The paper investigates the sensitivity of numerically computed flow fields to uncertainties in thermodynamic models for complex organic fluids. Precisely, the focus is on the propagation of uncertainties introduced by some popular thermodynamic models to the numerical results of a computational fluid dynamics solver for flows of molecularly complex gases close to saturation conditions (dense gas flows). A tensorial-expanded chaos collocation method is used to perform both a priori and a posteriori tests on the output data generated by thermodynamic models for dense gases with uncertain input parameters. A priori tests check the sensitivity of each equation of state to uncertain input data via some reference thermodynamic outputs, such as the saturation curve and the critical isotherm. A posteriori tests investigate how the uncertainties propagate to the computed field properties and aerodynamic coefficients for a flow around an airfoil placed into a transonic dense gas stream.

The purpose of this study is to simulate the distribution of a coarse granular material discharged in a hopper via a conveyor belt. This simulation is intended to be a model calibration for an optimization that will be later performed to obtain a proper material distribution device. From the hopper, the material is discharged in a blast furnace. Hence, achieving an adequate distribution in the hopper is crucial, since that distribution is directly linked to how the material spreads in the blast furnace, and this heavily influences the efficiency of the whole steel-making process. The apparatus is modeled by online three dimensional Computer-Aided Design software Onshape. Rocky DEM, a Computer-Aided Engineering software based on Discrete Element Method, is used to simulate the charge. The parameters of the numerical model are calibrated through an optimization algorithm. This phase is realized thanks to the optimization platform modeFRONTIER, using an algorithm that exploits meta-models to reduce the computational time of the optimization. By comparing the simulated results with the visual data obtained from blast furnace plant, the goal is to validate the model and to better understand the behavior of the whole charging process.