Professional Computational Fluid Dynamics CFD Analysis Simulation
What is Computational Fluid Dynamics (CFD)
Featured CFD Analysis Case Studies
Turbulence CFD Analysis of Antenna
Turbine Discharge CFD Analysis
Features & Benefits of Computational Fluid Dynamics (CFD)
1. Informed Engineering Decisions
The flexible option to offer easy & fast adjustment of test configuration makes it possible to save prototype testing time by having the ability to carry out parallel, Multiple-purposed model design Verification testing on practically any test configuration.
This is in contrast to Conventional single-purposed Physical testing which is usually Slow, Sequential.
3. Comprehensive & Accurate Test Input Parameters
Can Account for All Operational Environmental Conditions
Computational Fluid Analysis and FSI Simulation carried out by a CFD software offers a Realistic Quantitative simulation of fluid flow phenomena which takes into account all the desire input qualities.
Accurate Mathematical model
Simulation in a virtual time and space environment allows the creation of a Mathematical model which is High resolution and Full Scale (instead of sized-down models in physical experiments)
4. Lower Cost
1. Testing Cost
2. Capital Cost
Gain confidence in a design concept before committing large investment financially and in terms of project resources
This help organization to effectively reducing both capital and operational costs.
3. Operational Cost
5. Maximize Operation Uptime
We Help Our Clients Gain Valuable Insights to Optimize and Improve Product Performance, Reliability, and Efficiency.
1. Powerful CFD Simulation Software Tools
2. FEA Consultants with Extensive Research & Professional Experience
3. FEA projects Completed in a Timely and Cost-effective Manner
4. Proven Track Record
6. Full Knowledge Transfer
Computational Fluid Dynamics (CFD) Process
CFD is performed/carried out by means of using the following methods
1. Mathematical Model
Mathematical CFD modeling of fluid analysis challenge to be solved expressed using Partial differential equations (PDE) eg. IBVP = PDE + IC + BC
2. Discretization Process
The Discretization process involves having the PDE system mathematically transformed into a set of algebraic equations. Numerical analysis methods based on discretization includes
1. Mesh Generation
This involves decomposition of the model into Cell elements and Time instants.
The Node elements can be either Structured or unstructured, Triangular or Quadrilateral.
2. Space Discretization
This is the approximation of spatial derivatives based on coupled ODE/DAE systems
3. Time Discretization
Time discretization is the approximation of temporal derivatives using Algebraic system Ax = b
3. CFD Simulation Software
Involves implementation of CFD Computer simulation software algorithm tools (Iterative solvers, Discrete function values, Pre- and postprocessing utilities) and Computer hardware to solve mathematical equations
The computing time taken for a CFD flow simulation depend on
4. Post-processing Visualization, Analysis of data
The human (simulation consultant) component input involved in this CFD simulation analysis involves stating the problems & inspection.
Post-processing of the CFD simulation results involves interpretation of the computed flow field to extract the desired information. This calls for knowledge and good judgment.
This involves the calculation of Derived quantities (such as Streamfunction, vorticity) and Integral parameters (lift, drag, total mass)
Visualization involves representation of numbers as visual images
Using Statistical tools the simulation data is Systematically Analyzed to Verify the CFD model
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Customers will be provided with fully customisable CFD reports which detail the Methodology, in-depth analysis, and recommendations.
This insight allows our customers to optimize performance and make informed engineering decisions in a scientific, proven manner.
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What is Fluid Flow
Fluid flows can be classified into either the following flow types
Fluid Flow is happening all around us
It is a natural occurrence that happens in the physical environment in our day-to-day life.
• Natural meteorological phenomena (rain, wind, hurricanes, floods, fires)
• Use of HVAC system for indoor Air Heating, Air Ventilating circulation and Air Cooling of building interior environment, Automobile passenger compartments
Limitations of Computational Fluid Analysis Testing Methods
As a rule, CFD cannot fully replace the actual measurements completely but the amount of actual experimentation and the overall cost of testing can be significantly reduced.
The results of a CFD simulation are never designed to be a 100% representation because
1. Limitations of Mathematical modeling
The mathematical model of the analysis challenge at hand may be inadequate
The underlying assumptions mathematical model can also affect the quality of the simulation results
2. Approximate Discretization Process
The data of the input parameter may involve a certain level of guessing or inaccuracy due to the discretization process.
This can result in possible Errors due to Modeling, Discretization, Flow disturbances due to probes.
3. Constraints of Computer Processing Power
Accuracy and Speed of the simulation results is limited by the current computer processing power available
Applications of Computational Fluid Dynamics (CFD)
Some of the applications of CFD Numerical simulation analysis of fluid flow includes
This includes optimization of natural air flow to minimize of thermal hotspots in building indoor environment, such as Datacenter facility & Retail environments. This is done by using CFD thermal analysis to design the optimal layout configuration.
Other CFD Analysis Case Studies
CFD Ventilation Analysis of Indoor Environment when Subjected to Various Wind Parameters
CFD Design Optimization of Bubble Column Reactor for Miroalgae Cultivation
Modeling of Mass transfer of Oxygen from Air to water in a Stirred Tank Reactor
CFD behavior Analysis of the non-dimensional Navier-Stokes equation
Outcome & Conclusion :
Flow behavior of an Elastic and a Neo-Hookean particle in a Newtonian fluid using ABAQUS
Outcome & Conclusion –
Natural Convection-driven Flow in a Glass Loop Capillary Tube
Flow and Heat transfer characteristics of a non-newtonian fluid through a channel
Outcome & Conclusion
CFD Analysis of Condenser mixer
- To enhance the heat transfer between two fluids one can either go for active techniques or passive techniques. Since this is a parallel flow here we have chosen a passive technique for the Thermal Simulation
- In this CAE Simulation problem, we have considered only conduction and convection mode of heat transfer. And to achieve uniform temperature distribution at the condenser outlet without changing the size of the system, we changed the orientation of the hot fluid pipe as well as we have changed the position to achieve the fully developed condition. But this modification doesn’t give a satisfactory solution. By keeping the projection and changing the roughness of the pipe we could augment the momentum transfer but this leads to additional pressure drop. So we have introduced some holes in the inner annulus pipe where the cold fluid is flowing. This enhanced the mixing but this configuration couldn’t be manufactured as easily.so still we are working on this project to get the optimum design.
- The total number of mesh [tetra elements] used in the CFD Simulation is approximately 80 lakhs. And We have chosen the standard K-ω model to capture the near-wall physics. And we run the CFD Simulation for steady cases because the mass flow rate is constant.
Outcome & Conclusion:
Integrity Evaluation of dry stored nuclear fuel using CFD thermal Simulation Analysis.
Implementing SIMPLE family of CFD Algorithms
Related CFD research/ academic projects
CFD Aerodynamic Analysis of UAV
- Aerodynamic Analysis and Airflow Simulation of a UAV with propeller ON/OFF conditions.
- Estimation of Aerodynamic Loads on UAV using Wind Load Analysis Simulation
- Ground Effect study on the UAV
- Aerodynamic Analysis includes Estimation of Aerodynamic performance characteristics at a given speed (Subsonic Speed of 30-40 m/s)
- Simulation of the UAV’s with Propellor ON/OFF conditions and also perform simulations on Propellor failure conditions.
- Estimation of the Aerodynamic loads on UAV which will be used for structural analysis.
- Perform static ground effect study on UAV for various ground heights and wing settings.
- Hybrid unstructured grids were generated for the UAV with Propeller. There are multiple zones involved in the grids namely Stationary zone and Rotational zones to simulate the propeller effects.
- For the UAV with Propeller, simulations performed using Ansys CFD Fluent Software, Roe scheme with second-order discretization in space was used. Gradients are calculated using the Green-Gauss based reconstruction procedure. S-A and SST Turbulence model was chosen. The rotating zone around the propeller was created with an optimum gap and it was assigned as a Rotational zone condition with a rotational velocity of Propeller. Also, it was simulated using rotational wall BC on the propeller.
- For the Propeller OFF condition, the Rotational velocity was specified as zero to impose the no rotation of propellers.
- For Static Ground Effect simulations performed using Ansys Mechanical CFD, Grids were generated for different heights and different pitching angles of -8degs to 18degs. These works involve combinations of grids that need to be generated. This process was completely automated to minimize the grid generation effort.
- The Aerodynamic analysis results obtained from the 3D fluid Simulation Software were compared with experimental data without the propeller case. For the Propeller case, It was compared with the analytical solutions available.
- It was observed that The optimum domain distance of the Rotating zone from the propeller tip plays an important role while defining the rotating zone boundary condition.
- At a higher angle of attack grid also plays an important role along with propeller rotation.
- The performance data generated from the Ansys Fluent CFX software were compared with analytical values.
- The data obtaining by changing the UAV pitch angle needs to be corrected at higher alpha.
- After the correction, The Data can be used as a reference for control groups by comparing it with and without ground data.