Early studies on Cr-Coated Zircaloy-4 as enhanced accident tolerant nuclear fuel claddings for light water reactors

High temperature steam oxidation of chromium-coated zirconium-based alloys: Kinetics and process

High temperature oxidation and microstructural evolution of cold spray chromium coatings on Zircaloy-4 in steam environments

Microstructure, corrosion resistance and oxidation behavior of Cr-coatings on Zircaloy-4 prepared by vacuum arc plasma deposition

Porosity in plasma-sprayed coatings is vital for most engineering applications. Porosity has its merits and demerits depending on the functionality of the coating and the immediate working environment. Consequently, the formation mechanisms and development of porosity have been extensively explored to find out modes of controlling porosity in plasma-sprayed coatings. In this work, a comprehensive review of porosity on plasma-sprayed coatings is established. The formation and development of porosity on plasma-sprayed coatings are governed by set spraying parameters. Optimized set spraying parameters have been used to achieve the most favorable coatings with minimum defects. Even with the optimized set spraying parameters, defects like porosity still occur. Here, we discuss other ways that can be used to control porosity in plasma-sprayed coating with emphasis to atmospheric plasma-sprayed chromium oxide coatings. Techniques like multilayer coatings, nanostructured coatings, doping with rare earth elements, laser surface re-melting and a combination of the above methods have been suggested in adjusting porosity. The influences of porosity on microstructure, properties of plasma-sprayed coatings and the measurement methods of porosity have also been reviewed.

Porosity and its significance in plasma-sprayed coatings

To fabricate the oxidation resistance accident tolerance fuel cladding, Cr layer and FeCrAl alloys were deposited on Zircaloy-4 using atmospheric plasma spraying technology. The specimens were exposed under a simulated loss-of-coolant accident condition. XRD, SEM and EDS techniques were carried out to explore their high temperature behavior. According to the analysis results, both Cr coating and FeCrAl coating were successfully prepared by atmospheric plasma spraying system. Cr coating had superior oxidation resistance. Compact Cr2O3 scale forming on Cr coating acted as an oxygen diffusion barrier, whereas FeCrAl coating degraded due to inter-diffusion, leading to its poor protection for the cladding. Difference in standard Gibbs free energy of formation of Fe and Zr caused the prior oxidation of Zr. Accordingly, Zr-rich and Zr-depleted zones occurred in FeCrAl-coated specimens.

Behavior of plasma sprayed Cr coatings and FeCrAl coatings on Zr fuel cladding under loss-of-coolant accident conditions

Accident tolerant fuels are proposed for light water reactors to improve the safety margin in the event of severe accidents, especially under a LOCA scenario. In this work, Zr-Al-C coatings were synthesized by magnetron sputtering, with different aluminum concentrations incorporating binary carbide ZrC0.55. Based on the combined results of XRD, TEM, and XPS, 7.6 at.% Al coating resulted in a solid solution phase, while a further increase in the Al concentration to 17.7 at.% and 26.3 at.% led to the formation of nanocomposites. The average grain size of the Zr-Al-C coatings was in the range of 8–11 nm, and the hardness was on the order of 15–30 GPa. Meanwhile, the 7.6 at.% and 17.7 at.% Al coatings showed enhanced oxidation resistance against air up to 1000 °C due to formation of continuous oxide scale consisting of t-ZrO2 and Al2O3. Thus, in terms of the mechanical property and high-temperature oxidation resistance, the Zr-Al-C coatings had much better performance than the ZrC0.55 coatings.

Influence of Al concentration on mechanical property and oxidation behavior of Zr-Al-C coatings

To reduce the casualties caused by workers not wearing safety measures in time at construction sites. For the generic target detection model with high complexity and large model, which cannot perform helmet wearing detection in real-time, a real-time helmet wearing detection algorithm based on YOLOv3 is proposed, with the mobile end network as the feature extractor of the proposed method. For helmet wearing detection is often done outdoors, so to filter the environmental noise. The channel attention module is introduced to optimize the feature extraction when multiplexing the multi-scale feature maps. Finally, to weaken the problem of inadequate gradient back propagation brought by the IOU function, the CIOU loss function is used to optimize the gradient back propagation. The experimental results show that the method in this paper can balance the accuracy and detection rate of the detection model with an average accuracy of 82.47% (mAP). Compared with the model of the original YOLOv3 network, the model size of this method is only 22.3% of the original model, and the detection rate is significantly improved compared with the original method and can meet the requirements of real-time helmet wear detection.

Lightweight helmet wear detection algorithm based on YOLOv3

The aim of this study is to address the real-time requirements of forestry pest detection and the problem of a low detection rate caused by anchor box redundancy of existing detection methods. This paper proposes a real-time forestry pest detection method based on theanchor-free method that can balance the detection rate and detection accuracy. Based on the TTFNet method, a mobile feature extraction network is introduced, and the effective feature weights are increased by one-dimensional convolution before feature output to suppress invalid features. For pest detection, data are mostly small-scale targets. An enhanced feature fusion method is proposed to introduce an asymmetric convolution module in multi-scale feature fusion to feature-enhance the feature maps extracted by the backbone network and connect across layers to improve the detection accuracy. To address the degradation of the anchor box position regression loss in the original method, DIOULoss is introduced to optimize the position regression loss function of the anchor box. Finally, data augmentation is performed on a relatively small number of samples in the dataset, the accuracy of the model is improved by 1.94%, the FPS is improved to 1.6 times of the original one, and the training time is slightly increased compared with the preinnovation model. Ablation experiments are designed to demonstrate the effectiveness of the proposed algorithm while being more conducive to deployment on edge devices.

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Tong Liu

Papers

Behavior of plasma sprayed Cr coatings and FeCrAl coatings on Zr fuel cladding under loss-of-coolant accident conditions

Influence of Al concentration on mechanical property and oxidation behavior of Zr-Al-C coatings

Lightweight helmet wear detection algorithm based on YOLOv3

Real-Time Forestry Pest Detection Method Based on Enhanced Feature Fusion with Deep Learning

Synthesis and evaluation of phenolic capsaicin-derived self-polymers for antibacterial activity