Previous Issue
Volume 6, March
 
 

J. Nucl. Eng., Volume 6, Issue 2 (June 2025) – 3 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Select all
Export citation of selected articles as:
16 pages, 4118 KiB  
Article
Reinforcement Learning-Based Augmentation of Data Collection for Bayesian Optimization Towards Radiation Survey and Source Localization
by Jeremy Marquardt, Leonard Lucas and Stylianos Chatzidakis
J. Nucl. Eng. 2025, 6(2), 10; https://doi.org/10.3390/jne6020010 - 15 Apr 2025
Viewed by 128
Abstract
Safer and more efficient characterization of radioactive environments requires exploring intelligently, utilizing robotic systems which use smart strategies and physics-based statistical models. Bayesian Optimization (BO) provides one such statistical framework to explainably find the global maximum within noisy contexts while also minimizing the [...] Read more.
Safer and more efficient characterization of radioactive environments requires exploring intelligently, utilizing robotic systems which use smart strategies and physics-based statistical models. Bayesian Optimization (BO) provides one such statistical framework to explainably find the global maximum within noisy contexts while also minimizing the number of trials. For radiation survey and source location, the aid of such a machine learning algorithm could significantly cut down on time and health risks required for maintenance and emergency response scenarios. Maintaining the explainability while increasing the efficiency of the search has been found possible by including the high uncertainty data that is picked up while the agent is in transit. Now that the paths of transit matter to data acquisition they could be optimized as well. This paper introduces reinforcement learning (RL) to the BO search framework. The behavior of this RL additive is observed in simulation over three different datasets of real radiation data. It is shown that the RL additive can cause significant increases to the score of the maximum point discovered, but the computational time cost is increased by nearly 100% while the reconstructed radiation field root mean square error (RMSE) of the BO+RL algorithm matches BO performance within 1%. Full article
Show Figures

Figure 1

11 pages, 3315 KiB  
Article
Using Frozen Beads from a Mixture of Mesitylene and Meta-Xylene with Rupert’s Drop Properties in Cryogenic Neutron Moderators
by Maksim V. Bulavin and Ivan L. Litvak
J. Nucl. Eng. 2025, 6(2), 9; https://doi.org/10.3390/jne6020009 - 3 Apr 2025
Viewed by 171
Abstract
An experimental study was conducted on the feasibility of using frozen beads with the properties of Rupert’s drops—solid frozen beads with enhanced strength made from a mixture of aromatic hydrocarbons—in cryogenic neutron moderators utilizing bead technology. It is demonstrated that the use of [...] Read more.
An experimental study was conducted on the feasibility of using frozen beads with the properties of Rupert’s drops—solid frozen beads with enhanced strength made from a mixture of aromatic hydrocarbons—in cryogenic neutron moderators utilizing bead technology. It is demonstrated that the use of a new modification of the dosing device with a high discharge rate (approximately 6 units/s) significantly improves process efficiency. With standard pneumatic transport parameters maintained, it was possible to load solid frozen beads made from a mixture of mesitylene and meta-xylene into the cryogenic moderator chamber. The loading speed increased five-fold, while the beads remained intact during pneumatic transport. Full article
Show Figures

Figure 1

32 pages, 976 KiB  
Article
Introducing the Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integral Equations of the Volterra Type: Mathematical Methodology and Illustrative Application to Nuclear Engineering
by Dan Gabriel Cacuci
J. Nucl. Eng. 2025, 6(2), 8; https://doi.org/10.3390/jne6020008 - 29 Mar 2025
Viewed by 175
Abstract
This work presents the general mathematical frameworks of the “First and Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integral Equations of Volterra Type” designated as the 1st-FASAM-NIE-V and the 2nd-FASAM-NIE-V methodologies, respectively. Using a single large-scale (adjoint) computation, the 1st-FASAM-NIE-V enables the [...] Read more.
This work presents the general mathematical frameworks of the “First and Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integral Equations of Volterra Type” designated as the 1st-FASAM-NIE-V and the 2nd-FASAM-NIE-V methodologies, respectively. Using a single large-scale (adjoint) computation, the 1st-FASAM-NIE-V enables the most efficient computation of the exact expressions of all first-order sensitivities of the decoder response to the feature functions and also with respect to the optimal values of the NIE-net’s parameters/weights after the respective NIE-Volterra-net was optimized to represent the underlying physical system. The computation of all second-order sensitivities with respect to the feature functions using the 2nd-FASAM-NIE-V requires as many large-scale computations as there are first-order sensitivities of the decoder response with respect to the feature functions. Subsequently, the second-order sensitivities of the decoder response with respect to the primary model parameters are obtained trivially by applying the “chain-rule of differentiation” to the second-order sensitivities with respect to the feature functions. The application of the 1st-FASAM-NIE-V and the 2nd-FASAM-NIE-V methodologies is illustrated by using a well-known model for neutron slowing down in a homogeneous hydrogenous medium, which yields tractable closed-form exact explicit expressions for all quantities of interest, including the various adjoint sensitivity functions and first- and second-order sensitivities of the decoder response with respect to all feature functions and also primary model parameters. Full article
Previous Issue
Back to TopTop