INTRODUCTION
Object and Range of Study
The main research subject is a small room at the Thermal workshop of Ho Chi Minh
City University of Technology and Education Currently, the room is being installed with Daikin's air conditioner with a capacity of 2HP
This study examined three key factors affecting indoor climate control: the set temperature in the room, which ranged from 21 to 23 °C, the fan speed of the indoor unit, varying from levels 1 to 5, and the cooling conditions of the condenser, which included traditional condensers, evaporative condensers, and direct watering on the condenser.
Research Methods
In this study, I used three methodologies:
Aim of The Study
This thesis presents experiments and data analysis using Taguchi and Regression Analysis, developed with Minitab Software, to derive an equation for evaluating the Coefficient of Performance (COP) based on influential variables Additionally, Computational Fluid Dynamics (CFD) simulations using Ansys Software were conducted to observe the temperature distribution within the building.
Building Data
- Building: Laboratory in the thermal workshop
- Location: No 1, Vo Van Ngan Street, Linh Chieu Ward, Thu Duc City, HCM City
- The building area is 19.4 m 2 The wall material including Steel, Gypsum, Glass The floor material is concrete and the roof material is steel
- The area of roof, floor, glass, steel wall, gypsum wall is 18.36 m 2 , 18.36 m 2 , 8.3 m 2 , 11.32 m 2 , 15.45 m 2
- The furniture included 3 laptops, 3 tables, 1 cabinet, 1 shelf, 3 indoor units
- The number of people will be limited according to the table 1 3 – 4 people
Figure 1.3 Building Model Table 1 Factor of Floor Space (Table G9, Standard VN 06:2021/BXD) [2]
Space Usage Factor of Floor Space, m 2 /people
1 Covered play areas, halls, crowded places, clubs, dance floors,
Bars, Karaoke and similar areas 1.0
2 Grand lobby, atrium, reception area, waiting area, and the like 3.0
3 Meeting rooms, living rooms, conference rooms, dining rooms, reading rooms, study rooms, canteens, and similar spaces 1.5
5 Exhibition Hall or studio (film, broadcast, television, sound recording) 1.5
6 Shops that buy and sell services: department stores, barbers, curling irons, laundries, repair shops or the like 3.0
7 Art galleries, product galleries, museums or similar facilities 5.0
9 The stores sell large furniture such as tables and chairs, floor coverings, and the like 7.0
11 Bedroom or bedroom combined with a study room 8.0
13 Warehouse or place of storage 30.0
14 Car garage 2 people/parking box
- The ambient temperature is used in design:
+ The highest ambient temperature is 32.8 ℃
LITERATURE REVIEW
Obtained Data
The figure 2.40 describes information about all objects occupied in space room, also thermostats are arranged to obtain temperature data at point
Figure 2.35 Room space Actual measured room dimension is as follows:
Due to financial conditions, I would like to use the two indoor unit available in the room
Figure 2.36 2 models available in room
The trial simulation of air distribution revealed that indoor unit 2 provides a more even airflow compared to indoor unit 1 Based on the results illustrated in Figure 2.43, I have chosen indoor unit 2 for this research.
Figure 2.37 The wind distribution ability of
Figure 2.38 The wind distribution ability of
IDU – 2 This system uses an indoor unit with model FT50FVM and an outdoor unit with model R50BV1 The figure 2.46 is piping diagrams of the system
Figure 2.39 The properties of Indoor Unit
Figure 2.40 The properties of Outdoor Unit
Figure 2.41 The principle diagram of system [18]
Air velocity at the mouth is not uniform, necessitating measurements at four additional points for the simulation process, as illustrated in Figure 2.47.
Figure 2.42 4 Others point of the mouth
Figure 2.43 Velocity measuring instrument After I have measured the air velocity, I have collected the data and calculated the mean velocity
EXPERIMENT, NUMERICAL SIMULATION, AND DISCUSSION
Experimental Basis
Recent studies are increasingly focusing on utilizing artificial intelligence algorithms to enhance indoor environments while minimizing energy consumption Key variables influencing system power usage have been identified, particularly in air conditioning systems, where three primary factors play a significant role.
According to Standard for thermal comfortable zone [11] , I choose 3 levels that is 21 – 22 – 23
Following by the display of the remote, I choose 3 level that is 1 – 3 – 5
Base on the specialized knowledge about heat exchange, I propose 3 methods to cool the condenser:
- 2 – Evaporative condenser method with cool pack
- 3 – Watering directly on the condenser method
3.1.3 Design The Matrix Experiments (OAs)
Table 14 Parameters and Levels Determination
Orthogonal Array L9 is created by Minitab software after determined factors and levels
Table 15 L9 Table Was Created by Minitab Software
3.1.4 Assign Factors and Levels to The Orthogonal Arrays
Factors and levels will be assigned into the Orthogonal Array
Table 16 Assign Factors and Levels into Orthogonal Array
Experiments Temperature Air Velocity CCM
Conducting 9 experiments with 3 times being 10h, 13h, and 16h, respectively, the result of experiment was shown Appendix A
The data was analyzed by Minitab software
Response Table for Signal to Noise Ratios
Level TEMP AIR VELO CCM
Level TEMP AIR VELO CCM
Figure 3.1 Main Effects Plot for Means
The analysis of the data using the Taguchi method reveals that Air Velocity is the most significant factor affecting the Coefficient of Performance (COP), ranking first in influence To achieve a more objective evaluation, it is essential to conduct a further analysis through Regression Analysis.
Analysis of Variance Source DF Adj SS Adj MS F-Value P-Value
In the Analysis of Variance, the P-Value for the factors exceeds 0.05, indicating that none of the factors significantly influence the mean value of COP Consequently, further analysis will be conducted on independent trials, both with and without interactions among the factors.
Response Table for Signal to Noise Ratios
Larger is better Level TEMP AIR VELO CCM
Level TEMP AIR VELO CCM
Figure 3.3 Main Effects Plot for Means
Figure 3.4 Main Effects Plot for Ratios Regression Analysis with no interaction
Source DF Adj SS Adj MS F-Value P-Value Regression 3 0.81796 0.27265 2.26 0.109 TEMP 1 0.02479 0.02479 0.21 0.655 AIR VELO 1 0.67375 0.67375 5.57 0.027 CCM 1 0.11942 0.11942 0.99 0.331 Error 23 2.78023 0.12088
Lack-of-Fit 5 1.44389 0.28878 3.89 0.014 Pure Error 18 1.33634 0.07424
In a Taguchi Analysis, Air Velocity emerged as the most influential factor, ranking first, with a P-Value of less than 0.05, indicating that the differences among some of the means are statistically significant.
Source DF Adj SS Adj MS F-Value P-Value
=> The result of analysis saw that P-Value of CCM factor is 0.036 that of interaction between Temperature and CCM is 0.009 less than 0.05 Significance 3.1.7 Validation
From the figure 3.4, to evaluate the reliability of the analysis, conducting to validate as the following settings:
- CCM: Watering directly on the condenser
Table 17 The Result of Validation (Appendix B)
=>The result of validation shows that this setting has the highest COP value, this analysis is correct.
Experimental Setup
To ensure that temperature, velocity, and humidity levels meet the thermal comfort zone criteria and to verify the simulation's accuracy, I will strategically place thermostats around the occupant positions in the room, as well as in other essential locations, as illustrated in Figure 3.6.
Figure 3.5 Points being 500mm from gypsum wall
Figure 3.6 Point 7 being 800mm from inlet
Device Collection of Data Range
Table 21 shows specification measurement instrument used in experiments
Table 19 The temperature was obtained 400mm above the floor
Table 20 The temperature was obtained 800mm above the floor
Table 21 The temperature was obtained 1200mm above the floor
Numerical Simula on
The model is designed based on the actual size of the room, including:
- The floor, roof, walls, and objects in the room
- The volume of air in the room
Figure 3.7 The model was built by Autodesk REVIT Software 2019
The room sizes are as follows:
Figure 3.8 The inside room model was built by Autodesk REVIT Software 2019
Table 22 Detailed Dimension of The Room
No Name Quantity Dimension (mm)
In ANSYS software, the model of objects in a room is defined by calculating the air volume while excluding walls, floors, roofs, and other room objects.
Figure 3.10 The model of air volume in room
To be begin with, we start Ansys Workbench, then double-click geometry icon in order to import model, as follows:
- Step 1: Double-click to geometry icon
- Step 2: Right click to geometry, then choose import and select to the model
Figure 3.12 Geometry was imported in ANSYS Software
Next step is Boolean, which will make the objects not overlapped
- Step 1: Select Boolean function in Create
- Step 2: Select Subtract in Operating
- Step 3: Apply Air body to Target Bodies
- Step 4: Apply Objects body to Tool Bodies
Figure 3.14 Conduct Boolean function Next, we will process the model before going to the naming step as to manage the boundary condition
Figure 3.15 Name selection of inlet indoor face
- Step 1: Choose face which need to name it
- Step 2: Click tool tab, then choose Named Selection
- Step 3: To name in Detail View and click generate
We will do the same with the rest
After we have finished geometry step, switching to meshing
To begin, set the Physics Preference to CFD and the Solver Preference to Fluent for the mesh step Next, adjust the sizing to the inlet face and the body of air, ensuring that both Capture Curvature and Capture Proximity tabs are activated Finally, right-click on the mesh icon and select Generate Mesh to complete the process.
Figure 3.17 Physics and Solver Preference Setting
Figure 3.19 Capture Curvature and Capture Proximity Setting
After Generating Mesh, the result saw the picture below
Figure 3.21 The result after Meshing
Figure 3.23 The value of Skewness and Aspect Ratio Quality
For mesh quality, we will concern to Skewness and Aspect Ratio
Skewness is a crucial metric for assessing mesh quality, as it measures the deviation of a mesh element from the overall geometry Ideally, a grid element should have evenly balanced edges, while a mesh element with highly unbalanced edges represents the worst-case scenario.
Figure 3.24 The range of Skewness Quality [3]
According to ANSYS Meshing Application Introduction 2009, the maximum aspect ratio value for mesh quality in FLUENT is 14.266, which exceeds the required standards.
Next, we will move to the setup step of Fluid Flow (Fluent)
Figure 3.25 Fluid Flow (Fluent) setting
To set up Fluent Launcher, click on Setup and select the Double Precision option For faster processing, choose Parallel with a Solver setting of 6 in the Processing Options, then click OK.
The below is the internal interface
Figure 3.27 The interface inside Fluent
To check the mesh, click on "Check" and review the Volume statistics displayed in the Console If the value is positive, you can proceed to the next step; however, if it's negative, you must stop Throughout this process, ensure that Steady mode remains activated Next, select Gravity and input a z-axis value of -9.81.
- Energy: Due to simulation has temperature effects, we must to turn on Energy Equation
- Viscous: In this part, we activate the realizable 𝑘 − 𝜀 model In Options, because the air is compressible flow, we should tick on Viscous Heating
Figure 3.30 The realizable 𝒌 − 𝜺 model and Options Setting
The structure of building included some material such as steel, glass, concrete, gypsum We will add to Materials with thermal properties that is as follows:
Table 23 The thermal properties of Materials
We will replace material in this part We choose zone and click Edit
Figure 3.33 Cell Zone Conditions Setting
Figure 3.35 Click to Specified Operating Density
We select on zone and then click Edit
Figure 3.36 Edit to Inlet Boundary Conditions
- Choosing Inlet Edit Momentum tab Entering velocity value 5.1 and Thermal tab Entering Temperature value 14.3
Figure 3.37 Edit to Velocity Magnitude
- Selecting wall-foor Thermal tab Clicking on Convection, Heat Transfer Coefficient value 3.5, Free Stream Temperature Value 30.4, Wall Thickness Value 0.2, and Material Name is Concrete
Figure 3.39 Edit to Thermal Tab on wall-floor
- Selecting wall-roof Edit Thermal tab Clicking on Mixed, Heat Transfer Coefficient value 1.88, Free Stream Temperature Value 31.4, External Radiation Temperature Value 31.4 Wall Thickness Value 0.058, and Material Name is Steel
Figure 3.40 Edit to Thermal Tab on wall-roof
- Selecting wall-w Edit Thermal tab Clicking on Mixed, Heat Transfer Coefficient value 7.73, Free Stream Temperature Value 31.4, External Radiation Temperature Value 31.4, Wall Thickness Value 0.005, and Material Name is Steel
Figure 3.41 Edit to Thermal Tab on wall-w
- Selecting wall-glass Edit Thermal tab Clicking on Convection, Heat Transfer Coefficient value 7.55, Free Stream Temperature Value 30.4, Wall Thickness Value 0.003, and Material Name is Glass
Figure 3.42 Edit to Thermal Tab on wall-glass
- Selecting wall-thachcao Edit Thermal tab Clicking on Convection, Heat Transfer Coefficient value 6.7, Free Stream Temperature Value 30.4, Wall Thickness Value 0.01, and Material Name is Gypsum
Figure 3.43 Edit to Thermal Tab on wall-thachcao
- Double-Clicking on Methods Scheme SIMPLEC Pressure Standarrd
- Double-Clicking on Initialization Standard Initialization Compute from all- zones Clicking Initailize
- Double-Clicking on Run Calculation Entering Number of Iterations being 1000
Figure 3.46 Setting Number of Iterations
70 Figure 3.47 Temperature Field at plane 1 (XZ-axis)
Figure 3.48 Temperature Field at plane 2 (XZ-axis)
Figure 3.49 Temperature Field at plane 3 (XZ-axis)
Figure 3.50 Temperature Field at plane 4 (XZ-axis)
72 Figure 3.51 Temperature Field at plane 5 (XZ-axis)
Figure 3.52 Temperature Field at plane 6 (XZ-axis)
Figure 3.53 Temperature Field at plane 1 (YZ-axis)
Figure 3.54 Temperature Field at plane 2 (YZ-axis)
74 Figure 3.55 Temperature Field at plane 3 (YZ-axis)
Figure 3.56 Temperature Field at plane 4 (YZ-axis)
Figure 3.57 Temperature Field at the cross section from the floor (XY axis)
Figure 3.58 Temperature Field being 400mm from the floor (XY axis)
76 Figure 3.59 Temperature Field being 800mm from the floor (XY axis)
Figure 3.60 Temperature Field being 1200mm from the floor (XY axis)
Figure 3.61 Temperature Field being 1600mm from the floor (XY axis)
After simulation, the result show that the temperature field of some planes at different heights satisfies the conditions of the thermal comfort zone b) Velocity Field Value
Figure 3.62 Velocity Field at plane 1 (XZ-axis)
78 Figure 3.63 Velocity Field at plane 2 (XZ-axis)
Figure 3.64 Velocity Field at plane 3 (XZ-axis)
Figure 3.65 Velocity Field at plane 4 (XZ-axis)
Figure 3.66 Velocity Field at plane 5 (XZ-axis)
80 Figure 3.67 Velocity Field at plane 1 (YZ-axis)
Figure 3.68 Velocity Filed at plane 2 (YZ-axis)
Figure 3.69 Velocity Field at plane 3 (YZ-axis)
Figure 3.70 Velocity Field at plane 4 (YZ-axis)
82 Figure 3.71 Velocity Field at plane 5 (YZ-axis)
Figure 3.72 Velocity Field at the cross section from the floor (XY axis)
Figure 3.73 Velocity Field being 400mm from the floor (XY axis)
Figure 3.74 Velocity Field being 800mm from the floor (XY axis)
Figure 3.75 Velocity Field being 1200mm from the floor (XY axis)
Figure 3.76 Velocity Field being 1600mm from the floor (XY axis)
The results show that the velocity field of some planes at different heights satisfies the thermal comfort zone condition c) Streamline
Figure 3.77 Streamline starts from Inlet
Figure 3.78 Streamline Vertex start from Inlet
From the result of streamline, the air distribution is even, so the mounting position of the IDU 2 is reliable
To check grid independence, I have been re-established mesh method which is
Figure 3.79 Select to Method Mesh
Automatic Method Mesh Figure 3.84 Temperature Field with
Automatic Method Mesh Figure 3.86 Temperature Field with
Tetrahedrons Mesh After meshing, the result saw that temperature field is unchanged Its mean that it is the grid independence
DISCUSSION
Table 25 Experimental temperature value being 400mm from the floor
The temperature field of the experiment recorded values ranging from a maximum of 20.1℃ to a minimum of 18.1℃ at a height of 400mm In comparison, the simulation results showed a slightly higher maximum temperature of 20.3℃, while the minimum remained the same at 18.1℃.
Table 26 Experimental temperature value being 800mm from the floor
Te m pe ra tu re o C
Temperature Graph between Experiment and Simulation
Figure 4.2 Temperature graph following by table 28
At a height of 800mm, the experimental temperature field recorded a maximum of 23.1℃ and a minimum of 20.6℃, while the simulation temperature field observed a maximum of 22.9℃ and a minimum of 21℃.
Table 27 Experimental temperature value being 1200mm from the floor
Figure 4.3 Temperature graph following by table 29 From a height of 1200mm, Temperature field of experiment saw that the highest
Te m pe ra tu re o C
Temperature Graph between Experiment and Simuation
Te m pe ra tu re o C
Temperature Graph between Experiment and Simulation
90 value and the lowest is 22.4 (℃) and 20.3 (℃), respectively Temperature field of simulation witnessed that the highest value and the lowest value is 20.6 and 18.9, respectively
After simulation and experiment, the result shows a small deviation (fluctuation from 0 to 10 %) It shows that the simulation results are very reliable
Numerical calculations of airflow distribution, temperature, and velocity are conducted to assess thermal comfort during winter conditions The realizable 𝑘 − 𝜀 model is utilized for simulations, demonstrating significant success in verification studies.
In this study, with a set temperature of 22 degrees Celsius, wind speed of 5.1 m/s, the thermal comfort zone was achieved The temperature around occupants ranges from 21 to
CONCLUSIONS AND FUTURE WORK
Conclusions
Based on the processed data, the relationship between these parameters and the COP through Experiment, Simulation, Taguchi Analysis and Regression Analysis
The Taguchi optimal analysis method identifies Setting Air Velocity as the most significant factor influencing the Coefficient of Performance (COP) Based on the data presented in figures 3.3 and 3.4, the optimal setup plan has been determined.
- Condenser Cooling Method: Watering directly on the condenser
Regression analysis reveals that the P-Value for CCM is 0.036 and for TEMP*CCM is 0.009, indicating that both factors exhibit a significant interaction, as their values are below the 0.05 threshold.
After I perform the validation, the experimental results show that it is very similar to the data analyzed above The COP indicated the highest value in the surveyed case as shown in the table 19
To evaluate occupant satisfaction regarding thermal comfort, a simulation was conducted to analyze the temperature field within the space The results of this simulation closely matched experimental values, indicating that the temperature fields in the room and around the occupants met the thermal comfort zone conditions outlined in TCVN 5687 – 2010, with fluctuations between 21 to 23 degrees Celsius, as illustrated in Table 28 and Figure 3.91.
Future work
Future work should be conducted to included factors relate to occupant’s behavior
It is crucial to implement predictions for various school classes while considering the dynamic changes in indoor environments Ensuring high Indoor Environment Quality is vital, as students and staff are especially susceptible to these conditions Prioritizing thermal comfort and indoor air quality is essential, but it must be balanced with energy efficiency, highlighting the need for further research in this area.
Bài viết "Đánh giá, mô phỏng các yếu tố ảnh hưởng đến phân phối gió trong hệ thống điều hòa không khí vùng không gian nhỏ hẹp tại xưởng nhiệt" của các tác giả Nguyễn Nhật Tân, Hồ Thanh Tuấn, Nguyễn Khoa Duy Thức và Phạm Thanh Ca, được thực hiện trong khuôn khổ đồ án tốt nghiệp tại TP HCM vào tháng 01 năm 2023, nghiên cứu các yếu tố quan trọng tác động đến hiệu quả phân phối gió trong không gian hạn chế Nghiên cứu này nhằm cải thiện hiệu suất hệ thống điều hòa không khí trong các xưởng nhiệt, từ đó nâng cao môi trường làm việc cho công nhân.
[4] Prediction and optimization of thermal comfort, IAQ and energy – Fangli Hou, Jun Ma, Helen H.L Kwok, Jack C.P Cheng – Building and Environment – September 2022
[5] Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings - Jin Woo Moon , Sung Kwon Jung - Applied Thermal Engineering 103 (2016) 1135–1144
[6] Thermostat strategies impact on energy consumption in residential buildings – Jin Woo Moon, Seung-Hoon Han – Energy and Buildings 43 (2011) 338 – 346
[10] Hướng dẫn thiết kế hệ thống điều hòa không khí – PGS TS Nguyễn Đức Lợi, NXB Khoa học và kỹ thuật, 2005
[11] TCVN 5687 -2010 – Tiêu chuẩn thiết kế điều hòa không khí, thông gió
[16] TS Trần Văn Khiêm, Phương pháp Taguchi và ứng dụng tối ưu hóa chế độ cắt, 2017
[17] Chapter 7 Analysis of variance (ANOVA) – Statistics for Anthropology – Cambridge University Press – June 2012 – 1998
[19] An innovative analysis and experimental investigation on energy savings of a VAV system in hot and humid climates - K.H Yang, M.M Ting - Building and Environment 35
[20] A review on buildings energy consumption information - Luis Pe´rez-Lombard, Jose´ Ortiz, Christine Pout - Energy and Buildings 40 (2008) 394–398
[21] Numerical Investigation of Thermal Comfort and Indoor Air Quality In An Office Room Using Floor Heating System - Bahadır Erman YĩCE, Erhan PULAT - Conference Paper – April 2019
[22] Workplace productivity and individual thermal satisfaction - Shin-ichi Tanabe, Masaoki Haneda, Naoe Nishihara - Building and Environment 91 (2015) 42-50
[23] Thermal Comfort in Office Buildings - Ancuta Nadia JURCO, Iacob Liviu SCURTU
[24] Thermal Comfort Analysis for Office Room Using Computational Fluid Dynamics: A Review – Rajnish Kumar Gautam, Ravindra Mohan – IJO SCIENCE – OCTOBER 2018
[25] Simulation of thermal comfort on public space and buildings around river in Banjarmasin-Indonesia – A Rahman - IOP Conf Series: Materials Science and Engineering
[26] Exploring the impact of perceived control on thermal comfort and indoor air quality perception in schools – Giulia Torriani, Giulia Lamberti, Fabio Fantozzi, Francesco Babich – Journal of Building Engineering 63 (2023) 105419
The study by Vithanage, Jayathilaka, Jayamini, and Nimali focuses on identifying the optimal comfort zone within air-conditioned rooms, taking into account the dimensions of the room and the placement of the air conditioning unit The research, presented at a conference in September 2022, emphasizes the significance of room layout and A/C positioning in achieving efficient temperature regulation and enhancing occupant comfort.
Time No Ex TEMP AIR VELO CCM COP