CN104951628A - Engine thermodynamic simulation model calibration method based on multi-objective optimization - Google Patents

Engine thermodynamic simulation model calibration method based on multi-objective optimization Download PDF

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CN104951628A
CN104951628A CN201510413948.4A CN201510413948A CN104951628A CN 104951628 A CN104951628 A CN 104951628A CN 201510413948 A CN201510413948 A CN 201510413948A CN 104951628 A CN104951628 A CN 104951628A
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engine
objection optimization
model
thermodynamics
dimension
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卢兆强
赵霄鹏
孟令群
洪进
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Wal Wuxi Good Fortune Automotive Engineering Co Ltd
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Wal Wuxi Good Fortune Automotive Engineering Co Ltd
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    • 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
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    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/82—Elements for improving aerodynamics

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Abstract

The invention discloses an engine thermodynamic simulation model calibration method based on multi-objective optimization. The method comprises steps as follows: S101, acquiring engine structure parameters and engine test data; S102, establishing a one-dimensional engine thermodynamic model; S103, establishing a coupling calculation model of the one-dimensional engine thermodynamic model and multi-objective optimization software; S104, selecting calibration parameters and setting ranges of the calibration parameters; S105, setting a calibration objective; S106, selecting a corresponding test design and optimization algorithm; S107, executing simulation iteration; S108, performing result analysis by using decision-making tools in the multi-objective optimization software. The method is based on computer simulation analysis, a multi-objective optimization algorithm and a numerical simulation technique are combined, automation of the calibration process is realized, accurate models can be calibrated quickly, the follow-up engine development is supported, the development efficiency is improved, the development cost is saved, and the development cycle is shortened.

Description

A kind of engine thermodynamics realistic model scaling method based on multiple-objection optimization
Technical field
The present invention relates to engine thermodynamics simulation technical field, particularly relate to a kind of engine thermodynamics realistic model scaling method based on multiple-objection optimization.
Background technology
In Modern Engine exploitation, the utilization of computer-aided engineering (CAE) means, can shorten the construction cycle greatly, saves cost of development.Engine thermodynamics emulation can the design of Fast Evaluation engine subsystems good and bad, suggest improvements, fast shaping for each subsystem provides strong support, but these work are based under engine thermodynamics model accurately prerequisite, therefore, the staking-out work of engine thermodynamics model is exactly the most important thing in whole engine thermodynamics simulation process.Because in engine thermodynamics model, adjustable parameter is numerous, with test figure to marking length consuming time, efficiency is low, and may attend to one thing and lose sight of another, and being difficult to model calibration accurately, needs skilled addressee to have very rich experience.
Summary of the invention
The object of the invention is to, by a kind of engine thermodynamics realistic model scaling method based on multiple-objection optimization, solve the problem that above background technology part is mentioned.
For reaching this object, the present invention by the following technical solutions:
Based on an engine thermodynamics realistic model scaling method for multiple-objection optimization, it comprises the steps:
S101, acquisition engine structure parameter and engine test data;
S102, set up engine one dimension thermodynamical model;
S103, set up the model for coupling of described engine one dimension thermodynamical model and multiple-objection optimization software;
S104, choose calibrating parameters and set its scope;
S105, setting spotting;
S106, select corresponding experimental aerodynamic forces algorithm;
S107, execution iteration of simulations;
Decision tool in S108, utilization multiple-objection optimization software carries out interpretation of result.
Especially, in described step S101, engine structure parameter includes but not limited to engine cylinder-body structural parameters, entering and exhaust channel structured data, intake and exhaust manifold structured data, air filter hierarchical structure data, exhaust system structure data, upper airway flow coefficient of discharge; Described engine test data include but not limited to power, moment of torsion, oil consumption, charging efficiency, frictional work, cylinder pressure, ignition angle, air-fuel ratio, row's temperature, VVT data, entering and exhaust channel pressure surge curve.
Especially, described step S102 specifically comprises: in engine one dimension simulation Software Platform, engine one dimension thermodynamical model is set up according to engine structure parameter, and engine structure parameters input in engine one dimension thermodynamical model, debugging is carried out to can normally run to engine one dimension thermodynamical model.
Especially, described step S103 specifically comprises: the model for coupling setting up described engine one dimension thermodynamical model and multiple-objection optimization software, drive one dimension thermodynamics simulation software to run by multiple-objection optimization software, and read the result of calculation of one dimension simulation software.
Especially, in described step S104, calibrating parameters includes but not limited to inlet manifold length, inlet manifold cavity volume, inlet manifold's diameter, admission cam extended period coefficient, admission cam lift coefficient, exhaust cam extended period coefficient, exhaust cam lift coefficient, CA50, intake and exhaust phase angle.
Especially, in described step S105, spotting includes but not limited to charging efficiency, moment of torsion, power, air inflow, fuel consumption.
Especially, in described step S106, experimental aerodynamic forces algorithm comprises Sobol serial method, NSGAII genetic algorithm.
Especially, in described step S106, optimized algorithm selects NSGAII genetic algorithm.
Especially, described step S107 specifically comprises: multiple-objection optimization software progressively carries out iterative computation according to the determined prioritization scheme of Sobol sequential test method for designing.
Especially, in described step S108, decision tool comprises Responds Surface Methodology (RSM).
The engine thermodynamics realistic model scaling method based on multiple-objection optimization that the present invention proposes is based on simulation analysis of computer, multi-objective optimization algorithm is adopted to combine with numerical simulation technology, achieve the robotization of calibration process, model accurately can be gone out by Fast Calibration, the exploitation follow-up for engine provides support, improve development efficiency, saved cost of development, shortened the construction cycle.
Accompanying drawing explanation
The engine thermodynamics realistic model scaling method process flow diagram based on multiple-objection optimization that Fig. 1 provides for the embodiment of the present invention;
The final calibration result curve map that Fig. 2 provides for the embodiment of the present invention.
Embodiment
Below in conjunction with drawings and Examples, the invention will be further described.Be understandable that, specific embodiment described herein is only for explaining the present invention, but not limitation of the invention.It also should be noted that, for convenience of description, illustrate only part related to the present invention in accompanying drawing but not full content.
Please refer to shown in Fig. 1, the engine thermodynamics realistic model scaling method process flow diagram based on multiple-objection optimization that Fig. 1 provides for the embodiment of the present invention.
Engine thermodynamics realistic model scaling method based on multiple-objection optimization in the present embodiment specifically comprises the steps:
S101, acquisition engine structure parameter and engine test data.In practical application, engine structure parameter includes but not limited to engine cylinder-body structural parameters, entering and exhaust channel structured data, intake and exhaust manifold structured data, air filter hierarchical structure data, exhaust system structure data, upper airway flow coefficient of discharge.Wherein, inlet manifold, air intake duct, exhaust manifold, exhaust duct get according to three-dimensional digital-to-analogue utilization GEM3D instrument is discrete, ensure that the precision of computation model.Described engine test data include but not limited to power, moment of torsion, oil consumption, charging efficiency, frictional work, cylinder pressure, ignition angle, air-fuel ratio, row's temperature, VVT data, entering and exhaust channel pressure surge curve.Because frictional work includes pumping loss, therefore need to cut after pumping loss from use.
S102, set up engine one dimension thermodynamical model.In engine one dimension simulation Software Platform, engine one dimension thermodynamical model is set up according to engine structure parameter, air intake duct, exhaust duct, inlet manifold, exhaust manifold adopt the discrete one-dimensional model got, model buildings is complete, and engine structure parameters input in engine one dimension thermodynamical model, debugging is carried out to can normally run to engine one dimension thermodynamical model.Wherein, testing the data that cannot obtain needs related personnel rule of thumb to adjust.
Combustion model, adopt VIBE combustion model, three combustion parameters of VIBE combustion model, initial angle, extended period, form factor, have different impacts to Combustion Release Heat separately.Heat transfer model, this computation model adopts Woschni1978 heat transfer model.Circulating analog is calculated, heat transfer process between working medium and cylinder inner wall not only affects the carrying out of cylinder interior process, and affect the thermal load of Heating Components and the heat of cooling medium, the coefficient of heat transfer and the internal surface temperature of combustion gas side must be studied for this reason.For the calculating of heat transfer coefficient, adopt formula:
α w = 130 · D - 0.2 · p 0.8 · T - 0.53 · [ C 1 · c m + C 2 · V s · T c j p c j · V c j · ( p - p c 0 ) ] 0.8
Inlet and exhaust valve coefficient of flow, coefficient of flow is and flows through the actual flow of air flue and the ratio of theoretical delivery, and scope is between 0-1.Coefficient of flow is the important indicator weighing negotiability, and coefficient of flow is larger, and illustrate that negotiability is large, pressure loss when fluid passes through is little.The formula that the present embodiment Zhang Caiyong AVL recommends calculates:
μσ = m · actual m . theo
Wherein represent actual flow [kg/s], representation theory flow [kg/s]
Theoretical delivery computing formula is as follows:
m · t h e o = A V · ρ · 2 · Δ p ρ m ρ = ρ 0 · ( p 0 - Δ p p 0 ) 1 κ
ρ m = 1 2 ( ρ 0 + ρ ) A V = d v 2 π 4
Wherein, A vrepresent valve seating area [m 2], d vrepresent valve seating inner ring diameter [m], ρ mrepresent gas average density [kg/m 3, ρ represents gas in the jar density [kg/m 3], ρ 0represent environment gas density [kg/m 3], P 0represent environmental pressure [Pa], Δ P represents inlet outlet pressure differential [Pa].The definition of other parameters, except above major parameter, intake and exhaust pressure, temperature, the parameters such as valve timing, gas handling system pressure drop, exhaust back pressure adjust according to test figure.
S103, set up the model for coupling of described engine one dimension thermodynamical model and multiple-objection optimization software.Set up the model for coupling of described engine one dimension thermodynamical model and multiple-objection optimization software, drive one dimension thermodynamics simulation software to run by multiple-objection optimization software, and read the result of calculation of one dimension simulation software.
S104, choose calibrating parameters and set its scope.Inlet manifold length, inlet manifold cavity volume, inlet manifold's diameter, admission cam extended period coefficient, admission cam lift coefficient, exhaust cam extended period coefficient, exhaust cam lift coefficient, CA50, intake and exhaust phase angle is included but not limited in calibrating parameters described in the present embodiment.
S105, setting spotting.Spotting includes but not limited to charging efficiency, moment of torsion, power, air inflow, fuel consumption in the present embodiment.
S106, select corresponding experimental aerodynamic forces algorithm.Wherein, described experimental aerodynamic forces algorithm comprises Sobol serial method, NSGAII genetic algorithm.This method is also the one of random series method, than the sampling of Random Sequence consistent random series method evenly, this method of the problem for 2-20 input variable is better.Described optimized algorithm have selected the NSGA-II after improvement in the present embodiment.NSGA-II algorithm is that Srinivas and Deb proposed on the basis of NSGA in 2000, and it is more superior than NSGA algorithm, is one of current most popular multi-objective Evolutionary Algorithm.
S107, execution iteration of simulations.Multiple-objection optimization software progressively carries out iterative computation according to the determined prioritization scheme of Sobol sequential test method for designing.Whole calculation process drives robotization, without the need to artificial interference, for the change procedure of paid close attention to parameter, can adopt on-line monitoring module, monitor in real time, if note abnormalities, can stop halfway and adjust parameter, then continues to calculate.
Decision tool in S108, utilization multiple-objection optimization software carries out interpretation of result.In the present embodiment, decision tool comprises Responds Surface Methodology (RSM).Because utilize Approximation Technology, matching output region based on result of calculation, can high-speed optimization be carried out with the Response surface meth od (RSM) that fitting function is optimized, so for needing the optimizing process carrying out a large amount of computing time, the time of optimization greatly can be shortened.The advantage of neural network (RBF) model comprises the very strong ability of approaching complex nonlinear function, need not assumptions, has the feature of black box, and pace of learning is fast, has fabulous generalization ability, stronger fault tolerance.
Be described to carry out simulation analysis to certain gasoline engine cooling system below.By technical scheme of the present invention, be intended to optimize best inlet manifold length, inlet manifold volume, inlet manifold's diameter, intake and exhaust cam molded line.
The demarcation of certain 2.4L petrol engine thermodynamical model is as follows: set up engine one dimension realistic model according to engine drawing, wherein inlet manifold, air intake duct, exhaust manifold, exhaust duct use according to three-dimensional digital-to-analogue that instrument is discrete to be got, and ensure that the precision of computation model.Burning adopts weber model, and heat transfer adopts Woschni model, and air filter pressure drop and exhaust back pressure are arranged according to whole vehicle state.Find during the power of engine and oil consumption curve and test figure contrast, in most of rotating speed model, although the analogue value is identical with trial value trend, error is all more than 5%, and maximum reaches 8%, therefore needs to demarcate realistic model.The model for coupling setting up multiple goal software and one dimension simulation analysis software carries out model calibration, and input variable and objective function arrange as shown in the table:
Computer capacity from 1000rpm to 6000rpm, every 400rpm point.Initial number is 10, and evolutionary generation is 100, and total emulation mode is 1000, and workstation CPU is 32 cores, about 12 hours computing times.Through the calculating of 1000 design points, the error of moment of torsion and oil consumption and trial value is within 1%, and model calibration completes, and may be used for engine simulation analysis and Optimization Work.As shown in Figure 2, in figure, 201 refer to moment of torsion analogue value curve to final calibration result, and 202 refer to experiment value curve.
Technical scheme of the present invention is based on simulation analysis of computer, multi-objective optimization algorithm is adopted to combine with numerical simulation technology, achieve the robotization of calibration process, model accurately can be gone out by Fast Calibration, the exploitation follow-up for engine provides support, improve development efficiency, saved cost of development, shortened the construction cycle.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, to those skilled in the art, the present invention can have various change and change.All do within spirit of the present invention and principle any amendment, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (10)

1., based on an engine thermodynamics realistic model scaling method for multiple-objection optimization, it is characterized in that, comprise the steps:
S101, acquisition engine structure parameter and engine test data;
S102, set up engine one dimension thermodynamical model;
S103, set up the model for coupling of described engine one dimension thermodynamical model and multiple-objection optimization software;
S104, choose calibrating parameters and set its scope;
S105, setting spotting;
S106, select corresponding experimental aerodynamic forces algorithm;
S107, execution iteration of simulations;
Decision tool in S108, utilization multiple-objection optimization software carries out interpretation of result.
2. the engine thermodynamics realistic model scaling method based on multiple-objection optimization according to claim 1, it is characterized in that, in described step S101, engine structure parameter includes but not limited to engine cylinder-body structural parameters, entering and exhaust channel structured data, intake and exhaust manifold structured data, air filter hierarchical structure data, exhaust system structure data, upper airway flow coefficient of discharge; Described engine test data include but not limited to power, moment of torsion, oil consumption, charging efficiency, frictional work, cylinder pressure, ignition angle, air-fuel ratio, row's temperature, VVT data, entering and exhaust channel pressure surge curve.
3. the engine thermodynamics realistic model scaling method based on multiple-objection optimization according to claim 1, it is characterized in that, described step S102 specifically comprises: in engine one dimension simulation Software Platform, engine one dimension thermodynamical model is set up according to engine structure parameter, and engine structure parameters input in engine one dimension thermodynamical model, debugging is carried out to can normally run to engine one dimension thermodynamical model.
4. the engine thermodynamics realistic model scaling method based on multiple-objection optimization according to claim 1, it is characterized in that, described step S103 specifically comprises: the model for coupling setting up described engine one dimension thermodynamical model and multiple-objection optimization software, drive one dimension thermodynamics simulation software to run by multiple-objection optimization software, and read the result of calculation of one dimension simulation software.
5. the engine thermodynamics realistic model scaling method based on multiple-objection optimization according to claim 1, it is characterized in that, in described step S104, calibrating parameters includes but not limited to inlet manifold length, inlet manifold cavity volume, inlet manifold's diameter, admission cam extended period coefficient, admission cam lift coefficient, exhaust cam extended period coefficient, exhaust cam lift coefficient, CA50, intake and exhaust phase angle.
6. the engine thermodynamics realistic model scaling method based on multiple-objection optimization according to claim 1, it is characterized in that, in described step S105, spotting includes but not limited to charging efficiency, moment of torsion, power, air inflow, fuel consumption.
7. the engine thermodynamics realistic model scaling method based on multiple-objection optimization according to claim 1, it is characterized in that, in described step S106, experimental aerodynamic forces algorithm comprises Sobol serial method, NSGAII genetic algorithm.
8. the engine thermodynamics realistic model scaling method based on multiple-objection optimization according to claim 1, is characterized in that, in described step S106, optimized algorithm selects NSGAII genetic algorithm.
9. the engine thermodynamics realistic model scaling method based on multiple-objection optimization according to claim 1, it is characterized in that, described step S107 specifically comprises: multiple-objection optimization software progressively carries out iterative computation according to the determined prioritization scheme of Sobol sequential test method for designing.
10., according to the engine thermodynamics realistic model scaling method based on multiple-objection optimization one of claim 1 to 9 Suo Shu, it is characterized in that, in described step S108, decision tool comprises Responds Surface Methodology (RSM).
CN201510413948.4A 2015-07-14 2015-07-14 Engine thermodynamic simulation model calibration method based on multi-objective optimization Pending CN104951628A (en)

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CN115438551A (en) * 2022-10-10 2022-12-06 北京理工大学 CFD-FEM (computational fluid dynamics-finite element modeling) joint simulation method for calculating heat insulation efficiency of engine combustion chamber
CN117113551A (en) * 2023-07-11 2023-11-24 昆明理工大学 Engineering design-oriented diesel engine combustion system optimization design method

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CN105930552A (en) * 2016-02-22 2016-09-07 上海交通大学 Tolerance determining method based on digital simulation technique
CN105930552B (en) * 2016-02-22 2019-06-21 上海交通大学 Method is determined based on the tolerance of digital simulation technique
CN106021645A (en) * 2016-05-06 2016-10-12 北京航空航天大学 An aero-engine compressor performance reliability design method
CN106202641B (en) * 2016-06-29 2019-03-29 中国重汽集团济南动力有限公司 A kind of scaling method, system and the device of engine CFD Simulation Calculation
CN106202641A (en) * 2016-06-29 2016-12-07 中国重汽集团济南动力有限公司 Scaling method, system and the device of a kind of electromotor CFD Simulation Calculation
CN108170966A (en) * 2018-01-03 2018-06-15 青岛海聚仿真软件技术有限公司 A kind of EGR engine cooler optimization method based on finite element analysis
CN108170966B (en) * 2018-01-03 2021-02-23 青岛海聚仿真软件技术有限公司 EGR engine cooler optimization method based on finite element analysis
CN108804079A (en) * 2018-04-24 2018-11-13 无锡沃尔福汽车技术有限公司 A kind of EOL systems and its development approach
CN110608105A (en) * 2018-06-15 2019-12-24 上海汽车集团股份有限公司 Automatic calibration method and device for inflation efficiency
CN110608105B (en) * 2018-06-15 2021-11-23 上海汽车集团股份有限公司 Automatic calibration method and device for inflation efficiency
CN110377951A (en) * 2019-06-13 2019-10-25 东南大学 A kind of operating condition metering method of deep cooling high-pressure hydrogen storing system
CN111125909A (en) * 2019-12-24 2020-05-08 奇瑞汽车股份有限公司 Automatic calibration method of one-dimensional automobile thermal management model
CN111125909B (en) * 2019-12-24 2023-03-31 奇瑞汽车股份有限公司 Automatic calibration method of one-dimensional automobile thermal management model
CN111274673A (en) * 2020-01-07 2020-06-12 上海索辰信息科技有限公司 Optical product model optimization method and system based on particle swarm optimization
CN111274673B (en) * 2020-01-07 2021-02-23 上海索辰信息科技股份有限公司 Optical product model optimization method and system based on particle swarm optimization
CN111881505B (en) * 2020-08-04 2022-06-03 河北工业大学 Multi-objective optimization transformation decision method for existing building based on GA-RBF algorithm
CN111881505A (en) * 2020-08-04 2020-11-03 河北工业大学 Multi-objective optimization transformation decision method for existing building based on GA-RBF algorithm
CN112380613A (en) * 2020-09-11 2021-02-19 上汽通用五菱汽车股份有限公司 One-dimensional three-dimensional joint simulation method for automobile engine cooling system
CN112765798A (en) * 2021-01-08 2021-05-07 广西玉柴机器股份有限公司 Method and related device for generating engine model
CN113239533A (en) * 2021-04-28 2021-08-10 联合汽车电子有限公司 Engine exhaust system temperature model construction method and device and storage medium
CN115438551A (en) * 2022-10-10 2022-12-06 北京理工大学 CFD-FEM (computational fluid dynamics-finite element modeling) joint simulation method for calculating heat insulation efficiency of engine combustion chamber
CN115438551B (en) * 2022-10-10 2023-12-08 北京理工大学 CFD-FEM joint simulation method for calculating heat insulation efficiency of engine combustion chamber
CN117113551A (en) * 2023-07-11 2023-11-24 昆明理工大学 Engineering design-oriented diesel engine combustion system optimization design method
CN117113551B (en) * 2023-07-11 2024-06-11 昆明理工大学 Engineering design-oriented diesel engine combustion system optimization design method

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Application publication date: 20150930