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MOS resistor arrays are the core devices in infrared hardware-in-the-loop simulation, and the imaging quality is directly related to the accuracy and confidence of the final simulation results. At present, a series of problems, su...
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MOS resistor arrays are the core devices in infrared hardware-in-the-loop simulation, and the imaging quality is directly related to the accuracy and confidence of the final simulation results. At present, a series of problems, such as image degradation and coupling distortion, will occur when the simulated digital infrared signal enters the MOS resistor array. Therefore, it is necessary to analyze the imaging principles and energy transfer process of the MOS resistor array based on its imaging mechanism, and to establish a process and radiation model of a single pixel, to represent its own physical characteristics. A similarity framework between input and output image signals was constructed, and the relationship between signals was tested and verified by image similarity algorithms and a multi-attribute fusion algorithm. The proposed similarity framework could provide an objective evaluation method to measure the MOS resistor array imaging quality. These achievements could provide an important theoretical basis for future research on coupling characteristics, reverse correction models and non-uniformity correction of larger scale MOS resistor arrays, while having significant value in practical engineering applications.
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Given the significance of LBO phenomena for practical combustors, plenty of work can be found with different methodologies. However, based on the conceptual approaches, one may classify them into the following four categories: sem...
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Given the significance of LBO phenomena for practical combustors, plenty of work can be found with different methodologies. However, based on the conceptual approaches, one may classify them into the following four categories: semi-empirical methods, numerical simulations, Damköhler method, and hybrid methods. Hybrid methods are comparatively recent and are based on combination of semi-empirical and numerical simulations. Prediction of lean blowout (LBO) is imperative in the safety perspective of aero engines. LBO predictions based on semi-empirical correlations are the simplest and the most economical so far. Owing to insufficient modelling capability and depth of these correlations, the prediction accuracy is limited and needs improvement. Though, introduction of flame volume concept in semi-empirical correlation has brought improvement by taking into account the effects of geometric configuration, complex spatial interaction of mixing, turbulence, heat transfer and combustion processes within in primary combustion zone, its estimation at preliminary design stage is a difficult and subsequently poses challenge to LBO prediction loop. This work extends the hybrid LBO prediction previously based on cold flow simulations to reactive flow simulations. Reynolds-averaged Navier-Stokes based simulations were carried out in Fluent 15.0 to maintain the robustness in prediction loop. Based on criterion defined for identification of flame in solution domain, flame volume for each configuration was estimated and subsequently utilized to predict LBO. It was proved that flame volume near lean limit is a small region in inner recirculation region and is a strong function of combustor geometric configuration. Moreover, by the comparison with experimental data of 11 combustors, the lean blowout fit well with that obtained by experiments, having maximum and average errors of ±20 and ±6% between predictions and measurements. The improvement in prediction can be attributed to inclusion of hot flow physics and criterion defined for flame identification in reactive flow simulations.
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摘要 :
Given the significance of LBO phenomena for practical combustors, plenty of work can be found with different methodologies. However, based on the conceptual approaches, one may classify them into the following four categories: sem...
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Given the significance of LBO phenomena for practical combustors, plenty of work can be found with different methodologies. However, based on the conceptual approaches, one may classify them into the following four categories: semi-empirical methods, numerical simulations, Damko?hler method, and hybrid methods. Hybrid methods are comparatively recent and are based on combination of semi-empirical and numerical simulations. Prediction of lean blowout (LBO) is imperative in the safety perspective of aero engines. LBO predictions based on semi-empirical correlations are the simplest and the most economical so far. Owing to insufficient modelling capability and depth of these correlations, the prediction accuracy is limited and needs improvement. Though, introduction of flame volume concept in semi-empirical correlation has brought improvement by taking into account the effects of geometric configuration, complex spatial interaction of mixing, turbulence, heat transfer and combustion processes within in primary combustion zone, its estimation at preliminary design stage is a difficult and subsequently poses challenge to LBO prediction loop. This work extends the hybrid LBO prediction previously based on cold flow simulations to reactive flow simulations. Reynolds-averaged Navier-Stokes based simulations were carried out in Fluent 15.0 to maintain the robustness in prediction loop. Based on criterion defined for identification of flame in solution domain, flame volume for each configuration was estimated and subsequently utilized to predict LBO. It was proved that flame volume near lean limit is a small region in inner recirculation region and is a strong function of combustor geometric configuration. Moreover, by the comparison with experimental data of 11 combustors, the lean blowout fit well with that obtained by experiments, having maximum and average errors of ±20 and ±6% between predictions and measurements. The improvement in prediction can be attributed to inclusion of hot flow physics and criterion defined for flame identification in reactive flow simulations.
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The paper analyzes the main difficulties in constructing organization-oriented web public opinion monitoring system based on the core techniques of its implementation, and then proposes relevant solutions for real-time web informa...
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The paper analyzes the main difficulties in constructing organization-oriented web public opinion monitoring system based on the core techniques of its implementation, and then proposes relevant solutions for real-time web information acquisition, hot topic detection, sensitive news recognition and so on. Combining the requirements and features of organization in terms of web public opinion monitoring, the paper also analyzes the design steps and summarizes methods of system implementation.In the end, the paper implements an organizationoriented web public opinion monitoring system taking Wuhan University as an example,which is supposed to find out hot topics relevant to the organization and timely recognize sensitive news. The good performance of the system demonstrates the feasibility of the proposals found in this paper.
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摘要 :
The paper analyzes the main difficulties in constructing organization-oriented web public opinion monitoring system based on the core techniques of its implementation, and then proposes relevant solutions for real-time web informa...
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The paper analyzes the main difficulties in constructing organization-oriented web public opinion monitoring system based on the core techniques of its implementation, and then proposes relevant solutions for real-time web information acquisition, hottopic detection, sensitive news recognition and so on. Combi-ning the requirements and features of organization in terms of web public opinion monitoring, the paper also analyzes the design steps and summarizes methods of system implementation. In the end, the paper imple-ments an organization-oriented web public opinion monitoring system taking Wuhan University as an exam-ple , which is supposed to find out hot topics relevant to the organization and timely recognize sensitive news. The good performance of the system demonstrates the feasibility of the proposals found in this paper.
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The lean blowout (LBO) is a critical aspect of combustion performance for gas turbine combustors. During the past decades, three major prediction methodologies for the LBO limits, i.e. the semi-empirical model, the numerical predi...
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The lean blowout (LBO) is a critical aspect of combustion performance for gas turbine combustors. During the past decades, three major prediction methodologies for the LBO limits, i.e. the semi-empirical model, the numerical prediction method and the hybrid prediction method are proposed. The semi-empirical models are derived mainly based on two kinds of physics-based models, i.e. the characteristic time (CT) model and the perfect stirred reactor (PSR) model. Among these semi-empirical models, Lefebvre’s LBO model that is based on the PSR model had been validated on 8 different aero gas turbine combustors with the prediction uncertainty ±30\% and applied widely on the prediction of the LBO limits. Subsequently, a series of studies have been done to further develop Lefebvre’s LBO model. The numerical prediction methods are studied increasingly with the dramatically increase of the computing power. Based on the open literature, the best prediction uncertainty of the numerical prediction methods could be within 14\% for a fixed combustor configuration with 3 kinds of fuels. More validations of different combustor configurations, atomization and dispersion models are required for the further application of numerical prediction methods. The hybrid prediction methods combine the semi-empirical models and the numerical methods simultaneously and could be divided into 2 types, i.e. the numerical and the semi-empirical based hybrid methods. The numerical based hybrid prediction method requires more validations and some general criteria for different configurations and operating conditions. The semi-empirical based hybrid prediction method could achieve maximum and average prediction uncertainties about ±15\% and ±5\%, respectively, for 10 combustor configurations. In summary, all the prediction methodologies should be further developed to achieve much more accurate prediction for the LBO limits as well as ensure the good generality.
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摘要 :
The lean blowout (LBO) is a critical aspect of combustion performance for gas turbine combustors. During the past decades, three major prediction methodologies for the LBO limits, i.e. the semi-empirical model, the numerical predi...
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The lean blowout (LBO) is a critical aspect of combustion performance for gas turbine combustors. During the past decades, three major prediction methodologies for the LBO limits, i.e. the semi-empirical model, the numerical prediction method and the hybrid prediction method are proposed. The semi-empirical models are derived mainly based on two kinds of physics-based models, i.e. the characteristic time (CT) model and the perfect stirred reactor (PSR) model. Among these semi-empirical models, Lefebvre's LBO model that is based on the PSR model had been validated on 8 different aero gas turbine combustors with the prediction uncertainty ±30% and applied widely on the prediction of the LBO limits. Subsequently, a series of studies have been done to further develop Lefebvre's LBO model. The numerical prediction methods are studied increasingly with the dramatically increase of the computing power. Based on the open literature, the best prediction uncertainty of the numerical prediction methods could be within 14% for a fixed combustor configuration with 3 kinds of fuels. More validations of different combustor configurations, atomization and dispersion models are required for the further application of numerical prediction methods. The hybrid prediction methods combine the semi-empirical models and the numerical methods simultaneously and could be divided into 2 types, i.e. the numerical and the semi-empirical based hybrid methods. The numerical based hybrid prediction method requires more validations and some general criteria for different configurations and operating conditions. The semi-empirical based hybrid prediction method could achieve maximum and average prediction uncertainties about ±15% and ±5%, respectively, for 10 combustor configurations. In summary, all the prediction methodologies should be further developed to achieve much more accurate prediction for the LBO limits as well as ensure the good generality.
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摘要 :
The lean blowout (LBO) is a critical aspect of combustion performance for gas turbine combustors. During the past decades, three major prediction methodologies for the LBO limits, i.e. the semi-empirical model, the numerical predi...
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The lean blowout (LBO) is a critical aspect of combustion performance for gas turbine combustors. During the past decades, three major prediction methodologies for the LBO limits, i.e. the semi-empirical model, the numerical prediction method and the hybrid prediction method are proposed. The semi-empirical models are derived mainly based on two kinds of physics-based models, i.e. the characteristic time (CT) model and the perfect stirred reactor (PSR) model. Among these semi-empirical models, Lefebvre’s LBO model that is based on the PSR model had been validated on 8 different aero gas turbine combustors with the prediction uncertainty ±30\% and applied widely on the prediction of the LBO limits. Subsequently, a series of studies have been done to further develop Lefebvre’s LBO model. The numerical prediction methods are studied increasingly with the dramatically increase of the computing power. Based on the open literature, the best prediction uncertainty of the numerical prediction methods could be within 14\% for a fixed combustor configuration with 3 kinds of fuels. More validations of different combustor configurations, atomization and dispersion models are required for the further application of numerical prediction methods. The hybrid prediction methods combine the semi-empirical models and the numerical methods simultaneously and could be divided into 2 types, i.e. the numerical and the semi-empirical based hybrid methods. The numerical based hybrid prediction method requires more validations and some general criteria for different configurations and operating conditions. The semi-empirical based hybrid prediction method could achieve maximum and average prediction uncertainties about ±15\% and ±5\%, respectively, for 10 combustor configurations. In summary, all the prediction methodologies should be further developed to achieve much more accurate prediction for the LBO limits as well as ensure the good generality.
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ZigBee network technology is an emerging research field of information science, this paper proposes a miniature photovoltaic power generation system based on ZigBee network technology design scheme and implement, household photovo...
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ZigBee network technology is an emerging research field of information science, this paper proposes a miniature photovoltaic power generation system based on ZigBee network technology design scheme and implement, household photovoltaic power generation systems have been discussed in detail in the ZigBee network framework, including information collection, system control, modular design, and sensor network nodes deployment, etc., the system for the center with the coordinator build ZigBee star structure for the micro photovoltaic power generation system of intelligent management and working state real time monitoring. The results show that the ZigBee network technology application in household photovoltaic power generation system, not only improve the level of the information management system, further research on grid type household photovoltaic power station also has important research significance.
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摘要 :
ZigBee network technology is an emerging research field of information science, this paper proposes a miniature photovoltaic power generation system based on ZigBee network technology design scheme and implement, household photovo...
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ZigBee network technology is an emerging research field of information science, this paper proposes a miniature photovoltaic power generation system based on ZigBee network technology design scheme and implement, household photovoltaic power generation systems have been discussed in detail in the ZigBee network framework, including information collection, system control, modular design, and sensor network nodes deployment, etc., the system for the center with the coordinator build ZigBee star structure for the micro photovoltaic power generation system of intelligent management and working state real time monitoring. The results show that the ZigBee network technology application in household photovoltaic power generation system, not only improve the level of the information management system, further research on grid type household photovoltaic power station also has important research significance.
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