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Predicting the Hydrodynamic Performance of Inland Waterway Ships in Extreme Shallow Waters Using State-of-the-Art CFD
Get PDFNumerical modelling of cross-flow turbines: 2D blade-resolved against 2D actuator line method simulations
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Anti-Erosion Behavior and Mechanism of Novel Graphene-Modified WC Coatings under High-Velocity Solid-Liquid Impingement
No full text''':This study investigates the anti-erosion mechanisms of a novel graphene-modified tungsten carbide coating under high-speed solid-liquid two-phase flow conditions. A liquid-solid two-phase flow erosion simulation device was employed, and fluid dynamics simulations were conducted using FLUENT software to determine the maximum experimental flow velocity. The influence of flow velocity, sand particle diameter, sand concentration, and fluid temperature on the erosion rate of the coating was systematically analyzed using the control variable method. Experimental results reveal that the erosion rate follows a power-law exponential relationship with flow velocity. When the sand particle diameter is below 0.5 mm, the erosion rate remains relatively stable; however, when the diameter exceeds 0.5 mm, the erosion rate increases significantly with particle size. Furthermore, the erosion rate exhibits a slight increase with higher sand concentrations, while a notable rise in fluid temperature leads to a substantial increase in the erosion rate, with the difference in erosion rates between the 150°C and 200°C coatings becoming more pronounced at elevated temperatures. Scanning electron microscopy (SEM) and numerical simulations were utilized to further elucidate the anti-erosion mechanisms of the coating. The incorporation of tungsten carbide significantly enhances the coating's hardness, while the addition of graphene results in a denser microstructure, effectively reducing porosity and improving the coating's resistance to impact and erosion. The findings demonstrate that the novel graphene-modified tungsten carbide coating exhibits superior erosion resistance, making it highly suitable for enhancing the durability and performance of rigid PDC drill bits in complex downhole environments
Comparison with Different Bayesian and non-Bayesian Estimation Techniques for the Compound Rayleigh Exponential Distribution with Actuarial Measures and Applications
Get PDFMany lifetime analysis, such as engineering, biology, survival, actuarial and medical sciences, heavily rely on the two-parameter compound Rayleigh exponential distribution, which is well-known in statistical theory. Because of their ability to successfully handle small sample sizes and involve prior knowledge, Bayesian techniques are crucial for estimating the parameters of the compound Rayleigh exponential distribution. This study presents the estimation of the compound Rayleigh exponential distribution unknown parameters using Bayesian and non-Bayesian estimation techniques, including maximum likelihood estimation, maximum product spacing, least square estimator, weighted least square estimator, Cramer-Von-Mise estimator, Anderson-Darling estimator, and Bayesian techniques with informative and non-informative priors based on different loss functions. Additionally, we used several methods to obtain the confidence intervals for the unknown parameters, such as the approximate and Bootstrap methods. The effectiveness of these estimators is evaluated through a Monte Carlo simulation study. Furthermore, we are committed to investigating three widely recognized risk metrics: the value at risk, the tail value at risk, and the tail variance premium. These findings are helpful for actuarial risk researchers who depend on risk measurement fitting when evaluating Bayesian tools for effectively modeling actuarial sciences. Finally, different applications taken from several areas are examined to illustrate the practical usefulness of the compound Rayleigh exponential distribution. Using various model selection criteria, the introduced model is contrasted with that of several well-known distributions. Our empirical findings indicate that the suggested model has superior goodness-of-fit to the other models examined
