CN-121997709-A - Supercritical water reactor fuel bundle circumferential heat exchange coefficient analysis method and system

CN121997709ACN 121997709 ACN121997709 ACN 121997709ACN-121997709-A

Abstract

The invention provides a method and a system for analyzing circumferential heat exchange coefficients of fuel bundles of supercritical water, which relate to the technical field of supercritical water transient spraying prediction, and can be used for determining specific influence weights of non-linear influence factors of circumferential heat exchange non-uniformity of fuel bundles in a compact grid structure under the working condition of the supercritical water reactor in the vicinity of a quasi-critical line with the physical property changed severely by utilizing excellent prediction performance of a reverse propagation neural network on a non-linear relation, writing a known heat exchange coefficient empirical formula into a weight result obtained by the reverse propagation neural network, and programming a new empirical formula into a sub-channel program to realize more accurate and fine three-dimensional circumferential temperature distribution result in the bundles, thereby providing reference for the optimal design of the supercritical water reactor core structure and improving the economy and safety of the supercritical water reactor.

Inventors

LIU XIAOJING
FENG CHAOPENG
SONG MEIQI

Assignees

上海交通大学

Dates

Publication Date: 20260508
Application Date: 20251223

Claims (10)

1. A supercritical water reactor fuel bundle circumferential heat exchange coefficient analysis method is characterized by comprising the following steps: acquiring experimental data of circumferential temperature distribution of a supercritical water reactor fuel rod bundle and a circumferential heat exchange coefficient empirical relation, and programming the circumferential heat exchange coefficient empirical relation into a subchannel program; carrying out thermal hydraulic analysis on the tight grid structure bar bundles according to the subchannel program, and extracting dimensionless numbers affecting local heat exchange coefficients in the circumferential heat exchange coefficient empirical relationship; constructing a back propagation neural network, and training the back propagation neural network according to the experimental data; verifying the trained back propagation neural network, and obtaining the dimensionless number weight based on the trained back propagation neural network if the requirement is met; substituting the weight of the dimensionless number into the empirical relation of the circumferential heat exchange coefficient to obtain a new empirical relation, and compiling the new empirical relation into the subchannel program; determining the circumferential temperature distribution of the supercritical water reactor fuel bundles based on the sub-channel program.
2. The method of claim 1, wherein the dimensionless number comprises a grating diameter ratio, a prandtl number, a ratio of cladding to fluid thermal conductivity.
3. The method of claim 1, wherein said programming said circumferential heat exchange coefficient empirical relationship into a sub-channel program comprises: and establishing a calling relation between a subprogram and a subprogram for solving the three-dimensional temperature field, wherein the subprogram comprises the empirical relation of the circumferential heat exchange coefficients.
4. The method of claim 1, wherein the constructing a back propagation neural network, training the back propagation neural network based on the experimental data, comprises: Initializing the dimensionless number weight, performing forward propagation, and calculating network output; the loss function is calculated to measure the difference between the predicted value and the actual value, and the weight and bias of the network are updated through a back propagation algorithm.
5. The method of claim 1, wherein said validating the trained back propagation neural network comprises: Evaluating the performance of the back propagation neural network after training using the experimental data; model parameters and structures are adjusted based on the evaluation results and a regularization technique is used to prevent overfitting.
6. The method of claim 1, wherein the empirical relationship of the circumferential heat transfer coefficients is as follows: 。 Wherein, the Pr is the Plandter number and is the local heat exchange coefficient, Is the ratio of the gate diameter to the gate diameter, Is the ratio of the thermal conductivity of the envelope to the fluid.
7. The method of claim 1, wherein variable passing between the subchannel program and the subroutine comprises: The subprogram obtains the heat exchange coefficient of the faced subchannel and the temperature and physical properties of the fluid from the subchannel program; The sub-program returns the heat productivity of the fuel rod to the sub-channel program, and the fuel rod is arranged in the three-dimensional fuel model, wherein the part of the fuel rod J facing the sub-channel N is divided into K grids along the circumferential direction, and the height of a grid in a certain axial direction is The heat obtained from fuel rod J by sub-channel N at the axial position of (a) is: Wherein, the A circumferential length of a kth mesh of the cladding surface; when the subchannel program needs to call the surface temperature of the cladding, the wall temperature of the part of the fuel rod J facing the subchannel N is as follows: 。
8. the method of claim 1, wherein the subchannel program is a COBRA-IV subchannel model program.
9. A supercritical water reactor fuel bundle circumferential heat exchange coefficient analysis system, comprising: the preparation module is used for acquiring experimental data of circumferential temperature distribution of the supercritical water reactor fuel rod bundles and a circumferential heat exchange coefficient empirical relation, and programming the circumferential heat exchange coefficient empirical relation into a subchannel program; the analysis module is used for carrying out thermal hydraulic analysis on the tight grating structure bar bundles according to the subchannel program, and extracting dimensionless numbers affecting local heat exchange coefficients in the circumferential heat exchange coefficient empirical relation; The model training module is used for constructing a back propagation neural network and training the back propagation neural network according to the experimental data; The verification module is used for verifying the trained back propagation neural network, and obtaining the dimensionless number weight based on the trained back propagation neural network if the requirement is met; the compiling module is used for substituting the weight of the dimensionless number into the circumferential heat exchange coefficient empirical relation to obtain a new empirical relation and compiling the new empirical relation into the subchannel program; and the temperature distribution determining module is used for determining the circumferential temperature distribution of the supercritical water reactor fuel rod bundle based on the subchannel program.
10. The system of claim 9, wherein the dimensionless number comprises a grating radius ratio, a prandtl number, a ratio of cladding to fluid thermal conductivity.

Description

Supercritical water reactor fuel bundle circumferential heat exchange coefficient analysis method and system Technical Field The invention relates to the technical field of supercritical water reactor temperature distribution, in particular to a supercritical water reactor fuel bar bundle circumferential heat exchange coefficient analysis method and system. Background The design of the high-temperature and high-pressure operation condition of the supercritical water reactor (Supercritical Water Reactor, SCWR) as a fourth generation nuclear energy system with development potential brings high efficiency and simultaneously generates a plurality of technical difficulties to be solved. The heat exchange condition prediction analysis problem of the compact grid structure in the supercritical water reactor is closely related to the safety and economy of the whole system, and is one of the key technical problems for further perfecting the design of the supercritical water reactor. In the conceptual design of supercritical water reactors, tight grid structural material elements in conventional light water reactors are inherited. The compact grid structure fuel element has the advantages of improving the fuel conversion ratio and the like, but also has obvious temperature distribution non-uniformity in the circumferential direction in the compact grid bar bundles. When the supercritical water reactor runs near the pseudo-critical line, the physical property change of water is very severe, and the heat exchange coefficient distribution in the circumferential direction of the fuel rod is directly influenced, so that the temperature distribution of fuel cladding is influenced, and the overall safety of the reactor core is further influenced. In the prior art, the problem of heat exchange coefficient distribution in the tight grating bar bundle is solved, a two-dimensional simple sub-channel model is often adopted for estimation, the circumferential heat exchange coefficient distribution condition of the fuel bar bundle cannot be generated, and accurate three-dimensional temperature distribution estimation in the bar bundle is difficult to realize. When the existing reactor thermal hydraulic program is used for processing the temperature of the cladding of the fuel rod, a two-dimensional model is generally adopted to ignore the circumferential change of the temperature distribution of the fuel rod, in the actual situation, the circumferential heat exchange coefficient of the cladding has non-uniformity, and the non-uniformity is ignored when the grating diameter ratio is larger than 1.3, so that larger calculation errors are caused, and the highest value of the temperature of the cladding cannot be accurately estimated. In particular, in the case of supercritical water reactors operating in the vicinity of a critical line, conventional computational analysis methods are no longer suitable. Disclosure of Invention In order to solve the problems, the embodiment of the invention provides a circumferential heat exchange coefficient analysis method for a supercritical water reactor fuel bundle, which comprises the steps of obtaining experimental data of circumferential temperature distribution of the supercritical water reactor fuel bundle and a circumferential heat exchange coefficient empirical relation, compiling the circumferential heat exchange coefficient empirical relation into a subchannel program, carrying out thermal hydraulic analysis on a tight grid structure bundle according to the subchannel program, extracting dimensionless numbers affecting local heat exchange coefficients in the circumferential heat exchange coefficient empirical relation, constructing a counter-propagation neural network, training the counter-propagation neural network according to the experimental data, verifying the trained counter-propagation neural network, obtaining weights of the dimensionless numbers based on the trained counter-propagation neural network if the requirements are met, substituting the weights of the dimensionless numbers into the circumferential heat exchange coefficient empirical relation to obtain a new empirical relation, compiling the subchannel program, and determining the circumferential temperature distribution of the supercritical water reactor fuel bundle based on the subchannel program. According to the supercritical water reactor fuel bundle circumferential heat exchange coefficient analysis method provided by the embodiment of the invention, by utilizing the excellent prediction performance of the BPNN on the nonlinear relation, the specific influence weight of the non-linear influence factors of the circumferential heat exchange non-uniformity of the fuel bundle in the compact grid structure can be definitely determined under the operation condition of the SCWR stack type near the quasi-critical line with the severe change of physical properties, the weight result obtained by the BPNN can be written into