Aquilino Senra Martinez

TL;DR: This paper addresses a global optimization approach to nuclear reactor core design problems and applies the method based on genetic algorithms to two traditional test problems that have been considered in literature.

TL;DR: In this paper, an alternative analytic solution is presented for point kinetics equations in which the only approximation consists of disregarding the term of the second derivative for neutron density in relation to time.

TL;DR: Results reveal the ability of the method in dealing with both dynamic identification of transients and correct “don't know” response and another important point studied in this work is that the system has shown to be independent of a trigger signal which indicates the beginning of the transient, thus making it robust in relation to this limitation.

TL;DR: In this article, a new method for the solution of the inverse point kinetics equation is presented, based on the integration by parts of the integral of the INK equation, which results in a power series in terms of the nuclear power in time dependence.

TL;DR: This work presents a novel methodology for pattern recognition that uses genetic learning to get an optimized classification system, applied to a real problem, in which it is required to distinguish three nuclear accidents that may occur in a nuclear power plant.