Wagner F. Sacco

Direct and inverse analysis of diffusive logistic population evolution with time delay and impulsive culling via integral transforms and hybrid optimization

Abstract Motivated by the fact that several species act as a vector in the spread of human or livestock diseases, many works propose mathematical formulations for the modeling of these populations, most of them considering Fickian dispersion and logistic like growth rates. For the best use of these models in a real application of optimal population control, the model parameters should be identified as accurately as possible for a given species population. In this work, this parameter identification problem is formulated as an inverse problem, which is tackled with a combination of the Generalized Integral Transform Technique (GITT), for the direct problem solution, and a hybrid stochastic–deterministic procedure for the minimization of the defined objective function in the inverse analysis, employing the Differential Evolution and the Levenberg–Marquardt methods. The direct problem solution with GITT and the inverse analysis are critically investigated. In order to improve the computational performance of the inverse problem solution, a second order semi-analytical integration and a solution refinement scheme are proposed.

The Luus-Jaakola algorithm applied to a nuclear reactor core design optimisation

The Luus-Jaakola (LJ) algorithm is a random search optimisation method that has been successfully employed mainly in chemical engineering problems. In this paper, we apply this algorithm to an optimisation problem solved previously with genetic algorithms, particle swarm optimisation and Metropolis algorithms. This problem consists of adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimise the average peak factor in a three-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and submoderation. LJ is compared with the aforementioned optimisation algorithms and is shown to perform well, thus demonstrating its potential for other applications.

Prediction of Double Retrograde Vaporization by Hybrid Global-Local Optimization Using Fuzzy Clustering Means

Retrograde vaporization calculation is a hard problem, which possesses several solutions and demands a robust algorithm. In the present work, we solved the double retrograde vaporization problem by hybrid global-local optimization. In fact, we propose a new methodology that involves fuzzy clustering means together with Luus-Jaakola and Nelder-Mead algorithms. We applied the proposed methodology to solve the double retrograde vaporization problem for binary systems of methane + n-butane. For this mixture, the dew point curve exhibits an S-shape at temperatures slightly below the critical temperature of the more volatile component. This binary mixture was modeled using the original Peng-Robinson equation of state and the classical one-fluid van der Waals mixing rule.

A constrained ITGO heuristic applied to engineering optimization

Abstract Nonlinear optimization is an active line of research, given the wide range of scientific fields that benefit from its development. In the last years, the meta-heuristics proved to be one of the most effective methods to tackle difficult optimization problems, providing an alternative in cases where exact methods would be unfeasible. In this work, we present a method based on the Iterative Topographical Global Optimization meta-heuristic, which we call C-ITGO, incorporating specific mechanisms to solve nonlinearly constrained optimization problems. We use the method developed in this work to optimize eight complex engineering design problems and compare the results obtained here against those obtained with several other methods found in the literature. In the tests performed, C-ITGO outperforms all competing methods, achieving state of the art results for the problems considered.