No abstract

No abstract

A two-dimensional, chemical kinetics and aerosol growth model is developed for the comprehensive simulation of the reactive synthesis of ultra-fine particles in a thermal plasma reactor. The model solves the velocity and temperature fields that are coupled to the equations describing the electromagnetic induction field. Conservation equations for the plasma species consider the multi-component diffusion and chemical reactions. For the condensable product, an additional term is introduced, due to the phase change. Particle growth occurs via homogeneous nucleation and surface condensation, and an evaporation term is considered for concentrations below saturation. The model is applied to the synthesis of silicon metal powders by the dissociation of . The effect of the input power and that of the radial quenching are investigated by comparison with a simulation base case.

No abstract

An electrochemical Parameter Estimation (PE) study of lithium-ion batteries for different materials is presented. The PE methodology is developed in Part I of the study and the challenges on the different materials for the positive electrode including LiCoO 2 , LiMn 2 O 4 and LiFePO 4 are examined in Part II. The most influential electrochemical parameters of the Li-ion battery are estimated by means of an inverse method. The inverse method rests on five elements: the input parameters, a direct model, the reference data, an objective function and an optimizer. Eight electrochemical variables are considered as the target of the PE study. A simplified version of Pseudo-two-Dimensional (P2D) model is developed for the direct model. The P2D model predictions coupled to a random noise function are employed to generate the reference data. The data include the cell potential values with respect to the battery capacity at low and high discharge rates. The least-squared function and Genetic Algorithm are employed as the objective function and its optimizer, respectively. The best time domain for the estimation of each parameter is calculated by using a sensitivity analysis performed for different discharge curves. Results show that the methodology remains accurate and stable at both low and high discharge rates. The simultaneous high power and energy density of Lithium-ion (Li-ion) batteries have made it the preferred device for storing electricity. As a result, Li-ion batteries are increasingly used in various applications including electronics and the automotive industry. Regardless of the shape and of the battery pack arrangement, the internal structure of the battery usually comprises four main components: Two electrodes, positive and negative, an electrolyte, and a separator. These components are made of materials that have been gradually modified over the years so as to improve the efficiency, the safety and the performance of the batteries and to reduce their cost. 1-3A Battery Management System (BMS) is crucial for monitoring the operation of the battery pack. The BMS must interact with all the elements of the system in order to control it and to protect the Li-ion cells. The intelligence of the BMS is based on a mathematical model that simulates and predicts the different operating conditions of the Li-ion battery pack. In high tech and automotive industries, the BMS usually relies on empirical-based models. These models are simple and provide fast response. They cannot however predict the performance of the battery as it ages. Moreover, they are only applicable to a specific cell, i.e., they cannot be transposed to other battery packs without recalibration. [4][5][6] Electrochemical-based models of Li-ion batteries, on the other hand, overcome these shortcomings. These models rest on chemical/electrochemical kinetics and transport equations. These Li-ion battery models are more complicated and CPU time-consuming than empirical based models. They are, on the other hand, more versatile and they provide reliable a...