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Moreover, the continuum sensitivity and adjoint equation contribute to the quick numerical implementation of sensitivity analysis using software that can solve an arbitrary partial differential equation directly.
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Results of sensitivity analysis are presented in Table 4.
Figure 1 illustrates the results of sensitivity analysis.
Results of sensitivity analysis.
We performed three types of sensitivity analysis.
In summary, so as to specify the relative importance of each factor on the model response, the implementation of a sensitivity analysis is mandatory, while in the RSM model, the relative importance of each input parameter can be determined simply by looking at the coefficient of each factor in the model equation.
The practical implementation of the sensitivity analysis in a finite element code employing the Analytical, Semi-Analytical and Refined Semi-Analytical approaches is discussed in detail.
Finally, the major contributing factors in the design of BioSNG supply chains are identified through the implementation of a rigorous global sensitivity analysis (GSA).
To calculate the local sensitivity coefficient for an objective function, C, with respect to a parameter, μ, one uses the formula [ 22]: sensitivity coefficient = μ C (∂ ∂ μ C ) A major advantage of local sensitivity analysis is its ease of implementation [ 21].
Furthermore the numerical implementation into a finite element program of the resulting set of constitutive equations and aspects of the sensitivity analysis for parameter identification are addressed.
(a) Result of linear sensitivity analysis.
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