CN112446098B - Method for simulating ultimate performance of propeller in marine equipment - Google Patents

Method for simulating ultimate performance of propeller in marine equipment Download PDF

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CN112446098B
CN112446098B CN202011414100.0A CN202011414100A CN112446098B CN 112446098 B CN112446098 B CN 112446098B CN 202011414100 A CN202011414100 A CN 202011414100A CN 112446098 B CN112446098 B CN 112446098B
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propeller
performance
parameter
parameters
function
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CN112446098A (en
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蔡如桦
陈刚
邹遂丰
刘建阁
闻心怡
彭晓钧
贺梦豪
刘婷
陈梦珂
谢敏灵
周跃
李天鹞
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Wuhan No 2 Ship Design Institute No 719 Research Institute of China Shipbuilding Industry Corp
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Wuhan No 2 Ship Design Institute No 719 Research Institute of China Shipbuilding Industry Corp
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
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    • G06F30/17—Mechanical parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

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Abstract

The invention relates to a method for simulating the ultimate performance of a propeller in marine equipment, which comprises the following steps: collecting environmental parameters, material parameters and performance parameters of the propeller; establishing a neural network model for simulating the limiting performance of the propeller, and obtaining a limiting performance simulation coefficient of the propeller through the neural network; the neural network takes the related parameters of the environmental parameters and the performance parameters of the propeller and the related parameters of the material parameters and the performance parameters as input vectors, and outputs the performance parameter change function of the propeller; and obtaining the corresponding relation between the material parameter and the performance parameter in the limit environment by linear fitting of the performance parameter change function of the output propeller. The simulation method provided by the invention can simulate the structure and the performance coefficient of the propeller in the limit environment, thereby being beneficial to improvement on the propeller, avoiding potential safety hazards in the actual limit environment and providing data basis for the design of the propeller.

Description

Method for simulating ultimate performance of propeller in marine equipment
Technical Field
The invention relates to a propeller in marine equipment, in particular to a method for simulating the limit performance of the propeller in the marine equipment.
Background
Marine equipment, such as marine nuclear power platforms, are required to face various harsh environments in marine environments, and therefore, performance requirements for components in marine equipment are relatively high. The propeller in the ship is used as power equipment, is important equipment of a marine nuclear power platform, has important functions on performances such as thrust, power, rotating speed, navigational speed and the like and durability in various environments, and particularly has quite important influence on running and safety if the propeller can keep the performance in normal environments in extremely cold environments.
Therefore, in order to avoid the failure of the propeller in the limit environment, it is necessary to perform a simulation calculation of the performance of the propeller in the limit environment in order to solve the problems that may occur to the propeller according to the calculated condition of the propeller.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a simulation method for the ultimate performance of the propeller in marine equipment, which can simulate the structure and the coefficient of performance of the propeller in the ultimate environment, thereby being beneficial to improvement of the propeller and avoiding potential safety hazards in the actual ultimate environment.
The technical scheme adopted for realizing the purpose of the invention is as follows: a method of simulating the ultimate performance of a propeller in marine equipment, the method comprising:
collecting environmental parameters, material parameters and performance parameters of the propeller;
establishing a neural network model for simulating the limiting performance of the propeller, and obtaining a limiting performance simulation coefficient of the propeller through the neural network; the neural network takes a related parameter gamma of an environmental parameter and a performance parameter of the propeller and a related parameter xi of a material parameter and the performance parameter as input vectors, and outputs a performance parameter change function of the propeller;
and obtaining the corresponding relation between the material parameter and the performance parameter in the limit environment by linear fitting of the performance parameter change function of the output propeller.
In the above technical solution, the network output expression of the neural network model is:
Y＝f 2 [W 2 ·f 1 (W 1 ·X-B 1 )-B 2 ]
W 1 、B 1 respectively a weight matrix and a deviation matrix from an input layer to a hidden layer, W 2 、B 2 Respectively a weight matrix and a deviation matrix from a hidden layer to an output layer, f 1 、f 2 The output vector Y is the ultimate performance function of the propeller, and the input vector X is the function of the environmental parameter, the material parameter and the performance parameter, wherein:
X＝f(A，V，C，K)
wherein A is an environmental parameter function, V is a material parameter function, C is a performance parameter function, and K is a correlation function; wherein a=k 1 (Loc，T 1 ，T 2 ，S)，Loc，T 1 ，T 2 S is respectively expressed as geographic position, ambient temperature, air humidity and wind speed; v=k 2 (L, M, N, O), L, M, N and O are the hardness, strength, brittleness and plasticity, respectively, of the parts in the propeller; c=k 3 (F，P，S 1 ，S 2 )，F，P，S 1 ，S 2 The four parameters of the thrust, the power, the rotating speed and the navigational speed of the propeller are respectively; k= { γ, ζ }, γ being the correlation coefficient of the environmental parameter a and the performance parameter C, ζ being the correlation coefficient of the material parameter V and the performance parameter C, wherein,
represents the conjugation of C;
represents the conjugation of C.
The method takes the related parameter gamma of the performance parameter of the propeller and the related parameter zeta of the material parameter and the performance parameter of the propeller as input vectors, calculates the material parameter of each component in the propeller and the propeller performance change function of the performance parameter of the propeller under different environment parameters, and then obtains the corresponding relation between the material parameter and the performance parameter under the limit environment by linear fitting the output propeller performance parameter change function, thereby simulating the parameters of the materials required by each component in the propeller under the limit environment, whether the working performance of the propeller accords with the standard, whether potential safety hazards exist or not, and the like, and providing reliable data basis for improving and optimizing the propeller materials, processes and performances running under the limit environment.
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FIG. 1 is a flow chart of a method of simulating the ultimate performance of a propeller in marine equipment according to the present invention.
Detailed Description
The invention will now be described in further detail with reference to the drawings and to specific examples.
As shown in fig. 1, the method for simulating the ultimate performance of the propeller in the marine equipment comprises the following steps:
s1, collecting environmental parameters, material parameters and performance parameters of a propeller;
s2, establishing a neural network model for simulating the limiting performance of the propeller, and obtaining a limiting performance simulation coefficient of the propeller through the neural network; the neural network takes a related parameter gamma of the environmental parameter and the performance parameter of the propeller and a related parameter xi of the material parameter and the performance parameter as input vectors, and outputs a performance parameter change function of the propeller.
Specifically, the network output expression of the neural network established in this embodiment is:
Y＝f 2 [W 2 ·f 1 (W 1 ·X-B 1 )-B 2 ]
W 1 、B 1 respectively a weight matrix and a deviation matrix from an input layer to a hidden layer, W 2 、B 2 Respectively a weight matrix and a deviation matrix from a hidden layer to an output layer, f 1 、f 2 The output vector Y is the ultimate performance function of the propeller, and the input vector X is the function of the environmental parameter, the material parameter and the performance parameter, wherein:
X＝f(A，V，C，K)
wherein A is an environmental parameter function, V is a material parameter function, C is a performance parameter function, and K is a correlation function; wherein a=k 1 (Loc，T 1 ，T 2 ，S)，Loc，T 1 ，T 2 S is respectively expressed as geographic position, ambient temperature, air humidity and wind speed; v=k 2 (L, M, N, O), L, M, N and O are the hardness, strength, brittleness and plasticity, respectively, of the parts in the propeller; c=k 3 (F，P，S 1 ，S 2 )，F，P，S 1 ，S 2 The four parameters of the thrust, the power, the rotating speed and the navigational speed of the propeller are respectively; k= { γ, ζ }, γ being the correlation coefficient of the environmental parameter a and the performance parameter C, ζ being the correlation coefficient of the material parameter V and the performance parameter C, wherein,
represents the conjugation of C;
represents the conjugation of C.
The output vector Y is the limiting performance function of the propeller.
In the embodiment, the built neural network is trained by using the state parameters of the propeller under the previous normal working condition, and the hidden layer function, the neuron number and the weight are continuously adjusted according to the error between the output value and the output value under the normal working condition until the error meets the requirement; and the normal working condition is an environment in which the state parameters of the propeller meet the standard conditions.
The embodiment carries out network training on the neural network through a BP algorithm, and specifically comprises the following steps:
the BP algorithm is divided into a forward propagation stage and a backward propagation stage, in the forward propagation stage, information is transmitted to an output layer from an input layer through gradual transformation, and the process is also executed when the network normally runs after training is completed; the backward propagation stage adjusts the weight matrix stage according to the performance error;
taking the error measure of the network for the p-th sample:
wherein: m is the number of neurons of the output layer; y is pj Ideal output vector representing the jth element of the p-th sample, Y pj For the network output value of the jth element of the p-th sample, finally, the error of the network with respect to the whole sample set is measured as:
E＝∑E p
the error calculated to the whole sample set according to the above formula is within a specified threshold range, i.e. the performance index of the propeller under normal circumstances.
S3, obtaining the corresponding relation between the material parameter and the performance parameter in the limit environment through linear fitting of the performance parameter change function of the output propeller.
Calculated by the following formula:
wherein a is n As a function of environmental parameters, x i As a function of the material parameters, y, of the component i in the propeller i Sigma x is a function of the performance parameters of component i in the propeller i y i The method is a corresponding relation formula of material parameters and performance parameters under different environmental parameters of a component i in the propeller.
The corresponding relation function of the material parameters and the performance parameters in the extreme environment is obtained through the above formula fitting, so that the parameters of the materials required by each part in the propeller and whether the working performance of the propeller accords with the standard can be simulated. For example, in extremely cold environment (temperature-40 ℃), whether the parameters of the hardness, strength, brittleness and plasticity of the material parameters of each part in the propeller are within the normal standard data range or not is calculated through the corresponding relation function, if the parameters are out of range, potential safety hazards exist, the materials of the propeller need to be tested and improved, and meanwhile, whether the thrust, the power, the rotating speed and the navigational speed of the propeller are influenced or not is calculated through the performance change function of the propeller, so that data support is provided for the optimization design and improvement of the propeller.

Claims (4)

1. A method of simulating the ultimate performance of a propeller in marine equipment, comprising:
collecting environmental parameters, material parameters and performance parameters of the propeller;
establishing a neural network model for simulating the limiting performance of the propeller, and obtaining a limiting performance simulation coefficient of the propeller through the neural network; the neural network takes a related parameter gamma of an environmental parameter and a performance parameter of the propeller and a related parameter xi of a material parameter and the performance parameter as input vectors, and outputs a performance parameter change function of the propeller; the network output expression of the neural network model is:
Y＝f 2 [W 2 ·f 1 (W 1 ·X-B 1 )-B 2 ]
W 1 、B 1 respectively a weight matrix and a deviation matrix from an input layer to a hidden layer, W 2 、B 2 Respectively a weight matrix and a deviation matrix from a hidden layer to an output layer, f 1 、f 2 The output vector Y is the ultimate performance function of the propeller, and the input vector X is the function of the environmental parameter, the material parameter and the performance parameter, wherein:
X＝f(A，V，C，K)
wherein A is an environmental parameter function, V is a material parameter function, C is a performance parameter function, and K is a correlation function; wherein a=k 1 (Loc，T 1 ，T 2 ，S)，Loc，T 1 ，T 2 S is respectively expressed as geographic position, ambient temperature, air humidity and wind speed; v=k 2 (L, M, N, O), L, M, N and O are the hardness, strength, brittleness and plasticity, respectively, of the parts in the propeller; c=k 3 (F，P，S 1 ，S 2 )，F，P，S 1 ，S 2 The four parameters of the thrust, the power, the rotating speed and the navigational speed of the propeller are respectively; k= { γ, ζ }, γ being the correlation coefficient of the environmental parameter a and the performance parameter C, ζ being the correlation coefficient of the material parameter V and the performance parameter C, wherein,
represents the conjugation of C;
represents the conjugation of C;
and obtaining the corresponding relation between the material parameter and the performance parameter in the limit environment by linear fitting of the performance parameter change function of the output propeller.
2. The method for simulating the ultimate performance of a propeller in marine equipment according to claim 1, wherein: training the established neural network by using the state parameters of the propeller under the previous normal working condition, and continuously adjusting hidden layer functions, the number of neurons and weights according to the errors between the output values and the output values under the normal working condition until the errors meet the requirements; and the normal working condition is an environment in which the state parameters of the propeller meet the standard conditions.
3. A method of simulating the ultimate performance of a propeller in marine equipment as claimed in claim 2, wherein: the neural network is trained through a BP algorithm, the BP algorithm is divided into a forward propagation stage and a backward propagation stage, and in the forward propagation stage, information is transmitted to an output layer from an input layer through gradual transformation; the backward propagation stage adjusts the weight matrix stage according to the performance error;
taking the error measure of the network for the p-th sample:
wherein: m is the number of neurons of the output layer; y is pj Ideal output vector representing the jth element of the p-th sample, Y pj For the network output value of the jth element of the p-th sample, finally, the error of the network with respect to the whole sample set is measured as:
E＝∑E p
the error calculated to the entire sample set according to the above equation is within a prescribed threshold range.
4. A method for simulating the ultimate performance of a propeller in marine equipment according to any one of claims 1-3, wherein the corresponding relation between the material parameter and the performance parameter obtained by linear fitting the performance parameter variation function of the output propeller under different environmental parameters is calculated by the following formula:
wherein a is n As a function of environmental parameters, x i As a function of the material parameters, y, of the component i in the propeller i Sigma x is a function of the performance parameters of component i in the propeller i y i The method is a corresponding relation formula of material parameters and performance parameters under different environmental parameters of a component i in the propeller.
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