CN202487187U - Intelligent critical heat flux density measuring device - Google Patents
Intelligent critical heat flux density measuring device Download PDFInfo
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- CN202487187U CN202487187U CN2011205177419U CN201120517741U CN202487187U CN 202487187 U CN202487187 U CN 202487187U CN 2011205177419 U CN2011205177419 U CN 2011205177419U CN 201120517741 U CN201120517741 U CN 201120517741U CN 202487187 U CN202487187 U CN 202487187U
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E30/00—Energy generation of nuclear origin
- Y02E30/30—Nuclear fission reactors
Abstract
The utility model provides a novel intelligent measuring device of reactor core critical heat flux density. The intelligent critical heat flux density measuring device comprises a detecting unit, an input unit and a processing unit, wherein the detecting unit is used for measuring various parameters of a nuclear reactor through various sensors and detectors and converting relative parameters into electrical signals for outputting, the input unit is connected with a detecting device and saves the electrical signals of the detecting device into the processing unit, and the processing unit is an unit used for processing and outputting data signals and displays the result of critical heat flux density obtained by a computing unit. According to the intelligent critical heat flux density measuring device, a computer system is utilized for real-time processing. Therefore, the intelligent critical heat flux density measuring device is rapid in response speed, high in fault-tolerance, capable of accurately measuring critical heat flux density in real time and predicting departure nucleate boiling, and used for guaranteeing the safe operation of the reactor.
Description
Technical field
The utility model relates to the nuclear engineering field, particularly relates to a kind of critical heat flux density measurement mechanism.
Background technology
For solving the human energy demand that increases day by day, nuclear energy is by increasing use.When utilizing nuclear energy, safety problem can not be ignored.Critical heat flux density (Critical Heat Flux is abbreviated as CHF) is one of critical limitation property parameter of guaranteeing heat-exchange system safety.And, relate to a plurality of ambits such as nuclear reactor engineering, Engineering Thermophysics, safety engineering, nonlinear dynamic system, nonequilibrium thermodynamics to the research of CHF.Be necessary heat-exchange system is regarded as a power system, from the most basic mass-energy transitive relation formula, around its nonlinear characteristic; The rule that the announcement accident takes place; Utilize artificial intelligence technology then, comprehensive multiple correlative factor forms the relevant monitoring method of its security.And reactor CHF phenomenon has polytrope.In the CHF forming process, each physical quantity shows certain rules property on the whole, and the instantaneous value of a certain physical quantity or a plurality of physical quantitys often shows certain randomness.Physical dimension and physical propertys such as the shape of rod, size, cooling medium degree of supercooling, flow velocity, composition, multiple factors such as cluster screen work all have influence on CHF.
At present, people have carried out a large amount of experiments and research to the Forecasting Methodology of CHF, obtain a lot of rule-of-thumb relations, such as the W-3 experimental formula.But these rule-of-thumb relations lack the Physical Mechanism model supports, and range of application is narrow.The model that is used for the Safety Analysis System of some reactors depends on the mechanism of controlling CHF, and mechanism has very strong nonlinear relationship with the relative influence parameter often.In the reactor transient process, operational factor constantly changes, the measurement of CHF need set up coupling Different Effects parameter the NONLINEAR CALCULATION model.
Summary of the invention
The technical matters that the utility model solves provides that a kind of error rate is low, reaction velocity fast, the measurement mechanism of the CHF of applied range.
For solving the problems of the technologies described above; The technical scheme that the utility model adopts is: a kind of intelligent critical heat flux density measurement mechanism; Comprise: probe unit, through the various parameters of various sensors and detector measurement nuclear reactor, and change relevant parameter into electric signal output; Input block connects sniffer, deposits the electric signal of sniffer in pretreatment unit; Pretreatment unit screens data-signal, reject some wrong data after, deposit the critical heat flux density database in; Computing unit calculates corresponding critical heat flux density value in real time according to the measurement data after the pretreatment unit screening; The self study unit carries out computing to the data in the critical heat flux density database, according to the continuous corrected Calculation model of error minimum principle; Feedback unit, the result returns to computing unit with the self study process; Output unit shows the critical heat flux density result of computing unit gained.
Further, said sniffer comprises pressure detector, mass rate detector, steam quality detector and hygrosensor.
As one of improvement ground, said storage unit, pretreatment unit, computing unit, feedback unit and self study unit can be integrated in the computing machine.
As one of improvement ground, said critical heat flux density database storing has the data of critical heat flux density question blank, the measurement data of runtime system and the data that self study obtains.
As one of improvement ground, said self study unit adopts the support vector machine technology, has the function of inquiry, data analysis, Knowledge Discovery simultaneously.Feedback data in the database is carried out self study and data are carried out Knowledge Discovery, constantly revise sample set and computation model.
As one of improving ground, said computation model is trained critical heat flux density and measurable relative influence parameter thereof through the support vector machine technology and is obtained, and can set up the nonlinear relationship between critical heat flux density and the relative influence parameter well.These influence parameter and comprise pressure, mass rate, steam quality, temperature etc.
Compared with prior art, beneficial effect is:
(1) utilization support vector machine technology can be realized the real-time computation model of CHF, and the Nonlinear Mapping of output bounded to constitute the NONLINEAR CALCULATION model of describing CHF, has the height fault-tolerance;
(2) intelligent critical heat flux density measuring system provided by the invention can with the computer system parallel processing, travelling speed is fast, the reaction time is short;
(3) physical quantity of actual detection when CHF produces has very big variability, and this just requires mode identification procedure to have stronger fuzzy analogy, identification and fault-tolerant ability.Fuzzy theory, support vector machine technology have sample self-learning capability, high fault tolerance, are to realize complicated mode classification and the effective tool of debating knowledge.With combinations such as fuzzy logic theory, SVMs and above-mentioned CHF theory and basic datas, form real intelligent predicting technology.Database that this device is set up and computing unit thereof have self-learning capability preferably, become intelligent CHF measuring system;
(4) this device adopts fuzzy Judgment, can avoid the mistake brought because of the difference of differential responses heap CHF state and variation, increases its versatility.
Description of drawings
Fig. 1 is the structural representation of the utility model.
Embodiment
The utility model is intelligent critical heat flux density measurement mechanism, and in conjunction with Fig. 1, the utility model comprises: probe unit, through the various parameters of various sensors and detector measurement nuclear reactor, and change relevant parameter into electric signal output; Input block connects sniffer, deposits the electric signal of sniffer in pretreatment unit; Pretreatment unit screens data-signal, reject some wrong data after, deposit the critical heat flux density database in; Computing unit calculates corresponding critical heat flux density value in real time according to the measurement data after the pretreatment unit screening; The self study unit carries out computing to the data in the critical heat flux density database, according to the continuous corrected Calculation model of error minimum principle; Feedback unit, the result returns to computing unit with the self study process; Output unit shows the critical heat flux density result of computing unit gained.Wherein sniffer comprises pressure detector, mass rate detector, steam quality detector and hygrosensor.
The performing step of the critical heat flux density measurement mechanism of the utility model is following: detector is surveyed data such as the pressure P obtain, mass rate G, steam quality x, temperature T and converted to electric signal by probe unit; Deposit pretreatment unit in through input block; By pretreatment unit data are carried out pre-service; Reject some wrong data, when depositing data in the critical heat flux density database, calculate corresponding critical heat flux density value in real time according to measurement data by computing unit; The critical heat flux density result that at last will calculate gained by output unit shows.The self study unit carries out self study to the data in the critical heat flux density database, according to the continuous corrected Calculation model of error minimum principle; Feedback unit, the result returns to computing unit with the self study process.Wherein the error minimum principle is meant that square error is minimum.The computing unit that the present invention uses the non-linear mechanism of CHF, fuzzy theory and support vector machine technology to combine and set up; Have the ability of self study and the process of Knowledge Discovery; Obtain the high training sample set of reliable fault-tolerance; Constantly the oneself replenishes and is perfect, helps to improve CHF and measures intelligentized degree, improves accuracy, promptness and the reliability of measuring greatly.
Claims (1)
1. an intelligent critical heat flux density measurement mechanism is characterized in that, comprising: probe unit, through the various parameters of various sensors and detector measurement nuclear reactor, and change relevant parameter into electric signal output; Input block connects sniffer, deposits the electric signal of sniffer in processing unit; Processing unit is handled, is calculated data-signal; Output unit shows the critical heat flux density result of processing unit gained; Wherein, said probe unit comprises pressure detector, mass rate detector, steam quality detector and hygrosensor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011205177419U CN202487187U (en) | 2011-12-13 | 2011-12-13 | Intelligent critical heat flux density measuring device |
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| Application Number | Priority Date | Filing Date | Title |
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| CN2011205177419U CN202487187U (en) | 2011-12-13 | 2011-12-13 | Intelligent critical heat flux density measuring device |
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| CN202487187U true CN202487187U (en) | 2012-10-10 |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103928065A (en) * | 2014-04-17 | 2014-07-16 | 中国人民解放军陆军军官学院 | Reactor internal component critical heat flux real-time monitoring method based on sonic sensor |
| CN106055850A (en) * | 2016-07-18 | 2016-10-26 | 西安交通大学 | Method for acquiring departure from nucleate boiling type critical heat flux density |
| CN109285612A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of density measuring equipment |
| CN109284517A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of hot-fluid monitoring system |
| CN109285611A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of nuclear reactor early warning system |
| CN109284516A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of vector machine system |
| CN109283356A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of flow rate measuring device |
| CN110633454A (en) * | 2019-09-19 | 2019-12-31 | 中国核动力研究设计院 | CHF relational DNBR limit value statistical determination method based on correction method |
| CN110727920A (en) * | 2019-09-19 | 2020-01-24 | 中国核动力研究设计院 | CHF relational DNBR limit value statistical determination method based on grouping method |
| CN110751173A (en) * | 2019-09-10 | 2020-02-04 | 西安工程大学 | Critical heat flux density prediction method based on deep learning support vector machine |
-
2011
- 2011-12-13 CN CN2011205177419U patent/CN202487187U/en not_active Expired - Fee Related
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103928065A (en) * | 2014-04-17 | 2014-07-16 | 中国人民解放军陆军军官学院 | Reactor internal component critical heat flux real-time monitoring method based on sonic sensor |
| CN103928065B (en) * | 2014-04-17 | 2016-08-17 | 中国人民解放军陆军军官学院 | A kind of reactor inner part critical heat flux method of real-time based on sonic sensor |
| CN106055850A (en) * | 2016-07-18 | 2016-10-26 | 西安交通大学 | Method for acquiring departure from nucleate boiling type critical heat flux density |
| CN106055850B (en) * | 2016-07-18 | 2019-01-08 | 西安交通大学 | A method of obtaining departure nucleate boiling type critical heat flux density |
| CN109285611A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of nuclear reactor early warning system |
| CN109284517A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of hot-fluid monitoring system |
| CN109285612A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of density measuring equipment |
| CN109284516A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of vector machine system |
| CN109283356A (en) * | 2017-07-22 | 2019-01-29 | 周尧 | A kind of flow rate measuring device |
| CN110751173A (en) * | 2019-09-10 | 2020-02-04 | 西安工程大学 | Critical heat flux density prediction method based on deep learning support vector machine |
| CN110633454A (en) * | 2019-09-19 | 2019-12-31 | 中国核动力研究设计院 | CHF relational DNBR limit value statistical determination method based on correction method |
| CN110727920A (en) * | 2019-09-19 | 2020-01-24 | 中国核动力研究设计院 | CHF relational DNBR limit value statistical determination method based on grouping method |
| CN110727920B (en) * | 2019-09-19 | 2022-08-19 | 中国核动力研究设计院 | CHF relational DNBR limit value statistical determination method based on grouping method |
| CN110633454B (en) * | 2019-09-19 | 2022-10-21 | 中国核动力研究设计院 | CHF relational DNBR limit value statistical determination method based on correction method |
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| C14 | Grant of patent or utility model | ||
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Granted publication date: 20121010 Termination date: 20181213 |
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| CF01 | Termination of patent right due to non-payment of annual fee |