CN112800591B - Method for predicting engine performance parameter modifier and related device - Google Patents
Method for predicting engine performance parameter modifier and related device Download PDFInfo
- Publication number
- CN112800591B CN112800591B CN202110024183.0A CN202110024183A CN112800591B CN 112800591 B CN112800591 B CN 112800591B CN 202110024183 A CN202110024183 A CN 202110024183A CN 112800591 B CN112800591 B CN 112800591B
- Authority
- CN
- China
- Prior art keywords
- data
- supercharger
- performance parameter
- engine
- calculation result
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/12—Improving ICE efficiencies
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Combined Controls Of Internal Combustion Engines (AREA)
Abstract
The embodiment of the application provides a method for predicting a modifier of an engine performance parameter and a related device, which are used for predicting the modifier of an engine supercharger. The method in the embodiment of the application comprises the following steps: obtaining an engine model; acquiring a supercharger data sample, wherein the supercharger data sample is obtained by arranging and combining all data in a supercharger efficiency range interval and all data in a supercharger flow range interval; respectively substituting all data in the supercharger data sample into an engine model to generate a test engine model group; respectively operating the test engine models in the test engine model group to obtain performance parameter data; analyzing and calculating all performance parameter data to obtain a calculation result; identifying target parameters causing that the performance parameter data cannot reach the preset performance parameter data standard according to the calculation result; and comparing the target parameter with a target parameter in a performance parameter data standard preset by the engine model to determine a modifier predicted value of the target parameter.
Description
Technical Field
The embodiment of the application relates to the field of simulation, in particular to a method for predicting engine performance parameter modifiers and a related device.
Background
In practical situations, the performance of the engine after development can meet the development target. However, in actual production, the performance often changes or the dispersion is large, so that the performance of the engine cannot meet the preset requirement.
In the prior art, in order to reduce the problem, quality control is often enhanced on the quality of parts, but performance parameters cannot be controlled specifically, so that accurate countermeasures cannot be taken for rectification, and the engine is easy to generate a compliance risk during production.
Disclosure of Invention
The embodiment of the application provides a method for predicting a modifier of an engine performance parameter and a related device, which are used for predicting the modifier of an engine supercharger.
A first aspect of the present application provides a method of predicting a supercharger modifier for an engine, comprising:
obtaining an engine model;
acquiring a supercharger data sample, wherein the supercharger data sample is obtained by arranging and combining all data in a supercharger efficiency range interval and all data in a supercharger flow range interval;
respectively substituting all data in the supercharger data sample into an engine model to generate a test engine model group;
respectively operating the test engine models in the test engine model group to obtain performance parameter data;
analyzing and calculating all performance parameter data to obtain a calculation result, wherein the calculation result is distribution data of the performance parameter data when the engine model runs under the influence of different supercharger data samples, the calculation result comprises a calculation result of each data type in the supercharger data samples, and the data types comprise supercharger efficiency and supercharger flow;
identifying a target parameter which causes that the performance parameter data cannot reach a preset performance parameter data standard according to the calculation result;
and comparing the target parameter with a target parameter in a performance parameter data standard preset by the engine model to determine a modifier predicted value of the target parameter.
Optionally, before the obtaining the engine model, the method further includes:
acquiring N flow parameters in a supercharger efficiency range interval;
obtaining M efficiency parameters in a flow range interval of a supercharger;
combining the N flow parameters and the M efficiency parameters one by one to obtain N x M parameter results;
and collecting the N-M parameter results to obtain a supercharger data sample.
Optionally, substituting all data in the supercharger data sample into the engine model respectively, and generating a test engine model group includes:
setting the DOE test design models by taking the N-M parameter results as variables respectively, and generating test engine models respectively;
and collecting the test engine models to generate a test engine model group.
Optionally, the analyzing and calculating all the performance parameter data to obtain the calculation result includes:
collecting all performance parameter data to obtain a performance parameter data sample;
and calculating the average deviation and the standard deviation of the performance parameter data samples to obtain a calculation result.
Optionally, the identifying, according to the calculation result, a target parameter that causes the performance parameter data to fail to reach a preset performance parameter data standard includes:
determining the weight of the data type contained in the supercharger data sample on the performance parameter data according to the calculation result, wherein the weight is used for determining the influence of the data type contained in the supercharger data sample on the performance parameter data respectively;
and determining the target parameters according to the weights.
A second aspect of the present application provides an apparatus for predicting a modifier of a supercharger of an engine, comprising:
a first acquisition unit for acquiring an engine model;
the second acquisition unit is used for acquiring a supercharger data sample, and the supercharger data sample is obtained by arranging and combining all data in a supercharger efficiency range interval and all data in a supercharger flow range interval;
the generation unit is used for substituting all data in the supercharger data sample into the engine model respectively to generate a test engine model group;
the running unit is used for respectively running the test engine models in the test engine model group to obtain performance parameter data;
the data processing unit is used for analyzing and calculating all performance parameter data to obtain a calculation result, wherein the calculation result is distribution data of the performance parameter data when the engine model runs under the influence of different supercharger data samples, the calculation result comprises a calculation result of each data type in the supercharger data samples, and the data types comprise supercharger efficiency and supercharger flow;
the identification unit is used for identifying a target parameter which causes that the performance parameter data cannot reach a preset performance parameter data standard according to the calculation result;
and the comparison unit is used for comparing the target parameter with a target parameter in a performance parameter data standard preset by the engine model to determine a modifier predicted value of the target parameter.
Optionally, the apparatus further comprises:
the third acquisition unit is used for acquiring N flow parameters in the efficiency range interval of the supercharger;
the fourth acquisition unit is used for acquiring M efficiency parameters in the flow range interval of the supercharger;
the combination unit is used for combining the N flow parameters and the M efficiency parameters one by one to obtain N × M parameter results;
and the collecting unit is used for collecting the N-M parameter results to obtain a supercharger data sample.
Optionally, the generating unit includes:
the setting module is used for setting the DOE test design models by taking the N-M parameter results as variables respectively and generating test engine models respectively;
a first aggregation module is used for aggregating the test engine models to generate a test engine model group.
Optionally, the data processing unit includes:
the second set module is used for collecting all the performance parameter data to obtain performance parameter data samples;
and the data processing module is used for calculating the average deviation and the standard deviation of the performance parameter data samples to obtain a calculation result.
Optionally, the identification unit includes:
the first determination module is used for determining the weight of the data types contained in the supercharger data samples on the performance parameter data according to the calculation result, and the weight is used for determining the influence of the data types contained in the supercharger data samples on the performance parameter data respectively;
and the second determining module is used for determining the target parameters according to the weights.
A third aspect of the present application provides an apparatus for predicting a supercharger modifier for an engine, comprising:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the processor specifically performs the following operations:
obtaining an engine model;
acquiring a supercharger data sample, wherein the supercharger data sample is obtained by arranging and combining all data in a supercharger efficiency range interval and all data in a supercharger flow range interval;
respectively substituting all data in the supercharger data sample into an engine model to generate a test engine model group;
respectively operating the test engine models in the test engine model group to obtain performance parameter data;
analyzing and calculating all performance parameter data to obtain a calculation result, wherein the calculation result is the distribution data of the performance parameter data when the engine model runs under the influence of different supercharger data samples, the calculation result comprises the calculation result of each data type in the supercharger data samples, and the data types comprise supercharger efficiency and supercharger flow;
identifying a target parameter which causes that the performance parameter data cannot reach a preset performance parameter data standard according to the calculation result;
and comparing the target parameter with a standard target parameter in the performance parameter data preset by the engine model to determine a modifier predicted value of the target parameter.
According to the technical scheme, a test engine model group is generated through a supercharger data sample, a plurality of performance parameter data are obtained after operation, all the performance parameter data are analyzed and calculated to obtain a calculation result, the proportion of influence of each data type in the supercharger data sample on the performance parameter data when the engine operates is analyzed according to the calculation result, so that the target parameters causing the influence are identified, and the performance parameter data standard preset by the engine model are compared to determine the modification quantity predicted value of the target parameters. The sensitivity of each parameter of the supercharger on the performance of the engine is analyzed by the simulation method, and then the parameter which influences the performance of the engine to the maximum is identified, so that specific technical parameter requirements are obtained and serve as a basis for technical rectification, the problem of poor consistency of the engine is finally solved, and the existing compliance risk is reduced.
Drawings
FIG. 1 is a schematic flow chart illustrating one embodiment of a method for predicting an engine performance parameter modifier in an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating an embodiment of a method for predicting an engine performance parameter modifier according to an embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of an embodiment of an apparatus for predicting engine performance parameter modifiers in an embodiment of the present application;
FIG. 4 is a schematic structural diagram of another embodiment of an apparatus for predicting engine performance parameter modifiers in an embodiment of the present application;
FIG. 5 is a schematic structural diagram of another embodiment of an apparatus for predicting an engine performance parameter modifier in an embodiment of the present application.
Detailed Description
The embodiment of the application provides a method for predicting a modifier of an engine performance parameter and a related device, which are used for predicting the modifier of an engine supercharger.
The technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Specifically, the method can be used for equipment capable of performing simulation on the engine, such as a terminal and a system, and the like, and is not limited herein.
Referring to fig. 1, an embodiment of the present application provides an embodiment of a method for predicting a supercharger modifier of an engine, including:
101. obtaining an engine model;
the engine model is generated by modeling according to the entity of the engine to be tested and has the same characteristics and data as the engine to be tested.
In practical situations, the engine is put into production after being developed, and although test data during engine development meets an operation standard, performance changes or large dispersion may occur during operation of an actual product of the engine due to slight deviation in production, so that the engine needs to be modeled again according to the engine data during actual production for simulation test so as to adjust parameters of the engine, and then the cost for performing the test is reduced.
Specifically, the dispersion difference is that the working condition of a device is unstable when the engine runs, so that the performance data of the engine generates large fluctuation, and the operation standard mainly meets the national operation requirements on the running of the engine, such as the hard requirements on the influence of the engine on the surrounding environment in the running process, such as exhaust quality, noise decibel and the like.
102. Acquiring a supercharger data sample, wherein the supercharger data sample is obtained by arranging and combining all data in a supercharger efficiency range interval and all data in a supercharger flow range interval;
the engine parts of the same batch are obtained by performing reverse molding according to the models of the same size, and after the engines are produced in batches, performance data of a plurality of engines in operation can be obtained, so that the performance data suitable for the engine models in batches can be obtained, and sample data of the supercharger can be obtained from the performance data.
Specifically, when the engine actually runs, the engine controller can acquire running data of the engine in real time, so that whether the engine meets the performance parameter index set by the engine during testing is judged according to the running data, and the working condition of the engine can be reflected according to the working state change of the supercharger in the running process, so that when the running data of the engine does not meet the preset performance index, the running deviation of the engine can be judged according to the data of the supercharger, and similarly, the running data of the engine in actual running can be improved according to the changed production data of the supercharger of the engine, so that the modified engine can meet the preset performance parameter index.
103. Respectively substituting all data in the supercharger data sample into an engine model to generate a test engine model group;
the supercharger data sample is composed of a plurality of supercharger data, and only when enough supercharger data is obtained, enough data can support the test of parameter adjustment of the engine.
In practical situations, after a sufficient number of supercharger data samples are obtained, the supercharger data are input into the engine model one by one, and all generated test engine models are collected to obtain a test engine model group.
104. Respectively operating the test engine models in the test engine model group to obtain performance parameter data;
specifically, all the test engine models in the test engine model group are operated one by one, and performance parameter data of each test engine model are respectively obtained, the performance parameter data are contained in the operation data of the test engine model which is operated according to the supercharger data at present, and the performance parameter data are directly read by a controller which controls the test operation in the process of carrying out the test operation.
105. Analyzing and calculating all performance parameter data to obtain a calculation result, wherein the calculation result is the distribution data of the performance parameter data when the engine model runs under the influence of different supercharger data samples, the calculation result comprises the calculation result of each data type in the supercharger data samples, and the data types comprise supercharger efficiency and supercharger flow;
the analysis and calculation of all performance parameter data are used for more intuitively reflecting the influence of the change of supercharger data on the performance parameter data generated by the operation of an engine, specifically, the supercharger is provided with an air compression end and a turbine end, and the analysis and calculation of the performance parameter data can more accurately lock the reason that the performance parameter data does not meet the preset data through flow and efficiency.
106. Identifying a target parameter which causes that the performance parameter data cannot reach a preset performance parameter data standard according to the calculation result;
according to the steps, the supercharger is divided into an air compression end and a turbine end and is used for respectively processing air intake and exhaust of the engine, the supercharger can generate changes of air flow and efficiency in the air processing process, so that the operation data of the engine in the operation process deviates from the preset data, in actual situations, the air intake and the exhaust of the supercharger can be allowed to float at a threshold value under a standard situation, and the operation parameter data of a test engine model can be directly reflected according to the air flow and the efficiency of the air intake and/or the exhaust of the supercharger when the operation parameter data of the test engine model can not reach the preset performance parameter data standard, so that the target parameter of each data of the supercharger influencing the performance parameter data of the engine in operation is determined.
107. And comparing the target parameter with a target parameter in a performance parameter data standard preset by the engine model to determine a modifier predicted value of the target parameter.
After the target parameter is determined, the target parameter may be modified, and the modified parameter is set in the engine model to operate until the performance parameter data during the operation of the engine model meets the preset performance parameter data standard, at which time the supercharger data of the engine model is extracted and compared with the supercharger data in the performance parameter data in step 104 to obtain the predicted value of the modification amount suitable for the entity engine.
According to the technical scheme, a test engine model group is generated through a supercharger data sample, a plurality of performance parameter data are obtained after operation, all the performance parameter data are analyzed and calculated to obtain a calculation result, the proportion of influence of each data type in the supercharger data sample on the performance parameter data when the engine operates is analyzed according to the calculation result, so that the target parameters causing the influence are identified, and the performance parameter data standard preset by the engine model are compared to determine the modification quantity predicted value of the target parameters. The sensitivity of each parameter of the supercharger on the performance of the engine is analyzed by the simulation method, and then the parameter which influences the performance of the engine to the maximum is identified, so that specific technical parameter requirements are obtained and serve as a basis for technical rectification, the problem of poor consistency of the engine is finally solved, and the existing compliance risk is reduced.
Referring to fig. 2, another embodiment of a method for predicting a supercharger modifier for an engine is provided, including:
201. acquiring N flow parameters in a supercharger efficiency range interval;
202. obtaining M efficiency parameters in a flow range interval of a supercharger;
in step 201 and step 202, the operating condition of the supercharger according to the above description of the embodiment may be determined by the efficiency of the supercharger and the flow rate of the supercharger, during the operation of the physical engine, the change of the efficiency and the flow rate of the supercharger of the engine when the engine is running is obtained by the engine controller, when the change of the efficiency and the flow rate of the supercharger of a plurality of engines is obtained, the maximum value and the minimum value are extracted from the obtained data to generate the flow rate and the efficiency interval of the supercharger, and a sufficient amount of flow rate parameter and efficiency parameter are generated from the range interval according to the preset data amount required for generating the sample.
203. Combining the N flow parameters and the M efficiency parameters one by one to obtain N x M parameter results;
the acquired flow parameters and efficiency parameters are arranged and combined to generate supercharger working condition data under different conditions, and sufficient data support is provided for testing an engine model, so that the result obtained by testing the engine is more accurate.
204. Collecting the N-M parameter results to obtain a supercharger data sample;
and collecting all the generated parameter results to obtain a sample of supercharger data, wherein the sample of the supercharger data comprises all data combination modes in flow parameter and efficiency parameter intervals for testing.
205. Obtaining an engine model;
206. acquiring a supercharger data sample, wherein the supercharger data sample is obtained by arranging and combining all data in a supercharger efficiency range interval and all data in a supercharger flow range interval;
207. Setting the DOE test design models by taking the N-M parameter results as variables respectively, and generating test engine models respectively;
the DOE (DESIGN OF EXPERIMENT) model is a functional module capable OF carrying out EXPERIMENT DESIGN, and is used for parameterizing sample data OF the supercharger, setting the DOE model by taking the sample data OF the supercharger as variable data, and respectively generating a test engine model according to the DOE model with different parameters.
208. Assembling the test engine models to generate a test engine model group;
a plurality of engine models generated through the DOE model are collected to serve as test data to generate a test engine model group, the test engine model group is used for carrying out operation test on the test engine model in sequence, data omission can be prevented, and the integrity of data acquisition during testing is improved.
209. Respectively operating the test engine models in the test engine model group to obtain performance parameter data;
step 209 in this embodiment is similar to step 104 in the previous embodiment, and is not described herein again.
210. Collecting all performance parameter data to obtain a performance parameter data sample;
after all the performance parameter data are obtained, all the performance parameter data are collected, preliminary arrangement is carried out on the data, the data do not need to be extracted repeatedly in follow-up calculation, and follow-up calculation efficiency is improved.
211. Calculating the average deviation and the standard deviation of performance parameter data samples to obtain a calculation result, wherein the calculation result is the distribution data of the performance parameter data when the engine model runs under the influence of different supercharger data samples, the calculation result comprises the calculation result of each data type in the supercharger data samples, and the data types comprise supercharger efficiency and supercharger flow;
the average deviation and the standard deviation are calculated respectively according to the flow and the efficiency of a supercharger compressor end and a turbine end in a performance parameter data sample of the tested engine model, the difference value of the flow and the efficiency of the supercharger compressor end and the turbine end in the performance parameter data to the performance parameter data of the tested engine model is obtained by comparing the average deviation and the standard deviation calculation results, the larger the difference value is, the larger the influence on the performance parameter data is, and the higher the sensitivity of the value to the fact that the running data of the engine does not accord with the preset value is.
212. Determining the weight of the data type contained in the supercharger data sample on the performance parameter data according to the calculation result, wherein the weight is used for determining the influence of the data type contained in the supercharger data sample on the performance parameter data respectively;
after the sensitivities of the data to the performance parameter data during the running of the engine are determined, the importance degree of each data can be graded according to a preset weight, the performance parameter higher than the preset weight can be regarded as a parameter which causes the influence of the performance parameter data on the running of the engine, and after the weights are compared, the importance degree of the parameter is determined according to the height of a comparison result.
213. Determining the target parameters according to the weights;
in the embodiment of the present application, data greater than the weight is regarded as the target parameter, but the data is modified according to the priority of the degree of importance.
214. And comparing the target parameter with a target parameter in a performance parameter data standard preset by the engine model to determine a modifier predicted value of the target parameter.
Step 214 in this embodiment is similar to step 107 in the previous embodiment, and is not described herein again.
The supercharger data sample generation process, the data processing process of analysis and calculation and how to determine the target parameters are specifically described, and according to the supercharger data sample generation method and device, the number of times of experiments on the entity engine is reduced by predicting the modifier, and the cost of data correction on the engine is reduced.
Referring to fig. 3, the present application provides an embodiment of an apparatus for predicting a modifier of a supercharger of an engine, including:
a first acquisition unit 301 for acquiring an engine model;
a second obtaining unit 302, configured to obtain a supercharger data sample, where the supercharger data sample is obtained by permutation and combination of all data in a supercharger efficiency range interval and all data in a supercharger flow range interval;
the generation unit 303 is configured to substitute all data in the supercharger data sample into the engine model respectively to generate a test engine model group;
an operation unit 304, configured to operate the test engine models in the test engine model group respectively to obtain performance parameter data;
a data processing unit 305, configured to perform analysis and calculation on all performance parameter data to obtain a calculation result, where the calculation result is distribution data of the performance parameter data when the engine model operates under the influence of different supercharger data samples, and the calculation result includes a calculation result for each data type in the supercharger data samples, where the data type includes supercharger efficiency and supercharger flow;
an identifying unit 306, configured to identify, according to the calculation result, a target parameter that causes the performance parameter data to fail to reach a preset performance parameter data standard;
and a comparison unit 307, configured to compare the target parameter with a standard target parameter in performance parameter data preset by the engine model to determine a modifier predicted value of the target parameter.
In this embodiment, the functions of the units correspond to the steps in the embodiment shown in fig. 1, and are not described herein again.
Referring to fig. 4, another embodiment of the present application provides an apparatus for predicting a modifier of a supercharger of an engine, including:
a third obtaining unit 401, configured to obtain N flow parameters in a supercharger efficiency range interval;
a fourth obtaining unit 402, configured to obtain M efficiency parameters in a supercharger flow range interval;
a combining unit 403, configured to combine the N flow parameters and the M efficiency parameters one by one, so as to obtain N × M parameter results;
an aggregation unit 404, configured to aggregate the N × M parameter results to obtain a supercharger data sample.
A first acquisition unit 405 for acquiring an engine model;
a second obtaining unit 406, configured to obtain a supercharger data sample, where the supercharger data sample is obtained by permutation and combination of all data in a supercharger efficiency range interval and all data in a supercharger flow range interval;
a generating unit 407, configured to substitute all data in the supercharger data sample into the engine model respectively to generate a test engine model group;
an operation unit 408, configured to operate the test engine models in the test engine model group respectively to obtain performance parameter data;
the data processing unit 409 is configured to perform analysis and calculation on all performance parameter data to obtain a calculation result, where the calculation result is distribution data of the performance parameter data when the engine model operates under the influence of different supercharger data samples, the calculation result includes a calculation result for each data type in the supercharger data samples, and the data types include supercharger efficiency and supercharger flow;
an identifying unit 410, configured to identify, according to the calculation result, a target parameter that causes the performance parameter data to fail to reach a preset performance parameter data standard;
and the comparison unit 411 is used for comparing the target parameter with a standard target parameter in the performance parameter data preset by the engine model to determine a modifier predicted value of the target parameter.
In this embodiment of the present application, the generating unit 407 includes:
a setting module 4071, configured to set the N × M parameter results as variables respectively to set the DOE test design models, and generate test engine models respectively;
a first assembling module 4072 is configured to assemble the test engine models to generate a set of test engine models.
In this embodiment of the application, the data processing unit 409 includes:
a second set module 4091, configured to set all performance parameter data to obtain performance parameter data samples;
and the data processing module 4092 is used for calculating the average deviation and the standard deviation of the performance parameter data samples to obtain a calculation result.
In the embodiment of the present application, the identifying unit 410 includes:
a first determining module 4101, configured to determine, according to the calculation result, weights of data types included in the supercharger data sample on the performance parameter data, where the weights are used to determine influences of the data types included in the supercharger data sample on the performance parameter data, respectively;
a second determining module 4102, configured to determine the target parameter according to the weight.
In this embodiment, the functions of the units correspond to the steps in the embodiment shown in fig. 2, and are not described herein again.
Referring to fig. 5, another embodiment of an apparatus for predicting a modifier of a supercharger of an engine according to the present disclosure includes:
a processor 501, a memory 502, an input/output unit 503, and a bus 504;
the processor 501 is connected to the memory 502, the input/output unit 503, and the bus 504;
the processor 501 specifically executes operations corresponding to the method steps in fig. 1 to fig. 2.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Claims (8)
1. A method of predicting a supercharger modifier for an engine, comprising:
obtaining an engine model;
acquiring a supercharger data sample, wherein the supercharger data sample is obtained by arranging and combining all data in a supercharger efficiency range interval and all data in a supercharger flow range interval;
respectively substituting all data in the supercharger data sample into an engine model to generate a test engine model group;
respectively operating the test engine models in the test engine model group to obtain performance parameter data;
analyzing and calculating all performance parameter data to obtain a calculation result, wherein the calculation result is distribution data of the performance parameter data when the engine model runs under the influence of different supercharger data samples, the calculation result comprises a calculation result of each data type in the supercharger data samples, and the data types comprise supercharger efficiency and supercharger flow;
identifying a target parameter which causes that the performance parameter data cannot reach a preset performance parameter data standard according to the calculation result;
comparing the target parameter with a target parameter in a performance parameter data standard preset by the engine model to determine a modifier predicted value of the target parameter;
the identifying, according to the calculation result, the target parameter that causes the performance parameter data to fail to reach the preset performance parameter data standard includes:
determining the weight of the data type contained in the supercharger data sample on the performance parameter data according to the calculation result, wherein the weight is used for determining the influence of the data type contained in the supercharger data sample on the performance parameter data respectively;
and determining the target parameters according to the weights.
2. The method of claim 1, wherein prior to said obtaining an engine model, the method further comprises:
acquiring N flow parameters in a supercharger efficiency range interval;
obtaining M efficiency parameters in a flow range interval of a supercharger;
combining the N flow parameters and the M efficiency parameters one by one to obtain N x M parameter results;
and collecting the N-M parameter results to obtain a supercharger data sample.
3. The method of claim 2, wherein the substituting all data within the supercharger data samples into the engine model, respectively, to generate a set of test engine models comprises:
setting the DOE test design models by taking the N-M parameter results as variables respectively, and generating test engine models respectively;
and collecting the test engine models to generate a test engine model group.
4. The method according to any one of claims 1 to 3, wherein the performing the analysis calculation on all the performance parameter data to obtain the calculation result comprises:
collecting all performance parameter data to obtain a performance parameter data sample;
and calculating the average deviation and the standard deviation of the performance parameter data samples to obtain a calculation result.
5. An apparatus for predicting a supercharger modifier for an engine, comprising:
a first acquisition unit for acquiring an engine model;
the second acquisition unit is used for acquiring a supercharger data sample, and the supercharger data sample is obtained by arranging and combining all data in a supercharger efficiency range interval and all data in a supercharger flow range interval;
the generation unit is used for substituting all data in the supercharger data sample into the engine model respectively to generate a test engine model group;
the running unit is used for respectively running the test engine models in the test engine model group to obtain performance parameter data;
the data processing unit is used for analyzing and calculating all performance parameter data to obtain a calculation result, wherein the calculation result is distribution data of the performance parameter data when the engine model runs under the influence of different supercharger data samples, the calculation result comprises a calculation result of each data type in the supercharger data samples, and the data types comprise supercharger efficiency and supercharger flow;
the identification unit is used for identifying a target parameter which causes that the performance parameter data cannot reach a preset performance parameter data standard according to the calculation result;
the comparison unit is used for comparing the target parameter with a target parameter in a performance parameter data standard preset by the engine model to determine a modifier predicted value of the target parameter;
the identification unit includes:
a first determining module, configured to determine, according to the calculation result, weights of data types included in the supercharger data sample on the performance parameter data, where the weights are used to determine influences of the data types included in the supercharger data sample on the performance parameter data, respectively;
and the second determining module is used for determining the target parameters according to the weights.
6. The apparatus of claim 5, further comprising:
the third acquisition unit is used for acquiring N flow parameters in the efficiency range interval of the supercharger;
the fourth acquisition unit is used for acquiring M efficiency parameters in the flow range interval of the supercharger;
a combination unit, configured to combine the N flow parameters and the M efficiency parameters one by one, so as to obtain N × M parameter results;
and the aggregation unit is used for aggregating the N-M parameter results to obtain a supercharger data sample.
7. The apparatus of claim 6, wherein the generating unit comprises:
the setting module is used for setting the DOE test design models by taking the N-M parameter results as variables respectively and generating test engine models respectively;
a first aggregation module is used for aggregating the test engine models to generate a test engine model group.
8. The apparatus according to any one of claims 5 to 7, wherein the data processing unit comprises:
the second set module is used for collecting all the performance parameter data to obtain performance parameter data samples;
and the data processing module is used for calculating the average deviation and the standard deviation of the performance parameter data samples to obtain a calculation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110024183.0A CN112800591B (en) | 2021-01-08 | 2021-01-08 | Method for predicting engine performance parameter modifier and related device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110024183.0A CN112800591B (en) | 2021-01-08 | 2021-01-08 | Method for predicting engine performance parameter modifier and related device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112800591A CN112800591A (en) | 2021-05-14 |
| CN112800591B true CN112800591B (en) | 2023-03-21 |
Family
ID=75809413
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110024183.0A Active CN112800591B (en) | 2021-01-08 | 2021-01-08 | Method for predicting engine performance parameter modifier and related device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112800591B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110543715A (en) * | 2019-08-28 | 2019-12-06 | 东风汽车集团有限公司 | Two-stage supercharging system matching method based on simulation calculation |
| CN111788567A (en) * | 2018-08-27 | 2020-10-16 | 华为技术有限公司 | Data processing equipment and data processing method |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9551286B2 (en) * | 2011-04-22 | 2017-01-24 | Borgwarner Inc. | Turbocharger boost control using exhaust pressure estimated from engine cylinder pressure |
| US9964057B2 (en) * | 2015-10-19 | 2018-05-08 | General Electric Company | Method and system for adjusting engine cylinder operation |
| US10604278B2 (en) * | 2017-04-18 | 2020-03-31 | General Electric Company | Methods and apparatus to monitor health information of a turbine engine |
| CN107579893A (en) * | 2017-09-07 | 2018-01-12 | 广西玉柴机器股份有限公司 | Engine operating parameter measuring method based on SAEJ1939CAN buses |
| KR20200066751A (en) * | 2018-11-30 | 2020-06-11 | 현대자동차주식회사 | Engine control method for vehicle |
| CN110630393B (en) * | 2019-09-27 | 2022-04-26 | 潍柴动力股份有限公司 | Supercharger protection control method and device, controller, vehicle and equipment |
| CN110704509A (en) * | 2019-10-09 | 2020-01-17 | 北京百度网讯科技有限公司 | Data classification method, device, equipment and storage medium |
-
2021
- 2021-01-08 CN CN202110024183.0A patent/CN112800591B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111788567A (en) * | 2018-08-27 | 2020-10-16 | 华为技术有限公司 | Data processing equipment and data processing method |
| CN110543715A (en) * | 2019-08-28 | 2019-12-06 | 东风汽车集团有限公司 | Two-stage supercharging system matching method based on simulation calculation |
Non-Patent Citations (1)
| Title |
|---|
| 基于优化设计方法的超高负荷增压级气动设计;薛伟伟等;《科学技术与工程》;20190418(第11期);304-311 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112800591A (en) | 2021-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11436395B2 (en) | Method for prediction of key performance parameter of an aero-engine transition state acceleration process based on space reconstruction | |
| US7593804B2 (en) | Fixed-point virtual sensor control system and method | |
| US8793004B2 (en) | Virtual sensor system and method for generating output parameters | |
| JP4346291B2 (en) | Method and system for turbine engine diagnosis | |
| JP2010530179A (en) | Virtual sensor system and method | |
| US20090300422A1 (en) | Analysis method and system using virtual sensors | |
| CN110442911B (en) | High-dimensional complex system uncertainty analysis method based on statistical machine learning | |
| CN116975567B (en) | Method, system, equipment and storage medium for testing radiation interference resistance of server | |
| CN115373370A (en) | Method and system for monitoring running state of programmable controller | |
| CN113298162A (en) | Bridge health monitoring method and system based on K-means algorithm | |
| CN114926004B (en) | Method and system for evaluating fire resistance of ceramic matrix composite bus duct | |
| CN113191071B (en) | Method for virtually calibrating engine model and related device thereof | |
| CN114583767B (en) | Data-driven wind power plant frequency modulation response characteristic modeling method and system | |
| CN113098912B (en) | User account abnormity identification method and device, electronic equipment and storage medium | |
| CN112800591B (en) | Method for predicting engine performance parameter modifier and related device | |
| CN116450532B (en) | Multi-dimensional testing method for computer software performance | |
| CN108021739B (en) | Method for analyzing influence degree of parameters of real-time simulation model of high-power military diesel engine | |
| CN113642209B (en) | Structure implantation fault response data acquisition and evaluation method based on digital twinning | |
| CN115344495A (en) | Data analysis method and device for batch task test, computer equipment and medium | |
| CN113408076A (en) | Small sample mechanical residual life prediction method based on support vector machine model | |
| CN115146445A (en) | Method and related device for researching sensitivity of engine | |
| CN117271268B (en) | Cluster architecture performance evaluation method in digital computing platform | |
| CN114626234B (en) | Credibility evaluation method and system for digital twin combined model of equipment | |
| CN114329905B (en) | Method and device for evaluating reliability of full-range analog machine and computer equipment | |
| CN117850403B (en) | Dynamic detection method for initiating and controlling system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |