MoST Modelling Support Tool

Since its introduction in 1971, SPICE (Simulation Program with Integrated Circuit Emphasis) has become the most popular analog simulation tool in use today. In the last 15 years, we have seen explosive growth in the use of SPICE, with the addition of Berkeley SPICE 3 enhancements, and support for C code model and mixed-mode simulation using XSPICE (Cox et al. 1992, Kielkowski 1994).We have also seen many new companies emerge as developers of SPICE-based simulation tools, most of which are currently available for the PC platform. 

There is usually a big gap between the application layer and the layer of external models of tools (see again Fig. 1.6 separation line a) Tools only support minor or primitive parts of the work processes of developers, support does not fit to the task to be solved, the presentation of results is not evident, or the communication between developers is done outside of tool environments (phone, meetings, etc.). Work processes are mostly manual and tedious. 

Traditionally, electrical engineers have relied most on technical models/data, statistics and their own experience, and less on decision support models. However, because asset management decisions have become more complex, this trend is changing, and different types of models and tools used traditionally decoupled are now being integrated in order to offer the best available decision support. 

However, most of the other generic activities are not part of the process or the tool. Most of the methodologies listed above are not partieularly user friendly, and are very difficult and expensive to implement and maintain on the large scale project. As the complexity of the analysed system increases, the models produced using the existing techniques tend to increase in complexity at an even faster rate, resulting in extremely complex models that are difficult to validate and test Finally, due to their complexity, these end up not being used in support of the projects. 

As is noted by the authors at multiple points, the decision support modeling and optimization methods and tools advanced in this book depend critically on effectively managed, timely and reliable enterprise information. Even the most efficient and sophistical set of models and tools will be stymied and ineffective when provided with outdated and faulty data. The discussion of the information systems and tools required to accomplish this is quite beyond the scope of this book but is an important area for the serious reader to pursue further. 

Chemists seeking to use computational chemistry to support experimental efforts now have three generd theoretical tools available to them force field or molecular mechanics models, ab initio molecular orbital (MO) models and semiempirical MO models (1). Each of these tools have strengths and weaknesses which must be evaluated to determine which is most appropriate for a given applications. 

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