The above figure is a screen image of the confidence intervals for the length of a queue. The yellow line in the middle of the red area is the average queue length, and the othe lines (with red shadows) show the upper and lower limits of the area where the queue length belongs, with probability 0.9. Note that this is a dynamic analysis, and the values are shown as functions of time.
Animation is one of the ways to visualise the results of a simulation run. It is useful while testing the simulation program, and while presenting the results of the simulation to a target user. If you develop a simulation for someone (a person, a copany), you must make your client believe that the program really simulates what he wanted to be simulated. The best way to achieve this, is a good animation. The model shown below is a simple manufacturing cell. Products (compters) enter the model (In arrow) and are put on a conveyor. Machine 1, 2 and 3 are service operations. If the server is free, then the product enters the service, if not, it moves forward. If all servers are busy when the product passes near to it, then the product moves and returns to the beginning of the conveyor. After receving service, the product enters a buffer (orange line) and then the "quality check" operation. If it has a "good" quality, then it leaves the model, if not, it is being returned to the conveyor (red route). The animation is a nice way to see what happens in the model. The icons move rapidly on the screen and you can see the bottlenecks and the state of the server.
As mentioned before, Bluesss can be used to simulate a great variety of models. Below you can see an example of a Bluesss model given in the form of a signal flow diagram. This is a simple system of automatic control. The node U is the set point, the link E->Y is a PID controller, link Y->X is the controlled process, in this case an inertial object of the second order. The link X->V is the measurement instrument (first order inertia). The signal ate node E represents the control error (the difference U - V).
The following plot shows the results of a simulation run for the above model, in the "varying parameter" mode. In this simulation, the controller gain changes automatically between 4 and 14 in 25 steps. The curve 1 correspons to the lower value of the controller gain. Such simulation can be useful while looking for optimal setting of the controller.