{"title":"基于一维模型和规则的标定策略,改善涡轮增压火花点火发动机在整个发动机图谱上的性能","authors":"Dino Pirrello , Luigi Teodosio , Fabio Berni","doi":"10.1016/j.jestch.2024.101890","DOIUrl":null,"url":null,"abstract":"<div><div>This paper deals with the performance improvement of a turbocharged spark ignition (SI) engine redesigned by adopting a refined 1D model and a rule-based (RB) calibration strategy. The new SI engine operates without the throttle valve and combines an early intake valve strategy with a two-stage compression ratio device. The resulting SI unit represents a promising short-term technical solution and it is also suitable as a flexible fuel engine.</div><div>In a first phase, the engine model is validated with the experiments in the original configuration, and then it is virtually modified to obtain the redesigned solution. In this redesign process, the effects of the variations of engine geometry and valve strategy on combustion and performance are considered by phenomenological in-cylinder sub-models. Afterwards, the RB strategy is implemented into the re-designed engine model and assessed by comparing the results with the ones of an advanced calibration approach, based on an optimization with genetic algorithm performed by coupling the 1D model with an optimizer.</div><div>The RB method replicates with acceptable accuracy the numerical trends of performance and control parameters of the SI engine coming from the optimizer. Once the capability is verified, the RB strategy is adopted to compute the steady operating map of the redesigned engine, with significantly less effort than an optimization. The map provides noticeable benefits in terms of full load torque, fuel consumption at medium-to-low loads and a slight extension of the minimum fuel consumption region. The combustion stability is maintained at acceptable levels, although it is improvable at very low loads and speeds. The presented methodology has a general validity for conventional SI engines and can be efficiently exploited to support the redesign stage of SI units for improved performance, with reduced computational effort. It also offers a method to rapidly compute the operating map of SI engines for subsequent on-vehicle analyses.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"60 ","pages":"Article 101890"},"PeriodicalIF":5.1000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"1D model and rule-based calibration strategy to improve the performance of a turbocharged spark ignition engine over the whole engine map\",\"authors\":\"Dino Pirrello , Luigi Teodosio , Fabio Berni\",\"doi\":\"10.1016/j.jestch.2024.101890\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper deals with the performance improvement of a turbocharged spark ignition (SI) engine redesigned by adopting a refined 1D model and a rule-based (RB) calibration strategy. The new SI engine operates without the throttle valve and combines an early intake valve strategy with a two-stage compression ratio device. The resulting SI unit represents a promising short-term technical solution and it is also suitable as a flexible fuel engine.</div><div>In a first phase, the engine model is validated with the experiments in the original configuration, and then it is virtually modified to obtain the redesigned solution. In this redesign process, the effects of the variations of engine geometry and valve strategy on combustion and performance are considered by phenomenological in-cylinder sub-models. Afterwards, the RB strategy is implemented into the re-designed engine model and assessed by comparing the results with the ones of an advanced calibration approach, based on an optimization with genetic algorithm performed by coupling the 1D model with an optimizer.</div><div>The RB method replicates with acceptable accuracy the numerical trends of performance and control parameters of the SI engine coming from the optimizer. Once the capability is verified, the RB strategy is adopted to compute the steady operating map of the redesigned engine, with significantly less effort than an optimization. The map provides noticeable benefits in terms of full load torque, fuel consumption at medium-to-low loads and a slight extension of the minimum fuel consumption region. The combustion stability is maintained at acceptable levels, although it is improvable at very low loads and speeds. The presented methodology has a general validity for conventional SI engines and can be efficiently exploited to support the redesign stage of SI units for improved performance, with reduced computational effort. 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引用次数: 0
摘要
本文论述了采用改进的一维模型和基于规则(RB)的标定策略重新设计的涡轮增压火花点火(SI)发动机的性能改进。新型 SI 发动机工作时不使用节气门,并将早期进气阀策略与两级压缩比装置相结合。在第一阶段,发动机模型通过原始配置的实验进行验证,然后对其进行虚拟修改,以获得重新设计的解决方案。在这一重新设计过程中,发动机几何形状和气门策略的变化对燃烧和性能的影响是通过现象学缸内子模型来考虑的。然后,在重新设计的发动机模型中实施 RB 策略,并将其结果与先进的标定方法结果进行比较评估,该方法基于遗传算法优化,通过将一维模型与优化器耦合执行。一旦能力得到验证,就可以采用 RB 策略计算重新设计的发动机的稳定运行图,其工作量远远小于优化。该图谱在满负荷扭矩、中低负荷油耗和略微扩大最小油耗区域方面都有明显的优势。燃烧稳定性保持在可接受的水平,但在极低负荷和速度下仍可改善。所提出的方法对传统的 SI 发动机具有普遍的有效性,可以有效地用于支持 SI 装置的重新设计阶段,以提高性能,同时减少计算工作量。它还提供了一种快速计算 SI 发动机运行图的方法,以便进行后续的车载分析。
1D model and rule-based calibration strategy to improve the performance of a turbocharged spark ignition engine over the whole engine map
This paper deals with the performance improvement of a turbocharged spark ignition (SI) engine redesigned by adopting a refined 1D model and a rule-based (RB) calibration strategy. The new SI engine operates without the throttle valve and combines an early intake valve strategy with a two-stage compression ratio device. The resulting SI unit represents a promising short-term technical solution and it is also suitable as a flexible fuel engine.
In a first phase, the engine model is validated with the experiments in the original configuration, and then it is virtually modified to obtain the redesigned solution. In this redesign process, the effects of the variations of engine geometry and valve strategy on combustion and performance are considered by phenomenological in-cylinder sub-models. Afterwards, the RB strategy is implemented into the re-designed engine model and assessed by comparing the results with the ones of an advanced calibration approach, based on an optimization with genetic algorithm performed by coupling the 1D model with an optimizer.
The RB method replicates with acceptable accuracy the numerical trends of performance and control parameters of the SI engine coming from the optimizer. Once the capability is verified, the RB strategy is adopted to compute the steady operating map of the redesigned engine, with significantly less effort than an optimization. The map provides noticeable benefits in terms of full load torque, fuel consumption at medium-to-low loads and a slight extension of the minimum fuel consumption region. The combustion stability is maintained at acceptable levels, although it is improvable at very low loads and speeds. The presented methodology has a general validity for conventional SI engines and can be efficiently exploited to support the redesign stage of SI units for improved performance, with reduced computational effort. It also offers a method to rapidly compute the operating map of SI engines for subsequent on-vehicle analyses.
期刊介绍:
Engineering Science and Technology, an International Journal (JESTECH) (formerly Technology), a peer-reviewed quarterly engineering journal, publishes both theoretical and experimental high quality papers of permanent interest, not previously published in journals, in the field of engineering and applied science which aims to promote the theory and practice of technology and engineering. In addition to peer-reviewed original research papers, the Editorial Board welcomes original research reports, state-of-the-art reviews and communications in the broadly defined field of engineering science and technology.
The scope of JESTECH includes a wide spectrum of subjects including:
-Electrical/Electronics and Computer Engineering (Biomedical Engineering and Instrumentation; Coding, Cryptography, and Information Protection; Communications, Networks, Mobile Computing and Distributed Systems; Compilers and Operating Systems; Computer Architecture, Parallel Processing, and Dependability; Computer Vision and Robotics; Control Theory; Electromagnetic Waves, Microwave Techniques and Antennas; Embedded Systems; Integrated Circuits, VLSI Design, Testing, and CAD; Microelectromechanical Systems; Microelectronics, and Electronic Devices and Circuits; Power, Energy and Energy Conversion Systems; Signal, Image, and Speech Processing)
-Mechanical and Civil Engineering (Automotive Technologies; Biomechanics; Construction Materials; Design and Manufacturing; Dynamics and Control; Energy Generation, Utilization, Conversion, and Storage; Fluid Mechanics and Hydraulics; Heat and Mass Transfer; Micro-Nano Sciences; Renewable and Sustainable Energy Technologies; Robotics and Mechatronics; Solid Mechanics and Structure; Thermal Sciences)
-Metallurgical and Materials Engineering (Advanced Materials Science; Biomaterials; Ceramic and Inorgnanic Materials; Electronic-Magnetic Materials; Energy and Environment; Materials Characterizastion; Metallurgy; Polymers and Nanocomposites)