Thesis

Development of guidance for air flow network modelling to improve domestic ventilation design

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Awarding institution
  • University of Strathclyde
Date of award
  • 2025
Thesis identifier
  • T17205
Person Identifier (Local)
  • 201684597
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • The current domestic decentralised mechanical ventilation (dMEV) design, mandated by Scottish Building Regulations section 3.14, is aimed at providing occupants with adequate ventilation. However, studies have shown that in practice dMEV systems do not serve the intended purpose, with issues in design and construction process, and occupant interactions. This thesis directly addresses deficiencies in the ventilation design process. The use of Airflow Network (AFN) methods in design are common in non-domestic but not in domestic design. This is largely due to the perceived complexity of AFN modelling as well as a lack of standardised guidance of domestic applications. The primary aim of this thesis was to investigate the application of AFN in domestic design, establish a knowledge base and methods, and make recommendations and propose guidance for effective utilisation of AFN in domestic ventilation design. A literature review was carried out into current domestic ventilation performance, the use of AFN for ventilation design, standards and regulations, and relevant research, which highlighted poor performance, gaps, and ambiguities, plus a lack of studies directly relevant to the domestic design paradigm. A dataset was sourced from a domestic dMEV monitoring study which included design, construction, occupant behaviour, and carbon dioxide (CO2) monitoring. The monitoring was for two distinct periods, one with normal occupancy (logged through an occupant diary) and one with specified settings for occupant-controlled components. A modelling study was designed to investigate the application of AFN using this dataset as a reference point. Different configurations of AFN model and underpinning equations and parameter settings were investigated, reflecting the ambiguities found in literature, statistical analysis applied, and useful insights generated. The key results indicate that with an appropriately configured AFN, there is a significant increase in the model’s accuracy. The main findings suggest that the errors for the metabolic CO2 concentration can be up to 53%. The literature and the findings from the modelling study were both used to inform a set of recommendations and provide guidance on effective application of AFN to address the deficiencies in current design methods with a higher accuracy The primary focus in the thesis was the use of AFN in design for the provision of adequate ventilation using a dMEV system. However, applicability of the findings for the wider application of AFN for overheating and energy performance and applicability for different system types such as MVHR were discussed. This work contributes to the field by presenting guidance to assist modellers in conducting effective AFN-based ventilation design studies, directly addressing critical gaps in current practices. Limitations of the research include reliance upon a single dataset and this study would require broader validation across different building types and ventilation systems. Future work can focus on the expansion of applicability of AFN methods to other system types such as Mechanical Ventilation with Heat Recovery (MVHR) and exploring their role concerning overheating and energy performance issues
Advisor / supervisor
  • Tuohy, Paul
Resource Type
DOI

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