Transient thermal behavior across wall sections in Building 661
Architects rely on energy models to verify that buildings will perform to desired energy standards, but simulation alone does not provide a tacit understanding of the nuanced conditions buildings experience over time. To supplement energy models, we deploy sensor networks to monitor environmental attributes such as temperature, relative humidity, and carbon dioxide. This is particularly relevant to building retrofits, where the data informs performance modifications, targeting both energy efficiency and occupant comfort, all intended to improve existing building stock and advance our knowledge of the interaction of materials and climate.
From left: Fifth generation KieranTimberlake wireless hub prototype. Waterproof and rugged, the device connects up to 100 wired sensors to the internet; waterproof wired digital temperature sensors; KieranTimberlake Thermal Transfer Modeling Tool simulates thermal dynamics in complex wall assemblies
Past experience with the limitations of building monitoring technology—namely, battery replacement, lack of telemetry, and ease of deployment within various building material assemblies—motivated us to develop our own technology to simplify sensor deployment, receive data wirelessly, and view it in a web interface. Once sufficient data are captured, we can analyze the transient thermal profiles across sections of the building envelope to determine the performance of those assemblies in terms of thermal resistance and heat capacity.
The experiment
Our latest experiment with sensor networks undertaken as part of the retrofit of Building 661 for the Energy Efficient Buildings (EEB) Hub, one of three innovation clusters created by the Department of Energy in 2011, has yielded some interesting results. We launched a series of sensors concurrently with a heating and thermal imaging exercise that was performed in the building, which is not insulated and has been unoccupied since 1996. The empty building was heated for two days and recorded with infrared cameras to discover points of heat loss and moisture infiltration. This survey will provide a basis to compare the building envelope’s thermal performance before and after the retrofit.
Building 661 test locations on each facade and sensor depths within the masonry wall at each test location
We deployed fifty-seven sensors across Building 661’s envelope section at varying depths, with eight sensors at each location: two sensors to read interior ambient and surface temperatures, and six sensors placed at varying depths to read temperatures within the wall itself. The sensors captured the cool-down period for a total duration of three days. Data not only corroborated exterior surface temperature readings from the thermal imaging analysis, but also provides a cross section of temperatures to reveal the nature of the dynamic thermal transfer occurring through the envelope materials—information that is not perceptible with thermal imaging techniques.
View of sensors installed in interior masonry wall
Detail of sensors installed in interior masonry wall
The Analysis
Using a custom-developed thermal transfer tool, we determined the rate of heat transfer (thermal diffusivity) between each sensor location. Combining thermal resistance with heat capacity, these constants describe the relative ability of each wall section to inhibit thermal transfer and resist temperature change, expressed in units of time.
Using these calculated constants, we can accurately predict the thermal behavior of any wall section over time using adjacent sensor readings as boundary conditions, as the graph below indicates.
Comparison of interior surface temperature (green) with temperature predicted by thermal performance constants (magenta) calculated from adjacent sensor readings (black)
These diffusivity constants further our understanding of building performance in multiple ways. When we know boundary conditions, such as real-time weather data, we can predict the time-dependent behavior of a wall section using 1-d finite element analysis, as shown in the graph above. We can also compare the calculated diffusivities against the expected bulk material properties of each material to vary energy model inputs.
For example, common brick typically has a resistance of R-0.2 per inch, and a heat capacity of 3.6 Btu/ft²°F per inch, yielding an expected diffusivity of 0.72 hr x thickness². Therefore, our measurement of the brick layers at Building 661 with sensors four inches apart should have yielded diffusivities of 11.5 hours. In reality, our calculated diffusivities were often lower, ranging from 4.3 hours to 11.2 hours. This suggests that the existing wall construction, particularly in the High Bay and the First Floor of the Headhouse, offers less thermal benefit (in terms of both thermal resistance and thermal inertia) than would be predicted from bulk material properties alone.
Although this confirms the thermal value of the existing wall falls below expectations, we can also see that it holds onto a lot of heat. We typically think of a wall’s performance in terms of its thermal resistance, but our research suggests that the temperature profile depends just as much on the wall’s capacity to absorb heat (i.e. resist temperature change) as on its capacity to resist heat flow. If we can compare the performance of different wall assemblies, we can quality assure our energy models, ensuring the performance promised is the performance delivered.
