Predictive Modeling Network for Sustainable Human-Building Ecosystems

RCN Workshop 1 - March 18-19, 2014

UNT RCN-SEES-SHBE

RCN-SEES: Predictive Modeling Network for Sustainable Human-Building Ecosystems (SHBE)

Workshop on Physical Systems and Environment

This NSF-funded Research Coordination Network (RCN) in Science, Engineering and Education for Sustainability (SEES) announces its first workshop on one of its five themes: Physical Systems & Environment. Invited participants are requested to submit an abstract of about 500 words for engaging discussions and presentations describing how the network researchers should be collaborating in the following, but not limited to, aspects:

  • Parametric modeling, BIM, and energy modeling
  • Building equipment and systems for thermal and audio/visual comforts, microclimate
  • Cooling/heating/lighting/noise level control
  • Energy consumption patterns influencing design
  • Data validation and model interoperability problems
  • Collaborating needs for inputs/outputs to/from other themes*
  • Impact by climate change, water, resilience

Important Dates

RCN-SEES-SHBE Workshop (Invitation Only) March 18-19

Participants to Submit Abstract by February 18

Workshop RSVP Deadline March 1

Submit your RSVP and/or Abstract via email:

To Dr. Tingzhen Ming Tingzhen.Ming@unt.edu

Workshop Venue:

Holiday Inn Denton

1434 Centre Place Dr

Denton, TX 76205

940-383-4100 (Air Travel: DFW Int’l Airport)

Organizers:

Yong Tao, UNT
Thomas Spiegelhalter, FIU

Marilys Nepomechie, FIU

Wei Yan, TAMU

Kuruvilla John, UNT

Yiding Cao, FIU

Stan Ingman, UNT

March 18, 2014—Tuesday

Arrive-Holiday Inn Denton

Afternoon Half Day Workshop

Steering Committee Meeting

March 19, 2014—Wednesday

All Day Workshop

Including breakout sessions and Roundtable discussion and conclusion

For more information about RCN-SEES-SHBE, search ASME.ORG RCN-SEES-SHBE

Physical Systems & Environment (Theme I)

Building energy/water model (building monitoring data); Building envelope/materials model (BIM data); Indoor climate system/control (performance data); Livability (lighting and appliance data); Distributed power (solar, wind, CHP, biomass, etc. data); Passive features (daylight, green roof, solar chimney, shade, etc.); Outdoor microclimate models (local data).

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