• Energy Research
• other engineering and technologies close
• GB close
• IT close
• AU close

Abstract This paper presents a numerical study of the particle cluster behavior in a riser/downer reactor by means of combined computational fluid dynamics (CFD) and discrete element method (DEM), in which the motion of discrete particles is obtained by solving Newton's equations of motion and the flow of continuum gas by the Navier–Stokes equations. It is shown that the existence of particle clusters, unique to the solid flow behavior in such a reactor, can be predicted from this first principle approach. The results demonstrate that there are two types of clusters in a riser and downer: one is in the near wall region where the velocities of particles are low; the other is in the center region where the velocities of particles are high. While the extent of particle aggregation appears to be similar, the duration time for the first type in a downer is shorter than in a riser. Furthermore, it is demonstrated that the formation of clusters is affected by a range of variables related to operational conditions, particle properties, and bed properties and geometry. The increase of solid volume fraction, sliding and rolling friction between particles or between particles and wall, or damping coefficient can enhance the formation of clusters. The use of multi-sized particles can also promote the formation of clusters. But the increase of gas velocity or use of a wider bed can suppress the formation of clusters. The van der Waals force may enhance the formation of clusters when solid concentration is high but suppress the formation of clusters near the wall region when solid concentration is low.

Abstract This paper presents a numerical study of the particle cluster behavior in a riser/downer reactor by means of combined computational fluid dynamics (CFD) and discrete element method (DEM), in which the motion of discrete particles is obtained by solving Newton's equations of motion and the flow of continuum gas by the Navier–Stokes equations. It is shown that the existence of particle clusters, unique to the solid flow behavior in such a reactor, can be predicted from this first principle approach. The results demonstrate that there are two types of clusters in a riser and downer: one is in the near wall region where the velocities of particles are low; the other is in the center region where the velocities of particles are high. While the extent of particle aggregation appears to be similar, the duration time for the first type in a downer is shorter than in a riser. Furthermore, it is demonstrated that the formation of clusters is affected by a range of variables related to operational conditions, particle properties, and bed properties and geometry. The increase of solid volume fraction, sliding and rolling friction between particles or between particles and wall, or damping coefficient can enhance the formation of clusters. The use of multi-sized particles can also promote the formation of clusters. But the increase of gas velocity or use of a wider bed can suppress the formation of clusters. The van der Waals force may enhance the formation of clusters when solid concentration is high but suppress the formation of clusters near the wall region when solid concentration is low.

Abstract The use of simplifying techniques to obtain skeletal kinetic mechanisms with the required accuracy is often a necessary step when computationally demanding simulations are concerned. In this work, a novel approach for an automatic mechanism reduction, aimed at retaining accuracy on specific target species, is proposed. Starting from the consolidated coupling between flux analysis and sensitivity analysis, a methodology based on curve matching and functional data analysis was developed, through which the importance of a species in the target accuracy is assessed via a proper metric. The error associated with the removal of uncertain species from the detailed mechanism is quantified in terms of distance and similarity indices before and after such a removal, within a Species-Targeted Sensitivity Analysis (STSA) framework. A species ranking is then generated, and the original mechanism is progressively reduced. The whole algorithm also implements several improvements to enhance a faster convergence, and adds a novel criterion to remove unimportant reactions, based on sensitivity analysis to kinetic parameters. The capability of this algorithm was tested through two case studies in this work. A kinetic mechanism for a Toluene Reference Fuel (TRF) was first obtained, with the overall reactivity as reduction target. The numerical procedure allowed to obtain a compact skeletal mechanism (115 species and 856 reactions), able to retain good accuracy in ignition delay time and laminar flame speed predictions of both fuel mixture and pure compounds. More important, two skeletal mechanisms for methane combustion, including chemistry of nitrogen oxides (NO x ), were developed, with different degrees of reduction. The agreement between the original and the skeletal mechanisms in terms of NO formation was successfully assessed with satisfactory results. Attention was also dedicated to the choice of the type of reactor where undertaking reduction, which turned out to play a major role in the overall process.

Abstract The use of simplifying techniques to obtain skeletal kinetic mechanisms with the required accuracy is often a necessary step when computationally demanding simulations are concerned. In this work, a novel approach for an automatic mechanism reduction, aimed at retaining accuracy on specific target species, is proposed. Starting from the consolidated coupling between flux analysis and sensitivity analysis, a methodology based on curve matching and functional data analysis was developed, through which the importance of a species in the target accuracy is assessed via a proper metric. The error associated with the removal of uncertain species from the detailed mechanism is quantified in terms of distance and similarity indices before and after such a removal, within a Species-Targeted Sensitivity Analysis (STSA) framework. A species ranking is then generated, and the original mechanism is progressively reduced. The whole algorithm also implements several improvements to enhance a faster convergence, and adds a novel criterion to remove unimportant reactions, based on sensitivity analysis to kinetic parameters. The capability of this algorithm was tested through two case studies in this work. A kinetic mechanism for a Toluene Reference Fuel (TRF) was first obtained, with the overall reactivity as reduction target. The numerical procedure allowed to obtain a compact skeletal mechanism (115 species and 856 reactions), able to retain good accuracy in ignition delay time and laminar flame speed predictions of both fuel mixture and pure compounds. More important, two skeletal mechanisms for methane combustion, including chemistry of nitrogen oxides (NO x ), were developed, with different degrees of reduction. The agreement between the original and the skeletal mechanisms in terms of NO formation was successfully assessed with satisfactory results. Attention was also dedicated to the choice of the type of reactor where undertaking reduction, which turned out to play a major role in the overall process.

Lithium-ion batteries are fast becoming the battery of choice in applications such as electric/hybrid electric vehicles (EV/HEV) and renewable energy systems. This increasing usage demands an improved reliability of the battery systems, which in turn heavily relies on the control and optimization algorithms. Of particular importance is ensuring that each lithium-ion cell within a battery pack remains strictly within an acceptable charge range to avoid untimely degradation of the battery pack. Unfortunately, current battery models make the design of charge equalization circuitry difficult due to their limitations. The aim of this paper is to develop a component-wise control-oriented physics-based battery pack model to facilitate implementation of advanced model-based control and optimization algorithms. In the first stage some existing results are used to obtain a simplified electrochemical ODE model of an individual lithium-ion cell. Then, the cell model is used as the building block of the complete battery pack model. Different charge/discharge scenarios are presented to illustrate the potential of the modeling approach in facilitating the implementation of advanced control and optimization algorithms in improved power equalization and hence prolonging the battery pack lifetime.

Lithium-ion batteries are fast becoming the battery of choice in applications such as electric/hybrid electric vehicles (EV/HEV) and renewable energy systems. This increasing usage demands an improved reliability of the battery systems, which in turn heavily relies on the control and optimization algorithms. Of particular importance is ensuring that each lithium-ion cell within a battery pack remains strictly within an acceptable charge range to avoid untimely degradation of the battery pack. Unfortunately, current battery models make the design of charge equalization circuitry difficult due to their limitations. The aim of this paper is to develop a component-wise control-oriented physics-based battery pack model to facilitate implementation of advanced model-based control and optimization algorithms. In the first stage some existing results are used to obtain a simplified electrochemical ODE model of an individual lithium-ion cell. Then, the cell model is used as the building block of the complete battery pack model. Different charge/discharge scenarios are presented to illustrate the potential of the modeling approach in facilitating the implementation of advanced control and optimization algorithms in improved power equalization and hence prolonging the battery pack lifetime.

(2002). Electrolytic Deposition (Electroplating) of Metals. Transactions of the IMF: Vol. 80, No. 2, pp. 67-72.

(2002). Electrolytic Deposition (Electroplating) of Metals. Transactions of the IMF: Vol. 80, No. 2, pp. 67-72.

This paper presents a literature survey on the subject of voltage stability analysis of power systems. The survey describes several published methods and techniques used to determine voltage stability indices. These indices predict proximity to voltage instability and collapse problems. The Q-V and P-V curves; singular value decomposition; modal analysis; test function; reduced determinant; loading margin by multiple power flow solutions; local load margins; thevenin/load impedance; and energy function are the methods which have been decribed in the paper. The methods described are based on the original work that first proposed them. They are based on the power-flow system model, where the variation of real and reactive powers are assumed to be the main parameters driving the system to voltage instability. Some of the described methods were applied on the IEEE 30-bus power system.