Bayesian inferences of the thermal properties of a wall using temperature and heat flux measurements
Bibliography:
Iglesias, Marco, Zaid Sawlan, Marco Scavino, Raul Tempone, and
Christopher Wood. "Bayesian inferences of the thermal properties of a
wall using temperature and heat flux measurements." International Journal of Heat and Mass Transfer 116 (2018): 417-431.

Authors:
Iglesias, Marco, Zaid Sawlan, Marco Scavino, Raul Tempone, and Christopher Wood.

Keywords:
Heat Equation, Nuisance Boundary Parameters Marginalization, Heat Flux Measurements, Solid Walls, Bayesian Inference, Thermal Resistance, Heat Capacity, Experimental Design

Abstract:
Abstract

The assessment of the thermal
properties of walls is essential for accurate building energy
simulations that are needed to make effective energy-saving policies. These properties are usually investigated through in situ measurements of temperature and heat flux
over extended time periods. The one-dimensional heat equation with
unknown Dirichlet boundary conditions is used to model the heat transfer
process through the wall. In Ruggeri et al. (2017), it was assessed the
uncertainty about the thermal diffusivity
parameter using different synthetic data sets. In this work, we adapt
this methodology to an experimental study conducted in an environmental chamber , with measurements recorded every minute from temperature probes and heat flux sensors placed on both sides of a solid brick wall
over a five-day period. The observed time series are locally averaged,
according to a smoothing procedure determined by the solution of a
criterion function optimization problem ,
to fit the required set of noise model assumptions. Therefore, after
preprocessing, we can reasonably assume that the temperature and the
heat flux measurements have stationary Gaussian noise
and we can avoid working with full covariance matrices. The results
show that our technique reduces the bias error of the estimated
parameters when compared to other approaches. Finally, we compute the
information gain under two experimental setups to recommend how the user
can efficiently determine the duration of the measurement campaign and
the range of the external temperature oscillation.
No