bwr control rod design using tabu search

annals of

Annals of Nuclear Energy 32 (2005) 741–754

www.elsevier.com/locate/anucene

NUCLEAR ENERGY

BWR control rod design using tabu search

Jose Alejandro Castillo a,1, Juan Jose Ortiz a,Gustavo Alonso a,c,*, Luis B. Morales b, Edmundo del Valle c,2

a Instituto Nacional de Investigaciones Nucleares, Km 36.5 Carretera Mexico-Toluca,

Ocoyoacac 52045, Edo. de Mexico, Mexicob Universidad Nacional Autonoma de Mexico, Instituto de Investigaciones en Matematicas Aplicadas y en

Sistemas, Apartado Postal 70-221, Mexico, D.F. 04510, Mexicoc Instituto Politecnico Nacional, Escuela Superior de Fısica y Matematicas, Unidad Profesional ‘‘Adolfo

Lopez Mateos’’, ESFM, Edificio 9, C.P. 07738, D.F. Mexico

Received 18 August 2004; received in revised form 7 December 2004; accepted 7 December 2004

Available online 23 February 2005

Abstract

An optimization system to get control rod patterns (CRP) has been generated. This system

is based on the tabu search technique (TS) and the control cell core heuristic rules. The system

uses the 3-D simulator code CM-PRESTO and it has as objective function to get a specific

axial power profile while satisfying the operational and safety thermal limits. The CRP design

system is tested on a fixed fuel loading pattern (LP) to yield a feasible CRP that removes the

thermal margin and satisfies the power constraints. Its performance in facilitating a power

operation for two different axial power profiles is also demonstrated. Our CRP system is com-

bined with a previous LP optimization system also based on the TS to solve the combined LP-

CRP optimization problem. Effectiveness of the combined system is shown, by analyzing an

actual BWR operating cycle. The results presented clearly indicate the successful implementa-

tion of the combined LP-CRP system and it demonstrates its optimization features.

� 2005 Elsevier Ltd. All rights reserved.

0306-4549/$ - see front matter � 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.anucene.2004.12.004

* Corresponding author. Tel.: +52 55 53297233; fax: +52 55 53297340.

E-mail addresses: [email protected] (J.A. Castillo), [email protected] (J.J. Ortiz), galonso

@nuclear.inin.mx (G. Alonso), [email protected] (L.B. Morales), [email protected]. ipn.mx

(E. del Valle).1 Also a Ph.D. Student at Universidad Autonoma del Estado de Mexico.2 COFAA-IPN Fellow

mailto:[email protected]


mailto:@nuclear.inin.mx


mailto:[email protected].

742 J.A. Castillo et al. / Annals of Nuclear Energy 32 (2005) 741–754

1. Introduction

Optimization of BWR fuel reloads is a two stages task: The first one comprises

the allocation of fuel assemblies in the core to get maximum cycle length under a

specific power profile and it is a two edges calculation, at the beginning and at theend of the cycle. This array of assemblies is called the loading pattern (LP). The

objective of the second stage is to obtain a control rod pattern (CRP) that provides

sufficient thermal margin and a satisfactory axial power profile at any time during

the reactor cycle.

In a previous paper (Castillo et al., 2004), the first stage was solved using the tabu

search technique (TS), the LP obtained generates more energy than those designed

by using engineer expertise. However, the optimized LP needs to be tested to know

if it is feasible to operate without violating the thermal and safety operational limitsat any time during the whole operating cycle. For each LP obtained by TS there ex-

ists the possibility that it cannot be controlled by any CRP during the whole cycle, in

this case it will not be considered as a feasible LP.

Then, in this work, an optimization system based also on TS is introduced to

design a CRP that satisfies the thermal and operational constraints. This system

can be applied independently of the way that the LP was obtained or it can be

combined with our LP system (Castillo et al., 2004). Our combined system can

be considered as a tool to tackle the combined LP-CRP optimization problem,which, for a BWR, is a tightly coupled problem as it was noted by Turinsky

and Parks (1999). We will assess an actual BWR operating cycle to show the effec-

tiveness of the combined system. The results will be compared to those obtained

from engineer expertise.

Historically, the design of CRP is usually based on trial and error techniques. Re-

cently, this problem has been automated using IF-THEN rules (Lin and Lin, 1991),

heuristic rules and common engineering practices (Karve and Turinsky, 1999), gene-

tic algorithms (GA) (Montes et al., 2004), and fuzzy logic and heuristics (Francoiset al., 2004).

To test our CRP independent system, we search for the CRP for two different

problems, the first one is an operating cycle with a specific loading pattern, for this

problem two different axial power profiles are considered. The results are compared

against those obtained from a GA search (Montes et al., 2004) and engineering

expertise. The second problem is a loading pattern for an equilibrium cycle (Montes

et al., 2001), the cycle length obtained using the optimized CRP is compared against

the results of GA given by Montes et al. (2004) for the same problem and thoseobtained by using engineer expertise.

2. BWR control rod pattern

The reactor control system must be capable of compensating for all reactivity

changes that take place throughout a reactor operating cycle, and to do this at

a rate that roughly matches that of the reactivity changes. The movable control

Fig. 1. BWR control rod distribution and its 1/8 classification.

J.A. Castillo et al. / Annals of Nuclear Energy 32 (2005) 741–754 743

rods have a great effect on power distribution, and the interaction between control

rod arrangement and power distribution must be considered throughout the reac-

tor cycle.

Design of a CRP involves the control rod allocation in the core. An optimal CRP

will compensate for excess reactivity during the entire cycle, respecting the thermaland operational constraints with a minimal reduction in cycle length.

In this study, a BWR core with 444 fuel assemblies is analyzed to get a CRP that

satisfies operational constraints. The core has 109 control rods, and each one of these

can be placed in 25 different axial positions. A typical analysis of an operating cycle

is divided into 10 burnup steps (Total_Burnup_Steps), yielding ((25)109)10 possible

control rod patterns. If one assumes eighth core symmetry, this number is reduced

to ((25)19)10. Moreover, exploiting the control cell core (CCC) technique commonly

used in BWR plants, only control rods that have no fresh fuel are used for reactorcontrol. This reduces the number of possible control rod patterns to ((25)5)10 (see

Fig. 1).

The control rod axial positions are labeled as [00,02,04,06, . . ., 44,46,48]. Posi-tions 00–18 are considered ‘‘deep positions’’, positions 20–30 are considered ‘‘inter-

mediate positions’’, and positions 32–48 are considered ‘‘shallow positions’’. The

intermediate positions are forbidden during normal operation because if they are

used the axial power distribution shape is deformed (Almenas and Lee, 1992). There-

fore, only 19 of the possible 25 positions are allowed. Thus, the total number of pos-sibilities to generate all control rod patterns is ((19)5)10 � 1064.

The minimal critical power ratio (MCPR) and linear heat generation rate

(LHGR) must be satisfied. Furthermore, the reactor core must be critical, in this

case, the effective multiplication factor must be adjusted to an effective multiplication

factor target for each burnup step through the whole cycle, and the axial power dis-

tribution must be adjusted to a target axial power distribution. Thus, the objective

function that we propose is a function of the effective multiplication factor (keff),

axial power profile (P), linear heat generation rate (LHGR), and minimal critical

Table 1

Target and limits parameters

Symbol Meaning Limit value

kt Effective multiplication factor 1.0 (target)

LHGRt Linear heat generation rate 439 w/cm

MCPRt Minimal critical power ratio 1.45


power ratio (MCPR). Therefore, the CRP design problem can be formulated as the

following optimization problem:

minimizeF ðxÞ ¼ w1 keff ;x � keff ;tj j þ w2

X25

i¼1

Px;i � P t;ij j þ w3 LHGRx � LHGRtj j

þ w4 MCPRx �MCPRtj j;

where keff,x is the effective multiplication factor of the control rod pattern x; keff,t, tar-

get effective multiplication factor; Px,i, axial power distribution for node i of the con-

trol rod pattern x; Pt,i, target axial power distribution for node i; LHGRx, linear heat

generation rate of the control rod pattern x; LHGRt, maximum linear heat genera-

tion rate value permitted; MCPRx, minimal critical power ratio of the control rod

pattern x; MCPRt, minimal critical power ratio value permitted; and w1, . . .,w4 arecalled weighting factors and wi P 0, for i = 1, . . ., 4.

If the thermal limits LHGR and MCPR are satisfied, the corresponding weighting

factors will be zero, otherwise they will be the ones given by the user penalizing the

objective function. Thus, the objective function will have only the contribution due

to the effective multiplication factor and the axial power profile, when the thermal

limits are satisfied. Table 1 shows the MCPRt, LHGRt and kt values. Two different

target axial power distribution will be assessed, one obtained from the Haling calcu-

lation and a second one using spectral shift (Montes et al., 2004). These axial powerdistributions depend on the fuel reload studied. Moreover, we impose the following

constraints:

keff ;x � keff;tj j 6 d; d > 0;

Px;i � P t;ij j 6 eP t;i; e > 0; for i ¼ 1; . . . ; 25;

where d and e are the convergence criteria for the multiplication factor and the power

profile, respectively.

3. Methodology

TS is an iterative heuristic procedure for optimization. It has been designed to

overcome local optimality. It is distinguished from other methods because it incor-

porates a tabu list of length t of moves that forbids the reinstatement of certain attri-

butes of previously visited solutions, this tabu list is called short term memory,


because it stores information on the t most recent moves. These forbidden moves are

called tabu. For a more detailed presentation of TS, see Glover (1989).

Let us now describe how we use TS to get CRPs. In our approach, a feasible solu-

tion will be an octant of a reactor core and it is represented by a vector

(c1,c2,c3,c4,c5), where ci is the axial position of the ith control rod, for i = 1, . . ., 5(see Fig. 1). The range of each ci, avoiding intermediate positions, is

2,4, . . ., 18,32,34, . . ., 48.In our problem, a move is a transition from one CRP to another that is deter-

mined by the change of only one axial position. Whenever the ith rod is moved

to a different axial position, the tabu list forbids any movement of this rod to an

axial position considered in t preceding iterations. Formally, the tabu list consists

of vectors (i,ci), where the ith rod could not be allocated in that axial position ci.

During the process the tabu list is updated circularly. For this study, the lengthof the tabu list was randomly selected in the range (6 6 t 6 16). The value of a

move is the difference between the objective function (defined in Section 2) value

before and after the move. At each iteration the best move is choose, even if it

does not improve the objective function. The number of possible moves in each

iteration is 5 · 18 = 90. Since it is too expensive from a computational point of

view to evaluate all the moves, only a percentage of such moves will be consid-

ered in this study. The set M of these moves will be randomly generated, and the

first move that improves the objective function is done. However, if there is nomove that improves the objective function, then one must examine the whole sub-

set M.

In this study, the set M corresponds to 40% of the whole moves. This value was

chosen from previous experimental analysis where it was observed that for higher

values of moves there was no apparent improvement in the objective function value.

The range of moves analyzed was from 10% to 100%.

The long-term memory is a function that records moves taken in the past in order

to penalize those that are non-improving. The goal is to diversify the search by com-pelling regions to be visited that possibly were not explored before (Glover, 1989). In

our particular TS implementation, the long-term memory is an array, which will be

denoted F. The array has zeroes at the beginning of the procedure. When a control

rod is settled in an axial position at a given iteration, the array F changes as follows:

F i;ci ¼ F i;ci þ 2. The entry F i;ci is the frequency at which the axial position ci of the ith

control rod has been settled. The values of non-improving moves that switch the ax-

ial position ci of the ith control rod are then increased by F i;ci .

The two last concepts to explain are the aspiration and the stopping criteria used.The aspiration criteria cancels the status tabu of a move when it finds a feasible solu-

tion with a better function value than the best solution in the past. Our TS will be

stopped if the number of iterations used without improving the best solution is greater

than 40. This number was chosen from a statistical analysis as it was done with the

percentage of moves.

As it is set in Section 2, the objective function includes the thermal safety lim-

its, the axial power distribution and the effective multiplication factor. TS was

implemented along with the 3-D reactor core simulator CM-PRESTO (Scand-

END START

YES

SAVE BURNUP NO IS IT THE LAST STEP VALUES BURNUP STEP?

IS IT THEBURNUP STEP 1?

NO YES

YES NO END N-TH ITERATION CALCULATIONS

OR ITERATIONS? HALING

CALCULATION ASPIRATION FUNCTION EQUAL TO THE BEST

NEIGHBOR NO

CONTROL ROD PATTERN YES ASPIRATIONRANDOMLY GENERATED, FUNCTION > THE BEST

ASPIRATION FUNCTION = M NEIGHBOR?

NEW CONTROL ROD AXIAL POSITION UPDATE TABU TIME ARRAY,

FREQUENCIES AND THE BESTRETURN THE CONTROL ROD PATTERN

CONTROL ROD TO ORIGINAL AXIAL YES

POSITION HEURISTIC

RULE VIOLATED OR YES NO END40 % OF THE NEIGHBORHOOD NEIGHBORHOOD

ANALYZED? SEARCH? OBJECTIVE FUNCTION

EQUAL TO THE BEST NO NEIGHBOR

NO

CM-PRESTO OBJECTIVE CALCULATIONS YES FUNCTION PREVIOUS >

THE BEST NEIGHBORHOOD?

OBJECTIVE FUNCTION

CALCULATIONS RETURN THE UPDATE THE BEST CONTROL ROD NEIGHBORHOOD, TO ORIGINAL AXIALTHE BEST SAFETY POSITION

LIMITS AND THE BESTAXIAL POWER

OBJECTIVE DISTRIBUTION YES FUNCTION NO

< IS IT ASPIRATION A TABU

FUNCTION? NO MOVEMENT?

YES

YES OBJECTIVE FUNCTION

EQUAL TO OBJECTIVE IS IT FUNCTION PLUS AXIAL THE BESTCONTROL ROD POSITION NEIGBORHOOD?

FREQUENCY NO

Fig. 2. Flowchart of the CRP search.



power, 1995) to compose the Optimization Tabu Search System. This system is a

FORTRAN-77 based program implemented in an Alpha computer with UNIX

operating system. All Tabu Search runs were carried out with a random initial

solution.

Due to the way that the objective function is constructed, the system requires twodifferent runs of CM-PRESTO; the first one is to calculate the target axial power dis-

tribution in each burnup step, the other run is used to calculate the three parameters

and the axial power distribution. Fig. 2 shows the flowchart of the CRP optimization

process.

We now describe how to combine the CRP system with the LP system developed

by Castillo et al. (2004). Our combined system is a process consisting in two iterative

phases with two complimentary multi-objective functions. In the first phase, we use

the LP system to get an optimized LP. In the second phase, this LP is used as an in-put to our CRP system, a TS technique designed to obtain CRPs in certain burnup

steps previously defined along the whole cycle. If the CRP system does not succeed in

a burnup step then the process goes to the first phase again. On the contrary, if it

does succeed for all the burnup steps then the process is stopped, obtaining as a re-

sult a feasible LP-CRP. A pseudo-code for our iterative LP-CRP combined system

can be described as follows:

Phase 1: run the LP system

Phase 2:Input: the LP output from Phase 1

do burnup_step_number = 1 to Total_Burnup_Steps

run the CRP system

if (not succeed) then Phase 1

enddo

4. Problems analyzed and results

The CRP design system based on tabu search was applied to get the optimal CRP

for actual BWR operating cycles. To test the system the 3-D simulator CM-PRESTO

was used to analyze the corresponding BWR operating cycles.

In the first approach, a CRP was obtained for an actual BWR fuel assembly LP.

In the second approach, the combined LP-CRP optimization problem was solved for

another BWR operating cycle.

The results of the first stage are compared against the ones given by Montes et al.(2004) for the same problem. The objective is to get a CRP that satisfies the thermal

and operational limits for the whole throughout of the reactor cycle. In the first ap-

proach, we use BWR fuel reloads previously obtained using engineer expertise for

the actual cycle. These loading patterns use 120 fresh fuel assemblies in two batches

one of 116 fuel assemblies of 3.52 w/o of U-235, and a second one of four fuel assem-

blies of 3.03 w/o of U-235. Two different axial power profiles were considered, the

first one is a Haling profile (Graves, 1979) and the second one is a spectral shift pro-

file (Glasstone and Sesonske, 1994).

Fig. 3. Objective function behavior for burnup steps 1–3 for the CRP using TS.


Using the CRP design system based upon the TS, we obtained several control rodpatterns for this fuel reload. Less than 22,000 evaluations of the objective function

were required for each TS. This is a very small number compared with the

8.66 · 1063 permutations that are possible in one octant symmetry. In each burnup

step, we obtained a CRP where the objective function was minimized. The CRP that

provides the maximum cycle length for the Haling profile is reported through the fol-

lowing figures. Fig. 3 shows the objective function behavior to burnup steps 1–3, Fig.

4 shows the objective function behavior to burnup steps 4–6, and Fig. 5 shows the

objective function behavior to burnup steps 7–9. Table 2 shows the values obtainedfor keff, LHGR and MCPR in each burnup step. We ran our CRP TS system 10

times and the CRP with the longest cycle length is reported here.

The results for the GA were taken from Montes et al. (2004), where the problem

analyzed was the same that we use here. In this study, the cycle length given by the

CRP obtained by engineer expertise was 10023 MWd/TU. By using the optimized

control rod patterns obtained by TS and GA the cycle length are significantly greater

compared to the one obtained by the engineer expertise.

Figs. 6–8 show the CRP and axial power profiles for several burnup steps usingTS. Table 3 shows the cycle length, the number of control rod movements and the

number of rod movements for positions between deep and shallow.

For the same problem, using the spectral shift profile, the results for cycle length,

the number of control rod movements and the number of rod movements for posi-

tions between deep and shallow are shown in Table 4. Also the results obtained from

GA calculations for the same axial profile calculated by Montes et al. (2004) are

shown here, and again the cycle lengths obtained by using the tabu CRP system is

greater than the one obtained by using the GA system.



Table 2

Multiplication factor, LHGR and MCPR values in each burnup step for the first problem using a Haling

axial power profile

Burnup step number LHGR (w/cm) MCPR keff Number of iterations

1 390.16 1.51 1.00002 71

2 387.64 1.55 0.99996 73

3 374.67 1.58 0.99990 42

4 402.53 1.51 0.99995 89

5 398.49 1.58 0.99999 30

6 407.90 1.56 1.00127 23

7 401.86 1.57 1.00000 63

8 406.02 1.52 1.00014 26

9 390.43 1.53 1.00004 24

10 371.95 1.58 0.99935 1


Fig. 6. Axial power distribution at 1000 MWd/TU and its CRP using TS.




Table 3

Comparison of results between GA and TST for the first problem using a Haling axial power profile

Method Cycle length

(MWd/TU)

Control rod

movements

Movements between

deep and shallow positions

Genetic algorithms 10,157 44 20

Tabu search technique 10,321 41 18

Table 4

Comparison of results between GA and TST for the first problem using an spectral shift axial power

profile

Method Cycle length

(MWd/TU)

Control rod

movements

Movements between





In this stage, the second problem considered is the loading pattern for an equilib-

rium cycle loading, which uses 104 fresh fuel assemblies of 3.70 w/o of U-235 and the

axial power profile is an spectral shift profile (Montes et al., 2004). Results for this

problem are shown in Table 5. As can be seen from here, the results obtained from

the tabu CRP system produce more energy than those produced using GA.

Table 6

Cycle length comparison between the loading and control rod patterns given by engineer expertise and the

one generated by the TST coupled system

Method Cycle length

(MWd/TU)

Control rod

movements

Movements between deep

and shallow positions

Engineer expertise 9461.0 44 20

Tabu search technique 9943.7 41 18

Table 5

Comparison of results between GA and TST for the equilibrium cycle using an spectral shift axial power

profile

Method Cycle length

(MWd/TU)

Control rod

movements

Movements between





The second stage considers the combination of the CRP and LP (Castillo et al.,

2004) systems based on the TS. The cycle analyzed is different to the one used in

the first stage. We ran our LP-CRP combined system using a loading pattern of

112 fresh fuel assemblies of 3.53 w/o of U-235 and a Haling axial power profile.

The idea under this process is to minimize the objective function. The combined

LP-CRP that produces the maximum cycle length, which is 9756.86 MWd/TU (Hal-

ing calculation), is reported in Table 6, where the cycle length, the number of control

rod movements and the number of rod movements for positions between deep andshallow are shown. In this case the combined system provides more energy than the

one given by the LP-CRP generated by using engineer expertise.

5. Discussion and conclusions

A TS technique has been implemented successfully to the optimization of BWR

control rod patterns. The design system developed in this work generates controlrod patterns that satisfy the operational and safety constraints and keeps critical

the reactor through the whole cycle length. These results indicate that CRP designed

using TS yield fuel cycles that can produce more energy than those obtained using

engineer expertise or genetic algorithms.

In general, it is not possible to claim the superiority of one optimization method

over other because it depends on the specific problem analyzed and the implementa-

tion of the optimization method. In particular in this study, the results obtained sug-

gest that TS produce better results than those produced by the GA implemented byMontes et al. (2004) for the problem here considered.

On the other hand, an extensive comparative study for the job shop problem and

the flow shop problem has shown the significant superiority of TS over other ap-

proaches including iterative improvement, genetic algorithm, simulated annealing,


threshold accepting, constraint satisfaction, neural networks, and other local search

methods (see Vaessens et al., 1996).

Furthermore, Carter (1997) claims that TS produces very good results but it

has a downside because it needs to evaluate the whole neighborhood. This down-

side is solved by evaluating only a percentage of the whole neighborhood as it isdone in this study. Also, Carter (1997) mention that he is not aware of compar-

ative studies between GA and TS. Finally, for the CRP independent problem or

the combined LP-CRP problem will be necessary to perform a comparative study

to demonstrate the superiority of one method over the other but it is beyond the

goals of this paper.

Turinsky and Parks (1999) mention that for a BWR the LP and CRP optimiza-

tion must be considered together since the LP and CRP decisions are tightly coupled

problem, which is part of the purpose of this work. Turinsky (1999), also mentionthat use of Tabu Search for PWR LP produces results comparable to those from

GA but extensive testing must be performed before to give a firm conclusion.

Although some testing for the independent CRP problem has been done in this

work, and it shows better results for the TS than the GA, an extensive comparative

analysis must be performed to claim any superiority.

On the other hand, use of TS to generate CRP for an actual operating LP, in the

first problem using a Haling axial power profile, shows an increase in the cycle

length. This energy is 2.97% greater than was generated in the actual cycle (10023MWd/TU). It also produces more energy with fewer control rod movements than

the one designed using the GA.

This extra energy represents 13 days more of full power operation. To assess

the economical impact of this optimized fuel reload it will be necessary to per-

form a multi-cycle analysis to know the actual advantages of this fuel assembly

utilization.

Furthermore, for the others problems using the CRP system independently, TS

yield a cycle length with more energy than the one produced by the GA for both,the previous problem but now using the spectral shift profile and for the equilibrium

cycle problem. Although there were fewer control movements in the CRP designed

using GA than the one using TS, in both problems.

Finally, effectiveness of the TS combined system is shown by comparing the re-

sults for an actual BWR operating cycle with those obtained using engineer exper-

tise. The LP and CRP generated by our optimized combined system generate

more energy satisfying all the operational and safety constraints. The extra energy

produced represents 21 days more of full power operation. Thus, our system achievesits objective of getting more energy satisfying all the imposed constraints.

Acknowledgements

The authors acknowledge the support given by CONACyT through the research

Project 33806-U. The authors also acknowledge the significant comments of the re-

viewer which has contributed to improve our paper.


References

Almenas, K., Lee, R., 1992. Nuclear Engineering: An Introduction. Springer, New York.

Castillo, J.A., Alonso, G., Morales, L.B., Martın del Campo, C., Francois, J.L., del Valle, E., 2004. BWR

fuel reloads design using a tabu search technique. Annals of Nuclear Energy 31, 151.

Carter, J.N., 1997. Genetic algorithms for incore fuel management and other recent developments in

optimisation. Advances in Nuclear Science and Technology 25, 113.

Francois, J.L., Martın del Campo, C., Tavares, A., 2004. Development of a BWR control rod pattern

design based on fuzzy logic and heuristics. Annals of Nuclear Energy 31, 343.

Glover, F., 1989. Tabu search Part I. ORSA Journal of Computing 1, 190.

Glasstone, S., Sesonske, A., 1994. Nuclear Reactor Engineering, Reactor Systems Engineering. Chapman

& Hall, London.

Graves Jr., H.W., 1979. Nuclear Fuel Management. Wiley, New York.

Karve, A.A., Turinsky, P.J., 1999. Effectiveness of BWR control rod programming using heuristic and

mathematical methods. Nuclear Technology 48, 91.

Lin, L.S., Lin, C., 1991. A rule-based expert system for automatic control rod pattern generation for

boiling water reactors. Nuclear Technology 95, 1.

Montes, J.L., Perusquıa, R., Hernandez, J.L., 2001. Estudio del Ciclo de Equilibrio de 18 meses para la

CLV con combustible 10 · 10 usando el simulador 3D CM-PRESTO. Memorias del XII Congreso

Anual de Sociedad Nuclear Mexicana. Zacatecas, Mexico, 8–10 Octubre de 2001.

Montes, J.L., Ortiz, J.J., Requena, I., Perusquia, R., 2004. Searching for full power control rod patterns in

a boiling water reactor using genetic algorithm. Annals of Nuclear Energy 31, 1939.

Scandpower, 1995. User Manual CM-PRESTO-B/91.

Turinsky, P.J., 1999. Mathematical optimization of incore nuclear fuel management decisions: status and

trends. Internationale Zeitschrift fur Kernenergie 7, 454.

Turinsky, P.J., Parks, G.T., 1999. Advances in nuclear fuel management for light water reactors. Advances

in Nuclear Science and Technology 26, 137.

Vaessens, R.J.M., Aarts, E.H.L., Lenstra, J.K., 1996. Job shop scheduling by local search. INFORMS

Journal on Computing 8, 302–317.

bwr control rod design using tabu search

Documents