Showing 2 results for T. Shahrabi Farahani
H. Moharrami, M.t. Shahrabi Farahani and H. Shourabi,
Volume 26, Issue 1 (7-2007)
Abstract
Marine structures are one of the most important and susceptible facilities in Iran due to corrosion. The two methods of Cathodic Protection, namely, the cathodic protection with sacrificial anodes and cathodic protection using impressed current, are widely used for corrosion protection. According to the former, sacrificial anodes are installed at several points in the structure. Position of the anodes for achieving the required protection is a problem that engineers are very much interested in, and only empirical methods have so far been used to determine these positions. Empirical rules, however, might cause either overprotection or underprotection. A major goal of this research is to develop a systematic way for analysis and automated design of Cathodic Protection systems that not only deliver almost uniformly protected structures but also minimize the costs. To this end, a Genetic Algorithm (GA) routine is used to determine the optimal position of anodes on the structure such that a uniformly protected design with minimum cost is achieved. The percentage of protection in each design has been taken as its fitness criterion. To figure out the situation of corrosion protection on the structure, the entire offshore structure with its complex system at anodes and surrounding electrolyte is modeled and analyzed by a finite element algorithm. Employing GA gradually modifies the generation of designs. The design which completely protects the structure and whose cost is minimum is introduced as the optimum design. To show the capability of the proposed method in achieving the optimum design, two examples are offshore presented.
T. Shahrabi Farahani, V. Baigi and S. A. Lajevardi,
Volume 27, Issue 1 (7-2008)
Abstract
Prediction of SCC risk of austenitic stainless steels in aqueous chloride solution and estimation of the time to failure as a result of SCC form important and complicated topics for study. Despite the many studies reported in the literature, a formulation or a reliable method for the prediction of time to failure as a result of SCC is yet to be developed. This paper is an
effort to investigate the capability of artificial neural network in estimatiing the time to failure for SCC of 304 stainless steel in aqueous chloride solution and to provide a sensitivity analysis thereof. The input parameters considered are temperature, chloride ion concentration, and applied stress. The time to failure is defined as the output parameter and the key criterion to evaluate the effective parameters. The statistical performance of the neural network is expressed as the average of three learning and testing results. The SCC database is divided into two sections designated as the learning set and the testing set. The output results show that artificial neural network can predict the time to failure for about 74% of the variance of SCC experimental data. Furthermore, the sensitivity analysis also exhibits the effects of input parameters on SCC of 304 stainless steel in aqueous chloride solutions.
