Track 4: This New World: Preservation technology and emerging issues within our historic buildings and built landscapes
2 - Validation of Computational Analysis of a Historic Farmhouse
Tuesday, September 25
8:30 AM - 10:00 AM
The main house at Hardman Farm, an 1870's structure in northern Georgia, is rare in that it retains virtually all of its original materials and never had mechanical conditioning installed, continuing to rely on historic passive systems for heating and cooling. After considering a range of strategies for heating and cooling, the design team elected to install a very minimal mechanical system for heating only and to adopt an adaptive comfort approach to establish an acceptable interior temperature and humidity range. This strategy not only protected historic fabric, but offered an interpretive opportunity to provide an authentic thermal environment experience for the visitor. This innovative design decision was driven by predictive analysis and modeling to determine that mechanical cooling was not necessary for its new use. The analysis forecasted temperature stratification and thermal lag to justify continued reliance on passive systems. This analysis was presented at APT 2010.
Following project completion, the design team wished to evaluate the effectiveness of the design-phase analysis, which included computational fluid dynamics and energy modelling. A majority of the information regarding building performance analysis is tailored for the construction of new buildings and poses challenges for projects restoring existing fabric. Analysis in historic structures is often hindered by the fact that many materials are not manufactured and are of a composition which varies greatly between time period and locality. The assumptions made for Hardman Farm were based on the best judgment of the analysts and designers, but there is a complexity to passive systems which is difficult to capture within the confines of a model’s limited set of input variables. The analysis data predicted the farmhouse interior would be outside of the adaptive comfort range only a few hours a year during its established hours of operation. To corroborate these judgements, assumptions, and predictions the design team sought to collect actual building performance data to confirm the methodology from the design process.
A two year post-occupancy period of monitoring was funded by a grant from the National Center for Preservation Training and Technology to compare measured data at the site to the predicted performance during the design phase. Weather data, collected from an on-site station, provided measurement of external weather conditions. Correlating interior temperature and humidity data, and moisture content of interior surfaces, were also collected
When comparing the data as a daily and a monthly trend, the model’s predictive performance against the measured data proved very effective in forecasting the actual thermal conditions. This paper will highlight the array of tools used for predictive analysis, data inputs unique to historic structures, and the use of adaptive comfort models in historic structures.
- Upon completion, participant will understand the types of tools and equipment used for the building performance analysis in the design phase and for data collection during the post-occupancy phase.
- Upon completion, participant will understand the capability and accuracy of building performance analysis tools as they pertain to historic structures.
- Upon completion, participant will be familiar with data inputs in the building performance analysis tools that can be adjusted to reflect the characteristics of historic building envelopes.
- Upon completion, participant will be familiar with adaptive comfort as an alternative matrix for establishing goals for building performance and thermal environment goals for historic structures.