BWR fuel reloads design using a Tabu search technique

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<ul><li><p>Abstract</p><p>Annals of Nuclear Energy 31 (2004) 151161</p><p>www.elsevier.com/locate/anuceneWe have developed a system to design optimized boiling water reactor fuel reloads. This</p><p>system is based on the Tabu Search technique along with the heuristic rules of Control CellCore and Low Leakage. These heuristic rules are a common practice in fuel management tomaximize fuel assembly utilization and minimize core vessel damage, respectively. The system</p><p>uses the 3-D simulator code CM-PRESTO and it has as objective function to maximize thecycle length while satisfying the operational thermal limits and cold shutdown constraints. Inthe system tabu search ideas such as random dynamic tabu tenure, and frequency-basedmemory are used. To test this system an optimized boiling water reactor cycle was designed</p><p>and compared against an actual operating cycle. Numerical experiments show an improvedenergy cycle compared with the loading patterns generated by engineer expertise and geneticalgorithms.</p><p># 2003 Elsevier Ltd. All rights reserved.BWR fuel reloads design using a Tabu searchtechnique</p><p>Alejandro Castilloa,1, Gustavo Alonsoa,*, Luis B. Moralesb,Cecilia Martn del Campob, J.L. Francoisb, Edmundo del Vallec</p><p>aInstituto Nacional de Investigaciones Nucleares, Km 36.5 Carretera Mexico-Toluca, Ocoyoacac 52045,</p><p>Edo. de Mexico, MexicobUniversidad Nacional Autonoma de Mexico, Apartado Postal 70-221, Mexico, D.F. 04510, Mexico</p><p>cInstituto Politecnico Nacional, Escuela Superior de Fsica y Matematicas, Unidad Profesional</p><p>Adolfo Lopez Mateos, ESFM, Mexico, D. F., 07738, Mexico</p><p>Received 29 May 2003; accepted 29 June 20030306-4549/$ - see front matter # 2003 Elsevier Ltd. All rights reserved.doi:10.1016/S0306-4549(03)00214-7</p><p>E-mail addresses: jacm@nuclear.inin.mx (A. Castillo), galonso@nuclear.inin.mx (G. Alonso), lbm@</p><p>servidor.unam.mx (L.B. Morales), edmundo@esfm.ipn.mx (E. del Valle).1 Also Ph. D. student at Universidad Autonoma del Estado de Mexico.* Corresponding author. Tel.: +52-55-53297233; fax: +52-55-53297340.</p></li><li><p>energy. Thus, the fuel loading pattern plays a very important role to achieve that</p><p>goal. Design of BWR fuel reloads is usually based in engineer expertise, which is atechnique that uses human knowledge. This technique does not optimize the use ofthe fuel assemblies having in some cases under burnt fuel when it is discharged of thecore. To avoid this waste of energy a better design of fuel reloads can be donethrough optimization techniques.BWR fuel assembly reloads design can be considered a combinatorial problem,</p><p>which has been tackled using genetic algorithms (Francois and Lopez, 1999), simu-lated annealing (Moore et al., 1999) and recently tabu search (Jagawa et al., 2001).All of these techniques have as objective to maximize the cycle length while satisfy-ing the operational thermal limits and cold shutdown constraints. For the last tech-nique, Jagawa et al. (2001) designed an automatic system that uses a tabu searchmethod along with a simple linear perturbation method to avoid the extensive use ofthe 3-D simulator.A BWR presents strong three-dimensional material heterogeneities such as fuel</p><p>enrichment, burnable poison, coolant void and control rods, besides the number offuel assemblies embedded in the core in comparison with a PWR. These character-istics makes the loading pattern optimization problem very complex and it appealsfor the use of a licensed 3-D core simulator to achieve the goal proposed in com-parison with the use of 2-D simulators used for the PWR optimization problem.We develop a system named optimization tabu search system (OTSS) based on the</p><p>tabu search (TS) optimization technique, using the 3-D simulator code CM-PRE-STO to evaluate the objective function. Our TS uses a random tenure and long-termmemory whose purpose is to diversify the search of the optimal value making theprocess more ecient leading to explore more scenarios in less time than the originaltabu search. On the other hand, using this technique it is not necessary to give aninitial loading pattern; the system generates a random loading pattern automaticallyin contrast with the TS proposed by Jagawa et al. (2001), which starts from a refer-ence loading pattern.Furthermore, to follow the strategies used in many BWR plants, two heuristic</p><p>rules will be applied along the TS technique. These are the Control Cell Core (CCC)and Low Leakage (LL) techniques. The rst one does not allow the use of fresh fuelin Control Rod (CR) positions and the former does not allow also the use of freshfuel assemblies in the periphery to avoid damage to the core vessel.</p><p>2. BWR fuel reloads design problem</p><p>The problem to be solved is to get the best assembly distribution, makingshuing (permutations) of the fuel assemblies in the core. For a BWR having 4441. Introduction</p><p>From an economical point of view for a Boiling Water Reactor (BWR) it isnecessary to get as much fuel energy as we can to avoid under burnt fuel and waste</p><p>152 A. Castillo et al. / Annals of Nuclear Energy 31 (2004) 151161fuel assemblies, as the Laguna Verde reactors in Mexico, this problem requires the</p></li><li><p>heurTh</p><p>thermthro</p><p>objective function in terms of maximum possible energy value in the cycle(Energy), Mean Ratio of Nominal Power (MRNP), Radial Power Peaking Factor(RPPF), Linear Heat Generation Rate (XLHGR), Maximum Power GenerationRate (XMPGR), Minimal Critical Power Ratio (XMCPR), and Shutdown Margin(SDM):</p><p>f Energy w1 DMRNP w2 DRPPF w3 DXLHGR w4 DXMPGR w5 DXMCPR w6 DSDM</p><p>where:</p><p>Energy=cycle mean core burnupMRNP=MRNPmaxMRNPcRPPF=RPPFmaxRPPFcXLHGR=XLHGRmaxXLHGRcXMPGR=XMPGRmaxXMPGRcXMCPR=XMCPRcXMCPRminSDM=SDMcSDMminw1,. . .,w6 are called weighting factors and wi&gt;0, i=1,. . .,6</p><p>According to the s denition they will be negative if they are violating the safetylimits imposed in such case the corresponding weighting factors will be the onesgiven by the user in other case they would be zero not penalizing the objectivefunction. If all constraints are achieved then the objective function will be the energyistic rule.e main goal in this work is to obtain a maximized energy without violate theal operational limits and the cold shutdown constraints. It can be achieved</p><p>ugh the implementation of the tabu search technique using the followingarrangement of 444 positions, which means 444! fuel assembly permutations. Thus,the best assembly distribution is that which provides as much energy of the cycleas can be possible without violating the operational and safety limits and haveenough shutdown margin to not jeopardize the integrity of the core. As a rstsight this is a very complex problem which will require enormous computerresources.To reduce the complexity of the problem, one octant symmetry can be assumed,</p><p>then there will be only 60 dierent positions to allocate the fuel assemblies whichrepresent 8.321081 permutations or possible movements. Furthermore, if weintroduce the low leakage which means that only once and twice cycles burnt fuelassemblies can be used in the periphery (LL) and the control cell core rule whichmeans that we can not use fresh fuel in control cell positions, the optimizationproblem is reduced to 7.3611054 dierent permutations instead of the 444! fromthe original problem. Given the symmetry of the problem the fuel assemblies in thediagonal can be exchanged only among them as long as they do not violate any</p><p>A. Castillo et al. / Annals of Nuclear Energy 31 (2004) 151161 153produced by the core analyzed.</p></li><li><p>3. Tabu search technique</p><p>The tabu search method is an iterative heuristic method used for nding, in a set Xof feasible solutions, the solution that minimizes an objective function f based onneighborhood search (NS).In a neighborhood search, each feasible solution x has an associated set of neigh-</p><p>bors, N x 2 X, called the neighborhood of x. NS starts from an initial feasible solu-tion chosen randomly and explores the space X by moving from one solution toanother in its neighborhood. At each iteration of the process, a subset V of N(x) isgenerated and we move from the current solution x to the best one x* in V, whetheror not f(x*) is better than f(x). If N(x) is not large, it is possible to take V as theentire neighborhood. The method of examining the entire neighborhood becomesvery expensive as the problem size increases or its elements are expensive to evalu-ate. Thus, to reduce the sampling size of V one takes the rst move that improvesthe current solution; however, if there is no move that improves the current solution,then one has to examine all neighbors in V. Nevertheless, the main shortcoming ofNS algorithm is a cycling problem.Stopping rules must also be dened; in many cases a lower bound f * of the</p><p>objective function is known in advance. As soon as we have reached this bound, wemay interrupt the algorithm. In general, f * is not available with sucient accuracy,as it is the case of study; thus, the stop criterion is met whenever a xed maximumnumber of iterations is reached, or if a given maximum number of iterations havebeen performed without improving the best solution obtained so far.The tabu search algorithm oers another interesting possibility for overcoming</p><p>the above-mentioned obstacle of the NS technique. To prevent cycling, any movethat reinstates certain attributes of solutions recently visited is forbidden. This isaccomplished in a short-term memory framework by storing the forbidden (tabu)move in a tabu list. A move remains tabu during a certain period (or tabu tenure) tohelp aggressive search for better solutions. The tabu tenure may be xed or variable.In many TS implementations the short-term memory is complemented with a long-term memory, whose purpose is to diversify the search and to move unvisited regionsof the solution space; its function is usually based on the frequency criterion.Unfortunately, the tabu list may forbid certain interesting moves, such as those</p><p>that lead to a better solution than the best one found so far. An aspiration criterion isintroduced to cancel the tabu status of a move when this move is judged useful. Notethat neighborhood search is a tabu search method without an aspiration functionand where the length of the tabu list is zero.</p><p>4. Adaptation of Tabu search</p><p>In order to apply a tabu search algorithm to a combinatorial optimization prob-lem one has to dene the following elements:</p><p>(a) the representation of a feasible solution154 A. Castillo et al. / Annals of Nuclear Energy 31 (2004) 151161(b) the way to generate a starting solution</p></li><li><p>compose the core. Then an initial loading pattern is randomly generated taking intoaccount the low leakage and control cell core rules.</p><p>In general, TS starts from the hypothesis that it is possible to build up a neigh-</p><p>borhood along the iterative search process. In our problem, a neighbor of a feasiblesolution is obtained from this solution by exchanging (without infringe any heuristicrules) two dierent fuel assemblies settled in a 1/8-symmetry reactor core. Given thesymmetry of the problem the fuel assemblies in the diagonal can be exchanged onlyamong them as long as they do not violate any heuristic rule. Thus a move exchan-ges two assemblies in the 1/8-symmetry reactor core and is determined by the twopositions, p1 and p2 having assemblies a1 and a2, respectively. For each currentsolution x, considering one octant reactor core and taking into account the heuristicrules, there are 723 neighbors for each feasible solution. This number is calculatedusing a simple combinatorial counting. The set N(x) is considerably large andmoreover, its elements are expensive to evaluate. Thus, only subset V of N(x) of size0.1|N(x)| is randomly generated, and the move is made from x the rst solution in V(c) the moves (exchanges allowed without violating the restrictions imposed)(d) the form of the objective function and the method to calculate its values(e) the structure of the tabu list.</p><p>As it was mentioned, TS operates on a space of feasible solution and thus for ourproblem a feasible solution will be an octant of a reactor core and it is representedby an array of 60 positions to allocate the fuel assemblies (see Fig. 1). Furthermore,to follow the strategies used in many BWR plants, two heuristic rules will be appliedalong the TS technique. These are the CCC and LL techniques. The rst one doesnot allow the use of fresh fuel in Control Rod (CR) positions and the former doesnot allow also the use of fresh fuel assemblies in the periphery to avoid damage tothe core vessel.The process starts knowing the characteristics of the fuel assemblies that will</p><p>A. Castillo et al. / Annals of Nuclear Energy 31 (2004) 151161 155Fig. 1. Fuel reload design rules in one octant symmetry.</p></li><li><p>runsthatone</p><p>However for many solutions it is not necessary to have a full evaluation of the</p><p>objective function. OTSS makes a partial evaluation of the objective function toassess the energy and safety thermal limits and if they are violated then the shut-down margin runs are not performed. This reduces the calculation time because theobjective function will not be acceptable anyway.Our tabu list is implemented as a tabu time, which records the earliest iteration</p><p>that a move is removed from the list. The number of iterations tabu_tenure that amove or exchange will keep its tabu status is randomly selected in the range from 6to 14 (Glover, 1989). This random selection provides a more versatile search pro-cess. The tabu time is represented by the array tabu_time(p,a) where p is a positionand a is the assembly type allocated in p. The attributes of a swap move are stored inthe vector m=(p1,p2,a1,a2). Then, the tabu_time is updated as follows in order toimpose a tabu on the move m for tabu_tenure iterations:</p><p>tabu time p1; a2 tabu time p2; a1 iter tabu tenure</p><p>where iter is the current iteration number. Thus, the swap move m=(p1,p2,a1,a2) istabu if both tabu_time(p1,a2) and tabu_time(p2,a1) are greater or equal to the cur-rent iteration number. Note that the tabu_tenure value used in our case is a randomnumber between 5 and 10, this range comes from a trial and error test. Our tabutime forbids, during tabu_tenure iterations, any replacement of a2 and a1 in thepositions p1 and p2, respectively.The long term memory is a function that records moves taken in the past in order</p><p>to penalize those which are non-improving. The goal is to diversify the search bycompelling regions to be visited that possibly were not explored before (Glover,1989). In our particular TS implementation, the long-term memory is a vector whichwill be denoted F. The vector has zeroes at the beginning of the procedure. When apair of assemblies (a,b) are swapped at a given iteration, the vector F changes ascompose a full evaluation of the objective function. It is important to point outone Haling calculation takes around 1 tenth of the time that will take to do eachof the cold shutdown margin calculations.that improves the objective function. However, if there no solution that improves x,then one must to examine al...</p></li></ul>