This simulation is done to create an aero-thermal digital twin of a datacenter using CFD. The details of datacenter are taken from [1]. The datacenter CAD model is shown in Fig. 1.
The simulation employs κ − ε turbulence model with damping functions, SIMPLE-R (modified), as the numerical algorithm and second-order upwind and central approximations as the spatial discretization schemes for the convective fluxes and diffusive terms. The time derivatives are approximated with an implicit first-order Euler scheme. Flow simulation solves the Navier–Stokes equations, which are formulations of mass, momentum, and energy conservation laws for fluid flows. To predict turbulent flows, the Favre-averaged Navier–Stokes equations are used.
Fig. 1, Datacenter CAD
A Cartesian mesh with octree refinement, cut-cell method and immersed boundary methods is used. Special mesh refinements are deployed in the areas of interest i.e. inlets and outlets and sharp edges of server racks and CRAH units to accurately capture aero-thermal gradients and vortices. The resulting computational mesh has 2,698,156 cells. The computational domain and mesh are shown in Fig. 2.
Fig. 2, Computational mesh and domain
The results from the numerical analysis were compared with [1]. The results are in excellent agreement with previously published data. The animation in Fig. 3 shows thermal distribution inside datacenter using cut-plots. Fluid velocity distribution is also shown. The cut-plots are superimposed with streamlines and velocity vectors. These post processing features help identify hot-spots and recirculation zones. Design improvements can be made to reduce thee unwanted flow features. Within Fig. 3, Flow trajectories colored by air temperature are also shown. These show path the fluid takes between various inlets and outlets in the datacenter such as the CRAH system supply and return zones and inlets and outlets of servers. Fig. 4 shows various post processing tools available for diagnosing various issues from the aero-thermal perspective. These include iso-surfaces, cut-plots, flow trajectories and surface plots etc.
Fig. 3, The post processing animations
Fig. 4, The post processing images
A comparison of the results from present simulations with previously published literature is shown in Fig. 5. it can be seen that out results are in close agreement with previously published numerical and experimental data [1]! The locations at which the data is extracted is shown in Fig. 6. Within Fig. 5, solid lines indicate present study, dashed lines indicate published numerical results and filled circles represent published experimental results.
Fig. 5, Comparison of results
Fig. 6, Location of data extraction
If you want to collaborate on the research projects related to turbomachinery, aerodynamics, renewable energy, please reach out. Thank you very much for reading.
References
[1] Wibron, Emelie, Anna-Lena Ljung, and T. Staffan Lundström. 2018. "Computational Fluid Dynamics Modeling and Validating Experiments of Airflow in a Data Center" Energies 11, no. 3: 644. https://doi.org/10.3390/en11030644
This post is about a 2D NACA 0012 aero-foil undergoing forced aperiodic heaving. Heaving motion is achieved by the plot shown in Fig. 1. Plot within Fig. 1 represents position of airfoil at various time steps.
Fig. 1, The position of aero-foil
The animation of the vorticity contours are shown in Fig. 2. The velocity, pressure and vorticity for aperiodic heaving is shown in Fig. 3. A comparison will be made with heaving later, if ever 😀. As far as aerodynamic forces are concerned, per-cycle Cl, avg is at 0.63 as compared to 0.0 for periodic heaving. Cd, avg aperiodic heaving is at 0.162 as compared to 0.085 for periodic heaving. Of course, this is done on a coarse mesh. If ever I write a paper about this... 😀
Fig. 2, Top Row, Aperiodic, periodic heaving airfoil
Fig. 3, Top Row, L-R, Vorticity, pressure. Bottom Row, Velocity
If you want to collaborate on the research projects related to turbomachinery, aerodynamics, renewable energy, please reach out. Thank you very much for reading.
This post is about the CFD analysis of the DARPA Suboff at various speeds. The DARPA Suboff model is based on a generic submarine. The submarine geometry is shown in Fig. 1. The submarine in fully submerged in water. Refer to section "Update 01" for submarine sailing at the water level. The submarine geometry is availablehere. The geometry is made using equations from [1]. Machine Learning available here.
Fig. 1, DARPA Suboff submarine CAD
The simulations are validated with published literature [2-4]. SolidWorks Flow Simulation Premium software is employed for the simulations. Fig. 2 shows results of drag force at various cruise speeds. It can be seen that the results are in close agreement with the published experimental / numerical data.
Fig. 2, Comparison of results
The mesh has 656,714 cells in total. With 34,258 cells on the submarine surface. Special mesh refinements are added in the regions of interest i.e. regions with high gradients, the wake and on the control surfaces of the submarine. The mesh for is shown in Fig. 3.
Fig. 3, The mesh and computational domain
The results from the CFD post processing are presented next. Velocity cut-plots showing velocity distribution and wake of the submarine, surface pressure distribution on the submarine and vorticity around and in the wake of the submarine are also shown in Fig. 4. Within Fig. 4, the black arrows represent the direction of on coming flow.
Fig. 4, The post processing
Thank you for reading, If you would like to collaborate on projects, please reach out.
Update 01
This section presents the results from a simulation using volume of fluid method. The water line is just below the sail of the submarine, as shown in Fig. 5. The iso-surfaces are shown in Fig. 6. The black arrows in both Figs. 5-6 represent direction of on coming flow. These simulations allow for visualization of wake of submarines and then methods can be device to reduce the wake.
Fig. 5, The color blue represents water and white represents air
Fig. 6, 3D wake of a sailing submarine
References
[1] Groves, Nancy C. Huang, Thomas T. Chang, Ming S., "Geometric Characteristics of DARPA (Defense Advanced Research Projects Agency) SUBOFF Models (DTRC Model Numbers 5470 and 5471)", David Taylor Research Center, Bethesda MD, Ship Hydromechanics Dept, ADA210642, 1989 https://apps.dtic.mil/sti/citations/ADA210642
[2] Yu-Hsien Lin and Xian-Chen Li, "The Investigation of a Sliding Mesh Model for Hydrodynamic Analysis of a SUBOFF Model in Turbulent Flow Fields", Journal of Marine Science and Engineering, 8(10), 744, https://doi.org/10.3390/jmse8100744
[3] Liu, H.-L.; Huang, T.T. Summary of DARPA SUBOFF Experimental Program Data; Naval Surface Warfare Center Carderock Division, Hydromechanics Directorate: West Bethesda, MD, USA, 1998.
[4] Özden, Y.A.; Özden, M.C.; Çelik, F. Numerical Investigation of Submarine Tail Form on the Hull Eciency. In Proceedings of the Fifth International Symposium on Marine Propulsors, Espoo, Finland, 12–15 June 2017
This post is about the CFD analysis of the SACCON UCAV. Designed by NATO’s (North Atlantic Treaty Organization) RTO (Research and Technology Group) under Applied Vehicle Task Group (AVT-161) to assess the performance of military aircraft. The aircraft Geometry is shown in Fig. 1. The aircraft geometry is available here [1].
Fig. 1, SACCON UCAV The aircraft flight parameters and dimensions are given in [1]. The simulations are validated with published literature [1]. SolidWorks Flow Simulation Premium software is employed for the simulations. Fig. 2 shows results of Cl, Cd and Cm at various angles of attack. It can be seen that the results are agreement with the published experimental data.Fig. 2, Comparison of simulation results
The mesh has 3.7 million cells in total. Special mesh refinements are added in the regions of interest i.e. regions with high gradients, the wake and on the control surfaces of the aircraft. The computational domain and the mesh for 16° angle of attack is shown in Fig. 3.
Fig. 3, The computational mesh and domain
The mesh has 3.7 million cells in total. Special mesh refinements are added in the regions of interest i.e. regions with high gradients, the wake and on the control surfaces of the aircraft. The computational domain and the mesh for 16° angle of attack is shown in Fig. 3.
Fig. 4, CFD post processing
Thank you for reading, If you would like to collaborate on projects, please reach out.
References
[1] Andreas Schütte, Dietrich Hummel and Stephan M. Hitzel, “Flow Physics Analyses of a Generic Unmanned Combat Aerial Vehicle Configuration,” Journal of Aircraft, Vol. 49, No. 6, December 2012, https://doi.org/10.2514/1.C031386
