A formulation of ρ s,lim, calibrated on the results of the parametric survey is here proposed as a function of the non dimensional cover (c/∅ b ): rho_{{s,{ lim }}} = 2.86left( {frac{c}{{phi_{text{b}} }}} right)^{1.6} (33) Open image in new window Fig. 9 Influence of the stirrups amount on the parameter of the proposed bond-slip law.
By simultaneously fitting to three different measurements, only a minor amount of the parameter sets showed a good fit.
The DE algorithm is described as follows: Step 1. Initialization: Generate random initial population X G ={ X 1,G, X 2,G,…, X N,G }, where X i,G ={ X i1, G, X i2, G,…, X iD , G }. N is the population size, G is the generation, X i,G is a rate constant set and D is the amount of the kinetic parameters.
the amount of this parameter is depend on the scenario of detection.
The average amount of this parameter varied in the range of ten milli-Darcy to thousands of milli-Darcy regarding the types of reservoir and interconnection between cracks and fractures.
The amount of this parameter should be determined for ultrasonic assisted metal forming processes.
To measure average permeability, the amount of this parameter is calculated in each layer in Table 4.
By taking advantage of the disturbance model in the design of special infinite-impulse-response (IIR) filters, we can reduce the adaptation to identify the minimum amount of parameters, achieve accurate parameter estimation under noisy environments, and flexibly reject the narrow-band disturbances with clear tuning intuitions.
Particularly, the amount of additional parameters and the system complexity increase tremendously when the IκB/NFκB part of the model influences IKK or when the signaling cascade is extensively modeled.
One aspect with this approach is to balance the amount of parameters with the accuracy of the approach.
