Multi-parameter sensitivity analysis

Jemei et al. (2005) reported a Dynamic Recurrent Neural Network (DRNN) model of a PEMFC for a 500 W fuel cell. The proposed black box model can easily be extrapolated to more powerful fuel cell systems. For black-box models, simulation results are strongly dependant on the choice of input parameters. Thus, a sensitivity analysis is performed to assess the influences or relative importance of each input parameter on the output variable. Many different ways to perform sensitivity analysis are possible. A Multi Parameter Sensitivity Analysis (MPSA) is proposed to evaluate the relative importance of each input parameter independently on the fuel cell voltage. 

Jemei S, Hissel D, Pera M C and Kauffmann J M (2005) Multi-Parameter Sensitivity Analysis of a Proton Exchange Membrane Fuel Cell Model, 3rd European PEFC Forum, Lucerne, File No. P205. 

However, as a general observation, this study demonstrated the feasibility of the integrated modeling approach to couple an environmental multimedia and a PBPK models, considering multi-exposure pathways, and thus the potential applicability of the 2-FUN tool for health risk assessment. The global sensitivity analysis effectively discovered which input parameters and exposure pathways were the key drivers of Pb concentrations in the arterial blood of adults and children. This information allows us to focus on predominant input parameters and exposure pathways, and then to improve more efficiently the performance of the modeling tool for the risk assessment. 

The SPP general solvent seale, and the SA and SB speeifie solvent scales, are orthogonal to one another, as can be inferred from the small correlation eoefficients obtained in mutual fittings involving the 200 solvents listed in Table 10.3.1 [r (SPPvs. SA) = 0.13, r (SPP vs. SB) = 0.10 and r (SA vs. SB) = 0.01]. These results support the use of these seales for the multi-parameter analysis of other solvent scales or data sets sensitive to the solvent effeet on the basis of the following equation ... 

The application was supported by DSS (Lopes et al, 2009) designed for risk analysis using multi criteria decision analysis to consider three relevant risk dimensions in a real gas pipeline system. The DSS allows the decision makers to analyze the gas pipeline system considering several relevant variables and also to make a sensitivity analysis of the main parameters in order to verify the robustness of the analysis. 

SMART desalination plant incorporates the falling film multi-effect evaporation with horizontal tubes and a steam jet ejector (thermal vapour compressor). The desalination unit is designed with a plant life of 30 years, performance ratio of 19.6, acid cleaning to be performed once in 12 months, maximum brine temperature of 65°C, and the supplied seawater temperature of 33°C. Thermal vapour compressor is introduced to improve thermal efficiency of the process steam. The advantages of this design are high heat transfer coefficients and a relatively simple operation system. The performance ratio of desalination plant, one of the most important coupling parameters, was optimized based on the sensitivity analysis of water production cost and on the requirements to the SMART desalination plant. 

Sensitivity analysis thus provides a method by which the structural stability of multi-parameter models can be assessed and described in more detail. We can say for the reference solution studied here that the Lotka-Volterra oscillator is structurally unstable to variations in I<4 and I<5 but not to variations in k, k2 and I<3. These properties of the Lotka-Volterra oscillator are, of course, well-known. The success of sensitivity analysis in unambiguously (and quantitatively) verifying these facts suggests that it will be a useful tool for the study of models which are not so well-understood. 

Cukier, R.I., Levine, H.B., Shuler, K.E. Nonlinear sensitivity analysis of multi-parameter model systems. J. Comput. Phys. 26, 1-42 (1978)... 

The main advantage of a multi-frequency study is that it provides information on the frequency dispersion of magnetic resonance parameters. This approach (dispersion), for example, is the power in NMRD studies. Several laboratories pioneered in the application of multi-frequency EPR as a route to a more accurate evaluation of key spectroscopic parameters (g, A, Q, D, E), as well as a more sensitive methodology for studying dynamical processes, where an interplay between the frequency dependence of the spin process and the frequency dependence of the EPR observation often can provide exceptionally detailed information [64,65]. In order to take advantage of the method, the frequency dependence of spin systems must be understood. This has led to the development of several theoretical approaches for better analysis of multi-frequency data, and especially in BPCA research, for the analysis of the frequency dependence of geffective, Tle, T2e, and the overall EPR line shape in frozen glasses and in room-temperature aqueous solutions. 

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