Deflection is an important design parameter for structures subjected to service load. This paper provides an explicit expression for effective moment of inertia considering cracking, for uniformly distributed loaded reinforced concrete (RC) beams. The proposed explicit expression can be used for rapid prediction of short-term deflection at service load. The explicit expression has been obtained from the trained neural network considering concrete cracking, tension stiffening and entire practical range of reinforcement. Three significant structural parameters have been identified that govern the change in effective moment of inertia and therefore deflection. These three parameters are chosen as inputs to train neural network. The training data sets for neural network are generated using finite element software ABAQUS. The explicit expression has been validated for a number of simply supported and continuous beams and it is shown that the predicted deflections have reasonable accuracy for practical purpose. A sensitivity analysis has been performed, which indicates substantial dependence of effective moment of inertia on the selected input parameters.
The objective of this study was to analyze the fluid flow of molten steel in a continuous casting tundish using numerical simulations for better inclusion floatation and its separation. The tundish geometry was designed using Autodesk FUSION 360 and the analysis were performed on ANSYS FLUENT. The investigations were done on steady-state as well as transient conditions. To scale back vortexing and turbulence within the tundish, turbo stoppers and flow modulators, e.g. dam and weirs were placed for an optimized and efficient flow inside the tundish and its behavior on the spacious flow structure was explored. The strategic placements of the flow modifiers produced higher turbulence in the recess region of the tundish resulting in better turbulent flow withinside the inlet region of the tundish. Thereby a more homogeneous fluid flow is formed with better conditions for particle separation. Analysing the flow behavior we have determined the inclusion floatation using particle tracking method form dense discrete phase modelling along with multiphase eulerian-lagragian model. Reduction in dead volumes was achieved in the spatial flow due to better intermixing which further reduced the metal loss and increased the yield of the tundish using the fluid flow analysis. Analyzing eddy formations in the spatial geometry of the tundish structure made it easy to evenly distributes the flow-induced shear. This determined the lesser turbulence on the free surface of the steel flow resulting in less reduction of the liquid steel surface.
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