This study investigates the application of Direct Current Atmospheric Plasma Spraying (DC-APS) for depositing zirconium dioxide (ZrO2) coatings to enhance component performance and durability in advanced applications....
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ISBN:
(数字)9798331516772
ISBN:
(纸本)9798331516789
This study investigates the application of Direct Current Atmospheric Plasma Spraying (DC-APS) for depositing zirconium dioxide (ZrO2) coatings to enhance component performance and durability in advanced applications. ZrO2's excellent thermal and mechanical properties make it ideal for industries like aerospace and renewable energy. We focus on optimizing critical process parameters, such as plasma jet temperature and particle injection velocity, to improve coating characteristics. Using the Jets&Poudres (JP) simulation code, we analyze particle dynamics and heat transfer, revealing how process variations influence coating morphology and adhesion. Additionally, we employ artificial neural networks (ANNs) to model the deposition process, achieving high accuracy in predicting performance metrics. Our findings confirm that DC-APS is an effective method for producing high-quality ZrO2 coatings and underscore the importance of process optimization and AI integration for enhancing thermal spray applications.
This paper investigates the heat transfer enhancement in a two-compartment heat exchanger using nanofluids, employing numerical simulations and deep learning. The study systematically examines the influence of key par...
详细信息
ISBN:
(数字)9798331516772
ISBN:
(纸本)9798331516789
This paper investigates the heat transfer enhancement in a two-compartment heat exchanger using nanofluids, employing numerical simulations and deep learning. The study systematically examines the influence of key parameters: Rayleigh number $(Ra=10^{6}-10^{9})$ , conductivity ratio $(kr=1-15)$ , nanoparticle volume fraction $(\varphi=0-3\%)$ , nanofluid temperature (Temp=293-323K), and scaled heat exchanger wall thickness (0.02-0.05). The first compartment features internal heat generation, while the second incorporates baffles and nanofluids to optimize mixing and heat transfer. Computational Fluid Dynamics (CFD) is used to analyze Nusselt number, isotherms, streamlines, velocity vector magnitude, exergy loss, entropy generation, and the Bejan number. Deep learning models are developed to predict and optimize heat transfer performance based on these five input parameters. Results demonstrate that increasing the Rayleigh number and conductivity ratio significantly enhances heat transfer, while nanoparticles higher volume fractions improve performance, albeit with potential viscosity increases. Exergy analysis reveals opportunities for design optimization to minimize entropy generation. The integrated approach of CFD and deep learning provides a powerful tool for optimizing the design and operation of nanofluid-based heat exchangers for improved thermal management in various applications.
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