Status of research and development of learning-based approaches in nuclear science and engineering: A review

Gomez-Fernandez, Mario; Higley, Kathryn A.; Tokuhiro, Akira; Welter, Kent; Wong, Weng‐Keen; Yang, Haori

doi:10.1016/j.nucengdes.2019.110479

Cited by 71 publications

(25 citation statements)

References 158 publications

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“…All of these examples are potentially useful for radioecology but none to our knowledge have been applied at any scale with the exception of established population modelling methods (Vives i Batlle et al 2012;Alonzo et al 2016), or studies of Ra-226 characterization (Varley et al 2015). These tools are only recently being applied to the area of nuclear science and radiation protection (Gomez-Fernandez et al 2020) There are several questions:…”

Section: Discussion On Big Data Machine Learning Informatics and Modelling As New Approaches For Radioecologymentioning

confidence: 99%

From tangled banks to toxic bunnies; a reflection on the issues involved in developing an ecosystem approach for environmental radiation protection

Mothersill

Oughton

Schofield

et al. 2020

International Journal of Radiation Biology

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Section: Discussion On Big Data Machine Learning Informatics and Modelling As New Approaches For Radioecologymentioning

confidence: 99%

From tangled banks to toxic bunnies; a reflection on the issues involved in developing an ecosystem approach for environmental radiation protection

Mothersill

Oughton

Schofield

et al. 2020

International Journal of Radiation Biology

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“…The learning-based methods have been increasingly used in recent years. The authors of Reference [ 97 ] reviewed the machine learning algorithms in nuclear science and engineering highlighting the risks and opportunities of their application. Medhat et al [ 98 ] proposed the use of an artificial neural network to identify radioisotopes in natural gamma sources and determine the uncertainty of the corresponding activity.…”

Section: Mobile Radiation Detection Systemsmentioning

confidence: 99%

State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios

Marques

Vale

Vaz

2021

Sensors

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In the last decade, the development of more compact and lightweight radiation detection systems led to their application in handheld and small unmanned systems, particularly air-based platforms. Examples of improvements are: the use of silicon photomultiplier-based scintillators, new scintillating crystals, compact dual-mode detectors (gamma/neutron), data fusion, mobile sensor networks, cooperative detection and search. Gamma cameras and dual-particle cameras are increasingly being used for source location. This study reviews and discusses the research advancements in the field of gamma-ray and neutron measurements using mobile radiation detection systems since the Fukushima nuclear accident. Four scenarios are considered: radiological and nuclear accidents and emergencies; illicit traffic of special nuclear materials and radioactive materials; nuclear, accelerator, targets, and irradiation facilities; and naturally occurring radioactive materials monitoring-related activities. The work presented in this paper aims to: compile and review information on the radiation detection systems, contextual sensors and platforms used for each scenario; assess their advantages and limitations, looking prospectively to new research and challenges in the field; and support the decision making of national radioprotection agencies and response teams in respect to adequate detection system for each scenario. For that, an extensive literature review was conducted.

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“…In relation to nuclear science and engineering, ANNs and artificial intelligence methods have been applied for loading optimization in relation to in-core fuel management (e.g., Refs. [18][19][20][21]).…”

Section: Context Of Optimization Assumptions and Strategymentioning

confidence: 99%

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

Solans

Rochman

Brazell

et al. 2021

Neural Comput & Applic

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This paper presents an approach for the optimisation of geological disposal canister loadings, combining high resolution simulations of used nuclear fuel characteristics with an articial neural network and a genetic algorithm. The used nuclear fuels (produced in an open fuel cycle without reprocessing) considered in this work come from a Swiss Pressurised Water Reactor, taking into account their realistic lifetime in the reactor core and cooling periods, up to their disposal in the final geological repository. The case of 212 representative used nuclear fuel assemblies is analysed, assuming a loading of 4 fuel assemblies per canister, and optimizing two safety parameters: the fuel decay heat (DH) and the canister effective neutron multiplication factor k$$_{\mathrm{eff}}$$ eff . In the present approach, a neural network is trained as a surrogate model to evaluate the k$$_{\mathrm{eff}}$$ eff value to substitute the time-consuming-code Monte Carlo transport & depletion SERPENT for specific canister loading calculations. A genetic algorithm is then developed to optimise simultaneously the canister k$$_{\mathrm{eff}}$$ eff and DH values. The k$$_{\mathrm{eff}}$$ eff computed during the optimisation algorithm is using the previously developed artificial neural network. The optimisation algorithm allows (1) to minimize the number of canisters, given assumed limits for both DH and k$$_{\mathrm{eff}}$$ eff quantities and (2) to minimize DH and k$$_{\mathrm{eff}}$$ eff differences among canisters. This study represents a proof-of-principle of the neural network and genetic algorithm capabilities, and will be applied in the future to a larger number of cases.

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Status of research and development of learning-based approaches in nuclear science and engineering: A review

Cited by 71 publications

References 158 publications

From tangled banks to toxic bunnies; a reflection on the issues involved in developing an ecosystem approach for environmental radiation protection

From tangled banks to toxic bunnies; a reflection on the issues involved in developing an ecosystem approach for environmental radiation protection

State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios

Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm

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