KULI advanced offers an optimization toolbox which can be used for quick parameter variation on one hand, and automatic minimization, maximization or goal seek on the other hand. Based on those functions it is possible to perform statistical analysis as well.
This example shows the application of the Monte Carlo Method in KULI. The basic idea is that some input parameters do have a certain degree of uncertainty. In this example we assume that the charge air temperature has a temperature of 150°C, but we are not sure, so we apply a normal distribution with mean 150°C and a standard deviation of 10 K. Furthermore we assume that the built in resistance has a zeta of 400, again with some uncertainty resulting in a normal distribution with mean 400 and standard deviation of 10. And finally we assume that we don’t really know the exit cp-value; since we can’t even guess a mean value we apply a uniform distribution between -0.4 and -0.2. When we run this model we choose „Monte Carlo Simulation“ with a sample size of e.g. 500. A possible output is mean value and standard deviation of water and charge air temperatures.Usable from release: KULI 8.0-1.04