Multi-zone modeling of Thermal Comfort and Energy Consumption of a hospital ward -a summer case study

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Energy Engineering

Examiner: Mathias Cehlin

DEPARTMENT OF TECHNOLOGY AND BUILT ENVIRONMENT

Multi-zone modeling of Thermal Comfort and

Energy Consumption of a hospital ward

-a summer case study

Tian Xie

June 2010

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Acknowledgements

First of all, I would like to appreciate my supervisor, who gave me advices and suggestions both in technical and writing support. Without his patience and profuse encouragement, I could not finish it.

I am grateful to The Division of Energy and Mechanical Engineering of the University of Gävle, for having the opportunity to accomplish this study.

Many thanks to the teachers those taught me knowledge and enlightened the way of my further study.

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ABSTRACT

Hospital is of interest when consider its especial function. Because of the obviously different between the normal residential buildings, the requirement of hospitals’ indoor climate strictly differs from other buildings.

The author starts this report by briefly stating the building construction currently. Surrounded the topic of thermal comfort and energy consumption, many suggestion and options came out in this report to develop a better condition.

Firstly, the introduction of the hospital buildings requires the background of the hospital object and the purpose to this report will be stated.

Secondly, the simulation tool and how to use this tool simulate our real case are introduced.

Then, the summer case is investigated by this tool after the model is proved to be validated.

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1 Introduction

Several physical parameters influence the quality of the indoor climate. The design of a building, operational conditions and building services strongly affects these parameters and therefore the indoor climate. Airflow patterns affect the contaminant spatial distribution and comfort of building occupants in a ventilated air space. Improper indoor airflow patterns, air velocities and air temperatures are frequently described as air drafts, insufficient ventilation, poor distribution, stuffiness, etc. Location and design of the supply terminal as well as the extract terminal often significantly determine the airflow pattern and thus affect the air quality and the thermal comfort.

In recent years, the need for regarding the building as a whole has been widely manifested. It is assumed that the use of a system perspective on a building will lead to building more energy efficient buildings and, so, decreasing the energy demand in the building sector. Such a whole-building approach includes a modified building process with more interaction between different experts, minimizing the heat losses and consciousness over the occupants’ needs for healthy indoor climate.

In addition, the demand for energy efficient buildings will be pronounced in the future, for example the European energy performance directive. Buildings stand for about 40% of the energy demand in the world and there is a great potential to lower this by applying different energy efficient measures. However, these measures have to be made without deteriorating the health, well-being and comfort in buildings.

A broad perspective that includes the dimensions of energy, indoor climate, the occupants and the environmental performance can emphasize the aim of the building as a whole and be a piece of the puzzle in the achievement of sustainability1.

This thesis is an extension of a research project, which was taking place at the Division of Energy and Mechanical Engineering of the Department of Technology and Built Environment of the University of Gävle, Sweden. The main aim of that study was to present a systematic way to analyze the operation of the current Heating, Ventilation and Air Conditioning (HVAC) systems within the health care sector. Moreover, it was intended to achieve a global view of the energy systems within the health care sector,

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which will eventually lead to lower costs, less impact on the environment and a more efficient use of energy.

Thus, it is necessary to develop a method to analyze simultaneously energy systems and comfort in health care premises, to investigate how the energy systems can be used at their optimal capacity and how to reach the indoor climate that best adjusts to the needs of both the patients and the staff. This might serve in a near future as a well structured tool which will work as a manual and will present the potential of energy efficiency in every building.

The hospital studied is a typical hospital building, built in the early 1970´s, located in the middle of Sweden. The empirical data is collected in the orthopedic ward. The ward consists of around 20 patient rooms and an administrative area positioned at the inner parts of the building.

Therefore, the conclusions of the thesis should be suggestions for possible improvements of the indoor climate as well as energy saving measures at the orthopedic ward. These conclusions should be based on a sensitivity analysis of the model.

1.1 Indoor climate requirements in hospitals

Occupy the hospital building, they are staff and patients. It is important to provide a comfortable indoor environment to both groups of people with the consideration that they all have different physical, social and individual needs.

Indoor climate requirement in hospitals is special and distinguishing with other normal office buildings. In order to fulfill the indoor climate in hospital, it is necessary to focus on perception of different groups. Briefly speaking, the perceptions of different groups may depend on individual condition, behaviors and different climate or environment factors. Individual condition includes age, gender and health effect of each individual in the hospitals. Behaviors may be affected by changing metabolic rate, clothing or

medicine effects on thermal sort of things. Environment factors consist of temperature, air humidity, air movements, air quality, light and noise.

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As concluded by the study concentrated on thermal perception of patients who has distinguishing features on 50-80 years old, there is no significant difference between males and females in thermal sensation.2 Thermal sensation is indeed independent of age. But physical strength had a highly significant effect on thermal sensation.

Behaviors

Normally, the staff has to possess a larger metabolic rate compared to the patients, because the staff definitely generates more metabolic heat during their physical activities. At the same time, the patients are confined to their beds. What is more, the medicine patients taking may affect the metabolism of patient.

In summer, different activity levels and clothing may result in different optimal temperature for both patients and staff. The optimal temperature which showed in Figure1.1 for patients is 25 °C, and for staff is 18°C, under respective temperatures.

Figure 1.1 Staff and Patients Optimal Operative Temperature During Winter3

Environment factors

Temperature

2_{Ruey-Lung Hwanga, Tzu-Ping Linb, Ming-Jen Chengc, and Jui-Hung Chiena. “Patient thermal}

comfort requirement for hospital environments in Taiwan,” Int. J. Building and Environment, vol.42, p. 2985, 2007.

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Skoog J., Fransson N., Jagemar L., 2004 Thermal Environment in Swedish Hospitals. Summer

and Winter Measurements. Building Services Engineering, Department of Energy & Environment,

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Considering the study carried by Smith R.M., Rae A. The average temperature for patient rooms, as it were, is 22°C4, which is regarded as the most basic condition during the colder part of the year. That is about 1 to 4°C higher than normal buildings in order to reach a comfortable environment.

Variable approaches may be introduced to hospital so that the temperature can be restrict maintained in summer time, such as improving air handling unit so as to increase the air ventilation flow rate, install sun shadings and so on.

Air humidity

From the view of hygiene and thermal comfort, Air humidity must be controlled reasonable.

High humidity levels cause thermal discomfort. The ventilation requirements influence humidity control. Depending on climatic conditions, additional ventilation may increase the dehumidification load more than the sensible cooling, imposing a disproportionate demand on the HVAC equipment. Low humidity levels (dry air) favor blood coagulation, which is undesirable during a surgery.5

Consequently, optimal indoor climate depends on many factors. The optimal indoor climate can be created only if the entire requirement qualified. Even under the same condition the two different groups assist their own opinions with different perceptions. And there always be a perceive difference between patients and staff as long as their differences in physical, social and individual needs exist.

1.2 Purpose

The aim of this thesis is to examine the thermal comfort in the orthopedic ward during summer conditions and to create a feasible model of it with multi-zone modeling. This thesis studies the effects of different physical factors on the thermal climate and airflow

4

Smith R.M., Rae A., 1977 Thermal Comfort of Patients in Hospital Ward Areas. Int. J. Building and

Environment, vol. 12, issue 3, pp143-146, 1977.

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pattern inside a large hospital building. Computer simulations of multi-zone airflow and thermal transport are performed in order to improve the thermal climate and at the same time reduce the use of energy in the present hospital. By assuming that the energy prices in general and electricity in particular, will increase in the future and increasing environmental awareness, designing an energy-efficient building is more important than ever.

1.3 Limitation

the hypothesis that the staff and the patients are treated as one group coherent group of users with the same needs and preferences has been proved wrong in previous study researched by Jennie Skoog, Niklas Fransson, and Lennart Jagemar. In this study, one optimal environment was created for one group in this orthopedic ward, without considering the optimal solution for the other group.

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2 Method

The HVAC, lightning and equipment systems of a building interact with each other in a very complex way and it is not easy to integrate all of them in a model. In order to accomplish the actual thesis, the IDA Indoor Climate and Energy 3.0 software has been used. IDA Indoor Climate and Energy (ICE) is a software tool for simulation of thermal comfort, indoor air quality and energy consumption in buildings.

A visible and practical building is indispensable to be built in order to complete the simulation. Different factors in reality situation have to be led to the model so that the accuracy of the method can be made sure and trusted. Different parameters in the simulation, such as, HVAC system was necessary to be described in the model. The building characters not only in geometry but also in building construction should be displayed during the whole simulation process, that is, location, orientation and building material must be imitated as the same scale as the original building model. All those elements are detailed brought form the virtual hospital to our built model. After being created, the model can be run, find out and examine the optimal solution by changing variable parameter, for instance, air flow rate, shading conditions and so on.

2.1 IDA Indoor Climate and Energy (ICE)

IDA Indoor Climate and Energy (ICE) is a program for study of the indoor climate of individual zones within a building, as well as energy consumption for the entire building. 6

As a whole-building simulator, it can perform assessments of all issues fundamental to a successful building design: form, fabric, glazing, HVAC systems, controls, light, indoor air quality, comfort, energy consumption etc. for advanced energy and indoor climate analysis, program can simulate periodic or a dynamic climate and energy. The number of registered users is more than 900, mostly HVAC designers but also consultants, educators and researchers. They spread almost in Scandinavian countries. The original

development of IDA ICE was requested, specified and partly financed by a group of thirty leading Scandinavian AEC companies. The mathematical models were originally

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developed at the Royal Institute of Technology in Stockholm (KTH) and at Helsinki University of Technology, now both part of the ICE academic network. 7

The model we used here is IDA Indoor Climate and Energy 3.0, which is quite detailed and has been validated against measurements and other calculation software in several projects.

Although lots of programs can also be utilized as our simulation tool, based on the reasons followed, IDA was chosen to be our simulation tool.

Distinguished with most other similar program where normally the model is build from the view factor of the whole zone. But in IDA program, the operative temperature can be calculated through different points of view.

The correct dynamic temperature response of the building is calculated. In many other programs this completely fundamental element of the heat balance is treated by rough approximations. 8

Ida can implement different session of multi-rooms and building. Hospital is considered as a building with high quality of indoor climate and air quality. Many functions can be involved and simulated simultaneously.

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Due to the researchers and developers of this program is from the Royal Institute of Technology KTH and Helsinki University of Technology and most users from Scandinavian countries, when this program came into being, the designer must have considered the Scandinavian scenario.

Compared to other program, not only Swedish, but English can also be available in IDA program. Therefore, IDA is much easier to get started.

Moreover, the division of energy and mechanical engineering of University of Gävle was provided by the IDA license, which makes our simulation possible.

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Mathias Cehlin, 2010, Project in building simulation, Course material, Dept. of technology and built environment, Gävle University, Sweden.

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In our simulation, Thermal comfort and energy use are simulated and calculated only at building level. Validation tests also should not be undertaken until the reasonable results are acceptable.

2.2 Boundary conditions

2.2.1 Geometry

In the predefined layout, a large number of zones were presented as a specific scale. The map below showed is the hospital we were going to deal with (see Figure 2.1).

Figure 2.1 Falun Hospital floor plan layout

2.2.2 Building construction parts

The building components consist of different constitutes, see Table 2.1:

components constitute U- value ( W/m2*K)

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Internal walls Windows

Interior wall with insulation 3 pane glazing clear

0.619 2.000 Ground Ceiling Glass wall Concrete floor150

40mm Light insulation +250mm concrete 80mm glass

2.385 0.700 3.669

Table2.1 Building components

2.2.3 HVAC-systems

The heating system in the hospital is turned off when the outdoor temperature exceeds 16°C and is thus neglected in the model as the model represents summer conditions.

The whole building has a ventilation system that consists of a heat exchanger, a by-pass connection when heat exchanging is unnecessary and two parallel fans. For each floor there is a separate system for heating or cooling of the air. The supply air to the object of this study is between 17-19°C according to the ventilation control system. The preferred temperature is 22°C in the exhaust air. Between 6 p.m. and 6 a.m. the airflow is reduced by 50%.

2.2.4 Internal heat generation

The internal heat generation in this case is mainly produced by three parts: occupancy, light and equipment.

Occupancy

The occupancy consists of patients and staff. For the patients, Due to their areas,

different patient rooms accommodate variable number of patients from 1 to 6. Moreover, we assume that the number is constant. Normally, the patients stay at their own rooms around all day.

For the stuff, practically, they may emerge in different room at different time. It depends on the patients’ needs and staffs’ duty. Therefore it is hard to locate each staff at specific time. However, we can estimate that the number of staffs appear in each zone, practically. That is

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11.3.143A reception 1 None 11.3.163B Skot.exp 11.3.187 Skot.exp 2 2 None None 11.3.215C Dagrum Corridor 17_18 Openarea 18 2 2 2 None None None 11.3.190 korridor 3 1 11.3.177 korridor 2 1 11.3.121 korridor 2 1 11.3.139 korridor 2 1

Table.2.2 Number of occupant of the orthopaedic ward

The internal heat generation by occupants depends on the activity level of occupants. The activity level for each occupant is quite different. The patient is confined on the beds, which means a lower vale of activity level. We assume it to be 1 met (108 W/ occupant) for patients sitting and reading at daytime, 0.8 met (86 W/ occupant) for patients sleeping in the nights. The corresponding patients’ clothing was assumed to be 0.5clo

(Underpants, shirt with short sleeves, light trousers, light socks and shoes) at daytime, 1.0clo (Underwear with short sleeves and legs, shirt, trousers, jacket, socks and shoes) in the nights.

Meanwhile, we assume it be2 met (216 W/occupant) for staff at daytime due to

comparable high activity level, 1 met (108 W/occupant) for stuff sitting and reading in the nights. The corresponding occupant’s clothing was assumed to be 1.0clo (Underwear with short sleeves and legs, shirt, trousers, jacket, socks and shoes) at daytime, 2.0clo (Underwear with long sleeves and legs, thermo jacket, trousers, socks and shoes) in the nights.

Lights and equipment

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Figure 2.2 The whole floor can be divided into several areas

Area Lightning power division estimate

Patient17 Divided equally between the six rooms

Dayroom17 Divided equally between the three rooms, zero for the toilet Corridor17 Divided equally between two corridor zones

Openarea17 Divided proportionally to area between two open area zones Reception Divided proportionally to area between three reception zones

17admin Divided proportionally to area between seven zones, zero for bath room 18admin Divided proportionally to area between eight zones

Corridor18 Divided proportionally to area between four open area zones Dayroom18 The single room gets all the power

Open area 18 The single room gets all the power

Room1 Divided evenly between two equal patient rooms

Part A Corridors get as much light as corridor17 due to the same area, zero for passage rooms, and the rest is divided proportionally to area between the six patient rooms.

Part B Corridors get light proportionally to area corresponding to corridor17 and the rest is divided proportionally to area between the patient rooms.

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Table 2.3 The estimation rule light power distributed

Area Equipment power division estimate

Patient17 Divided equally between the six rooms

Dayroom17 Divided equally between the three rooms, zero for the toilet Corridor17 Divided equally between two corridor zones

Openarea17 Divided proportionally to area between two open area zones Reception Divided proportionally to area between three reception zones

17admin Room 11.3.185 gets 50% of the equipment, the rest is divided proportionally to the area between the remaining of the rooms.

18admin Room 11.3.167 gets 50% of the equipment, the rest is divided proportionally to the area between the remaining of the rooms.

Corridor18 Divided proportionally to area between four open area zones Dayroom18 The single room gets all the power

Open area 18 The single room gets all the power

Room1 Divided evenly between two equal patient rooms

PartA Zero for corridors and passage rooms. All the power is divided proportionally to area between the six patient rooms.

Part B Zero for corridors and passage rooms. All the power is divided proportionally to area between the six patient rooms.

Smallrooms17 Zero

Table 2.3 The estimation rule light power distributed

The daily energy consumption of equipment and light for the hospital building are 154829 kWh and 177600 kWh, respectively.

2.3 Building of model

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Along with the process that the model builds, we can catch the substance of using this program. For more information, explanatory videos are available on the official software web of this program.9

2.3.1 Inputting general data

Start a new building with a single zone

When ask a new document, the template building for IDA Indoor Climate and Energy has to be chosen in order to start building the model, see figure 2.3.

Figure 2.3 Creating a new IDA ICE document

General

After that the general window was presented as Fig2.4. All the options can affect the model when they are requested.

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Figure 2.4 General Properties of an IDA ICE file

Location

The location shows the geometric property of the project where it was carried out. In our case, the location of Falun has to be used. Usually, the location of represent cities over the world can be found in the resources form database, otherwise, more resources have to be found or created by the users.

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Figure 2.5 Location of Project

Climate

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Figure 2.6 Climate definition in Falun Climate

Wind profile

Here the wind profile has little affect the model. It is hard and not necessary to collect the data. So the default can be used in normal model.

Simulation data

Simulation date can be selected in this window. In coordination with the corresponding climate on the selected dates, the program can calculate different case know from different time. Periodic and dynamic calculation can be chosen dependent on the period the simulation goes through.

The difference between climate model and energy model in the Model fidelity has been stated in the previous study.

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The Energy model is simple and has a more conventional degree of accuracy based on a mean radiant temperature. If the rest of conditions are to remain the same, the calculation time for the Energy model will be shorter.10

For the startup tab, how long the startup phase should be depends on how heavy the building is. The heavier the building is, the longer startup phase is required.

For advanced tab, we can adjust the accuracy by altering the tolerance.

Figure 2.7 Simulation data of the project

Requested output

The catalog of requested objects can be chosen in this window bellowed, fig.2.8. It depends on which kind of simulated material the users request from the program. The output can be displayed either in diagram or report.

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Figure 2.8 List of requested output

Site and shading building and shading building definition

The site of the building can be adjusted corresponding to the coordinate at the lower left corner.

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A shading building is defined as a neighbouring building whose shading may affect the solar radiation into the studied building.

In order to set shading building, the surrounding neighbour building have to be investigated before be defined in the program. Especially the height of the neighbour buildings that always affect the solar radiation into our studied is an interesting factor.

By observing and measurements, the height of neighbour building is from 6m to 16m. The program will calculate the influence that generated by neighbour building.

Figure 2.10 the shading building of Falun hospital

All the resources used in the model was presented in the window IDA resourse. A change made in an IDA resource at the building level applies to all the instances in the building where it has been used.

Wind pressure definition

From the right below corner, the pressure coefficient is requested when the button is clicked.

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Relative to static pressure of the free wind, the pressure, , acting at any point on a surface can be approximated by:

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v

C

P

_w _p a

Here,

C

_p is a wind pressure coefficient

V is the windvelocity at specified refence height.

A negtive value of

C

_pmeans that there is a wind suction acting on the envelope. The pressure coefficient is a empirically derived parameter, largedly based on the results of wind tunnel experiments.11

After adding the pressure coefficients in the model, see fig.2.11. The model is more close to the real case, even though pressure makes little contribution to the whole indoor climate scenario.

Figure 2.11 Pressure Coefficients over building envelope

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Figure 2.12 IDA Resources Window

The project data is the basic information of describing this project. Our project is a model of the orhopaedic ward at Falun hospital.

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2.3.2 Design HVAC system in the Model

As an megnitude part both in the building system, and in our project, the air handling unit should be designed to satisfy the ventilation requirement. Based on actual situation, the Air handling unit has to be replaced as the one in reality instead of the default one.

According to the HVAC system utilized in the hospital, the control of exhaust temperature is applied to the building model rather than general condition of Air hangdling unit.

The main parameters are

Maximum supply air temperature is 19 ºC Minimum supply air temperature is 17 ºC Setpoint for exhaust air temperature is 22 ºC Efficiency of heat exchanger is 0.6

Temperature increase for the supply fan and system 1 ºC

Figure 2.14 The Air Handling Unit in Falun Hospital

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air flow was set up. Full load supply air between 6 o’clock to 18 o’clock. The half flow rate was set for the rest time of the days, as it is stated before.

Figure 2.15 Fan schedule for the operation of the fan

2.3.3 Design the floor plan

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Figure 2.16 Default floor plan

By right click on the default building’s shape and choose the option Edit. Then, the user can alter the shape and by clicking Done option when finished. In the actual thesis, the final building shape will look like Fig. 2.17.

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When click each object in the floor plan, we can alter the site of each zone by altering the coordinate.

Figure 2.18 Coordinate setting for each object

2.3.4 Creating a new zone with its properties

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Figure 2.19 General setting for a new zone

Air handling in each room

Air quality with high standard is significant in hospital. Consequently, air handling unit were installed in most rooms where it requires.

System type

Generally speaking, two types of air handling units as below in each zone are mostly selected from a range of options as below.

Constant AIR Volume AHU (CAV) is the less efficient of AHU. The fans in CAV do not have variable speed controls. Instead, CAVs open and close dampers and water supply valves to maintain temperatures in the building’s spaces. They heat or cool the spaces by opening or closing chilled or hot water valves that feed their internal heat exchangers. In the actual project. CAV has been used12.

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Variable Air Volume (VAV) incorporates one supply duct which provides a variable air flow at a constant temperature in order to meet the rising and falling heat gains or losses within the served thermal zone13.

In terms of our project, Constant air volume air handling unit (CAV) was defined. After consulted the information from pdf File LUFTFLÖDESPROTOKOLL, air flow rate information is picked into each room respectively. The air volume flow rate for each room was presented in the file clearly with supply air flow rate or exhaust air flow rate, even both.

Exhaust air for CAV

The method to input exhaust air flow into the program is entering the data (l/s m2 of floor area) into the first blank, see fig 2.20. The program can calculate by transferring the unit of different express for air flow. As stated in the dialog box, the first two units refer to total values for the whole room. The third one depends on the area and the last one, which shows air changes per hour (ACH), that is, how many times the air in the room is

exchanged in one hour.

Figure 2.20 Exhaust air for CAV

Supply air / Exhaust air

There is no supply blank to fill in so that it states supply air rate as well. However, the Supply air flow is defined in relation to exhaust air flow.

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The quotient can be set to zero when there is no exhaust ventilation in the room.

However, it is a pity that there are always some zones with supply air to be zero as well.

Even if it is not possible to input a mathematical infinite value for the supply air; it is possible to input a “physical” infinite. If values are input as shown in the table below, the mathematical problem is solved without losing physical rigor.14

It is not easy to enter some positive extremely figure instead of infinitive number. On the contrary, the only method to fix this problem is putting an extremely low value as exhaust air flow rate. Therefore we can get a value high enough compared to normal values.

Real values Input values

Supply air 0 -

Exhaust air 100 m3/h 100 m3/h

Supply air /Exhaust air 0 0

Real values Input values

Supply air 100 m3/h -

Exhaust air 0 0.0001

Supply air /Exhaust air ∞ 1.000.000

Table 2.4 & 2.5 The extreme case that supply and exhaust in each zone

Leak area at 4 Pa, 1m above floor

This leakage option has to be considered only when there is an external with a window; otherwise there is no air leakage.

Air leakage is a measure of the air tightness of the building envelope. In practical building design, the air tightness of the whole building or its components is expressed as a leakage rate (in air change per hour), or an air leakage area.

Building air leakage area is a physical property of a building determined by its design, construction, seasonal effects, and deterioration over time, the larger the air leakage, the

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larger its infiltration rate. However, no simple relationship exists between a building’s air tightness and its air exchange rate, although some empirical methods have been

developed to estimate the values.

The air leakage in buildings may be determined by pressurisation testing or tracer gas measurement. Ratings for air tightness have been established in some standards based on air flow rates predicted at particular reference pressures and test conditions. In some cases, the predicted air flow rate is converted to an equivalent or effective air leakage area using the following equation (which is derived from the Bernoulli equation for incompressible fluid flow):

where AL = effective air leakage area (cm2)

Qr = predicted air flow rate at pr (m3/s) = density of air (kg/m3)

pr = reference pressure difference (Pa)

CD = discharge coefficient

For the whole-building case, all the openings in the building envelope are combined into an overall opening area and discharge coefficient for the building when the effective air leakage area is calculated. Therefore, the air leakage area of a building is the area of an orifice (with an assumed CD value of 1 or 0.6) that would produce the same amount of leakage as the building envelope at the reference pressure.

From the study carried out by Persily, the mean leakage area for external wall of health care is 5.6 cm2/m2 for external walls15 in our model, at the same time, the air leakage form crack is 250 cm2 for those zones where the wall with a closed door16.

15

Persily, A.K. 1998. "Airtightness of Commercial and Institutional Buildings: Blowing Holes in the Myth of Tight Buildings." DOE/ASHRAE/ORNL/BETEC/NRCC/CIBSE Conference Thermal Performance of the Exterior Envelopes of Buildings VII, 829-837.

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Air velocity in the occupied zone

Air velocity may affect the operative temperature and PMV index. However for 0.1 m/s velocity in the occupied room is reasonable.

2.3.5 Drawing all the zones

New zones were inserting into the floor plan after they were created. By adjusting all the zones’ coordinate to suitable site. Finally the layout of the orthopedic ward which looks with 111 zones totally came into being.

However, the orthopaedic room with 111 zones may run out of dynamic memory. Especially when we consider the long period during the simulation period, it is even less effective. Therefore simplify the model is indispensable.

The objects needed to be simplified are those zones do neither matter the environment, nor occupy much space and function. Moreover, the supply and exhaust air in those zones have to be considered as air flow into the new zones so as to keep the total air flow rate constant over the whole project hospital building. The total power consumption also needs to keep constant after merging.

The types of zones needed to be simply are some corridor, toilet and storage room. The method is to merge two or multiple zones into one so as to simplify the model but little influence on the result.

The corridor cross merging

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Figure 2.21 Part B before corridor cross merging

Figure 2.22 Part B after corridor cross merging

After simplify the corridor, the corridor crossing were merged to reduce by 8 of zones, which makes the total zone number decrease down to 103.

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Amount of toilets are shown in the floor plan make the simulation complex. For the toilet attached by the northeast and southwest patient room, the toilets can be margined to a part of the patient rooms; For the toilets attached by TV room, (such as those cases in part A and part b, room138 is a toilet which is next to 137) they were merged to one room together; Also, for toilet rooms in a couple, (such as toilet179&180 and 172 &173) they can be margined as one toilet. Since the light & equipment consumed in the toilets has been considered zero throughout the paper and the toilet doors remain usually closed and the outlets are located in the toilets; it seems quite a good idea to merge the toilets in the model. In this way, the zone number can be reduced, the model will simulate faster and finally, very little verity is lost. Moreover, at the same time the amounts of air flow rate were also merged.

Figure 2.23 After merging of toilet 179&180 and 172 &173

After merging of toilet, the number of zones decreases the zone amount from 103 to 83. Now it is much easier to run the simulations.

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Figure 2.24 Floor Plan after zone merging

2.4 Validation of model

In order to apply our model into practise and simulate for necessary period, we have to assure that the model is coincident with the reality. Therefore the following validation has to be carried out.

• Validation of the temperate of the multiple rooms model compare with the empirical measurement on a specific date;

• Validation of air quality of the multiple rooms model compare with the empirical measurement on a specific date;

Then a train of data experimentally measured by Mathias Cehlin from the Division of Energy Systems of University of Gävle will be presented later.

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2.5 Thermal climate and Energy analysis

Once the model coincide the measurement in Section 2.3 of Chapter, we can simulate this model and use the program to calculate the requested output. The analysis was carried out by two aspects: thermal comfort and energy consumption.

Thermal Comfort

The temperature in the hospital was simulated at the validated model as well as PMV index and energy consumption as well. Especially some parts in the hospital are more attractive than the others due to their special usages, such as patient rooms and receptions when consider about the air temperatures, as well as people’s perception.

Therefore, the temperatures will be plotted later in the requested dates. Under such environment, PPD and PMV levels will be generated in reflection of the patients or staff’s feeling.

Energy Consumption

While, in terms of energy consumption, the energy consumption from different resources is more interesting rather than the detailed rooms.

To determine whether an energy gain can justify the consumption of resources, it must be possible to compare the future savings of energy with the consumption of materials use of energy and any work required when the investment is made. There must be possible a different and comparable measure of energy consumption in future compare to now. There are many approaches can be done before comparison.

Reducing surplus heat and internal heat generating offer the greatest opportunities for energy saving methods in our study. Only by reducing the heat surplus, can energy be saved.

Improvement to the insulation is a limited energy saving method. High level insulation is important for heat transition, But it has very little effect on energy saving. The significant savings potential is primarily found in the technical systems installed in the building.17

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Sun shading can be a good method. Effective sun shadings can reduce the solar irradiation therefore the required HVAC system can be reduced as well.

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3 Result and Discussion

3.1 Validation of the model

The experimental validation was based on the measurement performed at 12:00 on 15th September 2006. We used these collected data to compare with the result of our simulation. During the simulation we set the simulated date to be 15th September. We required comparing the corresponded values of the parameters at the same moment.

Room number EMPIRICAL TEMPERATURE MODEL TEMPERATURE TEMPERATURE DIFFERENCE (MODEL-EMPIRICAL) 11.3.168B 24 25.5 1.5 Corridor 17_18 25.1 24.3 -0.9 11.3.187 23.6 23.85 0.25 11.3.227 23.4 24.9 1.5 11.3.190 22.8 21.5 -0.3 11.3.121 25.2 25.5 0.3 11.3.129 24.5 22.8 -1.7 11.3.139 24.6 23.5 0.9 11.3.150 23.8 23.5 -0.3

Table3.1 The temperature comparison between empirical and the open door model

Room number EMPIRICAL CONCENTRATION MODEL CONCENTRATION CONCENTRATION DIFFERENCE (MODEL-EMPIRICAL) 11.3.168B 560 550 -10 Corridor 17_18 640 550 -90 11.3.187 540 530 -10 11.3.227 490 530 40 11.3.190 550 520 -30 11.3.121 650 520 -130 11.3.129 500 480 -20 11.3.139 590 540 -50 11.3.150 500 530 30

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Here, a big error may occur which might lead a difference between the on/ off of the door in each room. All doors set to be open in the case above. The schedules of the door for each room influence the temperatures of rooms. In order to simulate the real condition, the model which all doors set to be closed came into being. By requiring the responding values, the temperatures can be compared with the empirical ones as following.

Room number EMPIRICAL TEMPERATURE MODEL TEMPERATURE TEMPERATURE DIFFERENCE (MODEL-EMPIRICAL) 11.3.168B 24 27 3 Corridor 17_18 25.1 24.9 0.2 11.3.187 23.6 23.95 0.35 11.3.227 23.4 23.6 0.2 11.3.190 22.8 21.5 -1.3 11.3.121 25.2 26.95 1.75 11.3.129 24.5 22.3 2.2 11.3.139 24.6 24.3 -0.3 11.3.150 23.8 24.1 0.3

Table 3.3 The temperature comparison between empirical and the closed door model

Room number EMPIRICAL CONCENTRATION MODEL CONCENTRATION CONCENTRATION DIFFERENCE (MODEL-EMPIRICAL) 11.3.168B 560 620 60 Corridor 17_18 640 620 -20 11.3.187 540 590 50 11.3.227 490 500 10 11.3.190 550 540 -10 11.3.121 650 600 -50 11.3.129 500 480 -20 11.3.139 590 590 0 11.3.150 500 550 50

Table 3.4 The CO₂ concentration comparison between empirical and the closed door model

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Room number EMPIRICAL TEMPERATURE MODEL TEMPERATURE TEMPERATURE DIFFERENCE (MODEL-EMPIRICAL) 11.3.168B 24 25.7 1.7 Corridor 17_18 25.1 24.7 -0.3 11.3.187 23.6 23.8 0.2 11.3.227 23.4 24.6 1.2 11.3.190 22.8 21.8 -1.0 11.3.121 25.2 25.1 -0.1 11.3.129 24.5 23.5 -1.0 11.3.139 24.6 23.9 -0.7 11.3.150 23.8 23.8 0

Table 3.5 The temperature comparison between empirical and the reasonable model

Room number EMPIRICAL CONCENTRATION MODEL CONCENTRATION CONCENTRATION DIFFERENCE (MODEL-EMPIRICAL) 11.3.168B 560 550 -10 Corridor 17_18 640 620 -20 11.3.187 540 570 30 11.3.227 490 520 30 11.3.190 550 550 0 11.3.121 650 550 -100 11.3.129 500 500 0 11.3.139 590 580 -10 11.3.150 500 550 50

Table 3.6 The CO₂ concentration comparison between empirical and the reasonable model

Through the comparison between the three models by changing the schedules of the open/closed door, we found out that

• The variation trend of CO2 concentration is similar as temperature;

• From the third case, which parts of the doors are open parts of the doors are closed at specific time not only fits the reality situation, but also suits the empirical measurement.

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Figure 3.1 The temperature comparison between empirical and the reasonable model

Figure 3.2 The CO₂ concentration comparison between empirical and the reasonable model

Third model has proved make sense, so that it can be used for further simulation. For the further simulation and estimation, this model is to be based on.

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3.2 Thermal climate and Energy analysis

The simulation of thermal performances was carried out during the whole summer time in Sweden, which is from 1 June to 31August 2006. This section showed temperatures, thermal comfort, and energy consumption during the simulated days of each room in the orthopedic ward after simulation. The temperature varied during the whole period, as well as PMV index and energy consumption. They were all investigated throughout the entire period.

The mean air temperatures in the main ward of the hospital are more considered than anywhere else. Reception, the normal corridors or patient rooms and specific corridors or patient rooms in all direction or orientation of the hospital, are the most worthy parts to participate to be investigated within this hospital ward. The Table 3.7 shows the average temperature of the representative rooms overall the whole summer of 2006.

Region _{Temperature (℃)}

Reception (In Red, middle) 27.05

North corridor (In Black, up) 24.16

North patient room (In Blue, down) 23.71

Northeast corridor (In Red, up) 27.0

Northeast patient room (In Orange, up) 26.75

Southeast corridor (In Red, down) 27.29

Southeast patient room (In Orange, down) 25.55

Northwest patient room (In Blue, down) 24.74

Southwest patient room (In Purple, left) 24.60

South corridor (In Black, down) 25.13

South patient room (In purple, right) 24.85

Exhaust air 25.48

Table 3.7 The average temperature of representative rooms in different regions

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Figure 3.3 the main parts in the building geometry

Air temperature

It is obviously to see that the temperatures of the red region are higher than the others from Table3.7.

Thermal Comfort

We just selected one room in each orientation and have a glance at the PPT (Predicted Percentage of Dissatisfied) and PMV (Predicted Mean Vote) values.

Thermal Comfort

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Here, we picked 11.3.163A to represent the reception region, have a look at the results from mean air temperature duration diagram (see Figure3.4), PPT and PMV (see Figure 3.5and table 3.8) simulated by IDA program, respectively.

Figure 3.4 Mean air temperature duration diagram of reception11.3.163.A

24,5 25 25,5 26 26,5 27 27,5 28 28,5 29 29,5 3623 4123 4623 5123 5623 M e an ai r t e m p e ratu re Time

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Figure 3.5 PPD and PMV values of reception11.3.163A in the daytime (blue lines) and night (green lines)

The average PPD and PMV values of reception in the daytime and in the night are also shown in the Table 3.8 below.

Thermal Comfort PPD of daytime PPD of night PMV of daytime PMV of night

The average value 30.39% 4.67% 0.83 0.17

Table 3.8 The average PPD and PMV values of reception11.3.163A in daytime and nighttime

From the figures and the table, we can see that the temperature is almost over 25℃ all

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For the north corridor, we picked the region next to patient room 11.3.197 to represent the whole corridor 11.3.190, have a look at the result from mean air temperature duration(see Figure3.6), PPD and PMV values(see Figure3.7 and Table 3.9 ) simulated by IDA program,

Figure 3.6 Mean air temperature duration diagram of corridor 11.3.190

Figure 3.7 PPD and PMV values of corridor 11.3.190 in the daytime

20 21 22 23 24 25 26 27 28 29 30 3623 4123 4623 5123 5623 M e an ai r t e m p e ratu re Time

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The average amount of PMV and PPD are shown in the table below,

Thermal Comfort PPD of daytime PMV of daytime The average value 15.56% 0.55

Table3.9 the average PPD and PMV values of corridor 11.3.190 in daytime

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For north patient room, 11.3.206 was chosen to be investigated, have a look at the result from mean air temperature duration diagram (see Figure 3.8) PPD and PMV values(see Figure3.9 and Table3.10 ) simulated by IDA program,

Figure 3.8 Mean air temperature Duration diagram of patient room11.3.206

Figure 3.9 PPD and PMV values of patient room 11.3.206 in the daytime (blue lines) and night (green lines)

The average amount of PMV and PPD are shown in the table next,

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The average value 26.37% 26.08% -0.73 -0.70

Table 3.10The average PPD and PMV values of patient room 11.3.206 in daytime and night

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For northeast corridor11.3.177, we picked the region next to patient room 11.3.221and 224 representing the whole northeast corridor11.3.177, have a look at the results from mean air temperature duration diagram (see Figure 3.10), PPD and PMV values(see Figure 3.11 and Table 3.11) simulated by IDA program,

Figure 3.10 Mean air temperature Duration diagram of 11.3.177

23 24 25 26 27 28 29 30 31 3623 4123 4623 5123 5623 M e an air t e m p e rat u re Time

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Figure 3.11 PPD and PMV values of patient room 11.3.177 in the daytime

The average amount of PMV and PPD were shown in the table next,

Thermal Comfort PPD of daytime PMV of daytime

The average value 29.40% 0.81%

Table 3.11 The average PPD and PMV values of patient room 11.3.197 in daytime

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For northeast patient rooms, we picked the patient room 11.3.221 representing the whole northeast patient rooms, have a look at the result from the mean air temperature duration diagram (see Figure 3.12) PPD and PMV values (Figure 3.13 and Table 3.12) simulated by IDA program,

Figure 3.12 Mean air temperature Duration diagram of patient room 11.3.221

22 23 24 25 26 27 28 29 30 31 3623 4123 4623 5123 5623 M e an ai r t e m p e ratu re Time

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Figure 3.13 PPD and PMV values of 11.3.221 in the daytime (blue) and night (green)

Table 3.12 The average PPD and PMV values of patient room 11.3.221 in daytime and night

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For the southeast corridor11.3.121, we picked the region next to patient room 11.3.128 representing the whole corridor11.3.121, have a look at the result from mean air

temperature duration diagram ( see Figure 3.14), PPD and PMV values (see Figure 3.15 and Table 3.13) simulated by IDA program,

Figure 3.14 Duration diagram of mean air temperature of 11.3.121

22 24 26 28 30 32 34 3623 4123 4623 5123 5623 M e an ai r t e m p e ratu re Time

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Figure 3.15 PPD and PMV values of 11.3.121 in the daytime(blue) and night(green)

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For southeast patient room, 11.3.128 was chosen to be investigated, have a look at the result from mean air temperature duration diagram (see Figure 3.16), PPD and PMV values (see Figure 3.17 and Table 3.14) simulated by IDA program,

20 22 24 26 28 30 32 34 3623 4123 4623 5123 5623 M e an ai r t e m p e ratu re Time

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Figure 3.17 PPD and PMV values of 11.3.128 in the daytime (blue) and night(green)

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For northwest patient room, 11.3.200 was chosen to be investigated, have a look at the result from mean air temperature duration diagram (see Figure3.18), PPD and PMV values (see figure 3.19 and Table 3.15) simulated by IDA program,

Figure 3.19 PPD and PMV values of 11.3.200 in the daytime (blue) and night (green)

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For southwest patient room, 11.3.156 was chosen to be investigated, have a look at the result from mean air temperature diagram (see Figure 3.20), PPD and PMV values (see Figure 3.21 and Table 3.16) simulated by IDA program,

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For south corridor11.3.139, we picked the region next to patient room 11.3.159

representing the whole south corridor, have a look at the result from mean air temperature duration diagram (see Figure 3.22), PPD and PMV values(see Figure 3.23and Table 3.17) simulated by IDA program,

Figure 3.23 PPD and PMV values of 11.3.139 in the daytime

21 22 23 24 25 26 27 28 29 3623 4123 4623 5123 5623 M e an air t e m p e rat u re Time

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Thermal Comfort PPD of daytime PMV of daytime The average value 22.63% 0.68%

Table 3.17 The average PPD and PMV values of 11.3.139 in daytime

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For south patient room, 11.3.150 was chosen to be investigated, have a look at the result from mean air temperature duration diagram (see Figure3.24), PPD and PMV values (see Figure 3.25 and Table 3.18) simulated by IDA program,

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The average amount of PMV and PPD values were shown in the table next,

It seems good, but a little cooler than the optimal standard.

Obviously, two groups emerged after PMV and PPD were presented from the simulation. Occupants in this study feel either warm or cool compared to the optimal standard. In detail, the indoor climate always feels warm for staff in corridors and reception; on the contrary, it feels cool for patients. There is always a gap between the opinions of the two groups to reach the theoretical optimal climate for both at the same time.

Nevertheless, in this study adaptation has to play a significant role. The tendency of this study is to create a better environment for staff. The reason is that the patients can adjust and control their clothing value whenever they want by wearing more clothes and putting blankets on, while, it is not convenient to change clothes for staff.

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Energy consumption

Energy was mainly concluded by 5 major ways gained or used and contribute to the energy consumption in the hospital ward. The first four factors which should be got rid of our model, are generated from internal heat; the last one which should be control as low as possible was introduced to the model in order to cool down the temperature. They are listed as a Table 3.19 below.

Resources Sun Light Occupants equipment cooling

Gained or used Of energy(KWh)

11455.4 19473.0 20880.5 17579.0 13796.0

Table 3.19 Energy consumption from different resources

Hence, the method to lower energy consumption is to apply effective energy-saving approaches to this hospital ward.

3.3 Generation of improvement and alternatives

The thermal comfort values depends on clothing of the occupants, activity levels of occupants, air velocity, mean radiant temperature and air temperature. In order to perfection the PPD values and PMV values for staff. The way in our improvement is try to cool down the mean air temperature so as to reach a better indoor climate. How to cool down the air temperature was defined as the main strategy in our improvement.

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Method I

This simulation is to reduce the minimum inlet air flow temperature to be 17 instead of 18. After improved, the mean air temperature (see Figure 3.26) PPD and PMV values of reception (Figure 3.27 and Table 3.20) will decrease.

Figure 3.26 Comparison of corridor 11.3.177 mean air temperatures between before and after applied method I

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Figure 3.27 PPD and PMV values of reception in the daytime (blue) and night (green) after applied method I

Table 3.20 The average PPD and PMV values of reception in the daytime and night after applied Method I

Compared with the original model, after improvement,

The mean air temperature of reception decreased by nearly 1.5°C, (see Figure3.26); The PPD values have decreased by 3 .6 and 1.4 percent in daytime and night, respectively (see Table 3.20);

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The PPD and PMV values of corridor 11.3.177after improved:

Figure 3.28 PPD and PMV values of corridor 11.3.177 in the daytime (blue) and night (green) after applied method I

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The average value 26.38% 0.75

Table 3.21 The average PPD and PMV values of 11.3.177 in the daytime and night after applied Method I

The mean air temperature of corridor 11.3.177 decreased about 0.5°C (see Figure 3.27); The PPD value has decreased by 3 percent in daytime (see Table 3.21);

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The PPD and PMV values of corridor 11.3.121 after improved:

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Figure 3.30 PPD and PMV values of corridor 11.3.121 in the daytime (blue) and night (green) after applied method I

Table 3.22 The average PPD and PMV values of 11.3.121 in the daytime (blue) and night (green) after applied Method I

The mean air temperature of reception decreased by nearly 1.5°C (see Figure3.29); The PPD values have decreased by 2.4 and 8.5 percent in daytime and night, respectively (see Table 3.22);

And PMV values are 0.05 and 0.07 closer to zero than original model in the daytime and night, respectively (see Table 3.22).

Resouces Sun Light Occupants equipment cooling

11455.4 19473.0 20880.5 17579.0 13722.0

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Method II

The second simulation is to add external shadings on the windows. Adding an external shading definitely decrease the solar energy gained from the outside, which means lower temperature may be reached for patients. However, we can take the temperature of patient room 11.3.28 as an example.

Glance at the first week of July before improvement, we can see that at the noon of days (around 12o’clock), the temperature would be extremely high than the other time (see Figure 3.31). Several sharp peaks merged at those moments.

Figure 3.31 Mean air temperature of patient room 11.3.28 during the whole summer before applied method II

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Figure 3.32 Mean air temperature of patient room 11.3.28 during the whole summer after applied method II

Moreover, it can improve the thermal comfort for the staff more or less as well. The PPD and PMV values of reception after improved:

Figure 3.33 Comparison of reception mean air temperatures between before and after applied method II

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Figure 3.34 PPD and PMV values of reception in the daytime (blue) and night (green) after applied method II

Table 3.23 The average PPD and PMV values of reception in the daytime and night after applied Method II

The mean air temperature decreased by 0.5°C, (see Figure3.33);

The PPD values have decreased by 1.6 and 1.2 percent in daytime and night, respectively (see Table 3.23);

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The PPD and PMV values of corridor 11.3.177after improved:

Figure 3.35 Comparison of corridor 11.3.177 mean air temperatures between before and after applied method II

Figure 3.36 PPD and PMV values of corridor 11.3.177 in the daytime (blue) and night (green) after applied method II

23 24 25 26 27 28 29 30 31 3623 4123 4623 5123 5623 M e an ai r t e m p e ratu re Time

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mean air temperature after adding external shading

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The average value 25.29% 0.73

Table 3.24 The average PPD and PMV values of reception in the daytime after applied Method II

The mean air temperature decreased by around 1°C, (see Figure 3.35); The PPD value has decreased by 4.1 percent in daytime (see Table 3.24);

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The PPD and PMV values of corridor 11.3.121 after improved:

Figure 3.37 Comparison of corridor 11.3.121 mean air temperatures between before and after applied method II

Figure 3.38 PPD and PMV values of 11.3.121 in the daytime (blue) and night (green) after applied method II

22 24 26 28 30 32 34 3623 4123 4623 5123 5623 M e an ai r t e m p e ratu re Time

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mean air temperature after adding external shading

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Table 3.25 The average PPD and PMV values of 11.3.121 in the daytime and night after applied Method II

The mean air temperature decreased by 0.5°C, (see Figure3.37);

The PPD values have decreased by 5.8and 3.4 percent in daytime and night, respectively (see Table 3.25);

And PMV values are 0.11 and 0.16 closer to zero than original model in the daytime and night, respectively (see Table 3.25).

Resources Sun Light Occupants equipment cooling

3535.2 19474.0 21460.9 17580.0 13685.0

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Method III

The third simulation is to reduce the internal energy gained from lighting by installing motion detectors in those rooms in the centre part and toilets.

The PPD and PMV values of reception after improved:

Figure 3.39 Comparison of corridor reception mean air temperatures between before and after applied method III

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mean air temperature after using motion detector

Multi-zone modeling of Thermal Comfort and Energy Consumption of a hospital ward -a summer case study

Energy Engineering

Examiner: Mathias Cehlin

DEPARTMENT OF TECHNOLOGY AND BUILT ENVIRONMENT

Multi-zone modeling of Thermal Comfort and

Energy Consumption of a hospital ward

-a summer case study

Tian Xie

June 2010

Acknowledgements

ABSTRACT

Table of Contents

1 Introduction

1.1

Indoor climate requirements in hospitals

1.2

Purpose

1.3

Limitation

2 Method

2.1

IDA Indoor Climate and Energy (ICE)

2.2

Boundary conditions

2.2.1 Geometry

2.2.2 Building construction parts

The building components consist of different constitutes, see Table 2.1:

2.2.3 HVAC-systems

2.2.4 Internal heat generation

2.3 Building of model

2.3.1 Inputting general data

2

v

C

P

C

C

2.3.2 Design HVAC system in the Model

2.3.3 Design the floor plan

2.3.4 Creating a new zone with its properties

Air velocity in the occupied zone

2.3.5 Drawing all the zones

2.4

Validation of model

2.5

Thermal climate and Energy analysis

Thermal Comfort

Energy Consumption

3 Result and Discussion

3.1

Validation of the model

3.2

Thermal climate and Energy analysis

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3.3

Generation of improvement and alternatives

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