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[e.g. bell curve, linear distribution, etc.]. This is like rolling a dice. The outcome is always within the range of 1 to 6 and it follows a linear distribution -there is an equal opportunity for any number to be the outcome.
In Monte Carlo simulations, the random selection process is repeated many times to create multiple scenarios. Each time a value is randomly selected, it forms one possible scenario and solution to the problem. Together, these scenarios give a range of possible solutions, some of which are more probable and some less probable.
When repeated for many scenarios [10,000 or more], the average solution will give an approximate answer to the problem. Accuracy of this answer can be improved by simulating more scenarios. In fact, the accuracy of a Monte Carlo simulation is proportional to the square root of the number of scenarios used.
8.3.3 USE OF MONTE CARLO SIMULATION
Monte Carlo simulation is advantageous because it is a "brute force" approach that is able to solve problems for which no other solutions exist. Unfortunately, this also means that it is computer intensive and best avoided if simpler solutions are possible. The most appropriate situation to use Monte Carlo methods is when other solutions are too complex or difficult to use.
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8.4.1 INTRODUCTION
Noise Modelling is used to determine the noise distribution over a predetermined area as generated by a specific traffic pattern.
8.4.2 HOW NOISE MODELLING WORKS
Noise Modellers use an advanced form of fast-time simulator which are capable of calculating noise contours over a pre-defined area. These ‘noise-modelling’ functionalities are added to typical functionalities (such a flight trajectory calculation) included in ‘standard’ fast-time simulators.
In order to generate the noise contours for each simulated aircraft in addition to the flight trajectories, the noise modeller determines (according to the aircraft model) the estimated speed and engine power setting/thrust. Based on these data and taking into account the terrain contours and other environmental conditions (time of the day, meteorological condition, etc), the simulator calculates the noise distribution and noise level at predetermined check points.
The accuracy of the results very much depends upon the realism of the aircraft models used by the simulator and on the model used for calculating noise distribution. Aircraft trajectories can be directly derived from recorded Radar data from real-live operations. Even so, modelling individual aircraft is difficult even when using advanced computational technologies. Movements are allocated to different aircraft ‘types’ and aircraft that are noise ‘significant’ (by virtue of their numbers or noise level) are represented individually by aircraft type, e.g. B747-400. Some ‘types’ are grouped together with those having similar noise characteristics. For each ‘type’, average profiles of height and speed against track distance are calculated from an analysis of radar data. These average profiles are subdivided into appropriate linear segments.
Average ground tracks for each route are calculated based on radar data. Accurate noise exposure estimation requires a realistic simulation of the lateral scatter of flight tracks actually observed in practice. This is done by creating additional tracks which are a number of standard deviations either side of the central average track. The standard deviations and the proportions of traffic allocated to each route are determined by analysis of the radar data.
　
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本文鏈接地址：EUROCONTROL MANUAL FOR AIRSPACE PLANNING 2(116)