Minimization algorithms and experimental inverse transient leak detection

Abstract

The inverse transient method has been verified for leak detection from a laboratory pipeline using a least-squares minimization. The process determines the size and location of a leak. In previous numerical studies the Levenberg-Marquardt algorithm and genetic algorithms were used to perform this minimization. In the current experiment, the Levenberg-Marquardt algorithm yields incorrect leak locations and magnitudes. The complexity of the objective function causes the Levenberg-Marquardt algorithm to fail. The inverse transient method has been verified for leak detection from a laboratory pipeline using a least-squares minimization. The process determines the size and location of a leak. In previous numerical studies the Levenberg-Marquardt algorithm and genetic algorithms were used to perform this minimization. In the current experiment, the Levenberg-Marquardt algorithm yields incorrect leak locations and magnitudes. The complexity of the objective function surface causes the Levenberg-Marquardt algorithm to fail. The use of the shuffled complex evolution algorithm—a more global minimization algorithm—improves results, however convergence is slow. A systematic approach to the application of the Levenberg-Marquardt algorithm determines the correct leak location and magnitude more efficiently.