By James J. Nutaro
This publication bargains a concise advent to the artwork of creating simulation software program, amassing crucial recommendations and algorithms in a single position. Written for either participants new to the sector of modeling and simulation in addition to skilled practitioners, this consultant explains the layout and implementation of simulation software program utilized in the engineering of huge platforms whereas providing the suitable mathematical parts, notion discussions, and code improvement.
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Additional info for Building Software for Simulation: Theory and Algorithms, with Applications in C++
The addEventListener method puts an EventListener into the list of listeners that are notified of changes to the model. The getTime method returns the simulation time at which the state was last computed. The computeNextState method does four things: (1) computes the model’s output function if this has not already been done, (2) computes the model’s next state, (3) notifies listeners of these actions, and (4) tells the model to clean up objects created by its output function. The computeOutput method invokes the model’s output function and informs registered EventListeners of the consequent output values; it does not change the state of the model.
The tank’s computer and the tank itself are network models. The simulation engine operates on the interfaces provided by these two types of components. An atomic model has methods for computing its output from its current state and for computing its next state from its current state and input. A network model has methods for retrieving its set of components, for transforming output from its components to inputs for its other components and outputs from itself, and for transforming its inputs into input for its components.
From here on, it is assumed that our models are time-invariant. Suppose that we apply an input trajectory that is defined at a single point. For example, the trajectory x[t0 , t1 ) = x(t0 ). 8) where the function δ is the single-step state transition function or just the state transition function. 8 without referring to time at all. 9) This function gives us a convenient way to define the total state transition by describing how the system responds to any single input. The total state transition function is defined by recursive application of δ to the values in an input trajectory x[t0 , tn ).