New mathematical approaches to the quantification of uncertainty affecting the measurement of U-value
General Material Designation
[Thesis]
First Statement of Responsibility
De Simon, Lia
.PUBLICATION, DISTRIBUTION, ETC
Name of Publisher, Distributor, etc.
University of Nottingham
Date of Publication, Distribution, etc.
2017
DISSERTATION (THESIS) NOTE
Dissertation or thesis details and type of degree
Ph.D.
Body granting the degree
University of Nottingham
Text preceding or following the note
2017
SUMMARY OR ABSTRACT
Text of Note
This thesis describes the development and validation of a new computational procedure for the calculation of thermal transmittance (U-value) of existing building elements from the measurement of surface heat flux, and surface and nearby air temperatures. The U-value plays a key role in the determination of the final energy consumption of a dwelling, and, as in the current political scenario reducing carbon emissions is a growing concern, obtaining accurate and quick measurements of thermal transmittance is of particular relevance to the precise representation of the energy performance of the building sector. The calculation method developed is an extension of the RC network, a model based on the discretisation of building elements in resistors and capacitors in analogy with electrical circuits. The advances proposed in this work extend the discrete RC networks to a model based on the full heat equation, with continuous, spatially varying thermal prop- erties. The solution algorithm is inserted in a Bayesian framework that allows the reformulation of the problem in terms of probability distributions. Two solution schemes have been confronted: Markov Chain Monte Carlo and Ensemble Kalman Filters approximation. The model proposed has been validated on synthetic data, laboratory data collected in an environmental chamber on a solid and cavity wall, and in-situ data collected in 3 different locations (2 solid walls and 1 insulated steel frame construction). The results show that the model offers an improved characterisation of the heat transfer through the building elements, furthermore, the algorithm can be used to analyse different wall constructions without the necessity of changing the structure of the model, as opposed to the standard RC networks, and, finally, it offers the practical advantages of the uncertainty reduction on thermal transmittance (from 14-25% to 7-10%) and a diminution of the necessary monitoring period from a minimum of 3 days to 1 day or less. These advantages, in turn, benefit the building performance evaluation on different levels: in first instance, the practicality of measuring thermal transmittance in-situ is improved, thus making it easier to monitor the actual envelope performance and, secondly, the uncertainty reduction on the U-value leads to important reductions on the uncertainty surrounding the energy consumption predictions associated with a dwelling.