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Since modeling has gained wider acceptance and is increasingly relied upon to support important decisions, capturing and cataloging simulation models, the data used for their input, and the calculated results are very important. Managing simulation data has become a challenge for many organizations engaged in process modeling. A good simulation data management (SDM) system allows models to be “recycled” and used again for a different set of operating conditions, and allows different users to access archived models and their data. Furthermore, a good system has cataloging and search features that allow users in need of modeling data to access it quickly, even if they are not familiar with the previous modeling study. That is, not only should a SDM system assist individual analysts to organize their results, but it should serve a larger enterprise, with many people in different and changing roles.

The interactions and associated transfer of information of different organizations within a glass processing enterprise are sketched in Figure 10. Whether simulations are performed to assist a technical‐support team or a technology‐development effort in R&D, the ultimate benefactor is the manufacturing operation. Although there are direct interactions between any two of these subgroups, an SDM system can be used to manage data collected and generated by the modeling and simulation activities.

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.