Search

Tip leakage loss significantly influences aerodynamic performance of high-pressure turbines, with squealer tips serving as an effective control strategy. The development of a tip leakage loss model is crucial for evaluating and predicting turbine aerodynamic performance and guiding blade tip design. This study presents a novel leakage loss model for squealer tips, employing a hybrid approach that integrates physics-driven and data-driven methodologies, followed by comprehensive validation. The leakage flow within the squealer tip gap is modeled into three basic flows: Vena contracta motion in both the pressure-side and suction-side squealer tip gaps, and jet diffusion inside the cavity. The specific flow pattern and loss magnitude inside the cavity are intricately linked to the evolution of the scraping vortex. The paper introduces a methodology for modeling the scraping vortex through deep learning, grounded in the separation and reattachment theory of backstep flow. Subsequently, it presents a physical model of the leakage flow across the squealer tip gap, informed by the classical theories of three identified basic flows and the scraping vortex's behavior. The influences of complex factors that are challenging to address solely through physical modeling are also taken into consideration with the aid of machine learning. The proposed model enables a rapid and precise prediction of key flow features, such as scraping vortex characteristics, discharge coefficient, leakage flow rate and momentum, alongside total leakage flow rate and leakage loss. This model provides a reliable analytical tool for predicting leakage performance and guiding designs for the squealer tip. [ABSTRACT FROM AUTHOR]

Liver cirrhosis remains a significant global public health concern, with liver transplantation standing as the foremost effective treatment currently available. Therefore, investigating the pathogenesis of liver cirrhosis and developing novel therapies is imperative. Mitochondrial dysfunction stands out as a pivotal factor in its development. This study aimed to elucidate the relationship between mitochondria dysfunction and liver cirrhosis using bioinformatic methods to unveil its pathogenesis. Initially, we identified 460 co-expressed differential genes (co-DEGs) from the GSE14323 and GSE25097 datasets, alongside their combined datasets. Functional analysis revealed that these co-DEGs were associated with inflammatory cytokines and cirrhosis-related signaling pathways. Utilizing weighted gene co-expression network analysis (WCGNA), we screened module genes, intersecting them with co-DEGs and oxidative stress-related mitochondrial genes. Two algorithms (least absolute shrinkage and selection operator (LASSO) regression and SVE-RFE) were then employed to further analyze the intersecting genes. Finally, COX7A1 and IFI27 emerged as identifying genes for liver cirrhosis, validated through a receiver operating characteristic (ROC) curve analysis and related experiments. Additionally, immune infiltration highlighted a strong correlation between macrophages and cirrhosis, with the identifying genes (COX7A1 and IFI27) being significantly associated with macrophages. In conclusion, our findings underscore the critical role of oxidative stress-related mitochondrial genes (COX7A1 and IFI27) in liver cirrhosis development, highlighting their association with macrophage infiltration. This study provides novel insights into understanding the pathogenesis of liver cirrhosis. [ABSTRACT FROM AUTHOR]

The PCHE has been recognized as a promising heat exchanger for hypersonic precooled aero-engines due to its superior thermal performance, high compactness and robustness. The present work focuses on the experimental thermal-hydraulic performance study of a novel zigzag microchannel PCHE. The results indicate that the pressure loss caused by friction is the main source. The Fanning correlation proposed by Moisseysev shows more precise prediction results, whose prediction deviations are within 10.0%. The measured Nu is markedly 1.24 ~ 1.94 times higher than that of the straight channel and a new Nu correlation was developed and the maximum deviations do not exceed 5%. [ABSTRACT FROM AUTHOR]

Background and Aims: Accumulating evidence highlights the association between the gut microbiota and liver cirrhosis. However, the role of the gut microbiota in liver cirrhosis remains unclear., Methods: We first assessed the differences in the composition of the bacterial community between CCl4-induced liver cirrhosis and control mice using 16S rRNA sequencing. We then performed a two-sample Mendelian randomization (MR) analysis to reveal the underlying causal relationship between the gut microbiota and liver cirrhosis. Causal relationships were analyzed using primary inverse variance weighting (IVW) and other supplemental MR methods. Furthermore, fecal samples from liver cirrhosis patients and healthy controls were collected to validate the results of the MR analysis., Results: Analysis of 16S rRNA sequencing indicated significant differences in gut microbiota composition between the cirrhosis and control groups. IVW analyses suggested that Alphaproteobacteria, Bacillales, NB1n, Rhodospirillales, Dorea , Lachnospiraceae , and Rhodospirillaceae were positively correlated with the risk of liver cirrhosis, whereas Butyricicoccus , Hungatella , Marvinbryantia , and Lactobacillaceae displayed the opposite effects. However, the weighted median and MR-PRESSO estimates further showed that only Butyricicoccus and Marvinbryantia presented stable negative associations with liver cirrhosis. No significant heterogeneity or horizontal pleiotropy was observed in the sensitivity analysis. Furthermore, the result of 16S rRNA sequencing also showed that healthy controls had a higher relative abundance of Butyricicoccus and Marvinbryantia than liver cirrhosis patients., Conclusions: Our study provides new causal evidence for the link between gut microbiota and liver cirrhosis, which may contribute to the discovery of novel strategies to prevent liver cirrhosis., Competing Interests: The authors have no conflict of interests related to this publication., (© 2024 Authors.)