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Showing 2 results for Sensitivity Analysis

H. Moharrami, S.a. Alavinasab,
Volume 4, Issue 2 (6-2006)
Abstract

In this paper a general procedure for automated minimum weight design of twodimensional steel frames under seismic loading is proposed. The proposal comprises two parts: a) Formulation of automated design of frames under seismic loading and b) introduction of an optimization engine and the improvement made on it for the solution of optimal design. Seismic loading, that depends on dynamic characteristics of structure, is determined using "Equivalent static loading" scheme. The design automation is sought via formulation of the design problem in the form of a standard optimization problem in which the design requirements is treated as optimization constraints. The Optimality Criteria (OC) method has been modified/improved and used for solution of the optimization problem. The improvement in (OC) algorithm relates to simultaneous identification of active set of constraints and calculation of corresponding Lagrange multipliers. The modification has resulted in rapid convergence of the algorithm, which is promising for highly nonlinear optimal design problems. Two examples have been provided to show the procedure of automated design and optimization of seismic-resistant frames and the performance and capability of the proposed algorithm.
Gholamreza Asadollahfardi,
Volume 14, Issue 4 (6-2016)
Abstract

This paper presents a numerical model based on  the explicit finite difference method for contaminant transport under electrokinetic remediation process. The effect of adsorption, precipitation and water auto-ionization reactions were considered  to  set of algebraic equations. Also the effect of electrolysis reaction in anode and cathode cells was considered with appropriate boundary conditions. The model predictions are compared with experimental results of electrokinetic lead removal from kaolinite in the literature. The coefficient of determination and index of agreement between the lead concentration of experimental result and model prediction was 0.974 and 0.884, respectively. The coefficient of determination and index of agreement between the pH value of  the experiment and the pH prediction was 0.975 and 0.976, respectively



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