CN113312727B - Efficient online design optimization method for nuclear power station valve - Google Patents
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Abstract
The invention provides an efficient online design optimization scheme for a nuclear power station valve, which comprises the following steps: establishing a resource library of CFD calculation results of typical nuclear power station valves; developing a valve characteristic regression function resource library; optimizing the on-line design of the valve; according to the invention, the valve characteristics can be rapidly calculated by calling the valve regression function relation in the one-dimensional flow network; a CFD operation flow encapsulation module is established, three-dimensional modeling, grid division and turbulence model selection are not needed, and CFD calculation can be updated by simply modifying structural parameters, so that the working difficulty of users is greatly reduced, and the working efficiency is improved; the coupling calculation of the one-dimensional flow network and the three-dimensional valve flow field can be carried out, and the valve on-line optimization design is realized.
Description
Technical Field
The invention belongs to the technical field of valve design optimization of fluid mechanics, and particularly relates to a high-efficiency online design optimization method for a nuclear power station valve.
Background
In a nuclear power plant fluid network system (hereinafter referred to as a flow network), a valve is an important component device, and the most significant difference from a common valve is that: not only the working environment is exposed to the radiation, but also the requirements of various operating conditions of the nuclear power station, such as disturbance conditions, emergency conditions, accident conditions, experimental conditions and the like, are also met in the aspect of operating characteristics, which leads to very difficult experiment of the valve of the nuclear power station. The nuclear power station valve controls key elements such as pressure, flow, flowing direction and the like of fluid, has multiple functions such as adjustment, non-return, cutoff, safety, flow distribution, steam exhaust and the like, is a core control component in a flow network conveying system, and therefore, the design optimization research of the valve has important significance.
For the design optimization of valve elements in the flow network, one-dimensional fluid calculation methods were used in the early days. The method mainly focuses on system scale characteristics of the valve, such as the relationship between the opening degree of the valve and pressure drop and flow. The method is adopted to simulate and calculate the dynamic process of the steam inlet valve opening of a steam turbine in a second loop of a certain nuclear power station from 1 to 0.6, and reflects the transient process of real-time change of parameters such as pressure, flow, enthalpy value, temperature, dryness and the like of a high/low pressure cylinder, a reheater and a high/low pressure heater of the steam turbine along with the valve opening. However, the one-dimensional fluid calculation method is a valve characteristic function obtained based on curve fitting, and when the working condition deviates from the design working condition or is in variable working condition operation, the calculation error is large.
At present, three-dimensional fluid calculation methods are mostly adopted for the design optimization of valve elements. The method is mainly used for analyzing the influence of valve inlet and outlet parameters, internal structure parameters and the like on the distribution characteristics of the flow field, and further achieves the purposes of design optimization and partial replacement experiments. The method comprises the following steps of (1) taking a certain type of regulating valve as an object, introducing CFD (computational fluid dynamics) and CAD (computer-aided design) software to carry out numerical simulation on a flow field in a valve, and rapidly and accurately calculating pressure drop loss, wherein the document [ good pottery, Caidiong, Severe mine ] three-dimensional numerical simulation and experimental research [ J ]. engineering thermophysics, 2003(01):63-65 ]; the method is characterized in that CFD software CFX is adopted in a document [ Belgium, Jiangshan, Jiangsheng, numerical research on flow field characteristics of a large nuclear turbine high-pressure regulating valve [ J ]. Oriental electrical comment 2016,30(02):78-84], and the influence of structures of an upper silencing groove, a valve disc cavity and a valve casing splitter plate on the valve seat of the certain nuclear turbine high-pressure regulating valve on the flow field characteristics inside the valve is analyzed. However, the flow field calculation of the CFD software is very complicated, the calculation time is long, a large amount of computer resources are occupied, and the variable working condition of the valve cannot be responded to in time.
In addition to theoretical analysis and numerical calculations, optimization of valve element design based on physical experimentation remains the most conservative approach. According to different action forms on the valve, the method can be divided into a hydraulic pressure experiment, a mechanical experiment, a thermal fatigue experiment, a chemical experiment, a reliability experiment and the like; the experimental conditions can be divided into bench experiments and running experiments. Obviously, any kind of physical experiment will face very complicated operation flow and very high experiment cost.
Disclosure of Invention
Aiming at the defects of the method, the invention aims to provide an efficient method for optimizing the on-line design of the nuclear power station valve, which is beneficial supplement of valve design optimization and operation experiments.
The system structure schematic diagram of the method is shown in figure 1: firstly, establishing a packaged CFD calculation result resource library aiming at different types of valves; developing a valve characteristic regression function resource library by using a regression analysis method according to the CFD calculation result, and calling the regression function resource library according to the valve type in one-dimensional flow network simulation calculation when in use so as to achieve the purpose of quick calculation; finally, the operator can modify variables in the regression function to realize the on-line design and optimization of the valve performance; when the regression function calculation result does not meet the actual condition, the system supports an operator to directly call a CFD calculation result resource library to perform one-dimensional-three-dimensional coupling calculation of the flow network system and the valve so as to obtain accurate valve characteristic data.
The purpose of the invention is realized as follows:
firstly, establishing a CFD calculation result resource library of typical nuclear power station valves
(1) Selecting commonly used nuclear-grade valves such as a pressure reducing valve, a check valve, a protection valve, a gate valve and the like as research objects;
(2) for each type of valve, M main structural parameters are selected, and K is taken as each structural parameteri(i is more than or equal to 1 and less than or equal to M, and K is more than or equal to 2) different values are obtained by permutation and combination
Assembling valve structure combination data;
(3) using CFD software to perform the following operations on each of the N sets of valve objects described in (2): three-dimensional modeling, dividing a grid and setting initial parameters of a solver; running CFD software to carry out N times of calculation to obtain N groups of characteristic data of the distribution of the internal flow field of the valve;
(4) performing three-dimensional packaging on the calculation of the valve CFD, setting an external interface, and establishing a valve CFD calculation resource library; when the valve is used in the later period, only individual main parameters are required to be modified, the three-dimensional calculation of the valve can be updated, and modeling and grid division are not required to be carried out again.
Second, developing a regression function resource library of valve characteristics
(1) For each type of valve, performing stepwise regression analysis according to the characteristic data of N groups of flow field distribution calculated in the step (3) to obtain L (L is less than or equal to M) key structure parameters with strong sensitivity to the valve outlet data, and further obtaining the matched key structure parameters
The outlet data of the group valves is used as a dependent variable y;
(2) taking L structural parameters in N' groups of valves as independent variables xi(i is more than or equal to 1 and less than or equal to L), analyzing the relation between the dependent variable and the independent variable, and selecting a proper regression model;
(3) and performing regression analysis calculation on the N' groups of valve data to obtain a characteristic regression function of the valve, and forming a valve characteristic regression function resource library.
Third, on-line design optimization of valve
(1) Establishing a one-dimensional flow network where the valve is located, setting boundary parameters, and presetting a resistance coefficient of a valve calculation object;
(2) selecting a regression function relation matched with the valve characteristic regression function from a regression function resource library according to the type of the valve, and inputting an initial valve structure parameter;
(3) one-dimensional flow network calculation is carried out, so that inlet and outlet parameters of the valve are quickly obtained;
(4) and when the calculation result cannot meet the requirement, performing one-dimensional-three-dimensional coupled valve design optimization:
modifying structural parameters of the valve, directly calling a packaging module in a valve CFD calculation result resource library, updating three-dimensional calculation, and obtaining outlet section data and internal flow field distribution characteristics of the valve;
comparing with the one-dimensional calculation result obtained in the step (3), if the error is larger than a preset value, replacing the one-dimensional calculation result with outlet section data obtained through three-dimensional calculation, and then updating the valve resistance coefficient according to the following formula:
in the formula: zeta is the valve resistance coefficient; g is the acceleration of gravity, m/s2(ii) a Delta P is the valve pressure drop, MPa, calculated by three-dimensional fluid software; rho is the fluid density, kg/m3(ii) a v is the fluid flow velocity, m/s.
Thirdly, carrying out one-dimensional flow network calculation again by using the new valve resistance coefficient, and repeating the steps from the first step to the second step until the relative error is smaller than a preset value, thereby obtaining the final valve outlet section parameter;
the data exchange method of the one-dimensional-three-dimensional coupling calculation is shown in fig. 2, and the convergence determination method is shown in fig. 3.
Compared with the prior art, the invention has the beneficial effects that:
(1) the rapid calculation of the valve characteristics can be realized by calling a valve regression function relation in a one-dimensional flow network;
(2) a CFD operation flow encapsulation module is established, three-dimensional modeling, grid division and turbulence model selection are not needed, and CFD calculation can be updated by simply modifying structural parameters, so that the working difficulty of users is greatly reduced, and the working efficiency is improved;
(3) the coupling calculation of the one-dimensional flow network and the three-dimensional valve flow field can be carried out, and the valve on-line optimization design is realized.
Drawings
FIG. 1 is a schematic diagram of a system for optimizing valve design;
FIG. 2 is a diagram of a data exchange method for the one-dimensional-three-dimensional coupling method;
FIG. 3 is a diagram of a convergence determination method for the one-dimensional to three-dimensional coupling method;
FIG. 4 is an overall flow diagram of a valve design optimization method;
FIG. 5 is a flow diagram of valve characteristic regression function repository development;
FIG. 6a comparison of one-dimensional and three-dimensional pressure drop calculation accuracy;
FIG. 6b one-dimensional vs. three-dimensional calculated velocity;
FIG. 7a shows an optimization calculation graph of pressure drop calculation (opening 0.6, hole depth 0.045 m);
FIG. 7b shows an optimization calculation graph for pressure drop calculation (valve opening 0.6, pore diameter 0.023 m).
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The flow chart of the specific optimization process is shown in fig. 4:
firstly, establishing a CFD calculation result resource library
(1) 4 structural parameters in the pressure reducing valve are preliminarily selected: the valve opening degree, the aperture of the throttling hole plate in the valve, the hole depth of the throttling hole plate in the valve and the distance between the throttling hole plate in the valve and the valve outlet are calculated. 4, 4 × 3 × 3 × 3 — 108 valve structure combination data groups can be obtained through permutation and combination; the selected parameters and values are shown in table 1:
TABLE 1 structural parameters of the valve
(2) Modeling the 108 groups of valve data by using Ansys Workbench software (a Geometry module), grid division (a Mesh module) and solver setting (a Fluent module), calculating for 108 times to obtain 108 groups of valve outlet pressures, and further calculating 108 groups of valve pressure drops;
(3) the modeling, the grid and the solver arrangement of the pressure reducing valve are subjected to three-dimensional packaging, an external modification interface is designed, and a user can complete the three-dimensional calculation of the valve under a new working condition through simple updating without repeating the work;
(4) and matching and storing 108 groups of calculation results and the three-dimensional package into a warehouse, and building a CFD calculation result resource library.
Second, developing a regression function resource library of valve characteristics
(1) Performing stepwise regression analysis on 108 groups of calculation results to obtain 3 key structure parameters with strong sensitivity to valve outlet data (excluding 'orifice plate distance'), so as to obtain 4 × 3 × 3 ═ 36 groups of valve pressure drops;
(2) analyzing the relation between the dependent variable and the independent variable, and finding that the relation obeys an exponential model, so that the pressure drop of 36 groups of valves is taken as logarithmic pressure drop and is taken as the dependent variable y, MPa; the value of each structural parameter is taken as an independent variable xi(x1Representing the valve opening; x is the number of2Represents the orifice plate aperture in the valve; x is the number of3Representing the hole depth of the throttle orifice plate in the valve), and performing regression analysis;
(3) the valve pressure drop characteristic regression function obtained by calculation and arrangement is as follows:
(4) and storing the regression function into a warehouse, and building a valve pressure drop characteristic regression function resource library.
The process of developing the regression function library for valve characteristics is shown in FIG. 5.
In order to prove the accuracy of the method, any parameter combination different from the 36 parameter combinations is selected, 11 parameter combinations are counted, and the specific data are shown in table 2:
TABLE 2 parameter combinations
Calculating the 11 groups of data according to the regression function in the step (3) to obtain 11 groups of valve pressure drops;
calculating 11 groups of data according to the CFD method in the step one, and obtaining 11 groups of valve pressure drops;
comparing the errors of the two, as shown in fig. 6a, it is shown that the regression function is basically consistent with the calculation result of the CFD software, and the requirement of high precision can be met; when the two calculations are compared, as shown in fig. 6b, it is shown that the calculation time of the regression function is much shorter than that of the CFD software, and the requirement of high efficiency can be met.
Third, on-line design optimization of valve
By calling a pressure drop characteristic regression function of the pressure reducing valve, the pressure drop loss of the pressure reducing valve with the aperture of 0.016 m-0.030 m and the aperture depth of 0.03 m-0.06 m can be randomly calculated without additional modeling calculation;
(1) inputting initial boundary parameters of a flow network, operating one-dimensional fluid calculation software, and obtaining initial inlet data and outlet data of a valve;
(2) respectively modifying two independent variables of the aperture and the hole depth of the human-computer interaction interface: continuously inputting a plurality of groups of values of which the aperture is between 0.016 and 0.030 under the condition of keeping the opening and the depth unchanged; continuously inputting a plurality of groups of values with the hole depth between 0.03 and 0.06 under the condition of keeping the opening and the aperture unchanged;
(3) running one-dimensional fluid calculation software after input modification is finished every time, and calculating the pressure drop of the pressure reducing valve at the moment according to the regression function in S2-3;
(4) as shown in fig. 7 a: the throttling loss on the pressure reducing valve can be effectively reduced by increasing the aperture of the throttling orifice plate; as shown in fig. 7 b: the throttling loss on the pressure reducing valve can be effectively reduced by shortening the hole depth of the throttling orifice plate.
Claims (1)
1. An efficient online design optimization method for a nuclear power station valve is characterized by comprising the following steps:
the method comprises the following steps: establishing a resource library of CFD calculation results of typical nuclear power station valves;
1.1 selecting a commonly used nuclear-grade valve including a pressure reducing valve, a check valve, a protection valve and a gate valve as research objects;
1.2 for each type of valve, M main structural parameters are selected, each taking Ki(i is more than or equal to 1 and less than or equal to M, and K is more than or equal to 2) different values are obtained by permutation and combination
Assembling valve structure combination data;
1.3 Using CFD software, the N sets of valve objects described in 1.2 are all operated as follows: three-dimensional modeling, dividing a grid and setting initial parameters of a solver; running CFD software to carry out N times of calculation to obtain N groups of characteristic data of the distribution of the internal flow field of the valve;
1.4, three-dimensional packaging is carried out on the calculation of the valve CFD, an external interface is set, and a valve CFD calculation resource library is established; when the valve is used in the later period, the three-dimensional calculation of the valve can be updated only by modifying individual main parameters without modeling and dividing grids again;
step two: developing a valve characteristic regression function resource library;
2.1 for each type of valve, carrying out stepwise regression analysis according to the characteristic data of N groups of flow field distribution calculated in 1.3 to obtain L (L is less than or equal to M) key structure parameters with strong sensitivity to the valve outlet data, and further obtaining the matched key structure parameters
The outlet data of the group valves is used as a dependent variable y;
2.2 taking L structural parameters in N' valve groups as independent variables xi(i is more than or equal to 1 and less than or equal to L), analyzing the relation between the dependent variable and the independent variable, and selecting a proper regression model;
2.3, carrying out regression analysis calculation on the N' valve data to obtain a characteristic regression function of the valve, and forming a valve characteristic regression function resource library;
step three: optimizing the on-line design of the valve;
3.1 establishing a one-dimensional flow network where the valve is located, setting boundary parameters, and presetting a resistance coefficient of a valve calculation object;
3.2 according to the valve type, selecting a regression function relation matched with the valve characteristic regression function from a regression function resource library, and inputting an initial valve structure parameter;
3.3, one-dimensional flow network calculation is carried out, so that inlet and outlet parameters of the valve are quickly obtained;
3.4 when the calculation result can not satisfy the pore diameter of 0.016 m-0.030 m and the pore depth of 0.03 m-0.06 m, carrying out one-dimensional-three-dimensional coupling valve design optimization:
modifying structural parameters of the valve, directly calling a packaging module in a valve CFD calculation result resource library, updating three-dimensional calculation, and obtaining outlet section data and internal flow field distribution characteristics of the valve;
comparing with the one-dimensional calculation result obtained in the step 3.3, if the error is larger than a preset value, replacing the one-dimensional calculation result with outlet section data obtained through three-dimensional calculation, and then updating the valve resistance coefficient according to the following formula:
in the formula: zeta is the valve resistance coefficient; g is the acceleration of gravity, m/s2(ii) a Delta P is the valve pressure drop, MPa, calculated by three-dimensional fluid software; rho is the fluid density, kg/m3(ii) a v is the fluid flow velocity, m/s;
and thirdly, carrying out one-dimensional flow network calculation again by using the new valve resistance coefficient, and repeating the steps from the first step to the second step until the relative error is smaller than a preset value, thereby obtaining the final valve outlet section parameter.
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