Lesson 2 Summary

Lesson 2 , page 12 of 12

In Lesson 2, we briefly covered the basic concepts upon which GoldSim models are based:

• GoldSim represents parameters, processes, or events in a system A subunit of the world separated by a boundary from the rest of the world. The description of the system is comprised of the relations within the system as well as those characterizing the action of the outside world on the system. using objects called elements The basic building blocks with which a GoldSim model is constructed. Each element represents a feature, pararamer, process or event in the model..
• Each element has a symbol or graphical image to represent it.
• An element accepts input data and produces output data.
• There are six primary categories of elements: Inputs, Functions, Events, Stocks, Delays, and Results.
• GoldSim represents the links (dependencies) between elements using an arrow called an influence An arrow connecting two elements that indicates that one element influences the other..
• GoldSim ensures dimensional consistency and carries out all unit conversions for you.
• A special type of element, the Container An element that acts like a “box” or a “folder” into which other elements can be placed. It can be used to create hierarchical models., is used to hierarchically organize other elements.
• In order to simulate how a system might evolve over time in a program like GoldSim, it is necessary to discretize time into discrete intervals referred to as timesteps An element that generates discrete signals based on a specified rate of occurrence.. GoldSim then "steps through time" by carrying out calculations every timestep, with the values at the current timestep computed as a function of the values at the previous timestep.
• After running a model, GoldSim can generate and display different types of results, in either graphical or chart form. The most common results viewed are the time history result A chart or table showing how a model variable changes with time. and the distribution result A chart showing the uncertainty in the output of a probabilistic simulation. Distribution results can take the form of a Cumulative Distribution Function, a Complementary Cumulative Distribution Function, or a Probability Density Function.. A time history result simply shows how a model output is predicted to change with time. As such, it is the fundamental type of result produced by a dynamic simulation model. A distribution result is the fundamental type of result produced by a probabilistic simulation A simulation in which the uncertainty in the input parameters is explicitly represented by defining them as probabilility distributions. model. It shows a probability distribution of an output at a specific point in time (e.g., the end of the simulation).