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Browsing Aerospace and Aviation by Subject "34 Chemical sciences"

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    A review of hydrogen micromix combustion technologies for gas turbine applications
    (Elsevier, 2025-05-13) Singh, Gaurav; Schreiner, B. Deneys J.; Sun, Xiaoxiao; Sethi, Vishal
    Hydrogen micromix combustion is a promising technology for gas turbines, introducing rapid, miniaturized air-fuel mixing, significantly reducing combustion zone length and nitrogen oxides (NOx) emissions. This review evaluates the state-of-the-art hydrogen micromix combustion technologies, focusing on injector performance, flashback characteristics, and NOx reduction strategies. Injector designs are categorized based on premixing and flame stabilization techniques. While stationary gas turbines approach Technology Readiness Level (TRL) 9, aviation applications remain below TRL 4. This review identifies key design principles and predictive modelling challenges and presents a development roadmap for advancing hydrogen micromix combustion technology for aviation from TRL 4 to TRL 9 by 2040.
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    Advanced thermal imaging processing and deep learning integration for enhanced defect detection in carbon fiber-reinforced polymer laminates
    (MDPI, 2025-03-25) Garcia Rosa, Renan; Pereira Barella, Bruno; Garcia Vargas, Iago; Tarpani, José Ricardo; Herrmann, Hans-Georg; Fernandes, Henrique
    Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and first- and second-order derivatives were applied to refine thermographic signals, enhancing defect visibility and SNR. The U-Net architecture was used to assess segmentation performance on datasets with and without preprocessing. The results demonstrated that preprocessing significantly improved defect detection, achieving an Intersection over Union (IoU) of 95% and an F1-Score of 99%, outperforming approaches without preprocessing. These findings emphasize the importance of preprocessing in enhancing segmentation accuracy and reliability, highlighting its potential for advancing non-destructive testing techniques across various industries.