Evaluation of a Sodium–Water Reaction Event Caused by Steam Generator Tubes Break in the Prototype Generation IV Sodium-cooled Fast Reactor

AbstractThe prototype generation IV sodium-cooled fast reactor (PGSFR) has been developed by the Korea Atomic Energy Research Institute. This reactor uses sodium as a reactor coolant to transfer the core heat energy to the turbine. Sodium has chemical characteristics that allow it to violently react with materials such as a water or steam. When a sodium–water reaction (SWR) occurs due to leakage or breakage of steam generator tubes, high-pressure waves and corrosive reaction products are produced, which threaten the structural integrity of the components of the intermediate heat-transfer system (IHTS) and the safety of the primary heat-transfer system (PHTS). In the PGSFR, SWR events are included in the design-basis event. This event should be analyzed from the viewpoint of the integrities of the IHTS and fuel rods. To evaluate the integrity of the IHTS based on the consequences of the SWR, the behaviors of the generated high-pressure waves are analyzed at the major positions of a failed IHTS loop using a sodium–water advanced analysis method-II code. The integrity of the fuel rods must be consistently maintained below the safety acceptance criteria to avoid the consequences of the SWR. The integrity of the PHTS is evaluated using the multidimensional analysis of reactor safety-liquid metal reactor code to model the whole plant

An Improved Semi-Empirical Model for Radar Backscattering from Rough Sea Surfaces at X-Band

We propose an improved semi-empirical scattering model for X-band radar backscattering from rough sea surfaces. This new model has a wider validity range of wind speeds than does the existing semi-empirical sea spectrum (SESS) model. First, we retrieved the small-roughness parameters from the sea surfaces, which were numerically generated using the Pierson-Moskowitz spectrum and measurement datasets for various wind speeds. Then, we computed the backscattering coefficients of the small-roughness surfaces for various wind speeds using the integral equation method model. Finally, the large-roughness characteristics were taken into account by integrating the small-roughness backscattering coefficients multiplying them with the surface slope probability density function for all possible surface slopes. The new model includes a wind speed range below 3.46 m/s, which was not covered by the existing SESS model. The accuracy of the new model was verified with two measurement datasets for various wind speeds from 0.5 m/s to 14 m/s

Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning

This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input data set is the artificial clutter data that are generated via the Advanced Refractive Prediction System (AREPS) simulation software Ver. 3.6 in conjunction with random atmospheric refractive indices. The output of the RFC method is then predicted via binary classification, indicating whether the atmospheric conditions are duct or non-duct. For the cross-validation, the clutter power spectrum data are generated based on real atmospheric refractivity data. The results show that the DNN trained with 5600 pieces of data (validation accuracy of 95.99%) exhibits a binary classification accuracy of 98.36%. The deep neural network (DNN) trained with 28,000 pieces of data (validation accuracy of 98.20%) achieves a binary classification accuracy of 99.06% with an F1-score of 0.9921

Advanced thermal fluid leakage detection system with machine learning algorithm for pipe-in-pipe structure

Pipe-in-pipe (PIP) system is essential for high thermal and high pressure fluid transportation. However, in the existing PIP systems, fluid leakage between inner and outer pipe has been difficult to discover or detect, which has worked as bottle neck to utilize PIP system in high risk industries as nuclear reactor, chemical plant or oil drilling systems. Here, we propose a noble PIP leakage detection system utilizing distributed temperature sensing (DTS) with Machine Learning (ML). With the Fourier transformed spectrogram data from DTS, the ML assisted system was able to detect 0.2 similar to 7 ml/min liquid leakage between inner and outer pipe with the accuracy of 91.67% with a single embedded optical fiber. Under varying operating temperature, the system successfully distinguished leakage and non-leakage states using the optimized convolutional neural network. Our developed PIP leakage detection system can be deployed in safety-critical industrial systems for autonomous leakage detection

Graphene nanonet for biological sensing applications

We report a simple but efficient method to fabricate versatile graphene nanonet (GNN)-devices. In this method, networks of V2O5 nanowires (NWs) were prepared in specific regions of single-layer graphene, and the graphene layer was selectively etched via a reactive ion etching method using the V2O5 NWs as a shadow mask. The process allowed us to prepare large scale patterns of GNN structures which were comprised of continuous networks of graphene nanoribbons (GNRs) with chemical functional groups on their edges. The GNN can be easily functionalized with biomolecules for fluorescent biochip applications. Furthermore, electrical channels based on GNN exhibited a rather high mobility and low noise compared with other network structures based on nanostructures such as carbon nanotubes, which was attributed to the continuous connection of nanoribbons in GNN structures. As a proof of concept, we built DNA sensors based on GNN channels and demonstrated the selective detection of DNA. Since our method allows us to prepare high-performance networks of GNRs over a large surface area, it should open up various practical biosensing applications.close0

Nanotube-Bridged Wires with Sub-10 nm Gaps

We report a simple but efficient method to synthesize carbon nanotube-bridged wires (NBWs) with gaps as small as 5 nm. In this method, we have combined a strategy for assembling carbon nanotubes (CNTs) inside anodized aluminum oxide pones and the on-wire lithography technique to fabricate CNT-bridged wires with gap sizes deliberately tailored over the 5-600 nm range. As a proof-of-concept demonstration of the utility of this architecture, we have prepared NBW-based chemical and biosensors which exhibit higher analyte sensitivity (lower limits of detection) than those based on planar CNT networks. This observation is attributed to a greater surface-to-volume ratio of CNTs in the NBWs than those in the planar CNT devices. Because of the ease of synthesis and high yield of NBWs, this technique may enable the further incorporation of CNT-based architectures into various nanoelectronic and sensor platforms

Laser powder bed fusion for AI assisted digital metal components

This paper proposes a novel method to impart intelligence to metal parts using additive manufacturing. A sensor-embedded metal bracket is prototyped via a metal powder bed fusion process to recognise partial screw loosening or total screw missing or identify the source of vibration with the assistance of artificial intelligence (AI). The digital metal bracket can recognise subtle changes in the screw fixation state with 90% accuracy and identify unknown sources of vibration with 84% accuracy. The von Mises stress distribution in the prototyped metal bracket is evaluated using a finite element analysis, which is learned by AI to match the real-time deformation analysis of the metal bracket in augmented reality. The proposed prototype can contribute to hyper-connectivity for developing next-generation metal-based mechanical components.11Nsciescopu

Vertical and In-Plane Current Devices Using NbS₂/n-MoS₂ van der Waals Schottky Junction and Graphene Contact

A van der Waals (vdW) Schottky junction between two-dimensional (2D) transition metal dichalcogenides (TMDs) is introduced here for both vertical and in-plane current devices: Schottky diodes and metal semiconductor field-effect transistors (MESFETs). The Schottky barrier between conducting NbS₂ and semiconducting n-MoS₂ appeared to be as large as ∼0.5 eV due to their work-function difference. While the Schottky diode shows an ideality factor of 1.8–4.0 with an on-to-off current ratio of 10³−10⁵, Schottky-effect MESFET displays little gate hysteresis and an ideal subthreshold swing of 60–80 mV/dec due to low-density traps at the vdW interface. All MESFETs operate with a low threshold gate voltage of −0.5 ∼ −1 V, exhibiting easy saturation. It was also found that the device mobility is significantly dependent on the condition of source/drain (S/D) contact for n-channel MoS₂. The highest room temperature mobility in MESFET reaches to approximately more than 800 cm²/V s with graphene S/D contact. The NbS₂/n-MoS₂ MESFET with graphene was successfully integrated into an organic piezoelectric touch sensor circuit with green OLED indicator, exploiting its predictable small threshold voltage, while NbS₂/n-MoS₂ Schottky diodes with graphene were applied to extract doping concentrations in MoS₂ channel

Vertical and In-Plane Current Devices Using NbS₂/n-MoS₂ van der Waals Schottky Junction and Graphene Contact

A van der Waals (vdW) Schottky junction between two-dimensional (2D) transition metal dichalcogenides (TMDs) is introduced here for both vertical and in-plane current devices: Schottky diodes and metal semiconductor field-effect transistors (MESFETs). The Schottky barrier between conducting NbS₂ and semiconducting n-MoS₂ appeared to be as large as ∼0.5 eV due to their work-function difference. While the Schottky diode shows an ideality factor of 1.8–4.0 with an on-to-off current ratio of 10³−10⁵, Schottky-effect MESFET displays little gate hysteresis and an ideal subthreshold swing of 60–80 mV/dec due to low-density traps at the vdW interface. All MESFETs operate with a low threshold gate voltage of −0.5 ∼ −1 V, exhibiting easy saturation. It was also found that the device mobility is significantly dependent on the condition of source/drain (S/D) contact for n-channel MoS₂. The highest room temperature mobility in MESFET reaches to approximately more than 800 cm²/V s with graphene S/D contact. The NbS₂/n-MoS₂ MESFET with graphene was successfully integrated into an organic piezoelectric touch sensor circuit with green OLED indicator, exploiting its predictable small threshold voltage, while NbS₂/n-MoS₂ Schottky diodes with graphene were applied to extract doping concentrations in MoS₂ channel