Mohammed B* and Rachid S
Laboratory of Energetics and Applied Thermic (ETAP), Algeria
*Corresponding author: Mohammed B, Laboratory of Energetics and Applied Thermic (ETAP), Algeria
Submission: July 12, 2021 Published: July 23, 2021
ISSN:2694-4421
Volume2 Issue3
This paper presents the applications Artificial intelligence in the field of nanofluidic Hybrid AI algorithms. Are becoming beneficial as alternative methods to conventional approaches or as components of embedded systems. They have been used to solve complex applied problems in different fields and are becoming increasingly popular at present. In this survey, for the first time, a comprehensive review of AI algorithms developed to study different nanofluid related problems is conducted. The contributions presented in this paper reveal the strong potential of AI methods as prediction and optimization tools in nanofluids, investigated different aspects of nanofluids in applications such as electronic cooling, solar systems, heat exchangers, microchannels, refrigeration systems, and MCPs. In addition, challenges and directions for future research in the area of using AI techniques in nanofluids are presented and discussed.
Keywords: Artificial intelligence; Nanofluid; Hybrid AI algorithms
Abbreviations: ANFIS: Adaptive Neuro-Fuzzy Inference System; ANN: Artificial Neural Networks; GA: Genetic Algorithms
The development of artificial intelligence as an academic discipline dates back to the 1950s,
when scientists and researchers began to consider the possibility of machines processing
intellectual abilities similar to those of human beings. The term “artificial intelligence” itself
was coined in 1956 by a Massachusetts Institute of Technology professor, JOHN MCCARTHY.
MCCARTHY created the term for a conference that was held in the same year. The conference
was called the Dartmouth Conference by researchers in AI, made AI a distinct discipline.
The conference also defined the major goals of AI: to understand and model human thought
processes and to design machines that mimic that behaviour. Much of the AI research in
the period between 1956 and 1966 was theoretical in nature. The first AI program, Logic
Theorist (presented at the Dartmouth Conference) proved mathematical theorems. Several
other programs were developed later taking advantage of AI, such as “Sad Sam”, (written
by Robert K. Lindsay in 1960) which included simple English sentences and was able to
draw conclusions from facts learned in a conversation. The conclusions drawn depended
on data called a Knowledge Base (KB) in AI. Another was ELIZA, a program developed in
1967 by Joseph Weizenbaum at MIT that was able to simulate a therapist’s responses to
patients. With increasingly successful demonstrations of the feasibility of AI, the focus of AI
research changed. Researchers have focused on solving specific problems in areas of possible
AI application. This shift in research focus has resulted in the current definition of AI as “a
variety of research areas concerned with extending the ability of computers to perform tasks that resemble those performed by human beings,” as V. Daniel
Hunt puts it in his 1988 article “The Development of Artificial
Intelligence” (Andriole 52). Some of the most interesting areas of
current AI research include expert systems, neural networks, and
robotics [1]. On the other hand, there are several reasons why AI
paradigms are used in nanotechnology research. Nanotechnology
suffers from the physical limitations of its working scale, where the
physics is completely different from that of the macroscopic world.
This means that the correct interpretation of the results obtained
from any system or device at this scale is one of the problems faced
in nanotechnology [2].
Nanofluids have been introduced as a modern and interesting
type of nanotechnology-based heat transfer liquids and have been
developed considerably in the last two decades. They possess
the highest thermal conductivity and superior convective heat
transfer compared to conventional fluids. Therefore, nanofluids
have been studied significantly by many researchers to examine
their capabilities to overcome the challenges of cooling technology
and thermal management due to the excellent characteristics of
nanofluids. In this regard, many review articles have also been published in the last decade, which has studied different aspects of
nanofluids in applications such as electronic cooling, solar systems,
heat exchangers, microchannel, refrigeration systems and PCMs
[3]. The present of this paper can be considered as a guideline
applications Artificial intelligence in the field of nanofluids Hybrid
AI algorithms.
Various unique AI methods have been used in nanofluids, simple algorithms often have limitations such as slow prediction rate and large error. Recently, researchers have introduced methods that combine at least two AI approaches. These algorithms are called hybrid artificial intelligence systems and are implemented to overcome the limitations of single algorithms and to improve the efficiency of the predictor. For example, ANNs will only allow input to output reasoning and this can be overcome by applying Adaptive Neuro-Fuzzy Inference System (ANFIS) and by combining different AI algorithms, the advantages of each of the algorithms can be used combinations of ANNs with GA and combinations of GA with decision making approaches. Various combinations of AI algorithms have been employed in the field of nanofluids [3].
ANNs and fuzzy logic algorithms have advantages and
disadvantages. However, an ANFIS that is developed by combining
an ANN and a fuzzy logic method can recover the disadvantages
of both approaches and develop an effective algorithm for
engineering system modeling. ANFIS uses training methods
developed from an ANN to find appropriate fuzzy rules and fuzzy
membership functions. In ANFIS, a training approach is aligned
with an integrated training method. The ANFIS structure consists of five layers, in which the first hidden layer is used to map the input
parameter to each member. In the second layer, the normoperator
T is used to evaluate the antecedent of the rules. The third hidden
layer normalizes the strengths of the rules, followed by a fourth
hidden layer in which the realizations of the rules are computed.
Finally, the last layer computes the output and the sum of all signals
entering this layer. ANFIS has been used to study the characteristics
of nanofluids in some research contributions. Shanbedi et al. [4]
employed ANFIS to predict the thermal resistance and heat transfer
performance of a Two-Phase Closed Thermosiphon (TPCT).
Dispersions of pristine carbon nanotubes and functionalized
nanotubes containing ethylene diamine were applied as nanofluids.
The experimental results regarding the performance of TPCT were
modelled by the ANFIS approach. A comparison was made between
the results with 5 mathematical criteria. These criteria proposed
that the modelling via ANFIS is reliable, so that it can be developed
for other cases. In Study by Adio et al. [5] found the optimal energy
required to prepare MgO-ethylene glycol nanofluids by modifying
the ultrasonic energy input in the synthesis process. The nanofluids
were characterized and their viscosity was measured in terms of
volume concentration, temperature and particle size. The size
distribution of the nanoparticles and the Transmission Electron
Microscopy (TEM) image are shown in the Figure 1. The average
sizes of the nanoparticles were ~21, ~105 and ~125nm, which
are shown in the figure as MgO-I, MgO- II and MgO-III respectively.
ANFIS was used to model the viscosity as a function of the
mentioned parameters. The results had good consistency with the
experimental results.
Aminossadati et al. [6] numerically studied on mixed convection
in a cavity filled with an alumina-water nanofluid. The bottom
and top walls of the cavity were kept at different temperatures,
while the vertical walls were thermally isolated. An ANFIS method
was designed trained and validated using data obtained from
a numerical simulation. The results proved that ANFIS can be
effectively applied to estimate the fluid temperature, velocity, and
amount of heat exchange, with reduced computational time and
no reduction in accuracy. The aim of the study by Mehrabi et al.
[7] presented models to estimate the viscosity of nanofluids using
ANFIS with the use of experimental results. Apparent viscosity
was chosen as the target parameter, while temperature, volume
fraction and particle diameter were assumed as input parameters.
The estimated viscosities were compared with empirical results for
4 different nanofluids including alumina AL2O3, Titanium dioxide
TiO2, Copper Oxide CuO and Silicon dioxide SiO2, and with water
as the base liquid. The results obtained from the suggested ANFIS
model showed appropriate consistency with the empirical results.
Figure 1: TEM image of MgO and particle size distribution (a) MgO-I (b) MgO-II (c) MgO-III (d) particle size distribution [5].
The hybrid AI system combines ANN with GA have focused on creative methods to couple ANN modules with GA. Research efforts also focus on methods to introduce ANNs for easier tuning and development by GA, techniques to replace gas with neural training approaches, and probabilities to incorporate new methods from evolutionary computing. The combination of GA with ANN has also been used in the area of nanofluids. The study goal of Vakili et al. [8] modeled the viscosity of graphene nanofluid using MLP neural network and GA. To obtain the empirical data, the nanofluid was used at volume fractions of 0.025 to 0.1 wt% and temperatures of 20 to 60 °C. The combination of genetic algorithms with ANN was used to improve the learning process. The results revealed that the obtained model was consistent with the empirical results. Karimi et al. [9] introduced a GA-based ANN to predict the viscosity of nanofluids. GA was applied to optimize the ANN factors. The viscosity of eight nanofluids at temperatures between 238.15K and 343.15K with volume fraction up to 9.4% was studied. The results proved that the model shows good agreement with the experimental data. Moreover, the results revealed that the model has better accuracy than the usual correlations. Salehi et al. [10] presented the models for evaluating the thermal characteristics of a water-Ag nanofluid inside a closed two-phase thermosiphon employing an optimized ANN via a genetic algorithm. The ANN was used to predict the heat transfer performance and resistance of a thermosiphon under a magnetic field. The volume fraction, magnetic field strength, and input power were considered as input variables, while the thermal resistance and heat transfer efficiency were taken as the outputs. The results were compared with the experimental results and it was concluded that the results obtained from ANN are accurate. The trial and error technique is time consuming and the ANN architecture obtained may not be optimal. To solve this problem, as observed, ANNs based on genetic algorithms have been used in nanofluids, in which the ANN parameters are optimized by GA. In the field of nanofluids, in general, GA has been used to determine the weights of ANNs as well as to design their structure, while no studies have been conducted in which GA is used to find an optimal formation rule for ANNs. This idea can be followed in the future to find optimal learning algorithms to improve the prediction task in nanofluids.
In multi-attribute optimization methods, a group of states
is reached as the optimal case, while they have no preference
over each other. Thus, some researchers have combined GA with
different decision making methods to facilitate the selection
procedure between different optimal points. The study of Bahiraei
et al. [11] that attributes of water-Al2O3 nanofluid in a tube and
shell heat exchanger. The schematic of the helical baffles used
as well as the overlap parameter and helix angle are shown in
the Figure 2. The pressure drops and heat transfer increased
with increasing baffle overlap and volume fraction and reducing
helix angle. In order to determine the cases with the highest heat
transfer and lowest pressure loss, optimization was performed
on the ANN model by GA coupled with a decision making method
(i.e. Trade-off Programming). The results revealed that even when
a small pressure drop is considerably vital to the decision maker,
nanofluids with large volume fractions can be used. Furthermore,
when both low pressure drop, and high heat transfer are crucial,
low helix angles can be used.
The study by Bahiraei & Hangi [12] that conducts the efficiency
of a water-based Mn\Zn ferrite nanofluid inside a double tube heat
exchanger in the presence of a quadrupole magnetic field. The
nanofluid flowed on the tube side, while the hot water flowed on
the ring side (Figure 3). The particle distribution was not uniform
so that the concentration was lower near the walls. However, the
use of the magnetic field led to a more uniform distribution of the
nanoparticles. Optimization was performed via GA coupled with a
trade-off programming method, which is a decision approach, to
achieve the highest overall heat transfer coefficient with the lowest
pressure drop. The optimal values were determined against various
states for the relative importance of the objective functions to the
designer’s views.
Figure 2: Diagram of the helical deflectors [11].
Figure 3: The heat exchanger studied in [12] (a) longitudinal cross section and (b) Cross section.
Application of AI algorithms in solar energy applications with nanofluids
Recently, there has been an increasing demand in the use of
renewable energy. This type of energy is sustainable, generates
zero or minor greenhouse gas emissions and will never run out,
which has a negligible effect on the environment. Solar energy is
the oldest energy source ever used with a large supply. In some of
the studies that have used nanofluids in solar energy applications,
AI algorithms have been applied for modeling and optimization.
Mohammad Zadeh et al. [13] introduced efficient solar collector
optimization and modeling in a numerical study. The method used
in the modeling employed a parabolic trough collector absorber
tube, a fully developed flow, and a synthetic alumina oil nanofluid.
A hybrid optimization approach involving a genetic algorithm and
sequential quadratic programming was adopted in the optimization
procedure. The optimization problem involved maximizing a nondimensional
relationship including pressure drop and Nusselt
number with Reynolds and Richardson numbers applied as design
constraints.
The results indicated that the heat exchange increment shows
a direct relationship with volume fraction while it demonstrates an
inverse relationship with temperature. In an analytical research,
Risi et al. [14] proposed an innovative Transparent Parabolic Solar Collector (TPTC) operating with a nanofluid. A conventional
PTC is shown in the Figure 4. Transparent receivers as well as
nanofluid are able to directly adsorb solar radiation due to the
very large surface area of nanoparticles. The model was applied to
run an optimization method with the use of GA. The simulations
revealed that the highest solar/thermal transfer efficiency TPTC
was 62.5% for a volume fraction of 0.3% and an exit temperature
of 650 °C. Toghyani et al. [15] theoretically examined the efficiency
of an integrated Rankine power cycle equipped with a parabolic
trough solar system and a thermal storage system using 4 different
nanofluids. The impact of solar intensity, dead-state temperature as
well as particle concentration on the cycle efficiency was evaluated.
The GA was used to optimize the power output. It was shown that
by increasing the volume fraction, the exergy efficiency increases.
At higher dead-state temperatures, higher solar irradiation
caused a significant increase in power output. After performing
an analytical study, Ahmadi Boyaghchi et al. [16] optimized a new
refrigeration system combined with a flat plate solar collector. LiBr-
H2O was used as a fluid pair in the cascade absorption part, while
R134a, R1234ze, R1234yf, R407C and R22 coolants were used in
the vapor compression part and CuO-water nanofluid was used as
a heat transfer fluid in the collector subsystem. The thermal and
exergy coefficients and total cost rate were chosen as objective
functions. NSGA-II was implemented to obtain the final solutions.
The modeling showed that R134a was the optimal refrigerant from
the exergy and energy point of view.
Figure 4: Schematic of a solar PTC [14].
Application of artificial intelligence algorithms to electronic cooling with nanofluids
Power and solid-state electronic devices have found many applications in commercial, residential, military and space environments. Nowadays, these systems are commonly used in automobiles, televisions, computers, phones, etc. Due to their extensive use, electronic components must operate reliably under a wide range of environmental conditions. One of the main causes that influence reliability is thermal management. The difference between input and output energy in an electronic device is converted into heat, which must be dissipated properly to avoid overheating and chip failure. Proper cooling in electronics still remains a problem that needs to be studied as it becomes an important enabling industry for the next development of electronics. To this end, some researchers have used nanofluids in electronic cooling, and the capabilities of Al have been used in some of the studies conducted in this area. Mital et al. [17] analytically studied the thermal characteristics of a rectangular channel liquid block with nanofluid flow for electronics cooling (Figure 5). The model was applied to estimate the pumping power and thermal resistance as a function of flow velocity, channel width, particle concentration, and wall width. The variables were optimized using a genetic algorithm with a constant value of pumping power as a constraint, and the lowest thermal resistance as an objective function. Minimized thermal resistances had only a minor benefit because solid nanoparticles increased energy consumption. Kargar et al. [18] used a neural network and CFD to evaluate the efficiency of two microchips in a cavity filled with a copper-water nanofluid. Heat exchange occurred due to free convection between the heated microchips attached to the right and left walls. The results obtained from the CFD simulation were used to form the ANN. Comparison of the results obtained from the CFD and those obtained from the neural network proved that the neural network accurately estimates the cooling of the microchips. The geometries applied for electronics cooling are miniature and are often in the micrometer range. Therefore, the possibility of channel clogging and particle agglomeration in these systems is high and therefore nanofluids with appropriate stability must be used for this purpose. In addition, the pressure drop intensifies considerably by reducing the channel size and therefore, low viscosity nanofluids are suggested for use in electronics cooling. With respect to many of the parameters affecting electronics cooling with nanofluids, ANNs and other AI algorithms can be effective for prediction and optimization of attributes in this area.
Figure 5: The heat sink under study [17].
Application of AI algorithms in heat exchangers operating with nanofluids
Heat exchangers have already proven to be important equipment
for heat transfer systems in many technical fields. To improve the
efficiency of heat exchangers, nanofluids are newly applied as
working fluids. Due to the excellent attributes of nanofluids, research
in this area has seen considerable development. AI algorithms have
been applied to investigate the attributes of nanofluids inside heat
exchangers in some studies. In a numerical effort, Bahiraei et al.
[19] studied the thermal-hydraulic characteristics of alumina-water
nanofluid in a shell-and-tube heat exchanger equipped with helical
baffles. The influence of volume fraction and Reynolds number on
pressure drop and heat transfer was examined. Increasing Reynolds
number and particle concentration increased both pressure drop
and heat transfer. Friction factor and Nusselt number models were
developed as a function of particle concentration and Reynolds
number per ANN. The ANN predicted the output parameters with
appropriate accuracy.
Bahiraei et al. [20] numerically evaluated convective flow and
heat transfer of a non-Newtonian nanofluid in a double tube heat
exchanger model. Reducing the particle diameter and increasing
the volume fraction increased the pressure drop and convective
heat transfer. Using the data obtained from the simulations, an
ANN model was designed to estimate the pressure drop and
convective heat transfer coefficient as a function of radius ratio,
volume fraction, and nanoparticle diameter. In addition, GA was
used to achieve the optimal conditions regarding the designer’s
viewpoints. Exergy analysis of heat exchangers using AI algorithms
is suggested as a really suitable future area for optimization of
weight, size, baffle configuration and cost of heat exchangers
operating with nanofluids. Moreover, heat exchangers operating
with nanofluids are exposed to severe fouling which can depend
on many parameters such as flow velocity, particle concentration,
temperature, etc. To date, no studies have been conducted on fouling
of heat exchangers operating with nanofluids via AI algorithms.
Since many factors affect fouling, it is proposed to utilize the
capabilities of AI, especially ANNs in this area.
Application of AI algorithms for hybrid nanofluids
Hybrid nanofluids are novels that are synthesized by suspending
different nanoparticles as a mixture or composite in base liquids.
The motivation for the production of hybrid nanofluids is the
improvement of heat transfer with enhanced thermal conductivity
of these nanofluids as well as the use of some special features. For
example, functionalization of carbon nanotubes with other types of
nanoparticles can integrate the characteristics of CNTs with these
nanoparticles, which can lead to innovative materials with new
chemical, thermal and physical characteristics as well as interesting
applications. In some investigations, AI methods have been used to
study hybrid nanofluids. Hemmat E et al. [21] presented a model
for predicting the thermal conductivity of SWCNTs-MgO/EG hybrid
nanofluids through a neural network. The hybrid nanofluids were
experimentally synthesized and tested at concentrations between
0.05 and 2%.
Photographs and XRD patterns of MgO and SWCNT are shown
individually in the Figure 6. The ANN correlation revealed a high
degree of accuracy. Comparison of the results of this research
and single particle nanofluids indicated that the interaction of
particles in hybrid nanofluids acts in a positive direction, namely
the improvement of thermal conductivity. Rostamian et al. [22]
investigated the variations of thermal conductivity of CuO/
SWCNTs-EG/water hybrid nanofluid as a function of volume
fraction and temperature via neural network modeling. CNTs
were mixed with CuO nanoparticles in equal volume (50:50)
in an experimental work. Thermal conductivity modeling was
performed by ANN by applying experimental data. The ANN model
correctly estimated the thermal conductivity of the nanofluids.
Afrand et al. [23] obtained a neural network model to estimate
the relative viscosity of the MWCNTs-SiO2/AE40 hybrid nanofluid
using the experimental data. The concentration and temperature
of the particles were considered as input parameters. The results
obtained from ANN showed a margin of deviation of 1.5% from the
empirical results (Figure 7). It was concluded from comparisons
that the optimal neural network model is accurate in predicting the
outputs. It can be noted that studies that have used AI algorithms
for hybrid nanofluids, have often investigated the thermophysical properties of these nanofluids. With the further development of
hybrid nanofluids in the future, the use of different AI algorithms
is proposed to model and optimize the convective heart transfer of
these modern nanofluids as well.
Figure 6: XRD analysis of nanopowder [21].
Figure 7: Comparison between experimental data and ANN results [23].
Application of AI algorithms for magnetic nanofluids
Magnetic nanofluids or ferrofluids are dispersions produced
from non-magnetic base liquid and magnetic solid nanoparticles.
In this new group of dispersions that are also called smart fluids,
particle movement, heat exchange and fluid flow can be controlled
using magnetic fields. Due to the excellent attributes of magnetic
nanofluids, many researchers have conducted extensive research
on these nanofluids. Some research papers have been published
in which AI approaches have been implemented to evaluate
the characteristics of magnetic nanofluids. Selimefendigil et al.
[24] numerically investigated the effect of a magnetic dipole on
the convective heat transfer of magnetic nanofluids inside an
enclosure. A partial heating was attached to the left wall while the
right wall remained at invariant temperature. The magnetic source
was located outside the cavity. It was seen that the interaction
between magnetic and free convection influences the hydrothermal
characteristics. The magnetic field reduced local heat exchange in
some locations, while enhancing it in others. Ultimately, a model was
suggested to predict the heat transfer efficiency of the system using
ANN. By mixing carbon nanotubes and magnetic nanoparticles in
a base liquid, a new type of hybrid nanofluid can be synthesized.
Such a ferrofluid benefits from the high thermal conductivity of
CNTs in addition to the controllability of magnetite nanoparticles.
In other words, in addition to possessing high thermal conductivity,
such a hybrid nanofluid will also be controllable under magnetic
fields. The study by Shahsavar & Bahiraei [25] experimentally
measured the viscosity and thermal conductivity of a hybrid
ferrofluid comprising both magnetite nanoparticles and CNTs at
temperatures from 25 to 55 °C, Fe3O4 concentrations from 0.1% to
0.9% and CNT concentrations from 0 to 1.35%.
To avoid agglomeration, tetramethylammonium hydroxide
and gum arabic were used to coat the magnetite nanoparticles and
CNTs, respectively. The pictures of FeCl2 solution and FeCl3 solution,
which were applied to synthesize the ferrofluid, as well as the
picture of a hybrid ferrofluid instance are shown in the Figure 8.
Based on the experimental results, two ANN models were designed
to predict the viscosity and thermal conductivity. The obtained
models had a good ability to estimate these properties. The review
of the relevant literature indicates that correlations to predict the
thermophysical properties of magnetic nanofluids are very rare.
Therefore, it is essential to extend the models that can be used for
the thermophysical properties of these nanofluids. Accordingly, it is
suggested to apply ANRs to model the thermophysical properties
of ferrofluids using experimental data, as the results obtained from
numerical simulations are highly dependent on these properties.
Figure 8: Photos of a) FeCl2 solution, b) FeCl3 solution, c) hybrid nanofluid sample [25].
Application of AI algorithms in second law analysis of nanofluids
Most of the research done on nanofluids has used the first law of thermodynamics to evaluate the behavior or fundamental thermal applications, which is not sufficient to disclose the energy efficiency of these types of systems. Indeed, the processes of energy exchange result in an irreversible increase in entropy. Therefore, although the quantity of energy is invariant, its quality decreases with its conversion into another form of energy to which less work is available. As a result, the rate of thermal entropy production increases with increasing particle diameter and wall heat flux, while it decreases with increasing concentration. The friction entropy production rate increased with increasing concentration, and reduced with increasing nanoparticle enlargement, while it changed insignificantly with improving wall heat flux. Finally, a model was proposed to predict the entropy production rates by ANN using the numerical data as learning models [3].
Application of AI algorithms for nanofluids with respect to nanoparticle migration
Particle migration in nanofluids has been trivially investigated
in the studies performed and is certainly an open research
topic requiring more studies. Particle migration can affect the
characteristics of nanofluids significantly by disrupting the particle
distribution and altering the thermophysical properties. In fact,
particle migration occurs when nanoparticles do not follow the
streamlines of the base fluid. Particle migration in nanofluids has
been studied by some researchers. Some of these studies have been
done by applying AI algorithms. In a numerical study, Bahiraei &
Abdi [26] evaluated the entropy generation rates for the flow of a
titanium dioxide-water nanofluid in a minitube regarding particle
migration. A neural network model was designed to predict entropy
generation amounts as a function of volume fraction, Reynolds
number and particle diameter. The results revealed that particle
migration significantly affects entropy production, especially for
large particles and high volume fractions. The obtained ANN model
estimated the outputs with appropriate accuracy.
Bahiraei et al. [27] evaluated the impacts of particle migration
on the thermal characteristics of Al2O3 - water nanofluids by
considering Brownian and thermophoresis forces through a
numerical study. They presented an ANN model for the estimation
of the convective heat transfer coefficient. Taking into account the
influences of particle migration, a larger heat transfer coefficient
was obtained and also the volume concentration in the central
region of the tube was higher than that in the near wall. In addition,
the proposed ANN model effectively estimated the convective heat
transfer coefficient. It is also crucial to examine the attributes of
nanofluids based on the second law of thermodynamics. As a result,
some researchers have conducted the second law examination of
nanofluids, and AI capabilities have been applied in some of these
contributions.
Bahiraei & Mohammadi Majd [28] analyzed the second law of
thermodynamics for the flow of water-alumina nanofluids inside a
mini channel with triangular cross section via numerical simulations.
The impacts of wall heat flux, Reynolds number, volume fraction and
particle diameter were evaluated. Entropy generation due to heat
transfer decreased as the volume fraction and Reynolds number
were increased, while it increased with increasing nanoparticle
diameter or wall heat flux. Friction-induced entropy generation,
however, possessed an opposite trend. Entropy generation rate
patterns were obtained via an a MLP neural network. The contours
related to the total entropy generation rate obtained from this study
for a volume fraction of 5% at a channel cross section are shown in Figures 2 & 4 for three different Reynolds numbers. Heshmatian
& Bahiraei et al. [29] examined the irreversibilities due to friction
and heat transfer for the flow of a titania-water nanofluid inside a
circular microtube with numerically calculated entropy generation
rates.
It is concluded that in this review reveals that various AI
approaches including ANNs, fuzzy logic and hybrid systems have
been successfully used in the nanofluid domain. AI algorithms
present easy formulation without requiring complete knowledge
of the system, are simple to apply, adaptable and able to overcome
long delays. ANN is used due to its accuracy while fuzzy logic is
applied due to the simplicity of development and interpretation.
In addition, GA is used to allow multiple potential answers to be
produced. Hybrid AI algorithms in recent years show the notable
success of these methods. The key factor in this is the synergy
developed via intelligent mechanisms such as fuzzy logic, machine
learning, ANNs and GA. Each of these approaches results in hybrid
techniques with complementary reasoning and search methods
that allow the application of domain knowledge to solve different
problems.
The most important results of this paper as well as suggestions
for future research are as follows:
1. Among the different thermophysical properties of
nanofluids modeled by AI algorithms, most of the considerations
have been attributed to viscosity and thermal conductivity,
while less attention is given to other properties including
density and specific heat. The study of other properties of
nanofluids such as their optical properties using AI methods is
strongly suggested.
2. Unlike common fluids, sparse relationships exist to
predict convective heat transfer of nanofluids, and the need to
develop accurate correlations via AI methods is strongly felt.
3. GA approaches are promising while suffering from the
problem of too much complexity if the problem is too large.
Moreover, since the selection in GA is performed stochastically.
4. In the field of nanofluids, GA has been generally used
to determine the weights of ANNs as well as to design the
structure, while no studies have been conducted in which GA
is used to find an optimal training rule for ANNs. This idea can
be followed in the future to find optimal training algorithms to
improve the prediction task in nanofluids.
5. ANFIS has opened up a new avenue for understanding
complex behaviors and phenomena in different domains related
to nanofluids. However, ANFIS is not suitable for problems with
many inputs because under such conditions, the number of
fuzzy rules produced increases exponentially.
6. The results show that hybrid AI approaches are more
efficient than simple approaches. Therefore, hybrid approaches
can furthermore be tried for use in nanofluids.
7. The stability of nanofluids is a very critical issue that can
be evaluated using ANN models.
8. Exergy analysis of heat exchangers using AI algorithms is
suggested as a fantastic future area for optimizing the weight,
size, baffle configuration and price of heat exchangers operating
with nanofluids. Nanofluids via AI algorithms. Since there are
many factors that affect fouling, it is proposed to utilize the
capabilities of AI, especially ANNs.
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