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Progress in Nuclear Energy 53 (2011) 562e565

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Progress in Nuclear Energy

journal homepage: www.elsevier .com/locate/pnucene

Advanced ePrometheus� GNF fuel cycle optimization tool

Serkan Yilmaz a,*, Gerald Kvaall a, Steve Sutton a, Greg Huff b

aGlobal Nuclear Fuel e Americas, PO Box 780 M/C J70, Wilmington, NC 28402, United StatesbGE-Hitachi Nuclear Energy, PO Box 780 M/C J70, Wilmington, NC 28402, United States

a r t i c l e i n f o

Article history:Received 31 December 2009Accepted 13 September 2010

Keywords:OptimizationFuel cycle managementePrometheus

* Corresponding author.E-mail addresses: serkan.yilmaz@gnf.com (S. Yil

(G. Kvaall), steve.sutton@gnf.com (S. Sutton), greg.hu

0149-1970/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.pnucene.2010.09.007

a b s t r a c t

This paper presents the advanced version of ePrometheus� GNF Fuel Cycle Optimization Tool. ePro-metheus is Global Nuclear Fuel-Americas LLC (“GNF”) patented computer system used to optimizeBoiling Water Reactor (BWR) fuel management and operations. The advanced version of ePrometheusutilizes a new database structure (Oracle 11 g) and a new C#, WPF based graphical user interface (GUI),and employs a capability of performing refueling calculation. It also displays additional 2-D and 3-D coresimulation data that are necessary to improve the fuel cycle design process, and utilizes many moreadvanced features based on user experience feedback. The objective of this paper is to present newadvanced infrastructure of the system, and to provide overall information on the advanced features ofthe newly released version 3.0.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

ePrometheus is GNF’s patented computer system used to opti-mize Boiling Water Reactor (BWR) fuel management and opera-tions. ePrometheus system is a result of an extensive andcontinuous investment done by Global Nuclear Fuel over manyyears and it has a proven broad range of application experiences toboth domestic and world wide BWR cores. Publications are avail-able on the methodology, specific applications and benefits in theliterature (Asgari and Kropaczek, 2005; GNF, 2007; Kropaczek andRussell, 2003; Oyarzun et al., 2003). The advanced version ofePrometheus utilizes a new database structure (Oracle 11 g) anda new C# graphical user interface, and employs a capability ofperforming refueling calculation. It also displays additional 2-D and3-D core simulation data that are necessary to improve the fuelcycle design process, and utilizes many more advanced featuresbased on user experience feedback.

2. Methodology

The engine optimization algorithm is based on the iterativeimprovement of a single objective function that incorporatesconstraints derived from the set of system control variables and

maz), gerald.kvaall@gnf.comff@ge.com (G. Huff).

All rights reserved.

simulator outputs, which may be either discrete or continuous.Furthermore, constraints may be equalities or inequalities. BWRfuel management includes both plant operations, as it relates tocycle energy production, and core design.

In the optimization, the independent variables specific to plantoperations are the control blade placements, core flow, and controlblade sequence exchange times. For core design, the independentvariables include, in addition to those for operations, the exposedfuel-loading pattern and fresh bundle design selection. Theexposed fuel-loading optimization determines the placement ofexposed bundles for a user-provided fresh fuel-loading template.The fresh bundle design optimization determines the choice ofbundle type from a palette of possible fresh bundle designs withineach location of the fresh fuel-loading template. For a multi-streamfresh loading, the fresh bundle design optimization enables deter-mination of the optimal “split” among multiple streams. Withproblems typically containing 200e800 independent variables, thenumbers of combinatorial solutions that exist are, for all practicalpurposes, infinite (>10100 solutions).

The dependent variables specific to plant operations includethermal limits such as those on critical power and linear heatgeneration rate, reactivity limits such as shutdown margin, expo-sure limits, and core performance metrics such as End of Cycle Keff(EOCK). The dependent variables are the results of core simulatorcalculations while the independent variables are the basis of thecore simulator inputs.

The set of independent and dependent variables are used in theassessment of constraints for a given optimization problem.

Fig. 1. Physical infrastructure.

S. Yilmaz et al. / Progress in Nuclear Energy 53 (2011) 562e565 563

Constraints fall under one of two categories: “hard” or “soft”constraints. A hard constraint is physical constraint that must besatisfied always in order for a solution to be considered feasible.Examples of hard constraint include control blade notch exclusions,core loading symmetries, allowable control blade groupings, lockedbundle positions, and frequency of sequence exchange. Softconstraints are core performance criteria derived from the set ofsimulator output results and are satisfied through the use of anobjective function, combined with credit and penalty weightingfactors, within the context of the search algorithm. Soft constraintsinclude such parameters as minimum and maximum values ofthermal and reactivity limits, flow window, and exposure limits.The optimization methodology was addressed in the previous

Fig. 2. Operation c

works of Asgari and Kropaczek, 2005; GNF, 2007; Kropaczek andRussell, 2003; Oyarzun et al., 2003 are summarized under refer-ences section.

Oracle 11 g Enterprise Edition was utilized to better address thearchitectural issues experienced before. The decision was based ona comprehensive review of the capabilities offered by Oracle 9i,10 g, 11 g and Structured Query Language (SQL) Server 2008. Ofthese, only Oracle 11 g Enterprise Edition provides a completesolution to the issues identified. Of particular note in Oracle 11 gEnterprise Edition is the introduction of a new feature calledSecureFiles. The SecureFiles feature is a specifically engineered todeliver high performance for file data comparable to that of tradi-tional file systems while retaining the advantages of the Oracle

onfiguration.

Fig. 3. Blade group management view.

Fig. 4. Summary screen for objective function and its components.

S. Yilmaz et al. / Progress in Nuclear Energy 53 (2011) 562e565564

S. Yilmaz et al. / Progress in Nuclear Energy 53 (2011) 562e565 565

database. For example, Oracle automatically detects identicalSecureFiles data and stores only one copy. Further, access to the filesis also simplified since the system can be configured as a PortableOperating System Interface (POSIX) compliant file system and datacan be accessed, if allowed, through open data protocols such asNetwork File System (NFS) and File Transfer Protocol (FTP). Finally,management of the file data is conducted through the RDMS; thus,eliminating the need for external processes tomanage file clean-up.Again consider the security situation utilizing the SecureFilesfeature of Oracle 11 g Enterprise Edition as illustrated in Fig. 1. Notethat only one outbound Transmission Control Protocol (TCP) port isrequired from the Nuclear Customer Network (NCN) into the GEnetwork (Oracle connectivity) and the NetApp is no longer requiredfor access to the file system. Also, note that access to the files ishandled completely through the Relational Database ManagementSystem (RDMS) and no duplicate files are stored on disk.

3. Results

Fig. 1 illustrates the physical infrastructure of new ePrometheussystem hosted within GNF’s hardened Nuclear Customer Network(NCN). Customers access to the system is provided thoroughapubliclyaccessiblewebsite or via a dedicated business-to-businessVirtual Private Network (VPN) tunnel securely linking GNF to thecustomer. In the case of the later, access times and overall userexperience are greatly improved due to the reduced number of hopstraversed by the network traffic. Applicationpresentation is realizedthrough the use of Citrix virtualization technology that, amongstother things, allows GNF to deliver a suite of products directly to theusers’ desktop. Users have access to web-based online help andability to upload and download files to their local desktop. Diskstorage residingwithin the secureGEnetwork is alsomade availableto reduce latencies associated with large file transfers.

Fig. 2 displays the operation configuration menu for a samplesimulation case. This feature has been updated with refuelingoption available for any exposure step during core operation, whichallows users mid-cycle shuffle and re-start capability. A user alsohas the flexibility to perform any manual fuel movement within UIinterface. The advanced version stores core simulator wrap-up filein the database and can provide this information to users for the

use of multi-cycle discharge/load/shuffle capability for down-stream cycles.

Fig. 3 shows the blade management view for setting up controlblade groups, blade notch exceptions and insertion constraints.Fig. 4 shows a sample of summary screen for the objective functionand its components. Constraints lists, values and their contributionto overall objective function are summarized on the left part of thescreen. The user can see the evaluation of objective functionthroughout the optimization search in a graphical form on the rightside of the screen. The summary screen has help capability toprovide recommendations and tips to users for the possible solu-tion mechanism of violated design constraints available in the list.

4. Conclusions

The advanced version of ePrometheus utilizes a new infra-structure with Oracle 11 g database structure and a new C#, WPFbased graphical user interface, and it employs a capability of per-forming refueling calculation. It also displays additional 2-D and3-D core simulation data that are necessary to improve the fuelcycle design process, and utilizes many more advanced featuresbased on user experience feedback. The new infrastructure isa transition towards to multi-cycle capability. The new systemprovides a better reporting capability for release fuel cycle analysis,stores the wrap-up information for spent fuel discharge and freshfuel load performed by the designers, and defines a chain relationbetween cycles for a complete multi-cycle analysis.

References

Asgari, Mehdi, Kropaczek, Dave J, 2005. N-StreamingSM concept for boiling waterreactor fuel cycle design. Trans. Am. Nucl. Soc. 92, 612e614.

“GNF ePrometheusTM N-StreamingSM Application for Nine Mile Point Unit 2 NuclearPower Station”, (2007). Serkan Yilmaz and Gerald Kvaall, Global Nuclear Fuel eAmericas, Carl Lepine and Jeff Winklebleck, Nine Mile Point Nuclear Station,American Nuclear Society 2007 Annual Winter Meeting, Washington, DC, Nov11e15.

Kropaczek, D.J., Russell, William E, 2003. Method for Optimization of BWR FuelManagement and Plant Operations. Proc. Topical Meeting on Advances in FuelManagement II-117, Hilton Head.

Oyarzun, C.C., et al., 2003. The GNF Optimization System for BWR Fuel CycleManagement. Proc. Topical Meeting on Advances In Fuel Management II-118,Hilton Head.