Volume 6: Education; Electric Power最新文献

{"title":"Prediction of Gas Turbine Performance Using Machine Learning Methods","authors":"Vipul Goyal, Mengyu Xu, J. Kapat, L. Vesely","doi":"10.1115/GT2020-15232","DOIUrl":"https://doi.org/10.1115/GT2020-15232","url":null,"abstract":"\u0000 The current study is based on multiple machine learning algorithms to predict the normal behavior of operational parameters including power generated and blade path temperature spread. The predictions can be used to identify anomalies and probable failures in the gas turbine performance. The data used in the study is taken from multiple heavy-duty gas turbine units of combined cycled utility power plants which are known to contain operational failures. The predictors include operational parameters such as fuel flow, various thermodynamic variables, etc.\u0000 In the first step, we cluster the observations into different working modes, because of the heterogeneous behavior of the gas turbine parameters under various modes. Then we consider predicting the operational parameters under each mode respectively, via algorithms including random forest, generalized additive model, and neural networks. The models are trained and parameters are selected based on the overall prediction performance on the validation set.\u0000 The comparative advantage based on prediction accuracy and applicability of the algorithms is discussed for real-time use and post processing. The advantage of our method is that they achieve high predictive power and provide insight into the behavior of specific gas turbine variables, e.g.- turbine blade path temperature spread, which are not explicitly known to have any correlation with other thermodynamic variables.","PeriodicalId":436120,"journal":{"name":"Volume 6: Education; Electric Power","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114168333","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}

{"title":"Analytical Formulation of the Performance of the Allam Power Cycle","authors":"Y. Haseli","doi":"10.1115/GT2020-15070","DOIUrl":"https://doi.org/10.1115/GT2020-15070","url":null,"abstract":"\u0000 Thermal power plants operating on fossil fuels emit a considerable amount of polluting gases including carbon dioxide and nitrogen oxides. Several technologies have been developed or under development to avoid the emissions of, mainly, CO2 that are formed as a result of air-fuel combustion. While post-combustion capture methods are viable solutions for reduction of CO2 in the existing power plants, implementation of the concept of oxyfuel combustion in future power cycles appears to be a promising technique for clean power generation from fossil fuels. A novel power cycle that employs oxyfuel combustion method has been developed by NET Power. Known as the Allam cycle, it includes a turbine, an air separation unit (ASU), a combustor, a recuperator, a water separator, CO2 compression with intercooling and CO2 pump. (Over 90% of the supercritical CO2 flow is recycled back to the cycle as the working fluid, and the rest is extracted for further processing and storage. The present paper introduces a simplified thermodynamic analysis of the Allam power cycle. Analytical expressions are derived for the net power output, optimum turbine inlet temperature (TIT), and the molar flowrate of the recycled CO2 flow. The study aims to provide a theoretical framework to help understand the functional relationships between the various operating parameters of the cycle. The optimum TIT predicted by the presented expression is 1473 K which is fairly close to that reported by the cycle developers.","PeriodicalId":436120,"journal":{"name":"Volume 6: Education; Electric Power","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126267725","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}

{"title":"A Toolbox of Hardware and Digital Solutions for Increased Flexibility","authors":"N. Demougeot, A. Steinbrenner, A. Cires, M. Paskin","doi":"10.1115/GT2020-15289","DOIUrl":"https://doi.org/10.1115/GT2020-15289","url":null,"abstract":"\u0000 The power generation market has been changing rapidly with the injection of an ever increasing usage of renewable power sources. The cyclic and highly unpredictable nature of power generation output from renewable sources is forcing Gas Turbine (GT) operators to significantly increase the operational flexibility of their engines.\u0000 While the industry has been, for many years, developing and fielding solutions providing increased output at the high end of the operating range, the focus has shifted recently to solutions allowing for a safe decrease of the engines’ minimum operating load.\u0000 The AutoTune (AT) system was introduced at last year’s Turbo Expo conference [5], and the challenges of developing a safe Extended Turndown add-on are detailed herein. Other digital and hardware solutions presented include Part Load Performance, decreased start-up time for both simple and combined cycle units, disc cavity cooling modulation and Exhaust Bleed. Increased ramp rate is addressed with the associated significant difficulty of maintaining the mechanical integrity of the rotors and casings.\u0000 PSM has been working on a toolbox of both hardware and digital solutions to increase on GT operability both on the high and low ends of the load range and the technical issues faced are described in this paper.","PeriodicalId":436120,"journal":{"name":"Volume 6: Education; Electric Power","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116165359","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}

{"title":"Interactive Learning Platform for Turbine Design Using Reduced Order Methods","authors":"Igor Oliveira, G. P. Silva, D. Tonon, C. Bringhenti, J. T. Tomita","doi":"10.1115/GT2020-16028","DOIUrl":"https://doi.org/10.1115/GT2020-16028","url":null,"abstract":"\u0000 This work presents the implementation of an interactive learning platform for turbine design in an engineering teaching environment. Due to the abundance of strategies and problems encountered in a multidisciplinary iterative design process, presenting the student to the multitude of scenarios can be a laborious and time-consuming task, often not possible in one-semester courses for undergraduate students.\u0000 The developed computational program breaks down the preliminary design methodology into a step-by-step analysis of a single-stage axial turbine for aeronautical application. In it, the student is guided through velocity diagram construction, performance prediction, tridimensional and compressible effects considerations, blade designing as well as accounting for losses. In this interactive learning tool, it is possible to explore the sensitivity and effects of each design choice at various design steps, generating insight and hopefully a more intimate understanding.\u0000 This exploration generates real-time changes in the output interface, for example the velocity diagrams and major geometrical features, in which the student is able through different trials to observe and compare the impact of different approaches, choices and assumptions.\u0000 The program is written in Python language and the loss models chosen were Kacker and Okapuu; Dunham and Came; and Ainley and Mathieson. As the same set of design requirements can lead to different — yet optimal — configurations, the student will be given guidelines based on established design methodologies with the aid of graphs and the usual ranges of the calculated parameters found in practice.\u0000 At the end of this process, the student is able to harvest a final design from which it is possible to generate discussions among a class or examine the suitability of a final product in regards to a proposed assignment, objective or application.","PeriodicalId":436120,"journal":{"name":"Volume 6: Education; Electric Power","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125203617","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}

{"title":"An Ensemble of Recurrent Neural Networks for Real Time Performance Modelling of Three-Spool Aero-Derivative Gas Turbine Engine","authors":"I. Ibrahem, O. Akhrif, H. Moustapha, Martin Staniszewski","doi":"10.1115/GT2020-15756","DOIUrl":"https://doi.org/10.1115/GT2020-15756","url":null,"abstract":"\u0000 Gas turbine is a complex system operating in non-stationary operation conditions for which traditional model-based modelling approaches have poor generalization capabilities. To address this, an investigation of a novel data driven neural networks based model approach for a three-spool aero-derivative gas turbine engine (ADGTE) for power generation during its loading and unloading conditions is reported in this paper. For this purpose, a non-linear autoregressive network with exogenous inputs (NARX) is used to develop this model in MATLAB environment using operational closed-loop data collected from Siemens (SGT-A65) ADGTE. Inspired by the way biological neural networks process information and by their structure which changes depending on their function, multiple-input single-output (MISO) NARX models with different configurations were used to represent each of the ADGTE output parameters with the same input parameters. First, data preprocessing and estimation of the order of these MISO models were performed. Next, a computer program code was developed to perform a comparative study and to select the best NARX model configuration, which can represent the system dynamics. Usage of a single neural network to represent each of the system output parameters may not be able to provide an accurate prediction for unseen data and as a consequence, provides poor generalization. To overcome this problem, an ensemble of MISO NARX models is used to represent each output parameter. The major challenge of the ensemble generation is to decide how to combine results produced by the ensemble’s components. In this paper, a novel hybrid dynamic weighting method (HDWM) is proposed. The verification of this method was performed by comparing its performance with three of the most popular basic methods for ensemble integration: basic ensemble method (BEM), median rule and dynamic weighting method (DWM). Finally, the generated ensembles of MISO NARX models for each output parameter were evaluated using unseen data (testing data). The simulation results based on datasets consisting for experimental data as well as data provided by Siemens high fidelity thermodynamic transient simulation program show improvement in accuracy and robustness by using the proposed modelling approach.","PeriodicalId":436120,"journal":{"name":"Volume 6: Education; Electric Power","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134092711","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}

{"title":"Introduction of a Smartphone Application in an Aeroengine Technology Course","authors":"I. Aslanidou, A. Gaitanis, A. Kalfas","doi":"10.1115/GT2020-15958","DOIUrl":"https://doi.org/10.1115/GT2020-15958","url":null,"abstract":"\u0000 The main goal of an engineering course is for the students to achieve the defined educational goals, enhance their problem-solving capabilities and develop the essential engineering mindset. The continuous improvement of a course is essential to maintain its challenging nature while improving the course quality. Adapting the teaching methods used to new types of students can provide a significant improvement in student learning.\u0000 In that context, a digital tool is employed in an advanced course in Aeroengine Technology. A smartphone application that calculates gas turbine performance is introduced in the course to help students understand some of the key concepts. The purpose of the application is to provide the students with an interactive tool to understand the gas turbine thermodynamic cycle. An exercise regarding this application is assigned to note the performance of different engine technologies used in aircraft propulsion through the years. The assignment with the application is combined with a larger assignment on gas turbine performance. The application is also employed in the final exams of the course.\u0000 The purpose of this paper is to present the use of the application in the course and to address any challenges that arise in the implementation of the app in the learning process. The employed teaching methods received positive feedback from the students who indicated that the app assignment helped them understand some of the key concepts in the course. After all, the main aim of the use of novel teaching methods should be to make learning more interesting, so that students get more involved in a course.","PeriodicalId":436120,"journal":{"name":"Volume 6: Education; Electric Power","volume":"297 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127719711","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}

{"title":"How Is a Correct GT Combustor Heat Balance Established?","authors":"Hans E. Wettstein","doi":"10.1115/GT2020-14235","DOIUrl":"https://doi.org/10.1115/GT2020-14235","url":null,"abstract":"\u0000 The heat balance of gas turbine (GT) combustors is used for determining the average Combustor Exit Temperature (CET). It is important for designing the hot parts in this area. Sensor measurements of the CET are nearly impossible due to its high level up to above 1700°C. Therefore it is typically evaluated based on a 1-D cycle calculation, in which the combustor receives compressed air and fuel and it discharges the hot combustion gas at the temperature CET.\u0000 In the classic approach the fuel heat received in the combustor is evaluated based on the lower heating value (LHV) of the fuel and after the complete combustion the mixture of excess air and combustion products leaves the combustor at the temperature CET, which is calculated based on its specific enthalpy function.\u0000 So far so simple but this is tricky. The reaction energy is not the LHV but the higher heating value HHV, which includes additionally the discharged energy for condensing the combustion water at ambient temperature. The total heat comes into the flue-gas in the combustor, which is designed for a combustion efficiency of typically 99%+. There is no significant downstream reaction known, which could add the missing difference of HHV-LHV. In GT based power stations condensation is mostly avoided by sufficiently high stack temperature. For methane as a fuel the HHV is around 11% higher than the LHV.\u0000 Thus the CET derived with the LHV for a given fuel mass flow rate may be underestimated. The method comparison shown below indicates values around 10K. This is a “grey” issue. The intention of this paper is an attempt to understand this practice both technically and historically.\u0000 Gas turbine catalogues indicate performance data based on burning pure methane. This may have its historic roots in the fact that methane (only Methane, not higher hydrocarbons) burns with oxygen without a change of the specific volume. This simplified the cycle calculation in the sense that combustion could be modelled by adding the LHV to air and methane (assuming an equal temperature) and by calculating the expansion of air and methane separately (corresponding to mixed if no chemical reaction due to the high temperature is assumed) but with the same polytropic efficiency. At ambient temperature this fuel-air mixture is still gaseous and therefore the heat balance of the GT matches exactly with the LHV (used before in the combustor heat balance) because there is no condensation issue.\u0000 Another feature of the air may compensate the CET mistake partly when using the LHV. It is the effect of dissociation. This increases the specific heat and therefore reduces the calculated CET. In the older time the used specific heat function of air did not include the dissociation effect while nowadays it is mostly included assuming chemical equilibrium.\u0000 In this paper the good match of a cycle calculation considering the HHV and dissociation with published OEM data will be demonstrated. Indeed this method contradicts exis","PeriodicalId":436120,"journal":{"name":"Volume 6: Education; Electric Power","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115484849","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}

{"title":"Development of Web-Based Short Courses on Control, Diagnostics, and Instrumentation","authors":"I. Aslanidou, V. Zaccaria, A. Fentaye, K. Kyprianidis","doi":"10.1115/GT2020-14395","DOIUrl":"https://doi.org/10.1115/GT2020-14395","url":null,"abstract":"\u0000 As a consequence of globalization and advances in digital tools, synchronous or asynchronous distance courses are becoming an integral part of universities’ educational offers. The design of an online course introduces more challenges compared to a traditional on campus course with face to face lectures. This is true especially for engineering subjects where problem or project-based courses may be preferred to stimulate critical thinking and engage the learners with real-life problems. However, realizing this with distance learning implies that a similar study pace should be kept by the learners involved. This may not be easy, since individual pace is often a motivation for choosing a distance course. Student engagement in group projects, collaborations, and the proper design of examination tasks are only some of the challenges in designing a distance course for an engineering program.\u0000 A series of web-based courses on measurement techniques, control, and diagnostics were developed and delivered to groups of learners. Each course comprised short modules covering key points of the subject and aimed at getting learners to understand both the fundamental concepts that they do not typically learn or understand in the respective base courses and to build on that knowledge to reach a more advanced cognitive level.\u0000 The experience obtained in the courses on what strategies worked better or worse for the learners is presented in this paper. A comparison between the courses provides an interesting outlook on how the learners reacted to slightly different requirements and incentives in each course. The results from the evaluation of the courses are also used as a base for discussion.\u0000 The background and availability of the learners is closely linked to how a course should be designed to optimally fit the learning group, without compromising on the achievement of the learning outcomes. This series of courses is a good example of continuous professional development courses in the field of control, diagnostics, and instrumentation (CDI), and brings with it a number of challenges and opportunities for the development of online courses.","PeriodicalId":436120,"journal":{"name":"Volume 6: Education; Electric Power","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114245399","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}

{"title":"Balancing a High-Renewables Electric Grid With Hydrogen-Fuelled Combined Cycles: A Country Scale Analysis","authors":"P. Colbertaldo, G. Guandalini, E. Crespi, S. Campanari","doi":"10.1115/GT2020-15570","DOIUrl":"https://doi.org/10.1115/GT2020-15570","url":null,"abstract":"\u0000 A key approach to large renewable energy sources (RES) power management is based on implementing storage technologies, including batteries, power-to-hydrogen (P2H), pumped-hydro, and compressed air energy storage. Power-to-hydrogen presents specific advantages in terms of suitability for large-scale and long-term energy storage as well as capability to decarbonize a wide range of end-use sectors, e.g., including both power generation and mobility.\u0000 This work applies a multi-nodal model for the hourly simulation of the energy system at a nation scale, integrating the power, transport, and natural gas sectors. Three main infrastructures are considered: (i) the power grid, characterized by instantaneous supply-demand balance and featuring a variety of storage options; (ii) the natural gas network, which can host a variable hydrogen content, supplying NG-H2 blends to the final consumers; (iii) the hydrogen production, storage, and re-electrification facilities. The aim of the work is to assess the role that can be played by gas turbine-based combined cycles in the future high-RES electric grid. Combined cycles (GTCCs) would exploit hydrogen generated by P2H implementation at large scale, transported through the natural gas infrastructure at increasingly admixed fractions, thus closing the power-to-power (P2P) conversion of excess renewables and becoming a strategic asset for future grid balancing applications.\u0000 A long-term scenario of the Italian energy system is analyzed, involving a massive increase of intermittent RES power generation capacity and a significant introduction of low-emission vehicles based on electric drivetrains (pure-battery or fuel-cell). The analysis highlights the role of hydrogen as clean energy vector, not only for specific use in new applications like fuel cell vehicles and stationary fuel cells, but also for substitution of fossil fuels in conventional combustion devices. The study also explores the option of repowering the combined cycles at current sites and evaluates the effect of inter-zonal limits on power and hydrogen exchange. Moreover, results include the evaluation of the required hydrogen storage size, distributed at regional scale or in correspondence of the power plant sites. Results show that when extra hydrogen generated by P2H is fed to GTCCs, up to 17–24% H2 use is achieved, reaching up to 70–100% in southern regions, with a parallel reduction in fossil NG input and CO2 emissions of the GTCC plants.","PeriodicalId":436120,"journal":{"name":"Volume 6: Education; Electric Power","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126180798","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}

{"title":"Quality Key Numbers of Gas Turbine Combined Cycles","authors":"Hans E. Wettstein","doi":"10.1115/GT2020-14508","DOIUrl":"https://doi.org/10.1115/GT2020-14508","url":null,"abstract":"\u0000 The most relevant quality key numbers for the largest and most efficient Gas Turbine Combined Cycles (GTCC) are not (only) the data published by the original engine manufacturers OEM’s. Additional numbers are here evaluated with educated guesses based on published data of the latest announcements of the “big four OEM’s” [8]. Such data are of interest for potential customers but also for nailing down the current state-of-the-art for all kind of further cycle studies using turbomachinery components and also as a contemporary history record.\u0000 Making educated guesses means thermodynamic 1D simulation based on additional assumptions for pressure losses and other cycle data, which have a limited influence on the (unpublished) target quality numbers, such as:\u0000 • Mixed turbine inlet temperature Tmix. This is a key value describing the technology level. It can be derived independently of the (unpublished) TCLA value. It is a quality number for the general cooling design and for the secondary air systems.\u0000 • Polytropic efficiency of the compressor blading. This number describes the aerodynamic quality of the compressor blading.\u0000 • Polytropic efficiency of the turbine blading. It describes the quality level of both the blading aerodynamics and of the open air cooling design.\u0000 • Distribution of the exergy losses within the GT and in the bottoming cycle. The exergy losses describe the remaining opportunities for further improvements in the thermodynamic cycle design. But they also indicate its limits.\u0000 However already the determination of the Tmix is tricky. It depends on the analysis method and on the fluid data applied. The polytropic efficiency of the turbine blading and the exergy losses will depend both on the used methods and on the Tmix found. Achieving a trustable result therefore requires a transparent and reproducible method. In case of application of the found results for performance prediction of similar cycles the same method has to be applied in order to avoid mistakes.\u0000 In this paper real gas data with consideration of dissociation in equilibrium are used, while the polytropic efficiencies are determined with an incremental method based directly on the classic definitions of Stodola [3] and Dzung [4]. Therefore the still most used method using semi-perfect gas properties and corresponding formulas is bypassed.\u0000 In order to keep it as simple as possible the evaluation is limited to base load at ISO ambient condition (15°C, 60% relative humidity, sea level). The fuel is limited to pure methane according to the practice in current catalogue data.\u0000 The main focus is on the gas turbine with its components. The steam bottoming cycle is captured with its effect on the overall exergy and energy balance of the GTCC, which identifies exhaust and condensation losses.","PeriodicalId":436120,"journal":{"name":"Volume 6: Education; Electric Power","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128340266","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}