Question

2.21 Use IHT to perform the following tasks.
(a) Graph the thermal conductivity of pure copper,
2024 aluminum, and AISI 302 stainless steel over
the temperature range 300 T 600 K. Include
all data on a single graph, and comment on the
trends you observe.
(b) Graph the thermal conductivity of helium and air
over the temperature range 300 T 800 K.
Include the data on a single graph, and comment on
the trends you observe.
(c) Graph the kinematic viscosity of engine oil,

ethylene glycol, and liquid water over the tempera-
ture range 300 T 360 K. Include all data on

a single graph, and comment on the trends you
observe.
(d) Graph the thermal conductivity of a water-Al2O3
nanofluid at T 300 K over the volume fraction
range 0 0.08. See Example 2.2.

Answer 1

(a) IHT workspace is as follows

// Copper (pure) property functions : From Table A.1

// Units: T(K)

kCu = k_T("Copper",T) // Thermal conductivity,W/m·K

// Aluminum 2024 property functions : From Table A.1

// Units: T(K)

kAl = k_T("Aluminum 2024",T) // Thermal conductivity,W/m·K

// Stainless steel-AISI 302 property functions : From Table A.1

// Units: T(K)

kss = k_T("Stainless Steel-AISI 302",T) // Thermal conductivity,W/m·K

T = 300 // Temperature, K

Result

According to the result obtained, large difference between thermal conductivity of copper, aluminium and steel has been obtained. Copper conducts thermal energy effectively, while stainless steels are relatively poor thermal conductors. Also, for aluminium and steel thermal conductivity increases whwereas for copper its decreases with temperature

(b) IHT workspace is as follows

// Helium property functions : From Table A.4

// Units: T(K)

kHe = k_T("Helium",T) // Thermal conductivity, W/m·K

// Air property functions : From Table A.4

// Units: T(K); 1 atm pressure

kAir = k_T("Air",T) // Thermal conductivity, W/m·K

T = 300 // Temperature, K

Result

Note the high thermal conductivity of helium relative to that of air. The thermal conductivity of both gases increases with temperature.

(c) IHT workspace is as follows

// Engine Oil property functions : From Table A.5

// Units: T(K)

nuOil = nu_T("Engine Oil",T) // Kinematic viscosity, m^2/s

// Ethylene glycol property functions : From Table A.5

// Units: T(K)

nuEG = nu_T("Ethylene Glycol",T) // Kinematic viscosity, m^2/s

// Water property functions :T dependence, From Table A.6

// Units: T(K), p(bars);

xH2O =0 // Quality (0=sat liquid or 1=sat vapor)

nuH2O = nu_Tx("Water",T,xH2O) // Kinematic viscosity, m^2/s

T = 300 // Temperature, K

Result

The kinematic viscosities vary by three orders of magnitude between the various liquids. For each case the kinematic viscosity decreases with temperature.

(d) IHT workspace is as follows

// Water property functions :T dependence, From Table A.6

// Units: T(K), p(bars);

xH2O =0 // Quality (0=sat liquid or 1=sat vapor)

kH2O = k_Tx("Water",T,xH2O) // Thermal conductivity, W/m·K

kbf = kH2O

T = 300

j = 0.01 // Volume fraction of nanoparticles

//Particle Properties

kp = 36 // Thermal conductivity, W/mK

knf = (num/den)*kbf

num = kp + 2*kbf-2*j*(kbf - kp)

den = kp + 2*kbf + j*(kbf - kp)

Result

The increase in the thermal conductivity of the nanofluid with addition of more nanoparticles. The

solid phase usually has a higher thermal conductivity than the liquid phase.