A ship turbulence model constant correction and display method and system

Abstract

The present invention provides a method and system for correcting and displaying constants of a ship turbulence model. The method comprises the following steps: first, a k-ε turbulence model is used to perform numerical simulation to generate a virtual flow field of the ship, and a plurality of discrete velocity observation data are extracted from the virtual flow field. Then, a Monte Carlo sampling method is used to generate a plurality of set members of the model constants of the k-ω turbulence model. Based on the initial value of the state parameter vector of each set member, the k-ω turbulence model is used to calculate the predicted value of the state parameter vector of each set member, and then a set covariance matrix is ​​calculated. A Kalman gain matrix is ​​calculated based on the set covariance matrix. Then, data assimilation is performed on the state parameter vector of each set member to obtain a corrected state parameter vector of each set member. The average value of all the corrected state parameter vectors is calculated to obtain an optimal model constant vector, thereby effectively saving costs.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical simulation of ships and ocean engineering, and in particular to a method and system for correcting and displaying constants of a ship turbulence model. Background Art

[0002] The wake field of a ship, especially the wake field in the propeller operating area, is directly related to the ship's resistance and propulsion performance, the vibration excitation characteristics of the stern area, and crew comfort. Therefore, accurately predicting the stern flow and wake field distribution based on computational fluid dynamics (CFD) methods is fundamental to ship design and performance optimization. However, due to the significant geometric variations in the stern area and the complexity of the wake field flowing through the stern, including the turbulent characteristics of the wake boundary layer, the 3D separation characteristics of the hull flow, and the interference characteristics of the free surface waveform on the stern flow, existing CFD software and methods suffer from insufficient prediction accuracy and fail to meet practical requirements. Taking the Reynolds Average Navier-Stokes (RANS) equations as an example, the accuracy of RANS turbulence model calculations is highly dependent on model constants. The default values ​​of these constants are calibrated using classical flow models, such as flat-plate boundary layers and free shear flows. For complex flow fields such as those following a ship, using these default values ​​often results in significant errors between the calculated flow field results and experimental measurements. Numerous experiments have also demonstrated a certain deviation between the model constant values ​​corresponding to the observed data and the default values. However, selecting appropriate model constants for a specific calculation object is extremely difficult. Currently, there is no effective method to accurately identify the empirical constants of the turbulence model equations. In ship flow simulations, a suitable turbulence model constant is generally selected through extensive trial calculations. This process is time-consuming and labor-intensive, failing to meet the needs of increasingly in-depth research.

[0003] With the development of artificial intelligence technologies such as deep neural networks, data-driven algorithms are being applied to quantify uncertainty in turbulence models and to improve and construct models. For example, machine learning is being used to improve the accuracy of turbulence separation predictions on wing surfaces using the SA turbulence model. By embedding the network-reconstructed model into the SA model, the accuracy of lift coefficient and pressure predictions on the airfoil surface has been significantly improved. For example, supervised learning algorithms are used to establish a mathematical model of the closed-ended terms in turbulence models. This model, trained through machine learning, is then embedded into CFD numerical simulations, improving model performance.

[0004] In addition, optimizing turbulence model constants using experimental data-driven algorithms is a common optimization strategy. For example, stochastic optimization and Monte Carlo sampling techniques were used to optimize the parameters of the k-ε turbulence model. After parameter optimization, the prediction error of the reattachment length on the leeward side of buildings was significantly reduced. For example, an adjoint-driven inversion process combined with artificial neural networks and Gaussian process regression was used to correct turbulence model parameters and other closure coefficients to better predict the flow field in the turbulent boundary layer. For example, the Latin hypercube sampling (LHS) method was used to study the uncertainty contained in the k-ε turbulence model constants. By studying the parameter uncertainty in the reverse flow case, it was found that the model parameters have a significant impact on the calculated results such as the mean velocity of the fluid, turbulence intensity, reattachment point location, and wall pressure. For example, a data-driven method for obtaining the optimal model constants was proposed. By applying small perturbations, the minimum variance between the experimental and predicted values ​​was obtained. However, this method cannot obtain the globally optimal model constants, but only the local optimal constants. The final prediction results may still have a large error compared with the observed values.

[0005] The most commonly used method for correcting turbulence model constants is optimal constant estimation based on data assimilation. Data assimilation is the fusion of experimental observations and model predictions. Its three main elements are the prediction model, observational data, and the assimilation algorithm. This method incorporates observational data into model predictions, altering the model's trajectory, ultimately optimizing model performance and improving prediction accuracy. Data assimilation was first applied in meteorological forecasting and has since expanded to fields such as geology, hydrology, and system monitoring. In recent years, data assimilation has been introduced into computational fluid dynamics, becoming a key method for optimizing turbulence model constants. For example, data assimilation has been used to study the uncertainty of constants in different RANS models. For example, data assimilation was used to compare the prediction performance of different RANS models for free jets after optimizing turbulence model constants. The results showed that optimizing the model constants can improve the model's prediction accuracy. For example, a hybrid data assimilation method, the ensemble-variational method (EnVar), was studied to recalibrate the constants of the k-ωSST turbulence model. Results showed that it effectively improved the accuracy of the flow characteristics prediction model. Summary of the Invention

[0006] To address the current difficulty in selecting appropriate model constants in RANS numerical simulations of ships, the present invention provides a method for correcting and displaying ship turbulence model constants. This method utilizes the k-ε turbulence model within the RANS turbulence model for numerical simulation, combines discrete velocity observations with an ensemble Kalman filter algorithm to calculate the Kalman gain matrix, and then corrects the model constants of the k-ω turbulence model within the RANS turbulence model through data assimilation. This method eliminates the need for extensive training data and expensive computing resources, effectively saving costs and offering high flexibility and versatility. The present invention also relates to a system for correcting and displaying ship turbulence model constants.

[0007] The technical solutions of the present invention are as follows:

[0008] A method for correcting and displaying constants of a ship turbulence model, characterized by comprising the following steps:

[0009] Virtual flow field generation and velocity observation data extraction steps: setting the boundary conditions and initial conditions of a ship's flow field, performing numerical simulation based on the boundary conditions and initial conditions and using the k-ε turbulence model in the RANS turbulence model to generate the ship's virtual flow field, and extracting multiple discrete velocity observation data from the virtual flow field;

[0010] The steps for calculating the predicted value of the state parameter vector are as follows: a Monte Carlo sampling method is used to randomly sample the model constant space of the k-ω turbulence model in the RANS turbulence model to generate multiple set members of the model constant of the k-ω turbulence model, and based on the initial value of the state parameter vector of each set member, the predicted value of the state parameter vector of each set member is calculated using the k-ω turbulence model;

[0011] State parameter vector correction step: Based on the ensemble Kalman filter algorithm, the ensemble covariance matrix is ​​calculated according to the number of ensemble members, the predicted value of the state parameter vector, and the average value of the predicted values ​​of all state parameter vectors, and the Kalman gain matrix is ​​calculated according to the ensemble covariance matrix. The state parameter vector of each ensemble member is data assimilated according to the Kalman gain matrix, the velocity observation data, and the predicted value of the state parameter vector to obtain the corrected state parameter vector of each ensemble member, and the average value of all corrected state parameter vectors is calculated to obtain the optimal model constant vector;

[0012] Velocity field calculation and display steps: Input the optimal model constant vector into the k-ω turbulence model, calculate the optimized velocity field, compare the optimized velocity field with the initial velocity field in the initial conditions, and display the comparison results.

[0013] Preferably, in the step of calculating the predicted value of the state parameter vector, the Monte Carlo sampling method is used to perform random sampling in the model constant space of the k-ω turbulence model in the RANS turbulence model, including: the model constant of the k-ω turbulence model is a default value, the default value is used as the average value, and the Monte Carlo sampling method is used to perform random sampling in the average value to generate an initial set with multiple set members.

[0014] Preferably, in the step of calculating the predicted value of the state parameter vector, the state parameter vector in each set member will be brought into the k-ω turbulence model for calculation starting from the initial value until the turbulence numerical simulation calculation converges, thereby calculating the predicted value of the state parameter vector of each set member.

[0015] Preferably, in the state parameter vector correction step, data assimilation is performed on the state parameter vector of each set member, the mapping relationship is analyzed using a data assimilation algorithm, the inverse problem is solved by integrating relevant speed observation data, and the Kalman filter algorithm is used to analyze the process iteratively to obtain the optimal model constant vector.

[0016] Preferably, the state parameter vector includes velocity, pressure and temperature.

[0017] Preferably, in the virtual flow field generation and velocity observation data extraction steps, the boundary conditions include inlet boundary conditions, outlet boundary conditions and hull surface boundary conditions; and / or, the initial conditions include initial velocity field, initial pressure field and initial turbulence field.

[0018] A ship turbulence model constant correction and display system is characterized by comprising a virtual flow field generation and velocity observation data extraction module, a state parameter vector prediction value calculation module, a state parameter vector correction module and a velocity field calculation and display module connected in sequence.

[0019] The virtual flow field generation and velocity observation data extraction module sets the boundary conditions and initial conditions of a ship's flow field, performs numerical simulation based on the boundary conditions and initial conditions and adopts the k-ε turbulence model in the RANS turbulence model to generate a virtual flow field of the ship, and extracts multiple discrete velocity observation data from the virtual flow field;

[0020] The state parameter vector prediction value calculation module uses a Monte Carlo sampling method to perform random sampling in the model constant space of the k-ω turbulence model in the RANS turbulence model to generate multiple set members of the model constant of the k-ω turbulence model, and calculates the predicted value of the state parameter vector of each set member based on the initial value of the state parameter vector of each set member and using the k-ω turbulence model;

[0021] The state parameter vector correction module, based on the ensemble Kalman filter algorithm, calculates an ensemble covariance matrix according to the number of ensemble members, the predicted value of the state parameter vector, and the average value of the predicted values ​​of all state parameter vectors, and calculates a Kalman gain matrix according to the ensemble covariance matrix, performs data assimilation on the state parameter vector of each ensemble member according to the Kalman gain matrix, the velocity observation data, and the predicted value of the state parameter vector to obtain a corrected state parameter vector for each ensemble member, and calculates the average value of all the corrected state parameter vectors to obtain an optimal model constant vector;

[0022] The velocity field calculation and display module inputs the optimal model constant vector into the k-ω turbulence model, calculates the optimized velocity field, compares the optimized velocity field with the initial velocity field in the initial condition, and displays the comparison result.

[0023] Preferably, in the state parameter vector prediction value calculation module, the Monte Carlo sampling method is used to perform random sampling in the model constant space of the k-ω turbulence model in the RANS turbulence model, including: the model constant of the k-ω turbulence model is a default value, the default value is used as the average value, and the Monte Carlo sampling method is used to perform random sampling in the average value to generate an initial set with multiple set members.

[0024] Preferably, in the state parameter vector correction module, data assimilation is performed on the state parameter vector of each set member, the mapping relationship is analyzed using a data assimilation algorithm, the inverse problem is solved by integrating relevant speed observation data, and the Kalman filter algorithm is used to iterate the analysis process to obtain the optimal model constant vector.

[0025] Preferably, the state parameter vector includes velocity, pressure and temperature;

[0026] And / or, the boundary conditions include inlet boundary conditions, outlet boundary conditions and hull surface boundary conditions;

[0027] And / or, the initial conditions include an initial velocity field, an initial pressure field and an initial turbulence field.

[0028] The beneficial effects of the present invention are:

[0029] The present invention provides a method for correcting and displaying constants of a ship turbulence model. The method first sets the boundary conditions and initial conditions of a ship flow field, performs numerical simulation based on the boundary conditions and initial conditions and the k-ε turbulence model in the RANS turbulence model, generates a virtual flow field of the ship using the k-ε turbulence model, and extracts multiple discrete velocity observation data from the virtual flow field. Then, a Monte Carlo sampling method is used to perform random sampling in the model constant space of the k-ω turbulence model (Baseline example) in the RANS turbulence model to generate multiple set members of the ship turbulence model constants. Based on the initial value of the state parameter vector of each set member, the k-ω turbulence model is used to calculate the predicted value of the state parameter vector of each set member. Then, based on the set Kalman filtering algorithm, the predicted value of the state parameter vector and the average predicted value of all state parameter vectors are calculated. The ensemble covariance matrix is ​​calculated by the mean, and the Kalman gain matrix is ​​calculated based on the ensemble covariance matrix. The state parameter vector of each ensemble member is assimilated according to the Kalman gain matrix, the velocity observation data and the predicted value of the state parameter vector to obtain the corrected state parameter vector of each ensemble member, and the average value of all corrected state parameter vectors is calculated to obtain the optimal model constant vector. The state parameter vectors after the ensemble Kalman filter data assimilation are averaged to obtain the optimized model constant matrix. The optimal model constant vector is substituted into the original calculation model for confirmation and verification, that is, the optimal model constant vector is finally input into the k-ω turbulence model to calculate the optimized velocity field, and the optimized velocity field is compared with the initial velocity field in the initial conditions. The comparison results are displayed, which effectively improves the optimization speed, saves costs, and has high flexibility and versatility.

[0030] The present invention adopts the Ensemble Kalman filter (EnKF) algorithm, which has advantages in dealing with small sample, high dimensional and highly nonlinear problems. Compared with machine learning methods, such as neural networks, the EnKF algorithm can correct the turbulence model constants by using less discrete observation data without the need for a large amount of training data and expensive computing resources. This advantage is particularly obvious when the cost of ship model experimental measurement is high and observation data is scarce. This makes the EnKF algorithm a more efficient and economical choice. The EnKF algorithm performs data assimilation (also known as the EnKF assimilation algorithm) and becomes an effective method for solving complex fluid dynamics problems in actual ship flow fields. In addition, the EnKF assimilation algorithm is a non-invasive data assimilation algorithm that does not require explicit modification or adjustment of the original prediction model. Therefore, it is relatively simple to implement and can be coupled with different CFD software (Fluent, OpenFOAM, etc.). It is suitable for different types of prediction models and has high flexibility and versatility.

[0031] The present invention also relates to a ship turbulence model constant correction and display system, which corresponds to the above-mentioned ship turbulence model constant correction and display method, and can be understood as a system that implements the above-mentioned ship turbulence model constant correction and display method, including a virtual flow field generation and velocity observation data extraction module, a state parameter vector prediction value calculation module, a state parameter vector correction module and a velocity field calculation and display module connected in sequence. The modules work together to perform numerical simulation based on experimental observation data by adopting the k-ε turbulence model in the RANS turbulence model, combine discrete velocity observation data and use the ensemble Kalman filter algorithm to calculate the Kalman gain matrix, and use a non-invasive data assimilation algorithm to correct the model constants of the k-ω turbulence model in the RANS turbulence model. This system does not require a large amount of training data and expensive computing resources, effectively saves costs, and has high flexibility and versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart for solving the direct and inverse problems using the RANS turbulence model.

[0033] Figure 2 It is a flow chart of the ship turbulence model constant correction and display method of the present invention.

[0034] Figure 3 This is a preferred flow chart of the ship turbulence model constant correction and display method of the present invention.

[0035] Figure 4 It is a schematic diagram of the comparison results of the velocity field before and after optimization of the ship turbulence model constant correction and display method of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be described below with reference to the accompanying drawings.

[0037] The RANS model constant optimization method based on data assimilation is an effective method to improve the prediction accuracy of ship flow field. In order to study the feasibility and effectiveness of this method in ship flow field, the present invention proposes a ship turbulence model constant correction and display method, which can also be called a ship turbulence model constant correction (optimization) and display method based on data assimilation. The 14000TEU Panamax container ship is taken as the research object, and the k-ω model numerical simulation is adopted. The data assimilation is combined with discrete velocity observation data to correct the model constants of the k-ω turbulence model. The process of optimizing the turbulence model constants through data assimilation is essentially to solve the inverse problem, such as Figure 1The forward problem and inverse problem are shown in Figure 1. The prediction model (i.e., RANS turbulence model) establishes the mapping relationship between the predicted state parameters (velocity, pressure, and temperature) and the model constants, while data assimilation uses the assimilation algorithm to analyze the mapping relationship and integrates the relevant observation data to solve the inverse problem, that is, using the output value of the prediction model to optimize the input value, thereby inferring and calibrating the model constants. The flowchart of this method is shown in Figure 1. Figure 2 As shown, the following steps are included in sequence:

[0038] 1. Steps for generating a virtual flow field and extracting velocity observation data: set the boundary conditions and initial conditions of a ship's flow field, perform numerical simulation based on the boundary conditions and initial conditions and use the k-ε turbulence model in the RANS turbulence model to generate a virtual flow field of the ship, and extract multiple discrete velocity observation data from the virtual flow field.

[0039] Specifically, if Figure 3 As shown, first, the boundary conditions and initial conditions of a ship's flow field are set. The boundary conditions include inlet boundary conditions, outlet boundary conditions, and hull surface boundary conditions. The inlet boundary conditions include the flow velocity, flow direction, turbulence intensity, etc. at the inlet, and are set according to actual conditions or test data. The outlet boundary conditions are usually set to a free flow outlet, and the pressure or velocity of the fluid can also be set as needed. The hull surface boundary conditions are usually set to a no-slip wall, that is, it is assumed that the velocity of the fluid is equal to that of the hull surface; the initial conditions include the initial velocity field, initial pressure field, and initial temperature field. Then, through numerical simulation, the k-ε turbulence model will calculate the distribution of parameters such as the velocity field and turbulence intensity in the entire flow field based on the set boundary conditions and initial conditions, and then generate a virtual flow field for the ship. This virtual flow field is assumed to be a real flow field, and 100 velocity observation data y are extracted from the virtual flow field. exp , extract the velocity observation data y exp As the observation information input of the Ensemble Kalman filter (EnKF) algorithm.

[0040] 2. Steps for calculating the predicted value of the state parameter vector: Use the Monte Carlo sampling method to perform random sampling in the model constant space of the k-ω turbulence model (Baseline example) in the RANS turbulence model to generate multiple set members of the model constants of the k-ω turbulence model. Based on the initial value of the state parameter vector of each set member, the predicted value of the state parameter vector of each set member is calculated using the k-ω turbulence model.

[0041] Specifically, statistical information is obtained as follows: the model constants in the k-ω turbulence model (Baseline example) are set to default values, and the default values ​​are used as the mean. Random sampling is performed in the model constant space of the Baseline example through the Monte Carlo sampling method to generate N set members of the model constants of the k-ω turbulence model. Each set member contains a state parameter vector (velocity vector and k-ω turbulence model constant vector). Then, based on the initial value of the state parameter vector of each set member, the predicted value of the state parameter vector of each set member is calculated using the k-ω turbulence model in the RANS turbulence model, and the calculation is performed according to the following formula:

[0042] x f =F(x0,v) (1)

[0043] In the above formula, x f is the predicted value of the state parameter vector, x0 is the initial value of the state parameter vector, F is the k-ω turbulence model in the RANS turbulence model, and ν is the system noise.

[0044] Substitute the initial value x0 of the state parameter vector of each set member into the RANS turbulence model and calculate until convergence, and obtain the corresponding N groups of predicted values ​​x f , as the prediction information input of the Ensemble Kalman filter (EnKF). The state parameter vector of each ensemble member is in the form of x i =(U i ,θ i ) T , where U is the velocity vector, θ is the turbulence model constant vector, and i is the geometric member index.

[0045] Experimental data acquisition: Extract discrete velocity observation data y from the flow field exp Since the verification ship selected in this invention has no experimental data, a flow field is recalculated using the k-ε turbulence model, and the flow field is assumed to be a real flow field, from which 100 velocity observation points can be extracted. The extracted observation data y exp As the observation information input of the ensemble Kalman filter algorithm.

[0046] 3. State parameter vector correction step: Based on the ensemble Kalman filter algorithm, the ensemble covariance matrix is ​​calculated according to the number of ensemble members, the predicted value of the state parameter vector and the average value of the predicted values ​​of all state parameter vectors, and the Kalman gain matrix is ​​calculated according to the ensemble covariance matrix. The state parameter vector of each ensemble member is data assimilated according to the Kalman gain matrix, the velocity observation data and the predicted value of the state parameter vector to obtain the corrected state parameter vector of each ensemble member, and the average value of all corrected state parameter vectors is calculated to obtain the optimal model constant vector. Among the many data assimilation algorithms, the present invention selects the ensemble Kalman filter algorithm (Ensemble Kalman filter, EnKF) proposed by Evensen in 1994. The EnKF algorithm is developed from the classical Kalman filter and the extended Kalman filter algorithm, and is a Kalman filter based on the Monte Carlo theory. The principle can be simply understood as artificially creating a set with N set members, assuming a normal distribution, and correcting the N set members separately through filtering methods so that the state parameters corresponding to the N combined members are close to the true values ​​of the system under study. It is especially suitable for numerical model optimization of high-order nonlinear systems such as turbulence problems.

[0047] This step integrates the uncertainty information of the velocity observation data and the statistical information of the ensemble members to determine the Kalman gain matrix K and complete the update of the ensemble members. Specifically, based on the ensemble Kalman filter algorithm, the ensemble covariance matrix P is calculated based on the number of ensemble members, the predicted value of each state parameter vector, and the average value of the predicted values ​​of all state parameter vectors. The calculation is performed according to the following formula:

[0048]

[0049] In the above formula, P is the set covariance matrix, N is the number of set members, is the average value of the predicted values ​​of all state parameter vectors, i is the ensemble member index, T is the transposed matrix, and then the covariance matrix R of the ensemble measurement disturbance is obtained based on the velocity observation data.

[0050] Then the Kalman gain matrix is ​​calculated based on the collective covariance matrix P, which is calculated according to the following formula:

[0051] K=PH T (HPH T +R) -1 (3)

[0052] In the above formula, P is the collective covariance matrix, R is the covariance matrix of the collective measurement disturbance, H is the observation function, and the covariance matrix of the collective measurement disturbance is W=(ω 1 ,ω 2,…,ω n ), where ω is the observation error, which is an artificially given constant, and n is the number of velocity observation data.

[0053] Finally, the state parameter vector of each ensemble member is assimilated according to the Kalman gain matrix K, velocity observation data, and the predicted value of the state parameter vector (i.e., the model constants of the k-ω turbulence model are corrected) and corrected according to the following formula:

[0054]

[0055] In the above formula, ω represents the observation error, y exp represents the velocity observation data, Represents the predicted value of the state parameter vector of the i-th set member. According to the above formula, the corrected state parameter vector of each set member can be obtained. Then the average value of all corrected state parameter vectors is calculated to obtain the optimal model constant vector.

[0056] This step is to ensemble the Kalman filter algorithm to filter and correct the statistical information (predicted value x f ) and observation information (velocity observation data y exp ) is used as the input of the ensemble Kalman filter algorithm, and the Python program is used to implement the assimilation process shown in Equations (2) to (4) to correct the model constant values.

[0057] 4. Velocity field calculation and display step: This step recalculates the flow field, and brings the modified model constants back into the k-ω turbulence model of the RANS model to calculate and obtain the optimized velocity field, and compare the results of the velocity field before and after optimization. In other words, the optimal model constant vector is input into the k-ω turbulence model to calculate the optimized velocity field, and the optimized velocity field is compared with the initial velocity field in the initial conditions, and the comparison results are displayed. The comparison results are as follows: Figure 4 The figure shows the velocity distribution along the y-axis at different cross sections. The solid line represents the baseline before optimization (or the result before assimilation, i.e., the initial velocity field in the initial conditions), the hollow origin represents the true value (Truth), and the dashed line represents the result after optimization (Da, or the result after assimilation, i.e., the optimized velocity field). Comparison shows that the assimilated result is closer to the true value, thus verifying the effectiveness and feasibility of the ship turbulence model constant correction scheme based on data assimilation.

[0058] The present invention also relates to a ship turbulence model constant correction and display system, which corresponds to the above-mentioned ship turbulence model constant correction and display method, and can be understood as a system for implementing the above-mentioned method. The system includes a virtual flow field generation and velocity observation data extraction module, a state parameter vector prediction value calculation module, a state parameter vector correction module and a velocity field calculation and display module connected in sequence. Specifically,

[0059] The virtual flow field generation and velocity observation data extraction module sets the boundary conditions and initial conditions of a ship's flow field, performs numerical simulation based on the boundary conditions and initial conditions and adopts the k-ε turbulence model to generate a virtual flow field of the ship, and extracts a plurality of discrete velocity observation data from the virtual flow field;

[0060] The state parameter vector prediction value calculation module uses a Monte Carlo sampling method to perform random sampling in the model constant space of the k-ω turbulence model in the RANS turbulence model to generate multiple set members of the model constant of the k-ω turbulence model, and calculates the predicted value of the state parameter vector of each set member based on the initial value of the state parameter vector of each set member and using the k-ω turbulence model;

[0061] The state parameter vector correction module, based on the ensemble Kalman filter algorithm, calculates an ensemble covariance matrix according to the number of ensemble members, the predicted value of the state parameter vector, and the average value of the predicted values ​​of all state parameter vectors, and calculates a Kalman gain matrix according to the ensemble covariance matrix, performs data assimilation on the state parameter vector of each ensemble member according to the Kalman gain matrix, the velocity observation data, and the predicted value of the state parameter vector to obtain a corrected state parameter vector for each ensemble member, and calculates the average value of all the corrected state parameter vectors to obtain an optimal model constant vector;

[0062] The velocity field calculation and display module inputs the optimal model constant vector into the k-ω turbulence model, calculates the optimized velocity field, compares the optimized velocity field with the initial velocity field in the initial condition, and displays the comparison result.

[0063] Preferably, in the state parameter vector prediction value calculation module, the Monte Carlo sampling method is used to perform random sampling in the model constant space of the k-ω turbulence model in the RANS turbulence model, including: the model constant of the k-ω turbulence model is a default value, the default value is used as the average value, and the Monte Carlo sampling method is used to perform random sampling in the average value to generate an initial set with multiple set members.

[0064] Preferably, in the state parameter vector correction module, data assimilation is performed on the state parameter vector of each set member, the mapping relationship is analyzed using a data assimilation algorithm, the inverse problem is solved by integrating relevant speed observation data, and the Kalman filter algorithm is used to iterate the analysis process to obtain the optimal model constant vector.

[0065] Preferably, the state parameter vector includes velocity, pressure and temperature.

[0066] Preferably, the boundary conditions include inlet boundary conditions, outlet boundary conditions and hull surface boundary conditions.

[0067] Preferably, the initial conditions include an initial velocity field, an initial pressure field and an initial turbulence field.

[0068] The present invention provides an objective and scientific method and system for correcting and displaying constants of ship turbulence models. By adopting the k-ε turbulence model in the RANS prediction model for numerical simulation, combining discrete velocity observation data and adopting the ensemble Kalman filter algorithm to calculate the Kalman gain matrix, the model constants of the k-ω turbulence model in the RANS prediction model are corrected. This method does not require a large amount of training data and expensive computing resources, effectively saves costs, and has high flexibility and versatility.

[0069] It should be noted that the specific embodiments described above can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail with reference to the drawings and embodiments, those skilled in the art should understand that the present invention can still be modified or replaced with equivalents. In short, all technical solutions and improvements that do not depart from the spirit and scope of the present invention should be included in the scope of protection of the patent for the present invention.

Claims

1. A method for correcting and displaying constants of a ship turbulence model, characterized in that: The following steps are involved: Virtual flow field generation and velocity observation data extraction steps: setting the boundary conditions and initial conditions of a ship's flow field, performing numerical simulation based on the boundary conditions and initial conditions and using the k-ε turbulence model in the RANS turbulence model to generate the ship's virtual flow field, and extracting multiple discrete velocity observation data from the virtual flow field; The steps for calculating the predicted value of the state parameter vector are as follows: a Monte Carlo sampling method is used to randomly sample the model constant space of the k-ω turbulence model in the RANS turbulence model to generate multiple set members of the model constant of the k-ω turbulence model, and based on the initial value of the state parameter vector of each set member, the predicted value of the state parameter vector of each set member is calculated using the k-ω turbulence model; State parameter vector correction step: Based on the ensemble Kalman filter algorithm, the ensemble covariance matrix is ​​calculated according to the number of ensemble members, the predicted value of the state parameter vector, and the average value of the predicted values ​​of all state parameter vectors, and the Kalman gain matrix is ​​calculated according to the ensemble covariance matrix. The state parameter vector of each ensemble member is data assimilated according to the Kalman gain matrix, the velocity observation data, and the predicted value of the state parameter vector to obtain the corrected state parameter vector of each ensemble member, and the average value of all corrected state parameter vectors is calculated to obtain the optimal model constant vector; Velocity field calculation and display steps: Input the optimal model constant vector into the k-ω turbulence model, calculate the optimized velocity field, compare the optimized velocity field with the initial velocity field in the initial conditions, and display the comparison results.

2. The ship turbulence model constant correction and display method according to claim 1, characterized in that: In the step of calculating the predicted value of the state parameter vector, randomly sampling in the model constant space of the k-ω turbulence model in the RANS turbulence model using the Monte Carlo sampling method includes: the model constant of the k-ω turbulence model is a default value, the default value is used as the average value, and the Monte Carlo sampling method is used to randomly sample in the average value to generate an initial set with multiple set members.

3. The ship turbulence model constant correction and display method according to claim 1 or 2, characterized in that: In the step of calculating the predicted value of the state parameter vector, the state parameter vector in each set member will be brought into the k-ω turbulence model for calculation starting from the initial value until the turbulence numerical simulation calculation converges, thereby calculating the predicted value of the state parameter vector of each set member.

4. The ship turbulence model constant correction and display method according to claim 3, characterized in that: In the state parameter vector correction step, data assimilation is performed on the state parameter vector of each set member, a data assimilation algorithm is used to analyze the mapping relationship, the inverse problem is solved by integrating relevant speed observation data, and the Kalman filter algorithm is used to iterate the analysis process to obtain the optimal model constant vector.

5. The ship turbulence model constant correction and display method according to claim 1, characterized in that: The state parameter vector includes velocity, pressure and temperature.

6. The ship turbulence model constant correction and display method according to claim 1, characterized in that: In the virtual flow field generation and velocity observation data extraction steps, the boundary conditions include inlet boundary conditions, outlet boundary conditions and hull surface boundary conditions; and / or, the initial conditions include initial velocity field, initial pressure field and initial turbulence field.

7. A ship turbulence model constant correction and display system, characterized in that: It includes a virtual flow field generation and velocity observation data extraction module, a state parameter vector prediction value calculation module, a state parameter vector correction module and a velocity field calculation and display module, which are connected in sequence. The virtual flow field generation and velocity observation data extraction module sets the boundary conditions and initial conditions of a ship's flow field, performs numerical simulation based on the boundary conditions and initial conditions and adopts the k-ε turbulence model in the RANS turbulence model to generate a virtual flow field of the ship, and extracts multiple discrete velocity observation data from the virtual flow field; The state parameter vector prediction value calculation module uses a Monte Carlo sampling method to perform random sampling in the model constant space of the k-ω turbulence model in the RANS turbulence model to generate multiple set members of the model constant of the k-ω turbulence model, and calculates the predicted value of the state parameter vector of each set member based on the initial value of the state parameter vector of each set member and using the k-ω turbulence model; The state parameter vector correction module, based on the ensemble Kalman filter algorithm, calculates an ensemble covariance matrix according to the number of ensemble members, the predicted value of the state parameter vector, and the average value of the predicted values ​​of all state parameter vectors, and calculates a Kalman gain matrix according to the ensemble covariance matrix, performs data assimilation on the state parameter vector of each ensemble member according to the Kalman gain matrix, the velocity observation data, and the predicted value of the state parameter vector to obtain a corrected state parameter vector for each ensemble member, and calculates the average value of all the corrected state parameter vectors to obtain an optimal model constant vector; The velocity field calculation and display module inputs the optimal model constant vector into the k-ω turbulence model, calculates the optimized velocity field, compares the optimized velocity field with the initial velocity field in the initial condition, and displays the comparison result.

8. The ship turbulence model constant correction and display system according to claim 7, characterized in that: In the state parameter vector prediction value calculation module, the Monte Carlo sampling method is used to perform random sampling in the model constant space of the k-ω turbulence model in the RANS turbulence model, including: the model constant of the k-ω turbulence model is a default value, the default value is used as the average value, and the Monte Carlo sampling method is used to perform random sampling in the average value to generate an initial set with multiple set members.

9. The ship turbulence model constant correction and display system according to claim 7 or 8, characterized in that: In the state parameter vector correction module, data assimilation is performed on the state parameter vector of each set member, a data assimilation algorithm is used to analyze the mapping relationship, the inverse problem is solved by integrating relevant speed observation data, and the Kalman filter algorithm is used to iterate the analysis process to obtain the optimal model constant vector.

10. The ship turbulence model constant correction and display system according to claim 7 or 8, characterized in that: The state parameter vector includes velocity, pressure and temperature; And / or, the boundary conditions include inlet boundary conditions, outlet boundary conditions and hull surface boundary conditions; And / or, the initial conditions include an initial velocity field, an initial pressure field and an initial turbulence field.

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