{"title":"基于Moore-Greitzer模型的单级轴流压气机参数辨识及模糊控制器设计","authors":"Md Fahdul Wahab Chowdhury, M. Schoen, Ji-chao Li","doi":"10.1109/IETC47856.2020.9249210","DOIUrl":null,"url":null,"abstract":"This paper presents a novel approach to mitigate a long-standing instability problem in axial flow compressors. The instabilities known as stall and surge limits the operating range of these systems. Moore and Greitzer combined their work on modelling axial compressor systems, resulting into the Moore-Greitzer (MG) model. This model is built on the assumption of a specific compressor characteristic. However, the parameters of the characteristics are dependent on the compressor geometry and other factors. As each compressor exhibits different characteristics, the parameters of the characteristic equation of the MG model are not the same and difficult to estimate. Thus, the MG model is not suitable to provide a compressor's specific dynamics - rather it describes the general fluid dynamics of a compression system. Hence, addressing the fluid flow control problem using the MG model is difficult without the knowledge of the specific characteristics. In order to solve this problem, a new approach is proposed in this paper that allows for the extraction of a compressor's specific characteristic parameters using only experimental data. This approach employs a genetic algorithm-based optimization technique. The proposed approach is tested using simulated data from the MG model and experimental data from a one-stage axial compressor test system. The extracted parameters are then utilized to design a fuzzy logic controller for the specific one-stage axial compressor. The objective of the controller is to regulate the mass flow rate by varying the throttle of the compressor in order to maintain a specific operating point. The input into the controller is the error between the desired operating point and the actual operating point. The compressor - operating without control - becomes unstable at the maximum pressure rise coefficient. The operating point of the system is set just below the maximum pressure rise coefficient and the corresponding mass flow coefficient. From the simulation result of the pressure rise and mass flow coefficient, it is found that the compressor can be operated safely at this new operating point.","PeriodicalId":186446,"journal":{"name":"2020 Intermountain Engineering, Technology and Computing (IETC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Parameter Identification and Fuzzy Logic Controller Design for a One-Stage Axial Flow Compressor System based on Moore-Greitzer Model\",\"authors\":\"Md Fahdul Wahab Chowdhury, M. Schoen, Ji-chao Li\",\"doi\":\"10.1109/IETC47856.2020.9249210\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel approach to mitigate a long-standing instability problem in axial flow compressors. The instabilities known as stall and surge limits the operating range of these systems. Moore and Greitzer combined their work on modelling axial compressor systems, resulting into the Moore-Greitzer (MG) model. This model is built on the assumption of a specific compressor characteristic. However, the parameters of the characteristics are dependent on the compressor geometry and other factors. As each compressor exhibits different characteristics, the parameters of the characteristic equation of the MG model are not the same and difficult to estimate. Thus, the MG model is not suitable to provide a compressor's specific dynamics - rather it describes the general fluid dynamics of a compression system. Hence, addressing the fluid flow control problem using the MG model is difficult without the knowledge of the specific characteristics. In order to solve this problem, a new approach is proposed in this paper that allows for the extraction of a compressor's specific characteristic parameters using only experimental data. This approach employs a genetic algorithm-based optimization technique. The proposed approach is tested using simulated data from the MG model and experimental data from a one-stage axial compressor test system. The extracted parameters are then utilized to design a fuzzy logic controller for the specific one-stage axial compressor. The objective of the controller is to regulate the mass flow rate by varying the throttle of the compressor in order to maintain a specific operating point. The input into the controller is the error between the desired operating point and the actual operating point. The compressor - operating without control - becomes unstable at the maximum pressure rise coefficient. The operating point of the system is set just below the maximum pressure rise coefficient and the corresponding mass flow coefficient. From the simulation result of the pressure rise and mass flow coefficient, it is found that the compressor can be operated safely at this new operating point.\",\"PeriodicalId\":186446,\"journal\":{\"name\":\"2020 Intermountain Engineering, Technology and Computing (IETC)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Intermountain Engineering, Technology and Computing (IETC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IETC47856.2020.9249210\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Intermountain Engineering, Technology and Computing (IETC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IETC47856.2020.9249210","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parameter Identification and Fuzzy Logic Controller Design for a One-Stage Axial Flow Compressor System based on Moore-Greitzer Model
This paper presents a novel approach to mitigate a long-standing instability problem in axial flow compressors. The instabilities known as stall and surge limits the operating range of these systems. Moore and Greitzer combined their work on modelling axial compressor systems, resulting into the Moore-Greitzer (MG) model. This model is built on the assumption of a specific compressor characteristic. However, the parameters of the characteristics are dependent on the compressor geometry and other factors. As each compressor exhibits different characteristics, the parameters of the characteristic equation of the MG model are not the same and difficult to estimate. Thus, the MG model is not suitable to provide a compressor's specific dynamics - rather it describes the general fluid dynamics of a compression system. Hence, addressing the fluid flow control problem using the MG model is difficult without the knowledge of the specific characteristics. In order to solve this problem, a new approach is proposed in this paper that allows for the extraction of a compressor's specific characteristic parameters using only experimental data. This approach employs a genetic algorithm-based optimization technique. The proposed approach is tested using simulated data from the MG model and experimental data from a one-stage axial compressor test system. The extracted parameters are then utilized to design a fuzzy logic controller for the specific one-stage axial compressor. The objective of the controller is to regulate the mass flow rate by varying the throttle of the compressor in order to maintain a specific operating point. The input into the controller is the error between the desired operating point and the actual operating point. The compressor - operating without control - becomes unstable at the maximum pressure rise coefficient. The operating point of the system is set just below the maximum pressure rise coefficient and the corresponding mass flow coefficient. From the simulation result of the pressure rise and mass flow coefficient, it is found that the compressor can be operated safely at this new operating point.