Computational Fluid Analysis

Computational Fluid Analysis which is done during CFD consulting in our Singapore office at BroadTech Engineering involves replacing Partial differential equations (PDE) systems with a set of Algebraic equations which is solvable mathematically using computer CFD simulation software.
These Partial differential equations (PDE) are governing mathematical equations which represent physics laws for conservation of Mass, Momentum, and Energy.

Computational Fluid Dynamics (CFD)

Computational Fluid Dynamics (CFD) is a highly interdisciplinary area of engineering which lies at the intersection of Physics, Applied mathematics, and Computer science.
It allows CFD companies to provide a Qualitative (and sometimes Quantitative) simulation of fluid flow patterns.
Th enables Simulation Engineers, Green building consultants, ESD consultant and Scientists to execute Fluid dynamic analysis using ‘numerical experiments’ (i.e. computer simulations) in a ‘virtual flow environment’ to accurately predict engineering design performance, Optimize and Improve Design performance, and Validate product Fluid Dynamic behavior as early as possible in the engineering development process.

Featured Computational Fluid Analysis Case Studies

Automating Transmission CFD results and Linking them to a 1-D Hydraulic System Model

For most of our CFD Engineer at BroadTech Engineering, most of the analyses were time-consuming and involved running a Design of Experiments manually. This was done to characterize the hydraulic circuits at different temperatures and flow-rates. This pressure drop vs flow rate map is then linked to a 1-D system model to create a high fidelity hydraulics control system model.

To solve this repetition of labor, we developed a user-friendly JAVA GUI that links to Star CCM+ and automated this entire process and organized the results into an excel file. This reduced the analysis time by over 50%.

The CFD results enabled us to reduce the restrictions in the clutch circuits so as to optimize the clutch apply and de-apply times. The high fidelity 1-D hydraulic system model enabled us to accurately predict control system performance prior to hardware builds. This project saved client $12 per transmission and was recognized with the Global CAE Innovation Chief Engineer’s Award for 2016 and the Global CAE Innovation Excellence Award for 2015

CFD Analysis to Reduce the Side-loads on Spool Valves.

Spool valves in automatic transmissions operate in a bore with very tight tolerances. If designed incorrectly, the valve could experience high flow forces (side-loads) that cause the valve to rub along the bore surface and thus causing wear. This eventually leads to a degradation of the valve regulating performance and this is a warranty concern.

A modular Fluid-Structure Interaction model (FSI) was built in Star CCM+. Thus the ports and valves could be easily swapped out for different designs and could be tested over the entire operating range dynamically. Then the results could be compared to select the best designs.

Using this FSI model our CFD engineers prevented warranty concerns by reducing the side-loading on spool valves by ~60% to minimize wear.

Advantages of Computational Fluid Dynamics (CFD)

Our CFD consulting services (IL: CFD consulting services) at BroadTech Engineering gives an engineering insight into fluid flow pattern behaviors that are difficult, costly or impossible to recreate and analyze using conventional testing methods.
CFD simulation software offers many advantages such as

1. Cheaper Cost

Lower cost of testing and validation of new engineering designs by eliminating the need for expensive physical destructive testing of prototypes.
It also removes the need for the use of actual physical Test Equipment which is logistically difficult and costly to transport.
This allows for a larger number of test simulation studies to be conducted and provides a larger quantity of data points and time instants

2. Flexibility

The Flexibility option to offer an easy & fast modification of test parameters makes it possible to save prototype testing time by having the capability to conduct a parallel, Multiple-purposed model design validation testing on virtually any test configuration.
This is in contrast to Conventional single-purposed Physical testing which is usually Slow, Sequential.

3. Comprehensive & Realistic Test Input Parameters

Can Simulate for All Operating Environmental Conditions

Computational Fluid Analysis carried out by a CFD software offers an Accurate Quantitative prediction of fluid flow behavior which takes into account all the required input parameters.

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 Scale-down models in physical testing)

Overview

1. FEA Consulting

2. CFD Consulting

3. LS-DYNA Consulting

About Us

BroadTech Engineering is a Leading Engineering Simulation and Numerical Modelling Consultancy in Singapore.
We Help Our Clients Gain Valuable Insights to Optimize and Improve Product Performance, Reliability, and Efficiency.

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1. Powerful ANSYS FEA 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

5. Affordable

6. Full Knowledge Transfer

Call Us for a Free Consultation

Discover what Computational Fluid Analysis can do for your company today by calling us today at +6581822236 for a no obligation discussion of your needs.
If you have any queries our knowledgeable and friendly consultants will be glad to Assist and understand more about your simulation needs and requirements

Alternatively, for quote request, simply email us your technical specifications & requirements to info@broadtechengineeering.com

What is Fluid Flow

Fluid flows can be classified into either the following flow conditions/types

• Viscous or Inviscid

• Compressible or Incompressible

• Steady or Unsteady

• Laminar flow (slow) or Turbulent flow (fast)

• Single-phase or Multiphase flow

• Chemically inert flow systems or Reactive Flows

Fluid Flow is happening all around us

It is a physical phenomenon that we encountered in the natural environment in our everyday life.

• Natural Climatic phenomena (rain, wind, hurricanes, floods, fires)

• Assessment of Outdoor Environmental health risk (air pollution, transport movement of air pollutants)

• Use of HVAC system for Air Heating, Air Ventilation and Air Conditioning of building indoor environment, Car passenger compartments.

• Fuel Injection and Combustion in Automobile engines and other Propulsion systems

• Physical and Chemical interaction of various objects with the surrounding fluid air

• Complicated Thermal Heat flows in Combustion furnaces, Commercial heat exchangers, Industrial chemical reactors etc.

• Natural Biological processes in human body (eg. Heart pumping of blood flow, breathing and aerobic respiration process, drinking of fluids)

Applications of Computational Fluid Dynamics (CFD)

Numerical simulations of fluid flow (will) enable

• Building architects to design building indoor living environments for maximum Comfortable and safety of occupants

• Designers and engineers of vehicles to improve the Aerodynamic performance characteristics

• Chemical engineers to maximize the performance output from their equipment from a Business productivity point of view

• Petroleum engineers to maximize optimal oil recovery strategies

• Surgeons to diagnose and cure arterial disease conditions (computational hemodynamics)

• Meteorologists to forecast the weather climate conditions and issue pre-emptive warning of approaching natural disasters

• Safety consultants to mitigate possible health risks from exposure to radiation and other health hazards

• Military defense companies to accelerate the engineering development of weapons and estimate the blast damage

Computational Fluid Dynamics (CFD) Process

CFD is performed out by means of using the following methods

1. Mathematical Model

Mathematical modeling of fluid analysis problem to be resolved expressed based on Partial differential equations (PDE) eg. IBVP = PDE + IC + BC

2. Discretization Process

The Discretization process involves having the PDE system mathematically converted into a set of algebraic equations. Numerical analysis techniques using discretization includes

1. Mesh Generation

This involves breaking down of the model into Nodes elements and Time instants.
The Cell elements can be either Structured or unstructured, Triangular or Quadrilateral.

• Adaptive refinement of Mesh element size in Flow regions of interest

• CAD tools + grid generators (Delaunay, advancing front)

2. Space Discretization

This is the approximation of spatial derivatives using coupled ODE/DAE systems

• high- vs. low-order approximations

• finite differences/volumes/elements

3. Time Discretization

Time discretization is the approximation of temporal derivatives base on Algebraic system Ax = b

• Explicit vs Implicit schemes, Stability constraints

• Local time-stepping, Adaptive time step control

3. Iterative Solution Solving Approach/Strategy

The coupled nonlinear algebraic equations must be solved iteratively

• Outer iterations

The coefficients of the discrete problem are updated using the solution values from the previous iteration so as to

– get rid of the nonlinearities by a Newton-like method

– solve the governing equations in a segregated fashion

• Inner iterations

The resulting sequence of linear subproblems is typically solved by an iterative method (conjugate gradients, multigrid) because direct solvers (Gaussian elimination) are prohibitively expensive

• Convergence criteria

It is necessary to check the residuals, relative solution changes and other indicators to make sure that the iterations converge.
As a rule, the algebraic systems to be solved are very large (millions of unknowns) but sparse, i.e., most of the matrix coefficients are equal to zero.

4. CFD Simulation Software

Involves implementation of CFD Computer Fluid dynamics simulation (IL: Fluid dynamics simulation) software code tools (Iterative solvers, Discrete function values, Pre- and postprocessing utilities) and Computer hardware to solve mathematical formulations
The computation time taken for a CFD flow simulation depend on

• Type of Numerical algorithms and Data structures were chosen

• Discretization parameters (mesh quality, mesh size, time step)

• Run parameters and Stopping criteria

• Linear algebra tools, Stopping criteria for iterative solvers

• Programming language used for coding the CFD Code (most use Fortran)

• Cost per time step and convergence rates for outer iterations

• Vectorization, Parallelization

• Computer hardware

5. Post-processing Visualization, Analysis of data

The human (simulation analyst) component input involved in this CFD simulation analysis involves defining the problems & inspection.
Post-processing of the CFD simulation results involves interpretation of the computed flow field to extract the desired data. This calls for experience and good judgment.

1. Calculation

This involves the calculation of Derived quantities (such as Streamfunction, vorticity) and Integral parameters (lift, drag, total mass)

2. Visualization

Visualization involves representation of numeric data as visual images

– 3D data: cutlines, cut planes, isosurfaces, iso-volumes

– 1D data: function values connected by straight lines

– 2D data: streamlines, contour levels, color diagrams

– arrow plots, particle tracing, animations

3. Analysis

Through the use of Statistical tools, the simulation data is Systematically Analyzed to Validate the CFD mathematical model

Here you can create the content that will be used within the module.

Limitations of Computational Fluid Analysis Testing Methods

As a practice, CFD does not completely replace the actual measurements completely but the amount of experimentation and the overall cost can be significantly reduced.
The results of a CFD simulation are never meant to be a 100% representation as

1. Approximate Discretization Process

The data of the input parameter may contain/involve a certain degree of approximation or imprecision due to the discretization process. This can result in possible Errors due to Modeling, Discretization, Flow disturbances caused by probes.

2. Limitations of mathematical modeling

The mathematical model of the analysis problem at hand may be insufficient.
The underlying assumptions mathematical model can also affect the quality of the simulation results

3. Constraints of Computer Processing Power

Accuracy and Speed of the simulation results is constrained by the pre-existing computing power available