The variations of input parameters induce remarkable variations of output heat flux and total heat load.
The sensitivity of the optimal solution respect to variations of input parameters is analyzed.
In this situation, reference image will change according to the variations of input images that named it background learning [20].
In this respect, in order to develop a reliable numerical simulation, it is necessary to include uncertainty quantification due to the variations of input data [30].
The variations of input parameters (both individual and combined cases) are studied to portray their relative effect on the output quantity of interest.
The results also indicate that the approach is relatively robust in terms of sensitiveness to variations of input data and starting conditions.
A design matrix of 45 × 9 is generated for variations of input parameters with the Latin Hypercube Sampling and 45 code runs were taken.
Through parametric variations of input conditions, the proposed model/tool can be used to identify adsorbent adsorbate pairs for optimal performance.
Both individual and combined variations of input parameters have been considered to account for the effect of low and high dimensional input parameter spaces in the surrogate based uncertainty quantification algorithms including the rate of convergence.
The DWT scheme is a good approach in the name of optimizing frequency weights, but it does not yet consider temporal variations of input signal statistics because the optimized weights can be calculated only once through the whole training data.
