Page 185 - DCAP601_SIMULATION_AND_MODELING

Unit 10: Simulation of a PERT Network (II)

different answer for each execution. Although this might seem obvious, this is a special point of Notes
attention in stochastic simulations, where random numbers should actually be semi-random
numbers. An exception to reproducibility are human in the loop simulations such as flight
simulations and computer games. Here a human is part of the simulation and thus influences the
outcome in a way that is hard if not impossible to reproduce exactly.

Vehicle manufacturers make use of computer simulation to test safety features in new designs.
By building a copy of the car in a physics simulation environment, they can save the hundreds
of thousands of dollars that would otherwise be required to build a unique prototype and test it.
Engineers can step through the simulation milliseconds at a time to determine the exact stresses
being put upon each section of the prototype.
Computer graphics can be used to display the results of a computer simulation. Animations
can be used to experience a simulation in real-time e.g. in training simulations. In some
cases animations may also be useful in faster than real-time or even slower than real-time
modes. For example, faster than real-time animations can be useful in visualizing the
buildup of queues in the simulation of humans evacuating a building. Furthermore,
simulation results are often aggregated into static images using various ways of scientific
visualization.
In debugging, simulating a program execution under test (rather than executing natively) can
detect far more errors than the hardware itself can detect and, at the same time, log useful
debugging information such as instruction trace, memory alterations and instruction counts.
This technique can also detect buffer overflow and similar “hard to detect” errors as well as
produce performance information and tuning data.

Drawback

Although sometimes ignored in computer simulations, it is very important to perform sensitivity
analysis to ensure that the accuracy of the results are properly understood. For example, the
probabilistic risk analysis of factors determining the success of an oilfield exploration program
involves combining samples from a variety of statistical distributions using the Monte Carlo
method. If, for instance, one of the key parameters (i.e. the net ratio of oil-bearing strata) is
known to only one significant figure, then the result of the simulation might not be more
precise than one significant figure, although it might (misleadingly) be presented as having
four significant figures.

Model Calibration Techniques

The following three steps should be used to produce accurate simulation models: calibration,
verification, and validation. Computer simulations are good at portraying and comparing
theoretical scenarios but in order to accurately model actual case studies, it has to match what is
actually happening today. A base model should be created and calibrated so that it matches the
area being studied. The calibrated model should then be verified to ensure that the model is
operating as expected based on the inputs. Once the model has been verified, the final step is to
validate the model by comparing the outputs to historical data from the study area. This can be
done by using statistical techniques and ensuring an adequate R-squared value. Unless these
techniques are employed, the simulation model created will produce inaccurate results and not
be a useful prediction tool.
Model calibration is achieved by adjusting any available parameters in order to adjust how the
model operates and simulates the process. For example in traffic simulation, typical parameters
include look-ahead distance, car-following sensitivity, discharge headway, and start-up lost

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