Flow Simulation And Performance Analysis Of Centrifugal Compressor Using Cfd_Tool

This paper is concerned the flow simulation and performance analysis of the Centrifugal Compressor Using CFD – Tool. The complex internal flow of centrifugal compressor can be well analyzed, and the unique design system needs to be developed. It should be early to use the interface and also flexible for input and output. A 3-D flow simulation of turbulent – fluid flow is presented to visualize the flow pattern in-terms of velocity, streamline and pressure distribution on the blade surface are graphically interpreted. The standard K- e turbulence model and the simple model algorithm were chosen for turbulence model and pressure distribution well determined. The simulation was steady Heat transfer and moving reference frame was used to consider the impeller interaction under high resolution. Furthermore, A computational Fluid Dynamics (CFD) 3-D simulation is done to analyze the impeller head and efficiency required of centrifugal compressor. The impeller is rotated for a constant revolution and mass flow rate, in this study initially the geometry of centrifugal compressor impeller is created by an ANSYS Vista CCD, and the Blade modeller done by Bladegen, Finally, CFD analysis was performed in ANSYS CFX using the ANSYS Turbo grid meshing tool. According to the analysis, as the number of impeller blades increases, so does the value of the head and power imparted, as well as the impeller’s efficiency.

Mathematical modelling of fluid flow in electromagnetically stirred weld pool

In this paper, a mathematical model has been proposed to study the relationship between electromagnetic stirring (EMS) weld parameters and the mode of fluid flow on grain refinement of AA 6060 weldments. For this purpose, fluid flow modelling using Navier-Stokes equation is described first, and then, the proposed mathematical approach has been discussed in detail. For demonstration, calculations to determine the fluid velocity in the weld pool of thin plate AA6060 were performed. The application of the model on the experimental results indicates that the best grain refinement is achieved at a transition mode from laminar to turbulent fluid flow.

Investigation of Flow of the Disc-and-Doughnut Baffles and 40% Cut Segmental Baffles

Heat exchanger is usually used in manufacturing process. At present, many researchers have efforts to increase the performance of the heat exchanger with less of the cost. This research discussed about the performance of heat exchanger using 40% cut segmental baffles compared with modified double segmental baffles disc-and-doughnut type. In this study, the investigation of the computational results consisted of heat flux, velocity profile along the heat exchanger, pressure distribution and, theoretical heat transfer coefficient and heat exchanger effectiveness. The model was calculated using finite difference method forward modeling with Multiphysics Software and focuses on the performance evaluation of the small shell-and-tube heat exchanger (STHE) – laboratory type. The tubes are composed of 14 tubes with 0.583 m length, triangular 30° rotated pitch. The pipe radius of shell and tube are 0.055 m and 0.00635 m, respectively. The baffle radius of disc and doughnut are 0.055 m and 0.025 m, respectively, and the baffle radius of 40% cut segmental baffles are 0.055 m. Both types of the baffle have a distance of 0.116 m which is evenly distributed along the shell. The generalized minimal residual (GMRES) method for the fluid flow case used as an iterative method for solving some of the complex linear equations shows good performance as in reliability and validity. For the 40% cut segmental baffles, fluid flow makes a zigzag pattern, an Eddy or swirling of a fluid, and there was some back mixing of fluid stream which caused several dead zones along the shell. The occurrence of the dead zones caused the heat transfer to be ineffective and gave lower value compared to the double segmental disc-and-doughnut baffles. The 40% cut segmental baffles was also seen to have a higher pressure at the outlet region than the double segmental disc-and-doughnut baffles. The disc-and-doughnut baffles leads to a turbulent fluid flow which causes an increase in heat transfer characteristics and also lower pressure drop than the 40% cut segmental baffles. Based on the theoretical, both types of disc-and-doughnut baffles and the 40% cut segmental baffles of heat exchanger investigated have highest effectiveness at the lowest mass flow rate of the hot fluids (tube).

An energy-efficient pathway to turbulent drag reduction

Simulations and experiments at low Reynolds numbers have suggested that skin-friction drag generated by turbulent fluid flow over a surface can be decreased by oscillatory motion in the surface, with the amount of drag reduction predicted to decline with increasing Reynolds number. Here, we report direct measurements of substantial drag reduction achieved by using spanwise surface oscillations at high friction Reynolds numbers ($${{{\mathrm{Re}}}_{\tau }}$$ Re τ ) up to 12,800. The drag reduction occurs via two distinct physical pathways. The first pathway, as studied previously, involves actuating the surface at frequencies comparable to those of the small-scale eddies that dominate turbulence near the surface. We show that this strategy leads to drag reduction levels up to 25% at $${{{{{{{{\mathrm{Re}}}}}}}}}_{\tau }$$ Re τ = 6,000, but with a power cost that exceeds any drag-reduction savings. The second pathway is new, and it involves actuation at frequencies comparable to those of the large-scale eddies farther from the surface. This alternate pathway produces drag reduction of 13% at $${{{{{{{{\mathrm{Re}}}}}}}}}_{\tau }$$ Re τ = 12,800. It requires significantly less power and the drag reduction grows with Reynolds number, thereby opening up potential new avenues for reducing fuel consumption by transport vehicles and increasing power generation by wind turbines.

Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations

Computational Fluid Dynamics (CFD) simulations are useful to the field of engineering design as they provide deep insights on product or system performance without the need to construct and test physical prototypes. However, they can be very computationally intensive to run. Machine learning methods have been shown to reconstruct high-resolution single-phase turbulent fluid flow simulations from low-resolution inputs. This offers a potential avenue towards alleviating computational cost in iterative engineering design applications. However, little work thus far has explored the application of machine learning image super-resolution methods to multiphase fluid flow (which is important for important for emerging fields such as marine hydrokinetic energy conversion). In this work, we apply a modified version of the Super-Resolution Generative Adversarial Network (SRGAN) model to a multiphase turbulent fluid flow problem, specifically to reconstruct fluid phase fraction at a higher resolution. Two models were created in this work, one with a simple physics-constrained loss function and one without, and the results are discussed and analyzed. We found that both models were able to significantly outperform non-machine learning upsampling methods and can preserve an impressive amount of detail and nuance, showing the versatility of the SRGAN model for upsampling fluid simulations. However, the difference in accuracy between the two models is quite minimal. This indicates that, for these contexts studied here, the additional complexity of a physics-informed approach may not be justified.

Heat transfer intensification of nanomaterial with involve of swirl flow device concerning entropy generation

The thermal features of hybrid nano-powder turbulent motion through a pipe employing helical turbulator is numerically simulated via Finite Volume Method (FVM). The hybrid nanofluid (MWCNTs + Fe3O4 + H2O) is obtained by uniformly dispersing MWCNTs + Fe3O4 nanomaterials in H2O. The characteristics features of thermal energy transfer of hybrid nanofluid are investigated by varying the pitch ratio (P) of the helical turbulator and Reynolds number (Re) of the fluid. The outputs of the study are depicted in terms of contour plots of temperature, velocity, frictional irreversibility Sgen,f, and thermal irreversibility Sgen,th. The variation of Sgen,f, and Sgen,th with changing P and Re are also displayed by 3D plots. It is found that making the fluid more turbulent by increasing Re, the temperature of the fluid drops whereas the fluid velocity augments. The frictional irreversibility enhances, whereas the thermal irreversibility drops with the increasing turbulent motion. The decreasing P causes to drop the temperature of the higher turbulent fluid flow, while opposite effect is observed for smaller Re. The decreasing P causes to enhance the fluid mixing and thus augments the fluid velocity. Sgen,f and Sgen,th both augment with decreasing P. The comparison of current outputs with the older article shows an acceptable accuracy. The results of the present investigation will be useful in modelling of efficient thermal energy transfer systems.

Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression

Machine learning offers an intriguing alternative to first-principle analysis for discovering new physics from experimental data. However, to date, purely data-driven methods have only proven successful in uncovering physical laws describing simple, low-dimensional systems with low levels of noise. Here we demonstrate that combining a data-driven methodology with some general physical principles enables discovery of a quantitatively accurate model of a non-equilibrium spatially extended system from high-dimensional data that is both noisy and incomplete. We illustrate this using an experimental weakly turbulent fluid flow where only the velocity field is accessible. We also show that this hybrid approach allows reconstruction of the inaccessible variables – the pressure and forcing field driving the flow.