Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power最新文献_第5页

Survey of Calculation Methods for Polytropic Efficiencies 多向效率计算方法综述

Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power Pub Date : 2021-06-07 DOI: 10.1115/gt2021-59967

Hans E. Wettstein

{"title":"Survey of Calculation Methods for Polytropic Efficiencies","authors":"Hans E. Wettstein","doi":"10.1115/gt2021-59967","DOIUrl":"https://doi.org/10.1115/gt2021-59967","url":null,"abstract":"\u0000 Calculating polytropic efficiencies is a basic task used for quantifying performance of power cycles involving compression and/or expansion. The incremental definition of a “polytropic curve” of gases by Gustav Zeuner in 1905 may be the oldest mention of the word “polytropic” in a thermodynamic context [1].\u0000 In Turbomachinery blading, the typical changes of state are nearly adiabatic and polytropic. L. S. Dzung was probably the first defining an incremental polytropic efficiency in 1944 [3]. Recursive integration of this has become the best thermodynamic quality measure of a blading.\u0000 Both Zeuner and Dzung started their consideration with an incremental definition. However, they integrated analytically assuming ideal gas data. This resulted in the well-known formula (1) p v n = constant Most thermodynamic textbooks declare this the definition of a polytropic change of state. However, the incremental definition survived too. Stodola [2], Dzung and later scientists established it as another definition of a polytropic change of state.\u0000 Thus, we face now two definitions of a polytropic change of state, which are theoretically identical for ideal gases but different for real gases and vapors. In educational context, this is disturbing and forcing to a logical detour. We trace the historic roots and show that the initial incremental definition is the physically healthier one. Recursive integration allows direct application to turbomachinery with any finite pressure ratio and to any real fluid.","PeriodicalId":169840,"journal":{"name":"Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130671914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaboration Between Academia and Industry to Advance Industrial Gas Turbines 产学研合作推动工业燃气轮机发展

Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power Pub Date : 2021-06-07 DOI: 10.1115/gt2021-01335

Bernhard Winkelmann, R. Kurz, D. Voss, K. Thole

引用次数: 0

Aero-Thermal Characterization of Accelerating and Diffusing Passages Downstream of Rotating Detonation Combustors

Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power Pub Date : 2021-06-07 DOI: 10.1115/gt2021-59111

J. Braun, G. Paniagua, D. Ferguson

{"title":"Aero-Thermal Characterization of Accelerating and Diffusing Passages Downstream of Rotating Detonation Combustors","authors":"J. Braun, G. Paniagua, D. Ferguson","doi":"10.1115/gt2021-59111","DOIUrl":"https://doi.org/10.1115/gt2021-59111","url":null,"abstract":"\u0000 Cycle benefits of rotating detonation engines show up to five percentage points of efficiency gain for low-pressure ratio engines. An optimal integration between the combustor and the turbine needs to be guaranteed to realize this potential gain. The rotating detonation combustor (RDC) exhausts transonic flow with shocks rotating at frequencies ranging from a few to tens of kilohertz depending on the number of present waves. Hence, the turbine design requires precise knowledge of the fluctuations and losses downstream of the combustor. This paper focuses on the quantification of fluctuations and losses for accelerating and diffusing passages. The analysis of the combustor is performed via reactive unsteady Reynolds Averaged Navier-Stokes (URANS) simulations. The unsteady RANS equations are solved via CFD++ from Metacomp with a one-step reaction mechanism for an H2-air mixture. The resolving of the boundary layer is achieved with a structured mesh of around 36 million cells. Inlet pressure of 10 bar and two different back pressures are applied to the combustor to model the interconnection with downstream turbines. Finally, we present and assess a methodology to reduce the computational time to model these passages ten times.","PeriodicalId":169840,"journal":{"name":"Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122513709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Turboprop Engine Loading During High and Low Maneuver Intensity Flight Mode 涡桨发动机在高低机动强度飞行模式下的载荷

Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power Pub Date : 2021-06-07 DOI: 10.1115/gt2021-59668

I. Templalexis, Lambros Giachalis, Ioannis Lionis

{"title":"Turboprop Engine Loading During High and Low Maneuver Intensity Flight Mode","authors":"I. Templalexis, Lambros Giachalis, Ioannis Lionis","doi":"10.1115/gt2021-59668","DOIUrl":"https://doi.org/10.1115/gt2021-59668","url":null,"abstract":"\u0000 The life consumption rate of the aircraft engine is a vital input for aircraft operators who aim to an efficient fleet management. T6 aircraft, propelled by the PT6 turboprop engine, is operated by the Hellenic Air Force, both for training and aerobatic purposes. The current study focuses on quantifying and comparatively assessing the engine life consumption rate for the following missions: i) An “aerobatic” mission which is a typical high intensity maneuver flight and ii) a “training for patrol” mission, representing a typical low intensity maneuver flight. Missions were selected with the criterion of setting the lowest and the highest possible engine loading during a certain mission. In other words, the goal of the study is to define the extent of the loading the engine can encounter as a propulsion system of the T-6 aircraft during a certain mission. This is the first step before proceeding in setting up a methodology for continuously monitoring the engine life consumption rate in support of the squadron flight management plan. The study was based on real time data recorded during the respective flights. An engine model built using “GasTurb” gas turbine simulation software was used to fill in engine operating data at stations where recordings have not been taken. Engine life consumption was based on creep and low cycle fatigue failure mechanisms of the first gas generator turbine stage. Creep life fractions were calculated based on the Larson-Miller parameter curves and the fatigue cycles were counted using the rainflow method. The study showed that the life consumption is about 10 times lower when the aircraft is operated at a low loading mode as opposed to a high loading mode.","PeriodicalId":169840,"journal":{"name":"Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124754337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Thermodynamic Analysis of Waste Heat Recovery Systems in Large Waste Heat Generating Industries 大型余热发电工业余热回收系统的热力学分析

Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power Pub Date : 2021-06-07 DOI: 10.1115/gt2021-59194

Shantanu Thada, Yash T. Rajan, A. Pradeep, A. Sridharan

{"title":"Thermodynamic Analysis of Waste Heat Recovery Systems in Large Waste Heat Generating Industries","authors":"Shantanu Thada, Yash T. Rajan, A. Pradeep, A. Sridharan","doi":"10.1115/gt2021-59194","DOIUrl":"https://doi.org/10.1115/gt2021-59194","url":null,"abstract":"\u0000 The accelerating growth of electricity demand necessitates looking for potential waste heat recovery solutions in production industries. Significant potential for efficient waste heat recovery is observed in the cement manufacturing industry. Based on the waste heat source temperatures in a cement plant, two potential candidates, the supercritical CO2 Brayton (S-CO2) cycle or the Organic Rankine cycle (ORC), promises low capital cost and enhanced thermodynamic performance. The current study focuses on modelling and optimization of the S-CO2 and ORC cycles for a 1 MTPA cement plant, with the raw-clinker preheater as the waste-heat source. The primary objective is to maximize the net-power output using genetic algorithms. A comparative performance analysis of the two ORCs with working fluids: R134a and Propane, the simply recuperated S-CO2 cycle (RC) and recompressed-recuperated S-CO2 cycle (RRC) configurations is presented with varying number of preheaters. For all cases, ORC-R134a yields more power than the ORC-Propane, RC, and RRC configurations. In terms of the waste heat recovered, ORC-Propane marginally outperforms ORC-R134a. The ORC configurations recover 32%–38% of the available heat, while the S-CO2 configurations recover, at maximum, 25%–30% of the available heat.","PeriodicalId":169840,"journal":{"name":"Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121999321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Gas Path Fault Diagnostic Model for Gas Turbine Based on Explainable Convolutional Neural Network With LIME Method 基于可解释卷积神经网络的燃气轮机气路故障诊断模型

Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power Pub Date : 2021-06-07 DOI: 10.1115/gt2021-59289

Chen Yao, Xi Yueyun, Chen Jinwei, Zhang Huisheng

{"title":"A Novel Gas Path Fault Diagnostic Model for Gas Turbine Based on Explainable Convolutional Neural Network With LIME Method","authors":"Chen Yao, Xi Yueyun, Chen Jinwei, Zhang Huisheng","doi":"10.1115/gt2021-59289","DOIUrl":"https://doi.org/10.1115/gt2021-59289","url":null,"abstract":"\u0000 Gas turbine is widely used in aviation and energy industries. Gas path fault diagnosis is an important task for gas turbine operation and maintenance. With the development of information technology, especially deep learning methods, data-driven approaches for gas path diagnosis are developing rapidly in recent years. However, the mechanism of most data-driven models are difficult to explain, resulting in lacking of the credibility of the data-driven methods. In this paper, a novel explainable data-driven model for gas path fault diagnosis based on Convolutional Neural Network (CNN) using Local Interpretable Model-agnostic Explanations (LIME) method is proposed. The input matrix of CNN model is established by considering the mechanism information of gas turbine fault modes and their effects. The relationship between the measurement parameters and fault modes are considered to arrange the relative position in the input matrix. The key parameters which contributes to fault recognition can be achieved by LIME method, and the mechanism information is used to verify the fault diagnostic proceeding and improve the measurement sensor matrix arrangement. A double shaft gas turbine model is used to generate healthy and fault data including 12 typical faults to test the model. The accuracy and interpretability between the CNN diagnosis model built with prior mechanism knowledge and built by parameter correlation matrix are compared, whose accuracy are 96.34% and 89.46% respectively. The result indicates that CNN diagnosis model built with prior mechanism knowledge shows better accuracy and interpretability. This method can express the relevance of the failure mode and its high-correlation measurement parameters in the model, which can greatly improve the interpretability and application value.","PeriodicalId":169840,"journal":{"name":"Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123534718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2