ASHRAE IJHVAC 7-3：国际供暖、通风、空调与制冷研究期刊

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"ASHRAE IJHVAC 7-3.pdf" 是一份由美国采暖与制冷工程协会（ASHRAE）出版的国际期刊《加热、通风、空调与制冷研究》（International Journal of Heating, Ventilating, Air-conditioning and Refrigerating Research）的文档。该期刊由约翰·W·米切尔博士担任主编，他同时也是威斯康星大学麦迪逊分校机械工程学院的教授。期刊设有数位副编辑，包括迈克尔·J·布兰德穆尔博士、詹姆斯·E·布劳恩博士、阿尔贝托·卡瓦利尼博士、阿瑟·L·戴斯特博士以及莱昂·R·格利克斯曼博士，他们分别在能源管理、机械工程、物理学、工程科学等领域有深厚的学术背景。 ASHRAE（American Society of Heating, Refrigerating, and Air-Conditioning Engineers）是全球知名的暖通空调和制冷工程领域的专业组织，致力于推动行业的技术进步和标准制定。这份期刊是ASHRAE的重要出版物之一，旨在发表和分享关于加热、通风、空调及制冷技术的最新研究成果、创新技术和工程实践，涵盖的范围包括但不限于热力学、流体力学、传热、室内空气质量、能源效率、建筑环境以及可持续性设计等方面。文档可能包含多篇研究论文、技术报告、案例分析和评论文章，这些内容对于工程师、研究人员、教育工作者和相关行业从业者来说具有极高的参考价值。通过阅读这些材料，读者可以了解到当前暖通空调和制冷领域的最新发展趋势，以及如何应用新技术来提高系统的性能、节能效果和环境友好性。此外，ASHRAE的标准和指南也对规范行业实践、确保工程项目的质量和安全起到了关键作用。由于版权限制，该文档仅供Naval Aviation Depot使用，并禁止未经许可的复制或网络分发。这意味着获取和使用这份资料需遵守严格的版权规定，以尊重知识产权并维护ASHRAE和IHS（信息与分析服务提供商）的合法权益。对于那些希望深入了解和研究暖通空调与制冷技术的人来说，合法获取和使用此类专业资源是至关重要的。

322

COMPARISION

MODELS

AGAINST DATA

HVAC&R

RESEARCH

The values from the thermophysical property models were compared with an extensive

empirical thermophysical property data set compiled from the literature, shown in Table

The

composition data for the food items listed in Table

were obtained from the USDA (1996).

The specific enthalpy, apparent specific heat, and ice content with respect to temperature for a

variety of foods have been established by the well-known enthalpy-moisture content-tempera-

ture diagrams from Riedel (1951, 1956, 1957, 1960). Numerous researchers have used these

data to validate their thermophysical property models (Chen 1985a, 1985b, Pham 1987,

Schwartzberg 1976) and these data can also be found in other sources such as ASHRAE (1981),

Charm (1971), and Rolfe (1968).

Figure

Calorimeter from Riedel

(1951)

To obtain the enthalpy-moisture content-

temperature data, Riedel (195 1) constructed

an elaborate calorimeter, shown in Figure 1.

The upper portion of the device contains a

conic copper box

(K),

containing a small food

sample

g), that is brought into close

contact with a copper cylinder

(G).

The tem-

perature of the copper cylinder is held con-

stant through the use of liquid air

(D)

and

heating coils (not shown). The upper portion

of the apparatus is contained within a dewar

vessel

(B)

to ensure that it is thermally insu-

lated from the surroundings.

Once the food

sample has reached the desired temperature,

the small conical box containing the food

sample is released into the lower portion

the calorimeter, which consists of a copper

cylinder

(X)

in which the conical copper sam-

ple box rests.

The copper cylinder is surrounded by a

large iron block

(Y)

that provides a constant

ambient temperature and the lower portion is

insulated by a large dewar vessel (R). Tem-

perature changes of the copper cylinder are

measured with a platinum resistance ther-

mometer

(U)

and are used to obtain specific

heat and specific enthalpy data. Riedel

(195 1) claims that calibration experiments

performed with this calorimeter, using water

as the test substance, yielded specific heat capacity and latent heat of fusion values thatagreed

within

0.2%

of the best data given in the literature. Additional apparent specific heat data

were obtained from Fleming (1969) using adiabatic calorimeter that agreed within

of the

best data given in the literature.

The thermal conductivity data used in this evaluation were obtained from Pham (1990),

who conducted a critical analysis of data available in the literature. The criteria for the selec-

tion of these data included that the product composition (or at least water content) be known,

that there should be no obviously unusual trend that contradicts the majority of other data, and

that the measuring equipment must have been calibrated against a material with a known ther-

mal conductivity value. The thermal conductivity data selected by Pham were originally

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VOLUME

NUMBER

OCTOBER

2001

323

Table

Empirical Thermophysical Property Data Set

Thermal

No.

of Data

Property

Points

Material Reference

Ice Fraction 13

Apparent Specific Heat Capacity 10

Specific Enthalpy

Thermal Conductivity 32

Orange Juice

(wwo

0.89) Riedel (1951)

Lean Beef

(wwo

0.74) Riedel (1957), Rolfe (1968)

Cod Muscle

(wwo

0.82) Riedel (1960), Rolfe (1968)

Cod Muscle

(w,

0.82) Riedel (1956)

Lean Beef

(wwo

0.82) Riedel (1957)

Lamb Kidneys

(wwo

0.798) Fleming (1969)

Lean Lamb Loin

(w,

0.649) Fleming (1969)

Moderately Fatty Lamb Loin Fleming (1969)

Fatty Lamb Loin

(wwo

0.444) Fleming (1969)

Veal

(wwo

0.775) Fleming (1969)

Apple Juice

(wwo

0.872) Riedel (1951)

Orange Juice

(wwo

0.89) Riedel (195 1)

Cod

(w,

0.80) Riedel (1956)

Haddock

(w,

0.84) Riedel (1956)

Perch

(wwo

0.79) Riedel (1956)

Lean Beef

(wwo

0.74) Riedel (1957)

Cod Muscle

(wwO

0.82) Riedel (1960)

Beef Pham (1990)

Fish Pham (1990)

Lamb Pham (1990)

Poultv Pham (1990)

(wW0

0.525)

obtained by either the guarded hot plate method or the line source method. The evaluation by

Pham resulted in the selection of

203

thermal conductivity data points from 11 sources. These

thermal conductivity measurement techniques produced an error of

or less with a standard

deviation of

or less when tested with substances of known thermal conductivity.

Food composition data in the USDA Nutrient Database for Standard Reference were com-

piled from published and unpublished sources. Published sources include the scientific and tech-

nical literature while the unpublished data are from the food industry, other government

agencies and research conducted under contracts initiated by the Agricultural Research Service

(ARS) (USDA 1999). Protein content was calculated from the level of total nitrogen in the food,

using conversion factors recommended by Jones (1941). Fat content was determined by gravi-

metric methods, including extraction methods that employ ether or a mixed solvent system con-

sisting

chloroform and methanol, or acid hydrolysis. Carbohydrate content was determined as

the difference between 100% and the sum of the percentages of water, protein, fat, and ash

(USDA 1999). Although the analysis of error associated with the measurement of the composi-

tion data reported by the USDA is not complete, when such analysis is available, the error in the

amount of each food constituent is generally less than 10%.

Tables 4 through

summarize the statistical analyses that were performed on the thermo-

physical property models discussed in this paper. For each of the models, the average absolute

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324

HVACBrR

RESEARCH

Table

Statistical

Analysis

Ice Fraction

Models

Prediction Standard Confidence

Average Absolute

Prediction Method Error,

Deviation, % Range, %

Kurtosis

Skewness

Chen

(

1985a)a 4.04 8.86 13.00 31.2

5.44

Chen

(1985b)b

Miles

(1974)

Tchigeov

(1979)

10.6 7.45 12.52

-1.32 0.639

10.5 7.43 12.51

-1.32 0.653

4.75

8.55

12.89

12.7 3.60

aIce

fraction model

Chen (1985a) using Equations

(6)

and

(7)

calculate molar mass.

bIce fraction model

Chen (1985b) using Equation (8)

calculate molar mass.

prediction error

(%),

the standard deviation

(%),

the 95% confidence range of the mean

(%),

the

kurtosis, and the skewness are presented.

Ice Fraction

shown in Table 4, the method by Chen (1985b) for calculating ice fraction,

conjunction

with the empirical correlations by Chen (1985a) for effective molar mass, Equations (6) and (7),

produced an average absolute prediction error of 4.04%, with a 95% confidence range of

13.00%, as shown in Table 4. In addition, the distribution of prediction errors was sharply

peaked around the average absolute prediction error as evidenced by the large, positive value for

the kurtosis,

1.2. The method by Chen (1985a) predicted ice fractions for beef and orange juice

very well, producing average absolute prediction errors of less than 1.8%. The average absolute

prediction error for the

fish

data set was considerably larger, 8.2%.

Using Equation (8) (Schwartzberg 1976) to approximate effective molar mass reduces the

method

Chen (1985a) to that reported by Miles (1974).

Thus,

when

using

Equation (8) for

effective molar mass, both the method by Chen and Miles’ method produce identical results

with a large average absolute prediction error of 10.6% and a 95% confidence range of 17.45%.

The average absolute prediction errors for these two methods ranged from

4.5%

for the beef data

set to 20% for the orange juice data.

The ice fraction equation of Tchigeov (1979) produced an average absolute prediction error

of 4.75% with a

95%

confidence range of 12.89%.

addition, the distribution of prediction

errors was sharply peaked around the average absolute prediction error as evidenced by

the

large, positive value for the kurtosis, 12.7. Tchigeov’s equation performed consistently for

ail

the food types tested. Best results were with the fish data set, producing an average absolute pre-

diction error of 2.3%. The average absolute prediction error for the beef data set was

5.3%

while

for the orange juice data set, the average absolute prediction error was 7.1%.

Both Chen’s method and Tchigeov’s equation underestimated the ice fraction, and the error

tended to decrease as the temperature of the food item decreased. The maximum error occurred

near the initial freezing point of the food item. The method of Miles (1974) exhibited uniform

error as a function of temperature.

The performance of both the ice fraction method by Chen (1985b) and the simple ice fraction

equation reported by Miles (1974) validate the primary assumption upon which these models

were derived, namely, that high moisture content food items can be considered to behave

ideal dilute solutions. Thus, these methods would be expected to produce acceptable results for

foods of high moisture content.

Specific Heat

As shown in Table

the three apparent specific heat models produced large average absolute

prediction errors along with large prediction variations. All the models produced relatively smaíl

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VOLUME

NUMBER

OCTOBER

2001

325

Table

Statistical Analysis

Apparent Specific Heat Capacity Models

Average Absolute

Prediction Standard Confidence

Estimation Method Error,

% Deviation,

Range,

Kurtosis Skewness

Chen

(1985a) 20.5 25.6

16.93 23.0 4.19

Schwartzberg

(1976)

19.3 25.4 16.87

24.6 4.39

Schwartzberg

(1981)

19.7 25.1

16.80

25.5 4.51

prediction errors for the fish and beef data sets and very large prediction errors for the lamb and

veal data sets. The two models by Schwartzberg (1976, 1981) performed similarly, exhibiting

average absolute prediction errors of approximately 20% with large standard deviations of

approximately 25%. Their best performance was obtained with the fish data set, resulting in

average absolute prediction errors of lo%, while their worst agreement was obtained with the

veal data set, producing average absolute prediction errors of 24%. The method by Chen (1985a)

produced a slightly larger average absolute prediction error of 20.5% with a standard deviation

of 25.6%. The method by Chen performed best with the fish data set, producing an average

absolute prediction error of 6.9% and worst with the veal data set, yielding an average absolute

prediction error of 27%.

All of the apparent specific heat models exhibited large variations in prediction error, and the

absolute value of the prediction error decreased as the temperature decreased. The maximum

errors tended to occur near the initial freezing point of

the

food item.

Enthalpy

The specific enthalpy equation developed by Chen (1985a) produced an average absolute pre-

diction error of 5.09% along with a standard deviation of

3.98%,

as shown in Table 6. The aver-

age absolute prediction errors for the method by Chen method (1985b) ranged from 4.2% for the

fish data set to 15% for the orange juice data set. On average, the method by Chen (1985b)

tended to underpredict the specific enthalpy of foods.

The specific enthalpy equation developed by Miki and Hayakawa (1996) produced an abso-

lute average prediction error of 5.86% and exhibited more consistency for all the food types

tested as compared with the equation by Chen (1985a). For example, the specific enthalpy equa-

tion by Chen produced a very large absolute average prediction error for the orange juice data

set of 15%, while the

Miki

and Hayakawa method predicted the orange juice data very well, pro-

ducing an average absolute prediction error of 1.3%. The method of Miki and Hayakawa tended

to underpredict slightly the specific enthalpy for orange juice and overpredict the specific

enthalpy for all other food types tested.

The average absolute prediction error of the empirical specific enthalpy equation by Chang

and Tao (1981) was found to be 7.56% and the spread

the absolute prediction errors was

large, as evidenced by the relatively large standard deviation of 6.61%. The equation by Chang

and Tao performed consistently for

all

the data sets tested.

The specific enthalpy model presented by Schwartzberg (1976) produced an average absolute

prediction error of 6.48% with a moderate standard deviation'of 4.64%. The model by

Schwartzberg (1 976) model performed consistently, with average absolute prediction errors

ranging from 2.6% for the orange juice data set to 7.0% for the fish data set.

The specific enthalpy equation by Chen (1985a) produced its greatest errors near the initial

freezing point and the error was reduced as the temperature decreased. The specific enthalpy

equation by Miki and Hayakawa (1996) produced the greatest errors just below the initial freez-

ing point and near

-30°C.

The Schwartzberg (1976) specific enthalpy model tended to underes-

timate near the initial freezing point and near -40°C and overestimated temperatures between

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326

Table

Statistical Analysis

Specific Enthalpy Models

HVAC&R

RESEARCH

Average Absolute 95

Prediction Standard Confidence

Estimation Method

Error,

Deviation,

Range, %

Kurtosis Skewness

Chen

(1985a)

5.09

3.98

10.828

4.85

1.83

Chang and Tao

(1981)

7.56

6.61

11.38 0.813 1.11

Miki

and

Hayakawa

(1996)

5.86 3.03 10.632

-0.232 -0.0511

Schwartzberg

(1976)

6.48 4.64 10.966

2.30 1.10

40°C and the initial freezing point. The equation of Chang and Tao (1981), however, overesti-

mated near the initial freezing point and near 4O"C, while underpredicting at temperatures

between 40°C and the initial freezing point.

The specific enthalpy equation by Chen (1985a) produced good results for apple juice, fish,

and beef, but large prediction errors for orange juice. The specific enthalpy equation of Miki and

Hayakawa (1996) agreed well for both apple juice and orange juice and produced good results

for the fish and beef data sets. The model by Schwartzberg (1976) performed very well for both

apple and orange juice. It also produced good results for the beef and fish data sets. The equation

of Chang and Tao (1981) produced good results for apple juice and performed adequately for

fish, beef, and orange juice.

The good agreement for the specific enthalpy models of Chen (1985a), Miki and Hayakawa

(1996), and Schwartzberg (1976) validates the primary assumption upon which these models

were derived, namely, that high moisture content food items can be considered to behave as

ideal dilute solutions. Thus, these models would be expected to produce acceptable results for

foods of high moisture content. The empirical specific enthalpy equation of Chang and Tao

(1981) is based upon correlations developed from the analysis of data collected on high moisture

content foods, and thus, it too would be expected to produce acceptable results for foods of high

moisture content

(wwo

0.70).

Thermal

Conductivity

shown in Table 7, the thermal conductivity model developed by Levy (1981) produced an

average absolute prediction error of 6.86% and a standard deviation of 4.89%. The average

absolute prediction errors for the method by Levy (1981) ranged from 4.4% for the lamb data set

to 9.5% for the poultry data set. The model by Levy produced good results for the lamb, beef,

and fish data sets.

The Kopelman (1966) isotropic model produced an average absolute prediction error of

8.08% with a 95% confidence range of 11.47%, and this model performed consistently for all

data sets except for poultry. For the poultry data set, the Kopelman (1966) isotropic model

exhibited an average absolute prediction error of 12.6% while for the rest of the data sets, the

model produced average absolute prediction errors of 8.3% or less.

The Kopelman (1966) perpendicular model produced an average absolute prediction error

8.98% with a 95% confidence range of 11.42%. Similar to the isotropic model, the perpendicu-

lar model performed consistently for all data sets except poultry. Thermal conductivity was

underpredicted by 4.3% on average for the poultry data set, while overpredicting by no more

than 8.5% for the rest of the data sets.

The Eucken-Maxwell(l940) model and the Kopelman (1966) parallel model performed simi-

larly overall and these two models achieved their best results with the poultry data set. The abso-

lute average prediction error for the poultry data set for both these models was 10%. Overall, the

Eucken-Maxwell (1940) model and the Kopelman (1966) parallel model produced average

absolute prediction errors of approximately 16% with a 95% confidence range of 12.5%.

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