Rapid prediction method for deposition state in condenser pipe of nuclear power plant

Abstract

The invention discloses a rapid prediction method for a deposition state in a nuclear power plant condenser pipe, and the method comprises the following steps: 1, carrying out random sampling on deposition characteristic parameters in the pipe in a condenser cooling water pipe bundle enveloping region range, including a foreign matter deposition position and an equivalent blocking radius, and obtaining a plurality of groups of deposition simulation working conditions in the condenser pipe; 2, three-dimensional steady-state simulation is carried out based on each group of working conditions; 3, performing post-processing to obtain temperature monitoring point data at the secondary side M of the condenser under each group of working conditions; 4, building a back propagation neural network; 5, taking the post-processed data of the M temperature monitoring points as input, taking each group of in-pipe deposition simulation working condition characteristic parameters as target output, and training a neural network; 6, inputting new data measured by temperature monitoring points at all positions of the real condenser in real time into the trained neural network, and quickly predicting the deposition characteristic parameters in the current condenser pipe; the method can rapidly predict the deposition state in the nuclear power plant condenser pipe in the operation state, provides reference for dredging and cleaning the condenser blockage, greatly saves the operation and maintenance cost, and is of great significance to the safety operation monitoring of a nuclear power plant.

Description

Technical Field

[0001] The invention belongs to the technical field of virtual real-time simulation of nuclear power plant equipment, and in particular relates to a method for quickly predicting the deposition state in a condenser tube of a nuclear power plant. Background Art

[0002] The condenser of a nuclear power plant is a shell-and-tube heat exchanger that uses seawater as a coolant, and the deposition phenomenon in the tube is one of the major threats to its normal operation. Since seawater contains a large amount of inorganic salts and microorganisms, it will cause electrochemical corrosion and crystallization scaling when flowing through the heat exchange metal surface, resulting in the continuous thickening of the naturally accumulated scale layer on the inner surface of the cooling water pipe, reducing the fluid flow area, and eventually causing a condenser blockage accident; at the same time, in sea areas with more impurities or complex ecological environment, nuclear power plants often experience foreign matter deposition and blockage in the condenser cooling water pipe, which has a great impact on the overall safety and heat exchange efficiency of the nuclear power plant. In addition, due to the high uncertainty of the time and area of ​​deposition and blockage, it is greatly affected by the physical properties, water quality and flow rate of seawater, and it is difficult to predict through experience. Regular maintenance based on safety margins often results in time and cost losses. Therefore, it is urgent to develop a method for rapid prediction of the deposition state in the condenser tube of a nuclear power plant based on machine learning methods, and to achieve rapid evaluation of the current state of the condenser of the nuclear power plant based on the instant output signal of the limited distributed thermocouple outside the cooling water pipe, so as to provide a reference for the design, operation and maintenance of the condenser.

[0003] At present, the research on heat exchangers based on artificial neural network structures at home and abroad mainly focuses on the estimation of thermal hydraulic parameters and prediction of operating characteristics of different types of heat exchangers. There is a relative lack of research on the application of artificial neural networks in abnormal states of heat exchangers, especially in the rapid state prediction of heat exchanger accidents. At the same time, the existing research on the operation characteristics of heat exchanger tube deposition mainly simplifies the influence of heat transfer tube blockage on the heat transfer performance of the reactor system by proportionally reducing the heat exchange area or reducing the primary side flow, and pays less attention to the specific location of deposition in the primary tube. The existing calculation model used in nuclear power systems to predict the change of the blockage share of U-tube steam generator heat exchange tubes with operating time focuses on analyzing and statistically analyzing the relationship between the number of faulty heat transfer tubes and time during the operation of nuclear power systems. Although it can accurately predict the growth of the heat exchange tube blockage share with the operating time at the macro level, the local processing strategy is relatively rough and fails to fully consider the impact of different specific two-dimensional locations of blockage on the performance of the heat exchanger; at the same time, the model does not consider the sudden uncertain heat exchange tube blockage accidents caused by external environmental variables, and it is difficult to meet the requirements of tube deposition state prediction and operation monitoring of nuclear power plant condensers in different usage scenarios.

[0004] In summary, the existing nuclear power plant heat exchanger blockage share prediction method is difficult to achieve real-time and rapid prediction of the specific two-dimensional position of deposition in the condenser tube and is difficult to apply in various usage scenarios under complex environmental variables, and cannot meet the needs of providing a reference for the cleaning of deposition in the condenser tube of a nuclear power plant. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method for quickly predicting the deposition state in the condenser tube of a nuclear power plant, which can greatly save the operation and maintenance costs of the condenser during use of the nuclear power plant, and effectively reduce the time and cost spent on regular maintenance based on safety margin.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A method for quickly predicting the deposition state in a condenser tube of a nuclear power plant, characterized in that it comprises the following steps:

[0008] Step 1: Randomly sample the characteristic parameters of the deposition in the tube within the envelope area of ​​the condenser cooling water tube bundle of the nuclear power plant, including the deposition location and equivalent blockage radius, and obtain several groups of condenser tube deposition simulation conditions. The specific steps are as follows:

[0009] Step 1-1: Determine the characteristic parameters describing the deposition state in the cooling water tube of the condenser of the nuclear power plant: the abscissa a of the foreign matter deposition, the ordinate b of the foreign matter deposition, and the equivalent blockage radius c; determine the value range of each parameter within the envelope area of ​​the cooling water tube bundle of the condenser of the nuclear power plant, that is:

[0010] a∈A=[a min , a max ] (1)

[0011] b∈B=[b min , b max ] (2)

[0012] c∈C=[c min , c max ] (3)

[0013] |a|+c∈[0,R](4)

[0014] |b|+c∈[0,R](5)where A, B, and C represent the value range of the abscissa a (unit m) of foreign body deposition, the value range of the ordinate b (unit m) of foreign body deposition, and the value range of the equivalent blockage radius c (unit m), respectively; the subscript min represents the minimum value, and max represents the maximum value; R (unit m) represents the radius of the envelope area of ​​the condenser cooling water tube bundle of a nuclear power plant;

[0015] Step 1-2: Use the random function of Excel software to randomly sample each parameter within the value range of each in-tube deposition characteristic parameter to obtain N groups of in-tube deposition simulation conditions, namely:

[0016] c i =c min +RAND()·(c max -c min ), i=1,2,3...N (6)

[0017] a i =a min +c i +RAND()·(a max -a min -2c i ), i=1,2,3...N (7)

[0018] b i =b min +c i +RAND()·(b max -b min -2c i ), i=1,2,3...N (8)

[0019] Among them, a i , b i , c i They represent the abscissa of foreign body deposition, the ordinate of foreign body deposition, and the equivalent blockage radius of the i-th group of simulation conditions respectively; N represents the total number of simulation conditions for deposition in the condenser tube of a nuclear power plant; RAND() is a random function that takes random values ​​between 0 and 1 and is dimensionless;

[0020] Step 2: Based on the N groups of in-tube deposition simulation conditions sampled in step 1, a UDF file is written in the computational fluid dynamics simulation software ANSYS Fluent to set the characteristic parameters of the deposition state in the cooling water tube of the nuclear power plant condenser, and a three-dimensional steady-state numerical simulation calculation of the nuclear power plant condenser under the N groups of in-tube deposition simulation conditions is carried out; each group of in-tube deposition simulation conditions can obtain the secondary side three-dimensional temperature field distribution of the condenser under the corresponding in-tube deposition state;

[0021] Step 3: Post-process the three-dimensional temperature field distribution on the secondary side of the condenser under each tube deposition condition, and extract the temperature data of the M fixed positions that can characterize the temperature field change. The specific steps are as follows:

[0022] Step 3-1: Import the secondary side temperature field distribution under a typical tube deposition simulation condition into the post-processing software CFD-Post, and use the slicing function to cut K temperature distribution sections inside the secondary side of the nuclear power plant condenser;

[0023] Step 3-2: Select S sections from the K temperature distribution sections where the temperature distribution is severely distorted due to the deposition in the condenser cooling water pipe as temperature monitoring surfaces, and evenly arrange a total of M temperature monitoring points based on each temperature monitoring surface;

[0024] Step 3-3: import N temperature field distributions in sequence, and extract the temperature data of the temperature monitoring point M at the secondary side of the nuclear power plant condenser under each group of tube deposition simulation conditions;

[0025] Step 4: Build a back propagation neural network, including one input layer, E hidden layers and one output layer;

[0026] Step 4-1: Establish an input layer, which contains M neurons, to receive the temperature monitoring point data at M on the secondary side of the condenser obtained by post-processing the i-th group of in-tube deposition simulation conditions, and arrange them into a one-dimensional tensor D0 with a size of 1×M;

[0027] Step 4-2: Establish E hidden layers, each hidden layer is a fully connected layer, each containing F neurons; each layer of neurons only receives the output of the previous layer and outputs it to the next layer, and there is no feedback between layers; the first hidden layer is used to receive the one-dimensional tensor D0, and then outputs it as a one-dimensional tensor D1 after nonlinear mapping through the activation function σ, with a size of 1×F; and so on, the last hidden layer outputs a one-dimensional tensor D of size 1×F E ;

[0028] Step 4-3: Create an output layer to receive the one-dimensional tensor D E , after nonlinear mapping, we get the actual output of the back propagation neural network: a one-dimensional tensor D of size 1×3 E+1 , including the three characteristic parameters a of the deposition condition in the ith tube of the condenser of a nuclear power plant i , b i , c i ;

[0029] Step 5: Take the secondary side temperature monitoring point data of N groups of in-tube deposition simulation conditions as the input of the back propagation neural network, take the characteristic parameters of N groups of in-tube deposition conditions as the target output, and use the actual output D of the neural network as the target output. E+1 With the target output D real The root mean square error between the two is used as the convergence criterion, and the back propagation neural network built in step 4 is trained by the error back propagation algorithm until convergence, and a back propagation neural network suitable for rapid prediction of the deposition state in the condenser tube of the current nuclear power plant is obtained.

[0030] Step 6: Input the M new data measured in real time at the temperature monitoring point M in the condenser of the nuclear power plant into the trained back propagation neural network, and quickly obtain the three characteristic parameters describing the deposition state in the tube under the current operating conditions: the horizontal coordinate a of the foreign matter deposition under the current operating conditions now , the vertical coordinate b of foreign matter deposition under the current operating conditions now And the equivalent blocking radius c under the current operating conditions now , to achieve rapid prediction of the deposition status inside the condenser tubes of nuclear power plants.

[0031] By adopting the rapid prediction method of the present invention, the deposition state in the cooling water pipe of the condenser of a nuclear power plant can be obtained in real time during operation in any complex working environment and under any selected working conditions, and the current operating state of the condenser of the nuclear power plant can be quickly evaluated based on this, providing a reference basis for the regular unblocking and cleaning of condenser blockages. Compared with the operation and maintenance strategy of regular cleaning based on the design safety margin, the overall economy and safety of the nuclear power plant are improved.

[0032] Compared with the prior art, the method of the present invention does not rely on the actual operation data statistics and time experience accumulation of the target condenser, effectively reducing the initial investment cost and time in engineering practice, overcoming the shortcomings of traditional calculation models that are difficult to predict sudden uncertain blockage accidents caused by external environmental variables, and solving the problem that the local foreign matter deposition position is difficult to accurately locate and the affected flow channel range is difficult to quickly evaluate. The present invention combines the advantages of numerical simulation calculation methods, and uses virtual simulation technology to calculate a large number of secondary side limited position temperature data under different tube deposition states of nuclear power plant condensers, simplifying the time-consuming and costly experimental data acquisition process into a simple and repetitive computer simulation. At the same time, the selection of the limited number of secondary side temperature monitoring points meets the requirements of simple and reliable thermocouple layout in engineering practice, and has strong operability in practical applications. The present invention can provide a reference for the design, operation, and maintenance of nuclear power plant condensers in multiple usage scenarios, greatly saving unnecessary resource consumption during use, and is of great significance to the safe operation monitoring of nuclear power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Schematic diagram of the back-propagation neural network built in step 4.

[0034] Figure 2 The figures are comparison diagrams of the actual value of the deposition condition in the condenser tube and the predicted value of the deposition characteristic parameters in the tube by the method of the present invention, wherein (a) is a comparison diagram of the actual value and the predicted value of the horizontal coordinate of the foreign matter deposition in each sample, (b) is a comparison diagram of the actual value and the predicted value of the vertical coordinate of the foreign matter deposition in each sample, and (c) is a comparison diagram of the actual value and the predicted value of the equivalent blockage radius of the foreign matter deposition in each sample.

[0035] Figure 3 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0036] The following combination Figure 3 The flow chart of the present invention shown in the figure takes the rapid prediction of the deposition state in the tube of the shell and tube single-pass seawater condenser of a nuclear power plant as an example to further describe the present invention in detail.

[0037] The invention provides a method for quickly predicting the deposition state in a condenser tube of a nuclear power plant, comprising the following steps:

[0038] Step 1: Randomly sample the characteristic parameters of the deposition in the tube within the envelope area of ​​the cooling water tube bundle of the shell and tube single-flow seawater condenser in the nuclear power plant, including the deposition location and equivalent blockage radius, and obtain several groups of simulation conditions for deposition in the condenser tube. The specific steps are as follows:

[0039] Step 1-1: Determine the characteristic parameters describing the deposition state in the cooling water tube of the condenser of the nuclear power plant: the abscissa a of the foreign matter deposition, the ordinate b of the foreign matter deposition, and the equivalent blockage radius c; determine the value range of each parameter within the envelope area of ​​the cooling water tube bundle of the condenser of the nuclear power plant, that is:

[0040] a∈A=[-0.14, 0.14] (1)

[0041] b∈B=[-0.14, 0.14] (2)

[0042] c∈C=[0.01, 0.03] (3)

[0043] |a|+c∈[0,0.14](4)

[0044] |b|+c∈[0,0.14](5)where A, B, and C represent the value range of the abscissa a (unit m) of foreign body deposition, the value range of the ordinate b (unit m) of foreign body deposition, and the value range of the equivalent blockage radius c (unit m), respectively; the subscript min represents the minimum value, and max represents the maximum value; R (unit m) represents the radius of the envelope area of ​​the condenser cooling water tube bundle of a nuclear power plant;

[0045] Step 1-2: Use the random function of Excel software to randomly sample each parameter within the value range of each in-tube deposition characteristic parameter to obtain 480 sets of in-tube deposition simulation conditions, namely:

[0046] c i =0.01+RAND()·(0.03- i= 348 (6)

[0047] a i =-0.14+c i +RAND()·(0.14+0.14-2c i), i=1,2,3...480 (7)

[0048] b i =-0.14+c i +RAND()·(0.14+0.14-2c i ), i=1,2,3...480 (8)

[0049] Among them, a i , b i , c i They represent the abscissa of foreign body deposition, the ordinate of foreign body deposition, and the equivalent blockage radius of the i-th group of simulation conditions respectively; RAND() is a random function, which takes random values ​​between 0 and 1 and is dimensionless;

[0050] Step 2: Based on the 480 sets of in-tube deposition simulation conditions sampled in step 1, a UDF file is written in the computational fluid dynamics simulation software ANSYS Fluent to set the characteristic parameters of the deposition state in the cooling water tube of the nuclear power plant condenser, and a three-dimensional steady-state numerical simulation calculation of the nuclear power plant condenser under the 480 sets of in-tube deposition simulation conditions is carried out; each set of in-tube deposition simulation conditions can obtain the secondary side three-dimensional temperature field distribution of the condenser under the corresponding in-tube deposition state;

[0051] Step 3: Post-process the three-dimensional temperature field distribution on the secondary side of the condenser under each tube deposition condition, and extract the temperature data of 18 fixed positions that can characterize the temperature field change. The specific steps are as follows:

[0052] Step 3-1: Import the secondary side temperature field distribution under a typical tube deposition simulation condition into the post-processing software CFD-Post, and use the slicing function to cut 10 temperature distribution sections inside the secondary side of the nuclear power plant condenser;

[0053] Step 3-2: Select 2 sections from the 10 temperature distribution sections where the temperature distribution is severely distorted due to the deposition in the condenser cooling water pipe as temperature monitoring surfaces, and evenly arrange a total of 18 temperature monitoring points based on each temperature monitoring surface;

[0054] Step 3-3: import 480 temperature field distributions in sequence, and extract the temperature data of 18 temperature monitoring points on the secondary side of the nuclear power plant condenser under each group of tube deposition simulation conditions;

[0055] Step 4: Build a back propagation neural network such as Figure 1 As shown, it includes one input layer, four hidden layers and one output layer;

[0056] Step 4-1: Establish an input layer, which contains 18 neurons, for receiving the temperature monitoring point data of 18 locations on the secondary side of the condenser obtained by post-processing the i-th group of in-tube deposition simulation conditions, and arranging them into a one-dimensional tensor D0 with a size of 1×18;

[0057] Step 4-2: Establish four hidden layers. Each hidden layer is a fully connected layer, each containing 10 neurons. Each layer of neurons only receives the output of the previous layer and outputs it to the next layer. There is no feedback between layers. The first hidden layer is used to receive the one-dimensional tensor D0, and then pass the activation function After nonlinear mapping, it is output as a one-dimensional tensor D1 with a size of 1×10; and so on, the last hidden layer outputs a one-dimensional tensor D with a size of 1×10 E ;

[0058] Step 4-3: Create an output layer to receive the one-dimensional tensor D E , after nonlinear mapping, we get the actual output of the back propagation neural network: a one-dimensional tensor D of size 1×3 E+1 , including the three characteristic parameters a of the deposition condition in the ith tube of the condenser of a nuclear power plant i , b i , c i ;

[0059] Step 5: Use the secondary side temperature monitoring point data of 480 sets of in-tube deposition simulation conditions as the input of the back propagation neural network, and use the characteristic parameters of the 480 sets of in-tube deposition conditions as the target output. E+1 With the target output D real The root mean square error between the two is used as the convergence criterion, and the back propagation neural network built in step 4 is trained by the error back propagation algorithm until convergence, and a back propagation neural network suitable for rapid prediction of the deposition state in the condenser tube of the current nuclear power plant is obtained;

[0060] Step 6: Input the 18 new data measured in real time at the 18 temperature monitoring points in the condenser of the nuclear power plant into the trained back propagation neural network, and quickly obtain the three characteristic parameters describing the deposition state in the tube under the current operating conditions: the horizontal coordinate a of the foreign matter deposition under the current operating conditions now , the vertical coordinate b of foreign matter deposition under the current operating conditions now And the equivalent blocking radius c under the current operating conditions now ,like Figure 2 As shown in the figure, it is a comparison chart between the actual value of the deposition condition in the condenser tube and the predicted value of the characteristic parameters of the deposition in the tube by the method of the present invention. Figure 2It can be seen from (a), (b), and (c) that the method of the present invention can quickly predict the cooling water pipe blockage state of the shell-and-tube single-flow seawater condenser of a nuclear power plant under different tube deposition conditions. The predicted value of the method of the present invention is well consistent with the actual value of the tube deposition state, and the method of the present invention avoids the large amount of actual operation data statistics and time experience accumulation process required for the establishment of traditional calculation models, which shows the correctness and superiority of the method of the present invention.

[0061] Parts of the present invention that are not described in detail belong to common knowledge among those skilled in the art.

Claims

1. A method for rapidly predicting the deposition state in a condenser tube of a nuclear power plant, characterized in that: The steps include: Step 1: Randomly sample the characteristic parameters of the deposition in the tube within the envelope area of ​​the condenser cooling water tube bundle of the nuclear power plant, including the deposition location and equivalent blockage radius, and obtain several groups of condenser tube deposition simulation conditions. The specific steps are as follows: Step 1-1: Determine the characteristic parameters describing the deposition state in the cooling water tube of the condenser of the nuclear power plant: the abscissa a of the foreign matter deposition, the ordinate b of the foreign matter deposition, and the equivalent blockage radius c; determine the value range of each parameter within the envelope area of ​​the cooling water tube bundle of the condenser of the nuclear power plant, that is: a∈A=[a min , a max ] (1) b∈B=[b min , b max ] (2) c∈C=[c min , c max ] (3) |a|+c∈[0,R](4)|b|+c∈[0,R](5)where A, B, and C represent the value range of the abscissa a of foreign matter deposition, the value range of the ordinate b of foreign matter deposition, and the value range of the equivalent blockage radius c, respectively; the subscript min represents the minimum value, and max represents the maximum value; R represents the radius of the envelope area of ​​the cooling water tube bundle of the condenser of a nuclear power plant; Step 1-2: Use the random function of Excel software to randomly sample each parameter within the value range of each in-tube deposition characteristic parameter to obtain N groups of in-tube deposition simulation conditions, namely: c i =c min +RAND()·(c max -c min ), i=1,2,3...N (6) a i =a min +c i +RAND()·(a max -a min -2c i ), i=1,2,3...N (7) b i =b min +c i +RAND()·(b max -b min -2c i ), i=1,2,3...N (8) Among them, a i , b i , c i They represent the abscissa of foreign body deposition, the ordinate of foreign body deposition, and the equivalent blockage radius of the i-th group of simulation conditions respectively; N represents the total number of simulation conditions for deposition in the condenser tube of a nuclear power plant; RAND() is a random function that takes random values ​​between 0 and 1 and is dimensionless; Step 2: Based on the N groups of in-tube deposition simulation conditions sampled in step 1, a UDF file is written in the computational fluid dynamics simulation software ANSYSFluent to set the characteristic parameters of the deposition state in the cooling water tube of the nuclear power plant condenser, and a three-dimensional steady-state numerical simulation calculation of the nuclear power plant condenser under the N groups of in-tube deposition simulation conditions is carried out; each group of in-tube deposition simulation conditions can obtain the secondary side three-dimensional temperature field distribution of the condenser under the corresponding in-tube deposition state; Step 3: Post-process the three-dimensional temperature field distribution on the secondary side of the condenser under each tube deposition condition, and extract the temperature data of the M fixed positions that can characterize the temperature field change. The specific steps are as follows: Step 3-1: Import the secondary side temperature field distribution under a typical tube deposition simulation condition into the post-processing software CFD-Post, and use the slicing function to cut K temperature distribution sections inside the secondary side of the nuclear power plant condenser; Step 3-2: Select S sections from the K temperature distribution sections where the temperature distribution is severely distorted due to the deposition in the condenser cooling water pipe as temperature monitoring surfaces, and evenly arrange a total of M temperature monitoring points based on each temperature monitoring surface; Step 3-3: import N temperature field distributions in sequence, and extract the temperature data of the temperature monitoring point M at the secondary side of the nuclear power plant condenser under each group of tube deposition simulation conditions; Step 4: Build a back propagation neural network, including one input layer, E hidden layers and one output layer; Step 4-1: Establish an input layer, which contains M neurons, to receive the temperature monitoring point data at M on the secondary side of the condenser obtained by post-processing the i-th group of in-tube deposition simulation conditions, and arrange them into a one-dimensional tensor D0 with a size of 1×M; Step 4-2: Establish E hidden layers, each hidden layer is a fully connected layer, each containing F neurons; each layer of neurons only receives the output of the previous layer and outputs it to the next layer, and there is no feedback between layers; the first hidden layer is used to receive the one-dimensional tensor D0, and then outputs it as a one-dimensional tensor D1 after nonlinear mapping through the activation function σ, with a size of 1×F; and so on, the last hidden layer outputs a one-dimensional tensor D of size 1×F E ; Step 4-3: Create an output layer to receive the one-dimensional tensor D E , after nonlinear mapping, we get the actual output of the back propagation neural network: a one-dimensional tensor D of size 1×3 E+1 , including the three characteristic parameters a of the deposition condition in the ith tube of the condenser of a nuclear power plant i , b i , c i ; Step 5: Take the secondary side temperature monitoring point data of N groups of in-tube deposition simulation conditions as the input of the back propagation neural network, take the characteristic parameters of N groups of in-tube deposition conditions as the target output, and use the actual output D of the neural network as the target output. E+1 With the target output D real The root mean square error between the two is used as the convergence criterion, and the back propagation neural network built in step 4 is trained by the error back propagation algorithm until convergence, and a back propagation neural network suitable for rapid prediction of the deposition state in the condenser tube of the current nuclear power plant is obtained; Step 6: Input the M new data measured in real time at the temperature monitoring point M in the condenser of the nuclear power plant into the trained back propagation neural network, and quickly obtain the three characteristic parameters describing the deposition state in the tube under the current operating conditions: the horizontal coordinate a of the foreign matter deposition under the current operating conditions now , the vertical coordinate b of foreign matter deposition under the current operating conditions now And the equivalent blocking radius c under the current operating conditions now , to achieve rapid prediction of the deposition status inside the condenser tubes of nuclear power plants.

Images