Effect of T-groove Parameters on Steady-State Characteristics of Cylindrical Gas Seal

Gas film seal technology is becoming increasingly important as an advanced new rotary shaft seal technology in aviation engines and industrial gas turbines. In this paper the impacts of several parameters of T-groove cylindrical gas seal such as groove number, groove depth, groove width ratio, dam groove width ratio and floating ring length on the steady-state characteristics of cylinder film seal are studied in detail by the method of control variable using computational fluid dynamics software, and the focuses are on the pressure distribution, the gas film stiffness, and the leakage. Results show that with the increase of the number of grooves, the gas film stiffness increases gradually, but the leakage and leakage stiffness ratio decrease. The results also show that with the increase of groove depth, there is a maximum value for the gas film stiffness and a minimum value forleakage. This research plays an important role in guiding the design and the application of cylindrical gas seal

Numerical Analysis of Flow across Brush Elements Based on a 2-D Staggered Tube Banks Model

n order to improve efficiency in turbomachinery, brush seal replaces labyrinth seals widely in the secondary air system. A 2-d staggered tube bank model is adopted to simulate the gas states and the pressure character in brush seal, and computational fluid dynamics (CFD) is used to solve the model in this paper. According to the simulation results, the corrected formula of the Euler number and dimensionless pressure are given. The results show that gas expands when flow through the bristle pack, and the gas expansion closes to an isotherm process. The dynamic pressure increases with decreasing static pressure. The Euler number can reflect the seal performance of brush seals in leakage characteristics. Compared with increasing the number of rows, the reduction of the gap is a higher-efficiency method to increase the Euler number. The Euler number continually increases as the gap decreases. However, with the differential pressure increasing, Euler number first increases and then decreases as the number of rows increases. Finally, the pressure distribution on the surface of end rows is asymmetric, and it may increase the friction between the bristles and the back plat

A Feature Fusion Method with Guided Training for Classification Tasks

In this paper, a feature fusion method with guiding training (FGT-Net) is constructed to fuse image data and numerical data for some specific recognition tasks which cannot be classified accurately only according to images. The proposed structure is divided into the shared weight network part, the feature fused layer part, and the classification layer part. First, the guided training method is proposed to optimize the training process, the representative images and training images are input into the shared weight network to learn the ability that extracts the image features better, and then the image features and numerical features are fused together in the feature fused layer to input into the classification layer for the classification task. Experiments are carried out to verify the effectiveness of the proposed model. Loss is calculated by the output of both the shared weight network and classification layer. The results of experiments show that the proposed FGT-Net achieves the accuracy of 87.8%, which is 15% higher than the CNN model of ShuffleNetv2 (which can process image data only) and 9.8% higher than the DNN method (which processes structured data only)

Coupled Fluid–Solid Numerical Simulation for Flow Field Characteristics and Supporting Performance of Flexible Support Cylindrical Gas Film Seal

A new type of cylindrical gas film seal (CGFS) with a flexible support is proposed according to the working characteristics of the fluid dynamic seal in high-rotational-speed fluid machinery, such as aero-engines and centrifuges. Compared with the CGFS without a flexible support, the CGFS with flexible support presents stronger radial floating characteristics since it absorbs vibration and reduces thermal deformation of the rotor system. Combined with the structural characteristics of a film seal, an analytical model of CGFS with a flexible wave foil is established. Based on the fluid-structure coupling analysis method, the three-dimensional flow field of a straight-groove CGFS model is simulated to study the effects of operating and structural parameters on the steady-state characteristics and the effects of gas film thickness, eccentricity, and the number of wave foils on the equivalent stress of the flexible support. Simulation results show that the film stiffness increases significantly when the depth of groove increases. When the gas film thickness increases, the average equivalent stress of the flexible support first decreases and then stabilizes. Furthermore, the number of wave foils affects the average foils thickness. Therefore, when selecting the number of wave foils, the support stiffness and buffer capacity should be considered simultaneously

Data_Sheet_1_Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients.docx

Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.</p

A Partial Hierarchical Model for Online Low-Resolution Wear Particle Images Classification

Wear particle image analysis is an effective method to detect wear condition of mechanical devices. However, the recognition accuracy and recognition efficiency for online wear particle automatic recognition are always mutual restricted because the online wear particle images have almost no texture information and lack clarity. Especially for confusing fatigue wear particles and sliding wear particles, the online recognition is a challenging task. Based on this requirement, a super-resolution reconstruct technique and partial hierarchical convolutional neural network, SR-PHnet, is proposed to classify wear particles in one step. The structure of this network is composed by three modules, one is super-resolution layer module, the second is convolutional neural network classifier module, and the third is support vector machine (SVM) classifier module. The classification result of the second module is partial input to the third module for precision classification of fatigue and sliding particles. In addition, a new feature of radial edge factor (REF) is put forward to target fatigue and sliding wear particles. The test result shows that the new feature has the capability to distinguish fatigue and sliding particles well and time saving. The comparison experiments of the convolution neural network (CNN) method, support vector machine method (SVM) with and without REF feature, and integrated model of back-propagation (BP) and CNN are produced. The comparison results show that the online recognition speed and online recognition rate of the proposed SR-PHnet model in this paper are both improved markedly, especially for fatigue and sliding wear particles

Table_1_Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients.XLSX

Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.</p

Autophagy mediates the beneficial effect of hypoxic preconditioning on bone marrow mesenchymal stem cells for the therapy of myocardial infarction

Abstract Background Stem cell therapy has emerged as a promising therapeutic strategy for myocardial infarction (MI). However, the poor viability of transplanted stem cells hampers their therapeutic efficacy. Hypoxic preconditioning (HPC) can effectively promote the survival of stem cells. The aim of this study was to investigate whether HPC improved the functional survival of bone marrow mesenchymal stem cells (BM-MSCs) and increased their cardiac protective effect. Methods BM-MSCs, isolated from Tg(Fluc-egfp) mice which constitutively express both firefly luciferase (Fluc) and enhanced green fluorescent protein (eGFP), were preconditioned with HPC (1% O2) for 12 h, 24 h, 36 h, and 48 h, respectively, followed by 24 h of hypoxia and serum deprivation (H/SD) injury. Results HPC dose-dependently increased the autophagy in BM-MSCs. However, the protective effects of HPC for 24 h are most pronounced. Moreover, hypoxic preconditioned BM-MSCs (HPCMSCs) and nonhypoxic preconditioned BM-MSCs (NPCMSCs) were transplanted into infarcted hearts. Longitudinal in vivo bioluminescence imaging (BLI) and immunofluorescent staining revealed that HPC enhanced the survival of engrafted BM-MSCs. Furthermore, HPCMSCs significantly reduced fibrosis, decreased apoptotic cardiomyocytes, and preserved heart function. However, the beneficial effect of HPC was abolished by autophagy inhibition with 3-methyladenine (3-MA) and Atg7siRNA. Conclusion This study demonstrates that HPC may improve the functional survival and the therapeutic efficiencies of engrafted BM-MSCs, at least in part through autophagy regulation. Hypoxic preconditioning may serve as a promising strategy for optimizing cell-based cardiac regenerative therapy

Nebivolol Protects against Myocardial Infarction Injury via Stimulation of Beta 3-Adrenergic Receptors and Nitric Oxide Signaling

<div><p>Nebivolol, third-generation β-blocker, may activate β3-adrenergic receptor (AR), which has been emerged as a novel and potential therapeutic targets for cardiovascular diseases. However, it is not known whether nebivolol administration plays a cardioprotective effect against myocardial infarction (MI) injury. Therefore, the present study was designed to clarify the effects of nebivolol on MI injury and to elucidate the underlying mechanism. MI model was constructed by left anterior descending (LAD) artery ligation. Nebivolol, β3-AR antagonist (SR59230A), Nitro-L-arginine methylester (L-NAME) or vehicle was administered for 4 weeks after MI operation. Cardiac function was monitored by echocardiography. Moreover, the fibrosis and the apoptosis of myocardium were assessed by Masson's trichrome stain and TUNEL assay respectively 4 weeks after MI. Nebivolol administration reduced scar area by 68% compared with MI group (p<0.05). Meanwhile, nebivolol also decreased the myocardial apoptosis and improved the heart function after MI (p<0.05 vs. MI). These effects were associated with increased β3-AR expression. Moreover, nebivolol treatment significantly increased the phosphorylation of endothelial NOS (eNOS) and the expression of neuronal NOS (nNOS). Conversely, the cardiac protective effects of nebivolol were abolished by SR and L-NAME. These results indicate that nebivolol protects against MI injury. Furthermore, the cardioprotective effects of nebivolol may be mediated by β3-AR-eNOS/nNOS pathway.</p></div