The in-core fuel management of a nuclear power plant is a problem of optimization of core parameters such as operation cycle, average fuel burnup and shut down margin for determining a fuel loading pattern to meet the safety and economic aspects. The study is aimed to obtain an optimal fuel loading pattern. Two models of fuel burn up calculations were taken namely equilibrium and transition cores burn up models. The calculations will be carried out by means of computer codes SRAC2006 for cell calculation and PWR-FUEL for the fuel management. The results of keff values at BOC and EOC for each transition core are approximately 1.05 as the input data and the core cycle length is found to be 330 days. The keff values at both BOC and EOC are very near to critical at equilibrium core and the core cycle length is found 360 days. The results of the calculation of neutron flux distribution and power density using the NODAL and FDM methods of the PWR-FUEL the code has the same results. From the results of the neutronic parameter, it is shown that the optimal loading pattern of PWR core can be determined by the PWR-FUEL code either with equilibrium core search or with transition core burnup models. Key words: fuel loading pattern, PWR-FUEL code, operation safety.

1.
Salazar
D
,
Franceschini
F
,
Ferroni
P
,
Petrovic
B.
Fuel Consortium for Advanced Management Option Simulation of LWRs for an 18-Month Cycle Length
.
Adv Nucl Fuel Manag V (ANFM 2015)
.
2015
;
0
11
.
2.
Aghaie
M
,
Nazari
T
,
Zolfaghari
A
,
Minuchehr
A
,
Shirani
A.
Investigation of PWR core optimization using harmony search algorithms
.
Ann Nucl Energy [Internet].
2013
;
57
:
1
15
. Available from:
3.
Mahmoudi
SM
,
Aghaie
M
,
Bahonar
M
,
Poursalehi
N.
A novel optimization method, Gravitational Search Algorithm (GSA), for PWR core optimization
.
Ann Nucl Energy [Internet].
2016
;
95
:
23
34
. Available from:
4.
Oktavian
MR
,
Agung
A
,
Harto
AW
.
Fuel Loading Pattern Optimization with Constraint on Fuel Assembly Inventory Using Quantum-Inspired Evolutionary Algorithm.
2018
;
01007
:
42
9
.
5.
Liu
S
,
Cai
J.
A Studies of fuel loading pattern optimization for a typical pressurized water reactor (PWR) using improved pivot particle swarm method
.
Ann Nucl Energy.
2012
;
50
:
117
25
.
6.
Poursalehi
N
,
Zolfaghari
A
,
Minuchehr
A
,
Valavi
K.
Self-adaptive global best harmony search algorithm applied to reactor core fuel management optimization
.
Ann Nucl Energy.
2013
;
62
:
86
102
.
7.
Okumura
K
, K K, K T. SRAC2006: A Comprehensive Neutronics Calculation Code System.
Tokai
:
JAEA
.
2007
. p.
44
64
.
8.
PWR-FUEL
.
PWR In-Core Fuel Management Code.
User Guid.
2012
;(August).
9.
Pinem
S
,
Sembiring
TM
,
Surbakti T.
Pwr
Fuel Macroscopic Cross Section Analysis for Calculation Core Fuel Management Benchmark
.
S Phys Conf Ser.
2019
;(
1198
):
022065
.
10.
Pinem
S
,
Sembiring
TM
, Tukiran.
VERIFIKASI PROGRAM PWR-FUEL DALAM MANAJEMEN BAHAN BAKAR PWR
.
J Sains dan Teknol Nukl Indones.
2015
;
16
(
1
):
53
62
.
11.
Kuntoro
I
,
Pinem
S
,
Sembiring
TM
.
Validation of PWR-FUEL code for static parameters in the LWR core benchmark.
2020
;
22
(
3
):
111
22
.
This content is only available via PDF.