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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).
