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In the first of this two-part contribution, a methodology to assess the performance of an elbow-type draft tube is outlined. using Computational Fluid Dynamics (CFD) to evaluate the pressure recovery and mechanical energy losses along a draft tube design, while using open-source and commercial software to parameterise and regenerate the geometry and CFD grid. An initial validation study of the elbow-type draft tube is carried out, focusing on the grid-regeneration methodology, and the use of a steady-state assumption for evaluating the design's efficiency. The Grid Convergence Index (GCI) technique was used to assess the uncertainty of the pressure recovery to the grid resolution. It was found that estimating the pressure recovery through area-weighted averaging significantly reduced the uncertainty due to the grid.Simultaneously, it was found that this uncertainty fluctuated with the local cross-sectional area along the geometry. Subsequently, a study of the inflow cone and outer-heel designs on the flowfield and pressure recovery was carried out. Catmull-Rom splines were used to parameterise these components, so as to recreate a number of proposed designs from the literature. GCI analysis is also applied to these designs, demonstrating the robustness of the grid-regeneration methodology.

Large-Eddy Simulation of the flow around an elastically-mounted rectangular cylinder with an aspect ratio 4 was undertaken. 1DOF analysis of the heaving and torsional motions were performed under a free vibration. Various characteristics of the flow-field at lock-in are discussed. Subsequently, a divergence-free synthetic inflow generation approach was employed to analyse the effects of the freestream turbulence on the bridge response. The inflow turbulence intensity and integral length scales were systematically studied.The effect of turbulence intensity (up to 12%) was shown to deplete the structural response for both torsional and heaving motions. A variation of the tested integral length scales, which were order of the cylinder dimensions, had less effects (than a variation of the turbulence intensity) on the structure response.

In many product design and development applications, Computational Fluid Dynamics (CFD) has become a useful tool for analysis. This is particularly because of the accuracy of CFD simulations in predicting the important flow attributes for a given design. On occasions when design optimisation is applied to real-world engineering problems using CFD, the implementation may not be available for examination. As such, in both the CFD and optimisation communities, there is a need for a set of computationally expensive benchmark test problems for design optimisation using CFD. In this paper, we present a suite of three computationally expensive real-world problems observed in different fields of engineering. We have developed Python software capable of automatically constructing geometries from a given decision vector, running appropriate simulations using the CFD code OpenFOAM, and returning the computed objective values. Thus, users may easily evaluate a decision vector and perform optimisation of these design problems using their optimisation methods without developing custom CFD code. For comparison, we provide the objective values for the base geometries and typical computation times for the test cases presented here.

BNP levels rise progressively after DFT accompanied by early CK-MB increases and sustained increases in cTnI. These data suggest that DFT is associated with hemodynamic stress and left ventricular dysfunction, as evidenced by increases in BNP.