Sentence examples for reciprocal variables from inspiring English sources

For the planar 8-bar truss structure studied in Example 2, accuracy of polynomial-basis RSM with reciprocal variables is listed in Table 5.

For the 2-bar truss structure studied in Example 1, the computing accuracy of polynomial-basis RSM with reciprocal variables is listed in Table 4.

(2) Polynomial-basis RSM with reciprocal variables has a great improvement on the statically determinate structure, with relative errors less than 10−10.

This is mainly because internal forces are changing as the changes of design variables, and these changes don't be reflected in RSM equation, which lead to the difference in the form between RSM approximation with reciprocal variables and analytic solution.

The most commonly used RS approximation function of nodal displacements of truss structures can be obtained by taking the reciprocal variables x j as design variables and substituting them into Eqs.

In order to analyze the accuracy of the polynomial-basis response surface method with reciprocal variables, the accuracy analysis is carried out by reusing Example 1‒3 as the examples.

Instead, the proportion of drivers in each age group who would have been subject to each respective licensing policy in a given year was estimated by modeling the renewal period as its reciprocal and dividing variables representing vision, knowledge, and on-road driving test requirements by the renewal period.

We have proven that the hypergeometric function can be expressed as a finite expansion and that the integrand involving this series and a product of Bessel functions satisfies a linear differential equation with coefficients having a power series expansion in the reciprocal of the variable suitable for application of the nonlinear D- and D-transformations.

The effects of variables, namely reciprocal of substrate concentration (0.033 0.5 mM−1), reaction temperature (14.9 40.1 °C) and reaction pH (pH 4.4 8.5) on the reciprocal of initial reaction rate were evaluated and a second order polynomial model was fitted by a central composite circumscribed design (CCCD).

The reciprocal effects between the variables were favored by the data.

There was less support for a model in which the latter variables produced reciprocal paths to self-efficacy.