• Energy Research
• Embargo close
• US close
• AU close
• IN close

Filament seals, such as brush seals and leaf seals, are investigated as a potential improved seal for gas turbine applications. As these seals operate in contact with the rotor, a good understanding of their stiffness is required in order to minimize seal wear and degradation. This paper demonstrates that the filament and complete seal stiffness is affected in comparable magnitudes by mechanical and aerodynamic forces. In certain cases, the aerodynamic forces can also lead to an overall negative seal stiffness which may affect stable seal operation. In negative stiffness, the displacement of the seal or rotor into an eccentric position causes a resultant force, which, rather than restoring the rotor to a central position, acts to amplify its displacement. Insight into the forces acting on the seal filaments is gained by investigating a leaf seal, which consists of a pack of thin planar leaves arranged around the rotor, with coverplates on either side of the leaf pack, offset from the pack surfaces. The leaf seal is chosen due to its geometry being more suitable for analysis compared to alternative filament seals such as the brush seal. Data from two experimental campaigns are presented which show a seal exhibiting negative stiffness and a seal exhibiting a stiffness reduction due to aerodynamic effects. An empirical model for the forces acting on leaf filaments is developed based on the experimental data, which allows separation of mechanical and aerodynamic forces. In addition a numerical model is developed to analyze the flow approaching the leaf pack and the interleaf flow, which gives an insight into the causes of the aerodynamic forces. Using the empirical and numerical models together, a full picture of the forces affecting leaf stiffness is created. Validation of the models is achieved by successfully predicting seal stiffness for a further data set across the full range of operating conditions. The understanding of aerodynamic forces and their impact on filament and seal stiffness allows for their consideration in leaf seal design. A qualitative assessment of how they may be used to improve seal operation in filament seals is given.

Filament seals, such as brush seals and leaf seals, are investigated as a potential improved seal for gas turbine applications. As these seals operate in contact with the rotor, a good understanding of their stiffness is required in order to minimize seal wear and degradation. This paper demonstrates that the filament and complete seal stiffness is affected in comparable magnitudes by mechanical and aerodynamic forces. In certain cases, the aerodynamic forces can also lead to an overall negative seal stiffness which may affect stable seal operation. In negative stiffness, the displacement of the seal or rotor into an eccentric position causes a resultant force, which, rather than restoring the rotor to a central position, acts to amplify its displacement. Insight into the forces acting on the seal filaments is gained by investigating a leaf seal, which consists of a pack of thin planar leaves arranged around the rotor, with coverplates on either side of the leaf pack, offset from the pack surfaces. The leaf seal is chosen due to its geometry being more suitable for analysis compared to alternative filament seals such as the brush seal. Data from two experimental campaigns are presented which show a seal exhibiting negative stiffness and a seal exhibiting a stiffness reduction due to aerodynamic effects. An empirical model for the forces acting on leaf filaments is developed based on the experimental data, which allows separation of mechanical and aerodynamic forces. In addition a numerical model is developed to analyze the flow approaching the leaf pack and the interleaf flow, which gives an insight into the causes of the aerodynamic forces. Using the empirical and numerical models together, a full picture of the forces affecting leaf stiffness is created. Validation of the models is achieved by successfully predicting seal stiffness for a further data set across the full range of operating conditions. The understanding of aerodynamic forces and their impact on filament and seal stiffness allows for their consideration in leaf seal design. A qualitative assessment of how they may be used to improve seal operation in filament seals is given.

The lifetime and reliability of power transformers are primarily dependent on the hot-spot temperature in the windings, as temperature is the most important factor determining the insulation degradation rate. Key to removing the heat from the transformer is the radiator which must be carefully designed to keep the temperatures within limits under all operating conditions whilst minimizing the transformer size, weight and cost. This paper compares the analytical method used to predict the radiator performance with computational fluid dynamics (CFD) models in terms of heat dissipation. It is found that the analytical method and CFD models give similar results in the air natural (AN) cooling modes, whereas the analytical method overestimates the heat dissipation in the air forced (AF) cooling modes. Moreover, the thermal conduction effect in the radiator wall is investigated under different operating conditions and for different radiator sizes using the CFD models. The simulation results indicate that the radiator wall contributes to 6%-10% of the total heat dissipation under some circumstances and therefore should not be simply ignored in radiator models.

The lifetime and reliability of power transformers are primarily dependent on the hot-spot temperature in the windings, as temperature is the most important factor determining the insulation degradation rate. Key to removing the heat from the transformer is the radiator which must be carefully designed to keep the temperatures within limits under all operating conditions whilst minimizing the transformer size, weight and cost. This paper compares the analytical method used to predict the radiator performance with computational fluid dynamics (CFD) models in terms of heat dissipation. It is found that the analytical method and CFD models give similar results in the air natural (AN) cooling modes, whereas the analytical method overestimates the heat dissipation in the air forced (AF) cooling modes. Moreover, the thermal conduction effect in the radiator wall is investigated under different operating conditions and for different radiator sizes using the CFD models. The simulation results indicate that the radiator wall contributes to 6%-10% of the total heat dissipation under some circumstances and therefore should not be simply ignored in radiator models.

Modelling the economics of climate change is daunting. Many existing methodologies from social and physical sciences need to be deployed, and new modelling techniques and ideas still need to be developed. Existing bread-and-butter micro- and macroeconomic tools, such as the expected utility framework, market equilibrium concepts and representative agent assumptions, are far from adequate. Four key issues—along with several others—remain inadequately addressed by economic models of climate change, namely: (1) uncertainty, (2) aggregation, heterogeneity and distributional implications (3) technological change, and most of all, (4) realistic damage functions for the economic impact of the physical consequences of climate change. This paper assesses the main shortcomings of two generations of climate-energy-economic models and proposes that a new wave of models need to be developed to tackle these four challenges. This paper then examines two potential candidate approaches—dynamic stochastic general equilibrium (DSGE) models and agent-based models (ABM). The successful use of agent-based models in other areas, such as in modelling the financial system, housing markets and technological progress suggests its potential applicability to better modelling the economics of climate change.

Modelling the economics of climate change is daunting. Many existing methodologies from social and physical sciences need to be deployed, and new modelling techniques and ideas still need to be developed. Existing bread-and-butter micro- and macroeconomic tools, such as the expected utility framework, market equilibrium concepts and representative agent assumptions, are far from adequate. Four key issues—along with several others—remain inadequately addressed by economic models of climate change, namely: (1) uncertainty, (2) aggregation, heterogeneity and distributional implications (3) technological change, and most of all, (4) realistic damage functions for the economic impact of the physical consequences of climate change. This paper assesses the main shortcomings of two generations of climate-energy-economic models and proposes that a new wave of models need to be developed to tackle these four challenges. This paper then examines two potential candidate approaches—dynamic stochastic general equilibrium (DSGE) models and agent-based models (ABM). The successful use of agent-based models in other areas, such as in modelling the financial system, housing markets and technological progress suggests its potential applicability to better modelling the economics of climate change.

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.