Aspects Of Computational Wind Engineering Engineering Essay


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Computational Wind Engineering (CWE) is becoming more popular in the wind engineering community, for prediction of the wind loads on structures. The most important and basic requirement of CWE is successful modelling of turbulent flow in the simulated atmospheric boundary layer. Many turbulence models have been proposed and tested for a variety of flows, but they are not accurate enough to simulate the bluff body aerodynamics accurately.

Computational wind engineering is useful to study the wind loads, pressure, and flow field around the model completely with velocity, temperature and stress strain data which helps in better understanding the physical behaviour of flow and structure response.

In many branches of engineering, there has to be an understanding of the motion of fluids. One classic example of this is in the aircraft industry, where the aerodynamics of an aircraft must be

Determined; i.e. the lift, drag and side forces of a design must be estimated before a prototype flies. This ensures that the lift available will be sufficient to carry the weight of the loaded aircraft, that the required power of the engines can be determined together with the aircraft's fuel economy and that the motion of the aircraft can be predicted.

Separation from curved surfaces is one of the hardest aerodynamic processes to predict correctly. Yet, it is the key to determining the gross flow properties in and the operational performance of a wide variety of engineering devices, especially in aero-mechanical engineering.


Generally computational fluid dynamics can be defined as the analysis of system involving fluid flow, heat transfer and associated phenomena such as chemical reaction by computer based simulation. ( versteeg and Malalasekera, 1995 ).

This study relates generally to computer analysis and more particularly to CFD analysis. Computational Fluid Dynamics (CFD) is a branch of fluid dynamics in which the physics of motion of particles of gases or liquids are simulated using computers. The physical volume of fluid and bounding surfaces are represented using a finite set of discrete elements, and mathematical equations relating the motion of particles are computed at each element. Commercial CFD software is currently used in industry for a broad range of applications, including internal flow of liquids and gases in pipes, machinery, ventilation ducts, etc., as well as external flow of air or water for application to land, air and sea vehicles.

Computational fluid Dynamics, CFD, have not yet been accepted as the engineering tool in aerodynamic design processes. CFD is not regarded as trustworthy enough for this purpose. Thus aerodynamic design process still heavily depends upon experiments. However industrial and commercial CFD software's are being used more, but many of these have only simple turbulence models available such as models based on the eddy viscosity assumption. Thus the eddy viscosity models are often used in industry even though they are known to erroneously mimic the behaviour of the turbulence in complex flow situations such as curved flows including separation. However many results obtained using eddy viscosity models, EVMs, are surprisingly accurate is shown in research paper (Ramnefors, 1996).


Turbulence is encountered in most of our application today, In turbulent flow, unsteady, three dimensional, irregular, seemingly random and chaotic structures (eddies) appears on many scales and interact with each other. These eddies varies in sizes, from the largest comparable to the investigated geometry down to the smallest which size is order of millimetres or smaller (Kundu, 1990). Turbulence has furthermore two characteristic features, i.e. its ability to mix

a fluid and its ability to dissipate energy (Durbin & Reif, 2000). The former ability among other things prevents the occurrence of boundary layer separation (structure and location), while the latter increases the resistance (hydraulic losses) of internal flows. The latter since the turbulence consumes the mean flow energy in an energy cascade, from the largest developed by the mean flow to the smallest which is eventually dissipated by viscous forces. Accurate CFD predictions of turbulent flow field are therefore difficult to perform.



Reference: (MURAT UYGUN, 2004)


Nowadays turbulent flows may be computed using several different approaches. Either by solving the Reynolds-averaged Navier-Stokes equations with suitable models for turbulent quantities or by computing them directly. The main approaches are summarized below.



One assumes that the turbulent stress is proportional to the mean rate of strain. Furthermore eddy viscosity is derived from turbulent transport equations (usually k + one other quantity).


Turbulent stress is modeled as a non-linear function of mean velocity gradients. Turbulent scales are determined by solving transport equations (usually k + one other quantity). Model is set to mimic response of turbulence to certain important types of strain.


This category consists of Reynolds-stress transport models (RSTM) or second-order closure models (SOC). One is required to solve transport equations for all turbulent stresses.



One computes time-varying flow, but models sub-grid-scale motions.


Extend of modeling for certain CFD approach is illustrated in the following figure Figure 2. It is clearly seen, that models computing fluctuation quantities resolve shorter length scales than models solving RANS equations. Hence they have the ability to provide better results. However they have a demand of much greater computer power than those models applying RANS methods. (APSLEY, 2004) (BELL, 2003)

Figure 2 Extend of modelling for certain types of turbulent models


A variety of industrial sectors, such as aerospace, defense, power, process, automotive, electrical and civil engineering, there are many examples of areas where CFD is now used. For example, predictions can be made of the Lift and drag of aircraft. Here, as we have said, engineers need the data for performance prediction. CFD is used in conjunction with wind tunnel tests to determine the performance of various configurations. Flows over missiles, This, again, is an area where there is a need for lift; drag and side force data, so that simulations of performance can be made.


In general, however, there are two major sources of errors in CFD, numerical and modeling errors. The former kind of errors is estimated in the process of verification and the latter in the process of validation, i.e. "solve the equation right" and "solve the right equation", respectively. (Roache) (1997).


9.1. WHY CFD?

Traditionally, the design of this component has been based on simplified analytic methods, experimental rules of thumb and model tests (Gubin, 1973; Holmén, 1999). In the last decade or two, the usage of computational fluid dynamics (CFD) has dramatically increased in the design process and will continue to grow due to is flexibility and cost-effectiveness.

A CFD-based design search can further be aided with a robust and user-friendly optimization framework. Numerical prediction of the flow, on the other hand, challenging and time consuming, caused by its complex flow features, e.g. unsteadiness, turbulence, separation, streamline curvature, secondary flow, swirl, and vortex breakdown. Hence, there is a great need of developing both accurate and reliable CFD models, together with efficient and effective

Optimization frameworks.

9.2. Why PHOENICS?

The software PHOENICS used for modelling the turbulence flow with curved surface. It is the best software used to simulating the 2D models and for more accurate results compared to other software's of fluid flow. One of the most used versions of the law of the wall is that due to Spalding (1962) who also is one of the pioneers in CFD. Spalding was one of the first to device a viable CFD code and the first to generate a commercial CFD code (called PHOENICS) on which many other codes are based.


The curved surface is similar to the roof of the car and nose tip of the air craft models. By modelling the turbulence the drag reduction can be maximised in both the models.


This study relates to design of an aerodynamic drag reduction is maximised primarily by reducing the total pressure on the vehicle. When the air flow over the curved surface there will be a point where the flow separation and the meeting point of the air flow can be optimised to maximise the drag reduction.


It is a factor used for comparing the aerodynamic drag of different vehicle shapes independent of driving speed and size. It is dependent on shape, frontal area, and density, relative speed of air, turbulence properties and Reynolds number.

Drag = [(0.5) * rho * V2] * A * CD.


'[(0.5) * rho * V2]' is dynamic pressure.

'A' is frontal area.

'CD' is Coefficient of drag.


It is the product of the drag coefficient and frontal area.

Drag factor = CD * A

Drag depends on the projected frontal area and drag coefficient. It doesn't mean that if a vehicle having low CD results in less drag. It changes with the frontal area.


It is similar to that of a friction occurred between the two surfaces. When the air passes through the surface of the curve, surface friction is occurred by which the drag is produced known as surface friction drag.


It is the drag produced when the flow gets separated. It depends on where the flow separation occurs.

The flow throughout the car will not be continuous. Due to the pressure gradient, the flow gets separated. This occurs mainly at the rear because the average pressure on these parts is lower, when compared to the forward facing parts.




r density

u characteristic velocity (such as inflow)

L characteristic length (such as the length of an object in your flow)

µ viscosity

If the Reynolds Number is small, then the flow will be laminar, meaning that the flow progresses in layers of the fluid with no macroscopic mixing of the layers. If the Reynolds Number is large then the flow will be turbulent, meaning there will be a mixing of flow layers and the creation of large eddies at the start, breaking into smaller eddies as time progress. In between these two extremes, the flow is called transitional. These types of flow are illustrated by the familiar example of cigarette smoke.


A low Reynolds Number gives laminar flow while a high Reynolds Number gives turbulent flow. For both a laminar and a turbulent boundary layer increasing Reynolds Number gives lower skin friction drag. However, because of the higher energy loss in the boundary layer, a turbulent layer always has higher skin friction drag.

11.1. Navier-Stokes equations

The numerical solution of any fluid flow problem requires solution of general equation of fluid motion, the navier strokes and the continuity equation. Fluid flow problems are described mathematically by this equation which is set of coupled non linear partial differential equations with appropriate boundary conditions. These equations are derived from Newton's second law and describe the conservation of momentum in flow.

Where ui is the instantaneous velocity,r the fluid density, p the pressure and v the kinematic viscosity


The Reynolds-averaged Navier-Stokes (RANS) equations are time-averaged equations of motion for fluid flow. They are primarily used while dealing with turbulent flows. These equations can be used with approximations based on knowledge of the properties of flow turbulence to give approximate averaged solutions to the Navier-Stokes equations. For stationary, incompressible flow of Newtonian fluid, these equations can be written in Einstein notation as:


Reynolds number, in accurate modelling of wind flows aver curved surface

Natural ventilations or diffusion of pollutants with in partially open enclosure

Inability to model wind flows in atmosphere boundary layers with various stability conditions.

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