Digital twin-driven optimization of gas exchange system of 2-stroke heavy fuel aircraft engine

Abstract Multiple parts in gas exchange system of 2-stroke heavy fuel aircraft engine with poppet valves lead to complicated manufacturing and inefficient assembly. Besides, real experimental optimization wastes lots of time and cost due to the increased valve parameters. To address the above issues, the paper proposes a digital twin (DT)-driven optimization method with several DT modules for the system to virtually simulate and optimize the parameters, performance and manufacturing with data interaction and recorded. The DT modules receive real-time feedback data from manufacturing measurements and performance tests to conduct the correction throughout the optimization process. The results demonstrate that the virtual engine model with feedback and correction is quite precise and credible compared with test results, and iterative calculation for optimal parameters is performed efficiently. With the guidance of virtual manufacturing, real manufacturing and assembly are arranged more reasonably and efficiency has been promoted. Real-world test found both power and gas exchange performance improved about 4% under various engine speeds and loads, which verified the effectiveness of DT-driven optimization. The study achieves the integration between virtual and real worlds of the system performance and manufacturing, which facilitates the development of aircraft engine smart manufacturing.

[1]  Andrew Kusiak,et al.  Data-driven smart manufacturing , 2018, Journal of Manufacturing Systems.

[2]  Wang Yu,et al.  Research on sealing performance and self-acting valve reliability in high-pressure oil-free hydrogen compressors for hydrogen refueling stations , 2010 .

[3]  Ronay Ak,et al.  A Survey of the Advancing Use and Development of Machine Learning in Smart Manufacturing. , 2018, Journal of manufacturing systems.

[4]  Shuiting DING,et al.  Numerical and experimental investigation on dynamic performance of bump foil journal bearing based on journal orbit , 2020 .

[5]  Xiang-Rong Li,et al.  Evaluating the scavenging process by the scavenging curve of an opposed-piston, two-stroke (OP2S) diesel engine , 2019, Applied Thermal Engineering.

[6]  Yi Cai,et al.  Numerical and experimental investigation on thermohydrodynamic performance of turbocharger rotor-bearing system , 2017 .

[7]  Andrew Y. C. Nee,et al.  Digital twin driven prognostics and health management for complex equipment , 2018 .

[8]  Ben Richard Hughes,et al.  The development of modelling tools to improve energy efficiency in manufacturing processes and systems , 2019, Journal of Manufacturing Systems.

[9]  Kumaran Kadirgama,et al.  The two-stroke poppet valve engine. Part 1: Intake and exhaust ports flow experimental assessments , 2017 .

[10]  Ali Navid,et al.  Comparing multi-objective non-evolutionary NLPQL and evolutionary genetic algorithm optimization of a DI diesel engine: DoE estimation and creating surrogate model , 2016 .

[11]  Meng Zhang,et al.  Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing , 2017, IEEE Access.

[12]  Sandro Wartzack,et al.  Shaping the digital twin for design and production engineering , 2017 .

[13]  Shuiting Ding,et al.  Investigation on performance and implementation of Tesla turbine in engine waste heat recovery , 2019, Energy Conversion and Management.

[14]  Fei Tao,et al.  Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison , 2018, IEEE Access.

[15]  Yuhang Liu,et al.  Study on the synthetic scavenging model validation method of opposed-piston two-stroke diesel engine , 2016 .

[16]  Paul Conway,et al.  smaRTI—A cyber-physical intelligent container for industry 4.0 manufacturing , 2019, Journal of Manufacturing Systems.

[17]  Abdul-Ghani Olabi,et al.  Design of experiment study of the parameters that affect performance of three flow plate configurations of a proton exchange membrane fuel cell , 2010 .

[18]  Chih-Hsing Chu,et al.  Continuity-preserving tool path generation for minimizing machining errors in five-axis CNC flank milling of ruled surfaces , 2020 .

[19]  Zhang Zhen-y.  Influence of Scavenging Port Parameters on Scavenging Process in Opposed-Piston Two-Stroke Diesel Engine , 2014 .

[20]  Yu Zhang,et al.  Multi-Objective Optimization for High Strength Steel Sheet Metal Forming Process Based on Response Surface Methodology , 2010 .

[21]  Hua Zhao,et al.  Effects of valve timing, valve lift and exhaust backpressure on performance and gas exchanging of a two-stroke GDI engine with overhead valves , 2016 .

[22]  David Romero,et al.  Smart manufacturing: Characteristics, technologies and enabling factors , 2019 .

[23]  D. Axinte,et al.  Development of a novel system for in-situ repair of aeroengine airfoil via pulsed laser ablation , 2020 .

[24]  F Tao,et al.  Theories and technologies for cyber-physical fusion in digital twin shop-floor , 2017 .

[25]  Meng Zhang,et al.  Equipment energy consumption management in digital twin shop-floor: A framework and potential applications , 2018, 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC).

[26]  Ricardo Novella,et al.  Analysis of the combustion process, pollutant emissions and efficiency of an innovative 2-stroke HSDI engine designed for automotive applications , 2013 .

[27]  G Srinivas,et al.  Recent developments in turbomachinery component materials and manufacturing challenges for aero engine applications , 2018 .

[28]  Ming Zhou,et al.  Design and optimization of a novel electrically controlled high pressure fuel injection system for heavy fuel aircraft piston engine , 2018, Chinese Journal of Aeronautics.

[29]  Yan Wang,et al.  Experimental and numerical investigation on micro gas turbine as a range extender for electric vehicle , 2020, Applied Thermal Engineering.

[30]  Annamaria Gisario,et al.  Metal additive manufacturing in the commercial aviation industry: A review , 2019, Journal of Manufacturing Systems.

[31]  Macklini Dalla Nora,et al.  Experimental and numerical study of a two-stroke poppet valve engine fuelled with gasoline and ethanol , 2016 .

[32]  Yu Zhou,et al.  A novel blade tip clearance measurement method based on event capture technique , 2020 .

[33]  Cosmin E. Dumitrescu,et al.  Single and double Wiebe function combustion model for a heavy-duty diesel engine retrofitted to natural-gas spark-ignition , 2019, Applied Energy.

[34]  Andrew Y. C. Nee,et al.  Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison , 2019, Engineering.

[35]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[36]  Cosmin E. Dumitrescu,et al.  Flame development analysis in a diesel optical engine converted to spark ignition natural gas operation , 2018, Applied Energy.

[37]  Yong Zhou,et al.  Multi-Agent Based Hyper-Heuristics for Multi-Objective Flexible Job Shop Scheduling: A Case Study in an Aero-Engine Blade Manufacturing Plant , 2019, IEEE Access.

[38]  Shuiting Ding,et al.  High-altitude performance and improvement methods of poppet valves 2-stroke aircraft diesel engine , 2020 .

[39]  Ricardo Novella,et al.  Implementation of the Partially Premixed Combustion concept in a 2-stroke HSDI diesel engine fueled with gasoline , 2014 .

[40]  Yan Wang,et al.  Simulation and experimental investigation of swirl-loop scavenging in two-stroke diesel engine with two poppet valves , 2020 .

[41]  Zhao Rongli,et al.  Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system , 2020 .

[42]  Guo Li,et al.  Digital-twin-driven geometric optimization of centrifugal impeller with free-form blades for five-axis flank milling , 2020 .