Hard surface coating experimental evaluation and thermomechanical analysis of a seal with micro heat exchanger

Mechanical face seals are important components of many rotating machinery. Minimizing the friction between seal faces and efficiently removing the heat generated between seal surfaces are two crucial considerations in the design of mechanical seal. Thin film coating and MEMS technology hold great promise for improving the performance of mechanical seals from the viewpoint of reducing friction and heat at the interface in these two aspects. To reveal what effect the coating and MEMS technology can have on tribological properties of seals, friction and wear characteristics of Ti-C:H coatings on seal-like rings and heat transfer performances of a seal prototype implanted with micro heat exchangers were studied in this thesis. Implanted micro-heat exchangers were built using the MEMS technology in a previous work. Coating on seal-like rings was successfully implemented using CVD/PVD Friction and wear properties of coatings with different compositions were investigated through a series of unlubricated ring-on-disk experiments in a tribometer. The results showed the Ti-C:H coatings tend to improve the tribological performance. However, the experimental results did not reveal a direct relationship between coating composition and its tribological properties. Micro posts implanted seal prototype had been manufactured and tested in a previous work. In the present study, a finite element model was developed to simulate the experiment and evaluate the heat transfer characteristic of the seal prototype. The predictions of the model are in good agreement with the measured results. In addition, a method was developed for the calculation of the seal structure’s maximum stress under normal friction load. This method can be used for the structural analysis and failure prediction of seals with micro posts

Experimental and Analytical Study of the Surface Texturing Enhanced Lubrication Elements

Surface texturing is a method that modifies the frictional surface of a nominally flat tribocomponent by shallow patterns. It is found that with added patterns to the surface of a mechanical face seal or thrust bearing, their tribological performance improves, i.e. both friction and wear rate decrease. The current research concentrates on the analysis of hydrodynamic effect responsible for the performance enhancement of the spiral groove patterns and dimples on mechanical seal-like structures and the experimental evaluation of the tribological behavior of these structures. Surface textures considered are: dimple texture and spiral groove pattern. In the research on the dimple textures, the cavitation effect of the dimple enhanced friction pair is modeled using a mass-conservative theory – the Jakobsson-Floberg-Olsson (JFO) cavitation theory. Roughness effect is considered in the analysis of the dimple pattern performance. A thermohydrodynamic model is also developed to examine the influence of the temperature on the performance of the dimple textured frictional pair. The experiments on the dimple textured frictional pair are conducted on heat-treated 17-4 PH stainless steel specimens. The surface textures of the specimens are created by means of Nd:Ytterbium fiber laser. The laser surface textured specimens provide low coefficient of friction compared with plain (dimple free) surfaces. However, for the material used in the current experiments, the surface texture decreases the surface’s resistance to wear. In the research on the spiral groove pattern, the thermohydrodynamic model of the spiral groove surface seal is created. A commercially available CFD code – CFD-ACE+– is used for this purpose. The result shows that spiral grooves have significant influence on the seal’s thermal and load-carrying capacity behaviors. The experimental specimens on the spiral groove patterned friction pair research are made the same way as the dimple textured frictional pair. In this research spiral groove thrust bearings with variety of spiral angles subjected to different loads and speeds are tested. The frictional behaviors of the spiral groove thrust bearings are analyzed. In addition, a theoretical model is developed to gain further insight into the frictional characteristics of spiral grooves in both the hydrodynamic regime and the mixed lubrication regime

Sleep pattern disruption of flight attendants operating on the Asia–Pacific route

Jet lag is a common issue with flight attendants in international flights, as they have to cross several time zones back and forth, while their sleep patterns get disrupted by the legally required rest times between flights, which are normally carried out at different locations. This research aimed to investigate the sleep quality of a sample of flight attendants operating between New Zealand and Asia. Twenty flight attendants were surveyed in this research. The research found that flight attendants typically took a nap immediately after arriving into New Zealand, reporting a sound sleep time of about 6 hours. After the nap, however, they had problems falling sleep in subsequent nights. After their first nap, some flight attendants try to adapt to local light conditions, while others prefer to keep the sleep patterns they had back home. Both groups report different trends of sleep quality

Quantum Transport and Band Structure Evolution under High Magnetic Field in Few-Layer Tellurene

Quantum Hall effect (QHE) is a macroscopic manifestation of quantized states which only occurs in confined two-dimensional electron gas (2DEG) systems. Experimentally, QHE is hosted in high mobility 2DEG with large external magnetic field at low temperature. Two-dimensional van der Waals materials, such as graphene and black phosphorus, are considered interesting material systems to study quantum transport, because it could unveil unique host material properties due to its easy accessibility of monolayer or few-layer thin films at 2D quantum limit. Here for the first time, we report direct observation of QHE in a novel low-dimensional material system: tellurene.High-quality 2D tellurene thin films were acquired from recently reported hydrothermal method with high hole mobility of nearly 3,000 cm2/Vs at low temperatures, which allows the observation of well-developed Shubnikov-de-Haas (SdH) oscillations and QHE. A four-fold degeneracy of Landau levels in SdH oscillations and QHE was revealed. Quantum oscillations were investigated under different gate biases, tilted magnetic fields and various temperatures, and the results manifest the inherent information of the electronic structure of Te. Anomalies in both temperature-dependent oscillation amplitudes and transport characteristics were observed which are ascribed to the interplay between Zeeman effect and spin-orbit coupling as depicted by the density functional theory (DFT) calculations

Real-Time Neural Video Recovery and Enhancement on Mobile Devices

As mobile devices become increasingly popular for video streaming, it's crucial to optimize the streaming experience for these devices. Although deep learning-based video enhancement techniques are gaining attention, most of them cannot support real-time enhancement on mobile devices. Additionally, many of these techniques are focused solely on super-resolution and cannot handle partial or complete loss or corruption of video frames, which is common on the Internet and wireless networks. To overcome these challenges, we present a novel approach in this paper. Our approach consists of (i) a novel video frame recovery scheme, (ii) a new super-resolution algorithm, and (iii) a receiver enhancement-aware video bit rate adaptation algorithm. We have implemented our approach on an iPhone 12, and it can support 30 frames per second (FPS). We have evaluated our approach in various networks such as WiFi, 3G, 4G, and 5G networks. Our evaluation shows that our approach enables real-time enhancement and results in a significant increase in video QoE (Quality of Experience) of 24\% - 82\% in our video streaming system

Neural Video Recovery for Cloud Gaming

Cloud gaming is a multi-billion dollar industry. A client in cloud gaming sends its movement to the game server on the Internet, which renders and transmits the resulting video back. In order to provide a good gaming experience, a latency below 80 ms is required. This means that video rendering, encoding, transmission, decoding, and display have to finish within that time frame, which is especially challenging to achieve due to server overload, network congestion, and losses. In this paper, we propose a new method for recovering lost or corrupted video frames in cloud gaming. Unlike traditional video frame recovery, our approach uses game states to significantly enhance recovery accuracy and utilizes partially decoded frames to recover lost portions. We develop a holistic system that consists of (i) efficiently extracting game states, (ii) modifying H.264 video decoder to generate a mask to indicate which portions of video frames need recovery, and (iii) designing a novel neural network to recover either complete or partial video frames. Our approach is extensively evaluated using iPhone 12 and laptop implementations, and we demonstrate the utility of game states in the game video recovery and the effectiveness of our overall design

Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS)

The Research & Development (R&D) phase of drug development is a lengthy and costly process. To revolutionize this process, we introduce our new concept QMLS to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD. For Hit Generation, Machine Learning Molecule Generation (MLMG) generates possible hits according to the molecular structure of the target protein while the Quantum Simulation (QS) filters molecules from the primary essay based on the reaction and binding effectiveness with the target protein. Then, For Lead Optimization, the resultant molecules generated and filtered from MLMG and QS are compared, and molecules that appear as a result of both processes will be made into dozens of molecular variations through Machine Learning Molecule Variation (MLMV), while others will only be made into a few variations. Lastly, all optimized molecules would undergo multiple rounds of QS filtering with a high standard for reaction effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs. This paper is based on our first paper, where we pitched the concept of machine learning combined with quantum simulations. In this paper we will go over the detailed design and framework of QMLS, including MLMG, MLMV, and QS.Comment: 13 pages, 6 figure

Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections

Neural implicit methods have achieved high-quality 3D object surfaces under slight specular highlights. However, high specular reflections (HSR) often appear in front of target objects when we capture them through glasses. The complex ambiguity in these scenes violates the multi-view consistency, then makes it challenging for recent methods to reconstruct target objects correctly. To remedy this issue, we present a novel surface reconstruction framework, NeuS-HSR, based on implicit neural rendering. In NeuS-HSR, the object surface is parameterized as an implicit signed distance function (SDF). To reduce the interference of HSR, we propose decomposing the rendered image into two appearances: the target object and the auxiliary plane. We design a novel auxiliary plane module by combining physical assumptions and neural networks to generate the auxiliary plane appearance. Extensive experiments on synthetic and real-world datasets demonstrate that NeuS-HSR outperforms state-of-the-art approaches for accurate and robust target surface reconstruction against HSR. Code is available at https://github.com/JiaxiongQ/NeuS-HSR.Comment: 17 pages, 20 figure

ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation

While language-guided image manipulation has made remarkable progress, the challenge of how to instruct the manipulation process faithfully reflecting human intentions persists. An accurate and comprehensive description of a manipulation task using natural language is laborious and sometimes even impossible, primarily due to the inherent uncertainty and ambiguity present in linguistic expressions. Is it feasible to accomplish image manipulation without resorting to external cross-modal language information? If this possibility exists, the inherent modality gap would be effortlessly eliminated. In this paper, we propose a novel manipulation methodology, dubbed ImageBrush, that learns visual instructions for more accurate image editing. Our key idea is to employ a pair of transformation images as visual instructions, which not only precisely captures human intention but also facilitates accessibility in real-world scenarios. Capturing visual instructions is particularly challenging because it involves extracting the underlying intentions solely from visual demonstrations and then applying this operation to a new image. To address this challenge, we formulate visual instruction learning as a diffusion-based inpainting problem, where the contextual information is fully exploited through an iterative process of generation. A visual prompting encoder is carefully devised to enhance the model's capacity in uncovering human intent behind the visual instructions. Extensive experiments show that our method generates engaging manipulation results conforming to the transformations entailed in demonstrations. Moreover, our model exhibits robust generalization capabilities on various downstream tasks such as pose transfer, image translation and video inpainting