Research Update: This research has developed a method called the BIM-Centric Daylight Profiler for Simulation, or BDP4SIM. The general concept of the BDP4SIM method is to step through each space in a building to determine space with “similar” daylighting conditions such that the results from one space can be accurately applied to another “similar” space.  The method analyzes each space sequentially, creating a profile of critical daylighting information as it goes and checking each new space against the growing database of daylighting profiles.

In order to properly describe the daylighting conditions of space, along with general building information relative to site location, climate and orientation that is common to all spaces in a given building, the following additional daylighting profile information is often required:

  • Geometric description and material surface characteristics of all keys elements in the space.
  • Window treatment characteristics and usage patterns for all windows in the space.
  • Geometric description of external obstructions.
  • Space usage and lighting power schedules

The methodology for gathering this daylighting profile information is described in the following subsections in the order that they are checked.  If a space does not pass one of the checks, further checks are not performed to improve simulation time although all the checks discussed only take a fraction of the simulation time.  The comparisons are performed in this order:

  1. Is the space daylit?
  2. Does the space have similar interior geometry and material conditions?
  3. Does the space have interior windows looking towards non-daylit spaces?
  4. Are the transmittances for all windows in the space similar?
  5. Is the exterior daylight resource between matched windows similar?
  6. Do the windows have the same treatment and control options?
  7. Does the space have similar usage schedules and illuminance tagets?

A number of similarity tolerances or “dials” have been implemented in the software when gathering daylight profile information.   This allows a user to adjust the amount of similarity between grouped “similar” spaces.  The different tolerance parameters implemented are listed below and are described throughout the different daylight profile sections that follow.

  • Spherical map resolutions
  • Spherical map miss counts
  • Reflectance and Transmittance tolerance
  • Schedule tolerance

Once similar spaces are matched up, the first found space is simulated and the results are then applied, weighted according to the areas of the similar spaces, to all other similar spaces and used in the building daylight metric totals. 

The figure above shows an example of the various methods available to view the space. First is hemispherical, next is angular, and finally stereographic. The figure below shows how the rays generated from the spherical maps identify unique properties of hte space.
The diagram below shows the three views of the hemispherical maps: up, down, and exterior.
The BDP4SIM methodology is currently being tested on a test building with a variety of shading configurations, both adjacent to the building as well as shading due to nearby buildings. The next phase of the research is to test the method on a real industry case study.

Fischer, M. Iaccarino, G., Welle, B., and Druzgalski, C.  (2011). "Improving the Cost-Effectiveness and Scalability of Multidisciplinary Design Optimization (MDO) for Daylighting Simulation using Artificial Intelligence, Distributed Computing, and Uncertainty Analysis." CIFE SEED Proposal 2011.

Problem: The cost-effectiveness and scalability of an MDO process for today’s large and complex high-performance building projects is limited by excessive computational requirements for sustainable analyses, such as daylighting simulation.

Proposed Research: We propose to develop three related methodologies to drastically improve the scalability and cost-effectiveness of daylighting MDO: (1) develop an artificial intelligence-based methodology/knowledge-based system for daylighting design; (2) a product decomposition methodology for parallel and distributed computing; and (3) a methodology for optimization uncertainty analysis. 

Design for a Sustainble Future

From current practice, to zero energy design, to zero emission design of built environments. Success is our moral obligation.

CIFE SEED Project 2011

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