Block /lab/sbs/ en Improving Data Center Energy Efficiency through End-to-End Cooling Modeling and Optimization /lab/sbs/doe-datacenter Improving Data Center Energy Efficiency through End-to-End Cooling Modeling and Optimization Anonymous (not verified) Sat, 10/01/2016 - 00:00 Research Block DOE Data center FFD High Performance Buildings

 

 

Led by Dr. Zuo, this is a joint research project with Lawrence Berkeley National Laboratory and Schneider Electric. We have developed an open-source, free (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 is 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 have demonstrated the usage of our tool and identified 53% energy saving potentials in a Florida data center and 74% in the Massachusetts data center.

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: .

The model development branch is at .

This project has also developed a reduced order model “ISAT-FFD” trained by simulations in Fast Fluid Dynamics to predict airflow in data centers: .

See this page for more information on FFD.

The development site of the ISAT module in Modelica Buildings library is at: .

The ISAT data center case can be found at: .

Please see this page for more information on tools related to this project.

Commerical Tool 

Based on our open source FFD models, Schneider Electric developed a commerial software "", which is a cloud-based data center CFD design software.  

Technical Advisory Group
 

Name Institution
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.

Collaborators

Press Release

Publications

Project Report

  • W. Zuo, M. Wetter, J. VanGilder, X. Han, Y. Fu, C. Faulkner, J. Hu, W. Tian, M. Condor 2021. “.” pp. 1-109, Report for US Department of Energy, DOE-CUBoulder-07688.

Recorded Presentations

  • W. Zuo 2020 "", American Modelica Conference 2020, September.

Ph.D. Theses

Journal Articles

Conference Proceedings

Off

Traditional 0 On White ]]> Sat, 01 Oct 2016 06:00:00 +0000 Anonymous 96 at /lab/sbs BIGDATA: Collaborative Research: IA: Big Data Analytics for Optimized Planning of Smart, Sustainable, and Connected Communities /lab/sbs/nsf-bigdata-scc BIGDATA: Collaborative Research: IA: Big Data Analytics for Optimized Planning of Smart, Sustainable, and Connected Communities Anonymous (not verified) Thu, 09/01/2016 - 00:00 Research Block HGV NSF Smart & Resilient Communities/Cities Smart city Urban scale modeling

The goal of this project is to develop a new planning framework for smart, connected and sustainable communities that allows meeting such zero energy, zero outage, and zero congestions goals by optimally deciding on how, when, and where to deploy or upgrade a community's infrastructure. By bringing together interdisciplinary domain experts from data science, electrical engineering, and civil and architectural engineering, this research will yield several innovations: 

  1. Novel big data techniques for faithfully creating spatiotemporal models for smart communities that integrate data from heterogeneous sources and shed light on the composition and operation of a given smart community;
  2. Novel, data driven performance metrics that advance powerful mathematical tools from stochastic geometry to explicitly quantify the health of smart communities via tractable notions of zero energy, zero outage, and zero congestion;
  3. Advanced analytical tools that bring forward novel ideas from optimization theory to devise the most effective strategies for deploying, upgrading, and operating various community infrastructure nodes, given the scale, dynamics, and structure of both the data and the community;
  4. A virtual smart community testbed that can accurately reconstruct, simulate, and evaluate the theoretical framework by leveraging open nonproprietary real world big data sets.

Project Team

Faculty:

Wangda Zuo, Ph.D. 
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder
wangda.zuo@colorado.edu

 

Graduate 鶹Ժ:

Jing Wang, M.S. 
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder
jing.wang@colorado.edu

 

Katy Hinkelman, M.S., EIT 
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder
kathryn.hinkelman@colorado.edu

 

Saranya Anbarasu, M.S. 
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder
saan1256@colorado.edu

 

Mingzhe Liu, Ph.D. 
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder
mingzhe.liu@colorado.edu

 

Chengnan Shi, M.S. 
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder
chengnan.shi@colorado.edu

 

Yingli Lou, M.S. 
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder
yingli.lou@colorado.edu

 

Yizhi Yang, M.S,
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder
yizhi.yang@colorado.edu

 

Collaborators

Resulted Open Source Libraries

This project has developed two open source Modelica libraries:

Press Release

Publications

Journal articles

  • J. Wang, S. Huang, W. Zuo, D. Vrabie 2021. “.” Energy and Buildings, 252, pp. 111399.
  • S. Huang, J. Wang, Y. Fu, W. Zuo, K. Hinkelman, R. M. Kaiser, D. He, D. Vrabie 2021. “”. Sustainable Cities and Society, 75, pp. 103255.
  • Y. Ye, K. Hinkelman, Y. Lou, W. Zuo, G. Wang, G., J. Zhang 2021. "" Building Simulation, pp. 1-17.
  • S, Huang, Y. Ye, Di. Wu, W. Zuo 2021. “” Energy, 221, pp. 119571.
  • Y. Ye, Y. Lou, W. Zuo, E. Franconi, G. Wang 2020. "" Energy and Buildings, 224, pp. 110267.
  • YY. Ye, Y. Lou, M. Strong, S. Upadhyaya, W. Zuo, G. Wang 2020. "" Science and Technology for the Built Environment, 26 (9), pp. 1321-1336.
  • W. Tian, X. Han, W. Zuo, Q. Wang, Y. Fu, M. Jin 2019. “” Energy and Buildings, 199, pp. 342-251.
  • S. Huang, X. Lu, W. Zuo, X. Zhang, C. Liang 2019. "." Building and Environment, 160, pp. 106199.
  • W. Tian, J.W. VanGilder, X. Han, C.M. Healey, M.B. Condor, W. Zuo 2019. “.”&Բ;ASHRAE Transactions, 125, pp. 141-148.
  • X. Lu, K. Hinkelman, Y. Fu, J. Wang, W. Zuo, Q. Zhang, W. Saad 2019. “.” IEEE Access, 7, pp. 55458-55476.
  • S. Huang, Y. Ye, X. Han, W. Zuo, X. Zhang, L. Jiang 2019. “.” Energy Conversion and Management, 186, pp. 500-515.
  • S. Huang, W. Zuo, H. Lu, C. Liang, X. Zhang 2019. “.” Energy Conversion and Management, 180, pp. 1039-1054.
  • G. Zhou, Y. Ye, W. Zuo,  X. Zhou, X. Xu 2018. “.” Applied Thermal Engineering, 145, pp. 133-146.
  • G. Zhou, Y. Ye, J. Wang, W. Zuo, Y. Fu, X. Zhou 2018. “.” Applied Thermal Engineering, 143, pp. 137-148.

Conference Proceedings

  • S. Anbarasu, K. Hinkelman, W. Zuo. 2022. “”&Բ;The 5th International Conference on Building Energy and Environment (COBEE 2022). July 25-29, Montreal, Canada.
  • Y. Lou, Y. Ye, W. Zuo, J. Zhang 2021. "." Proceeding of the 17th Conference of International Building Performance Simulation Association (Building Simulation 2021), September 1-3, Bruges, Belgium.
  • J. Wang, K.N. Garifi, K.A. Baker, W. Zuo, Y. Zhang 2020. “”&Բ;2020 Building Performance Modeling Conference and SimBuild, Virtual Conference, September 29-October 1.
  • K. Hinkelman, S. Huang, J. Wang, J. Lian, W. Zuo 2019. “” 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. “” The Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm 2019), May 28-31, Las Vegas, NV. 
  • Q. Wang, Y. Pan,  Z. Huang, W. Zuo, P. Xu 2018. “.”&Բ;The 4th Asia Conference of International Building Performance Simulation Association - ASim2018, December 3-5, Hong Kong. 
  • X. Lu, Y. Fu, W. Zuo 2018. “.” 2018 ASHRAE Building Performance Analysis Conference and SimBuild (BPACS 2018), pp. 250-257, September 26-28, Chicago, IL.

Presentations

K. Hinkelman 2020 "", American Modelica Conference 2020, September.

Workshop

J. Wang, S. Huang, W. Zuo 2020 "", the free workshop on "Cyber-physical System Modeling using Modelica for Smart and Sustainable Communities" on September 18.

 

Off

Traditional 0 On White ]]> Thu, 01 Sep 2016 06:00:00 +0000 Anonymous 102 at /lab/sbs Energy Modeling of Typical Commercial Buildings in Support of ASHRAE Building Energy Quotient Energy Rating Program /lab/sbs/ashrae-building-energy-quotient-energy-rating Energy Modeling of Typical Commercial Buildings in Support of ASHRAE Building Energy Quotient Energy Rating Program Anonymous (not verified) Fri, 04/01/2016 - 00:00 Research ASHRAE Advanced Modeling Techniques Block Building stock simulation


The objective of this research is to reconcile the differences between the empirical and modeled baselines for energy performance comparison for new and existing commercial buildings, allowing seamless translation of building energy performance metrics among LEED, Standard 90.1, Standard 189.1, Standard 100, and the bEQ As Designed and In Operation ratings. This research is to:

  1. Evaluate and characterize the variability of EUI with building characteristics for standard building types using both statistical analysis of CBECS data and parametric variation of prototype energy models.
  2. Develop a set of modeling assumptions and correction procedures that provide consistent baselines for energy performance metrics for new and existing commercial buildings based on measured EUI of existing buildings.
  3. Validate the modeling assumptions and correction procedures by demonstrating that energy models of buildings with average construction characteristics predict median energy performance.
  4. Using the methods developed above, develop a procedure to relate the EUI of an ASHRAE Standard 90.1-2004 compliant building to the median EUI of existing buildings of the same type and in the same climate.

Collaborators

University of Miami

Open Source Release

During the project, we have develop new prototype building energy models based on the CBECS data. Those models have been open source released at Building Energy Models for Commercial Buildings Based on CBECS Data

Press Release

Publications

Ph.D. Thesis:

  • Y. Ye 2019. "" Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder.

Journal Articles

  • Y., Ye, K. Hinkelman, Y. Lou, W. Zuo, G. Wang, G., J. Zhang 2021. "" Building Simulation, pp. 1-17.
  • Y. Ye, Y. Lou, W. Zuo, E. Franconi, G. Wang 2020. "" Energy and Buildings, 224, pp. 110267.
  • Y. Ye, Y. Lou, M. Strong, S. Upadhyaya, W. Zuo, G. Wang 2020. "" Science and Technology for the Built Environment, 26 (9), pp. 1321-1336.
  • Y. Ye, K. Hinkelman, J. Zhang, W. Zuo, G. Wang 2019. “.” Energy and Buildings, 194, pp. 351-365.
  • Y. Ye, W. Zuo, G. Wang 2019. “.” Energy and Buildings, 186, pp. 126-137.

Conference Proceedings

  • Y. Ye, K. Hinkelman, J. Zhang, Y. Xie, W. Zuo 2019. “” Proceeding of the 16th Conference of International Building Performance Simulation Association (Building Simulation 2019), September 2-4, Rome, Italy.
  • Y. Ye, K. Hinkelman, W. Zuo, G. Wang 2019. “” ASHRAE 2019 Annual Conference, June 22-26, Kansas City, MO.
  • Y. Ye, G. Wang, W. Zuo, P. Yang, K. Joshi 2018. “” 2018 ASHRAE Building Performance Analysis Conference and SimBuild (BPACS 2018), pp. 573-580, September 26-28, Chicago, IL.
  • Y. Ye, G. Wang, W. Zuo 2018. “.” Proceedings of the 4th International Conference on Building Energy and Environment (COBEE2018), pp. 373-378, February 5-9, Melbourne, Australia. 

Off

Traditional 0 On White ]]> Fri, 01 Apr 2016 06:00:00 +0000 Anonymous 110 at /lab/sbs