Now showing items 443-462 of 518

      Authors Name
      Tasnim, Syed [1]
      Tasnim, Syeda [2]
      Telichev, Igor [1]
      Tepylo, Nick [1]
      Thakkar, Rohan [1]
      Therriault, Daneil [1]
      Therriault, Daniel [2]
      This research work presents the results of a comparative study conducted to compare the coatings properties of WC-10Co-4Cr coats produced by two different Oxy-fuel coating process; high velocity oxy-gas fuel (HVOF) and high velocity oxy-liquid fuel (HVOLF) thermal spraying techniques. The coats were deposited directly on low carbon steel substrate without bonding coats. Scanning electron microscopy (SEM) was performed to study microstructural analysis and to quantify the porosity and cross-sectional coat thickness. Furthermore, the mechanical properties of both coating processes were defined in terms of bond strength and micro hardness. The results show that the liquid fuel sprayed coatings (HVOLF) produced higher adhesion strength coats (~ 73 MPa) compared to (~ 68 MPa) for HVOF. Similar results observed for micro-hardness of 1255 VHN and 1032 VHN, respectively. The surface roughness and porosity were less for HVOLF 4.32μm/0.85% compared to HVOF results of 5.26μm/1.29% porosity. This superior result in coats properties of HVOLF compared to HVOF was attributed due to less decarburization in HVOLF and hence less production of hard secondary phases of W2C. [1]
      Timofeev, Evgeny [1]
      Ting, D.S-K [1]
      Ting, David S-K [1]
      Togue, Honoré Kuate [1]
      Toyserkani, Ehsan [1]
      Tsai, Scott [2]
      Turbulent flow inside the urban roughness sublayer, despite its complexities, plays a crucial role in the microclimate of the built environment. The parameterization of flow in the urban roughness sublayer provides a better understanding of turbulent exchange process leading to accurate weather forecasting. This study focused on developing relationships between turbulent quantities, including momentum and heat fluxes, and mean quantities such as mean wind speeds. Field data, including wind directions, wind speeds, and thermal stability conditions, were collected from an urban canopy in Guelph, Ontario, Canada during the summer 2017. Comparative data was obtained from a nearby rural station. A systematic scaling analysis was performed to identify a range of quantities highly related to turbulent fluxes. All combinations of quantities leading to dimensionless groups were evaluated. Linear and nonlinear correlation coefficients between different groups of variables identified when mean and turbulent quantities were related. Significant improvement in correlation coefficients was observed using high order polynomial regression, revealing the challenge of developing a robust model for predicting nonlinear behavior of turbulence. This study also used artificial neural networks (ANNs) to find nonlinear relationships between turbulent and mean quantities. As used here, an ANN is a multivariable function which attempts to approach the exact value of turbulent flux based on independent variables, properly chosen from dimensionless groups. Results showed that these approaches can successfully relate most, but not all, turbulent quantities to mean quantities. [1]
      Ulrich, Alvin [1]
      Upadhyaya, Rohit [1]
      Urbanic, Dr. R. Jill [1]
      Urbanic, Jill [1]
      Vakil, Ali [1]

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