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Computational Fluid Dynamics

Understanding Computational Fluid Dynamics

Applications and Methodologies

Computational Fluid Dynamics is a branch of fluid mechanics that uses numerical methods and algorithms to solve and analyze problems related to fluid flow.CFD methods have become an integral part of the design process of new products in the past years. CFD is used to optimize designs, improve efficiency, and analyze fluid flow in fields like aerospace, automotive, energy, HVAC, environmental engineering, process industry, and biomedical applications.

Key Considerations

From System Modeling to Computational Resources

Problem Setup:

Clearly defining objectives, domain, boundaries, and initial conditions.

Grid/Domain Resolution:

Choosing a suitable grid/mesh resolution capturing relevant flow features.

Numerical Methods:

Select accurate, stable schemes for discretization and solving equations.

Turbulence Modeling:

Choose appropriate turbulence models based on flow characteristics and available resources.

Convergence Criteria:

Establish criteria for determining solution convergence.

Validation and Verification:

Validate against experimental/analytical data, perform grid refinement studies.

Sensitivity Analysis:

Assess the effects of uncertain parameters and boundary conditions.

Post-Processing:

Analyze results, extract relevant quantities, and visualize them.

Uncertainty and Error Estimation:

Consider sources of uncertainty and estimate errors.

Verification with Physical Reality:

Validate results against real-world observations

Key Steps in Conducting CFD

A Detailed Guide

Problem Definition

Clearly define objectives, geometry, and conditions.

Preprocessing

Create a suitable computational mesh/grid.

Governing Equations

Formulate equations for fluid flow.

Numerical Descretization

Apply numerical methods to discretize equations.

Boundary Conditions

Specify conditions at domain boundaries.

Turbulance Modeling

Choose a suitable turbulence model.

Solver Selection

Select an appropriate solver algorithm

Solutions Computation

Solve equations to obtain numerical solutions.

Post- Preprocessing

Analyze and visualize results.

Validation & Verification

Compare results with experiments or analytical solutions.

Iteration & Optimization

Refine analysis if necessary.

Reporting & Documentation

Summarize findings and conclusions in a report.

Methodological Frameworks

From System Modeling to Optimization Strategies

Conjugative Heat Transfer Analysis (CHT)

CHT or Conjugative Heat transfer Analysis can accurately predict by simultaneously solving heat transfer through solids and fluids.

(CHT) is a combination of heat transfer in solids and fluids by exchanging energy at the interface between them. In solids conduction always dominates and in fluids convection always dominates.

Effectively combining heat transfer in solids and fluids is the key to designing several Industrial applications. This study enables to understand the heat transfer co-efficient, heat flux rates, identification of localized hot-spots in systems, evaluation of temperature peaks etc.

Transient Analysis

Transient or time-dependent or time stepping analysis is conducted when the system’s boundary condition changes with respect to time. Almost every system changes with time, it is used to capture insight into time-varying flow features or phenomena which would otherwise be difficult to capture in physical testing.

For any transient simulations, it is important to select appropriate time step required to capture the flow physics, too large a time step will result in lost detail and a time step which is too small will increase computational efforts. A right physical model, time step size and user-defined functions (UDF) is appropriate to capture the physics with fewer approximations.

Internal Flow Analysis

CFD internal flow analysis is most widely performed analysis type which can accurately predict the behaviour of fluid flow in any bounded volume system for example – pipe lines, plenums, valves, HVAC ducts, heat exchangers, orifices etc.

This type of CFD study can provide comprehensive set of data which gives significant insight into performance of the system and optimizing it.

The following objectives can be identified with CFD internal flow analysis –

Flow distribution and optimization

Pressure drop analysis

Evaluating heat transfer rates

Identification of flow circulation and seperation region

Evaluating air exchange rates or ACPH

External flow analysis

CFD external flow analysis can accurately predict flow behavior of fluids over bodies. It has very wide range of applications and it ranges from evaluation of building emission dispersion to a highly compressible flow phenomena of fighter jet.

A full-scale CFD simulations can be performed over complex geometries generating insights into flow behavior around bodies thus providing significant advantages when it comes to validating or optimizing designs.

The following objectives can be evaluated in this study –

Identifying load incluced on the system due to wind

Virtual Wind tunnel study

Visualization of bounded flow over objects

Evaluation of drag and lift induced forces

Evaluating temperature fields over objects

Moving Geometries

It is the simplest steady-state numerical modelling methodology available to predict the behavior of fluid in rotating or stationary systems. Moving reference frame (MRF) is a technique employed to predict fluid flows in turbine, impellers, blowers, stator, compressors etc.

The analysis helps developers to understand from the bulk of information –

Visualization of flow over turbine blades

Pressure distribution on blade surface

Performance analysis

windage loss prediction

Multi-phase flows

CFD can be used to accurately predict Multi-phase flow behavior of fluids discretely dispersed in continuous phase of main fluid domain. Multi-phase flow studies are inherently complex to model in CFD and hence more understanding is required in approximating boundary conditions when it comes to simulating and optimizing these flow systems. We have performed particle to particle and phase dispersion transport analysis for clients across industries.

We have expertise in Industrial applications such as –

Nozzle spray

Contaminant migration inside buildings

Dirt desposition

Erosion analysis

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Species transport

CFD simulations of reactive flows are necessary to understand the kinetics and analysing this behaviour helps designers to gain insight and more informed design decisions. Species transport is among the complex numerical models with chemical reactions, multi-phase flows and particle tracking (Discrete phase).

With prior experience in solving for Combustion chambers, furnaces, burners and heaters. Our project expertise includes solving for –

Reacting flows

Combustion in IC engines, furnces and burners

Formation of emission or burnt mixture

Temperature fields and heat transfer rate in flame zone

Discrete Phase

A Discrete Phase numerical model in CFD is developed to understand the flow of particles (Discrete phase) in a continuum or a fluid medium. Various Industrial applications and processes involves fluid and particles in one system.

Our prior experience in solving includes –

Air filteration

Spray coating

Catalytic converters