Uma nova metodologia para determinar a divergência de um feixe de nêutrons

• Ano de defesa: 2018
• Autor(a) principal: Souza, Erica Silvani
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio de Janeiro; Brasil; Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia; Programa de Pós-Graduação em Engenharia Nuclear; UFRJ
• Palavras-chave em Português: Divergência do feixe; Nêutrons térmicos; Razão L/D; CNPQ::ENGENHARIAS::ENGENHARIA NUCLEAR
• Link de acesso: http://hdl.handle.net/11422/13741

This work posits a novel approach to characterize the divergence of a neutron beam, defined as the deviation from an ideal parallel beam expressed by its Rocking Curve – RC. After this concept, every point of the source emits neutrons in a nonhomogeneous fashion following a bell-shaped profile. With the RC it is possible to assess the effect of degrading agents upon the quality of neutron radiographs. In this work an inverse procedure is applied, by using a neutron radiograph to find the RC. For that, experimental images are compared with synthetic ones generated under similar conditions. The RC semi-width of the synthetic image exhibiting the closest resemblance with the experimental one defines the beam divergence. An equivalent procedure has been employed to evaluate the L/D. Both procedures yielded consistent results with previous different methods. Yet, the RC forecasts better the neutron pattern hitting the detector and does not need a precisely machined test-object as required by the L/D approach. All data treatment is embedded into an ad hoc written Fortran 90 program. The technique has been applied to determine the divergence of the neutron beam at the main port of the Argonauta reactor in Instituto de Engenharia Nuclear. The related RC has been used to get the curve PSF x Object-detector Gap an essential data to restore images by deconvolution. The better quality of radiographs restored by the Richardson-Lucy algorithm with the correct PSF – rather than any other one – ratified once more the soundness and robustness of the developed algorithm.