Читать книгу Encyclopedia of Glass Science, Technology, History, and Culture - Группа авторов - Страница 164

The use of mathematical modeling in other glass industry segments has also increased over the years. Examples of such work can be found in the container, specialty, and float‐glass industry for simulations of processes such as refining, homogenizing, tempering, shaping, gas generation [4, 16–18]. Striking results have, for instance, been obtained for containers for which simulations allow the shape and fabrication process to be optimized many times more rapidly (and less expensively) than in the traditional way. Not surprisingly, constructing a model to simulate a glass melting furnace is a larger, more time‐consuming task. Obtaining a converged solution while considering the uncertainties associated with material properties is also challenging. Small changes to a validated model can be applied, however, and new simulation results can be computed quickly.

The amount of information that can be extracted from simulation results is appreciated for its value in assessing conditions not possible without computational modeling. Sometimes, a particular post‐processing analysis is not desired until well after a simulation has been completed (i.e. weeks, months, or years later), but as long as the simulation data has been preserved, the analysis can be completed quickly.

Although advanced, modeling and simulation of glass processes can be improved. Some of the improvements are related to numerical implementation, but it is often the case that required transport properties cannot be measured without inordinate expense. Some improvements are related to achieving more accurate solutions, whereas other are related to improve post‐processing. For example, improved knowledge of heat transfer in a foam could significantly reduce the time required to tune and validate a simulation model (i.e. improved accuracy and efficiency), whereas more information about refractory dissolution or wear could improve post‐processing assessments of furnace life.

The batch layer represents an important area whose physics needs to be better understood. The effects of batch constituents (as well as the size and shape of individual particles), the manner in which the batch moves, transmits energy, reacts, and melts into glass need to be understood in a way that can be implemented in a numerical simulation. A similar comment can be made for foam. Despite these shortcomings, simulation results are very useful when applied and interpreted properly. When significant uncertainties exist or if validation fails to reconcile all metrics satisfactorily, then comparisons between simulations cases can be made in a semiquantitative manner, where the simulation results reveal general trends (e.g. increased recirculation, or lowering of exhaust gas temperatures). Results such as these provide guidance that would otherwise be unavailable.