ANALYSIS OF INSULATION OF MATERIAL
Group Member:
Wang Deyu, Li Dejun, Zhong Haoyuan
Xu Shanshan, Li Yaqiong, Yan Li
—— PROJECT OF DESIGN OF EXPERIMENT
1 Literature Review ................................................ 3
2 Executive Summary............................................. 4
3 Preparation ......................................................... 5
4 Choice of Experimental Design ............................ 8
5 Performing the Experiment ............................... 10
6 Eliminating Noise .............................................. 11
7 Data analysis ..................................................... 14
8 Reference ......................................................... 27
CATALOG
1 Literature Review
The problem of interest in our project is about how a specific insulation material, the cloth,
could affect the cooling rate of water. We first need to define how various factors would
accelerate or decelerate the cooling rate.
We searched for several periodicals and find two articles discussing insulation materials [1]
and cooling rates of water [2], in Chinese and English respectively.
In the article, we could learn that the most important factors that affect the rate of heat emission
of an object are contact areas of the heat source, the properties of the object, either physical or
chemical, and the heat conduction rate in the object itself. From our daily experience and some
fundamental physics knowledge, we expected that the color of the object may also contribute to
the heat emission of the object.
As for the insulation material, an article about garments suggests that the thermal
conductivity and evaporative resistance are more important among others in affecting the
comfortableness of garments. As this article discusses in particular about the garment design,
which involve more about the direct contact of the body, the conclusion should be for reference
only.
In summary, we would expect the cooling rate of the water in our project to be affected
mainly by: properties of the liquid, physical properties of insulation material, size of the container,
heat conduction property of the container, contact of the air, color of the material, and thickness
of the material.
We first propose a brief model to define the cooling rate of the water. It should be like this:
Δ𝑇 = 𝑓(𝐿𝑃, 𝑆,𝑀, 𝐶, 𝑇, 𝐻𝐶, 𝐶𝐴)
where LP=liquid properties, S=size of the container, M=material, C=color of the material,
T=thickness of the material, HC=heat conduction, CA=contact of the air.
LITERATURE REVIE
2 Executive Summary
2.1 Problem Statement
The experiment is aimed to compare the performance of different kinds of heat
insulation materials under normal conditions. The results of the experiment would be
quantified into the details including the texture, thickness, exterior color and
ventilation.
2.2 Regression Model
Temp Diff
= e i = igh = hi e
e = i g e i i =
e i = igh = hi e
e i = igh e i i =
= hi e e i i =
e = i g e i i =
Cause and Effect Diagram – Fishbone Diagram
EXECUTIVE SUMMARY
3 Preparation
3.1 Material and Measuring Equipment
3.1.1 Material
We select two clothing type with different texture, one is cotton which is
more tightened weaved, and the other is flax. For each type of material, we
choose two articles of different color, one is black and the other is white. Our
material is show as follows:
PREPARATION
Figure 1 Material
3.2 Container: Beaker
We use beaker to hold water. Each beaker is 150ml. In order to reduce the
impact of cool beaker, in each experiment, the beaker is warmed-up. To reduce
Flax, Black
Flax, White
Cotton, Black
Cotton, White
noise caused by desk, we put a paper bowl under the beaker. The paper bowl has
low specific heat capacity, so it absorbs heat at a low speed, which will favor our
experiment. The beaker is show as follows:
Figure 2 Beaker
3.2.1 Kerosene thermometer
To measure the temperature before and after experiment, we use two piece
of Kerosene thermometer. The scales of thermometers used in this experiment are
different, one is 1 centigrade and the other is 2 centigrade. The Kerosene
thermometer is shown as follows:
Beaker,150ml
Figure 3 Thermometer
3.3 Experiment Location
This experiment is done in C Builiding, Room 300, Tshinghua University. The
room temperature is 26 centigrade.
4 Choice of Experimental Design
4.1 Design of Experiment
4.1.1 Variable Selection
In the second chapter, the cause and effect diagram shows various factors that
could affect the response variable, the change of temperature. To perform the
experiment in a more efficient and more accurate way, we need to carefully select
the critical variables and the way to distinguish the levels of these variables.
The four major factors we choose are: Material, Color, Layer, and Ventilation.
For each of the variables, we choose to have two levels, and these two levels
should be distinguishable. For material, we find two kinds of cloth, one of which
has dense threads and is slightly thicker, the other one has relatively loose threads
and is lighter. To achieve larger difference between the two levels, we choose
black and white cloth of each kind in the experiment as the two levels in of the
color variable. Another factor that may significantly affect the cooling rate of the
water is the thickness of the insulation material. We decide to wrap 3 layers of
CHOICE OF EXPERIMENTAL DESIGN
cloth as the high level and single layer as the low level. Finally, whether to use
a covering for the beaker during cooling of the water determine the level of
ventilation in the experiment.
4.1.2 Setting Variables
The four variables and the corresponding settings to their levels are
determined. To be more explicit, we list them in Table 1.
Factor Material Color Layer Ventilation
+ Heavy Black Multiple Yes
- Light White Singular No
Table 1 Variables in the insulation experiment
The experiment could then be designed on these four variables.
4.1.3 Blocking
In the experiment, we use two thermometers to measure the temperature of the
cooling water. Though the two thermometers are both kerosene thermometer,
they have different calibration. Thus, to mitigate the influence of the
measurement itself, we should develop two blocks to apply the two thermometers.
For each treatment of the experiment, there will be two replications, each of
which is in one block.
4.1.4 Experiment Design
The experiment has the following properties:
4 variables;
2 levels per variable;
2 replications per treatment;
2 blocks;
Full factorial.
Use Minitab 15 to generate an experiment design, we would have 32 runs, as
has been shown in
Appendix 1.
5 Performing the Experiment
According to the design, we could start the experiment. We boil tap water to
approximately 100 degrees Celsius, and then quickly pour 200 ml boiling water into the two
beakers and two experimenters would use the thermometer to read the temperature of the
water. To ensure that the temperature is accurately measured, we begin reading when we first
see the temperature is steady and begin to drop. At a certain temperature, the experimenter
would write down the reading on the meter and count 3 minutes before a second reading is
acquired. Using the two readings with 3-minute interval, the drop of temperature within the
timespan could be calculated.
The two experimenters read the meter individually. The difference between the two
meter and between the readings by the two experimenters would be mitigated through
blocking.
In the treatment with no ventilation, a paper plate is used to cover the beaker. In the
center of the plate, a hole is left for the thermometer to be placed right in the beaker. Paper is
a kind of poor heat conductor. Thus, the noise could be minimized.
PREFORMING THE EXPERIMENT
6 Eliminating Noise
6.1 Warm up of the beakers and the thermometers
To ensure that the boiling water will not lose its heat through channels we are not
interested in, the beakers and the thermometers themselves are to be preheated before
data is sampled.
6.2 Wrap the cloth tightly to the beaker
The clothes are wrapped around the beaker, no matter one-layer or three-layer is
applied, the clothes are fixed by using a hair clip. The slim clip would also ensure that
the least width is overlapped.
6.3 Pad the cup with a paper dish underneath
The bottom of the beaker should not directly contact the table, which is a good heat
conductor. We put another paper plate beneath the beaker to minimize the heat
conducted through the bottom.
The experiment is conducted under a condition as shown in
ELIMINATING NOISE
.
Appendix
StdOrder RunOrder CenterPt Blocks Material Color Layer Ventilation
1 1 1 1 Light White Singular No
2 2 1 1 Heavy White Singular No
3 3 1 1 Light Black Singular No
4 4 1 1 Heavy Black Singular No
5 5 1 1 Light White Multiple No
6 6 1 1 Heavy White Multiple No
7 7 1 1 Light Black Multiple No
8 8 1 1 Heavy Black Multiple No
9 9 1 1 Light White Singular Yes
10 10 1 1 Heavy White Singular Yes
11 11 1 1 Light Black Singular Yes
12 12 1 1 Heavy Black Singular Yes
13 13 1 1 Light White Multiple Yes
14 14 1 1 Heavy White Multiple Yes
15 15 1 1 Light Black Multiple Yes
16 16 1 1 Heavy Black Multiple Yes
17 17 1 2 Light White Singular No
18 18 1 2 Heavy White Singular No
19 19 1 2 Light Black Singular No
20 20 1 2 Heavy Black Singular No
21 21 1 2 Light White Multiple No
22 22 1 2 Heavy White Multiple No
23 23 1 2 Light Black Multiple No
24 24 1 2 Heavy Black Multiple No
25 25 1 2 Light White Singular Yes
26 26 1 2 Heavy White Singular Yes
27 27 1 2 Light Black Singular Yes
28 28 1 2 Heavy Black Singular Yes
29 29 1 2 Light White Multiple Yes
30 30 1 2 Heavy White Multiple Yes
31 31 1 2 Light Black Multiple Yes
32 32 1 2 Heavy Black Multiple Yes
Appendix 1 The design of experiment
Figure 4 The experiment equipment
7 Data analysis
7.1 Regression model
In this chapter, we will generate a model and solve it in Minitab.
First, we formulate a model with combination of all the four major factors, namely
Material, Color, Layer, Ventilation, Material*Color, Material*Layer,
Material*Ventilation, Color*Layer, Color* ventilation, Layer*Ventilation,
Material*Color*Layer, Material*Color*Ventilation, Material*Layer*Ventilation,
Color*Layer*Ventilation, Material*Color*Layer*Ventilation
We use these 15 factors in a GLM and calculate the coefficients in Minitab
来源 自由度 Seq SS Adj SS Adj MS F P
Material 1 6.570 6.570 6.570 22.73 0.000
Color 1 3.445 3.445 3.445 11.92
0.003
Layer 1 2.820 2.820 2.820 9.76
0.007
Ventilation 1 122.853 122.853 122.853 425.00 0.000
Material*Color 1 5.200 5.200 5.200 17.99 0.001
Material*Layer 1 0.263 0.263 0.263 0.91 0.355
DATA ANALYSIS
Material*Ventilation 1 3.063 3.063 3.063 10.60 0.005
Color*Layer 1 0.015 0.015 0.015 0.05
0.821
Color*Ventilation 1 5.040 5.040 5.040 17.44 0.001
Layer*Ventilation 1 5.200 5.200 5.200 17.99 0.001
Material*Color*Layer 1 0.578 0.578 0.578 2.00 0.177
Material*Color*Ventilation 1 0.000 0.000 0.000 0.00 0.974
Material*Layer*Ventilation 1 0.008 0.008 0.008 0.03 0.871
Color*Layer*Ventilation 1 0.383 0.383 0.383 1.32 0.267
Material*Color*Layer*Ventilation 1 0.015 0.015 0.015 0.05 0.821
误差 16 4.625 4.625 0.289
合计 31 160.080
We delete Material*Color*Layer*Ventilation, and then recalculate the coefficients.
来源 自由度 Seq SS Adj SS Adj MS F P
Material 1 6.570 6.570 6.570 24.07 0.000
Color 1 3.445 3.445 3.445 12.62 0.002
Layer 1 2.820 2.820 2.820 10.33 0.005
Ventilation 1 122.853 122.853 122.853 450.08 0.000
Material*Color 1 5.200 5.200 5.200 19.05 0.000
Material*Layer 1 0.263 0.263 0.263 0.96 0.340
Material*Ventilation 1 3.063 3.063 3.063 11.22 0.004
Color*Layer 1 0.015 0.015 0.015 0.06 0.816
Color*Ventilation 1 5.040 5.040 5.040 18.47 0.000
Layer*Ventilation 1 5.200 5.200 5.200 19.05 0.000
Material*Color*Layer 1 0.578 0.578 0.578 2.12 0.164
Material*Color*Ventilation 1 0.000 0.000 0.000 0.00 0.973
Material*Layer*Ventilation 1 0.008 0.008 0.008 0.03 0.868
Color*Layer*Ventilation 1 0.383 0.383 0.383 1.40 0.253
误差 17 4.640 4.640 0.273
合计 31 160.080
We delete Material*Color*Layer, and then recalculate the coefficients.
来源 自由度 Seq SS Adj SS Adj MS F P
Material 1 6.570 6.570 6.570 25.48 0.000
Color 1 3.445 3.445 3.445 13.36 0.002
Layer 1 2.820 2.820 2.820 10.94 0.004
Ventilation 1 122.853 122.853 122.853 476.52 0.000
Material*Color 1 5.200 5.200 5.200 20.17 0.000
Material*Layer 1 0.263 0.263 0.263 1.02 0.326
Material*Ventilation 1 3.063 3.063 3.063 11.88 0.003
Color*Layer 1 0.015 0.015 0.015 0.06 0.810
Color*Ventilation 1 5.040 5.040 5.040 19.55 0.000
Layer*Ventilation 1 5.200 5.200 5.200 20.17 0.000
Material*Color*Layer 1 0.578 0.578 0.578 2.24 0.152
Material*Layer*Ventilation 1 0.008 0.008 0.008 0.03 0.864
Color*Layer*Ventilation 1 0.383 0.383 0.383 1.48 0.239
误差 18 4.641 4.641 0.258
合计 31 160.080
We delete Material* Layer*Ventilation, and then recalculate the coefficients.
来源 自由度 Seq SS Adj SS Adj MS F P
Material 1 6.570 6.570 6.570 26.86 0.000
Color 1 3.445 3.445 3.445 14.08 0.001
Layer 1 2.820 2.820 2.820 11.53 0.003
Ventilation 1 122.853 122.853 122.853 502.15 0.000
Material*Color 1 5.200 5.200 5.200 21.26 0.000
Material*Layer 1 0.263 0.263 0.263 1.07 0.313
Material*Ventilation 1 3.063 3.063 3.063 12.52 0.002
Color*Layer 1 0.015 0.015 0.015 0.06 0.805
Color*Ventilation 1 5.040 5.040 5.040 20.60 0.000
Layer*Ventilation 1 5.200 5.200 5.200 21.26 0.000
Material*Color*Layer 1 0.578 0.578 0.578 2.36 0.141
Color*Layer*Ventilation 1 0.383 0.383 0.383 1.56 0.226
误差 19 4.648 4.648 0.245
合计 31 160.080
We delete Color*Layer*Ventilation, and then recalculate the coefficients.
来源 自由度 Seq SS Adj SS Adj MS F P
Material 1 6.570 6.570 6.570 26.12 0.000
Color 1 3.445 3.445 3.445 13.70 0.001
Layer 1 2.820 2.820 2.820 11.21 0.003
Ventilation 1 122.853 122.853 122.853 488.36 0.000
Material*Color 1 5.200 5.200 5.200 20.67 0.000
Material*Layer 1 0.263 0.263 0.263 1.04 0.319
Material*Ventilation 1 3.063 3.063 3.063 12.18 0.002
Color*Layer 1 0.015 0.015 0.015 0.06 0.808
Color*Ventilation 1 5.040 5.040 5.040 20.04 0.000
Layer*Ventilation 1 5.200 5.200 5.200 20.67 0.000
Material*Color*Layer 1 0.578 0.578 0.578 2.30 0.145
误差 20 5.031 5.031 0.252
合计 31 160.080
We delete Material*Color*Layer, and then recalculate the coefficients.
来源 自由度 Seq SS Adj SS Adj MS F P
Material 1 6.570 6.570 6.570 24.60 0.000
Color 1 3.445 3.445 3.445 12.90 0.002
Layer 1 2.820 2.820 2.820 10.56 0.004
Ventilation 1 122.853 122.853 122.853 459.95 0.000
Material*Color 1 5.200 5.200 5.200 19.47 0.000
Material*Layer 1 0.263 0.263 0.263 0.98 0.333
Material*Ventilation 1 3.063 3.063 3.063 11.47 0.003
Color*Layer 1 0.015 0.015 0.015 0.06 0.813
Color*Ventilation 1 5.040 5.040 5.040 18.87 0.000
Layer*Ventilation 1 5.200 5.200 5.200 19.47 0.000
误差 21 5.609 5.609 0.267
合计 31 160.080
We delete Color*Layer, and then recalculate the coefficients.
来源 自由度 Seq SS Adj SS Adj MS F P
Material 1 6.570 6.570 6.570 25.70 0.000
Color 1 3.445 3.445 3.445 13.48 0.001
Layer 1 2.820 2.820 2.820 11.03 0.003
Ventilation 1 122.853 122.853 122.853 480.54 0.000
Material*Color 1 5.200 5.200 5.200 20.34 0.000
Material*Layer 1 0.263 0.263 0.263 1.03 0.322
Material*Ventilation 1 3.063 3.063 3.063 11.98 0.002
Color*Ventilation 1 5.040 5.040 5.040 19.72 0.000
Layer*Ventilation 1 5.200 5.200 5.200 20.34 0.000
误差 22 5.624 5.624 0.256
合计 31 160.080
We delete Material*Layer, and then recalculate the coefficients.
来源 自由度 Seq SS Adj SS Adj MS F P
Material 1 6.570 6.570 6.570 25.67 0.000
Color 1 3.445 3.445 3.445 13.46 0.001
Layer 1 2.820 2.820 2.820 11.02 0.003
Ventilation 1 122.853 122.853 122.853 479.96 0.000
Material*Color 1 5.200 5.200 5.200 20.32 0.000
Material*Ventilation 1 3.063 3.063 3.063 11.97 0.002
Color*Ventilation 1 5.040 5.040 5.040 19.69 0.000
Layer*Ventilation 1 5.200 5.200 5.200 20.32 0.000
误差 23 5.887 5.887 0.256
合计 31 160.080
Also we get
S = 0.505930 R-Sq = 96.32% R-Sq(调整) = 95.04%
项 系数 系数标准误 T P
常量 6.65313 0.08944 74.39 0.000
Material
Light -0.45313 0.08944 -5.07 0.000
Color
White 0.32813 0.08944 3.67 0.001
Layer
Singular -0.29688 0.08944 -3.32 0.003
Ventilation
No -1.95938 0.08944 -21.91 0.000
Material*Color
Light White -0.40312 0.08944 -4.51 0.000
Material*Ventilation
Light No 0.30938 0.08944 3.46 0.002
Color*Ventilation
White No -0.39687 0.08944 -4.44 0.000
Layer*Ventilation
Singular No 0.40313 0.08944 4.51 0.000
We also draw some plot in function DOE in Minitab to show the effect of left factors.
Figure 5 The pareto plot
C
AD
B
BD
AB
CD
A
D
2520151050
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标准化效应
2.07
A Material
B Color
C Layer
D Ventilation
因子 名称
标准化效应的 Pareto 图(响应为 TempDiff,Alpha = .05)
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百分
比
A Material
B Color
C Layer
D Ventilation
因子 名称
不显著
显著
效应类型
CD
BD
AD
AB
D
C
B
A
标准化效应的正态图(响应为 TempDiff,Alpha = .05)
Figure 6 The residual plot
Figure 7 The probability plot for the residual
We find that most residual fit well yet some out liers occur.
We delete 2 points (11th run and 24th run) and redo the job.
And the result is shown below.
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比
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残差
正态概率图 与拟合值
直方图 与顺序
TempDiff 残差图
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残差1
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均值 -2.49800E-16
标准差 0.4358
N 32
AD 0.831
P 值 0.029
残差1 的概率图正态 - 95% 置信区间
拟合因子: TempDiff 与 Material, Color, Layer, Ventilation
TempDiff 的效应和系数的估计(已编码单位)
项 效应 系数 系数标准误 T P
常量 6.7542 0.05487 123.08 0.000
Material 0.9398 0.4699 0.05507 8.53 0.000
Color -0.4542 -0.2271 0.05487 -4.14 0.000
Layer 0.6273 0.3136 0.05507 5.70 0.000
Ventilation 3.8852 1.9426 0.05507 35.28 0.000
Material*Color -0.7727 -0.3864 0.05507 -7.02 0.000
Material*Ventilation 0.4167 0.2083 0.05487 3.80 0.001
Color*Ventilation -0.8273 -0.4136 0.05507 -7.51 0.000
Layer*Ventilation 0.6042 0.3021 0.05487 5.50 0.000
S = 0.298239 PRESS = 3.81306
R-Sq = 98.71% R-Sq(预测) = 97.38% R-Sq(调整) = 98.23%
对于 TempDiff 方差分析(已编码单位)
来源 自由度 Seq SS Adj SS Adj MS F P
主效应 4 129.498 129.425 32.3564 363.77 0.000
2因子交互作用 4 13.964 13.964 3.4909 39.25 0.000
残差误差 21 1.868 1.868 0.0889
失拟 7 0.368 0.368 0.0526 0.49 0.826
纯误差 14 1.500 1.500 0.1071
合计 29 145.330
TempDiff 的系数估计,使用未编码单位的数据
项 系数
常量 6.75417
Material 0.469886
Color -0.227083
Layer 0.313636
Ventilation 1.94261
Material*Color -0.386364
Material*Ventilation 0.208333
Color*Ventilation -0.413636
Layer*Ventilation 0.302083
Figure 8 The pareto plot
AD
B
CD
C
AB
BD
A
D
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A Material
B Color
C Layer
D Ventilation
因子 名称
标准化效应的 Pareto 图(响应为 TempDiff,Alpha = .05)
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百分
比
A Material
B Color
C Layer
D Ventilation
因子 名称
不显著
显著
效应类型
CD
BD
AD
AB
D
C
B
A
标准化效应的正态图(响应为 TempDiff,Alpha = .05)
Figure 9 The residual plot
At this time, the residuals fit fine in a normal distribution, and the main effects and all the 4
interactions are significant. We
Temp Diff = e i = igh = hi e
e = i g e i i =
e i = igh = hi e e i = igh e i i =
= hi e e i i = e = i g e i i =
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残差
正态概率图 与拟合值
直方图 与顺序
TempDiff 残差图
Figure 10 The interaction plot for tempdiff
From this interaction plot we see only Material-Layer and Color-Layer have no obvious
interaction, which fits fine with the model.
Figure 11 The effect plot proof the positive/negative of coefficients of each factor
.What’s more, we used to try to transform the response factor to look for better model.
We transform TempDiff into logarithm form, and we find it not any better.
BlackWhite MultipleSingular YesNo10.0
7.5
5.010.0
7.5
5.010.0
7.5
5.0
Material
Color
Layer
Ventilation
Light
Heavy
Material
White
Black
Color
Singular
Multiple
Layer
Interaction Plot for TempDiffData Means
HeavyLight
9
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6
5
BlackWhite
MultipleSingular
9
8
7
6
5
YesNo
Material
平均
值
Color
Layer Ventilation
TempDiff 主效应图数据平均值
We transform TempDiff into Exponential form, and get the residual plot as below
Figure 12 The residual plot
We see some obvious patterns, we don’t recommend to transform the data in this way.
7.2 Results explanations
7.2.1 No ventilation can remarkably maintain the high level of heat preservation
7.2.1.1 From the main effects graph, D has the most significance, which means the
ventilation-absence condition nearly plays the determinant role of heat
preservation.
7.2.1.2 Any two-order interactions containing D, that is A*D, B*D, C*D, are also
significant, indicating D indeed have main effect.
7.2.1.3 Moreover, from the original data we can find any combination of treatment
with no ventilation has the better heat preservation relatively to that with
ventilation, which in turn confirm the result.
7.2.1.4 The negative coefficient of ventilation=no means the rate of temperature
decreasing will accelerate. And the absolute value of the coefficient is the
largest, indicating the main effect of ventilation or not.
7.2.2 Materials have main effect of heat preservation as well
7.2.2.1 From the main effects graph, A has relatively large significance, which
means the materials have effects on maintaining heat.
7.2.2.2 Some two-order interactions containing A, that is A*D, A*B, are also
significant, indicating A indeed has main effect.
7.2.2.3 The negative coefficient of material=light means heavy material does
better in maintaining heat.
7.2.3 Colors of material have main effect of heat preservation as well
7.2.3.1 From the main effects graph, B has relatively large significance, which
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0
残差
频率
3230282624222018161412108642
30000
20000
10000
0
-10000
观测值顺序
残差
正态概率图 与拟合值
直方图 与顺序
Exp(Diff) 残差图
means the different colors have different abilities to avoid heat loss.
7.2.3.2 Some two-order interactions containing B, that is B*D, A*B, are also
significant, indicating B indeed have main effect
7.2.3.3 The positive coefficient of color=white means white material has prior
ability in maintaining heat, which may be contrary to our concept.
7.2.4 Thickness of material has less but also main effect of heat preservation as well
7.2.4.1 From the main effects graph, C has relatively large significance, which
means the different layers have different abilities to avoid heat loss.
7.2.4.2 Only one two-order interaction containing C, that is C*D, has main effect,
indicating layers have the least effect among all the main effect on heat
loss rate.
7.2.4.3 The negative coefficient of layer=singular means thicker material has prior
ability in maintaining heat, consistent with our common sense.
7.2.5 Interaction explanation:
7.2.5.1 Colors have less effect than materials do, and these two have interaction.
7.2.5.2 The relatively parallel lines of interactions containing layers mean in the
combination of layer and color, and layer and material, layer has the same
effect with the other one and has no interaction.
7.2.5.3 Interactions containing ventilation are evident, which means when
ventilation condition changes, the result changes much.
7.3 Possible causes
7.3.1 Ventilation-absence condition has the best ability of maintaining heat may result
in that in this experiment condition the heat is lost mostly from the top of the cup,
more that from the wall of cup. Thus, if the top of the cup is covered, more heat
will be maintained inside, leading to less temperature difference.
7.3.2 White color surprisingly has better ability of maintaining heat can be explained as
this: although darker materials can absorb more heat radiation from the
surroundings such as when put in the sunlight, however, in room condition heat
radiation can be neglected and instead, darker materials absorb more heat from
the water inside. Thus, more heat from the water wrapped by black cloth is loss.
This indicates that not all the common senses are right.
7.3.3 Heavy cloth has better heat maintaining ability, which corresponds to our
intuition. However, layers have less effect. The results may be explained by our
design of “heavy or light” and “number of layers”, which means only attributes
are introduced, no quantity ensure the validity of appropriate number of layers to
have more effect on the results.
7.4 Error sources:
7.4.1 Inequity of preliminary heating results the different original conditions of
materials such as cloth and the cups.
7.4.2 Two thermometers have different abilities of measuring such as sensitivity to
temperature changes and measurement resolution.
7.4.3 System errors from two experimenters reading the thermometers such as view
angular.
7.4.4 Water incrustation or impurities in later treatments because of repetitive uses.
7.4.5 Impurities in water may affect the temperature decrease rates
7.4.6 Room temperature may change during the relatively long period time during the
experiment process.
8 Reference
[1]. 水压机泵站工作液体降温问题分析 , Ma Shaomin, Shenyang Heavy Machine Factory,
Forging Shop.
[2]. Fabric Selection for a Liquid Cooling Garment, Huantian Cao; Donna H Branson; Semra Peksoz;
Jinhee Nam; Cheryl A Farr, Textile Research Journal; Jul 2006; 76, 7; ProQuest Agriculture
Journals.
REFERENCE