CN116757242A - Diesel engine intelligent parameter calibration method and device using improved gray wolf algorithm in digital twin model - Google Patents
Diesel engine intelligent parameter calibration method and device using improved gray wolf algorithm in digital twin model Download PDFInfo
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Abstract
A diesel engine intelligent parameter calibration method and device using an improved gray wolf algorithm in a digital twin model relates to the field of diesel engine combustion performance analysis. In order to solve the technical problems in the prior art that the traditional wolf algorithm is only selected randomly in the initialization process, and the calculation resources are less, the convergence is easier, but the local optimization is easy to fall into, the invention provides the technical scheme that: the intelligent parameter calibration method for the diesel engine in the digital twin model by using the improved gray wolf algorithm comprises the following steps: collecting and preprocessing parameters of a diesel engine cylinder to obtain at least three points which are used as input quantity of a gray wolf algorithm; collecting preset input quantity, objective function and iteration stopping condition of a pre-calibration model; solving an initial iteration point of the gray wolf algorithm, and starting iteration to eliminate weak gray wolves until reaching a preset stop iteration condition; and obtaining the position of the wolf according to the current wolf algorithm, and taking the position as a calibration result. The method is suitable for intelligent calibration of the diesel engine model parameters.
Description
Technical Field
The intelligent calibration method for the diesel engine model parameters relates to the field of diesel engine combustion performance analysis, in particular to an intelligent calibration method for the diesel engine model parameters.
Background
Diesel engines, a key power plant for marine propulsion, undergo a series of energy conversions, converting chemical energy of diesel fuel into thermal energy by combustion. The thermal energy is then further converted by expansion into mechanical energy which is output to perform work. In view of the ever-increasing restrictions on environmental and energy policies, optimizing diesel engine operating conditions to meet green, energy saving and safety requirements has been a major concern for researchers in this field. With the advanced convergence of technology such as artificial intelligence, internet of things (IoT) and big data, the shipbuilding industry is particularly faced with unprecedented challenges and opportunities. As ships move toward autonomy, exploration of intelligent ships has become a key research direction in the industry. In the context of intelligent development, digital twin technology is receiving increasing attention, and the core of the digital twin technology is to integrate a multi-physical and multi-scale model with high fidelity characteristics. Based on the high-precision models, dynamic and interactive data streams are used for drawing comprehensive life cycle activities of objects in the physical and virtual fields, and finally, the realization of intelligent development targets is promoted. Digital twinning technology has great promise because it supports deep fusion of the real world and the virtual world. Although the application of the digital twin technology in the field of marine diesel engines is still limited, the prospect provided by the digital twin technology has great value, which highlights the necessity of comprehensive research measures.
Digital twinning technology combines a high-fidelity model with real world data, and has attracted attention as a means to support intelligent development of various systems. Digital twin technology is a promising means for promoting the seamless integration of the real world and the virtual world, and although the application in the field of marine diesel engines is still limited, the future application potential of the digital twin technology has great value and deserves comprehensive research. The digital twin technology has the advantages of reducing cost, reducing risk, improving efficiency, monitoring and predicting engine performance in real time, and the like. However, digital twin diesel modeling involves many parameter assumptions that are difficult to directly measure, resulting in lower accuracy of experimental data. Therefore, it is necessary to calibrate multiple parameters simultaneously to achieve accurate calibration of the digital twin model. As diesel engine model complexity increases, calibration effort grows exponentially, and manual calibration becomes challenging. To address these challenges, the wolf algorithm may be used for fast intelligent calibration of digital twin models. The gray wolf algorithm has the advantages of simplicity, balance of exploration and development, simplicity of parameter algorithm involved in the use process, high convergence speed and the like. However, the traditional wolf algorithm is only randomly selected in the initialization process, so that although the called computing resources are less and are easier to converge, the initial random placement of the wolves can significantly influence the search process. In many cases, multiple different initializations are required to run to obtain satisfactory results, and meanwhile, the algorithm is poor in robustness, and different random inputs easily cause great differences in optimization effect and easily fall into local optimum.
Disclosure of Invention
In order to solve the problems in the prior art, the traditional wolf algorithm is only selected randomly in the initialization process, so that although the called computing resources are fewer and are easier to converge, the initial random placement of the wolves can obviously influence the searching process; in many cases, satisfactory results can be obtained by multiple times of different initialized operations, meanwhile, the algorithm robustness is poor, different random inputs easily cause great difference in optimization effect and easily fall into the technical problem of local optimization, and the technical scheme provided by the invention is as follows:
a diesel engine intelligent parameter calibration method using an improved gray wolf algorithm in a digital twin model, the method comprising:
collecting and preprocessing parameters of a diesel engine cylinder to obtain at least three points as input quantity of a gray wolf algorithm;
collecting preset input quantity, objective function and iteration stopping condition of a pre-calibration model;
solving an initial iteration point of the gray wolf algorithm, and starting iteration to eliminate weak gray wolves until reaching a preset stop iteration condition;
and obtaining the position of the wolf according to the current wolf algorithm, and taking the position as a calibration result.
Further, there is provided a preferred embodiment, the preprocessing comprising:
and processing the acquired diesel engine cylinder parameters by using three-point secondary interpolation, and enabling the difference between the parameters and the parameter interpolation obtained by simulation to be not larger than a preset value.
Further, there is provided a preferred embodiment, the diesel cylinder parameters including, but not limited to: cylinder pressure frequency and crank angle.
Further, a preferred embodiment is provided wherein the initial iteration point is solved by a Monte Carlo algorithm and the starting iteration is implemented using a greedy algorithm.
Further, a preferred embodiment is provided, the objective function being specifically:
wherein N is the total number of discrete points to be compared, j represents the number, j=1, 2,3,representing the j-th model simulation data, +.>Represents the j-th experimental data.
Further, a preferred embodiment is provided wherein each iteration preserves the first three best solutions currently obtained and improves the performance of other wolf individuals by updating the population's location.
Based on the same inventive concept, the invention also provides a diesel engine intelligent parameter calibration device using an improved gray wolf algorithm in a digital twin model, which comprises:
collecting and preprocessing parameters of a diesel engine cylinder to obtain at least three points, wherein the three points are used as modules of input quantity of a gray wolf algorithm;
the module is used for collecting preset input quantity, objective function and iteration stopping condition of the pre-calibration model;
solving an initial iteration point of the gray wolf algorithm, and starting iteration to eliminate weak gray wolves until a module of a preset stop iteration condition is reached;
and obtaining the position of the wolf according to the current wolf algorithm, and taking the position as a module of a calibration result.
Based on the same inventive concept, the invention also provides a digital twin diesel engine model, which is calibrated by the method.
Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program, which when read by a computer performs the method.
Based on the same inventive concept, the present invention also provides a computer comprising a processor and a storage medium, the computer performing the method when the processor reads a computer program stored in the storage medium.
Compared with the prior art, the technical scheme provided by the invention has the following advantages:
according to the intelligent parameter calibration method for the diesel engine, provided by the invention, the improved gray-wolf algorithm is used in the digital twin model, and the intelligent calibration of the model parameters is completed by adopting the gray-wolf algorithm, so that the research and development times of the model are shortened. The wolf optimization algorithm is a relatively mature algorithm that inspires a wolf population from north america.
According to the intelligent parameter calibration method for the diesel engine, the traditional gray-wolf algorithm is improved, the problem of premature convergence in the gray-wolf algorithm is solved, intelligent calibration of the diesel engine model facing the digital twin can be completed, and compared with the traditional manual parameter adjustment process, the time and the manual participation degree of calibration in the modeling process are greatly reduced.
The intelligent calibration work suitable for the diesel engine model parameters is suitable for the intelligent calibration work of the diesel engine model parameters.
Drawings
FIG. 1 is a schematic flow chart of an improved wolf algorithm according to one embodiment;
fig. 2 is a flow chart of a method for calibrating intelligent parameters of a diesel engine using a modified gray wolf algorithm in a digital twin model according to an embodiment.
Detailed Description
In order to make the advantages and benefits of the technical solution provided by the present invention more apparent, the technical solution provided by the present invention will now be described in further detail with reference to the accompanying drawings, in which:
in a first embodiment, the present embodiment is described with reference to fig. 1 and 2, and the present embodiment provides a diesel engine intelligent parameter calibration method using an improved wolf algorithm in a digital twin model, the method comprising:
collecting and preprocessing parameters of a diesel engine cylinder to obtain at least three points as input quantity of a gray wolf algorithm;
collecting preset input quantity, objective function and iteration stopping condition of a pre-calibration model;
solving an initial iteration point of the gray wolf algorithm, and starting iteration to eliminate weak gray wolves until reaching a preset stop iteration condition;
and obtaining the position of the wolf according to the current wolf algorithm, and taking the position as a calibration result.
The three points are specifically: and acquiring and preprocessing parameters of a diesel engine cylinder to obtain a cylinder pressure curve of the diesel engine cylinder, and dispersing the cylinder pressure curve into at least three points.
In particular, the method comprises the steps of,
the method comprises the following steps:
1. data preprocessing: the frequency of the experimental sensor for collecting cylinder pressure is delta exp The collection of all the collected cylinder pressure data isThe crank angles corresponding to the above are +.>m is the number of points of cylinder pressure acquired by experiments, wherein m=1, 2,3, when a digital twin-oriented diesel engine model is constructed, cylinder pressure data output by model simulation is +.>The crank angles corresponding to the above are +.>n is the number of cylinder pressures output by model simulation comparison, n=1, 2, 3.; wherein f is a function, the function is used for explaining that the algorithm can be calibrated for any parameter to be checked, if the heat dissipation rate is checked, f is the heat dissipation rate, if the cylinder pressure is checked, f is the cylinder pressure, and if the other parameters are checked, f is the other parameters; discretizing an experimental cylinder pressure curve into I points; (is the output of the diesel engine model, is the input of the particle swarm optimization) the cylinder pressure corresponding to each crank angle is +.>If the sampling frequency of the experimental data is not consistent with the crank angle interval of the simulation output, adopting three-point secondary Newton interpolation to approximately obtain the cylinder pressure under the crank angle, and calculating in the form of difference quotient:
the second order difference quotient formula and the third order difference quotient formula are as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the second order difference quotient of the two adjacent points,representing a third order difference quotient adjacent to the three points, < ->The function value corresponding to the crank angle is generally the cylinder pressure collected by experiments, and can also be the heat release rate, the gas flow energy, the heat dissipation rate of the cylinder wall and the like. The embodiment mainly analyzes data processing and model calibration, is not specific to a specific diesel engine model, and can be applied to the output of any theoretical diesel engine model.
The three-point quadratic newton interpolation formula is:
step 2: setting an objective function
In the traditional wolf algorithm, the objective function is a certain function capable of writing a mathematical expression, and in order to enable the wolf algorithm to be applied to intelligent calibration of a digital twin-oriented diesel engine model, the objective function is set as follows:
realizing;
wherein N is the total number of discrete points to be compared, j represents the number, j=1, 2,3,representing the j-th model simulation data, +.>Represents the j-th experimental data.
Step 3: the initial stop iteration condition is preset, and the following condition is generally satisfied:
1. the population of the residual wolves is less than epsilon n ；
2. In the two continuous initialization iterative processes, the arithmetic average value of the objective function of the optimal wolf population and the arithmetic average value of the objective function of the worst wolf population meet the following formulas:
wherein Y is ini_Best Is the arithmetic average value of the objective function corresponding to the optimal gray wolf population, Y ini_Worst Is the arithmetic average value epsilon of the objective function corresponding to the worst wolf population n For the self-defined positive integer, usually 4-10, epsilon is a positive number larger than 0 and smaller than 1, and usually 0.1-0.3.
Step 4: grey wolf algorithm initialization
The Monte Carlo method and the greedy partitioning method are combined to solve an initial solution, N gray wolves are selected, if k parameters need to be calibrated, each gray wolf carries k-dimensional information, each dimension is equally divided into 2 k Each population, calculating the arithmetic average value of objective functions corresponding to different small Monte Carlo blocks, eliminating 2 k-1 The arithmetic average of the objective functions is larger, the weak wolf population is short, and the rest excellent wolf populations are reserved. Stopping iteration when the initial iteration condition is met, ending initialization, and setting the objective function value corresponding to the stored current optimal calibration parameter as Y ini_best_temp 。
Step 5: the gray wolf algorithm performs parameter calibration on the diesel engine model,
where t represents the current number of iterations,and->Is a coefficient vector, ++>Is an intermediate variable +.>Is the position vector of the prey, i.e. +.>Representing the position vector of the wolf omega, t represents the current number of generations.
(Vector)And->Is calculated as follows:
wherein the method comprises the steps ofIn the iterative process from 2 to 0,/in the iterative process>Is [0,1 ]]Is a random vector in (a).
The position update formula of the three-head wolves in the wolf algorithm is as follows:
wherein the subscripts 1,2,3 respectively represent the positions of the updated alpha wolves, beta wolves and delta wolves, the subscripts alpha, beta, delta respectively represent the positions of the alpha wolves, beta wolves and delta wolves at the last iteration,are all intermediate coefficients.
In the wolf algorithm, the first three best solutions obtained so far are preserved and other wolf individuals are forced to do, namely: the agent (including omega) is searched for and its location is updated based on the optimal location.
The wolf attacks the hunting when it stops moving, thereby completing hunting. In order to mathematically model the proximity of prey, the reduction in the iterative processIs a value of (2); />The fluctuation range of (2) is also->Decrease, |A|<1 forcing the wolf to attack the prey, and outputting the parameter value and the objective function value of the calibrated diesel engine model at the end of iteration.
The wolf algorithm simulates the leader class and hunting mechanism of the wolves in nature; four types of wolves, alpha, beta, delta and omega, were used to simulate the leadership. The gray wolf algorithm can provide very competitive results in optimization processes, especially in the engineering field.
The second embodiment and the present embodiment are further defined on the method for calibrating intelligent parameters of a diesel engine using the modified wolf algorithm in the digital twin model provided in the first embodiment, where the preprocessing includes:
and processing the acquired diesel engine cylinder parameters by using three-point secondary interpolation, and enabling the difference between the parameters and the parameter interpolation obtained by simulation to be not larger than a preset value.
In a third embodiment, the present embodiment is further defined by a method for calibrating intelligent parameters of a diesel engine using an improved wolf algorithm in the digital twin model provided in the first embodiment, where the diesel engine cylinder parameters include: cylinder pressure frequency and crank angle.
In a fourth embodiment, the present embodiment is further defined on the method for calibrating intelligent parameters of a diesel engine using an improved gray wolf algorithm in the digital twin model provided in the first embodiment, the initial iteration point is solved by a monte carlo algorithm, and the initial iteration is implemented by a greedy algorithm.
In a fifth embodiment, the present embodiment is further defined on the method for calibrating intelligent parameters of a diesel engine using an improved wolf algorithm in the digital twin model provided in the first embodiment, where the objective function specifically is:
wherein N is the total number of discrete points to be compared, j represents the number, j=1, 2,3,representing the j-th model simulation data, +.>Represents the j-th experimental data.
In a sixth embodiment, the present embodiment is further defined by the method for calibrating intelligent parameters of a diesel engine using an improved wolf algorithm in the digital twin model provided in the first embodiment, where in the iterative process, each iteration stores the first three best solutions obtained currently, and improves the performance of other wolf individuals by updating the positions of the groups.
An embodiment seven, the present embodiment provides a diesel engine intelligent parameter calibration device using an improved gray wolf algorithm in a digital twin model, the device comprising:
collecting and preprocessing parameters of a diesel engine cylinder to obtain at least three points, wherein the three points are used as modules of input quantity of a gray wolf algorithm;
the module is used for collecting preset input quantity, objective function and iteration stopping condition of the pre-calibration model;
solving an initial iteration point of the gray wolf algorithm, and starting iteration to eliminate weak gray wolves until a module of a preset stop iteration condition is reached;
and obtaining the position of the wolf according to the current wolf algorithm, and taking the position as a module of a calibration result.
An eighth embodiment, the present embodiment, provides a digital twin diesel engine model, the model being calibrated by the method provided by the first embodiment.
Embodiment nine, the present embodiment provides a computer storage medium storing a computer program that, when read by a computer, performs the method provided in any one of embodiments one to six.
In a tenth embodiment, a computer is provided, including a processor and a storage medium, where the computer performs the method provided in any one of the first to sixth embodiments when the processor reads a computer program stored in the storage medium.
The technical solution provided by the present invention is described in further detail through several specific embodiments, so as to highlight the advantages and benefits of the technical solution provided by the present invention, however, the above specific embodiments are not intended to be limiting, and any reasonable modification and improvement, combination of embodiments, equivalent substitution, etc. of the present invention based on the spirit and principle of the present invention should be included in the scope of protection of the present invention.
In the description of the present invention, only the preferred embodiments of the present invention are described, and the scope of the claims of the present invention should not be limited thereby; furthermore, the descriptions of the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, for example, two, three, etc., unless specifically defined otherwise. Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention. Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory. It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments. In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
Claims (10)
1. The intelligent parameter calibration method for the diesel engine by using the improved gray wolf algorithm in the digital twin model is characterized by comprising the following steps of:
collecting and preprocessing parameters of a diesel engine cylinder to obtain at least three points as input quantity of a gray wolf algorithm;
collecting preset input quantity, objective function and iteration stopping condition of a pre-calibration model;
solving an initial iteration point of the gray wolf algorithm, and starting iteration to eliminate weak gray wolves until reaching a preset stop iteration condition;
and obtaining the position of the wolf according to the current wolf algorithm, and taking the position as a calibration result.
2. The method for intelligent parameter calibration of a diesel engine using a modified gray wolf algorithm in a digital twin model according to claim 1, wherein the preprocessing comprises:
and processing the acquired diesel engine cylinder parameters by using three-point secondary interpolation, and enabling the difference between the parameters and the parameter interpolation obtained by simulation to be not larger than a preset value.
3. The intelligent parameter calibration method for diesel engine using modified gray wolf algorithm in digital twin model according to claim 1, wherein the diesel engine cylinder parameters include but are not limited to: cylinder pressure frequency and crank angle.
4. The intelligent parameter calibration method for the diesel engine using the improved gray wolf algorithm in the digital twin model according to claim 1, wherein the initial iteration point is solved by a Monte Carlo algorithm, and the starting iteration is realized by adopting a greedy algorithm.
5. The intelligent parameter calibration method for the diesel engine using the modified gray wolf algorithm in the digital twin model according to claim 1, wherein the objective function is specifically:
wherein N is the total number of discrete points to be compared, j represents the number,representing the j-th model simulation data, +.>Represents the j-th experimental data.
6. The intelligent parameter calibration method for the diesel engine using the modified wolf algorithm in the digital twin model according to claim 1, wherein in the iterative process, the first three best solutions obtained currently are saved in each iteration, and the performance of other wolf individuals is improved by updating the positions of the groups.
7. An intelligent parameter calibration device for a diesel engine using an improved gray wolf algorithm in a digital twin model, which is characterized by comprising:
collecting and preprocessing parameters of a diesel engine cylinder to obtain at least three points, wherein the three points are used as modules of input quantity of a gray wolf algorithm;
the module is used for collecting preset input quantity, objective function and iteration stopping condition of the pre-calibration model;
solving an initial iteration point of the gray wolf algorithm, and starting iteration to eliminate weak gray wolves until a module of a preset stop iteration condition is reached;
and obtaining the position of the wolf according to the current wolf algorithm, and taking the position as a module of a calibration result.
8. A digital twin diesel engine model, characterized in that the model is calibrated by the method of claim 1.
9. Computer storage medium for storing a computer program, characterized in that the computer performs the method according to any one of claims 1-6 when the computer program is read by the computer.
10. Computer comprising a processor and a storage medium, characterized in that the computer performs the method according to any of claims 1-6 when the processor reads a computer program stored in the storage medium.
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