**Abstract**

The chapter comprises of two sections: the first concerns with the nanofluids, and the second is about the computational applications in medicine and biology. Nanotechnology is a novel logical methodology that includes materials and gear equipped for controlling the physical just as chemical properties of a substance at subatomic dimensions. This innovation can possibly expel the evident limits between biology, physics, and chemistry to some degree and shape up our present thoughts and comprehension. Consequently, numerous new difficulties and bearings may likewise emerge in education, research, and diagnostics in parallel by the extensive use of nanobiotechnology with the progression of time. Blood flow modeling in various arteries is an important topic of CFD biomechanics. Regardless of these endeavors and advances, there are as yet confounded inquiries around, for example, the interaction between blood flow and various artery diseases.

**Keywords:** nanofluidics, computational fluid dynamics, fluid-structure interaction, multiphysics systems coupling, multiphase flow

## **1. Introduction**

The use of solid particles, like particles having millimeter or micrometer size, as additives suspended into the base fluid has been well recognized for numerous years. Nevertheless, they have not been of attention for the practical uses owing to problems, like the sedimentation that leads to increase the pressure drop in the channel of flow. The recent progress in the technology of material has done it able to engender an innovative nanofluid via the suspension of the particles having nanometer size in the base fluids that can vary the fluid movement and some properties of the base fluid. The nanofluids are solid-liquid composite alloys comprising solid nanofibers or nanoparticles having sizes distinctively from 1 to 100 nm suspended in a fluid. Various base fluids are commonly used. These are water, organic liquids (e.g., ethylene, triethylene glycols, refrigerants, etc.), oils and lubricants, bio-fluids, and polymeric solutions. The nanoparticles utilized in nanofluids include chemically stable metals (gold, copper, aluminum), metal oxides (alumina, silica, zirconia, and titania), metal carbides (SiC), metal nitrides (AIN, SiN), various forms of carbon (diamond, graphite, carbon nanotubes, fullerene), and functionalized nanoparticles. It is not a simple liquid-solid blend; the highly significant criterion of nanofluid is the agglomeration of freely steady suspension

for long periods without resulting in any chemical variations in the base fluid. That can be done via increasing the liquid viscosity, via preventing the particles from agglomeration, and via using particles having nanometer size. The particle settling can prevent or minimize the density between solids and liquids [1].

Classical science and engineering disciplines already provide a wide, wellestablished base of knowledge for the understanding of these phenomena of nanofluids. Examples of areas that deal intensively with nanoscale phenomena include tribology, surface sciences, and colloid sciences (**Figure 1**).

Another classical research field that previously dealt with nanofluidic phenomena is the surface science, which studies the phenomena occurring at the interface of two phases, such as solid-liquid interfaces, solid-gas interfaces, and liquid-gas interfaces (**Figure 2**).

Computational fluid dynamics (CFD) is the using of computer-based simulation to analyze the systems that involve fluid flow, heat transfer, and connected phenomena. A numerical model is initially built utilizing a set of mathematical equations describing the flow. Then, such equations are solved employing a computer program for obtaining the flow parameters within the domain of flow. The development and application of CFD have undergone a considerable growth, and as a result, it has become a powerful tool in the design and analysis of engineering and other processes [2]. The governing equations of the models are partial differential equations (PDEs). Because the digital computers can merely recognize and handle the numerical data, such equations cannot be solved straightforward. Thus, the partial differential equations must be converted into numerical equations including merely the numbers and no derivatives. Such operation of making a numerical analogue to the partial differential equations is named "numerical discretization." The discretization operation includes an error because the "numerical" terms are merely the approximations to the initial "partial differential" terms. Such error, nevertheless, can be much reduced to low and thus acceptable levels. The main

technique utilized for discretization is the "finite volume method." This method is likely the highly famous one employed for the "numerical discretization" in CFD. In some ways, it's similar to the "finite difference method (FEM)," but certain of its tools drag the features taken from the FEM. Such method includes discretization of spatial domain into the finite control volumes. The control volume overlaps with numerous mesh elements, and thus it can be split into sectors, each one backs to

The governing differential equations are integrated over every control volume.

The obtained laws of integral conservation are precisely met for every control volume and for the whole domain, which is a discrete benefit of the FEM. Then, every integral term is changed into a separate form, therefore providing discretized

**2. Some advantages and applications of using nanofluid and CFD**

because of the size of nanoparticles, the liquid is considered one fluid.

Due to the size of nanoparticles, the pressure drop is minimal, and a strong change in the properties of the main fluid, by the suspension of nanofluids and

equations at the nodal points, or centroids, of control volumes.

various mesh elements, as depicted in **Figure 3**.

*A control volume (Vi) surrounded by mesh elements.*

*Classical areas of science and engineering related to nanofluidics [1].*

*Nanofluids and Computational Applications in Medicine and Biology*

*DOI: http://dx.doi.org/10.5772/intechopen.88577*

**Figure 2.**

**Figure 3.**

**95**

#### **Figure 1.**

*Length scales and volume scales of nanofluidics, microfluidics, common microtechnologies, and common objects [1].*

*Nanofluids and Computational Applications in Medicine and Biology DOI: http://dx.doi.org/10.5772/intechopen.88577*

for long periods without resulting in any chemical variations in the base fluid. That can be done via increasing the liquid viscosity, via preventing the particles from agglomeration, and via using particles having nanometer size. The particle settling

Classical science and engineering disciplines already provide a wide, wellestablished base of knowledge for the understanding of these phenomena of nanofluids. Examples of areas that deal intensively with nanoscale phenomena

Another classical research field that previously dealt with nanofluidic phenomena is the surface science, which studies the phenomena occurring at the interface of two phases, such as solid-liquid interfaces, solid-gas interfaces, and liquid-gas

Computational fluid dynamics (CFD) is the using of computer-based simulation to analyze the systems that involve fluid flow, heat transfer, and connected phenomena. A numerical model is initially built utilizing a set of mathematical equations describing the flow. Then, such equations are solved employing a computer program for obtaining the flow parameters within the domain of flow. The development and application of CFD have undergone a considerable growth, and as a result, it has become a powerful tool in the design and analysis of engineering and other processes [2]. The governing equations of the models are partial differential equations (PDEs). Because the digital computers can merely recognize and handle the numerical data, such equations cannot be solved straightforward. Thus, the partial differential equations must be converted into numerical equations including merely the numbers and no derivatives. Such operation of making a numerical analogue to the partial differential equations is named "numerical discretization." The discretization operation includes an error because the "numerical" terms are merely the approximations to the initial "partial differential" terms. Such error, nevertheless, can be much reduced to low and thus acceptable levels. The main

*Length scales and volume scales of nanofluidics, microfluidics, common microtechnologies, and common*

can prevent or minimize the density between solids and liquids [1].

include tribology, surface sciences, and colloid sciences (**Figure 1**).

interfaces (**Figure 2**).

*Applications of Nanobiotechnology*

**Figure 1.**

*objects [1].*

**94**

*Classical areas of science and engineering related to nanofluidics [1].*

#### **Figure 3.**

technique utilized for discretization is the "finite volume method." This method is likely the highly famous one employed for the "numerical discretization" in CFD. In some ways, it's similar to the "finite difference method (FEM)," but certain of its tools drag the features taken from the FEM. Such method includes discretization of spatial domain into the finite control volumes. The control volume overlaps with numerous mesh elements, and thus it can be split into sectors, each one backs to various mesh elements, as depicted in **Figure 3**.

The governing differential equations are integrated over every control volume. The obtained laws of integral conservation are precisely met for every control volume and for the whole domain, which is a discrete benefit of the FEM. Then, every integral term is changed into a separate form, therefore providing discretized equations at the nodal points, or centroids, of control volumes.
