bluuebunny/crossref_metadata_2025 huggingface dataset:
Overview of kinetic Monte Carlo methods used to simulate microstructural evolution of materials under irradiation
Authors: Adjanor Gilles , Athènes Manuel , Domain Christophe , Mousseau Normand
Published: October 2024

Kinetic Monte Carlo (KMC) methods are commonly used to simulate the microstructure evolution of metals under irradiation due to their ability to generate the random walks underlying defect-mediated diffusion processes at the atomic scale. However, the range of applicability of KMC methods is severely limited by the kinetic trapping of the simulated trajectories within low energy basins presenting small intra-basin barriers. This results in dramatically reducing the efficiency of the classical KMC algorithm. Kinetic trapping can be alleviated by implementing non-local jumps relying on the theory of absorbing Markov chains. A factorisation of an auxiliary absorbing transition matrix then allows to generate escaping paths and first-passage times out of trapping basins. Although, the speed-up can be of several orders of magnitudes, this is sometimes not enough for very long-term prediction. We must then turn to homogenised rate-equation formulation of the problem. Usually solved deterministically, the corresponding large ordinary differential equation system often suffers from the curse of dimensionality. Dedicated Monte Carlo schemes can simulate the coarse-grained rate equations based on a chemical master equation. Finally, we show the relevance of relaxing the rigid-lattice assumption in the calculation of the free energy barriers and attempt frequencies to capture elastic effects that are important for certain systems, such as high entropy alloys. The activation-relaxation technique can be used for this purpose in kinetic Monte Carlo studies of slow diffusion processes.
Investigation of Uncertainty Propagation in the Resolved Resonance Range
Authors: Sole Pierre , Jaiswal Vaibhav , Basavaraja Allannavar Aparna , Jouanne Cédric , Petillon Barthelemi , Brovchenko Mariya
Published: October 2024

This paper presents a comparative study using the first-order formula (also called “sandwich”) and the multivariate sampling methodologies for propagating uncertainty in the resolved resonance region. A distinctive aspect of this work is the generation of random cross sections by sampling Resonance Parameters (RPs) taking in consideration their correlations provided in nuclear data libraries via the Resonance Parameter Covariance Matrix (RPCM). SCOOBY (Sampling COvariance OBservatorY), a newly developed sampling tool, is presented in this paper. This tool relies on the GAIA-2 nuclear data processing code to read and correct the RPCM and generate random cross sections.

The study compares the sandwich method that relies on sensitivity coefficients and covariance matrices, with the sampling method, which involves numerous Monte Carlo simulations with random cross sections. The comparison is tested on an ICSBEP benchmark, PU-MET-MIXED-002, chosen for its sensitivity to 239Pu cross sections. The results indicate that both methods quantify similar uncertainties, confirming the reliability of both the SCOOBY module and the GAIA-2 code.

By using the PU-MET-MIXED-002 benchmark and sampling 239Pu resonance parameters, this work demonstrates the effectiveness of these methodologies in estimating uncertainty in criticality safety calculations. The findings highlight the robustness of these approaches in uncertainty propagation, suggesting the need for further research on additional benchmarks and expanded uncertainty propagation studies.
Preliminary Generation of Windowed Multipole Covariance Data
Authors: Barlow Jack , Forget Benoit
Published: October 2024

A methodology for the generation of covariance data in the windowed multipole formalism for resolved resonance data was developed. Using this process, covariance matrices were created for oxygen-16, uranium-235, and uranium-238, such that sampled cross-sections would match a user-selected average standard deviation, set at 5% ± 0.5% for this work. Windowed multipole datasets were then sampled and used on a simple critical benchmark to demonstrate the use of WMP data in conjunction with the Total Monte Carlo method of uncertainty propagation.
Fast simulation of neutron noise using the Transient Fission Matrix approach and validation on the CROCUS reactor
Authors: Laureau Axel , Begue Max , Lamirand Vincent , Merle Elsa , Pakari Oskari
Published: October 2024

This article presents the development of a neutron noise calculation technique with a reduced calculation time based on the TFM (Transient Fission Matrix) approach. The latter contains different information such as the system transfer function (fission and absorption probability density functions and corresponding neutron multiplicity) of prompt and delayed neutrons, and the corresponding propagation time. This information is used to reconstruct neutron showers based on precursor decays up to the shower disappearance by absorption, leakage or delayed neutron precursor creation. Then pseudo-detectors such as fission chamber count rate histograms are reconstructed from these neutron showers and the αprompt is calculated from the cross-correlation function. A very good agreement has been obtained on the uranium-fueled light water reactor CROCUS between the model developed and the reference value.
Automatic Importance Biasing in FLUKA: Application Scenarios
Authors: Ruiz Francisco Ogallar , Theis Christian , Vincke Helmut
Published: October 2024

In the context of Monte Carlo simulation, importance biasing is a well-established variance reduction technique. It is typically employed to compensate for particle attenuation and is commonly used in FLUKA simulations. However, implementing importance biasing in a simulation can be a rather time-consuming exercise, especially for complex geometries as it requires the segmentation of solids. This document demonstrates the effectiveness of a newly developed tool designed to automate the implementation of problem specific importance biasing schemes in FLUKA simulations, with a focus on user-friendliness. By minimizing user intervention, the tool significantly reduces the amount of time required to implement the biasing scheme. Additionally, it reduces the risk of potential errors by eliminating the need of geometry modifications and manual attribution of importances. The document briefly outlines the tool and focuses on several examples to illustrate its usage and scope. It is important to mention that it does not aim for fully replacing the traditional region-based FLUKA importance biasing. In the hands of an expert the latter might indeed achieve faster convergence, depending on the problem. Yet, the automated solution’s overall benefits become apparent when considering the time investment required for manual implementation and fine-tuning of importance factors.
A Non-parametric Bootstrap Method for Kinetic Monte Carlo Variance Reduction
Authors: Skretteberg Martin , Cosgrove Paul , Kowalski Mikolaj
Published: October 2024

A new variance reduction technique for Monte Carlo transport methods is investigated. This approach is based on non-parametric bootstrapping, a statistical inference and resampling method which is used to generate simulated samples of Monte Carlo scores to estimate the statistical properties of underlying integral response distributions. It is also used to quantify the uncertainty of the corresponding estimates as well as any bias introduced by bootstrapping, which then can be corrected. In this context, non-parametric bootstrapping is used to estimate the first and second-order moments of estimations of Monte Carlo responses for time-dependent neutron transport problems, which are particularly time-consuming. This variance reduction technique was implemented in SCONE - Stochastic Calculator Of Neutron transport Equation, developed at the University of Cambridge. Results show that the non-parametric bootstrap method can improve the trade-off between simulation time and variance of integral response estimates, especially when the number of responses is kept to a moderate level. As the number of responses increases, however, challenges with bias, memory and computational efficiency become more prominent, which is addressed.
The CADIS-DXTRAN method in NECP-MCX for Radiation Shielding Simulations
Authors: Huang Zhanpeng , Cao Liangzhi , He Qingming , Wu Hongchun
Published: October 2024

This paper discusses the implementation of the Consistent Adjoint-Driven Importance Sampling-Deterministic Transport (CADISDXTRAN) method in NECP-MCX to speed up Monte Carlo simulations for neutron radiation shielding applications. It provides a brief theoretical overview of the CADIS-DXTRAN method and describes the coding process in NECP-MCX. The study compares the performance of the CADIS-DXTRAN method with the CADIS method and the DXTRAN method in a labyrinth problem and a channel shielding problem. The results of the labyrinth problem reveal that the CADIS-DXTRAN method is highly efficient with a Figure of Merit (FOM) value 45 times that of DXTRAN and 17 times that of the CADIS method. While there is no significant improvement of the CADIS-DXTRAN method in the channel shielding problem. The particle behavior in the two problems is analyzed in detail to explain the performance of the CADIS-DXTRAN method.
Towards a highly efficient and unbiased population-control algorithm for kinetic Monte Carlo simulations
Authors: Montecchio Cecilia , Lamirand Vincent , Mancusi Davide , Monange Wilfried , Zoia Andrea
Published: October 2024

Population-control methods are key to non-stationary Monte Carlo simulations of multiplying systems: they prevent either the unbounded growth or the disappearance of neutrons, occurring respectively in supercritical and subcritical conditions; furthermore, they contribute to an efficient allocation of computational resources by addressing the unbalance between the neutron and the precursor populations. In this paper, we present two alternative populationcontrol algorithms: the legacy implementation in TRIPOLI-4®, the Monte Carlo code developed at CEA, and an improved version that is currently under investigation, based on the use of a simplified point-kinetics solver. We assess the performance of these methods through the simulation of a $2.2 step reactivity insertion in a fast system (Flattop-Pu), leading to an increase of the neutron population by a factor 200, which is benchmarked against point kinetics. We show that the new implementation not only suppresses the slight bias that was present in the legacy method due to a stochastic normalization factor, but also outperforms the previous algorithm in terms of variance reduction and improvement of the figure of merit.
Development of an Adjoint Flux Calculation Technique for Exact Perturbation Theory in Monte Carlo Code MCS
Authors: Jo Yunki , Lee Deokjung
Published: October 2024

A calculation technique computing the adjoint flux of perturbed system is developed for the exact perturbation theory in Monte Carlo transport. By using a correlated sampling and iterated fission probability methods together, the adjoint flux of perturbed system is calculated during a forward Mote Carlo simulation of an unperturbed system. In the method, no additional particle simulation is required to compute the adjoint flux of perturbed system. The exact perturbation method is implemented in the Monte Carlo code MCS. The results of perturbation method for k-eigenvalue change are compared against differential operator sampling, adjoint-weighted perturbation, and direct perturbation methods for the Godiva benchmark and PLUS7 fuel assembly.
GPU-Accelerated Ray Tracing for Visualizing Monte Carlo Models
Authors: Nease Brian , Corser Joel , Burke Paul
Published: October 2024

Monte Carlo codes are naturally suited to act as computational kernels for ray tracing visualization applications because of the similarities between the ray tracing algorithm and the Monte Carlo neutral particle tracking algorithm. This paper presents a simplified ray tracing algorithm suitable for engineering applications, as well as optimizations to make the algorithm more amenable to computation on a GPU. This algorithm was implemented in a GPU-based ray tracing code called MantaRay. This code has been used as a backend kernel in MCVIZ, a real-time, interactive 3D visualization tool for MC21 models. MCVIZ+MantaRay allows for interactive rendering of extremely large models at 1024x768 resolution at framerates of 15 fps and greater.
Valjean, a universal post-processing tool for neutronics
Authors: Le Ménédeu Ève , Mancusi Davide
Published: October 2024

Valjean is an open-source framework for workflow scheduling, post-processing and data analysis in verification and validation of neutronics codes. It provides native support for reading TRIPOLI-4® and APOLLO3® output files, but can be easily extended to other codes. We present here the structure of the Valjean package, together with a few typical usage examples.
Time Dependent Energy Deposition Model in RMC Kinetics Calculation
Authors: Luo Hao , Jia Conglong , Liu Zhaoyuan , Li Zeguang , Ma Tao , Huang Shanfang , Wang Kan
Published: October 2024

Calculating or predicting the energy deposition in the reactor is an important task for reactor design and safety analysis. Rigorous energy deposition estimation is a difficult task because of the energy dependence, spatial dependence, and time dependence of in-core nuclear heating. This paper develops a time-dependent energy deposition model in the RMC kinetics calculation to explicitly account for the time-delay effect of delayed in-core nuclear heating. An explicit sampling method (ESM) is proposed to sample the decay time of the delayed photon and beta precursors based on a new precursor library processed from the ENDF/B-VIII.0 library. The new model is verified by comparing it with the typical equilibrium model using the Godiva experiment and the C5G7- TD benchmark. Maximum 2.5% and 5.4% relative deviations are observed in GODIVA and C5G7-TD cases respectively, which indicates that the equilibrium model underestimates the energy deposition over the transient time. The analysis of the energy deposition results demonstrates that the new model can provide more precise energy deposition estimation in the transient calculation.
CGMF & FREYA Verification in Monte Carlo Code RMC
Authors: Gou Yuanhao , Liu Zhaoyuan , Jia Conglong , Wang Kan
Published: October 2024

Monte Carlo simulation has become a crucial method internationally for simulating neutron multiplicity counting devices. This method requires sampling of fission neutron multiplicities, energies, and directions, making accurate simulation of fission events highly important. To meet this demand, the Fission Reaction Event Yield Algorithm (FREYA) and the Cascading Gammaray Multiplicity with Fission (CGMF) models have been integrated into RMC. These models can simulate individual fission events, preserving momentum, energy, and angular momentum, and then emit correlated particles. In addition to simulating fission neutrons, CGMF and FREYA models have been employed to compute fission photons, laying the foundation for subsequent photon multiplicity counting. Simultaneously, neutron multiplicity counting has been calculated using CGMF and FREYA, and point model equations have been used to invert Pu material, yielding favorable results.
Upgrading the neutronic version of the PSI cycle check-up methodology for full core PWR Monte Carlo simulations
Authors: Berry Louis , Vasiliev Alexander , Rochman Dimitri , Hursin Mathieu , Frankl Matthias , Ferroukhi Hakim
Published: October 2024

Full core Monte Carlo (MC) simulations are of high interest and can be used to study the beginning of live conditions of reactors. However, current hardware limitations make it difficult to perform such studies for other cycles, as the whole irradiation history of the fuel assemblies (FAs) loaded must be considered on a time scale of several years using MC depletion capabilities. Therefore, at PSI, a cycle check-up methodology (CHUP) is currently being developed to address this challenge. By extracting operating conditions and isotopic compositions from validated reference deterministic core-follow models (using CASMO5/SIMULATE5), MC neutron transport models are generated for selected operating points. These models can then be used to estimate quantities beyond the capabilities of deterministic solvers. This article presents the verification performed on a hot zero power (HZP) operating condition of a Swiss PWR using the newly updated CHUP methodology. This update consists of extracting information generated automatically from reference core models, minimising as much human intervention as possible. The performance of the method was first evaluated against the reference deterministic lattice code. Then, the memory requirements of the full core simulation were studied under different approximations. Finally, verification was performed against the nodal reference scheme.
Application of Combined Fission Matrix to Plate-type Research Reactor
Authors: Li Yuehang , He Donghao , Liu Xiaojing
Published: October 2024

The Combined Fission Matrix (CFM) method entails the instantaneous estimation of the entire system's fission matrix by amalgamating fission matrices from a pre-existing database. This approach allows for the rapid and accurate determination of the effective multiplication factor (keff) and the distribution of fission rates across the reactor system. This work applies the CFM method to the Japan Research Reactor-3 (JRR-3M), a water-cooled plate-type reactor. The CFM method demonstrates a high level of agreement with Monte Carlo (MC) simulation results, completing calculations within a minute. In the two cases of 'All Absorbers In' and 'All Absorbers Out', this method achieved a low root-mean-square (RMS) error of only 0.4% and 1% , respectively, with keff-error of 44 pcm and 24 pcm .
Sensitivity and Uncertainty Calculations in Support of the Noise Analysis of the TAPIRO Reactor
Authors: Aimetta Alex , Dulla Sandra
Published: October 2024

In the framework of an assessment of the application of neutron noise methodologies to the TAPIRO reactor, sensitivity and uncertainty analyses on impacting nuclear data for this experimental reactor have been performed using the Serpent Monte Carlo code. Integral parameters relevant to the noise analysis of TAPIRO have been investigated, namely the effective multiplication factor, the effective delayed neutron fraction and the effective generation time. The nuclear data uncertainty has been assessed using different data libraries (ENDF/B-VIII.0 and JEFF-3.3), different energy grids (ECCO-33 and XMAS- 172) and different techniques (Generalised Perturbation Theory and Unscented Transform). A ranking of the most impacting nuclides and reactions in terms of uncertainties has been performed, pointing out that the impact of nuclear data uncertainties on the responses is non-negligible and must be taken into account also in the noise analysis of the reactor.
Hybrid Resolution Method for Efficient Depletion Analyses using Explicit Geometry Multigroup Monte Carlo
Authors: Kim Inhyung , Lujan Vidor H. , Painter Bailey , Kotlyar Dan , Fratoni Massimiliano
Published: October 2024

The hybrid resolution (HR) method is a recently developed calculational algorithm tailored for efficient nuclear reactor analysis by leveraging multigroup (MG) Monte Carlo (MC) coupled with continuous energy MC calculations. While the HR method has demonstrated promising results in various scenarios, an anticipated challenge arises from inconsistencies of MG MC results caused by spatial homogenization when relatively coarse mesh is adopted, particularly in heterogeneous problems characterized by a strong flux gradient. To address this issue, the HR method incorporates MG MC calculations on the same explicit geometry used in continuous energy MC calculations. In other words, this approach preserves accuracy in geometry handling while solely simplifying energy treatment. In our investigation, a 3-dimensional BWR-type fuel assembly was focused on for a comparative study regarding the different geometry treatments in MG MC calculations. Various reactor parameters such as k-effective, power, flux, and atomic densities were estimated, and numerical performance was also compared in terms of the computing time and figure-of-merits. Our evaluation revealed that the explicit geometry-based HR method yielded more consistent and reliable parameters compared to the simplified coarse mesh-based scheme, reducing errors in power and flux distributions by about 10%.
High Fidelity Whole Core Reactor Eigenvalue Calculation using the Hybrid Monte Carlo and Deterministic RAPID Code System
Authors: Stroh Brian , Al Hajj Walid , Haghighat Alireza
Published: October 2024

This paper examines the accuracy and performance of the RAPID (Real-time Analysis for Particle transport and In-situ Detection) code system for whole core reactor simulation. The RAPID code is an implementation of the Multistage Response function Transport (MRT) methodology resulting in a hybrid Monte Carlo and deterministic technique. This paper utilizes the Watts Bar Unit 1 benchmark as the computational model to exam the accuracy and efficiency of RAPID compared to Serpent. The RAPID calculated eigenvalue and 3-D fission density distribution are compared to the results from Serpent. This paper shows that RAPID is able to obtain pin-wise, axially-dependent fission density in 7 minutes using one computer core, as opposed to assembly-wise, axially dependent fission density in 8 days on 40 computer cores.
Count Rate Analysis of the Source-range Detector for M310
Authors: Guo Jiandi , Liu Zhouyu , He Qingming , Huang Danbin , Lin Ziwei , Cao Liangzhi , Wu Hongchun
Published: October 2024

This paper analyses the count rate of the ex-core source-range detector for Unit 4, M310 Pressurized Water Reactor (PWR), at Fujian Fuqing Nuclear Power Co., Ltd (FQNPC). The analysis encompasses power states during Cycle 1 startup and Cycle 3 fuel unloading. The analytical process involves analysing the count rate of the source-range detector, simulating secondary neutron source intensity and calculating nuclide information and neutron source intensity of irradiated fuel assemblies, etc. The maximum error between the theoretically calculated and measured count rate of the source-range detector is found to be 8.318%, which confirms the accuracy and validity of the count rate analysis method applied to the ex-core source-range detector for M310.
Gamma-ray measurement for classification of fuel debris and waste
Authors: Shiba Tomooki , Okumura Keisuke , Tanaka Takahiro
Published: October 2024

The detection of prompt fission gamma-rays and Tl-208 decay gamma-rays, which are unique to nuclear materials, could be applied to sorting nuclear fuel debris and radioactive waste from the Fukushima Daiichi Nuclear Power Plant. To confirm its technical feasibility, particle transport calculations using the Monte Carlo N-Particle code were performed.
Selection method for observation points using Bayesian LASSO at estimating radiation source distribution from air dose rates
Authors: Yamada Susumu , Machida Masahiko , Tanimura Naoki
Published: October 2024

When we decommission a reactor building, it is essential to identify the radiation source distribution for safety. It has been reported that the source distribution can be predicted from the measured air dose rates at appropriate observation points by minimizing an evaluation function using the Least Absolute Shrinkage and Selection Operator (LASSO). However, it is challenging to decide on suitable points in advance. Therefore, we estimate the posterior distribution from the prior distribution of the source amounts, calculated by the standard LASSO, using the Bayesian LASSO. We then assess the predictive distribution of the air dose rates at the candidate observation points from the posterior distribution. We select the additional observation points based on the variances of the predictive distributions. We confirmed that the method can estimate the source distribution with fewer additional observation points than when adding them randomly in most cases.
Research Directions on AI and Nuclear
Authors: Cancila Daniela , Daniel Geoffrey , Sirven Jean-Baptiste , Chihani Zakaria , Chersi Fabian , Vinciguerra Regis
Published: October 2024

The development of applications and systems for the nuclear domain involves the interplay of many different disciplines and is, therefore, particularly complex. Additionally, these systems and their innovations have to be compliant with strict international regulations and recommendations. The scientific and industrial communities have been studying, developing and applying advanced Artificial Intelligence (AI) techniques and tools in several (non-nuclear) application domains. Their encouraging results have pushed the nuclear community to pay increasing attention to the field of AI. Among the expected benefits of AI is the simplification of complex procedures, the reduction in the execution of time-consuming operations, the increase of safety levels, and the reduction in the overall cost. At the French Atomic Energy Commission (CEA), we have identified and have started to address several open questions, such as: where in the nuclear domain can AI-based techniques be implemented in the most productive way? What do the nuclear standards and recommendations say about its use? Can we identify some core challenges and issues common to multiple areas of the nuclear domain? In this paper we provide a first analysis and answers to the above questions and we conclude by emphasizing some cross-domain high priority challenges.
A Modular Heuristic-Based Solution for Large-Scale Outage Management in Nuclear Power Plants
Authors: Ninpan Khwansiri , Huang Shuzhang , Moro Maxime , Malhomme Olivier , Aumont Arthur , Assaad Mohamad Ali , Vitillo Francesco , Bureau Nicolas , Bechet Lies Benmiloud , Plana Robert
Published: October 2024

The Resource Constrained Project Scheduling Problem (RCPSP) is a prominent combinatorial optimization problem in academia. It involves scheduling project activities while considering constraints arising from logical relationships between tasks, resource capacity limitations, and other projectspecific factors. In this paper, we propose a modular heuristic method for efficient outage planning and execution within Nuclear Power Plants (NPPs). The project contains more than 24,000 tasks with specific industrial-oriented constraints such as the notion of prioritization, the event of pausing tasks in progress, handling of resources incompatibility, and adjusting dynamic units of resources based on their availability. The execution speed of the entire solution has been extensively revised to reach an average of 10 to 15 minutes on a standard computer. This enables project managers to promptly evaluate various scenarios with minimal data adjustments, facilitating informed decision-making. The solutions effectiveness has been validated by domain experts, demonstrating its potential for real-world application.
Sensitivity studies of PWR MOX fuel management to the plutonium initial vector using Artificial Neural Networks
Authors: Eveillard Sarah , Courtin Fanny , Doligez Xavier , Ernoult Marc , Thiollière Nicolas , Tirel Kévin , Vidal Jean-François
Published: October 2024

This paper presents new metamodels based on artificial neural networks trained on full core 3D depletion simulations performed with APOLLO2 and CRONOS2. They are used to estimate the irradiation cycle length, discharge burn-up of each fuel assembly type and radial power factor of a PWR loaded with 30% of MOX fuels, as a function of the initial plutonium composition. They allow to explore the impact of the plutonium isotopic vector on the reactor characteristics and can be used for scenarios studied for future fuel cycle. Some exclusion domains in the plutonium isotopic vector phase space are identified as a function of the cycle length. As an example, the potentialities of such fuel management for plutonium recycling from MOX spent fuel are studied.
Reactor Optimization Benchmark by Reinforced Learning
Authors: Schwarcz D. , Schneider N. , Oren G. , Steinitz U.
Published: October 2024

Neutronic calculations for reactors are a daunting task when using Monte Carlo (MC) methods. As high-performance computing has advanced, the simulation of a reactor is nowadays more readily done, but design and optimization with multiple parameters is still a computational challenge. MC transport simulations, coupled with machine learning techniques, offer promising avenues for enhancing the efficiency and effectiveness of nuclear reactor optimization. This paper introduces a novel benchmark problem within the OpenNeoMC framework designed specifically for reinforcement learning. The benchmark involves optimizing a unit cell of a research reactor with two varying parameters (fuel density and water spacing) to maximize neutron flux while maintaining reactor criticality. The test case features distinct local optima, representing different physical regimes, thus posing a challenge for learning algorithms. Through extensive simulations utilizing evolutionary and neuroevolutionary algorithms, we demonstrate the effectiveness of reinforcement learning in navigating complex optimization landscapes with strict constraints. Furthermore, we propose acceleration techniques within the OpenNeoMC framework, including model updating and cross-section usage by RAM utilization, to expedite simulation times. Our findings emphasize the importance of machine learning integration in reactor optimization and contribute to advancing methodologies for addressing intricate optimization challenges in nuclear engineering. The sources of this work are available at our GitHub repository: RLOpenNeoMC.
PWR Core Loading Pattern Design Assisted by Artificial Intelligence
Authors: Pallarés Pablo , Miró Rafael , Barrachina Teresa , Quintana-Ortí Enrique S.
Published: October 2024

Core design is the a priori study of the behavior of a reactor core throughout a cycle between two fuel reloads. There is a growing interest in using Artificial Intelligence (AI) tools to accelerate this type of calculation. Coupled thermal-hydraulic/neutronic calculations allow access to many variables of special interest to develop a digital twin (metamodel), which can be used, for instance, for pattern optimization, since it presents restrictions from the point of economics, safety and licensing. This work presents a neural network (NN) trained to obtain a metamodel, which will be used to determine different optimal configurations of the fuel elements based on certain criteria.
D-Shaped Photonic Crystal Fiber SPR Sensor for Humidity Monitoring in Oils
Authors: Romeiro Amanda F. , Rodrigues Hudson J.B. , Miranda Cauã C. , Cardoso Markos P. , Silva Anderson O. , Costa João C.W.A. , Giraldi M. Thereza R. , Santos Jose L. , Guerreiro Ariel
Published: October 2024

This theoretical study presents a D-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) based sensor designed for humidity detection in transformer oil. Humidity refers to the presence of water dissolved or suspended in the oil, which can affect its dielectric properties and, consequently, the efficiency and safety of the transformer’s operation, failures in the sealing system and the phenomenon of condensation can be the main sources of this humidity. This sensor leverages the unique properties of the coupling between surface plasmons and fiber guided mode at the Au-PCF interface to enhance the sensitivity to humidity changes in the external environment. The research demonstrated the sensor’s efficacy in monitoring humidity levels ranging from 0% to 100% with an average sensitivity of measured at 1106.1 nm/RIU. This high sensitivity indicates a substantial shift in the resonance wavelength corresponding to minor changes in the refractive index caused by varying humidity levels, which is critically important in the context of transformer maintenance and safety. Transformer oil serves as both an insulator and a coolant, and its humidity level is a key parameter influencing the performance and longevity of transformers. Excessive humidity can lead to insulation failure and reduced efficiency and, therefore, the ability to accurately detect and monitor humidity levels in transformer oil can significantly enhance preventive maintenance strategies, reduce downtime, and prevent potential failures, ensuring the reliable operation of electrical power systems.
Manipulation of Microparticles in Optofluidic Devices Fabricated by Femtosecond Laser Micromachining
Authors: Cameira Carolina , Maia João M. , Marques P.V.S.
Published: October 2024

This study reports the fabrication of three-dimensional microfluidic channels in fused silica, using femtosecond laser micromachining, to achieve two-dimensional hydrodynamic flow focusing in either the horizontal or the vertical directions. Spatial focusing of 3 μm polystyrene particles was successfully demonstrated, showing the ability of the fabricated devices to confine microparticles within a 6 μm layer over a channel width of 420 μm and within a 5 μm layer over a channel height of 260 μm. Integration of laser-direct written optical waveguides inside a microfluidic chip and orthogonal to the channel also enabled the implementation of a dual-beam optical trap, with trapping of polystyrene microparticles using a 1550 nm beam being demonstrated.
Fully Connected Feedforward Neural Network for the Prediction of Amorphous Silicon Grating Couplers Efficiency
Authors: Almeida Daniel , Fantoni Alessandro , Costa João , Vieira Manuela , Fonseca José
Published: October 2024

Photonic circuits are an enabling technology for the development of novel solutions in different fields such as healthcare, quantum computing, neural networks, communications, and manufacturing. Interconnections between devices and systems require low-loss light coupling strategies. Grating couplers are a promising solution to couple light between photonic circuits and optical fibres due to their off-plane coupling capabilities. Hydrogenated amorphous silicon (a-Si:H), which can be deposited by PECVD over a substrate of silica or glass, is a suitable low-cost solution for the production of such light coupling devices. In this work we developed, trained and tested a fully connected feedforward neural network for coupling efficiency prediction in a-Si:H grating couplers. The light coupling gratings were simulated by twodimensional finite-difference time-domain (FDTD) analysis and field distributions were analysed with the Finite Element Method (FEM). Simulated gratings include non-apodized, linear and quadratic refractive index variation designs featuring full or partial etching, operating at 1550 nm. Not featuring any type of bottom reflector, the couplers exhibit coupling efficiencies up to about 40 % (~ -4 dB). The neural network multiclass grating coupler efficiency classifier was trained with over 3000 simulation results, reaching an accuracy over 85%, for coupling efficiencies between 0 and 30%+.
High photoinduced birefringence in thermally treated layers of the azopolymer PAZO with significantly changed absorbance spectrum
Authors: Mateev Georgi , Dimov Deyan , Nazarova Dimana , Stoykova Elena , Hong Keehoon , Nedelchev Lian
Published: October 2024

In this experimental work we present the effect of thermal treatment of azopolymer thin films on their absorbance spectra and how that influences the photoinduced birefringence of the azopolymer. The azopolymer we use – PAZO (poly[1-[4-(3-carboxy-4-hydroxyphenylazo)benzenesulfonamido]-1,2ethanediyl, sodium salt]) is commonly investigated material for polarization holography because it is easily accessible and has well pronounced photoanisotropic properties. In a recent study it was discovered that its absorbance spectrum gradually changes upon heating to 250°C and as a result the absorbance in the range 400-600 nm is increased. That raises the question is it possible to increase the photoinduced birefringence for wavelengths that are far away from the peak of absorbance of the azopolymer PAZO, located approximately at 360 nm. For example, there are powerful lasers at 532 nm, which could be used for optical recording instead of traditionally used lasers in the blue and UV range. To answer this question, we investigated thin film samples of PAZO deposited on quartz substrate and measured continually their spectra of absorbance while heating the samples from room temperature to 250°C. We also measured the photoinduced birefringence using pump laser at 532 nm before and after the thermal procedure. Thus, we report significant increase of the birefringence at 532 nm after the thermal treatment and mention some potential applications.
Nanoparticles Enhanced Laser-Induced Breakdown Spectroscopy for Characterisation and Discrimination of Gemstones
Authors: Al-Juboori Haider , Haider Zhaib , Hang Wei , Roslan Sufi
Published: October 2024

Laser-induced breakdown spectroscopy (LIBS) is an excellent technique for rapid on-site investigations that attracts interest from diverse research areas. Gemmology is no exception. The application of LIBS for gemstone characterisation is limited due to ineffective ablation and crack formation, even more so with cost-effective, non-gated LIBS systems. Nanoparticle-enhanced LIBS (NELIBS) has proven to improve the effectiveness of LIBS by minimizing sample damage and enhancing the spectral features. Therefore, this study is dedicated to exploring the advantages of NELIBS, for characterising Sapphire and Opal and discrimination based on spectral differences. Our objective is to explore enhancing spectral features and performing discriminant analysis using the PLS-DA algorithm. Nanoparticles (NPs) were deposited in two layers by sequentially drying two drops (2µL) of a colloidal solution of 20nm gold nanoparticles (AuNPs) on the sample surface. Targeted areas were shot with 3 pulses of Nd:YAG laser (~350mJ, 10ns, 1064nm, 1Hz) for collecting NELIBS spectra with the OceanOptics HR4000 spectrometer. The procedure was repeated without NPs for comparative analysis with conventional LIBS (CLIBS). Results have shown a significant enhancement in spectral features, i.e., the emergence of several new spectral lines of major gemstone elements in the UV-Vis regions of the NELIBS spectra, while the CLIBS spectra were devoid of any meaningful spectral information. The PLS-DA model was trained and validated using a 4fold cross-validation approach. The model discriminated gemstones with 99.48% accuracy at the 4th fold and exhibited a mean cross-validation accuracy of 98.97%. This preliminary investigation demonstrates the effectiveness of NELIBS for characterization and the potential for onsite identification of gemstones.
Comparative Analysis of Ethanol Gas Sensors Based on Bloch Surface Wave and Surface Plasmon Resonance
Authors: Carvalho João P.M. , Almeida Miguel A.S. , Mendes João P. , Coelho Luís C.C. , De Almeida José M.M.M.
Published: October 2024

Ethanol plays a crucial role in modern industrial processes and consumer products. Despite its presence in human activity, short and long-term exposure to gaseous ethanol poses risks to health conditions and material damage, making the control of its concentration in the atmosphere of high importance. Ethanol optical sensors based on electromagnetic surface waves (ESWs) are presented, with sensitivity to ethanol vapours being achieved by the inclusion of ethanol-adsorptive zinc oxide (ZnO) layers. The changes in optical properties modulate the resonant conditions of ESWs, enabling the tracking of ethanol concentration in the atmosphere. A comprehensive comparative study of sensor performance is carried out between surface plasmon resonance (SPR) and Bloch surface wave (BSW) based sensors. Sensor efficiency is simulated by transfer matrix method towards optimized figures of merit (FoM). Preliminary results validate ethanol sensitivity of BSW based sensor, showcasing a possible alternative to electromagnetic and plasmonic sensors.
Development of a High-Precision, Portable and Automated Mobile Laser Scanner for the Recording and Digitation of Texture and Micro-marks in Archaeological and Heritage Stone
Authors: Pardiñas Pablo , Canzobre David S. , Lamas Javier , Gómez-Coronel Micaela , Ramil Alberto , Freire Lista David M. , López Ana J.
Published: October 2024

The use of laser tools to digitise cultural heritage items is becoming more widespread but is generally limited to manual systems that rely on the user’s movement or expensive automated equipment that is designed to scan large items but is not always able to accurately measure or reproduce specific details of small size. In this paper we present the novel high-precision system PALLAS, which allows the automatic digitisation of three-dimensional detailed surfaces. The equipment has been built using a set of motors and linear axes, control electronics and a line laser scanner that can be moved along the XY working plane. It is battery-powered for ease of use in the field and uses a standard laptop to communicate via Wi-Fi network. Custom software has been developed to automatically calculate paths, control the movement and store the profiles generated by the scanner. The software can also be used to reconstruct the surface from the set of scanner profiles, allowing the XYZ coordinates of any point on the surface to be measured and to obtain areal roughness parameters to characterise the finish and texture of the stone surface. Preliminary results obtained from masons’ marks, rock engravings and petroglyphs are presented.
Monitoring Reinforced Concrete Structures Using Iron Thin Film Coated Optical Fibre Sensors
Authors: Da Silva Pedro M. , Carvalho João P.M. , Mendes João P. , De Almeida José M.M.M. , Coelho Luís C.C.
Published: October 2024

Structural health monitoring (SHM) of reinforced concrete structures (RCS) is crucial for mitigating the consequences of their deterioration. By identifying and addressing the issues early, SHM helps reduce environmental impact, safeguard lives, and enhance economic resilience. Rebar corrosion is a leading cause of early RCS decay and optical fibre sensors (OFS) have been employed for its monitoring. Reflection optrodes using optical fibres where the tip is coated with iron (Fe) thin films offer a robust, longlasting and straightforward solution. This study investigates the tracking of spectral changes during the Fe thin film corrosion, which has been neglected in the literature, in favour of tracking reflection changes from thin film spalling. A multimode fibre tip, coated with a thin Fe layer embedded in concrete, allows spectral changes to be observed during corrosion. A 100 nm thick Fe film was deposited using radio frequency magnetron sputtering on polished fibre tips. Corrosion was induced by applying salted water drops and allowing the fibre tip to dry. Corrosion monitoring was successful for both air-exposed and cementembedded tips, with results compared to reflection simulations of Fe, Fe2O3, and Fe3O4 thin films. This study supports monitoring at different wavelengths, enhancing robustness, cost-effectiveness and earlier detection.
An affordable optical detection scheme for LSPR sensors
Authors: Mansour Rima , Serafinelli Catarina , Fantoni Alessandro , Jesus Rui
Published: October 2024

Biosensing technologies are essential for advancing healthcare by enabling rapid point-of-care (POC) testing and diagnosis, potentially saving lives. Biosensors, such as Localized Surface Plasmon Resonance (LSPR) sensors with gold nanoparticles (AuNPs), show promise in early disease diagnosis due to their simple structure and high sensitivity. However, their commercialization is limited by high production costs and the need for precise optoelectronic systems. This article proposes an affordable optical detection scheme for LSPR sensors named BioColor. BioColor uses a color CMOS camera to capture images of light passing through the sensor elements, which are plasmonic papers composed of AuNPs. Variations in the refractive index over the sensor’s surface cause changes in the color of the transmitted light. This color change can be detected through image processing algorithms and the detection results are visualized on the BioColor mobile app, providing instant automated access to sensing outcomes.
Contexte et retombées de l’engagement de patients partenaires en milieu clinique : une étude qualitative descriptive
Authors: Caron Marie-Ève , Ouellet Nicole , Bergeron Dave
Published: October 2024

Introduction : Engager les patients comme partenaires en milieu clinique est une approche pertinente pour répondre aux enjeux des systèmes de santé. Peu d’études explorent l’accompagnement de patients partenaires (PP) auprès de patients vivant avec une maladie chronique similaire. Objectifs : Cette étude permet de décrire le contexte d’engagement des PP dans un milieu clinique, les facteurs influençant cet engagement et ses retombées. Méthodes : Nous avons utilisé un devis qualitatif descriptif, des entrevues individuelles semi-dirigées suivi d’une analyse thématique des données. Résultats : Au total, 10 personnes ont participé aux entrevues : 1 patient, 5 PP et 4 professionnels. La majorité des PP sont engagés principalement dans le secteur des soins et certains dans l’enseignement. Les participants perçoivent plusieurs facteurs qui favorisent l’engagement des PP, comme avoir suivi un programme de formations d’éducation thérapeutique et l’acceptation de la maladie ainsi que des facteurs limitants comme la timidité et le lieu de l’intervention. L’engagement des PP favoriserait l’autogestion de la maladie en plus de briser l’isolement des patients. Conclusion : Ces données préliminaires permettent de mieux saisir l’engagement des PP en milieu clinique et les retombées positives. L’engagement de PP est un incontournable pour favoriser l’autonomie et le bien-être des personnes vivant avec une maladie chronique.
Der venöse Schleifenshunt 3
Authors: Krönung Gerhard
Published: October 2024

Loop-Konfigurationen der Shuntvene sind punktionstechnisch und hämodynamisch grundsätzlich günstig und bei der Revision eines Shunts mit 2 maturierten Leitvenen oft auch gut realisierbar. Aber auch, wenn bei ungünstiger Anatomie aufwendigere gefäßchirurgische Techniken für ihre Anlage erforderlich sind, gibt es hierfür bei entsprechender Symptomatik (z. B. Steal-Syndrom oder Herzvolumenbelastung) erfolgversprechende und damit indizierte Vorgehensweisen.
Aspekte der Immunsuppression nach allogener Nierentransplantation
Authors: Matevossian Edouard , Göß Rüdiger , Garhammer Katharina , Jungwirth David , Renders Lutz , Thorban Stefan , Aßfalg Volker
Published: October 2024

Die postmortale Organspende und die Vermittlung von Organen berühren fundamentale medizinische, ethische und rechtliche Facetten des gesellschaftlichen Zusammenlebens. Die Nierentransplantation stellt heute – mehr als ein halbes Jahrhundert nach ihrer Einführung in die klinische Behandlung – ein etabliertes und sicheres Therapieverfahren mit guten Langzeitergebnissen dar. Der langfristige Erfolg einer Transplantation ist multifaktoriell bedingt und hängt entscheidend von der dauerhaften und gewissenhaften Einnahme der Immunsuppressiva ab. Die optimale immunsuppressive Therapie sichert langfristig die Funktion der transplantierten Organe, indem sie die akute Transplantatabstoßung und eine chronische Transplantatdysfunktion verhindert. Die individualisierte Immunsuppression und qualifizierte Nachsorge können entscheidend dazu beitragen, die Funktionalität des Transplantats und seine Überlebenszeit relevant zu verlängern.