Investigation of an ICP torch plasma for the SiO2 deposition utilizing 3D MHD-simulations

Summary form only given. Thermal plasma processes are frequently accompanied with large energy consumption and environmental pollution. Optimum design and operation conditions help to reduce these concomitants and the production costs. Such an optimization can be considerably supported by modeling. An inductively coupled plasma torch for the silicon oxide deposition as it is typically applied for fiber preform production is studied by simulations. The analysis focuses on the induction process, the flow and plasma properties as well as the deposition process. A burner made by three cylindrical quartz tubes and a copper coil with five turns operated with 25 kW RF power at 3 MHz is considered. Oxygen (30 slm) and Nitrogen (120 slm) are used as plasma and sheath gas, respectively, at atmospheric pressure. For the outside vapor deposition process the precursor SiCl4 (2 kg/h) is fed through a nozzle in the torch center below the heating zone. The cylindrical preform target rotates (1 s"1) and laterally moves (0.18 m/min) in a distance of 4.2 cm above the burner. A 3D MHD-simulation method has been developed for the torch plasma based on the CFD-ACE+ commercial package. The steady state continuity, momentum and enthalpy equations are solved assuming local thermal and chemical equilibrium and laminar flow. This system is coupled with the electromagnetic equations to describe the induction process self-consistently. For all species considered in the system a transport equation is solved. The surface reaction governing the deposition process provides a boundary condition for the species mass fractions in the fluid.The simulation provides inside into the realistic spatial behavior of temperature, flow and mixing of the species. The calculated deposition rate on the substrate is illustrated in Fig. 1. In addition, the deposition on critical parts of the burner has been studied. The simulation method developed is suitable for burner design optimization as well as for deducing i- proved operation conditions.