Quantification of tolerance limits of engineering system using uncertainty modeling for sustainable energy

Measurements always associate a certain degree of uncertainty. In order to achieve high precision measurement in presence of uncertainty an efficient computation is desired. Statistical definition of precision of any measurement is defined as one standard deviation divided by the square root of the sample size taken for measurements. Accordingly, tolerance limits are statistical in nature. Therefore, measurements are required to repeat large number of times to obtain better precision. Hence, the target is to establish the tolerance limits in presence of uncertainty in computer and communication systems. Nonparametric method is applied to establish the tolerance limits when uncertainty is present in measurements. The basic aim of the present paper is to explore order statistics based nonparametric method to estimate the appropriate number of samples required to generate the realizations of the uncertain random parameters which further will facilitate user to establish the tolerance limits. A case study of solute transport model is experimented where tolerance limits of solute concentration at any spatial location at any temporal moment is shown. Results obtained based on the nonparametric simulation are compared with the results obtained by executing traditional method of setting tolerance limits using Monte Carlo simulations using computer and communication systems.