Novel thermal barrier coatings resistant to molten volcanic ash wetting

Molten environmental deposits primarily emanating from volcanic ash pose a serious threat to aviation safety. When ingested into a jet engine, the volcanic ash melts and adheres to the surface of hot regions (i.e., combustion chamber, turbine blade, and nozzle guide vanes) of jet engines. Virtually, these hot zones in jet engines comprise a two-layer thermal barrier coating (TBCs). These ceramic TBCs provide thermal insulation to the underlying nickel-based super alloy substrate, but these coatings are more vulnerable to the damage caused by molten volcanic ash deposits. Particularly, in the pursuit of high output efficiency, turbine operating temperatures increasingly exceed 1250°C, leading to detrimental effects on the TBCs. Introducing rare-earth oxides (eg. Gadolinium oxide) into TBCs is regarded as one of the main migratory approach to prevent the damage by ash, because the infiltration silica-rich molten volcanic ash deposit is slowed down by crystallising the melt, preventing deeper infiltration into the coating. However, the initial phase of the damage progression of volcanic ash into the porous texture of TBC has become unavoidable. Here, we utilised thermal spray technology to produce a novel thermal barrier coating consisting of the mixture of the hexagonal boron nitride (h-BN, 30 vol.%) and yttria stabilized zirconia (YSZ, 70 vol. %) (BN-YSZ coating). Please click Additional Files below to see the full abstract

Gradient damage spreading of molten volcanic ash on thermal barrier coatings

Aviation safety and aero engine life are always threatened by dust or ash suspending in the air route which derive from inevitable natural phenomena (volcanic eruption and sand storm) and human productive activity (run way debris, industrial fumes, and coal ash emission). Those floating silicate ash with the low melt temperature (lower than 1100 ºC) will be easily ingested into jet engine and quickly melted due to the fact that the turbine inlet temperature of the current advanced jet engine at cruising altitude (1200-1450 ºC) far exceed the melting point of those silicate ash. Subsequently, these molten ash are deposited on the surface of thermal barrier coatings (TBCs). TBCs is a refractory ceramic layer deposited on the surface of super alloy and can protect these metal at the hot parts (such as combustion chamber, blade and nozzle) from high temperature. However, these silicate deposits will lead to serious spallation and even failure of TBCs. Once the TBCs exfoliate under stress or chemical corrosion because of ash deposition, the engine may stop running during the flight and cause air disaster. Therefore, silicate ash deposition undoubtedly pose a huge obstacle in the development of jet engine. Here, to comprehensively understand the effect of silicate deposits on TBCs, we investigated the subsurface-transverse spreading ring of re-melted volcanic ash (obtained from Tungurahua Volcano, Ecuador, 2014) with various droplet size on the APS TBCs and EB-PVD TBCs respectively at the temperature from 1200 ºC to 1600 ºC over a wide range of duration (Figs. 1a and b). Our results demonstrate that the gradient change of concentration of volcanic ash melt onto TBCs directly leads to the formation of spreading ring in the subsurface-transverse of molten volcanic ash located in the edge of main spreading area (Fig. 1c). These observations imply that the interaction process of molten silicate ash with TBCs is driven not only by vertical infiltration due to gravitation but also by horizontal spreading owing to capillary force. Notably, the infiltration depth of the ring area was deeper than that of the main liquid area, which closely resembles previously observed in ceramic plate (Figs. 1d and e). Overall, we summaries the influence of temperature, holding time and size of droplet on spreading radius and conclude the mechanism of vertical infiltration. Those work is the first step to improving the TBCs and serve as the basic of developing the new generation of aeroengines. Please click Additional Files below to see the full abstract

ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth interval

Multi-View Stereo~(MVS) is a fundamental problem in geometric computer vision which aims to reconstruct a scene using multi-view images with known camera parameters. However, the mainstream approaches represent the scene with a fixed all-pixel depth range and equal depth interval partition, which will result in inadequate utilization of depth planes and imprecise depth estimation. In this paper, we present a novel multi-stage coarse-to-fine framework to achieve adaptive all-pixel depth range and depth interval. We predict a coarse depth map in the first stage, then an Adaptive Depth Range Prediction module is proposed in the second stage to zoom in the scene by leveraging the reference image and the obtained depth map in the first stage and predict a more accurate all-pixel depth range for the following stages. In the third and fourth stages, we propose an Adaptive Depth Interval Adjustment module to achieve adaptive variable interval partition for pixel-wise depth range. The depth interval distribution in this module is normalized by Z-score, which can allocate dense depth hypothesis planes around the potential ground truth depth value and vice versa to achieve more accurate depth estimation. Extensive experiments on four widely used benchmark datasets~(DTU, TnT, BlendedMVS, ETH 3D) demonstrate that our model achieves state-of-the-art performance and yields competitive generalization ability. Particularly, our method achieves the highest Acc and Overall on the DTU dataset, while attaining the highest Recall and $F_{1}$-score on the Tanks and Temples intermediate and advanced dataset. Moreover, our method also achieves the lowest $e_{1}$ and $e_{3}$ on the BlendedMVS dataset and the highest Acc and $F_{1}$-score on the ETH 3D dataset, surpassing all listed methods.Project website: https://github.com/zs670980918/ARAI-MVSNe

Quartz sand surface morphology of granitic tafoni at Laoshan, China

43-48In this study, a SEM method was used to analyze the surface morphology of the quartz sand granitic tafoni at Laoshan, for the purpose of exploring the weathering process of this tafoni. Present study showed that granitic tafoni at Laoshan, the quartz sand roundness was dominated by angular and sub-angular morphologies. Massive Hydrodynamic features had been developed on the quartz sand surfaces, as well as wind and chemistry forms, which were more developed. It was determined that granitic tafoni at Laoshan, the quartz sand had suffered long-term rainy and windy mechanical erosion, as well as chemical dissolution from residual pit water. These findings differed from the earlier views that the tafone was formed by the glacial melt water

Volcanic ash melting under conditions relevant to ash turbine interactions

The ingestion of volcanic ash by jet engines is widely recognized as a potentially fatal hazard for aircraft operation. The high temperatures (1,200-2,000 degrees C) typical of jet engines exacerbate the impact of ash by provoking its melting and sticking to turbine parts. Estimation of this potential hazard is complicated by the fact that chemical composition, which affects the temperature at which volcanic ash becomes liquid, can vary widely amongst volcanoes. Here, based on experiments, we parameterize ash behaviour and develop a model to predict melting and sticking conditions for its global compositional range. The results of our experiments confirm that the common use of sand or dust proxy is wholly inadequate for the prediction of the behaviour of volcanic ash, leading to overestimates of sticking temperature and thus severe underestimates of the thermal hazard. Our model can be used to assess the deposition probability of volcanic ash in jet engines

Learning Dense UV Completion for Human Mesh Recovery

Human mesh reconstruction from a single image is challenging in the presence of occlusion, which can be caused by self, objects, or other humans. Existing methods either fail to separate human features accurately or lack proper supervision for feature completion. In this paper, we propose Dense Inpainting Human Mesh Recovery (DIMR), a two-stage method that leverages dense correspondence maps to handle occlusion. Our method utilizes a dense correspondence map to separate visible human features and completes human features on a structured UV map dense human with an attention-based feature completion module. We also design a feature inpainting training procedure that guides the network to learn from unoccluded features. We evaluate our method on several datasets and demonstrate its superior performance under heavily occluded scenarios compared to other methods. Extensive experiments show that our method obviously outperforms prior SOTA methods on heavily occluded images and achieves comparable results on the standard benchmarks (3DPW)

Volcanic ash melting under conditions relevant to ash turbine interactions

The ingestion of volcanic ash by jet engines is widely recognized as a potentially fatal hazard for aircraft operation. The high temperatures (1,200-2,000 degrees C) typical of jet engines exacerbate the impact of ash by provoking its melting and sticking to turbine parts. Estimation of this potential hazard is complicated by the fact that chemical composition, which affects the temperature at which volcanic ash becomes liquid, can vary widely amongst volcanoes. Here, based on experiments, we parameterize ash behaviour and develop a model to predict melting and sticking conditions for its global compositional range. The results of our experiments confirm that the common use of sand or dust proxy is wholly inadequate for the prediction of the behaviour of volcanic ash, leading to overestimates of sticking temperature and thus severe underestimates of the thermal hazard. Our model can be used to assess the deposition probability of volcanic ash in jet engines

Crypto-ransomware Detection through Quantitative API-based Behavioral Profiling

With crypto-ransomware's unprecedented scope of impact and evolving level of sophistication, there is an urgent need to pinpoint the security gap and improve the effectiveness of defenses by identifying new detection approaches. Based on our characterization results on dynamic API behaviors of ransomware, we present a new API profiling-based detection mechanism. Our method involves two operations, namely consistency analysis and refinement. We evaluate it against a set of real-world ransomware and also benign samples. We are able to detect all ransomware executions in consistency analysis and reduce the false positive case in refinement. We also conduct in-depth case studies on the most informative API for detection with context

Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL

Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. Real-world data collection is often expensive and uncontrollable, leading to small and narrowly covered datasets and posing significant challenges for practical deployments of offline RL. In this paper, we provide a new insight that leveraging the fundamental symmetry of system dynamics can substantially enhance offline RL performance under small datasets. Specifically, we propose a Time-reversal symmetry (T-symmetry) enforced Dynamics Model (TDM), which establishes consistency between a pair of forward and reverse latent dynamics. TDM provides both well-behaved representations for small datasets and a new reliability measure for OOD samples based on compliance with the T-symmetry. These can be readily used to construct a new offline RL algorithm (TSRL) with less conservative policy constraints and a reliable latent space data augmentation procedure. Based on extensive experiments, we find TSRL achieves great performance on small benchmark datasets with as few as 1% of the original samples, which significantly outperforms the recent offline RL algorithms in terms of data efficiency and generalizability.Comment: The first two authors contributed equall