Desenvolvimento de um algoritmo genético paralelo utilizando MPI e sua aplicação na otimização de um projeto neutrônico.

Este trabalho apresenta o desenvolvimento de um algoritmo genético paralelo [1] distribuído aplicado a otimização de um projeto neutrônico. Para a implementação do paralelismo, utilizou-se a biblioteca “Message Passing Interface” (MPI) [2], padrão para computação paralela em ambientes de memória distribuída com intercambiamento de mensagens. Outra característica importante do MPI é sua portabilidade para qualquer arquitetura. Como principais objetivos deste artigo têm-se: i) validação dos resultados obtidos pela aplicação deste algoritmo na otimização de um projeto neutrônico, através de comparações com resultados apresentados na literatura. [3][4] e ii) teste de performance do cluster do Instituto de Engenharia Nuclear (IEN) [5] em problemas de otimização aplicados ao cálculo de física de reatores. Os experimentos demonstraram que o algoritmo genético paralelo utilizando a biblioteca MPI, implantado no cluster do IEN, apresentou ganhos significativos nos resultados obtidos, bem como no tempo de processamento. Tais resultados ratificam a utilização do cluster do IEN com algoritmos genéticos paralelos para resolução de problemas de otimização de projetos neutrônicos

Two stochastic optimization algorithms applied to nuclear reactor core design

Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck’s Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak factor in a three-enrichment-zone reactor, considering restrictions on the average thermal flux, criticality and sub-moderation. Results show that the PCA and the GDA perform very well compared to the canonical Genetic Algorithm and its variants, and also to Simulated Annealing, hence demonstrating their potential for other optimization applications

Nuclear Plants and Emergency Virtual Simulations based on a Low-cost Engine Reuse

Our industrialised society comprises many industrial processes that are very important for everyone, in a wide range of fields. Activities related to these industrial processes, though, involve, in higher or lower degrees, some risk for personnel,  besides risk for the general public in some cases. Therefore, efficient training programs and simulations are highly required, to improve the processes involved, increasing safety for people. To cite an example, nuclear plants pose high safety requirements in operational and maintenance routines, to keep plants in safe operation conditions and reduce personnel exposure to radiation dose. Besides operational and maintenance in nuclear plants, there are also other situations where efficient training is required, as in evacuation planning from buildings in emergency situations. Also, rescue tasks play similar role. These apply specially for nuclear sites. Another situation that requires efficient training is security, what has special meaning for plants that involve dangerous materials, such as nuclear plants. Nuclear materials must be kept under high security level, to avoid any misuse

Volume fraction calculation in multiphase system such as oil-water-gas using neutron

Multi-phase flows are common in diverse industrial sectors and the attainment of the volume fraction of each element that composes the flow system presents difficulties for the engineering process, therefore, to determine them is very important. In this work is presented methodology for determination of volume fractions in annular three-phase flow systems, such as oil-water-gas, based on the use of nuclear techniques and artificial intelligence. Using the principle of the fast-neutron transmission/scattering, come from an isotopic 241Am-Be source, and two point detectors, is gotten measured that they are influenced by the variations of the volumec fractions of each phase present in the flow. An artificial neural network is trained to correlate such measures with the respective volume fractions. In order to get the data for training of the artificial neural network without necessity to carry through experiments, MCNP-X code is used, that simulates computational of the neutrons transport. The methodology is sufficiently advantageous, therefore, allows to develop a measurement system capable to determine the fractions of the phases (oil-water-gas), with proper requirements of each petroliferous installation and with national technology contributing, possibly, with reduction of costs and increase of productivity

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost