D. Minchev, R.A. Varbanets, Oleksandr Shumylo, V.I. Zalozh, N. Aleksandrovska, Pavlo Bratchenko, Thanh Hai Truong
{"title":"Digital Twin Test-Bench Performance for Marine Diesel Engine Applications","authors":"D. Minchev, R.A. Varbanets, Oleksandr Shumylo, V.I. Zalozh, N. Aleksandrovska, Pavlo Bratchenko, Thanh Hai Truong","doi":"10.2478/pomr-2023-0061","DOIUrl":null,"url":null,"abstract":"Abstract The application of Digital Twins is a promising solution for enhancing the efficiency of marine power plant operation, particularly their important components – marine internal combustion engines (ICE). This work presents the concept of applying a Performance Digital Twin for monitoring the technical condition and diagnosing malfunctions of marine ICE, along with its implementation on an experimental test-bench, based on a marine diesel-generator. The main principles of implementing this concept involve data transmission technologies, from the sensors installed on the engine to a server. The Digital Twin, also operating on the server, is used to automatically process the acquired experimental data, accumulate statistics, determine the current technical state of the engine, identify possible malfunctions, and make decisions regarding changes in operating programs. The core element of the Digital Twin is a mathematical model of the marine diesel engine’s operating cycle. In its development, significant attention was devoted to refining the fuel combustion model, as the combustion processes significantly impact both the engine’s fuel efficiency and the level of toxic emissions of exhaust gases. The enhanced model differs from the base model, by considering the variable value of the average droplets’ diameter during fuel injection. This influence on fuel vapourisation, combustion, and the formation of toxic components is substantial, as shown. Using the example of calibrating the model to the test results of a diesel engine under 27 operating modes, it is demonstrated that the application of the improved combustion model allows better adjustment of the Digital Twin to experimental data, thus achieving a more accurate correspondence to a real engine.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.2478/pomr-2023-0061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract The application of Digital Twins is a promising solution for enhancing the efficiency of marine power plant operation, particularly their important components – marine internal combustion engines (ICE). This work presents the concept of applying a Performance Digital Twin for monitoring the technical condition and diagnosing malfunctions of marine ICE, along with its implementation on an experimental test-bench, based on a marine diesel-generator. The main principles of implementing this concept involve data transmission technologies, from the sensors installed on the engine to a server. The Digital Twin, also operating on the server, is used to automatically process the acquired experimental data, accumulate statistics, determine the current technical state of the engine, identify possible malfunctions, and make decisions regarding changes in operating programs. The core element of the Digital Twin is a mathematical model of the marine diesel engine’s operating cycle. In its development, significant attention was devoted to refining the fuel combustion model, as the combustion processes significantly impact both the engine’s fuel efficiency and the level of toxic emissions of exhaust gases. The enhanced model differs from the base model, by considering the variable value of the average droplets’ diameter during fuel injection. This influence on fuel vapourisation, combustion, and the formation of toxic components is substantial, as shown. Using the example of calibrating the model to the test results of a diesel engine under 27 operating modes, it is demonstrated that the application of the improved combustion model allows better adjustment of the Digital Twin to experimental data, thus achieving a more accurate correspondence to a real engine.