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ThermalOpt: A methodology for automated BIM-based multidisciplinary thermal simulation for use in optimization environments

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  • Advances in Modeling and Simulation Tools
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Abstract

This paper describes ThermalOpt—a methodology for automated BIM-based multidisciplinary thermal simulation intended for use in multidisciplinary design optimization (MDO) environments. ThermalOpt mitigates several technical barriers to BIM-based multidisciplinary thermal simulation found in practice today while integrating and automating commercially available technologies into a workflow from a parametric BIM model (Digital Project) to an energy simulation engine (EnergyPlus) and a daylighting simulation engine (Radiance) using a middleware based on the open data model Industry Foundation Classes (IFC). Details are discussed including methods for: automatically converting architectural models into multiple consistent thermal analytical models; integration/coordination of analysis inputs and outputs between multiple thermal analyses; reducing simulation times; and generating consistent annual metrics for energy and daylighting performance. We explain how ThermalOpt can improve design process speed, accuracy, and consistency, and can enable designers to explore orders of magnitude larger design spaces using MDO environments to better understand the complex tradeoffs required to achieve zero energy buildings.

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Authors and Affiliations

  1. Center for Integrated Facility Engineering (CIFE), Department of Civil and Environmental Engineering, Stanford University, Environment and Energy Building, 473 Via Ortega, #291, MC:4020, Stanford, CA, 94305, USA

    Benjamin Welle

  2. Design Process Innovation, 477 Vermont St., San Francisco, CA, 94107, USA

    John Haymaker

  3. Daylighting Innovations, Lafayette, CO, 80026, USA

    Zack Rogers

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  1. Benjamin Welle
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  2. John Haymaker
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  3. Zack Rogers
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Correspondence to Benjamin Welle.

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Welle, B., Haymaker, J. & Rogers, Z. ThermalOpt: A methodology for automated BIM-based multidisciplinary thermal simulation for use in optimization environments. Build. Simul. 4, 293–313 (2011). https://doi.org/10.1007/s12273-011-0052-5

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