Персона: Нахабов, Александр Владимирович
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Институт ядерной физики и технологий
Цель ИЯФиТ и стратегия развития - создание и развитие научно-образовательного центра мирового уровня в области ядерной физики и технологий, радиационного материаловедения, физики элементарных частиц, астрофизики и космофизики.
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Нахабов
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Александр Владимирович
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- ПубликацияТолько метаданныеAnalysis of the vibrational monitoring data regarding VVER type reactor installation(2020) Sanam, S. A.; Zihad, Ul, Haque, M.; Nakhabov, A.; Нахабов, Александр Владимирович© Published under licence by IOP Publishing Ltd.Vibrational data monitoring system is designed to observe, detect and study any vibration caused in the NPP working cycle by different factors. The objective of the work is to analyze vibrational monitoring data of the primary circuit installation of VVER reactor to acquire knowledge of the characteristics of equipment vibration. Data has been collected using displacement sensors and pressure fluctuation sensors attached to the equipment. Data processing is carried out in R software. The work gives an overall idea of the vibrational data processing, visualization and how to present it by different graphics which will help the NPP operator for taking necessary steps.
- ПубликацияТолько метаданныеNuclear Reactor Technology Development and Utilization: A volume in Woodhead Publishing Series in Energy(2020) Khan, S. U. -D.; Nakhabov, A.; Нахабов, Александр Владимирович© 2020 Elsevier Ltd All rights reserved.Nuclear Reactor Technology Development and Utilization presents the theory and principles of the most common advanced nuclear reactor systems and provides a context for the value and utilization of nuclear power in a variety of applications both inside and outside a traditional nuclear setting. As countries across the globe realize their plans for a sustainable energy future, the need for innovative nuclear reactor design is increasing, and this book will provide a deep understanding of how these technologies can aid in a region’s goal for clean and reliable energy. Dr Khan and Dr Nakhabov, alongside their team of expert contributors, discuss a variety of important topics, including nuclear fuel cycles, plant decommissioning and hybrid energy systems, while considering a variety of diverse uses such as nuclear desalination, hydrogen generation and radioisotope production. Knowledge acquired enables the reader to conduct further research in academia and industry, and apply the latest design, development, integration, safety and economic guidance to their work and research.
- ПубликацияТолько метаданныеPreface(2020) Khan, S. U. -D.; Nakhabov, A.; Нахабов, Александр Владимирович
- ПубликацияТолько метаданныеPrediction of spent nuclear fuel isotopic composition for the VVER-1000 reactor utilizing regression tree(2024) Tarequzzaman, M.; Nakhabov, A.; Нахабов, Александр ВладимировичIn this paper, a predictive model using regression trees is represented, which can predict the depleted isotopic composition (IC) of 21 isotopes of the VVER-1000 reactor based on effective days, initial enrichments, percentage of gadolinium absorber, and zones of fuel elements within the fuel assembly (FA). First, multiple regression trees (RT) are generated for a particular isotopic, and predictions of the isotopic composition of that isotope have been made by averaging the results from all trees. Multiple regression trees (RT) models have been generated by applying optimal cross-validation (CV) fold number, which is identified by investigating four parameters of the model. The model’s performance has been evaluated by the root mean squared error (RMSE), R2 value, and p-value for the heteroscedasticity test. It has been found that the percentage of RMSE for the training set and test set is less than 0.15%, the R2 value is close to unity, and the p-value for the heteroscedasticity test is higher than 5% with a 95% confidence interval. After completing the training process takes a fraction of a second to compute isotopic composition.
- ПубликацияОткрытый доступPrediction of a reactivity margin for partial refueling of nuclear reactor using artificial neural networks(2020) Nakhabov, A.; Kolesov, V.; Soglaev, P.; Нахабов, Александр Владимирович© 2020 The Authors. Published by Elsevier B.V.For some types of nuclear reactors (especially research ones) partial refueling is a routine operation when fuel rods are reloading during reactor operation to improve its physical characteristics. Reactivity margin or effective neutron multiplication factor are crucial parameters there because they determine nuclear safety of a facility. Thus for a safety reason a value of effective neutron multiplication factor should be calculated before refueling. A common method consists in neutron physical calculations-simple, but with rather high error. From the other side, precise computer modelling based on Monte-Carlo approach can be used, but this way is very time consuming. In this paper the new approach proposed when artificial neural network used to predict a value of effective neutron multiplication factor using information about fuel burnup in the reactor core as a input data. Training dataset is provided through Monte-Carlo modelling. Optimal layout and parameters selection is considered as well. Results obtained are very perspective for using this approach in real practice at nuclear facilities.