Vibrational analysis of planetary gear trains by finite element method

Planetary gear trains produce several advantages, including high speed reduction, compactness, greater load sharing and higher torque to weight ratio, which are used widely in wind turbine, automobiles, robot and other applications. In some important transmission applications, the noise and vibration are key concerns in design. In this paper, a 3D dynamic contact and impact analysis model of planetary gear trains has been proposed. Tooth surface friction, backlash, tolerance of peg hole, and time-varying stiffness were considered in this dynamic model. The ANSYS / LS-DYNA were utilized to analyze the dynamic responses of gear transmission of the planetary gears. The vibration behavior of an actual gear set under dynamic loading was simulated in the dynamic model. The stiffness and elastic deformation of gear teeth are calculated using the finite element method with actual geometry and positions of the gears. The time-varying position of the carrier defined as the vibration and noise source. After impact analysis, the numerical results of vibration of carrier involved with the transient and steady states. Through the Fast Fourier Transform (FFT) methods, frequency spectrums of the transient and steady states of the calculated vibration of planet carrier are obtained for the gearbox designer to avoid the resonance zone

Vibrational analysis of planetary gear trains by finite element method

Planetary gear trains produce several advantages, including high speed reduction, compactness, greater load sharing and higher torque to weight ratio, which are used widely in wind turbine, automobiles, robot and other applications. In some important transmission applications, the noise and vibration are key concerns in design. In this paper, a 3D dynamic contact and impact analysis model of planetary gear trains has been proposed. Tooth surface friction, backlash, tolerance of peg hole, and time-varying stiffness were considered in this dynamic model. The ANSYS / LS-DYNA were utilized to analyze the dynamic responses of gear transmission of the planetary gears. The vibration behavior of an actual gear set under dynamic loading was simulated in the dynamic model. The stiffness and elastic deformation of gear teeth are calculated using the finite element method with actual geometry and positions of the gears. The time-varying position of the carrier defined as the vibration and noise source. After impact analysis, the numerical results of vibration of carrier involved with the transient and steady states. Through the Fast Fourier Transform (FFT) methods, frequency spectrums of the transient and steady states of the calculated vibration of planet carrier are obtained for the gearbox designer to avoid the resonance zone

Starch gelatinization under shearless and shear conditions

This article reviews the development of studying starch gelatinization under shear and shearless conditions, in particular the technologies used to detect the degree of gelatinization. Advantages and disadvantages of each technology were discussed and then some examples were presented to demonstrate their application. A new technology RheoScope, an instrument that can measure viscosity under shear stress and simultaneously observes variation of starch particles using a microscope, was also introduced. It was found the definition of "gelatinization" could be different for different detection technologies. Under shearless condition full gelatinization of starch needs about ratio of water 3/starch 1, while the gelatinization under shear condition requires less water content since shear stress enhances the processing. The number of endotherm and enthalpy of gelatinization depends on amylose/amylopectin, moisture and lipid content

Rapid and sensitive insulated isothermal PCR for point-of-need feline leukaemia virus detection

Objectives: Feline leukaemia virus (FeLV), a gamma retrovirus, causes diseases of the feline haematopoietic system that are invariably fatal. Rapid and accurate testing at the point-of-need (PON) supports prevention of virus spread and management of clinical disease. This study evaluated the performance of an insulated isothermal PCR (iiPCR) that detects proviral DNA, and a reverse transcription (RT)-iiPCR that detects both viral RNA and proviral DNA, for FeLV detection at the PON. Methods: Mycoplasma haemofelis, feline coronavirus, feline herpesvirus, feline calicivirus and feline immunodeficiency virus were used to test analytical specificity. In vitro transcribed RNA, artificial plasmid, FeLV strain American Type Culture Collection VR-719 and a clinical FeLV isolate were used in the analytical sensitivity assays. A retrospective study including 116 clinical plasma and serum samples that had been tested with virus isolation, real-time PCR and ELISA, and a prospective study including 150 clinical plasma and serum samples were implemented to evaluate the clinical performances of the iiPCR-based methods for FeLV detection. Results: Ninety-five percent assay limit of detection was calculated to be 16 RNA and five DNA copies for the RT-iiPCR, and six DNA copies for the iiPCR. Both reactions had analytical sensitivity comparable to a reference real-time PCR (qPCR) and did not detect five non-target feline pathogens. The clinical performance of the RT-iiPCR and iiPCR had 98.82% agreement (kappa[κ] = 0.97) and 100% agreement (κ = 1.0), respectively, with the qPCR (n = 85). The agreement between an automatic nucleic extraction/RT-iiPCR system and virus isolation to detect FeLV in plasma or serum was 95.69% (κ = 0.95) and 98.67% (κ = 0.85) in a retrospective (n = 116) and a prospective (n = 150) study, respectively. Conclusions and relevance: These results suggested that both RT-iiPCR and iiPCR assays can serve as reliable tools for PON FeLV detection

Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa rich variety of NLP problems that can be best expressed with a graph structure. As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder models. We further introducea large number of NLP applications that are exploiting the power of GNNs and summarize thecorresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, we discussvarious outstanding challenges for making the full use of GNNs for NLP as well as future researchdirections. To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.Comment: 127 page

HiFi-123: Towards High-fidelity One Image to 3D Content Generation

Recent advances in text-to-image diffusion models have enabled 3D generation from a single image. However, current image-to-3D methods often produce suboptimal results for novel views, with blurred textures and deviations from the reference image, limiting their practical applications. In this paper, we introduce HiFi-123, a method designed for high-fidelity and multi-view consistent 3D generation. Our contributions are twofold: First, we propose a reference-guided novel view enhancement technique that substantially reduces the quality gap between synthesized and reference views. Second, capitalizing on the novel view enhancement, we present a novel reference-guided state distillation loss. When incorporated into the optimization-based image-to-3D pipeline, our method significantly improves 3D generation quality, achieving state-of-the-art performance. Comprehensive evaluations demonstrate the effectiveness of our approach over existing methods, both qualitatively and quantitatively

Capacity of Bicycle Platoon Flow at Two-Phase Signalized Intersection: a Case Analysis of Xi’an City

Although much is known about the operation of signalized intersections, little or no empirical research has been conducted regarding bicycle capacity at these locations and the correspondent contributory factors. The purpose of this study is to accurately quantify the capacity of bikeway at signalized intersection through a fluid dispersion approach, and ultimately the lane group capacity. Using this total dispersion of bicycle flow, a relationship is also described between bicycle volume per hour and per unit width, signal parameters (length of signal cycle and green time), bicycle flow (arrival rate, density, moving velocity) and geometric intersection distance. Through the videotaping of four intersections that have significant bicycle traffic around Xiaozhai in Xi’an, China, it is ascertained that bicycle capacity varies linearly (but limited by an asymptote domain) associated with the adjustment of these parameters. The analytical results indicate that the impact saturation flow of lane groups containing right-turning vehicles and pedestrian flow at signalized intersections on bicycles is being underestimated. If this is the case, then capacity is being overestimated through the HCM 2000 capacity model and JJ37-90 approach and intersections are not being adequately designed, due to the neglect of conflict nature of mixed traffic arrivals in competing for space