Encyclopedia of Glass Science, Technology, History, and Culture. Группа авторов
alt="Schematic illustration of the results of particle tracking post-processing: (a) calculated distribution of particle residence times, (b) paths taken by fastest 0.1percent particles, and (c) typical paths taken for median residence time. Same number of particles tracked in both cases."/>
Figure 8 Results of particle tracking post‐processing: (a) calculated distribution of particle residence times, (b) paths taken by fastest 0.1% particles, and (c) typical paths taken for median residence time. Same number of particles tracked in both cases.
Source: Courtesy of Glass Service, Inc.
An SDM system improves productivity by keeping data well organized and accessible to authorized individuals at various locations, who may be interested in a summary of results, build a new model based on a previous model, or mine data in search of correlations between certain operating conditions. By keeping official records, the SDM reduces problems of duplicated files or duplicated names of slightly different files. It also contributes to security by controlling access, minimizing the chance of inadvertently deleting files, and centralizing the backup of these data.
There are numerous ways to implement some kind of SDM system, ranging from a highly structured and well‐organized network‐attached storage system to highly sophisticated commercial offerings that either resemble product lifecycle management (PLM) systems or are even integrated into a PLM. In addition to administering simulation data, many of these systems also can be used to manage the simulation process, where workflow is defined within collaborative groups, computational resources are shared, and sequential, parametric simulations (e.g. a design optimization exploration) can be conducted with improved effectiveness. These SDM systems are designed to extract metadata from model files. Examples of commonly extracted metadata are methods to characterize viscous effects (i.e. laminar, turbulent k‐ε, turbulent RSM, etc.), mesh size, mesh type (i.e. hexahedra, tetrahedral, etc.), software version, etc. For glass processing, it is possible and recommended to provide a way to extract metadata associated with the glass process; examples of these include furnace footprint size, pull rate, glass type, batch characteristics, etc. Any organization which has collaborative efforts involved with modeling and simulation should thus implement some kind of data management system.
Figure 9 Pathways of sand particles dissolving in glass.
Source: courtesy of Glass Service, Inc.
Figure 10 Central role of simulation data management in sharing of information.
6 Perspectives
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.
4.2 Acknowledgements
The authors thank Glass Service Inc. for sharing nonproprietary model data from which some of the examples presented were taken. Also, they are grateful to their employer, Owens Corning, for supporting their effort to contribute to this volume.
References
1 1 Bird, R.B., Stewart, W.E., and Lightfoot, E.N. (1960). Transport Phenomena. New York: John Wiley & Sons.
2 2 Incropera, F.P. and DeWitt, D.P. (1996). Fundamentals of Heat and Mass Transfer, 4the. New York: John Wiley & Sons.
3 3 Modest, M.F. (1993). Radiative Heat Transfer. New York: McGraw‐Hill.
4 4 Loch, H. and Krause, D. (eds.) (2002). Mathematical Simulation in Glass Technology. Berlin: Springer Verlag.
5 5 Barnes, H.A., Hutton, J.F., and Walter, K. (1993). An Introduction to Rheology. Amsterdam: Elsevier.
6 6 Crochet, M.J., Davies, A.R., and Walters, K. (1984). Numerical Simulation of Non‐Newtonian Flows. Amsterdam: Elsevier.
7 7 Patankar, S.V. (1980). Numerical Heat Transfer and Fluid Flow. Washington, DC: Hemisphere.
8 8 Reddy, J.N. (1993). An Introduction to The Finite Element Method, 2nde. New York: McGraw‐Hill.
9 9 Glicksman, L.R. (1968). The dynamics of a heated free jet of variable viscosity liquid at low Reynolds number. J. Basic Eng. Trans. ASME, Series D 90: 343–354.
10 10 Purnode, B.A. and Rubin, Y. (1998). Two dimensional finite element analysis of glass fiber forming. In: Proceedings of the. International Congress on Glass. San Francisco: ACerS.
11 11 Purnode, B.A. (2000). Transient axisymmetric study of glass fiber forming. In: Proceedingsof the ASME 2000 Fluids Engineering Division Summer Meeting. Boston: ASME.
12 12 Pierrot, L. (2004). Accuracy of the Rosseland approximation in toy models of glass tanks. In: Proceedings of the 20th International Congress on Glass. Kyoto: The Ceramic Society of Japan.
13 13 Choudhary, M.K., Purnode, B.A., Lankhorst, A.M., and Habraken, A.F. (2017). Radiative heat transfer in processing of glass‐forming melts.