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A comparative performance analysis of the CFD platforms OpenFOAM and FLOW-3D is presented, focusing on a 3D swirling turbulent flow: a steady hydraulic jump at low Reynolds number. Turbulence is treated using RANS approach RNG k-ε. A Volume Of Fluid (VOF) method is used to track the air–water interface, consequently aeration is modeled using an Eulerian–Eulerian approach. Structured meshes of cubic elements are used to discretize the channel geometry. The numerical model accuracy is assessed comparing representative hydraulic jump variables (sequent depth ratio, roller length, mean velocity profiles, velocity decay or free surface profile) to experimental data. The model results are also compared to previous studies to broaden the result validation. Both codes reproduced the phenomenon under study concurring with experimental data, although special care must be taken when swirling flows occur. Both models can be used to reproduce the hydraulic performance of energy dissipation structures at low Reynolds numbers.
Previous studies optimized the dimensions of coaxial heat exchangers using constant mass fow rates as a boundary condition. They show a thermal optimal circular ring width of nearly zero. Hydraulically optimal is an inner to outer pipe radius ratio of 0.65 for turbulent and 0.68 for laminar fow types. In contrast, in this study, fow conditions in the circular ring are kept constant (a set of fxed Reynolds numbers) during optimization. This approach ensures fxed fow conditions and prevents inappropriately high or low mass fow rates. The optimization is carried out for three objectives: Maximum energy gain, minimum hydraulic efort and eventually optimum net-exergy balance. The optimization changes the inner pipe radius and mass fow rate but not the Reynolds number of the circular ring. The thermal calculations base on Hellström’s borehole resistance and the hydraulic optimization on individually calculated linear loss of head coefcients. Increasing the inner pipe radius results in decreased hydraulic losses in the inner pipe but increased losses in the circular ring. The net-exergy diference is a key performance indicator and combines thermal and hydraulic calculations. It is the difference between thermal exergy fux and hydraulic efort. The Reynolds number in the circular ring is instead of the mass fow rate constant during all optimizations. The result from a thermal perspective is an optimal width of the circular ring of nearly zero. The hydraulically optimal inner pipe radius is 54% of the outer pipe radius for laminar fow and 60% for turbulent fow scenarios. Net-exergetic optimization shows a predominant infuence of hydraulic losses, especially for small temperature gains. The exact result depends on the earth’s thermal properties and the fow type. Conclusively, coaxial geothermal probes’ design should focus on the hydraulic optimum and take the thermal optimum as a secondary criterion due to the dominating hydraulics.
Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, “Time series aggregation for energy system design: Modeling seasonal storage”, has developed a seasonal storage model to address this issue. Simultaneously, the paper “Optimal design of multi-energy systems with seasonal storage” has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.
This thesis aims at the presentation and discussion of well-accepted and new
imaging techniques applied to different types of flow in common hydraulic
engineering environments. All studies are conducted in laboratory conditions and
focus on flow depth and velocity measurements. Investigated flows cover a wide
range of complexity, e.g. propagation of waves, dam-break flows, slightly and fully
aerated spillway flows as well as highly turbulent hydraulic jumps.
Newimagingmethods are compared to different types of sensorswhich are frequently
employed in contemporary laboratory studies. This classical instrumentation as well
as the general concept of hydraulic modeling is introduced to give an overview on
experimental methods.
Flow depths are commonly measured by means of ultrasonic sensors, also known as
acoustic displacement sensors. These sensors may provide accurate data with high
sample rates in case of simple flow conditions, e.g. low-turbulent clear water flows.
However, with increasing turbulence, higher uncertainty must be considered.
Moreover, ultrasonic sensors can provide point data only, while the relatively large
acoustic beam footprint may lead to another source of uncertainty in case of
relatively short, highly turbulent surface fluctuations (ripples) or free-surface
air-water flows. Analysis of turbulent length and time scales of surface fluctuations
from point measurements is also difficult. Imaging techniques with different
dimensionality, however, may close this gap. It is shown in this thesis that edge
detection methods (known from computer vision) may be used for two-dimensional
free-surface extraction (i.e. from images taken through transparant sidewalls in
laboratory flumes). Another opportunity in hydraulic laboratory studies comes with
the application of stereo vision. Low-cost RGB-D sensors can be used to gather
instantaneous, three-dimensional free-surface elevations, even in flows with very
high complexity (e.g. aerated hydraulic jumps). It will be shown that the uncertainty
of these methods is of similar order as for classical instruments.
Particle Image Velocimetry (PIV) is a well-accepted and widespread imaging
technique for velocity determination in laboratory conditions. In combination with
high-speed cameras, PIV can give time-resolved velocity fields in 2D/3D or even as
volumetric flow fields. PIV is based on a cross-correlation technique applied to small
subimages of seeded flows. The minimum size of these subimages defines the
maximum spatial resolution of resulting velocity fields. A derivative of PIV for
aerated flows is also available, i.e. the so-called Bubble Image Velocimetry (BIV). This
thesis emphasizes the capacities and limitations of both methods, using relatively
simple setups with halogen and LED illuminations. It will be demonstrated that
PIV/BIV images may also be processed by means of Optical Flow (OF) techniques.
OF is another method originating from the computer vision discipline, based on the
assumption of image brightness conservation within a sequence of images. The
Horn-Schunck approach, which has been first employed to hydraulic engineering
problems in the studies presented herein, yields dense velocity fields, i.e. pixelwise
velocity data. As discussed hereinafter, the accuracy of OF competes well with PIV
for clear-water flows and even improves results (compared to BIV) for aerated flow
conditions. In order to independently benchmark the OF approach, synthetic images
with defined turbulence intensitiy are used.
Computer vision offers new opportunities that may help to improve the
understanding of fluid mechanics and fluid-structure interactions in laboratory
investigations. In prototype environments, it can be employed for obstacle detection
(e.g. identification of potential fish migration corridors) and recognition (e.g. fish
species for monitoring in a fishway) or surface reconstruction (e.g. inspection of
hydraulic structures). It can thus be expected that applications to hydraulic
engineering problems will develop rapidly in near future. Current methods have not
been developed for fluids in motion. Systematic future developments are needed to
improve the results in such difficult conditions.