text
stringlengths
0
1.75k
Þ
ð18Þ

QL ¼ γL τL  τLC
ð
Þ
ð19Þ

Q0 ¼ 1  γH  γL
ð
Þ τ0C  1
ð
Þ
ð20Þ

QH ¼ B τst  τH
ð
Þ
ð21Þ

QH þ 
QL  
Q0 ¼ 0
ð22Þ
d
Sin
dθ ¼

Q0
τ0C


QH
τHC


QL
τLC
ð23Þ
d
Sin
dθ ¼ β1 τHC  τ0C
ð
Þ þ β2 τ0C  τLC
ð
Þ
ð24Þ
d
Stot
dθ ¼ 
Q0 

QH
τH


QL
τL
ð25Þ
dτL
dθ ¼ γw 1  τL
ð
Þ þ 
Qload  
QL
ð26Þ
3
Thermodynamic Optimization
The problem consists of integrating Eqs. (25) and (26) in time and solving the
nonlinear system (18), (19), (20), (21), (22), (23), and (24) at each time step. The
objective is to minimize the time θset to reach a specified refrigerated space
temperature, τLset, in transient operation. To generate the results shown in Figs. 2,
3, 4, 5, 6, and 7, some selected parameters were held constant and others were
varied. The numerical method calculates the transient behavior of the system,
starting from a set of initial conditions, then the solution is marched in time and
checked for accuracy until a desired condition is achieved (temperature set points or
steady state). The equations are integrated in time explicitly using an adaptive time
step, 4th–5th order Runge–Kutta method (Yang et al. 2005). Newton–Raphson’s
method with appropriate initial guesses was employed for solving the above set of
nonlinear equations.
During the integration of the ordinary differential equations, once the set of fixed
parameters τH , τst, B, γH , γL , γW and 
Qload is defined Eqs. (18) and (18) give τHC.
The system of Eqs. (18), (19), (20), (21), (22), (23), and (24), at each time step of
integration of Eqs. (25) and (26), delivers 
Q0, 
QL, τ0C, and τLC.
1184
B. Yasmina et al.
To test the model and for conducting the analysis presented in this section,
assuming a small absorption refrigeration unit with a low total thermal conductance
(UA ¼ 400 W/K), we considered a total heat exchanger area A ¼ 4 m2 and an
average global heat transfer coefficient U ¼ 0.1 kW/m2 K in the heat exchangers
and Uw ¼ 1.472 kW/m2 K across the walls, which have a total surface area
0
5
10
15
20
25