Modelling heat transfers in a supermarket for improved understanding of optimisation potential

UoM administered thesis: Phd

  • Authors:
  • Frances Hill

Abstract

Energy demand attributable to the operation of supermarkets on-site is thought to be responsible for 1% of UK greenhouse gas emissions. In use data show a performance gap approaching a factor of three for overall energy use, with a gap of a factor of six in energy demand for heating. This performance gap indicates significant faults in the conventional modelling route. Current building regulations in the UK require the "building related" energy use of new commercial buildings to comply with particular requirements. Supermarket buildings are therefore modelled according to these protocols to establish their predicted energy demand. The impact on this predicted energy demand of the exclusion of process energy (eg for refrigeration) from these protocols is explored by modelling a supermarket retail floor with heat transfers related to refrigerated cabinets, and comparing the sensitivities of such models with those of models compliant with regulatory protocols.Whereas models compliant with regulatory protocols indicate an advantage of limiting the level of insulation and airtightness, and allowing stratification, to facilitate heat loss through the store envelope; models that include heat transfers around the refrigerated cabinets are found to show that energy demand may be decreased by up to 40% by doubling both insulation and airtightness, and by destratification. This will, however, only apply if rates of air change in buildings in use match those modelled.This shows the importance of including heat transfers around refrigerated cabinets in design modelling, so that appropriate decisions may be taken with respect to building envelope parameters. Compliance modelling protocols should be changed to reflect this. In order to facilitate this change and enable modelling of refrigerated cabinets within a compliance model through a few simple inputs, a set of data and associated algorithms is derived and offered for inclusion in compliance modelling tools.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award date1 Aug 2016