Knowledge-based method for the validation of complex simulation models

In traditional research, the validation of simulation models is mainly based on statistical analysis and simulation error evaluating. As simulation models become more and more complex, simulation behavior is more complicated and heavily dependent on simulation conditions, traditional method cannot be applied directly. On the other hand, measured data needed for traditional methods cannot always be available. What's more, the validation of complex models is usually costly and time-consuming. This research makes effort to settle the problems above. It provides three aspects of advantage for model validation. First, complicated simulation behavior is abstracted and classified as five categories, among which behavior relationship and aggregative behavior is unique for complex simulation models. And the analysis and validation method of each kind of behavior is proposed. Second, besides measured data of real system, it proposed to utilize experience of expert and other kind of domain knowledge for validation task. And third, the simulation analysis and validation method proposed can be implemented in knowledge system and accomplish validation task automatically. Simulation output analysis is the most important step in model validation. Besides classical continuous dynamic fitting and statistical consistency analysis methods, we propose a domain knowledge-based method for the validation of relationship among behavior segments and aggregative behavior which is unique for complex simulation models. Domain knowledge takes the role of reference in model validation, and it varies from measured data of real system to qualitative experience of experts. Knowledge-based system is implemented based on domain knowledge and validation techniques proposed. Validation of electromagnetic rail gun simulation models are introduced as an example. It has been found that this method can provides an automatic validation way for complex simulation models, and validation tasks can be accomplished efficiently.