian W.||Carrier Corporation|
|Plamondon, David||University of Massachusetts Medical School|
|Sartor, Dale||Lawrence Berkeley National Laboratory|
|Sorell, Vali||Syska Hennessy Group, Inc.|
- Department of Energy Press Release "Energy Department Invests $19 Million to Improve Efficiency of Nation’s Buildings"
- UM COE News "Improving Energy Savings Through Data Center Cooling Systems"
- Y. Fu 2020. "Modeling and Control for Grid-interactive Efficient Data Centers." Department of Civil, Environmental, and Architectural Engineering, Univeristy of Colorado Boulder.
- W. Tian, X. Han, W. Zuo, Q. Wang, Y. Fu, M. Jin 2019. “An Optimization Platform Based on Coupled Indoor Environment and HVAC Simulation and Its Application in Optimal Thermostat Placement.” Energy and Buildings, 199, pp. 342-251.
- Y. Fu, W. Zuo, M. Wetter, J. W. VanGilder, P. Yang 2019. "Equation-Based Object-Oriented Modeling and Simulation of Data Center Cooling Systems." Energy and Buildings, 198, pp. 503-519.
- W. Tian, J.W. VanGilder, X. Han, C.M. Healey, M.B. Condor, W. Zuo 2019. “A New Fast Fluid Dynamics Model for Data-Center Floor Plenums.” ASHRAE Transactions, 125, pp. 141-148.
- Y. Fu, W. Zuo, M. Wetter, J. W. VanGilder, X. Han, D. Plamondon 2019. “Equation-Based Object-Oriented Modeling and Simulation for Data Center Cooling: A Case Study.” Energy and Buildings,186, pp. 108-125.
- W. Tian, X. Han, W. Zuo, M. Sohn 2018. "Building Energy Simulation Coupled with CFD for Indoor Environment: A Critical Review and Recent Applications." Energy and Buildings, 165, pp.184-199.
- W. Tian, T. A. Sevilla, D. Li, W. Zuo, M. Wetter 2018. "Fast and Self-Learning Indoor Airflow Simulation Based on In Situ Adaptive Tabulation." Journal of Building Performance Simulation, 11(1), pp. 99-112.
- W. Tian, T. A. Sevilla, W. Zuo, M. Sohn 2017. "Coupling Fast Fluid Dynamics and Multizone Airflow Models in Modelica Buildings Library to Simulate the Dynamics of HVAC System." Building and Environment, 122, pp. 269-286.
- X. Han, W. Tian, W. Zuo, J.W. VanGilder 2019. “Optimization of Workload Distribution of Data Centers Based on a Self-Learning In Situ Adaptive Tabulation Model.” Proceeding of the 16th Conference of International Building Performance Simulation Association (Building Simulation 2019), September 2-4, Rome, Italy.
- W. Tian, J.W. VanGilder, M.B. Condor, X. Han, W. Zuo 2019. “An Accurate Fast Fluid Dynamics Model for Data Center Applications.” The Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm 2019), May 28-31, Las Vegas, NV.
- Y. Fu, M. Wetter, W. Zuo 2018. “Modelica Models for Data Center Cooling Systems.” 2018 ASHRAE Building Performance Analysis Conference and SimBuild (BPACS 2018), pp. 438-445, September 26-28, Chicago, IL.
- W. Tian, W. Zuo, T. A. Sevilla, M. Sohn 2017. “Coupled Simulation Between CFD and Multizone Models Based on Modelica Buildings Library to Study Indoor Environment Control.” Proceedings of the 12th International Modelica Conference, pp. 55-61, May 15-17, Prague, Czech Republic.