CN115017821B - Matching design method and system for linear internal combustion power generation system - Google Patents
Matching design method and system for linear internal combustion power generation system Download PDFInfo
- Publication number
- CN115017821B CN115017821B CN202210705467.0A CN202210705467A CN115017821B CN 115017821 B CN115017821 B CN 115017821B CN 202210705467 A CN202210705467 A CN 202210705467A CN 115017821 B CN115017821 B CN 115017821B
- Authority
- CN
- China
- Prior art keywords
- parameters
- user
- design
- matching
- parameter
- 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
- 238000013461 design Methods 0.000 title claims abstract description 164
- 238000002485 combustion reaction Methods 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000010248 power generation Methods 0.000 title claims abstract description 36
- 239000000446 fuel Substances 0.000 claims abstract description 55
- 238000005457 optimization Methods 0.000 claims abstract description 48
- 238000004088 simulation Methods 0.000 claims abstract description 31
- 230000009467 reduction Effects 0.000 claims description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 claims description 9
- 230000004044 response Effects 0.000 claims description 9
- 239000007789 gas Substances 0.000 claims description 7
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 6
- 239000003502 gasoline Substances 0.000 claims description 4
- 230000002068 genetic effect Effects 0.000 claims description 4
- 230000003993 interaction Effects 0.000 claims description 4
- 230000002452 interceptive effect Effects 0.000 claims description 4
- 230000006835 compression Effects 0.000 claims description 3
- 238000007906 compression Methods 0.000 claims description 3
- 239000001257 hydrogen Substances 0.000 claims description 3
- 229910052739 hydrogen Inorganic materials 0.000 claims description 3
- 125000004435 hydrogen atom Chemical class [H]* 0.000 claims description 3
- 230000006872 improvement Effects 0.000 description 12
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005094 computer simulation Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
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
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
-
- G—PHYSICS
- 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
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- 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)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Physiology (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- General Health & Medical Sciences (AREA)
- Genetics & Genomics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a matching design method and a matching design system for a linear internal combustion power generation system, wherein the matching design method comprises the following steps: determining working medium parameters and fuel parameters, and inputting target frequency and target travel; setting upper and lower boundaries of parameters to be designed of the system, and optimizing multiple target parameters by taking user matching setting requirements as targets to obtain multiple groups of system optimization parameters meeting the requirements; giving quality and electromagnetic coefficient options according to the optimization parameters, and selecting expected quality and electromagnetic coefficient from the given options by a user; determining a plurality of groups of system optimization parameters according to the selection of a user; setting the boundary of system parameters; performing multi-objective optimization in parallel to obtain a plurality of groups of matching design parameters, and selecting a group of matching design schemes by a user; setting system parameters according to a matching design scheme selected by a user, and performing time domain and frequency domain simulation on the system; if the user requirements are not met, returning to reselecting the design scheme; if the user requirement is met, the system outputs the matching design parameter and the performance simulation of the system under the parameter.
Description
Technical Field
The invention relates to the technical field of linear internal combustion power generation systems, in particular to a matching design method and system of a linear internal combustion power generation system.
Background
In the field of new energy, the linear internal combustion power generation system is used as a future power form with huge potential and becomes a research hotspot for students and enterprises; the linear internal combustion power generation system mainly comprises a linear internal combustion engine and a linear motor.
Unlike traditional internal combustion system, the linear internal combustion power generation system has no crank-link mechanism, and has less mechanical limitation condition in the operation process, and the traditional internal combustion power matching design mode does not use the linear internal combustion power generation system. The running frequency, running stroke and motor performance of the linear internal combustion power generation system directly influence the performance of the linear internal combustion power system.
Among the prior art, there are the following disclosed techniques:
chinese patent CN202110024251.3, a free piston linear generator matching optimization method based on engine;
chinese patent CN202110024233.5, a free piston linear motor matching optimization method based on system output.
According to the technical scheme, only the power matching of an engine (internal combustion power) and a linear motor is considered, the quality selection of a moving part is optimized, and whether the engine parameters and the motor parameters which are matched and designed have running conditions or not is not considered; and the calculation of the internal combustion power part calculates the power through the rotating speed and the torque in the traditional engine power calculation mode. For the free piston linear motor (linear internal combustion power generation system) to move into linear reciprocating motion, the rotational motion is not generated, the accuracy of calculating power by using the rotational speed and the torque is low, the actual operation characteristics of the linear internal combustion power generation system are not considered, the matching design is not only needed to carry out power matching, but also the matching design is needed to meet the matching of the movement performance of the linear internal combustion power generation system (meet certain operation characteristics).
The linear internal combustion power generation system is used as a multi-physical field coupling system, has numerous matching design parameters, relates to multiple fields of internal combustion engine science, thermodynamics, electromagnetism and the like, and needs to consider the motion performance and the output performance of the system in matching design, and meanwhile, considers the actual software and hardware conditions and simplifies the design flow. The field does not form a feasible matching design method of the system, the linear internal combustion power generation system is used as a future power form, and the matching design method and the matching system of the system need to be formed so as to advance the research of the linear internal combustion power generation power system.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a matching design method and a matching design system for a linear internal combustion power generation system.
The invention discloses a matching design method of a linear internal combustion power generation system, which comprises the following steps:
step 1, a user sets working media and fuel, and working media parameters and fuel parameters are determined;
Step 2, a user inputs matching design requirements, wherein the matching design requirements comprise a target frequency com f and a target travel com s;
Step 3, setting the upper and lower boundaries of a system to-be-designed parameter k 1、k2、k3、k4 according to an expert system;
Step 4, carrying out multi-objective parameter optimization by taking user matching setting requirements as targets to obtain a plurality of groups of system optimization parameters k 1、k2、k3、k4 meeting the requirements;
step 5, giving a quality M and an electromagnetic coefficient K v option according to the optimization parameters, and selecting an expected quality and an electromagnetic coefficient (optional) from the given option by a user;
step 6, determining a plurality of groups of k 1、k2、k3、k4 according to the selection of a user;
step 7, setting the boundary of system parameters;
step 8, multi-objective optimization is performed in parallel to obtain a plurality of groups of matching design parameters;
Step 9, selecting a group of matching design schemes from the user;
step 10, setting system parameters according to a matching design scheme selected by a user, and performing time domain and frequency domain simulation on the system;
Step 11, if the user is not satisfied with the simulation result under the current matching design scheme, returning to reselect the matching design scheme; if the user requirement is met, the system outputs the matching design parameter and the performance simulation of the system under the parameter.
As a further improvement of the present invention, the step 1 includes:
The user selects working media, and the set selectable options include ideal gas and air;
The user selects fuel, and the set selectable options comprise gasoline, methanol, ethanol and hydrogen, and the user can select one working medium and one fuel;
after determining the fuel and working medium, the system calls corresponding attribute parameters; the fuel parameters comprise a fuel low heat value Q lhv, combustion efficiency eta c and a compression ratio CR, and the working medium parameters comprise a gas constant R, an isobaric specific heat capacity C p and an adiabatic coefficient gamma.
As a further improvement of the present invention, the step 2 includes:
The user inputs a design target frequency and a target travel, the target frequency is assigned to com f, and the target frequency is assigned to com s.
As a further improvement of the present invention, the step 4 includes:
considering the light weight design and the energy output maximization design of the system, the system design target is set as follows:
Taking the upper and lower boundaries of the user matching design requirements and parameters to be designed as constraints:
Where l a is the lower limit of the parameter k 1 to be designed, u a is the upper limit of the parameter k 1 to be designed, l b is the lower limit of the parameter k 2 to be designed, u b is the upper limit of the parameter k 2 to be designed, l c is the lower limit of the parameter k 3 to be designed, u c is the upper limit of the parameter k 3 to be designed, l d is the lower limit of the parameter k 4 to be designed, u d is the upper limit of the parameter k 4 to be designed, o(s) is the actual operation stroke in the system simulation, o (f) is the actual operation frequency in the system simulation, δ 1 is the design stroke allowable error, and δ 2 is the design frequency allowable error;
Based on a multi-objective genetic algorithm (NSGA-II algorithm), developing global optimization for the multi-objective optimization problem; under the conditions of given iteration times, population scale and crowding degree, iterative cross variation is carried out, and finally the pareto solution set of the system is obtained.
As a further improvement of the present invention, the step 5 includes:
The pareto solution set is a set of non-inferior solutions and consists of a plurality of groups of solutions, wherein the first three groups of solutions are selected as user options; wherein in each option:
M＝k2
kv＝k3
The user can select 1-3 options from the three options as constraint conditions of the subsequent matching design.
As a further improvement of the present invention, the step 6 includes:
The system determines solutions for continuing to execute subsequent operations according to the M and k v selected by the user, and if the user selects a plurality of options at the same time, the system executes the subsequent operations on all solutions selected by the user.
As a further improvement of the present invention, the step 7 includes:
The set boundary does not precisely limit the parameters, and the boundary setting is only carried out on the parameters from the aspect of the rationality of the physical meaning of the parameters; wherein the system parameters include structural parameters and operational parameters, i.e., { AFR, L max,D,p0,T0 }, etc.
As a further improvement of the present invention, the step 8 includes:
If the user selects a plurality of options in advance, starting parallel operation, and simultaneously developing a plurality of optimization problems, and if the user selects only one option, adopting single problem serial operation for subsequent optimization;
Aiming at each optimization problem, taking light weight, compactness and fuel economy as optimization targets, and solving AFR, D and L max、kt、T0、p0 parameters.
Considering the light weight, compactness and fuel economy design of the system, the system design targets are set as follows:
Constraint of boundary optimization problem determined according to user selection and expert system is:
Where L aa is a lower limit of the intake air temperature T 0, u aa is an upper limit of the intake air temperature T 0, L bb is a lower limit of the intake air pressure p 0, u bb is an upper limit of the intake air pressure p 0, L cc is a lower limit of the maximum stroke L max, u cc is an upper limit of the maximum stroke L max, L dd is a lower limit of the air-fuel ratio AFR, u dd is an upper limit of the air-fuel ratio AFR, L ee is a lower limit of the piston diameter D, and u ee is an upper limit of the piston diameter D;
based on NSGA-II algorithm, optimizing the multi-objective planning problem; under the conditions of given iteration times, population scale and crowding degree, iterating cross variation to finally obtain a pareto solution set of the system;
the obtained pareto solution set is a set of non-inferior solutions, and consists of a plurality of groups of solutions, and the first 3 groups of solution sets are selected for the selection of a subsequent user scheme.
As a further improvement of the present invention, the step 9 includes:
Three sets of solution sets The user can select a solution set from the solution sets as the subsequent simulation scheme.
As a further improvement of the present invention, the step 10 includes:
after the user selects, the parameters of the selected solution set are assigned to the corresponding variables in the model, and the system parameters are completely determined;
obtaining the running frequency and running stroke of the system under the current parameter matching scheme through time domain analysis of the system;
The system pole under the current system parameter matching scheme is obtained through frequency domain analysis of the system to judge the system stability, and the amplitude frequency and the phase frequency response of the system are obtained, so that a user can evaluate the current system design from the frequency domain angle.
As a further improvement of the present invention, the step 11 includes:
if some matching parameters have a difference greater than a preset threshold value from the expected parameters of the user or the hardware of the current user cannot meet the matched parameters in the current matching scheme, the user can select unsatisfactory current design, return to reselection, execute subsequent simulation and perform system time domain and frequency domain analysis;
If the user is satisfied with the current design, the system outputs all matching parameters in the current design, including structural parameters { M, k v,Lmax, D }, fuel parameters { Q lhv,ηc, CR }, operation parameters { AFR, p 0,T0,kt }, working medium parameters { R, C p, gamma } according to classifications, and simultaneously outputs the time domain characteristics { o(s), o (f) }, frequency domain responses (pole, bode patterns) of the system.
The invention also discloses a matching design system of the linear internal combustion power generation system, which comprises:
The linear internal combustion power generation system analysis module mainly comprises system modeling and analysis and parameter dimension reduction, is used for system analysis, completes system description and modeling by using a plurality of mutually uncorrelated design parameters, fully analyzes the model and the design parameters, and realizes parameter dimension reduction; design parameters can be divided into four types of structural parameters, fuel parameters, operating parameters and working medium parameters. The fuel parameter and the working medium parameter are set by a user independently, and the fuel type and the combustion improver type applied by the current design system are determined and can be used as matching basis. Under the condition that the fuel parameter and the working medium parameter are determined, the structural parameter and the operation parameter directly determine the motion performance and the output performance of the system, the two parameters are generated by matching the method, a system model is fully analyzed to convert the design parameter into four parameters { k 1,k2,k3,k4 }, and the matching design of the system is facilitated;
The relationship between the generated four parameters to be optimized and the original parameters is as follows:
The interactive design module of the linear internal combustion power generation system mainly comprises a system matching design (1), a system matching design (2) and a system matching design (3) which are used for matching design structural parameters and operation parameters, determining an optimization scheme through interaction with a user and performing system performance simulation. The system matching design (1) is used for matching the parameters to be optimized, and four parameters to be optimized are designed and matched according to the requirements of users on the operation characteristics of the system; the system matching design (2) is used for parameter restoration, and the parameters to be optimized are restored into structural parameters and operation parameters; the system matching design (3) is used for system simulation verification, and the fuel parameters and working medium parameters set by a user, the structural parameters interactively selected by the user and other structural parameters and operation parameters generated by the matching design are brought into a system model for system time domain and frequency domain simulation analysis.
As a further improvement of the present invention, the parameter dimension reduction and parameter restoration include:
all the parameters which are not related to each other in the design parameters are screened out, and the parameters are classified according to the following categories: structural parameters, fuel parameters, operating parameters, and working medium parameters;
Parameter dimension reduction is carried out to form four parameters to be designed: k 1、k2、k3、k4;
and restoring the parameters into four types of parameters by utilizing four design parameters matched with the design and user setting, wherein the four types of parameters can be further divided into user setting parameters, user selection parameters and system matching parameters.
Compared with the prior art, the invention has the beneficial effects that:
Aiming at the characteristics of the linear internal combustion power generation system, the invention adopts a layered matching design thought to complete the parameter matching design of the linear internal combustion power generation system, thereby greatly simplifying the complexity of the matching design and reducing the time cost of the matching design; through deep analysis of the model, design parameters are classified, parameter dimension reduction is carried out, the matching design essence of the linear internal combustion power generation system is grasped, the design complexity is simplified, and the calculation amount of the matching design is reduced; through optimization and optimizing modes, various solutions are provided for users; fully focusing on man-machine interaction in the matching design process, opening the selection right to the user, and making interactive selection by the user in consideration of actual software and hardware conditions, wherein the matching design parameters have more practical prototype development values; the method integrally adopts a reverse matching method, and reversely plans and deducts the system design parameters according to the operation characteristic and the output characteristic requirements, so that the method can be widely applied to the design and matching of the parameters of the linear internal combustion power generation system under the requirements of variable fuel, variable mechanical structure, variable motor structure, variable working condition and the like.
Drawings
FIG. 1 is a flow chart of a matching design method for a linear internal combustion power generation system according to an embodiment of the present invention;
FIG. 2 is a block diagram of a matched design system for a linear internal combustion power generation system in accordance with one embodiment of the present invention;
FIG. 3 is a schematic diagram of parameter dimension reduction and parameter restoration according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the amplitude-frequency and phase-frequency response of a system according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is described in further detail below with reference to the attached drawing figures:
as shown in fig. 1, the present invention provides a matching design method for a linear internal combustion power generation system, including:
step 1, a user sets working media and fuel, and working media parameters and fuel parameters are determined;
The method specifically comprises the following steps:
The user selects working media, and the set selectable options include ideal gas and air;
The user selects fuel, and the set selectable options comprise gasoline, methanol, ethanol and hydrogen, and the user can select one working medium and one fuel;
after determining the fuel and working medium, the system calls corresponding attribute parameters; the fuel parameters comprise a fuel low heat value Q lhv, combustion efficiency eta c and a compression ratio CR, and the working medium parameters comprise a gas constant R, an isobaric specific heat capacity C p and an adiabatic coefficient gamma.
Step 2, a user inputs matching design requirements, wherein the matching design requirements comprise a target frequency com f and a target travel com s;
The method specifically comprises the following steps:
The user inputs a design target frequency and a target travel, the target frequency is assigned to com f, and the target frequency is assigned to com s.
Step3, setting the upper and lower boundaries of the parameters to be designed of the system according to an expert system;
The method specifically comprises the following steps:
the upper and lower boundaries of the parameter k 1、k2、k3、k4 to be designed are set according to the expert system.
Step 4, carrying out multi-objective parameter optimization by taking user matching setting requirements as targets to obtain a plurality of groups of system optimization parameters k 1、k2、k3、k4 meeting the requirements;
The method specifically comprises the following steps:
considering the light weight design and the energy output maximization design of the system, the system design target is set as follows:
Taking the upper and lower boundaries of the user matching design requirements and parameters to be designed as constraints:
Where l a is the lower limit of the parameter k 1 to be designed, u a is the upper limit of the parameter k 1 to be designed, l b is the lower limit of the parameter k 2 to be designed, u b is the upper limit of the parameter k 2 to be designed, l c is the lower limit of the parameter k 3 to be designed, u c is the upper limit of the parameter k 3 to be designed, l d is the lower limit of the parameter k 4 to be designed, u d is the upper limit of the parameter k 4 to be designed, o(s) is the actual operation stroke in the system simulation, o (f) is the actual operation frequency in the system simulation, δ 1 is the design stroke allowable error, and δ 2 is the design frequency allowable error;
Based on a multi-objective genetic algorithm (NSGA-II algorithm), developing global optimization for the multi-objective optimization problem; under the conditions of given iteration times, population scale and crowding degree, iterative cross variation is carried out, and finally the pareto solution set of the system is obtained.
Step 5, giving a quality M and an electromagnetic coefficient K v option according to the optimization parameters, and selecting an expected quality and an electromagnetic coefficient (optional) from the given option by a user;
The method specifically comprises the following steps:
the pareto solution set is a set of non-inferior solutions and consists of a plurality of groups of solutions, wherein the first three groups of solutions are selected as user options; wherein in each option:
M＝k2
kv＝k3
The user can select 1-3 options from the three options as constraint conditions of the subsequent matching design.
Step 6, determining a plurality of groups of k 1、k2、k3、k4 according to the selection of a user;
The method specifically comprises the following steps:
The system determines solutions for continuing to execute subsequent operations according to the M and k v selected by the user, and if the user selects a plurality of options at the same time, the system executes the subsequent operations on all solutions selected by the user.
Step 7, setting the boundary of system parameters;
The method specifically comprises the following steps:
The set boundary does not precisely limit the parameters, and the boundary setting is only carried out on the parameters from the aspect of the rationality of the physical meaning of the parameters; the system parameters include structural parameters and operation parameters, namely { AFR, L max,D,p0,T0 }, and the like.
Step 8, multi-objective optimization is performed in parallel to obtain a plurality of groups of matching design parameters;
The method specifically comprises the following steps:
If the user selects a plurality of options in advance, starting parallel operation, and simultaneously developing a plurality of optimization problems, and if the user selects only one option, adopting single problem serial operation for subsequent optimization;
Aiming at each optimization problem, taking light weight, compactness and fuel economy as optimization targets, and solving AFR, D and L max、kt、T0、p0 parameters.
Considering the light weight, compactness and fuel economy design of the system, the system design targets are set as follows:
Constraint of boundary optimization problem determined according to user selection and expert system is:
Where L aa is a lower limit of the intake air temperature T 0, u aa is an upper limit of the intake air temperature T 0, L bb is a lower limit of the intake air pressure p 0, u bb is an upper limit of the intake air pressure p 0, L cc is a lower limit of the maximum stroke L max, u cc is an upper limit of the maximum stroke L max, L dd is a lower limit of the air-fuel ratio AFR, u dd is an upper limit of the air-fuel ratio AFR, L ee is a lower limit of the piston diameter D, and u ee is an upper limit of the piston diameter D;
Based on NSGA-II algorithm, optimizing the multi-objective planning problem; under the conditions of given iteration times, population scale and crowding degree, iterating cross variation to finally obtain a pareto solution set of the system;
the obtained pareto solution set is a set of non-inferior solutions, and consists of a plurality of groups of solutions, and the first 3 groups of solution sets are selected for the selection of a subsequent user scheme.
Step 9, selecting a group of matching design schemes from the user;
The method specifically comprises the following steps:
Three sets of solution sets The user can select a solution set from the solution sets as the subsequent simulation scheme.
Step 10, setting system parameters according to a matching design scheme selected by a user, and performing time domain and frequency domain simulation on the system;
The method specifically comprises the following steps:
after the user selects, the parameters of the selected solution set are assigned to the corresponding variables in the model, and the system parameters are completely determined;
obtaining the running frequency and running stroke of the system under the current parameter matching scheme through time domain analysis of the system;
The system pole under the current system parameter matching scheme is obtained through frequency domain analysis of the system to judge the system stability, and the amplitude frequency and the phase frequency response of the system are obtained, so that a user can evaluate the current system design from the frequency domain angle.
Step 11, if the user is not satisfied with the simulation result under the current matching design scheme, returning to reselect the matching design scheme; if the user requirements are met, the system outputs the matching design parameters and the performance simulation of the system under the parameters;
The method specifically comprises the following steps:
if some matching parameters have a difference greater than a preset threshold value from the expected parameters of the user or the hardware of the current user cannot meet the matched parameters in the current matching scheme, the user can select unsatisfactory current design, return to reselection, execute subsequent simulation and perform system time domain and frequency domain analysis;
If the user is satisfied with the current design, the system outputs all matching parameters in the current design, including structural parameters { M, k v,Lmax, D }, fuel parameters { Q lhv,ηc, CR }, operation parameters { AFR, p 0,T0,kt }, working medium parameters { R, C p, gamma } according to classifications, and simultaneously outputs the time domain characteristics { o(s), o (f) }, frequency domain responses (pole, bode patterns) of the system.
As shown in fig. 2, the present invention provides a matching design system of a linear internal combustion power generation system, comprising:
The linear internal combustion power generation system analysis module mainly comprises system modeling and analysis and parameter dimension reduction, is used for system analysis, completes system description and modeling by using a plurality of mutually uncorrelated design parameters, fully analyzes the model and the design parameters, and realizes parameter dimension reduction; design parameters can be divided into four types of structural parameters, fuel parameters, operating parameters and working medium parameters. The fuel parameter and the working medium parameter are set by a user independently, and the fuel type and the combustion improver type applied by the current design system are determined and can be used as matching basis. Under the condition that the fuel parameter and the working medium parameter are determined, the structural parameter and the operation parameter directly determine the motion performance and the output performance of the system, the two parameters are generated by matching the method, a system model is fully analyzed to convert the design parameter into four parameters { k 1,k2,k3,k4 }, and the matching design of the system is facilitated;
The relationship between the generated four parameters to be optimized and the original parameters is as follows:
The interactive design module of the linear internal combustion power generation system mainly comprises a system matching design (1), a system matching design (2) and a system matching design (3) which are used for matching design structural parameters and operation parameters, determining an optimization scheme through interaction with a user and performing system performance simulation. The system matching design (1) is used for matching the parameters to be optimized, and four parameters to be optimized are designed and matched according to the requirements of users on the operation characteristics of the system; the system matching design (2) is used for parameter restoration, and the parameters to be optimized are restored into structural parameters and operation parameters; the system matching design (3) is used for system simulation verification, and the fuel parameters and working medium parameters set by a user, the structural parameters interactively selected by the user and other structural parameters and operation parameters generated by the matching design are brought into a system model for system time domain and frequency domain simulation analysis.
As shown in fig. 3, the parameter dimension reduction and parameter restoration of the present invention includes:
all the parameters which are not related to each other in the design parameters are screened out, and the parameters are classified according to the following categories: structural parameters, fuel parameters, operating parameters, and working medium parameters;
Parameter dimension reduction is carried out to form four parameters to be designed: k 1、k2、k3、k4;
and restoring the parameters into four types of parameters by utilizing four design parameters matched with the design and user setting, wherein the four types of parameters can be further divided into user setting parameters, user selection parameters and system matching parameters.
Examples:
a matching design method of a linear internal combustion power generation system comprises the following steps:
S1, a user sets working media and fuel, working media parameters and fuel parameters are determined, ideal gas is selected by the working media, gasoline is selected by the fuel, and corresponding fuel parameters and working media parameters are determined:
S2, the user input matches the design requirement, wherein the design requirement comprises a target frequency and a target stroke, the design target frequency com f is 15Hz, and the stroke com s is 0.016m.
S3, setting a boundary of a parameter k 1、k2、k3、k4 to be designed of the system according to an expert system;
S4, carrying out multi-objective parameter optimization by taking a user set requirement as a target to obtain a plurality of groups of system optimization parameters k 1、k2、k3、k4 meeting the requirement;
The system design targets were set as follows:
The upper and lower boundaries of the parameters to be designed, which are set by the expert system, are used as constraints for matching the design requirements of users:
Based on a multi-objective genetic algorithm (NSGA-II algorithm), developing global optimization for the multi-objective optimization problem; under the conditions of the given iteration times 50, population scale 1000 and crowding degree, iterative cross variation is carried out, and a pareto solution set of the system is obtained.
S5, giving a quality M and an electromagnetic coefficient K v option according to the optimization parameters, and selecting an expected quality and an electromagnetic coefficient from the given option by a user; selecting:
s6, determining k 1、k2、k3、k4 according to the selection of a user, wherein the value is as follows:
S7, setting the boundary of system parameters such as AFR, L max、D、p0、T0、kt and the like according to an expert system;
S8, carrying out multi-objective optimization in parallel to obtain a plurality of groups of matching design parameters;
The system design targets were set as follows:
constraints on the optimization problem are:
based on NSGA-II algorithm, optimizing the optimization problem; under the conditions of the given iteration times 100, population scale 100 and crowding degree, iterative cross variation is carried out, and finally the pareto solution set of the system is obtained.
S9, selecting a group of matching design schemes from the user; the scheme is selected:
S10, setting system parameters according to a matching design scheme selected by a user, and performing time domain and frequency domain simulation on the system; obtaining the running frequency o (f) of the system under the current parameter matching scheme to be 14.6Hz through time domain analysis of the system, wherein the running stroke o(s) is 0.016m;
The poles of the current system are:
the pole is positioned at the left side of the virtual axis, so that the system is stable; as shown in fig. 4, the amplitude and phase frequency response of the system is presented to facilitate the user's evaluation of the current system design from a frequency domain perspective.
S11, users are satisfied with the simulation result of the current matching design, all matching parameters in the current design are output, the matching parameters comprise structural parameters { M, k v,Lmax, D }, fuel parameters { Q lhv,ηc, CR }, operation parameters { AFR, p 0,T0,kt }, working medium parameters { R, C p, gamma } according to classifications, and the time domain characteristics { o (S), o (f) }, frequency domain responses (pole, bode pictures) of the system are output simultaneously.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1.A matching design method of a linear internal combustion power generation system is characterized by comprising the following steps:
Setting working medium and fuel by a user, and determining working medium parameters and fuel parameters; the method specifically comprises the following steps: the user selects working media, and the set selectable options include ideal gas and air; the user selects fuel, and the set selectable options comprise gasoline, methanol, ethanol and hydrogen, and the user can select one working medium and one fuel; after determining the fuel and working medium, the system calls corresponding attribute parameters; the fuel parameters comprise a fuel low heat value Q lhv, combustion efficiency eta c and a compression ratio CR, and the working medium parameters comprise a gas constant R, an isobaric specific heat capacity C p and an adiabatic coefficient gamma;
The user inputs matching design requirements, including a target frequency com f and a target travel com s;
Setting the upper and lower boundaries of a system to-be-designed parameter k 1、k2、k3、k4;
performing multi-objective parameter optimization by taking user matching and setting requirements as targets to obtain a plurality of groups of system optimization parameters k 1、k2、k3、k4 meeting the requirements; the method specifically comprises the following steps: considering the light weight design and the energy output maximization design of the system, the system design target is set as follows:
Taking the upper and lower boundaries of the user matching design requirements and parameters to be designed as constraints:
Where l a is the lower limit of the parameter k 1 to be designed, u a is the upper limit of the parameter k 1 to be designed, l b is the lower limit of the parameter k 2 to be designed, u b is the upper limit of the parameter k 2 to be designed, l c is the lower limit of the parameter k 3 to be designed, u c is the upper limit of the parameter k 3 to be designed, l d is the lower limit of the parameter k 4 to be designed, u d is the upper limit of the parameter k 4 to be designed, o(s) is the actual operation stroke in the system simulation, o (f) is the actual operation frequency in the system simulation, δ 1 is the design stroke allowable error, and δ 2 is the design frequency allowable error;
Based on a multi-objective genetic algorithm, developing global optimization for the multi-objective optimization problem; under the conditions of given iteration times, population scale and crowding degree, iterating cross variation to finally obtain a pareto solution set of the system;
Giving a quality M and an electromagnetic coefficient K v option according to the optimization parameters, and selecting an expected quality and an electromagnetic coefficient from the given option by a user; the method specifically comprises the following steps: the pareto solution set is a set of non-inferior solutions and consists of a plurality of groups of solutions, wherein the first three groups of solutions are selected as user options; wherein in each option:
M＝k2
kv＝k3
The user can select 1-3 options from the three options as constraint conditions of subsequent matching design;
Determining a plurality of groups of k 1、k2、k3、k4 according to the selection of a user; the method specifically comprises the following steps: the system determines solutions for continuing to execute subsequent operations according to M and k v selected by the user, and if the user selects a plurality of options at the same time, the system executes the subsequent operations on all solutions selected by the user;
Setting the boundary of system parameters; the method specifically comprises the following steps: the set boundary does not precisely limit the parameters, and the boundary setting is only carried out on the parameters from the aspect of the rationality of the physical meaning of the parameters; wherein the system parameters include structural parameters and operational parameters;
Performing multi-objective optimization in parallel to obtain a plurality of groups of matching design parameters; the method specifically comprises the following steps: considering the light weight, compactness and fuel economy design of the system, the system design targets are set as follows:
Constraint of boundary optimization problem determined according to user selection and expert system is:
Where L aa is a lower limit of the intake air temperature T 0, u aa is an upper limit of the intake air temperature T 0, L bb is a lower limit of the intake air pressure p 0, u bb is an upper limit of the intake air pressure p 0, L cc is a lower limit of the maximum stroke L max, u cc is an upper limit of the maximum stroke L max, L dd is a lower limit of the air-fuel ratio AFR, u dd is an upper limit of the air-fuel ratio AFR, L ee is a lower limit of the piston diameter D, and u ee is an upper limit of the piston diameter D;
based on NSGA-II algorithm, optimizing the multi-objective optimization problem; under the conditions of given iteration times, population scale and crowding degree, iterating cross variation to finally obtain a pareto solution set of the system;
The obtained pareto solution set is a set of non-inferior solutions and consists of a plurality of groups of solutions, and the first 3 groups of solution sets are selected for the selection of a subsequent user scheme;
the user selects a set of matching designs from the set of matching designs;
Setting system parameters according to a matching design scheme selected by a user, and performing time domain and frequency domain simulation on the system; the method specifically comprises the following steps: after the user selects, the parameters of the selected solution set are assigned to the corresponding variables in the model, and the system parameters are completely determined; obtaining the running frequency and running stroke of the system under the current parameter matching scheme through time domain analysis of the system; the system pole under the current system parameter matching scheme is obtained through frequency domain analysis of the system to judge the system stability, and the amplitude frequency and the phase frequency response of the system are obtained, so that a user can evaluate the current system design from the frequency domain angle;
if the user is not satisfied with the simulation result under the current matching design scheme, returning to reselect the matching design scheme; if the user requirements are met, the system outputs the matching design parameters and the performance simulation of the system under the parameters; the method specifically comprises the following steps: if some matching parameters have a difference greater than a preset threshold value from the expected parameters of the user or the hardware of the current user cannot meet the matched parameters in the current matching scheme, the user can select unsatisfactory current design, return to reselection, execute subsequent simulation and perform system time domain and frequency domain analysis;
If the user is satisfied with the current design, the system outputs all matching parameters in the current design, including structural parameters { M, k v,Lmax, D }, fuel parameters { Q lhv,ηc, CR }, operation parameters { AFR, p 0,T0,kt }, working medium parameters { R, C p, gamma } according to classifications, and outputs the time domain characteristics { o(s), o (f) }, frequency domain responses of the system simultaneously.
2. A system for implementing the matching design method for a linear internal combustion power generation system as set forth in claim 1, comprising:
The linear internal combustion power generation system analysis module is used for carrying out analysis modeling on the system operation characteristics and the output characteristics and analyzing the influence of design parameters on the system performance; performing parameter dimension reduction on the design parameters from the model angle to generate four parameters { k 1,k2,k3,k4 } to be optimized; wherein,
The relationship between the generated four parameters to be optimized and the original parameters is as follows:
The interactive design module of the linear internal combustion power generation system mainly comprises a system matching design, a system matching design and a system matching design, and is used for matching design structural parameters and operation parameters, determining an optimization scheme through interaction with a user and performing system performance simulation.
3. The system of claim 2, wherein the parameter dimension reduction and parameter restoration comprises:
all the parameters which are not related to each other in the design parameters are screened out, and the parameters are classified according to the following categories: structural parameters, fuel parameters, operating parameters, and working medium parameters;
Parameter dimension reduction is carried out to form four parameters to be designed: k 1、k2、k3、k4;
and restoring the parameters into four types of parameters by utilizing four design parameters matched with the design and user setting, wherein the four types of parameters can be further divided into user setting parameters, user selection parameters and system matching parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210705467.0A CN115017821B (en) | 2022-06-21 | 2022-06-21 | Matching design method and system for linear internal combustion power generation system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210705467.0A CN115017821B (en) | 2022-06-21 | 2022-06-21 | Matching design method and system for linear internal combustion power generation system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115017821A CN115017821A (en) | 2022-09-06 |
| CN115017821B true CN115017821B (en) | 2024-07-05 |
Family
ID=83076534
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210705467.0A Active CN115017821B (en) | 2022-06-21 | 2022-06-21 | Matching design method and system for linear internal combustion power generation system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115017821B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111431212A (en) * | 2020-02-19 | 2020-07-17 | 国电新能源技术研究院有限公司 | Multi-controller-multi-target coordinated optimization method for wind power generation system |
| CN111581746A (en) * | 2020-05-11 | 2020-08-25 | 中国矿业大学 | Novel multi-objective optimization method for three-phase cylindrical switched reluctance linear generator |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101080048B1 (en) * | 2010-03-29 | 2011-11-07 | 동아대학교 산학협력단 | Optimal Design Algorithm of Direct-driven PM Wind Generator And Knowledge-Based Optimal Design Method for The Same |
| CN113627000B (en) * | 2021-07-30 | 2024-06-07 | 江苏大学 | Permanent magnet motor layering robust optimization design method based on parameter sensitive domain |
| CN113657033B (en) * | 2021-08-13 | 2023-07-28 | 哈尔滨工程大学 | Model-based gas turbine multi-objective optimization method |
-
2022
- 2022-06-21 CN CN202210705467.0A patent/CN115017821B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111431212A (en) * | 2020-02-19 | 2020-07-17 | 国电新能源技术研究院有限公司 | Multi-controller-multi-target coordinated optimization method for wind power generation system |
| CN111581746A (en) * | 2020-05-11 | 2020-08-25 | 中国矿业大学 | Novel multi-objective optimization method for three-phase cylindrical switched reluctance linear generator |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115017821A (en) | 2022-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dai et al. | Chamnet: Towards efficient network design through platform-aware model adaptation | |
| US20210382445A1 (en) | Control sequence generation system and methods | |
| CN111523749B (en) | Intelligent identification method for hydroelectric generating set model | |
| CN111931413A (en) | Method for predicting performance of high-altitude diesel engine of optimized extreme learning machine based on chaotic particle swarm with extreme disturbance | |
| CN116804706B (en) | Temperature prediction method and device for lithium battery of electric automobile | |
| Cao et al. | PSO-Stacking improved ensemble model for campus building energy consumption forecasting based on priority feature selection | |
| CN115169235A (en) | Super surface unit structure inverse design method based on improved generation of countermeasure network | |
| CN117311141A (en) | Modeling method for thermal power unit coordination control system integrating hierarchical parameter optimization and fuzzy neural network | |
| CN115017821B (en) | Matching design method and system for linear internal combustion power generation system | |
| Zhou et al. | Hierarchical surrogate-assisted evolutionary optimization framework | |
| Wei et al. | MOO-DNAS: Efficient neural network design via differentiable architecture search based on multi-objective optimization | |
| CN113935124A (en) | Multi-target performance optimization method for biodiesel for diesel engine | |
| Chen et al. | A Spark-based Ant Lion algorithm for parameters optimization of random forest in credit classification | |
| CN117913806A (en) | LightGBM-GRU wind power combination prediction method driven by fusion knowledge | |
| Verma et al. | A case study on the application of a genetic algorithm for optimization of engine parameters | |
| CN114934848B (en) | Fuzzy neural network modeling method for optimizing control of combustion performance of diesel engine | |
| CN116227753A (en) | Water resource optimal allocation method under variable environment | |
| CN116070498A (en) | Handheld rapid damage assessment device | |
| Saetta et al. | AbbottAE: an autoencoder for airfoil aerodynamics | |
| Luchun et al. | Application of multiobjective optimization and multivariate analysis in multiple energy systems: A case study of CGAM | |
| Lu et al. | Integration of simulation, learning and optimization to support engineering design | |
| Wang et al. | Improved artificial bee colony algorithm and its application in classification | |
| CN117077508B (en) | Performance prediction and optimization method for thermoelectric generator | |
| Pierret et al. | An integrated optimization system for turbomachinery blade shape design | |
| CN114336696B (en) | Capacity configuration method and system for megawatt hydrogen energy storage power station |
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 |