A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis

Kumar, Anil; Tsvetkov, Pavel V.

doi:10.1016/j.anucene.2015.04.028

Cited by 45 publications

(13 citation statements)

References 10 publications

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“…The concept of GA is obtained from the fact that its operations are based on the mechanics of genetic adaptation in biological systems. The efficiency of the genetic algorithm has been proven in many respects such as nuclear power plant safety and fuel loading (Wang et al 2007, Ayoobian and Mohsendokht 2016, Kumar and Tsvetkov 2015, Saber et al 2015. The GA approach starts by considering the bias and weight values of the neural fuzzy diagnosis system as the initial population.…”

Section: Genetic Algorithm Methodsmentioning

confidence: 99%

Application of soft computing techniques in WWER nuclear power plant safety

Abbasi¹,

Sadıkoğlu²

2019

NUCET

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Nowadays, Nuclear Power Plant (NPP) is one of the intended energy resources for the world requirement energy in future, and nuclear power plants provided 11 percent of the world's electricity production in 2014. Meanwhile, nuclear power plant safety has always been one of the most critical issues in the world. In this paper, the nuclear power plant safety improvement using Soft Computing Techniques were analyzed. For this purpose, the support system based on Neuro-Fuzzy Diagnosis System (NFDs) method and Genetic Algorithms (GAs) approach were used. The obtained result showed that the first symptom is P3 (pressurizer pressure) and second order symptom is P2 (core coolant average temperature) in both approaches. The comparison between the NFDs method and the GAs approaches indicated that the GAs in data test results was faster than the NFDs results.

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Section: Genetic Algorithm Methodsmentioning

confidence: 99%

Application of soft computing techniques in WWER nuclear power plant safety

Abbasi¹,

Sadıkoğlu²

2019

NUCET

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“…However, in the current work, we are focusing on a fuel combination of natural uranium as the fuel and enriched thorium (ThO 2 þ UO 2 ) as the blanket. Previous work related to optimization, in problems related to nuclear engineering using GA includes, core design (Pereira and Lapa, 2003;do Nascimento Abreu Pereira et al, 1999;Haibach and Feltus, 1997), plant design (Cantoni et al, 2000;Kumar and Tsvetkov, 2015), nuclear system availability and maintenance scheduling (Lapa et al, 2000;Marseguerra and Zio, 2000), fuel management (Chapota et al, 1999;Dechaine and Feltus, 1995), and spent fuel management (Omori et al, 1997). However, coupled predictive analysis and optimization using GA in parametric analysis has not been extensively explored in reactor design problems.…”

Section: Previous Workmentioning

confidence: 98%

“…Fig. 1 presents a graphical view of GA (Kumar and Tsvetkov, 2015) that we have used in this research.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Optimization of U–Th fuel in heavy water moderated thermal breeder reactors using multivariate regression analysis and genetic algorithms

Kumar

Tsvetkov

2015

Annals of Nuclear Energy

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“…In addition, a neutron transport and depletion code like MCNP (X-5 Monte Carlo Team, 2008), SERPENT take a significantly long execution time if higher accuracy of results is desired. And, in a global optimization technique like GA (Kumar and Tsvetkov (2015)), simulated annealing, particle swarm optimization the code needs to be run for a significant number of input samples before a desired solution is obtained. Hence, regression methods could be useful to emulate the physics solver in the code, and preform a predictive analysis for test data.…”

Section: Background and Motivationmentioning

confidence: 99%