Solar Load Ratio Parameters for a Passive Solar Heat Pipe System

Abstract

The Solar Load Ratio (SLR) method is a performance prediction algorithm for passive solar space heating systems developed at Los Alamos National Laboratory. Based on curve fits of detailed thermal simulations of buildings, the algorithm provides fast estimation of monthly solar savings fraction for direct gain, indirect gain (water wall and concrete wall) and sunspace systems of a range of designs. Parameters are not available for passive solar heat pipe systems, which are of the isolated gain type. While modern computers have increased the speed with which detailed simulations can be performed, the quick estimates generated by the SLR method are still useful for early building design comparisons and for educational purposes. With this in mind, the objective of this project was to develop SLR predictions for heat pipe systems, which use heat pipes for one-way transport of heat into the building. A previous thermal network was used to simulate the heat pipe system with Typical Meteorological Year (TMY3) weather data for 13 locations across the US, representing ranges of winter temperature and available sunshine. A range of (nonsolar) load-to-collector ratio LCR = 1–15 W/m2K was tested for each location. The thermal network, along with TMY3 data, provided monthly-average-daily absorbed solar radiation and building load to calculate SLR. Losses from the solar aperture in a heat pipe system are very low compared to conventional passive solar systems, thus the load-to-collector ratio of the solar aperture was neglected in these preliminary calculations. Likewise, nighttime insulation is unnecessary for a heat pipe system, and was not considered. An optimization routine was used to determine an exponential fit (the heart of the SLR method) to simulated monthly solar savings fraction (SSF) across all locations and LCR values. Accuracy of SSF predicted by SLR compared to the thermal network results was evaluated. The largest errors (up to 50%) occurred for months with small heating loads (< 80 K days), which inflated SSF. Limiting the optimization to the heating season (October to March), reduced the error in SSF to an average of 4.24% and a standard deviation of 5.87%. These results expand the applications of the SLR method to heat pipe systems, and allow building designers to use this method to estimate the thermal benefits of heat pipe systems along with conventional direct gain, indirect gain and sunspace systems.