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Smooth Rough Contact Formulation for Crack Planes

Table of Contents



Table of Contents

Table of Figures

List of Tables


1 Introduction

1.1 Rationale for this thesis

1.2 Hypothesis

1.3 Structure of thesis

2 State of The Art Analysis

2.1 Thermal Comfort

2.1.1 Air Temperature

2.1.2 Radiant Temperature

2.1.3 Relative Humidity

2.1.4 Air Velocity

2.1.5 Activity (Metabolic Rate)

2.1.6 Clothing

2.2 Indoor Air Quality (IAQ)

2.2.1 Contributing Pollutants to IAQ

2.3 Fluid Dynamics

2.3.1 Governing Equations

2.3.2 Analytical Fluid Dynamics

2.3.3 Computational Fluid Dynamics

2.3.4 Stages of CDF

2.3.5 Computational Fluid Dynamics Software

2.3.6 Autodesk CFD

3 Software Architecture

3.1 Software Flow Diagram

3.2 Proposed software description

4 Methodology Approach




5 Case Study – ERI Building

5.1 Experimental Plan



6 Discussions & Results




7 Findings & Conclusion

8 Appendix

9 Bibliography

Table of Figures

Figure 3 Material Nonlinearity

List of Tables

Table 1 Concrete Material Properties

Table 2 Reinforcement Properties


Although many consistent models have been created to accurately model the linear behaviour of concrete, the same cannot be said for the non-linear behaviour of concrete.  The complex nature of the softening behaviour of concrete cracking under loading and the contact that occurs between rough crack surfaces when they are closed or subjected to shear loading makes the development of such a model a truly difficult task.  One model, which aims to achieve this goal is a multi-crack model called the Craft Model developed by A.D.Jefferson. This model which is defined as a plastic-contact-model uses planes of degradation that can undergo damage and separation but can also regain contact.  Recent updates to the model introduce smooth rough contact formulation for crack planes to simulate closing under both normal and shear loading.

1       Introduction

1.1      Rationale for this thesis

Humans spend on average 90,000 hours at work over their lifetime with much of this time spent in the office, so it is imperative that the quality of air in these office spaces should be delivered at an optimal standard to enhance productivity and comfort conditions for the occupants in their work environment.

With the world leaning towards energy savings, it’s not just as simple as fitting out the premises with a top of the range air conditioning system, as the carbon foot print left behind for the next generation would be catastrophic. The need to correctly implement design features during the design phase of the structure should be met with open mind, such to limit unnecessary energy wastage.

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Description generated with high confidenceNatural ventilation, mechanical ventilation, and full air conditioning are some of a few methods of maintaining a building in relation to thermal comfort, but which design to implement is the burning question as the flow of air, be it via natural ventilation or otherwise is difficult to predict.

Figure 1 – Natural Ventilation in Buildings

The need for accurate prediction of air movement in occupied spaces is essential, as copious quantities of air supplied to a space need to be evenly distributed. Without adequate air distribution, unwarranted air movement (drafts) may occur in some zones, whilst stagnant air may occur in other zones of the same area. Poor air distribution can affect the indoor climate and degrade the air quality leading to unsatisfied occupants [1].

Computational Fluid Dynamics (CFD) is now being applied to provide in-depth analysis of intricate fluid flows, including detailed flow features like velocity, turbulence, pressure and temperature for internal and external flows. By incorporating CFD into initial design procedures, the engineer tasked with designing the   ventilation system can limit if not eliminate the unwanted event of dissatisfaction amongst occupants arising [2].

However, implementing CFD properly is time consuming, with a good knowledge of fluid dynamics a necessity. CFD output is only as good as human input.

1.2      Hypothesis

The need to provide computational fluid dynamics analysis during the design stage of a project is a costly measure due to high user knowledge input and the time allocated to running simulations as the speed at which the simulation runs varies in relation to the process power of the machine being used. This study aims to provide future designers the knowledge of what are the maximum and minimum requirements for implementation of a CFD analysis, the ratio of window apertures to room volume necessary to maintain a high standard when specifying natural ventilation to the design based on building layout, orientation and design parameters set out by governing bodies.

1.3      Structure of thesis

Chapter 2 – In the state of the art analysis the author begins with a literature review of the basic requirements set out; what defines thermal comfort and the ventilation requirements associated with indoor air quality. A further analysis looks at the requirements of computational fluid dynamics, drilling down to the categories of ventilation modelling.

Chapter 3 –    Discusses the software environment associated with computational fluid dynamics and their ability to perform analysis based on indoor air quality, thermal comfort, risk of overheating and essentially the ability to mimic the room air flow effectively enough so that it appears to resemble the real thing. Based on this software analysis the author defines the chosen software most suitable for the impending study

Chapter 4 –  This chapter discusses the methodology approach, specifying standards used, ventilation types, software implemented, reference material available, governing equations associated with air flow movement and all assumptions made during this study.

Chapter 5 –  Chapter 4 looks at the case study based on the Environmental Research Institute (ERI) building. This study will provide sufficient information to determine the ventilation method acceptable within the defined space/zone whilst keeping to standards laid down by governing bodies.

Chapter 6 – This chapter discusses the granularity of the simulation results, indicating the estimated data processing time and input parameters required to produce adequate simulations for design discussion. Further discussions involve detailed analysis on improvements that could be made to improve the IAQ of similar building based on the various ventilation methods.

Chapter 7 – Findings & Conclusions



2       State of The Art Analysis

The following chapter looks at the importance of thermal comfort within an occupied space, the required air quality necessary to meet the thermal comfort criteria and the methods used during the design stage to implement these findings based on computational analysis. The ability to analyse the level of thermal comfort and the state of the indoor air quality is of the utmost importance to maintaining a good working environment where occupants can avoid thermal stress.

2.1      Thermal Comfort

Thermal comfort is defined as “that condition of mind which expresses satisfaction with the thermal environment and is assessed by subjective evaluation” [3].

The main influencing parameters for thermal comfort are as follows;

  • Temperature (Air, Surface, Asymmetry)
  • Air Speed/turbulance
  • Radiant Temperature
  • Activity (metabolic rate)
  • Relative Humidity
  • Clothing (clo-value)

Thermal comfort is essentially the way in which people interact with their thermal environment. When a person’s states that they are too hot or too cold, what is really happening is they are responding to the heat transfer from their body to the surrounding environment [4].Cf

The human body operates at a core temperature around 37oC for the internal organs to function properly. Any fluctuations in this temperature requires the body to self-regulate. Slight increases or decreases in this temperature can cause implications – a sudden drop can cause hypothermia whilst a sudden rise can result in hyperthermia [5].

Predicted Mean Vote (PMV) is a thermal sensation scale employed by Ashrae that incorporates a formula developed by Fanger to achieve thermal comfort amongst the majority of people in the occupied space. Although Fanger’s method is the most common method of calculating PMV, certain points should be noted:

  • Fanger’s study were based exclusively from climate chambers where conditions were kept at a constant, resulting in a steady state environment. This does not take for the everyday patterns of a building where door openings and closing takes place several times a day.
  • Clothing insulation [5] studies were determined in an experiment which used heated manikins
  • Humidity, air movements air temperatures and radiant temperatures have to be estimated when using Fanger’s equation.
  • Metabolic rates used in the equation are assumed, not really knowing what activity is actually taking place within the specified space. In addition to this, many spaces will have multiple activities.

Figure 2 – Ashrae and Bedford Comfort Descriptors [TM52: 2013]

The following formula for PMV as expressed by P.O. Fanger


PMV = Predicted Mean Vote Index
M = Metabolic rate
L = Thermal load – defined as the difference between the internal heat production and the heat loss to the actual

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Description generated with very high confidenceThe term Percentage persons dissatisfied (PPD) is a representation of the way a group of occupants would judge their level of comfort.

Figure 4 – Sample Software Calculation of PMV / PPD (Course Notes)

2.1.1      Air Temperature

What temperatures are comfortable? CIBSE Guides proposes a temperature of 20oC to 22oC is ok for some sedentary situations, but where activities are taking place, it is proposed a lower temperature is necessary, but these levels of activities may vary depending on the level of work taking place. A prime example of this is where one office space may consist of sedentary work and require say 21oC, whereas another office space might have a high level physical work requiring a lower air supply temperature. The following table indicates responses to various temperatures.

Temperature (oC) Response
18 Physically inactive people begin to shiver. Active people are comfortable.
21 Optimum for performance of metal work.
22 Most comfortable year-round indoor temperature for sedentary people.
24 People feel warm, lethargic and sleepy. Optimal for unclothed people
25 Optimal for bathing, showering. Sleep is disturbed.

1 – Typical Response to Various Temperatures [CIBSE Guide A]

2.1.2      Radiant Temperature

With ever office space there will be office furniture which produce heat, be it computers, printers, radiators, lights and even humans. The heat emitted from these objects radiates to the surrounding air. This radiant head in turn heats the surrounding air to produce a radiant temperature. To manually calculate this temperature would be a time-consuming process. With the development of advanced computer software, it is now possible to feed information into this software and receive the output data which can be critical to the future design of a structure.

2.1.3      Relative Humidity

When relative humidity is kept around 50%, occupants tend to have fewer respiratory problems. Higher humidity levels tend to make the occupied area stuffy whilst increasing the chances of bacterial and fungal growth. Humidity levels with fall below 50% tend to cause discomfort among occupants in the form of drying out skin causing skin rashes.  CIBSE Guide A recommends that relative humidity within the range 40-70% RH are generally accepted[6]

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Figure 5 – The effect of humidity on environmental factors that affect occupants comfort and health

[ASHRAE Systems 2008; chapter 22, figure 1]

2.1.4      Air Velocity

Large air movement in occupied spaces can cause considerable discomfort especially during the winter period if the air is cold. For the human body, the two most susceptible parts are the neck and the ankles, cooler air tends to travel at floor level which may cause discomfort around the ankles. During design stages, air diffusion within the occupied space should be carefully considered as high-level supply can cause considerable discomfort on the back of the neck for people working at desks, while low level supply as stated earlier can cause stress around the ankles. CIBSE recommend the following air velocities during seasonal periods; Winter 0.1(m/s), Summer 0.3(m/s)

2.1.5      Activity (Metabolic Rate)

Metabolic heat production is largely dependent on the occupant’s activity. In simple terms, the more physical work undertaken by the occupant, the more heat they produce. Adding to this, the more heat produced, the greater the need to lose heat to prevent the body from overheating.

When incorporating the metabolic rate into our thermal comfort equations, certain characteristics of the human study need to be noted;

  • Size
  • Weight
  • Age
  • Sex

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Description generated with high confidenceAll these factors have an impact on the persons perception of thermal comfort and should not be omitted from design factors. The following table taken from Ashrae 55-2010 gives typical metabolic rates and heat generation per unit area of body surface for office activities.

Figure 6 – Typical metabolic rates and heat generation

Activity Metabolic Rate/Met Heat Generation / W.m-2

2.1.6      Clothing

Clothing worn by the occupant alters accordingly to the time of year. During summer months, typical clothing may consist of a light form of dress, blouse, pants and even short sleeve shirt. Over compensating in clothing during warm months could result in the occupant overcoming to heat stress.

During winter, people tend to wear thicker, heavier and even more layers. If  the level of clothing does not provide enough insulation, the occupant is at risk of causing themselves injuries, even hypothermia.

The insulation effect of clothes is measured in the unit “CLO”. The following table are typical values for various items of clothing [ sample values taken from ]

Clothing Insulation
Clo m2K/W
Nude 0 0
Trousers Shorts 0.06 0.009
Walking shorts 0.11 0.017
Light trousers 0.20 0.031
Normal trousers 0.25 0.039
Flannel trousers 0.28 0.043
Overalls 0.28 0.043
Skirts, dresses Light skirt 15 cm. above knee 0.01 0.016
Light skirt 15 cm. below knee 0.18 0.028
Heavy skirt knee-length 0.25 0.039
Light dress sleeveless 0.25 0.039
Winter dress long sleeves 0.40 0.062

Table 2 – Thermal Insulation Values for typical Clothing

2.2      Indoor Air Quality (IAQ)

Indoor Air Quality (IAQ) refers to the air quality within a building. The need for indoor air quality must be addressed to meet the needs and requirements of the buildings occupants. Maintaining a good IAQ leads to a more happier and productive workforce, and a happier workforce reduces the work time which is lost due to unsatisfied occupants. Neglecting the standard of IAQ in a building can lead to high repair costs of the mechanical systems if adjustments must be made. The following table is an extract from CIBSE KS17 – Indoor Air Quality and Ventilation

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Table 3 – Approximate thermal comfort variables and their affect on IAQ

By incorporating good practice of IAQ during the design stage of a project will result in a building that is more successful in meeting its design goals and achieving the desired levels of performance throughout its occupied life[7].

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The ventilation of an occupied space is necessary to maintain good IAQ levels. Ventilation is needed for many reasons;

  • Providing fresh air to occupants to enhance their sense of thermal comfort.
  • The removal of contaminants and pollutants such as carbon dioxide, volatile organic compounds (VOC’s), odours and particulates which can lead to medical problems further down the line.

2.2.1      Contributing Pollutants to IAQ

Understanding and having the ability to control indoor air pollutants can help reduce the risk of indoor health concerns.

The following is a list of common pollutants that may exist in your current surroundings.     Carbon Dioxide (CO2)

Carbon dioxide is the most common pollutant

Critical Outcome: High concentrations can cause irritation to the eyes and throat. Elevated levels can have an adverse effect on the levels of concentration sustained by occupants.

Source: Carbon Dioxide is exhaled as part of the metabolic process and is also emitted from everyday appliances such as boilers and cookers.

The following table indicates concentrations associated with air quality classifications [8]

Classification Indoor Air Quality Standard Fresh Air Ventilation Range Fresh Air Default Value (L/s/p) Approximate indoor CO2 Concentration (ppm)
IDA 1 High >15 20 700 to 750
IDA2 Medium 10-15 12.5 850 to 900
IDA3 Moderate 6-10 8 1,150 to 1,200
IDA4 Low <6 5 1,500 to 1,600

Table 4 – Indoor Air Quality Classification in BS EN 13779     Carbon Monoxide (CO)

Critical Outcome: Acute exposure-related reduction of exercise, with an increase in symptoms of heart disease.

Source: Carbon monoxide is produced both indoors and outdoors by combustion sources such as poorly installed heating systems, poorly ventilated cooking areas and is a product of petrol and diesel vehicles.     Benzene (C6H6)

Critical Outcome: Acute myeloid leukaemia and genotoxicity.

Source: Benzene can originate in building materials, furniture and other such artefacts. The presence of combustion sources and other human activities will be the main determinant of the concentration of benzene indoors.     Formaldehyde (CH20)

Critical Outcome: Sensory Irritation

Source: The source of formaldehyde in the indoor environment are; furniture and wooden products containing formaldehyde based resins (plywood, insulation). Other sources include man made products such as paint, glue and varnish.     Nitrogen Dioxide (NO2)

Critical Outcome: Respiratory symptoms, airway inflammation and a decrease in immune system.

Source: Indoor sources include smoking and the burning of natural resources (coal, gas and wood) in stoves where inadequate ventilation is applied.     Radon (Rn)

Critical Outcome: Lung cancer and has association with other forms of cancer such as leukaemia.

Source: From the earth beneath the structure, the higher the uranium content of the soil the greater the levels of radon possess a risk to the indoor air quality.

2.3      Fluid Dynamics

Fluid Dynamics is the process used to model the behaviour of fluids. In building’s it is typically used to model the flow of air within an occupied space. This can be invaluable to the designer, allowing them to predict internal conditions ever before the building is built, allowing them to simulate options and select the most applicable to the design.

Fluid dynamics can be subdivided into two initial topics, analytical and computational. Both topics require the use of three governing equations, the conservation of mass, conservation of momentum and the conservation of energy.

2.3.1      Governing Equations

The three governing equations used within CFD, are written as seen below in partial differential format[9].

Conservation of Mass:

The equation is derived by considering a fixed volume in a space and that the flow of air entering the space is equal to that which is exiting it. This air flow is expressed as the mass per unit volume per second. (kg/m3/s)

δρδt+δδxj ρuj=0

Equation 1 – Conservation of mass

In short, this equation states that the change of mass within the volume is equal to the overall flow of mass across the boundaries of the fixed volume.

Conservation of Momentum:

This equation is derived from Newton’s second law; force = mass x acceleration. The following equation gathers all the force elements to the right-hand side.

δδtρui+ δδxiρuiuj= μδτijδj- ϱδpijδxj+Bi

Equation 2 – Conservation of Momentum

Broken down from left to right as follows;

1 The change in momentum over time
2 Mass x acceleration
3 Force due to shear stress
4 Force due to pressure gradient in the fluid
5 Generic term representing all other body forces.

Conservation of Energy:

Is also known as the first law of thermodynamics, states that the rate of change of internal energy of a volume of air is equal to that of the heat supplies to the air less the work done by the volume of air on its surroundings. It is expressed using enthalpy as an indicator of energy.

δhδt+ δδxiρuih- δδxiλCpδhδxj=0

Equation 3 – Conservation of Energy

Broken down from left to right as follows;

1 Represents Enthalpy. (heat)
2 Change in enthalpy due to air movement.
3 Change in enthalpy caused on the air outside the volume being considered

2.3.2      Analytical Fluid Dynamics

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Description generated with high confidenceUsing long-hand calculation to predict the air flow within a defined space can become tedious, as the equations to be implemented (stated above) can be intricate and most time consuming. The zone in question would need to be broken up into c complex mesh, the more complex the mesh the greater the prediction on the aerodynamic flow within the space. Below is a simple 2-D mesh (Figure 7) with some typical terminology.

Figure 7 – A Simple 2-D Mesh

To establish a detailed picture of the flow patterns within the occupied space the user should consider using a 3-dimensional grid (Figure 8), this method increases the workload of the user as it becomes painstakingly tiresome to formulate hundreds if not thousands of equations.

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Figure 8 – A Simple 3-D Mesh

Establishing a grid size is of the utmost importance when simulating the air stream flow in a space, a grid with large cells will output little useful data, but will be less time consuming, whereas implementing a 3-dimensional grid with small cell sizes will produce vast amounts of useful data for the user to study, such complex grids would result in days of complex calculations. It is with these unwanted constraints, engineers have moved away from the analytical form of fluid dynamics and moved towards the ever-advancing process of Computational Fluid Dynamics, where simulations can be tested and verified to suit the needs of the client.

Analytical Fluid Dynamics (AFD) cannot be independently verified and is very difficult for complex flows[1].


2.3.3      Computational Fluid Dynamics

With the evolving progress in computer technology in recent years, computational fluid dynamics has advanced to a level where intricate flows can be simulated using numerous variables which can hypothetically overcome the difficulties in predicting vital flow structures such as thermal stratification in natural ventilation.

CFD is arguably the most intricate air-flow modelling technique in use today and probably the least well understood. With the evolution of modern computers and full-bodied numerical algorithms, a division between the user and the governing equations being solved have left several users detached with limited knowledge on how the software established the results, this in turn leaves the user unable to diagnose problems in the event of unexpected or non-compliant results being returned by the software.

With a background knowledge in fluid dynamics CFD can be a powerful tool to any user with benefits that include;

  • Creating scenarios, with the model already build the user will only need to change the settings (variables) in order to create a “what if” analysis.
  • With advances in cloud computing, models can be generated in 1:1 scale which the unnecessary implication of scaling results to suit scaled down models.
  • CFD models are limitless as to the number of measurement sensors required during analysis.
  • Process time of analysis is only limited by the process power of the computer being used and is far more efficient than that of an analytical process.

2.3.4      Stages of CDF

Pre-Processing Stage

  • Defining the model geometry.
    • Import initial model into CFD software, unless model was created using the same software for modelling as for analysis.
  • Define the computational domain.
    • Select the space(s) within the model to be analysed.
  • Define the boundary and initial conditions.
    • Define flow rates to be used for study purpose
    • Specify design temperature, both internal and external
    • Create profiles for building occupancy, night-time cooling modes and heating/cooling set points
  • Define grid / mesh.
    • The space being analysed is subdivided into small blocks (the smaller the blocks the more defining results will be returned, note: the smaller the blocks, the more time consuming the process will be.) these blocks are to which the computational equations are applied.
  • Define all the necessary solver parameters.
    • This is dependent upon the computational models that are required based on the expected conditions of the flow.

Processing Stage

  • Inspect the progress of the run.
    • On completion of step 1, the model will be put forward for simulation to the solver. It is during this time the user should track and take note of the progress during the simulation run. Inconsistencies such as ambiguous temperatures (spikes) should be dealt with accordingly. It is at the user’s discretion to proceed or not with simulation runs if runs are deemed unrealistic.
    •  If simulation runs are stopped, the user may adjust the solver criteria and re-run the simulation to achieve convergence. It may be of benefit to return to step one and redefine the setup parameters, making changes in relation to the observations. Simulations are repeated until such time as the user deems results satisfactory.

Evaluation Stage

  • Upon completion of simulation where the user deems the results to be satisfactory, the user will analyse and generate a report based on the findings. Should the design criteria not me met, the user has the option of adjusting the initial model and progressing again with step one.

2.3.5      Computational Fluid Dynamics Software

With the increasing interest of airflow within occupied spaces during the building design, whole building CFD simulation programs are increasingly employed in the design process to help both engineers and architects determine fundamental design criteria to satisfy the occupants need for thermal comfort and in turn reducing the cost of additional plant where natural ventilation is acceptable within the building framework. The following are three simulation software packages on the market which can aid the designer to provide first-rate working conditions for its occupants.     EnergyPlus

EnergyPlus is an open source software and is defined as a whole building energy simulation program, used to model energy consumption in heating, cooling, lighting and ventilation. Some of the prominent features and facilities of EnergyPlus are:

  • Integrated, simultaneous solution of thermal zone conditions and HVAC system response that does not assume that the HVAC system can meet zone loads and can simulate un-conditioned and under-conditioned spaces.
  • Heat balance-based solution of radiant and convective effects that produce surface temperatures thermal comfort and condensation calculations.
  • Sub-hourly, user-definable time steps for interaction between thermal zones and the environment; with automatically varied time steps for interactions between thermal zones and HVAC systems. These allow EnergyPlus to model systems with fast dynamics while also trading off simulation speed for precision.
  • Combined heat and mass transfer model that accounts for air movement between zones.
  • Advanced fenestration models including actuated window blinds, electrochromic glazings, and layer-by-layer heat balances that calculate solar energy absorbed by window panes.
  • Illuminance and glare calculations for reporting visual comfort and driving lighting controls.
  • Component-based HVAC that supports both standard and novel system configurations.
  • A large number of built-in HVAC and lighting control strategies and an extensible runtime scripting system for user-defined control.
  • Functional Mockup Interface import and export for co-simulation with other engines.
  • Standard summary and detailed output reports as well as user definable reports with selectable time-resolution from annual to sub-hourly, all with energy source multipliers. [10]

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EnergyPlus is free, open-source, and cross-platform, latest release – v8.7.0 and is compatible with the following platforms;

Windows 7&8   32 and 64-bit versions

Mac OSX 10.9  64-bit version

Linux  (Ubuntu 14.04) 64 bit version

Item Description
Userface Complex
User Manual Difficult to follow for new users to CFD
Coverage of CFD Consists of only a small documented area within the user manual and is dependant of prior knowledge of fluid dynamics.
Cost EnergyPlus is an open source software (zero cost to user)
CPU requirements Standard amongst other CFD softwares
Plugins with Revit Yes
Cloud option Yes
Customer Support Slow response

Table 5 – EnergyPlus Software Capabilities     IES VE (MicroFlo)

MicroFlo is a vender specific software used to simulate air flow and heat transfer, both internally and externally. MicroFlo has the ability to define boundary conditions such as internal energy sources, environmental conditions and HVAC systems. It can be used to predict occupancy thermal comfort prior to construction, investigate in detail natural and mixed mode ventilation strategies.

System Requirements [11]

Supported Environments:

  • Windows Vista
  • Windows 7
  • Windows 8
  • Winows installation on Mac OS via (Link to ac FAQ & videos)
    • VMware Fusion
    • Parallels Desktop
    • Coot Camp


Software Required:

  • Microsoft® Internet Explorer 7.0 or higher [link FAQ item]
  • .NET Framework 2.0 from Microsoft


Minimum Hardware:

  • 1280 x 960 screen resolution
  • 256Mb OpenGL v2.1 compliant graphics card (or greater)
  • Dual core CPU (Intel Core 2, or AMD x2 series)
  • 4Gb RAM


Unsupported Environments:

  • Linus


Table 6 – IES System Requirements

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Item Description
Userface User Friendly, all software sub-programs confined in the one package. (ModelBuilder, Solar, Energy, Compliance and Rating, Lighting, Cost&Value, Egress and CFD)
User Manual Overall package broken down into sections, making it easier to find material necessary for desired consumption
Coverage of CFD CFD is covered in two separate manuals, each as a stepping stone to the next. (ApachieSim and MicroFlo)
Cost IES is a vender specific software with student prices of €60
CPU requirements Standard amongst other CFD softwares, but the better the ram the faster the simulation run time, the more powerful graphics card the greater the graphical output of simulations.
Plugins with Revit Yes
Cloud option Yes
Customer Support Yes, with next day response

Table 7 – IES Software Capabilities

The following are some capabilities of IES VE during the analysis of CFD:

  • Measurement of velocities, temperatures, humidity and CO2 along with air change effectiveness with the defined space.
  • Ability to optimise the design of a system to increase savings on a project.
  • Utilize dynamic simulation models to derive realistic boundary conditions.
  • Using environmental and design variables available, configure correct locations for supply/extract duct grilles, correct sizing of window apertures including their orientation.
  • Predetermine areas where limited air movement may occur prior to building construction.
  • Based on weather data available, define time allocations for night-time cooling and setpoint for night-time duration based on internal and external temperatures.
  • Check compliance of design parameters in Compliance and Rating section of the software.

2.3.6            Autodesk CFD

Autodesk CFD is a vender specific software used in the simulation of airflow both internally and externally of buildings. It provides fast, precise and flexible thermal simulation tools to predict airflow performance. Autodesk CFD enables users to easily explore and compare design alternatives by creating scenarios.

Supported Environments:

  • Windows 8 and 8.1 (Standard, Pro, or Enterprise)
  • Windows 7 Home Premium, Professional, Ultimate, or Enterprise edition
  • Windows 10 unofficially supported
  • HPC: Windows Server 2008 R2, Windows Server 2012, or Windows Server 2012 R2
  • Languages: English, Chinese Simplified, Chinese Traditional, Japanese, German, French, Italian



  • 24-bit color setting at 1280 x 1024 or higher screen resolution
  • 512 MB DRAM or higher OpenGL-capable graphics card (recommended)
  • 256 MB on-board memory (minimum)


Minimum Hardware:

  • Model: Dell M4700 with Intel i7-4710MQ @ 2.5 GHz (4 Cores)
  • RAM: 16 GB
  • Video: Nvidia K2000M
  • Hard Drive: 256 GB SSD


Table 8 – Autodesk CFD System Requirements

Item Description
Userface User Friendly, quite similar to all Autodesk products.
User Manual Non-existent, only source of information comes from product videos via their website, registration is necessary.
Coverage of CFD CFD is covered in two separate manuals, each as a stepping stone to the next. (ApachieSim and MicroFlo)
Cost IES is a vender specific software with student free licencing for 3-year
CPU requirements Standard amongst other CFD softwares, but the better the ram the faster the simulation run time, the more powerful graphics card the greater the graphical output of simulations.
Plugins with Revit No – Revit models have to be imported into SimStudio first and from there into Autodesk CFD.
Cloud option Yes
Customer Support Yes, Response time 2-3 days via email, user interface has compatibility with live chat.

Table 9 – Autodesk CDF Software Capabilities

The following are some capabilities of Autodesk CFD during the analysis of airflow:

  • Surface Wrapping –  Improved workflow for better mesh quality
  • Solver technology –  Advances scalable solver technology
  • Particle Tracing –  Better understand circulation and low
  • High-quality visualization –   Use visualization tools to create stunning imagery and near photorealistic rendering.
  • Architectural and MEP applications – Improve building efficiency with optimized design
  • Design Study Environment – Use intuitive workflow to study design iterations
  • Intelligent automatic mesh sizing – Use geometry and mesh automation
  • Thermal management – Use digital prototyping for thermal designs

3       Software Architecture

When analysing the movement of air within an occupied space, there are many software tools available which predict these flows, in the past few years these software programs have become increasing sophisticated, enabling the user to mimic the movement of such air-flow with a space prior to construction. Figure 9 below shows a high-level flow diagram of data exchanged between modelling tools and thermal simulation tools. The data exchange is conducted in various formats, primarily by IFC and gbXML. Figure 10 references the work flow diagram implemented in chapter 5

3.1      Software Flow Diagram

Figure 9 – High Level Software Flow Diagram [ ]

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Undefined Spaces

(Revert to Revit Model)

Unrealistic results

(Revert to ApacheSim to modify variables)

Realistic results

(Proceed to analyse results)

Figure 10 – Software Flow Diagram

3.2      Proposed software description

Autodesk Revit is building information modelling software used across the construction / engineering industry. It allows users to design, construct and manage a building and its components in 3D, annotate the model with 2D drafting elements, and access building information from the building model’s database. Revit is 4D BIM capable with tools to plan and track various stages in the building’s lifecycle, from concept to construction and later maintenance and/or demolition.

IES Virtual Environment (VE) is an energy analysis and performance modelling software that is used to design energy efficient buildings. IES is subdivided in eight software categories:  ModelBuilder, Solar, Energy, Compliance and Rating, Lighting, Cost & Value, Egress and CFD.

The following descriptions are solely extracted due to their future use in this thesis, further knowledge of additional IES software can be found in the following link;

ModelIt enables the designer to build a 3D analysis model with or without AutoCAD data. It is the principle modelling tool within the Virtual Environment where models can be drawn from scratch or amended to suit new design criteria.

ApacheSim: Central simulation processor which enables you to assess every aspect of thermal performance as well as share results and input across a wide variety of other VE for Engineers modules

MacroFlo: Is a program for analysing infiltration and natural ventilation in buildings. It uses a zonal airflow model to calculate bulk air movement in and through the building, driven by wind and buoyancy induced pressures.

MicroFlo: Is a subcategory software program based in the IEV VE system. It allows the user to look in detail at airflow at microscopic level. Using MacroFlo can help the user to optimise the design of the system to achieve a better performance levels whilst in the design stage.

MicroFlo allows the user to simulate internal conditions in as little as six steps.

  • Run ApacheSim to generate boundary conditions.
  • In MicroFlo, select the rooms that need to be simulated, define surface boundary conditions if ApacheSim has not been implemented.
  • Add additional components that make up the itinerary of the proposed space for simulation, this can be computers, occupants, printers or radiators.
  • MicroFlo’s automatic grid generation tool will create a grid based on the shape of the model, windows, surface objects and components.
  • Run and monitor simulation progress.
  • Analyse results.

Variables to be considered during implementation are Inputs, Outputs and Analysis options.

Input Options
Boundary Conditions
  • Air temperatures including moisture content
  • Surface, windows and door temperatures
  • Incoming and outgoing air flow temperatures and air flow rates.
  • Room gains from lighting underfloor heating, occupants and machinery
Surface Objects Properties
  • Supply Air Grilles
  • Extract Air Grilles
  • Heat flus patches
  • Temperature patches
  • Shading surfaces
CFD Components
  • Radiators
  • Air heat sources
  • Solid heat sources
Automatic Grid Generation
  • Decrease grid sizes to increase detail of certain aspects of the design parameters.
  • Non-fixed grids can be removed.
  • Grid regions are editable to change density between fixed gridlines.
CFD Control Settings
  • Turbulence Models: K-e and Constant effective viscosity.
  • User defined/MicroFlo calculated surface convective heat transfer coefficients.
  • Default initial surface temperatures.
Analysis Options
  • Simulate both internal and external air flow and thermal problems.
  • Pre-set initial conditions for quicker convergence.
  • Discretisation options
    • Upwind
    • Hybrid
    • Power law
  • A simulation monitor enables you to run, pause and re-start calculations.
Output Options
  • MicroFlo predicts
    • Internal air flow temperature, direction and velocity.
    • External air flow direction and velocity.
    • External static pressure.
  • Results are accessed via graphical views of 2D slices at any grid line.
  • The range, scale and colour of contours and vectors can be adjusted to improve results interpretation.



4       Methodology Approach

Methodology Aim:

To create a methodology so someone else can easily replicate this study based on the information I have included in this section and in the appendices.

Should help reader to anticipate the results.


4.1      Acquiring Knowledge

Acquisition of knowledge within the engineering sector with regards to ventilation can be long and tedious, as there is a mountain of information readily available via the internet. Filtering through this information for value data to implement into your design can exhaustive and time consuming.

Two international bodies used within the structure of this thesis are that of CIBSE (Chartered Institute of Building Services Engineers) and ASHRAE (American Society of Heating, Refrigeration and Air-Conditioning Engineers). By implementing both these international standards the engineer can limit their time associated with researching the necessary requirements.

The following is a list of criteria critical in the design of air flow, indoor air quality and thermal comfort extracted from the previously stated standards. The list includes publication name, section and page. As all designs are unique, various tables showing specific variables depending on specifications required.

The highlighted parameters identified below are those which are to be implemented in the case study in chapter 5

4.1.1      Ventilation Rates

The following contains extracts from CIBSE Guide A – Table 1.5 [12]

Building/Room Type Suggested air supply rate Unit
Office Type
Board Rooms, large conference rooms 10[2] L.s-1/person
General, small conference room, executive-office 10[2] L.s-1/person
Open-plan 10[2] L.s-1/person
Educational Buildings
Corridor 10[2] L.s-1/person
Gymnasium 10[2] L.s-1/person
Laboratory 10[2] L.s-1/person
Lecture Hall 10[2] L.s-1/person
Seminar Rooms 10[2] L.s-1/person
Teaching Spaces 10[2] L.s-1/person

Table 10 – Suggested Air Supply Rates (L.s-1/person)

[2] Assume no smoking

 [14] As required for industrial process, otherwise based on occupant’s requirements

4.1.2      Dry resultant temperature

The following contains extracts from CIBSE Guide A – Table 1.5 [12]

Building/Room Type Winter Operative Temperatures Summer Operative Temperatures
Office Type oC oC
Board Rooms, large conference rooms 21-23 22-25
General, small conference room, executive-office 21-23 22-25
Open-plan 21-23 22-25
Educational Buildings
Corridor 19-21 21-25
Gymnasium 19-21 21-25
Laboratory 19-21 21-25
Lecture Hall 19-21 21-25
Seminar Rooms 19-21 21-25
Teaching Spaces 19-21 21-25

Table 11 – Winter/Summer design Temperatures (oC)

4.1.3      Metabolic Rates

The following contains extracts from CIBSE Guide A – Table 1.4 [12]

Activity Metabolic Rate Heat Generation
Office Work met W/m-2
Reading, seated 1.0 58
Writing 1.0 58
Typing 1.1 64
Filing, seated 1.2 70
Filing, standing 1.4 81
Lifting, packing 2.1 122
For office activities, an average for the metabolic rate and heat generation will be used for implementation throughout the case study in chapter 5. 1.3 75.5

Table 12 – Metabolic Rates for office environments

4.1.4      Clothing Factor

Based on the space usage (office/laboratory) the following breakdown of table 1.3 from CIBSE Guide A is used to incorporate the clothing level and its corresponding change in operative temperatures environments as seen below in Table 13

Description Insulation Level (clo) Corresponding change in operative temperature
Shirts/Blouses – medium weight (long sleeve) 0.25 1.5
Trousers – lightweight 0.20 1.2
Trousers – mediumweight 0.25 1.5
Skirt/dresses – lightweight 0.15 0.2
Skirt/dresses – mediumweight 0.25 1.5
Ankle socks 0.02 0.1
Stockings 0.03 0.2

Table 13 – Extract from CIBSE Guide A Table 1.3 for clothing levels

The average from Table 13 is used for summer/winter design parameters as indicated below in Table 14 for use during the case study in chapter 5

Building/Room Type Winter Clothing Summer Clothing
Office Type clo clo
Board Rooms, large conference rooms 0.9 0.7
General, small conference room, executive-office 0.9 0.7
Open-plan 0.9 0.7
Educational Buildings
Corridor 1.0 0.6
Laboratory 1.0 0.6
Lecture Hall 1.0 0.6
Seminar Rooms 1.0 0.6
Teaching Spaces 1.0 0.6

Table 14 – Clothing factor for office

4.1.5      Relative Humidity

Using the ratio of vapour pressure to saturation vapour with the same dry bulb temperature. Effects of relative humidity has previously been explained in Figure 5.

Activity Relative Humidity  (RH) Range
In normal circumstances, humidity in the following range is acceptable. [13] 40 – 70

Table 15 – Relative Humidity (RH) recommendations

4.1.6      Local Air Speed

Variable Value Effect on IAQ of increasing or decreasing these values
  Winter Summer
Local air speeds (m/s) ~0.1 ~0.3 Increasing air speed may improve IAQ but increases the risk of discomfort. 

Decreasing the air speed can cause ankle level draughts. [14]

Table 16 – Local Air Speed for office environments

4.1.7      Floor Surface Temperatures

Under Floor Heating Temperature Range
Localised discomfort can be caused where floor surfaces are either too hot or too cold, CIBSE recommends the value over. 19-29

Table 17 – Floor Surface Temperatures

4.1.8      Internal Surface Resistance

Building Element Direction of Heat Flow Surface Resistance
Walls Horizontal 0.13
Ceilings or roofs (flat or pitched), floors Upwards 0.10
Ceilings or floors Downwards 0.17

Table 18 – Internal Surface Resistances [15]

4.2      Mathematical Equations

The following are a list of equations used during the analysis within the case study, whilst some calculations were produced by hand, others were implemented using the specified software. These software calculations would be deemed time consuming and tedious, but never the less an understanding of such equations is imperative, as such avoiding the whole “black box” association. The ability to understand gives the designer an increased knowledge of where results have come from. These equations were specified earlier on in chapter 2 “State of the Art Analysis”.

When dealing with thermal comfort and indoor air quality, the ability to determine where and when to implement equations can save time during the analysis. The following is an itemised list of such equations and a breakdown of how they are derived.

Based on the case study where the geometry of the structure incorporates two number ventilation stacks, the building will be classed as “single-cell building, with uniform temperature, as demonstrated in Figure 11

A screenshot of a video game

Description generated with high confidence

Figure 11 – Single cell building with uniform temperature

The spaces are all connected to two stairwells which act as atriums, the aim being to induce fresh air into all of the occupied spaces (as shown above) with all stale air passing out through the upper outlet.

4.2.1      Stack Driven Ventilation Calculations

The easiest calculation when considering stack ventilation is that where wind effects are not considered. This equation is as follows;

Calculating the opening area:

A=QCd[2rin . rin . g . (hnpl-h)( Tin-ToutTin)

Equation 4 – Open area required in stack ventilation


Q = Air flow rate through a large opening (m3/s)

Cd = Discharge coefficient (0.61 for large openings)

A = Opening area (m2)

rin = Air density inside stack (kg/m3)

g = Acceleration due to gravity (9.81 m/s2)

hnpl = Height of neutral pressure level above datum (m)

h  = Height of opening above datum (m)

Tout = Temperature of air outside stack (oK)

Tin   = Temperature of air inside stack (oK)

4.2.2      Flow through openings



Equation 5 – Flow through openings


qv = the volumetric flow rate through the openings (m3/s)

C = the flow coefficient (m3/s/Pa-n)

Δp = pressure difference across the opening (Pa)

n = flow exponent

4.2.3      Height of stack above roof level

For buildings that are isolated and the airflow is uninterrupted, the minimum height of the stack above roof level can be calculated using the following formula. This minimum height level is used to eliminate back-draughting.


Equation 6 – Stack height level


h = height above roof level (m)

θ = roof pitch (degrees)

a = horizontal distance between the outlet and the roofs highest point (m)

4.2.4      Basic equation for openings

The following equation is expressed with the relationship of the flow rate through the opening and that of the pressure difference across it, by means of the discharge coefficient and the specified geometric area

  1. Units used – example (imperial or metric)
  2. Software used and required CPU specification
  3. Previous case studies/Surveys
  4. Acquired data.
  5. Assumptions.
  1. Specify the source documentation used in the main frame of the study (CIBSE Guides, ASHRAE, software user guides) along with regulations based on the location of the study.
  1. Breakdown of the governing equations used in the analysis of CFD and specify how these differential equations are derived.
  1. As CFD is the study fluid behaviour it is imperative that the correct units be used through the field of study. Such examples of this would be as follows;
    • Using a timestep of hours (hr) would be to large when analysing data over a brief period whilst seconds (sec) might be to fine and time consuming.
    • Grid mesh sizing of meters (m) might be too large to generate eligible readings whilst milometers (mm) might be too small and again result in time consuming analysis.
  1. Specified software used in the application of the study and the minimum requirements of CPU specification required.
  1. Where similar case studies are available, catalogue in order of relevance, stating where common techniques where used.
  1. Where shared (software) data is used, specify where the user predefined the model location, example (weather data from Africa is of no benefit to a field study based in Ireland).
  1. Due to the complexity of the study it will be common that assumptions will be made, on this basis evidence of origin of these assumptions should be stated, example (derived film coefficients of internal surfaces and furniture surfaces should be stated and their source given if assumption are to be made)







5       Case Study – ERI Building

The following study was based on the Environmental Research Institute (ERI) Building, located on the Lee road Cork, adjacent to the county hall overlooking the river lee. The building contains state of the art laboratories, environmental control room, seminar rooms and office spaces which houses over 100 researchers with a combined floor area of 3000m2.

5.1      Experimental Plan

The following table consists of a breakdown method for implementation over the duration of the study, listing areas which need to be analysed, standards which need to be met (more to follow)

Start Date / Duration Area of Study Description Resulting Outcome
04/07/2017 ERI Building Inconsistencies between physical building and record drawings/digital model. (Room layouts / walk-thru’) Floor depth to be amended
04/07/2017 ERI Building Locate mechanical supply and extract duct grilles, motorised dampers Complete
04/07/2017 ERI Building Factor in room types to be analysed, stating room number, floor level and orientation 50%
04/07/2017 South facing façade. Free area calculation of louvre windows, room floor area, room profile, required fresh air/flow rates recorded. 0%
04/07/2017 North facing façade. Room areas, window types, positions of MD’s, means of ventilation.
05/07/2017 Areas to be analysed Schematic flow of air distribution in defined spaces
06/07/2017 Areas to be analysed Means of air flow-in (louver windows) to air flow-out (stack)
06/07/2017 IES Model Analyse model for inconsistencies
06/07/2017 IES Model Input data gathered during survey
06/07/2017 IES Model Input standards to be implemented during design
06/07/2017 IES Model Choose analysis criteria to be implemented (grid spacing, iterations, internal/external temperatures)





6       Discussions & Results








7       Findings & Conclusion

8       Appendix

9       Bibliography

[1] H. Awbi, Ventilation Systems Design and Performance, 2008.
[2] G. Harvie, “Computational Fluid Dynamics for buildings,” 2016.
[3] ASHREA_Standard_55, “Standard 55,” 2004.
[4] Health and Safety Executive, [Online]. Available:
[5] G. I. S. Organization, “Ergonomics of the thermal environment. Estimation of thermal insulation and water vapour resistance of a clothing ensemble,” BS EN ISO 9920, 2009.
[6] T. C. I. o. B. S. E. London, “CIBSE Knowledge Series,” KS06 – Comfort, 2006.
[7] ASHRAE, “Indoor Air Quality Guide – Best Practices for Design, Construction and Commissioning,” 2016.
[8] B. E. 13779, “Indoor Air Quality Classification,” 2007.
[9] CIBSE, “AM11_Building Performance Modelling,” 2015.
[10] “Energy Plus,” U.S. Department of Eneryg’s, [Online]. Available:
[11] I. VE, “IES Virtual Environments,” IES Virtual Environments, 2016. [Online]. Available:
[12] C. I. o. B. S. Engineers, “CIBSE Guide A,” in Environmental Design, 2016, p. 403.
[13] R. F. S. W. a. F. A. Nevins RG, “Temperature-humidity chart for thermal comfort of seated persons,” ASHRAE, 1996.
[14] A. D. Jefferson and I. C. Mihai, “A Smoothed Constitutive Model with Directional Cracking for the Finite Element Analysis of Quasi-brittle Materials,” Unpublished, Birmingham, 2013.
[15] C. G. Sih and A. DiTommaso, “Fracture mechanics of concrete: Structural application and numerical calculation,” Martinus Nijhoff Publishers, Dordrecht/Boston/Lancaster, 1985.
[16] A. D. Clark and P. Thorogood, “Serviceability Behaviour of Reinforced Concrete Half Joints,” Transport and Road Research Laboratory, Berkshire, 1987.
[17] L. A. Clark, “Design requirements for short cantilever elements in bride structures,” Unpublished, NA, 1982.
[18] Department of Transport (UK), Interim Advice Note 53/04, Department of Transport (UK), 2004.
[19] LUSAS, Module ADV-N1 Introduction to Nonlinear Analysis, LUSAS.
[20] V. B. Nguyen and A. H. Chan, “Comparisons of smeared crack models for RC bridge pier under cyclic loading,” in 13th ACME Conference , Sheffield, 2005.
[21] A. D. Jefferson, “Craft – a plastic-damage-contact model for concrete. 1. Model theory and thermodynamic considerations,” International Journal of Solids and Structures, 2003.
[22] A. D. Jefferson, “Craft––a plastic-damage-contact model for concrete.2. Model implementation with implicit return-mapping algorithm and consistent tangent matrix,” International Journal of Solids & Structures.
[23] A. D. Jefferson, “A constitutive model for aggregate interlock on formed crack planes,” Int. J. Numer. Anal. Meth. Geomech., pp. 26, 1-21, 2002a.
[24] A. D. Jefferson, “Tripartite cohesive crack model,” J. Eng. Mech. ASCE 128 (6), pp. 644-653, 2002b.
[25] J. Lubiner, J. Oliver, S. Oller and E. Onate, “A plastic-damage model for concrete,” Int. J. Solids Struct, pp. 25 (3), 299-326, 1989.
[26] K. William and E. Warnke, “Constitutive models for triaxial behavior of concrete.,” Proceedings of the International Association, Switzerland, 1975.
[27] P. Pettersson, “Crack growth and development of failure zones in plain concrete and similar materials,” Lund Institute of Technology, Sweden, 1981.
[28] A. D. Jefferson and B. I. Barr, “Unified Test Procedure for evaluating the fracture characteristics of concrete,” in Proceedings for the Second International Conference on Fracture Mechanics of Concrete Structures, Zurich, 1995.
[29] M. Arrea and A. R. Ingraffea, “Report No. 81-83 Mixed-mode crack propogation in mortar and concrete,” Departmet of Structural Engineering, Cornell University, 1982.
[30] B. Bresler and A. Scordelis, “Shear strength of reinforced concrete beams,” J. ACI, pp. 51-72, 1963.
[31] W. Kaufmann, PhD Thesis, Zurich: Swiss Federal Institute of Technology, 1998.
[32] D. Boothman, S. Leckie, I. MacGregor and A. Brodie, “Assessment of concrete half-joints using non-linear analysis,” ICE Institution of Civil Engineers, pp. 141-150, 2008.
[33] V. B. Nguyen and H. C. Chan, “Comparisons of smeared crack models for RC bridge pier under cyclic loading,” in 13th ACME conference, 2005.

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