Computational fluid dynamics or CFD analysis is one of the key analysis methods used in engineering applications. The origins of CFD lies in the mankind’s efforts to better understand the power of natural elements like wind, storms, floods, or sea waves.
What Do We Know About Flows?
The physical discipline of fluid dynamics evolved as the sciences started to classify the natural power and associated reaction of air, water or gases. This provided a systematic structure that embraced empirical laws and was derived from the idea of flow measurement that is used to solve practical problems. A typical fluid dynamics problem involves basic fluid properties like flow velocity, pressure, density, and temperature, in relation to time and space.
In everyday life, we can find fluid flows in meteorology (rain, wind, floods, hurricanes), heating, ventilation and air conditioning, aerodynamic design, engines combustion, industrial processes, or the human body—for example, blood flow—and so on. Fluid dynamics has a wide range of applications, including calculating forces on aircraft, determining the mass flow rate of petroleum through pipelines, and predicting weather patterns.
What is CFD?
Gas and liquid flow behavior is quantified by partial differential equations representing conservation laws for mass, momentum, and energy. Computational fluid dynamics is a branch of fluid mechanics that uses numerical analysis and algorithms to solve fluid flows situations. High-performing computers are used to conduct the calculations required to simulate the interaction of liquids and gases with surfaces defined by boundary conditions. 
CFD is based on the Navier-Stokes equations. Arising from applying Newton’s second law to fluid motion, together with the assumption that the stress in the fluid is the sum of a diffusing viscous term and a pressure term, these equations describe how the velocity, pressure, temperature, and density of a moving fluid are correlated. 
The development of CFD has been closely associated with the evolution of high-speed computers.
1922 – Basis of modern CFD and numerical meteorology made by Lewis Fry Richardson in a weather forecasting scheme using differential equations and finite differences ;
1933 – Earliest numerical solution for flow past a cylinder developed by A. Thom 
1950 – First 24-hour weather forecast performed by the ENIAC modern computer 
1955 – Particle-in-cell simulation method for transient 2D fluid flow developed by Los Alamos National Lab 
1963 – Vorticity-stream-function method for 2D, transient, incompressible flow 
1965 – Marker-and-cell method for time-dependent viscous flow developed by Los Alamos National Lab 
1966 – Fluid-in-cell method developed for unsteady compressible flow problems 
1967 – First 3D model based on panels discretization published by Douglas Aircraft 
1968 – First lifting Panel Code (A230) described by Boeing Aircraft 
1970 – First description of Full Potential equations published by Boeing 
1981 – 3D FLO57 code based on Euler equations for transonic flows 
After 1981 – Many of the fundamental pieces of research that contributed to CFD 2D and 3D methods focused on airfoil design and analysis. NASA research dedicated to the Navier–Stokes equations developed 2D codes ARC2D and 3D codes. These included codes like ARC3D, OVERFLOW, and CFL3D, which were the main sources for modern commercial CFD packages.
Why is It Important to Use CFD Analysis?
With a CFD analysis, we can understand the flow and heat transfer throughout a design process. The basic methodology for any engineering CFD analysis is based on a few procedures:
• Understanding flow model — Flow separations, transient effect, physical interactions;
• Proving assumed model — Experimental results validation, parametric studies, structural simulations;
• Model optimizing — Reducing pressure drops, flow homogenization, improving laminar and turbulent mixing.
Without numerical simulations of fluid flow, it is very difficult to imagine how:
• Meteorologists can forecast the weather and warn of natural disasters;
• Vehicle designers can improve aerodynamic characteristics;
• Architects can design energy-saving and safe-living environments;
• Oil and gas engineers can design and maintain optimal pipes networks;
• Doctors can prevent and cure arterial diseases by computational hemodynamic.
Performing Sophisticated CFD Simulations with SimScale
In relation to mechanical fluid simulation, Fluid Dynamics is one of the key analyses methods used by the SimScale platform together with Solid Mechanics, Thermodynamics, and Particles Analyses. The SimScale platform offers a wide variety of CFD analysis capabilities, including:
• Multiple laminar and turbulence models, which are based on the Reynolds number for the fluid flow;
• Steady-state applications and transient solvers setup;
• Mass transport within fluid flows;
• Access to multiple incompressible and compressible fluids solvers;
• Single- and multiphase flows simulation;
• Advanced modeling concepts, such as porous media or rigid body movement of fluid domains
In the course of a football or basketball game, the ball can often take unpredictable trajectories. This project shows how a flow analysis can be used to investigate the aerodynamic behavior of a football.
During his free session on Formula 1 aerodynamics, Nic Perrin provided a lot of great insights into the fascinating aerodynamics of a race car, such as the interaction between the front wing and the wheels, or how the vortices help to improve the downforce.
CFD is widely applicable in industrial areas. Below we have an example of a water purification process simulation. A maze model is used for multiple analyses of the mean flow field and the change in contamination distribution for the purification process.
The flow simulations for valves are particularly interesting in this case, with a wide applicability in the water purification industry. Here is a simple flow simulation of a globe valve where the results can be analyzed and visualized in the integrated post-processing environment.
The projects related to theoretical models research are extremely important as these assumptions are the foundation for real cases studies.