ASHRAE IJHVAC 7-4: 国际暖通空调与制冷研究期刊

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"ASHRAE IJHVAC 7-4.pdf" 是一份由ASHRAE（美国采暖与制冷工程协会）发布的专业期刊文章，属于《国际供热、通风、空调及制冷研究》（International Journal of Heating, Ventilating, Air-conditioning and Refrigerating Research）。该期刊由John W. Mitchell博士担任主编，汇聚了来自全球知名学术机构和大学的专家作为副编辑，如Michael J. Brandemuehl、James E. Braun、Alberto Cavallini、Arthur L. Dexter和Leon R. Glicksman等。 ASHRAE是北美地区在建筑环境科学和技术领域的权威组织，致力于推动行业的标准制定、研究和教育。其期刊内容涵盖广泛的学科，包括但不限于： 1. **热力学与传热**：深入探讨热量传递的基本原理，包括传导、对流和辐射，以及如何在 HVAC 系统设计中应用这些原理。 2. **流体力学与空气动力学**：研究空气流动和压力分布，对于理解和优化通风系统至关重要，包括风道设计和气流组织。 3. **制冷技术**：涵盖了制冷剂的选择、制冷循环的工作原理、能效比以及环保制冷剂的开发与应用。 4. **能源效率**：关注 HVAC 系统如何高效利用能源，减少能源消耗，包括节能设备、智能控制系统和可再生能源的集成。 5. **室内空气质量**：研究如何通过通风策略和空气净化技术改善室内环境质量，以提升居住者健康和舒适度。 6. **建筑围护结构**：讨论建筑外壳对 HVAC 系统性能的影响，如保温材料、窗户设计和热桥效应。 7. **控制系统与自动化**：介绍现代 HVAC 系统中的自动控制技术，包括传感器、控制器和楼宇管理系统。 8. **声学与振动**：分析 HVAC 系统产生的噪声和振动，以及如何进行减振和隔音设计。 9. **系统设计与分析**：提供关于不同类型的 HVAC 系统（如全空气系统、水环系统、变风量系统等）的设计和性能评估方法。 10. **标准与规范**：ASHRAE 制定了一系列行业标准和指南，这些标准在 HVAC 设计和施工中具有广泛的应用。这份期刊的文章通常会包含最新的研究成果、技术发展和创新实践，旨在促进 HVAC 领域的技术进步和知识分享，对工程师、研究人员、教育工作者和行业从业者来说具有很高的参考价值。

320

HVAC&R

RESEARCH

thermal conductivity. For heat flow parallel to the food fibers, Kopelman (1966) proposed the

following expression for thermal conductivity,

iven as

and for heat

flow

perpendicular to the food fibers where

is the volume fraction of the discon-

tinuous phase in fibrous food product:

-k[

'-'

]

l-P(l-N)

Levy (1981) introduced a modified version of the Eucken-Maxwell equation as follows:

k2[(2

2(h

1)F1]

A)-(A

1)Fl

(49)

where

is the thermal conductivity ratio

kl/k2),

is the thermal conductivity of compo-

nent

and

is the thermal conductivity of component

The parameter,

F,,

introduced by

Levy (1981) is given as

where

(h-

(A+l)

and

is the volume fraction of component

-1

[1+

1)(3]

(53)

When foods consist of more than two distinct phases, the two-component methods for the pre-

diction of thermal conductivity must be applied successively to obtain the thermal conductivity

of the food product. For example, in the case of frozen food, the thermal conductivity of the ice

and liquid water system is calculated first using one of the above mentioned methods. The

resulting thermal conductivity of the ice/water system is then combined successively with the

thermal conductivity predicted for each remaining food constituent to determine the thermal

conductivity of the food product.

For

multi-component systems, numerous researchers have proposed the use of parallel and

series thermal conductivity models based upon the analogy with electrical resistance (Murakami

and

Okos

1989). The parallel model is simply the sum of the thermal conductivities of the food

constituents multiplied by their volume fractions:

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322

HVACBrR

RESEARCH

COMPARISION

MODELS

AGAINST DATA

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

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

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 of

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

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

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 3. Empirical Thermophysical Property Data Set

Thermal

Property

No.

Data

Points

Material

Reference

Specific Enthalpy

Thermal Conductivity

Ice Fraction 13

Apparent Specific Heat Capacity 10

Orange Juice

(wW0

0.89) Riedel (195

Lean Beef

(wW0

0.74) Riedel (19S7), Rolfe (1968)

Cod Muscle

(wwo

0.82) Riedel (1960), Rolfe (1968)

Cod Muscle

(wwO

0.82) Riedel (1956)

Lean Beef

(wwO

0.82) Riedel (1957)

Lamb Kidneys

(wwo

0.798) Fleming (1969)

Lean Lamb Loin

(wWO

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

(wW0

0.872) Riedel (195 1)

Orange Juice

(wWo

0.89) Riedel

(

195 1)

Cod

(wW0

0.80) Riedel (1956)

Haddock

(w,

0.84) Riedel (1956)

Perch

(wWO

0.79) Riedel (1956)

Lean

Beef

(wW0

0.74) Riedel (1957)

Cod Muscle

(w,

0.82) Riedel (1960)

Beef Pham (1990)

Fish Pham (1990)

Lamb Pham (1990)

Poultry 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, includhg extraction methods that employ ether or a mixed solvent system con-

sisting of 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

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

HVAC&R

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

10.6 7.45 12.52

-1.32 0.639

Miles (1974)

10.5 7.43 12.51

-1.32 0.653

Tchizeov

(1979)

4.75

8.55

12.89 12.7

3.60

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

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

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%. 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, 12.7. Tchigeov’s equation performed consistently for all

the food types tested. Best results were with the fish data set, producing

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 as

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 small

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