Computational fluid dynamics modelling of displacement natural ventilation.

Natural ventilation is widely recognised as contributing towards low-energy building design. The requirement to reduce energy usage in new buildings has rejuvenated interest in natural ventilation. This thesis deals with computer modelling of natural displacement ventilation driven either by buoyancy or buoyancy combined with wind forces. Two benchmarks have been developed using computational fluid dynamics (CFD) in order to evaluate the accuracy with which CFD is able to model natural displacement ventilation flow. The first benchmark considers the natural ventilation of a single ventilated space with high and low level openings connected to the exterior driven by combined forces of wind and buoyancy. The second benchmark considers natural ventilation flow in a single space connected to an atrium driven by pure buoyancy. Simulation results of key ventilation parameters (stratification depth, temperature gradient and ventilation flow rate) have been compared with analytical and experimental models and close agreements have been achieved. The two benchmarks are defined using the RNG k-epsilon turbulence model. A pressure boundary is applied onto the ventilation openings directly and a porous medium boundary is used to assist the development of the thermal plume. This method has proved to be robust and the close agreement between the three modelling techniques indicates that CFD is able to model natural ventilation flows in simple geometries with acceptable accuracy and reliability. Using the benchmarks the influences of key CFD modelling parameters and building design issues have been investigated. For example, representing openings, heat source representation, stack height, and air inlet strategies. Natural displacement ventilation of a multi-storey building comprising an atrium is also addressed. Simple analytical models have been developed to describe the key air flow features within the ventilation system.

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

De Montfort University

Year dc:date.issued

2005

Author dc:creator

• Ji, Yingchun

Licence

• https://dora.dmu.ac.uk/bitstreams/a8cd9442-dfcd-4e3b-96b4-bc4296d8db81/download

Dc Identifier Grantnumber

GR/N37346

OAI identifier oai:identifier

oai:dora.dmu.ac.uk:2086/4951