Patent Grant
7200541
1. Field of the Invention
This invention relates generally to nuclear reactors, and more particularly to determining a reactor core design for the reactor.
2. Related Art
A core of a nuclear reactor such as boiling water reactor (BWR) or pressurized water reactor (PWR) has several hundred individual fuel bundles (fuel assemblies) of fuel rods (BWR) or groups of fuel rods (PWR) that have different characteristics. These bundles (fuel rod groups) are preferably arranged so that interaction between rods within a fuel bundle (rod group), and between fuel bundles (fuel rod groups) satisfies all regulatory and reactor design constraints, including governmental and customer-specified constraints. Additionally, the core design must be determined so as to optimize core cycle energy. Core cycle energy is the amount of energy that a reactor core generates before the core needs to be refreshed with new fuel elements, such as is done at an outage.
In the case of a BWR, for example, the number of potential bundle arrangements within the core and individual fuel element arrangements within a bundle may be in excess of several hundred factorial. From these many different possible configurations, only a small percentage of core designs may satisfy all applicable design constraints. Further, only a small percentage of these core designs, which do satisfy all applicable design constraints, are economical.
Traditionally, core design determinations have been made on a trial and error basis. Specifically, and based on only the past experience of the engineer or designer, in designing a core design an initial core design was identified. The initially identified design was then simulated in a computer. If a particular design constraint was not satisfied, then the arrangement was modified and another computer simulation was run. Many weeks of resources typically were required before an appropriate core design was identified using the above-described procedure.
For example, a current process being used is a stand-alone manual design process that requires a designer to repeatedly enter reactor plant specific operational parameters into an ASCII text file, which is an input file. Data entered into the input file includes blade notch positions of control blades (if the evaluated reactor is a boiling water reactor (BWR)), core flow, core exposure (e.g., the amount of burn in a core energy cycle, measured in mega-watt days per short time (MWD/st), etc.
A Nuclear Regulatory Commission (NRC) licensed core, simulation program reads the resulting input file and outputs the results of the simulation to a text or binary file. A designer then evaluates the simulation output to determine if the design criteria have been met, and also to verify that no violations of margins to thermal limits have occurred. Failure to meet design criteria (i.e., violations of one or more limits) require a manual designer modification to the input file. Specifically, the designer would manually change one or more operation parameter and rerun the core simulation program. This process is repeated until a satisfactory core loading pattern is achieved.
This process is extremely time consuming. The required ASCII text files are laborious to construct, and often are error prone. The files are fixed-format and extremely long, sometimes exceeding five thousand or more lines of code. A single error in the file results in a crash of the simulator, or worse, results in a mildly errant result that may be hard to initially detect, but will profligate with time and iterations to perhaps reduce core cycle energy when placed in an actual operating nuclear reactor core.
Further, no assistance is provided via the manual iterative process in order to guide a designer toward a more favorable core loading pattern solution. In the current process, the responsible designer or engineer's experience and intuition are the sole means of determining a core design solution.
A method and arrangement for determining a core design to be used in a fuel cycle in a reactor core of a nuclear reactor is described. In the method, a set of limits applicable to determining a core design are defined and a reference core design is generated based on the defined limits. The reference core design includes an initial loading pattern of current fresh fuel bundles arranged in a plurality of fuel locations therein. A unique subset of fresh fuel bundles is selected, to be evaluated in the reference core design and a first iterative improvement process is performed. The first iteration may include replacing, at each fuel location, at least one of the current fresh fuel bundles with at least one of the selected fresh fuel bundles, and simulating reactor operation on the reference core design to produce a plurality of outputs. Each output may correspond to the reference core design containing one or more of the selected fresh fuel bundles. The outputs may be ranked based on the defined set of limits, and the highest ranked output may be selected as an accepted core design for the nuclear reactor.
The present invention will become more fully understood form the detailed description given herein below and the accompanying drawings, wherein like elements are represented like reference numerals which are given by way of illustration only and thus are not limitative of the present invention and wherein:
A method and arrangement for determining a core design for a nuclear reactor is described. As used herein, the phrase “core design” refers to a core configuration of fuel assemblies that may include of specified loading pattern for fresh fuel assemblies that are to be loaded in a core of a nuclear reactor at a next scheduled outage, for example.
In an aspect, the arrangement may include an interface for communicating with the user and interacting with a computer-based system and/or processing medium (e.g., software-driven computer program, processor, applications driven by application servers, etc.). This enables the user to virtually create and evaluate core designs, in order to determine a desired fuel assembly configuration that may be implemented in a particular reactor core at the reactor plant's next scheduled outage, for example. The method and arrangement enables a user to provide feedback based on how closely a core design meets user-input constraints. The core design may be characterized by one or more fresh fuel assemblies, as well as information related to the placements of these assemblies in the core, to exposed fuel placements, to control blade patterns, etc.
The method and arrangement of the present invention provide several advantages. The method enables the determining of types and placements of fresh fuel bundles within a nuclear reactor core design, without regard to bundle complexity or number of fresh fuel bundle designs. In contrast to current core designs, which typically utilize one or two fresh fuel types (i.e., a one or two stream solution), any number or combinations of fresh fuel bundle designs (e.g., “N streams”) may be utilized in order to determine the desired fuel bundles for placement. The method and arrangement allow for greater efficiency in core design with improved safety margins. The method allows for selection of N candidate fresh fuel bundles from a palette of fresh fuel bundle designs, to be evaluated in a reference core design, followed by successive iterative improvements in fresh fuel bundle “loading” patterns (e.g., patterns as to how the fresh fuel is to be loaded in the reactor during the next outage, for example).
Additionally, the method and arrangement utilize a computing environment to effect a substantial reduction in the amount of time needed to create desirable core designs for a nuclear reactor. The method adheres perfectly to a user's input constraints or design limits (e.g., if the calculated objective function value is not equal to zero, the particular core design may still be improved upon). The method and arrangement offer greater operational flexibility to change core designs rapidly and simulate the altered designs, as compared to the conventional manual iterative process. Further, errors are no longer made in attempting to generate a simulator input file, as described with respect to the manual iterative process.
A plurality of external users 300 may communicate with application server 200 over a suitable encrypted medium such as an encrypted 128-bit secure socket layer (SSL) connection 375, although the present invention is not limited to this encrypted communication medium. A user 300 may connect to the application server 200 over the internet or from any one of a personal computer, laptop, personal digital assistant (PDA), etc., using a suitable interface such as a web-based internet browser. Further, application server 200 is accessible to internal users 350 via a suitable local area network connection (LAN 275), so that internal users 350 have access over an intranet for example. The application server 200 is responsible for online security, for directing all calculations and accessing of data in order to calculate objective function values, and for the creation of suitable graphical representations of various features of a core design that a user may review. The graphical information is communicated over the 128-bit SSL connection 375 or LAN 275 (to be displayed on a suitable display device of the users 300/350. Hereinafter, the term user refers to both an internal user 300 and an external user 300. For example, the user may be any of a representative of a nuclear reactor plant accessing the website to determine a core design for his or her nuclear reactor, and/or a vendor hired by a reactor plant site to develop core designs by using the method and arrangement of the present invention.
Application server 200 may also include a host processor 210, which may be constructed with conventional microprocessors such as currently available PENTIUM processors. Host processor 210 represents a central nexus from which all real time and non-real functions in application server 200 are performed, such as graphical-user interface (GUI) and browser functions, directing security functions, directing calculations such as calculation of the objective functions for various limits, etc., for display and review by the user. Accordingly, host processor 210 may include a GUI 230 which may be embodied in software as a browser. Browsers are software devices which present an interface to, and interact with, users of the arrangement 1000. The browser is responsible for formatting and displaying user-interface components (e.g., hypertext, window, etc.) and pictures.
Browsers are typically controlled and commanded by the standard hypertext mark-up language (HTML). Additionally, or in the alternative, any decisions in control flow of the GUI 230 that require more detailed user interaction may be implemented using JavaScript. Both of these languages may be customized or adapted for the specific details of a given application server 200 implementation, and images may be displayed in the browser using well known JPG, GIF, TIFF and other standardized compression schemes, other non-standardized languages and compression schemes may be used for the GUI 230, such as XML, “home-brew” languages or other known non-standardized languages and schemes. Host processor 210 may be operatively connected to a cryptographic server 260. Accordingly, application server 200 implements all security functions by using the cryptographic server 260, so as to establish a firewall to protect the arrangement 1000 from outside security breaches. Further, cryptographic server 260 secures all personal information of registered users.
Application server 200 may be also operatively connected to a plurality of calculation servers 400. The calculation servers 400 may perform all the calculations required to process user entered data, direct simulation of a core design, calculate values for comparison as to be described in further detail below, and to provide results which may be displayed via, the GUI 230, under the direction of application server 200.
The calculation servers 400 may be embodied as WINDOWS 2000 servers, for example. More particularly, the calculation servers 400 may be configured to perform a multitude of complex computations which may include, but are not limited to, configuring the objective function and computing objective function values, executing a 3D simulator program to simulate reactor core operation on a particular core design and to generate outputs from the simulation, providing results data for access and display by a user via GUI 230, and iterating an optimization routine as to be described in further detail below.
Additionally, relational database server 250 may include a queue database 253, which stores all parameters for a core design that are to be simulated in the 3D simulator, and a historical core loading pattern design database 254, which includes historical reactor core designs that may be selected in generating a reference core design that is most consistent with defined limits. All simulator results may be stored in simulator results database 255. The simulator results database 255 (and limits database 251) may be accessed by the calculation servers 400 in order to calculate a number of objective function values that are applicable to a particular core design. These objective function values may be stored in an objective function values database 257 within relational database server 250. A 3D simulator input parameters database 259 may also be included within relational database server 250. Database 259 may include the positions of control blades and reactor operating parameters for all exposure steps. As the calculation servers 400 is operatively connected to, and may communicate with, relational database server 250, each of the subordinate databases described in
In general in the method, all limits (which may include anything a customer, designer or user indicates is critical to a particular desired core design) should be understood and incorporated into a reference core design. Initial fresh fuel types and loading pattern for the reference core design may be determined based on an estimate from historical, similar cycles for example. An iterative process of improvement may then be performed on the reference core design. Results from each iteration may be viewed, if desired, and a user may determine whether the limits were met and if maximum energy output was obtained. If the results of the iterative improvement process are satisfactory, a report may be generated and provided to a customer. If the results are not satisfactory (e.g., even after the iterative process is performed, an acceptable core design solution has not been determined) a designer or user may determine modifications to be made to the reference core design (i.e., new fresh fuel loading pattern, including the introduction of additional fresh bundles, etc.). The iterative improvement process may then be repeated, as to be described in further detail below, until a satisfactory result is obtained. In an aspect, the method and arrangement utilize a GUI that enables rapid viewing of results at each process step, as stored and retrieved from one or more of the databases in the relational database server 250.
Referring now to
Limits which are to used in a simulation for core design of the selected plant are defined (Step S10). These limits may be related to key aspects of the design of the particular reactor being evaluated and design constraints of that reactor. The limits may be applicable to variables that are to be input for performing a simulation of the reference core design, and may be limit applicable only to the results of the simulation (e.g., on the outputs). For example, the input limits may be related to client-inputted reactor plant specific constraints and core performance criteria (e.g., energy content). Limits applicable to outputs from simulation may be related to one or more of operational parameter limits used for reactor operation, core safety limits, margins to these operational and safety limits and the other client-inputted reactor plant specific constraints.
A reference core design with initial fresh fuel loading pattern is generated (Step S15) for the selected reactor. For example, historical core loading pattern design database 254 may be accessed to find a historical reactor core design most consistent with the defined limits. A historical core design may be consistent if it is of a similar core size and power output rating, has similar cycle energy, and has similar operational performance characteristics to the core design being developed for the selected reactor plant. Using the similar historical design as a basis, the total energy content of the historical core may be calculated and a difference from the required energy content (e.g., the desired energy output from the determined core design, as based on customer requirements for example) defined. The difference in energy between historical core energy content and the energy content desired should be supplied by the loading of fresh fuel assemblies.
Thus, to generate the reference core design, the user should select (Step S20) fresh fuel bundle type(s) for the reference core design that can best meet the energy requirement(s) (which may be included in the limits) for the reactor core design to be developed. The bundles designs may be selected from fresh fuel bundle design database 252, which provides a wide variety of fresh fuel bundle designs (or N streams) that have been previously created and modeled.
With the fresh bundle types selected, core loading symmetries should be accounted for, with some plants requiring octant, quadrant, or half-core loading symmetry. This may be done by clicking on a suitable drop down menu and the like. By selecting octant symmetry, the user may model the reactor assuming that all 8 octants (where an octant is a group of fuel bundles for example) are similar to a modeled octant of fuel assemblies. Consequently, simulator time is generally increased by a factor of eight. Similarly, by selecting “quadrant symmetry”, the user can model the reactor assuming each of the 4 quadrants are similar to the modeled quadrant. Hence, simulator time is generally increased by a factor of four. If asymmetries in bundle properties prevent octant or quadrant symmetry, the user can also specify no symmetry. The core is thus loaded accounting for symmetries and the defined limits.
One or more current fresh fuel bundles in the reference core design may then be replaced (Step S25) with one or more of the selectable fresh fuel bundles 905 during an iterative improvement process. The selection may be performed via GUI 230, which provides the user with a summary of each bundle's performance characteristics. Once the “N-streaming” (selected fresh fuel bundles) have been defined, a looping process described in terms of Steps S25 and S30 is initiated, whereby a systematic process of replacement and analysis for fresh fuel bundles is performed.
At an outermost level (“outer loop”) each fresh fuel location in the current reference core design is examined in sequence. By “examined”, reactor core operation is simulated (Step S30) for the reference core design with each particular loading pattern, and performance characteristics of the bundle are reviewed to determine whether a reference core design that can best meet the energy requirement(s) (which may be included in the limits) for the reactor core design has to be developed. At the innermost level, each “replacement” fresh fuel bundle 905 selected from page 900 is examined in each fuel location. During this process, a current fresh fuel bundle in the reference core design is replaced with each new “N-streaming” fresh fuel bundle 905.
Reactor operation may be simulated (Step S30) on the reference core design containing one or more of the select fresh fuel bundles, in order to produce a plurality of simulated results, or outputs. The simulation may be executed by calculation servers 400; however, the simulation may be a 3D simulation process that is run external to the arrangement 1000. The user may employ well-known executable 3D simulator programs such as PANACEA, LOGOS, SIMULATE, POLCA, or any other known simulator software where the appropriate simulator drivers have been defined and coded, as is known. The calculation servers 400 may execute these simulator programs based on input by the user via GUI 230.
Thus, the user may initiate a 3D simulation at any time using GUI 230, and may have a number and different means to initiate a simulation. For example, the user may select a “run simulation” from a window drop down menu, or could click on a “RUN” icon on a webpage task bar, as is known. Additionally, the user may receive graphical updates or status of the simulation. Data related to the simulation may be queued in queue database 253 within relational database server 250. Once the simulation is queued, the user may have an audio and/or visual indication as to when the simulation is complete, as is known. The iterative steps of replacement and simulation are repeated (output of Step S35 is NO) until all selected fresh fuel bundles have been inserted at each fuel location and each “derivative” reference core design has been simulated (e.g., output of Step S35 is YES). Substitution of all selected fresh fuel bundles 905 into each of the fresh fuel locations is therefore complete upon exiting the inner and outer loops.
The iterative improvement process described above is beneficial in that it enables the user to fine tune a core design, and to perhaps extract even more energy out of an acceptable core design than was previously possible of doing with the conventional, manual iterative process. Further, incorporation of the relational database server 250 and a number of calculation servers 400 expedite calculations. The iterative improvement process as described in
The outputs from simulation are ranked based on the limits (Step S40). A user may display data related to each of the outputs, if desired. This enables a user to make a comparison against the reference core design to determine whether there was any improvement, where improvement may be defined in terms of not exceeding the defined limits, or meeting certain energy requirements, for example.
If the top ranked output is an improvement (output of Step S50 is YES) the core design corresponding to that highest ranked output is set (Step S70) as the new reference core design with the results stored (Step S80) in relational database server 250, such as in simulator results database 255. This completes one iteration of the iterative improvement process. Steps S25, S27, S30, S40 and S50 are repeated (e.g., N iterations), with each “improvement” becoming the new reference core design for a subsequent iteration. The defined limits are applicable to the reference core design in each of the N iterations. If, for a given iteration, there is no improvement in the top ranked output, the iterative process is complete, and data relating to the reference core design at that point, since it is the top ranked design may be displayed and interpreted (Step S60) by the user. The data may also provide the user with an indication of which location in a simulated core were the largest violators or largest contributors to a limit violation. At Step S60, the user may be inclined to initiate a modify subroutine (Step S90). This is an optional step which typically is required only if the above iterative improvement process fails to determine a core design that is acceptable to the user. The modify subroutine will be described in further detail below.
Concurrently, a program running on each of the available calculation servers 400 scans every few seconds to look for available jobs to run (Step S37). If a job is ready to run, one or more of the calculation servers 400 obtains the data from the queue database 253 and runs the appropriate 3D simulator. As described above, one or more status messages may be displayed to the user. Upon completion of the simulation, all results of interest may be stored in one or more subordinate databases within the relational database server 250 (e.g., simulation results database 255). Accordingly, the relational database server 250 may be accessed in order to calculate the objective function values for the test core design.
The objective function may be stored in relational database server 250 for access by calculation servers 400. Objective function calculations, which provide objective functions values, may also be stored in the relational database server 250, such as in a subordinate objective function value database 257. Referring to
Although the method and arrangement of the present invention envision any number of objection function formats that could be utilized, one embodiment includes an objective function having three components: (a) the limit for a particular constraint parameter (e.g., design constraint for reactor plant parameter), represented as “CONS”; the simulation result from the 3D simulator for that particular constraint parameter, represented as “RESULT”, and a multiplier for the constraint parameter, represented by “MULT”. A set of predefined MULTs may be empirically determined from a large collection of BWR plant configurations, for example. These multipliers may be set at values that enable reactor energy, reactivity limits, and thermal limits to be determined in an appropriate order. Accordingly, the method of the present invention utilizes a generic set of empirically-determined multipliers, which may be applied to over thirty different core designs. However, GUI 230 permits manual changing of the multipliers, which is significant in that user preference may desire certain constraints to be “penalized” with greater multipliers than the multipliers identified by the preset defaults.
An objective function value may be calculated for each individual constraint parameter, and for all constraint parameters as a whole, where all constraint parameters represent the entity of what is being evaluated in a particular core. An individual constraint component of the objective function may be calculated as described in Equation (1):
OBJpar=MULTpar*(RESULTpar−CONSpar); (1)
where “par” may be any of the client-inputted constraints listed in
OBJTOT=SUM(par=1,31){OBJpar} (2)
Referring to Equation 1, if RESULT is less than CONS (e.g. there is no violation of a constraint), the difference is reset to zero and the objective function will be zero. Accordingly, objective function values of zero indicate that a particular constraint has not been violated. Positive values of the objective function represent violations that may require correction. Additionally, the simulation results may be provided in the form of special coordinates (i, j, k) and time coordinates (exposure step) (e.g., particular time in a core-energy cycle). Therefore, the user can see at which time coordinate (e.g., exposure step) the problem is located. Hence, the core is modified only at the identified exposure step.
In addition, objective function values may be calculated as a function of each exposure step, and totaled for the entire core design problem (Step S43). The objective function values calculated for each constraint, and the objective function values per exposure step, may be further examined by normalizing each objective function value to provide a percentage contribution of a given constraint to a total objective function value (Step S45). Each result or value of an objective function calculation is stored in a subordinate objective function value database 257 within relational database server 250.
The objective function values may be utilized in the manual determination of core development. For example, the values of the objective function calculations may be viewed graphically by the user in order to determine parameters that violate limits. Additionally, any change in objective function values over successful iterations of core design provides the user with a gauge to estimate both improvement and detriment in their proposed design.
Increases in an objective function value over several iterations indicate that the user's changes are creating a core design that is moving away from a desired solution, while successive iterations of lesser objective functions values (e.g., the objective function value decreasing from a positive value towards zero) may indicate improvements in the iterative core design. The objective function values, limits and simulation results over successive iterations may be stored in various subordinate databases within relational database server 250. Therefore, designs from past iterations may be quickly retrieved, should later modifications prove unhelpful.
Upon completion of the objective function calculation, the user may be provided with data related to the objective function calculations, which may include limits that have been violated during the simulation of an evaluated core design.
Although the individual core modifications may alternatively be left to the desires of the user, procedural recommendations may be provided in the form of a pull down menu, for example. These recommendations may be divided into three categories: energy beneficial moves, energy detrimental moves, and converting excessive margin (from thermal limit) into additional energy. A preferred technique is to address problems using energy beneficial moves rather than energy detrimental moves. Even if the core design meets all of the limits (client-inputted plant specific constraints, design limits, thermal limits,.etc.) the user may verify that any excessive margin to a particular limit is converted into additional energy. Accordingly, the following logic statements may represent the above procedural recommendations:
Energy Beneficial Moves
The data resulting from the objective function calculations may be interpreted on a suitable display device. For example, this data may be displayed as a list of constraints with denoted violators, as described with respect to
In one embodiment, the user can manually direct this modifying subroutine, with the help of GUI 230. In another embodiment, the subroutine may be performed within the bounds of an optimization algorithm that automatically iterates modifying of the reference core design, simulation, calculation of objective function and evaluation of the results or values of the objective function calculations for a number of core design iterations.
The user determines, based on the displayed data, whether any limits are violated (Step S91). If no limits are violated, the user determines if any identifiers indicate that characteristics of maximum energy are obtained from the core design. For example, these identifiers may include an indication of good thermal margin utilization (such as margins on MFLCPR and LHGR) by moving fuel so as to maximize plutonium generation for cycle extension. Energy requirements may be shown to be met when the minimum EOC eigenvalue is obtained for the core design to be used for the fuel cycle (eigenvalue search) or the desired cycle length is determined at a fixed EOC eigenvalue. If there is an indication that maximum energy has been obtained from a core design (the output of Step S92 is YES), an acceptable core design has been determined, and the user may access a report of the results related to the core design (Step S93).
If limits are violated (the output of Step S91 is YES) or limits are not violated but there is an indication that maximum energy has not been obtained from the core design (the output Step S92 is NO) then the user determines a fresh fuel loading pattern modification to be made to the current reference core design (Step S94). This is where the user may either make individual core modifications, or use the system-provided procedural recommendations described above (energy beneficial moves, energy detrimental moves, and converting excessive margin (from thermal limit) into additional energy) by accessing a pull down menu, for example. Additionally, if several iterations of core design changes have been attempted and there has been no real improvement to the objective function, this is a further indication that an alternative core design with a different fresh fuel loading pattern might need to be explored.
In making a modification to the fresh fuel loading pattern, and based on the recommendations from above, the user may alter the fresh bundle loading via the GUI. For example, and using a suitable input device and GUI 230, a designer may identify the bundle symmetry option of any potential fresh bundle(s) in the reference core design to be moved, and may select the “target” fresh fuel bundle(s), the destination(s) where the target bundle(s) is/are to be moved. The identified target bundles are then “shuffled” according to the required symmetry (mirror, rotational, etc.). This process may be repeated for any fresh bundle shuffle that is required to re-load the core reference pattern in the desired manner.
These reference core design modifications may be saved in relational database 250, such as in 3D Simulator input parameters database 259, for example. A user may repeat steps S30 to S50 (Step S95) incorporating the design modifications. The resultant highest ranked output establishes a new reference core design from which the iterative improvement process of
If an iteration still indicates that limits are violated (the output of Step S160 is YES) then the modify subroutine in Step S90 is iteratively repeated until all limits are satisfied, or until all limits are satisfied within a margin that is acceptable, as determined by the user (Step S190). The iterative process is beneficial in that it enables the user to fine tune a core design, and to perhaps extract even more energy out of an acceptable core design than was previously possible of doing with the conventional, manual iterative process. Further, incorporation of the relational database server 250 and a number of calculation servers 400 expedite calculations. The iterative process as described in
To this point, the method and arrangement of the present invention have been described in terms of a user or designer interpreting data via GUI 230 and modifying a reference core design iteratively, by hand, based on displayed feedback (the data from the objective function) in order to get a desired design. However, the aforementioned steps of
Optimize bundle selection means making an optimal determination of fresh bundle types within the reference core design. As a result of the optimization, each fresh location may contain any one of a number of fresh bundle types (e.g., IAT types as shown in
Using a suitable input device (e.g., keyboard, mouse, touch display, etc.), the user may select, via GUI 230, one or more of the optimization parameters by clicking in the selection box 1342 associated with an optimization parameter 1240. When selected, a check appears in the selection box 1342 of the selected optimization parameter. Clicking in the selection box 1342 again de-selects the optimization parameter. For example, to perform an optimization, a user might select the optimize fuel loading and optimize bundle selection boxes 1342, as illustrated in
Memory (relational database server) 250 may also store constraint parameters associated with the optimization problem. These may be stored in limits database 251 for example. The constraint parameters are parameters of the optimization problem that must or should satisfy a constraint or constraints, where a constraint may be analogous to the limits described above.
Each optimization parameter may have a predetermined credit term and credit weight associated therewith stored in relational database server 250. Similarly, each optimization constraint has a predetermined penalty term and penalty weight associated therewith, which may be stored in relational database server 250, such as in limits database 251 and/or objective function values database 257. As seen in
Once the above selections have been completed, a calculation server 400 retrieves the selections above from relational database server 250 and configures the objective function according to the generic definition discussed above and the selections made during the selection process. The resulting configured objective function equals the sum of credit components associated with the selected optimization parameters plus the sum of penalty components associated with the selected optimization constraints.
Additionally, this embodiment provides for the user to select a method of handling the credit and penalty weights. For example, the user is supplied with the possible methodologies of static, death penalty, dynamic, and adaptive for the penalty weights; is supplied with the possible methodologies of static, dynamic and adaptive for the credit weights; and the methodology of relative adaptive for both the penalty and credit weights. The well-known static methodology maintains the weights at their initially set values. The well-known death methodology sets each penalty weight to infinity. The well-known dynamic methodology adjusts the initial weight value during the course of the objective function's use in an optimization search based on a mathematical expression that determines the amount and/or frequency of the weight change. The well-known adaptive methodology is also applied during the course of an optimization search. In this method, penalty weight values are adjusted periodically for each constraint parameter that violates the design value. The relative adaptive methodology is disclosed in U.S. patent application Ser. No. 10/246,718, entitled METHOD AND APPARATUS FOR ADAPTIVELY DETERMINING WEIGHT FACTORS WITHIN THE CONTEXT OF AN OBJECTIVE FUNCTION, by the inventors of the subject application, filed on Sep. 19, 2002.
Optimization Using the Objective Function
For the purposes of explanation only, the optimization process of
Each input parameter set of values is a candidate solution of the optimization problem. The core simulator as described above runs a simulated operation and generates a simulation result for each input parameter set of values. The simulation result includes values (i.e., system outputs) for the optimization parameters and optimization constraints. These values, or a subset of these values, are values of the variables in the mathematical expressions of the objective function.
Then, in step S1514, a calculation processor 400 uses the objective function and the system outputs to generate an objective function value for each candidate solution. In step S1516, the calculation processor 400 assesses whether the optimization process has converged upon a solution using the objective function values generated in step S1514. If no convergence is reached, then in step S1518, the input parameter sets are modified, the optimization iteration count is increased and processing returns to step S1512. The generation, convergence assessment and modification operations of steps S1512, S1516 and S1518 are performed according to any well-known optimization algorithm such as Genetic Algorithms, Simulated Annealing, and Tabu Search. When the optimization is utilized to determine an acceptable core design, the optimization is run until convergence (e.g., acceptable results as in steps S93/S193 of
The method and arrangement offer several advantages. As described above, the number of unique fresh fuel bundles within a design refers to the number of streams. Thus, a single-stream design utilizes one customized bundle; a two-stream design utilizes two customized bundles; and so on. Increasing the number of possible streams to N streams, as described above, may correlate to improved fuel cycle costs and safety margin. The greater the customization, the greater the ability of the designer to address local issues, while at the same time maintaining the highest core efficiency.
The present invention therefore allows a user to target specific ‘problem’ locations in a core design for a fuel-cycle in a systematic manner by perturbing one or several fresh fuel bundles within a core design solution. For example, to ‘fix’ a shutdown margin problem, one might simply exchange a fresh fuel bundle in the problem location for a fresh bundle containing more gadolinium content. By ‘fix’, it may be implied that a limiting location in the core may be made non-limiting with some level of margin to a constraint limit. To fix a MFLPD problem, for example, one might target the problem location with a fresh fuel bundle containing axial zoning changes to shift the axial power profile locally.
By targeting and fixing specific problem locations, global changes to the core design my be made that target cycle energy (e.g., a new fresh fuel loading pattern). The magnitude of such global changes may be such that new local problems arise. Using the method and arrangement of the invention, a process of local and global change can then be repeated. With each iteration of local/global change, an increasing number of local problem locations may arise as the design is ‘pushed’ towards improved energy. When no further local changes are possible, the core design solution is complete.
The technical effect of the invention is a computer-based arrangement that provides a way to efficiently develop a core design for a nuclear reactor, as well as a computer-based method for providing internal and external users the ability to quickly develop, simulate, modify and perfect a core design with a specified loading pattern for fresh fuel assemblies that are to be loaded in a core of a nuclear reactor at a next scheduled outage.
The invention being thus described, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the invention, and all such modifications as would be obvious to one skilled in the are intended to be included within the scope of the following claims.
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| 5790618 | Fawks, Jr. | Aug 1998 | A |
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