Gamma Meccanica’s R&D department is constantly researching new solutions to improve the overall performance of equipment, production capacity and reliability while developing environmentally sustainable processes and applications to benefit its customers.
The most recent example is the study of a new electric melter (see
Fig. 1a). The company purchased the ANSYS Fluent software to
perform a computational fluid dynamics (CFD) simulation of an
electric melter for the fusion of basalt and dolomite rocks (see
The use of a mathematical model allows an in-depth understanding of the operation, optimizing the geometric characteristics such as the center-to-center spacing of electrodes or of their depth of immersion in the melt.
Gamma Meccanica conducted a CFD simulation on a 7 ton/h electric melter (see Fig. 2a). The actual geometry was imported via a STEP in SPACECLAIM. Reasonable simplifications were applied to reduce the mesh size. The resulting geometry (Fig. 2b) was parameterized. Several geometric parameters are the subject of these studies;
The parameters were configured in ANSYS Workbench and a
complete tetrahedral mesh of about 3.9M, which is well-suited to
a parametric geometry, was created. The mesh was converted into
a polymesh (of 0.8M) in ANSYS Fluent. Only one fluid domain,
called the melt domain, was used (see Fig. 3). The Air domain was
considered as a solid with the thermal properties of air.
A single Fluent case is able to include the electrical physics, the thermal physics and the melt fluid dynamics, if an appropriate configuration, as described in the following paragraph, is used.
One of the most important properties is the electrical conductivity of the melt. Electrical resistance is a function of:
A specific combination of these three parameters defines the electric resistance (Rele) (see Fig. 4a). The Joule effect generated by one electrode is:
The electrical model considers that the three electrodes are at a steady state and have a constant Icurrent value (their inflow to the melt domain), which is equal for all the electrodes. Kele is constant and temperature and zero voltage are fixed throughout the solid domain (except in the melt and in the air). The resulting electrical potential is shown in Fig. 4b. These hypotheses derive from some preliminary studies, in which different boundary conditions were tested. Once the electrode distance (d) is set, the Kele was corrected via the preliminary studies to obtain the expected Pele. The electrical potential is shown in Fig. 5.
In this model, only the melt domain is fluid. The air domain is considered solid, with the standard properties of air. However, the contribution of thermal radiation, due to the high temperature of the melt, is important. A Monte Carlo radiation model was chosen, which allows the solid domain to participate in the radiation. The thermal source term, due to the joule effect (Fig. 5a) is concentrated near the electrode tips, inducing the temperature increase in the melt (Fig. 5b). Appropriately convective boundary conditions (heat exchange coefficient and free flow temperature) are imposed on the external surface of the refractory. The roof of the furnace includes a cooling system that reduces the temperature of the air inside the furnace and controls the formation of a crust on the top of the molten rock. This system is modelled using a negative energy source (to extract the estimated thermal power).
In the real furnace, solid material is added from seven different inlets (Fig. 6a) at different times during the process (controlled by local measurement of the melt temperature). Quantities of the melt are then removed (Fig. 6b) via a single outlet to maintain a prescribed melt level. A prescribed constant mass flow rate was set in the numerical model in order to use a steady-state simulation. The solidification/melting model in Fluent was used to include the latent heat absorbed by the inflow from these inlets as a result of the phase change from solid to liquid in the melt. These hypotheses allow the modelling of a continuous flow, and the evaluation of the average velocity field (see Fig. 7) from the inlet to the outlet of the melt domain.
After setting a base case (DP0), three different design points (DP1-DP3) were tested. Fig. 8 shows the distribution of the temperature fields in these four different cases:
It is possible to see that a reduction of the distance between
the electrode tips and the iron domain induces a reduction of
the temperature near the electrodes (due to the lower electrical
The relative position between the inlet and the electrodes also appears to be important to achieve a homogenous distribution of the temperature inside the melt. Fig. 9 shows the temperature ranges of the melts on the z-x plane. The flow lines from the inlet, colored according to the intensity of the joule effect, show that the DP1 configuration was able to generate a more uniform temperature in the highlighted area, compared to the DP0.
This paper presents a numerical model for a melting furnace for
stone wool. The numerical model was designed using ANSYS
FLUENT. The model includes all the main aspects of the real
process (the electrical field, the heat transfer process and the fluid
dynamics of the melt).
The CFD simulation provided a lot of information about the operating conditions of the electric melter while considering the hypothesis of the dissipation terms. A proper validation on site in the field will enable us to obtain a more reliable setup of the model, to reflect reality as closely as possible.
Gamma Meccanica is one of the world leaders in mineral wool production lines, both of individual machines and of complete lines for the production of mineral wools, namely stone wool and glass wool. The company also manufactures special lines to produce pipe sections and stitched mattresses, lamellar production lines, ceramic fiber machinery, and stone wool and glass wool hydroponic production lines. Gamma Meccanica’s machinery offers a combination of high performance and advanced technology. It meets and exceeds customer requests by constantly improving quality and energy efficiency, by means of technological evolution and high levels of technical support, in compliance with the strictest environmental standards..
The challenge was to find the optimal aerodynamic design while substantially reducing the costs associated with traditional CFD modeling.
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