Next Article in Journal
Factors That Influence Sustainable Selection and Reselection Intentions Regarding Soluble/Instant Coffee—The Case of Serbian Consumers
Previous Article in Journal
Effects of Online Learning Support Services on University Students’ Learning Satisfaction under the Impact of COVID-19
Previous Article in Special Issue
A Novel Approach for the Biological Desalination of Major Anions in Seawater Using Three Microalgal Species: A Kinetic Study
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Computational Fluid Dynamic Applications for Solar Stills Efficiency Assessment: A Review

by
Saleem S. AlSaleem
1,*,
Ebrahim Al-Qadami
2,
Hussein Zein Korany
3,4,
Md. Shafiquzzaman
1,
Husnain Haider
1,
Amimul Ahsan
5,6,
Mohammad Alresheedi
1,
Abdullah AlGhafis
7 and
Abdulaziz AlHarbi
8
1
Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Qassim, Saudi Arabia
2
Department of Civil and Environmental Engineering, University of Nottingham, Semenyih 43500, Selangor, Malaysia
3
Department of Mechanical Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
4
Mechanical Design and Production Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
5
Department of Civil and Environmental Engineering, Islamic University of Technology (IUT), Gazipur 1704, Bangladesh
6
Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne 3122, Australia
7
Department of Mechanical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia
8
Department of Plant Production and Protection, Agriculture College and Veterinary Medicine, Qassim University, Buraydah 51452, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(17), 10700; https://doi.org/10.3390/su141710700
Submission received: 30 July 2022 / Revised: 17 August 2022 / Accepted: 17 August 2022 / Published: 28 August 2022

Abstract

:
Even though water is a renewable resource, the majority of the available water on the planet is unfit for human use. Moreover, the drinkable water demand is ever-increasing as a result of rising population, urbanization, and life standards, which makes the needs for sustainable, economic, and environment-friendly treatment alternatives of utmost importance. Seawater desalination using solar stills has been proposed as a promising alternative that may help to solve drinkable water scarcity issues. In the past decades, many studies have been conducted to assess the performance of different types of solar stills aiming to enhance their productivity. Computational fluid dynamic (CFD) numerical simulation is one of the approaches that have been used recently to assess the performance of solar stills. The present study performed a systematic review and bibliometric analysis to provide a comprehensive overview of CFD numerical simulation uses as a tool to assess solar stills performance. A total of 486 publications were collected initially from different databases for the period between 2012 and 2022. The collected publications were filtered through several stages reaching 43 publications of highest significance. The collected data were analyzed descriptively, and the bibliometric mapping was presented. Furthermore, the basics and principles of CFD numerical simulation of solar stills efficiency were described and discussed. Later, the previous studies were analyzed to understand the algorithms, methods, and still types used. Finally, future research scopes and conclusions were stated. The presented knowledge in this study can help to provide a deep overview of using CFD in studying the efficiency of solar stills and inspire researchers to identify future research ways and gaps.

1. Introduction

Nowadays, water scarcity is thought to be one of the most important issues all around the world [1,2]. Several factors have contributed to creating and maximizing water shortage issues such as rapid population increment, environmental pollution, urbanization, nation economic progress, changes in land use, and climate changes [3,4,5,6,7,8]. The United Nations [9] reported that the global population living in urban areas increased from 0.8 billion to 4.4 billion between the years 1950 and 2020. Furthermore, the population of urban areas is expected to reach 6.7 billion by 2050. According to He et al. (2021) [10], one-third of the world population of urban areas (933 million people) was facing water scarcity; this value is expected to increase to nearly half of the global urban population by 2050 (1693–2373 billion people). According to the World Water Assessment Program (WWAP), around 1.10 billion people do not get sufficient potable water, whereas 2 billion people of 40 countries across the world suffer from water shortages [4]. In the future, it is expected that the water shortage issue will become more serious, especially with the climate change issue that has noticeable effects on water availability [11,12].
With the enormous availability of saline water in the seas and oceans, desalination was recognized as one of the most promising alternatives to overcome the water shortage issue and cover the water demand [13]. Consequently, numerous technologies and methods including multi-stage flash (MSF) desalination, multiple effect distillation (MED), thermal vapor compression (TVC), membrane distillation (MD), and reverse osmosis (RO) have been developed [14,15,16]. However, these techniques use extensive energy and require highly qualified workers and technicians for operations and maintenance [13]. Solar still (SS) is one of the most attractive seawater desalination technologies to produce safe drinkable water [17,18,19]. It is more economical as it can be fabricated using locally accessible low-cost materials and applies low maintenance and operational cost [20,21]. Besides its economic benefits, solar still is an environmentally friendly technique that can produce freshwater with low carbon dioxide (CO2) emissions [22,23].
The essential concept of the SS is based on evaporation and condensation processes at which sunlight passes through a transparent cover and is absorbed by a black basin filled with salty water. The water heats up and the water vapor condenses on the top of inclined covers, and later the pure water is drained out through the collection channels [24,25,26]. Three modes of heat transfer occur during the evaporation and condensation processes, including conduction, convection, and radiation. Conduction occurs when the heat flows from inside the solar still to the surrounding environment through the walls and glass cover, while convection occurs while the heat transfers from the basin material to the water. The radiation process exists when the solar heat flows from the sun to the solar still unit [27]. With all the economic and environmental benefits of solar stills, low water productivity remains one of the major challenges that need to be improved and enhanced [28,29,30].
Over the past years, several experimental, numerical, and analytical studies have been performed to improve the solar still’s productivity and various types of stills have been produced [31]. The developed solar still can be categorized into two main types, namely single effect stills and multi-effect stills. Based on the heat sources, these stills can be further classified into active and passive stills [31]. The passive solar still (PSS) is a desalination unit that totally depends on the direct solar energy to heat up the raw water inside the basin, while the active solar still has an external source of heating, such as solar collectors, concentrating collectors, and external and internal heaters [32,33,34]. An extensive review on solar still types and categorize can be seen in the study by Kumar et al. [31].
The science of computational fluid dynamics (CFD) can be considered as a useful tool to investigate solar still efficiency, especially with its ability to undertake essential analysis on the progress of the physical portions of computer system as well as numerical approaches. However, such studies are limited compared to the published experimental and theoretical investigations [35]. The present work aims to provide a comprehensive review of the most important studies on the CFD approach as a promising tool for studying the performance of solar stills. Systematic and bibliometric analysis assessed the level of achievements using the science of CFD in producing valuable results to enhance solar still research.

2. Methodology

This study aims to provide an extensive overview of using the art of CFD as an alternative and powerful tool to assess the performance of solar stills. The principles and guidelines of the systematic [36] and bibliometric [37] reviews were followed to conduct this study and archive its objectives. In general, a systematic literature review (SLR) is defined as a scientific approach to analyzing, determining, and selecting high-quality literature to come out with concise results [38,39]. On the other hand, to specify the structural, interconnections, and dynamic behavior of scientific research, bibliometric review and mapping can be used [40]. Coupling systematic and bibliometric methods can help to strengthen the outcomes and reduce the weakness of systematic and bibliometric reviews when conducted alone [41]. Therefore, in this study, a mixed method was followed and presented.
In this study, the PRISMA guidelines and scientometric analysis were used to review the previous literature. The process started by defining the scope of the study in which the aim of this study should be achieved at the end of the literature. Afterward, the most appropriate arrangement of the keywords was chosen and designed, with the aim that only the most relevant publications should be collected. Next, several refining processes were conducted to reach the final number of the collected publications. The selected publications were then thoroughly analyzed and discussed. Figure 1 illustrates the detailed description of the methodology adopted in the present study.

2.1. Data Collection

For data collection, the protocol was designed according to the main objectives of this study. The scope was defined to include “solar still”, “seawater desalination”, “water treatment”, “CFD”, and “numerical simulation”. The relevant publications were taken from the last 10 years (2012 and 2022). Three of the most reliable databases were considered as publication sources, including Web of Science (WoS), Scopus, and Science Direct (SD). Additional related papers published outside these databases were collected from Research Gate and Google Scholar. While searching, different combinations of keywords were designed and developed. Table 1 summarizes the keywords combinations and the terms used and the collected publications from the targeted databases. According to the initial search, a total of 486 publications were collected from journals, conferences proceeding, reviews, and dissertations. Figure 2 presents the yearly distribution of the collected data. It can be noticed that using CFD to study the efficiency of solar stills increased gradually over the past years reaching the maximum by 2021 with a total of 105 publications. Higher numbers of publications are expected in 2022 due to the availability of the advanced CFD tools and solver algorithms.

2.2. Data Refining

Before conducting reviewing and analysis processes, the collected data underwent different types of filtrations and scanning. The filtration process was performed using the following four steps. The first step excluded the repeated papers, in which the publications with the same titles from the same or different database were not considered. A total of 156 publications were removed. The second step excluded the non-English publications, in which a total of 3 publications were removed. The third step excluded the unrelated publications through the initial screening process; in this stage, only the title and abstract were investigated to check whether the collected publications lie in the scope of the present study. In this stage, a total of 246 publications were removed. The fourth step excluded the reviews and out-of-scope publications through deep screening. Complete manuscripts were evaluated, in which 38 unrelated publications were removed at this stage. Finally, a total of 43 publications meeting the scope were selected for detailed review and analysis. Table 2 summarizes each filtration step and the exclusion process.

3. Data Analysis

The collected publications were analyzed under three primary criteria to understand the CFD and solar stills research pattern and its development within the past few years: (i) yearly distribution of the publications, (ii) publication distribution per journal, and (iii) bibliometric mapping of the keywords co-occurrence.

3.1. Yearly Publication Distribution

Figure 3 shows the annual distribution of the collected publications between 2012 and 2022. A smaller number of publications can be observed between the years 2012 and 2019. Around 46.5% (20 articles out of 43) of the total works were published during the first 8 years of the selected period with a maximum publication of five articles in 2018, while highest number (12) of articles were published in year 2020. In addition, it can be noticed that over 53% of the articles were published during the past 3 years (2020–2022), which is higher than the total publications of the previous period (2012–2019). Thus, the use of CFD in studying solar still efficiency has been increasing over the recent years. This was due to the noticeable developments in the numerical approaches and the enormous improvements in the computer’s capacity.

3.2. Publication Distribution per Journals

In this study, a total of 29 different sources (journals) were observed from which the refined publications were published. It can be seen that the research topic of evaluating solar stills performance by using CFD approaches is a hot research trend, and several sources are interested to accept and publish related articles. The analysis found that eight sources were published with more than two articles while the rest were published with only one article each. Desalination and Water Treatment Journal published the highest with a percentage of 14.0% (six articles). Desalination Journal ranked second with a publication percentage of 9% (four articles). On the other hand, Energy, Energy Conversion and Management, International Journal of Ambient Energy, IOP Conference Series: Materials Science and Engineering, Materials Today: Proceedings, and Solar Energy published two articles each with a percentage less than 5% and total publications of 12. Other journals published 21 articles with a percentage of 49% as shown in Figure 4.

3.3. Publication Distribution by Locations

Content analysis was used to group the gathered publications based on the study locations and find the geographical locus. It is worth mentioning that several countries are interested to conduct research works on assessing the performance of solar stills using CFD. The collected publications were found to be conducted in 12 different countries across the world. Iran ranked first with a total publication of 13 articles (30%) followed by India with total publications of 12 articles (28%). These observations reveal that the most interesting countries on this research topic are commonly suffering from water shortage issues and have plenty of solar energy during the whole year. China and Egypt published four articles each with a percentage value of 9%. Mexico and Morocco came next with a publication percentage of 5% each (four articles). Other countries including Algeria, Iraq, Jordan, Pakistan, Saudi Arabia, and USA published only one article each, with a total percentage of 14% as shown in Figure 5.

3.4. Keywords Bibliometric Mapping and Analyses

According to Su et al. [42] and He et al. [43], keywords can be used to assist scholars in comprehending mainstream subjects and highlighting potential study areas. Moreover, Eck and Waltman [44] claimed that the research domain knowledge can be demonstrated and displayed through the visualizations and understanding of the keywords’ connections. In addition, keyword co-occurrence analysis can help to provide an overview of the research topic’s progress over time [45]. In this study, a keywords co-occurrence bibliometric mapping was performed by using VOSviewer software. The minimum frequency threshold of keywords co-occurrence was chosen to be 2. Among the total keywords (130 keywords), only 30 met the designed thresholds which were represented in Figure 6. In Figure 6, the circle and font sizes represent the keyword co-occurrence rate, with a greater size indicating a higher rate of co-occurrence. Different colors represent different clusters at which four clusters can be noticed in Figure 6. Links width represents the strength of the relationship and connections between each keyword at which the connection between terms “solar still” and” CFD” was the highest as can be seen in Figure 6. It was found out that the terms “solar still” and “CFD” are the most common keywords that have been used with co-occurrence rate of 16 and 11, respectively, for the average publication year of 2019. The terms “CFD simulation”, “desalination”, “solar desalination”, and “solar energy” scored next with co-occurrence rate of 8, 7, 5, and 5, respectively. Table 3 summarizes the keywords co-occurrence in terms of tabular form.

4. Computational Fluid Dynamics; Basics and Principles

The art of CFD is a well-known method for analyzing and solving fluid flow issues in a variety of settings. It is a simulation tool that uses applied and computational mathematics to predict heat, mass, and momentum transfer behavior in fluid flow regimes. This analysis, which is based on a simulation procedure, is carried out using computing machines [46]. The accuracy of CFD models has improved in recent years, indicating that CFD is a valid technique for performance analysis and design development for a wide range of thermal-hydraulic systems. This enables researchers to employ it in the simulation of evaporation and condensation processes with a vapor-liquid phase change process, as well as mass and heat transfers, which is one of the most pressing challenges in the development and study of solar stills. CFD is also defined as the analysis of systems, such as fluid flow, heat transfer, and associated phenomena, using computer simulation and numerical investigation to solve mathematical equations. The continuity, momentum, and energy equations, which compose a system of nonlinear partial differential equations, are the governing equations of fluid flow. CFD is a discretization method that uses a set of algebraic equations instead of the differential equations that regulate fluid flow due to the presence of non-linear components. Moreover, there are several discretization methods for CFD; the most utilized methods are the finite difference method (FDM), finite volume method (FVM), finite element method (FEM), and boundary element method [35,47].
Both experimental and CFD methods are commonly used in analyzing the performance of SS, as well as its design and optimization. The CFD technique has numerous advantages over experimental approaches to the design of various fluid systems, including a significant reduction in required time and cost, the ability to investigate systems when experiments are difficult or impossible to conduct (e.g., large systems), the ability to investigate systems under hazardous conditions (e.g., studies for safety and incident purposes), and the ability to provide practically limitless result details. In comparison to experimental work, CFD uses a fairly basic technique to accomplish numerical solutions such as pressure distribution, temperature fluctuations, and flow characteristics in a short period and at a low cost. Various software has been developed in light of the CFD codes that have been implemented, a few of which are: CFDRC-Esi, STAR-CD, CFX, FLUENT, PHOENICS, FLOVENT, and CFDRC-Esi [35].
In general, the processes of conducting numerical simulations to assess SS efficiency in all software are the same. Figure 7 shows a flow chart that illustrates the steps that are commonly followed to conduct the numerical runs. Basically, developing a proper computational grid is the first step to create a numerical model. The grid is comprised of many cells and elements connected to each other in which they represent the original physical domain. The main function of the developed grid is to divide the whole physical domain into smaller volumes for the purpose of solving the fluid governing equations in each grid. Moreover, the unknown parameters values are usually stored in these grids [48,49]. There are several types of computational grids that can be used to develop the numerical models such as tetrahedral, pyramidal, prismatic, and hexahedral (rectangular) grids. Selection of proper grid type is one of the main factors that can help to generate more accurate results and more stable simulations. Beside the grid type, adopting proper grid size is important to produce more accurate results and stable simulations. The second step in creating a numerical model is choosing the solver algorithm that will be used to solve the governing equations. Later, material properties and boundary conditions should be defined accurately to represent the real physical domain. Finally, proper simulation controls such as time steps and iteration numbers should be defined before running the solver [50,51].

5. Solar Stills and CFD

Recently, CFD numerical simulations have been widely used to investigate the working mechanism of different solar still systems due to several reasons such as (i) significant reducing time and cost required, (ii) providing more details about the targeted parameters, and (iii) producing results within the complex systems at which the experimental work is hard to be conducted [35]. Hence, many researchers have established using CFD numerical modeling to study the efficiency of solar stills under different climatic conditions and configurations. For instance, El-Sebaey et al. (2020) [13] investigated the performance of single slope solar still experimentally and numerically. Ansys Fluent was used as a CFD tool to conduct the numerical runs. Temperature variations of the glass cover, solar collector, and mixture were presented in 3-D and 2-D renderings at different times during the day as shown in Figure 8. In addition, hourly and cumulative solar still productivity was evaluated and calculated numerically. Later, the numerical outcomes were compared with the experimental investigations and good agreements were observed. A daily efficiency study was performed on the numerical and experimental outcomes, and it was noticed that the numerical daily was higher with a percentage value of 8.3%.
Rahbar and Esfahani [52] developed a 2-D CFD numerical simulation model to evaluate the effects of natural convection within a single-slope solar still. Figure 9 shows a simple sketch and configuration of the adopted solar still at which L is the length, Hl and Hr are the lift and right heights, respectively, Tw and Tg are the bottom and top temperature, respectively, g is the gravity, and θ is the glass cover angle. As a result, a new correlation for determining the convective heat transfer coefficient was proposed. In addition, it was reported that the heat transfer coefficient rises as the Rayleigh number increases for a certain aspect ratio (A = L/H) as presented in Figure 10.
Rahbar and Esfahani [46] employed the 2-D computational fluid dynamic simulation technique to evaluate the productivity of a single-slope solar still and optimize its geometrical and operating parameters. It was reported that the productivity increases with decrement of the specific height at a fixed length of the solar still. Moreover, it was found that there is an optimum length in which the still productivity is maximized (see Figure 11). Furthermore, it was found that the rise of freshwater productivity follows the trend of the convective heat transfer coefficient (see Figure 11). The obtained results were compared with the results of well-known models of Chiltone-Colburn analogy and Bulk-motion, and good agreement between both results was reported.
Double-diffusive free convection and surface thermal radiation of an inclined solar still unit were simulated by Alvarado-Juárez et al. [53]. Finite volume method was employed to solve the 2-D steady-state and laminar flow governing equations. Various angles of the glass cover were tested at which the streamlines, isotherms, isolines of vapor, water mass flow rate, and average Nusselt (Nu) and Sherwood (Sh) numbers were reported. According to the results, it was found that surface thermal radiation varies the fluid flow from a one-cell to a multi-cellular pattern because it increases the velocity near the walls. As a result, the average convective Nusselt number, total Nu, and Sh were all increased by around 25%, 17%, and 15%, respectively. Furthermore, it was noticed that the distillate mass flow rate increases with the increment of the aspect ratio and inclined glass cover angle.
Rahbar et. al. [54] studied the capability of a 2-D CFD simulation technique in predicting the heat and mass transfer in tubular solar still. Figure 12 shows a simple sketch of the adopted model. Based on the obtained results, a novel relationship to estimate tubular solar still parameters (water productivity, heat, and mass transfer coefficients) were introduced. Moreover, a characteristic curve to evaluate water productivity in various operating situations was presented. Furthermore, it was reported that the glass temperature has an inverse influence on the performance of tubular solar still, whereas water temperature has a direct effect as shown in Figure 13. The contours of vapor mass fraction at different values of buoyancy ratio and Rayleigh number were investigated and presented as well (see Figure 14). Rapid changes in vapor mass fraction due to the evaporation and condensation process were observed near to the water surface and glass cover as shown in Figure 14.
Rashidi et al. [55] conducted a 2-D numerical simulation to optimize the position and size of a partition that was proposed to be placed inside a single slope solar still. Figure 15 shows the schematic view of the adopted solar still and its dimensions. A 2-D square mesh was adopted to capture the fluid and solid domains as shown in Figure 16. The mesh was refined close the boundaries where the velocity and other parameters are expected to be changed rapidly. The sensitivity analysis is used to determine the response’s sensitivity (Nusselt number) to the partition position and height. Figure 17 shows the contours for several parameters after optimizing the position and height of the proposed partition. It can be seen that installation of partition helps to increase the vortices number in small sizes which provides a sufficient time to heat exchange process and increase the still efficiency. Furthermore, rapid changes in temperature close to the water surface and glass cover can be noticed which was occurred due to the phenomena of condensation and evaporation.
Using the ANSYS-CFX solver, Malaiyappan and Elumalai [56] performed a 3D numerical analysis based on CFD simulations to investigate the performance of a single slope solar still with three different basin materials, namely galvanized iron, glass, and aluminum. The phase change interactions between water evaporating into vapor at the liquid-gas interface and vapor condensing into water droplets on the top glass surface were represented in their model. Figure 18 shows a comparison between the contours of the liquid water fraction for the three different stills. It was reported that the solar still with the aluminum basin material produced the highest amount of freshwater. A comparison between the obtained numerical results and experimental results was performed and the average errors between both found to be 5.26% under the same boundary conditions as presented in Figure 19.
Using ANSYS FLUENT software, Taamneh [57] developed a 3D two-phase numerical model to study the evaporation and condensation process inside a pyramid-shaped solar still as displayed in Figure 20. Two types of raw water were placed inside two identical pyramid solar stills including Jordanian zeolite-seawater and seawater. Figure 21 shows the hourly water temperature variation for both samples, and it can be seen that the zeolite-seawater temperature was higher during the whole day when compared with normal seawater. Moreover, it was reported that the freshwater that was collected from the still with zeolite-seawater is higher than the freshwater collected from the other still as shown in Figure 22. The obtained numerical results were compared with the experimental results and a good agreement between both were observed.
Bait and Ameur [58] used SOLIDWORKS and ANSYS FLUENT software to perform a 3D numerical analysis of the geometrical dimensions of a multi-stage solar still in order to track multi-physics evaporation and condensation mechanisms inside the distillation tower (four stages) as shown in Figure 23. The main objective of the analysis was to improve the thermal performance of the solar still system as well as its productivity in order to manufacture it locally. Their parametric investigation has shown that the four-stage distillation system, which is completely isolated from the external medium, can operate at high temperatures of roughly 80.96 °C.
Rashidi et al. [59] used a 2-D computational fluid dynamic simulation to study the impacts of nanofluid on the freshwater productivity of stepped solar still as shown in Figure 24. A sensitivity analysis was utilized to investigate the sensitivity of hourly productivity to step height and length. The shape of the steps inside the still were optimized by utilizing the response surface methodology (RSM). According to the findings, raising nanoparticle concentration from 0% to 5% leads to a 22% increase in hourly productivity. It was concluded that when the stair length is increased for medium and high values of the stair height, the sensitivity of the hourly productivity to the stair height improves, but when the stair length is increased for small values of the stair height, it decreases. Finally, CFD simulation results were compared with RSM results, and the maximum error was found to be 2.4%.
Panchal and Patel [60] estimated the performance of a single slope solar still by using a 3-D CFD simulation model through simulating condensation and evaporation processes. An unstructured mesh type tetrahedral was adopted to capture both fluid and solid domains as shown in Figure 25. It was noticed that the temperature reaches to the highest value near to the glass cover as shown in Figure 26. Furthermore, it was reported that the solar still productivity is highly affected by the temperature of the inner glass, outside glass cover, basin water temperature, and vapor temperature. A comparison between experimental and the obtained numerical results was performed in terms of hourly productivity and a good agreement was observed as shown in Figure 27.
Arunkumar et al. [61] used CFD modeling to investigate the effect of using carbon impregnated foam (CIF) as a floating absorber with bubble-wrap (BW) insulation material on the freshwater productivity in a single slope solar still (SSSS) desalination unit. The performance of four identical SSSSs was studied under the same environmental conditions as presented in Figure 28. The CIF was permitted to float on the water surface, with a diameter of 170 mm and a thickness of 15 mm. The floating absorbers worked as heat storage, increasing the evaporative surface area of the basin because the CIF was open pore and hydrophilic as shown in Figure 29. According to the results, it was noticed that the freshwater productivity of the SSSS without insulation and with BW insulation were 1.9 L/m2/day and 2.3 L/m2/day, respectively, whereas the productivity was better (3.1 L/m2/day) when using SSSS-CIF with BW insulation. A conventional solar still with sawdust insulation achieved a daily productivity of 2.2 L/m2.
Rahbar et al. [62] conducted experimental and CFD modeling to study the influence of geometry on the performance of triangular and tubular solar stills as shown in Figure 30. Second law analysis is used to estimate the local entropy generation inside the cavity of these solar stills. The results showed that the tubular had a 20% advantage over the triangular in terms of performance. Furthermore, simulation results demonstrated that the stronger recirculating zones and reduced entropy generation are the major reasons for the tubular still producing more freshwater as presented in Figure 31. In addition, life cost analysis was used to estimate the cost of generated freshwater. As a result, two correlations were given to forecasting the solar stills’ efficiency and production under diverse weather conditions.
Maheswari and Reddy [63] conducted an experimental and CFD numerical study to evaluate the efficiency of six different configurations of solar stills including single slope solar still with 15° cover angle, single slope solar still with 20° cover angle, steps solar still, fins solar still, single slope solar still with phase modification materials (PCM), and double slope solar still. A tetrahedral mesh was generated for each type of solar still as shown in Figure 32. Water mass fraction was analyzed, and the 2-D and 3-D contours were generated for all the simulated models. It was found that the productivity of the single slope solar still with a 20° cover angle and integrated with PCM is the highest when compared to other types.
Tiantong et al. [64] used CFD modeling to investigate the effects of operating pressure Pop and geometrical parameters on the efficiency of a tubular solar still (TSS), with an emphasis on vapor flow in the enclosure. Figure 33a,b show the general overview of the adopted solar still geometry and the computational fluid mesh domain, respectively. Tiantong et al. [60] reported that vacuum operation led to higher humid air-flow velocities and greater water vapor mass diffusion within the TSS as shown in Figure 34. At operating pressures less than 60 kPa, the freshwater yield rate was enhanced by more than 50% as displayed in Figure 35. In addition, they show that the yield rate was observed to decrease as the water depth and radius ratio (r/R) decreased. Its value was reduced by 30% when the water depth was reduced from r to 0.8 r, and by 38% when the radius ratio (r/R) was varied from 0.6 to 0.9 at an operating pressure of 20 kPa.
Table 4 summarizes the most common studies that have been published investigating solar still efficiency using the CFD approach. Based on the table it can be seen that the efficiency of almost all solar still types such as single slope, double slope, pyramid, etc. can be studied and analyzed using CFD. Moreover, several parameters can be investigated and visualized using the powerful tools of CFD numerical modeling such as raw water temperature variation, fluid and vapor fraction, productivity, mixture velocity, heat, mass transfer coefficients, and solar still components temperatures. 2-D and 3-D contour rendering can be presented and introduced as well to provide a close look at the evaporation and condensation process and the temperature variations with time for each solar still component. Ansys fluent and Ansys CFX with the advantage of the Volume of Fluid (VOF) function was found to be the most common software that has been used in the previous studies which makes it the most recommended CFD tool when conducting future analysis. Furthermore, comparisons between the CFD results and experimental results were performed. According to Table 4, it was found that the differences between experimental and CFD numerical approaches are small and neglectable.

6. Future Ways and Research Gapes

The state-of-the-art literature review highlights the following future research directions and recommendations to enhance CFD applications in studying the performance of solar still systems.
  • The impact of the most common parameters that affect solar still productivity can be investigated and studied using CFD such as cover material, its angle, and its thickness, raw water type (lake, seawater), basin material and its absorptivity, and isolation materials between the system and the surrounding environment.
  • Active solar still that is integrated with a solar system or internal heater can be simulated and investigated using the CFD tool.
  • Solar still geometry optimization can be conducted by employing the CFD tool.
  • Complicated solar still systems such as solar still with separate condensers or those with external heater systems can be investigated by utilizing the CFD tool.
  • The impact of phase change materials (PCM) on solar still systems efficiency can be evaluated by using CFD.

7. Conclusions

The present study evaluated the use of CFD numerical simulations in assessing the performance of solar stills through systematic and bibliometric reviews coupled with content analysis. Among the 486 collected publications, 43 articles were further considered and analyzed. It was found that the CFD approach is a powerful tool that can be used to investigate and validate the performance of solar still systems under different climate conditions considering both evaporation and condensation processes. Moreover, CFD tools were found to be an applicable method to simulate different types of solar still such as single slope, double slope, pyramid, and tubular solar stills. Furthermore, detailed results, such as 2-D and 3-D renderings, can be produced from the CFD numerical modeling which can help to provide a better results visualization. Therefore, the CFD approach is recommended to be further used in assessing solar still systems and validating the experimental investigation for better improvement of their performances and efficiencies.

Author Contributions

Conceptualization, equal; methodology, E.A.-Q.; software, E.A.-Q.; validation, M.S., H.H. and A.A. (Amimul Ahsan); formal analysis, S.S.A., E.A.-Q. and H.Z.K.; investigation, S.S.A., E.A.-Q. and H.Z.K.; resources, S.S.A., E.A.-Q., H.Z.K., M.S. and A.A. (Abdullah AlGhafis); data curation, E.A.-Q. and H.Z.K.; writing—original draft preparation, S.S.A. and E.A.-Q.; writing—review and editing, M.S., H.H., A.A. (Amimul Ahsan), A.A. (Abdulaziz AlHarbi), and M.A.; visualization, E.A.-Q. and M.S.; supervision, S.S.A.; project administration, S.S.A.; funding acquisition, S.S.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Deputyship for Research & Innovation, Ministry of Education, Saudi Arabia project number (QU-IF-1-3-2).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All required data are included in the main manuscript.

Acknowledgments

The author gratefully acknowledges Deputyship for Research & Innovation, Ministry of Education, Saudi Arabia for funding this research work through the project number (QU-IF-1-3-2). The authors also thank the technical support of Qassim University.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Al-Qadami, E.H.H.; Abdurrasheed, A.S.; Mustaffa, Z.; Mugahed, A.Y.; Yusof, K.W.; Ahsan, A. Productivity Enhancement of a Double Slope Solar Still Coupled with a Solar System. J. Ecol. Eng. 2020, 21, 255–263. [Google Scholar] [CrossRef]
  2. Van Vliet, M.T.; Jones, E.R.; Flörke, M.; Franssen, W.H.; Hanasaki, N.; Wada, Y.; Yearsley, J.R. Global water scarcity including surface water quality and expansions of clean water technologies. Environ. Res. Lett. 2021, 16, 24020. [Google Scholar] [CrossRef]
  3. Al-Qadami, E.H.H.; Ahsan, A.; Abdurrasheed, A.S.I.; Mustaffa, Z.; Yusof, K.W.; Takaijudin, H.; Malek, M.A. Yield efficiency evaluation of double slope solar stills connected with external spiral copper for potable water production. J. Ecol. Eng. 2019, 20, 176–186. [Google Scholar] [CrossRef]
  4. Shamseddini, A. Reducing the Risk of Water Scarcity by Optimizing Water Allocation: A Review. International Con-ference on Disaster Management: From Polar Region to the Local Communities Social and Environmental Development National Institute of Development Administration (NIDA), India, 2016. Available online: http://ssed.nida.ac.th/images/PDF/inter_conf_2016/No.6.pdf (accessed on 29 July 2022).
  5. Kubiak-Wójcicka, K.; Machula, S. Influence of climate changes on the state of water resources in Poland and their usage. Geosciences 2020, 10, 312. [Google Scholar] [CrossRef]
  6. Iaccarino, M. Why there is water scarcity. AIMS Geosci. 2021, 7, 529–541. [Google Scholar] [CrossRef]
  7. Mehrazar, A.; Bavani, A.R.M.; Gohari, A.; Mashal, M.; Rahimikhoob, H. Adaptation of water resources system to water scarcity and climate change in the suburb area of megacities. Water Resour. Manag. 2020, 34, 3855–3877. [Google Scholar] [CrossRef]
  8. Wu, Z.; Zhang, Y.; Hua, Y.; Ye, Q.; Xu, L.; Wang, S. An Improved System Dynamics Model to Evaluate Regional Water Scarcity from a Virtual Water Perspective: A Case Study of Henan Province, China. Sustainability 2020, 12, 7517. [Google Scholar] [CrossRef]
  9. United Nations (UN). Revision of World Urbanization Prospects; UN: New York, NY, USA, 2018; Available online: https://population.un.org/wup (accessed on 14 August 2022).
  10. He, C.; Liu, Z.; Wu, J.; Pan, X.; Fang, Z.; Li, J.; Bryan, B.A. Future global urban water scarcity and potential solutions. Nat. Commun. 2021, 12, 4667. [Google Scholar] [CrossRef]
  11. Greve, P.; Kahil, T.; Mochizuki, J.; Schinko, T.; Satoh, Y.; Burek, P.; Fischer, G.; Tramberend, S.; Burtscher, R.; Langan, S.; et al. Global assessment of water challenges under uncertainty in water scarcity projections. Nat. Sustain. 2018, 1, 486–494. [Google Scholar] [CrossRef]
  12. Schewe, J.; Heinke, J.; Gerten, D.; Haddeland, I.; Arnell, N.W.; Clark, D.B.; Dankers, R.; Eisner, S.; Fekete, B.M.; Colón-González, F.J.; et al. Multimodel assessment of water scarcity under climate change. Proc. Natl. Acad. Sci. USA 2014, 111, 3245–3250. [Google Scholar] [CrossRef] [Green Version]
  13. El-Sebaey, M.S.; Ellman, A.; Hegazy, A.; Ghonim, T. Experimental analysis and CFD modeling for conventional basin-type solar still. Energies 2020, 13, 5734. [Google Scholar] [CrossRef]
  14. Catrini, P.; Cipollina, A.; Giacalone, F.; Micale, G.; Piacentino, A.; Tamburini, A. Thermodynamic, exergy, and thermoeconomic analysis of multiple effect distillation processes. In Renewable Energy Powered Desalination Handbook; Elsevier: Amsterdam, The Netherlands, 2018; pp. 445–489. [Google Scholar] [CrossRef]
  15. Tiwari, G.; Sahota, L. Review on the energy and economic efficiencies of passive and active solar distillation systems. Desalination 2017, 401, 151–179. [Google Scholar] [CrossRef]
  16. Wang, Y.-N.; Wang, R. Reverse osmosis membrane separation technology. In Membrane Separation Principles and Applications; Elsevier: Amsterdam, The Netherlands, 2019; pp. 1–45. [Google Scholar]
  17. Abimbola, T.O.; Yusof, K.W.; Takaijudin, H.; Abdurrasheed, A.S.; Al-Qadami, E.H.H.; Ishola, S.A.; Owoseni, T.A.; Akilu, S. A Concise Review of Major Desalination Techniques: Features and Limitations. In Proceedings of the International Conference on Civil, Offshore and Environmental Engineering, Kuching, Malaysia, 13–15 July 2021; pp. 154–162. [Google Scholar]
  18. Xu, Z.; Zhang, L.; Zhao, L.; Li, B.; Bhatia, B.; Wang, C.; Wilke, K.L.; Song, Y.; Labban, O.; Lienhard, J.H.; et al. Ultrahigh-efficiency desalination via a thermally-localized multistage solar still. Energy Environ. Sci. 2020, 13, 830–839. [Google Scholar] [CrossRef]
  19. Ayoub, G.M.; Malaeb, L. Developments in solar still desalination systems: A critical review. Crit. Rev. Environ. Sci. Technol. 2012, 42, 2078–2112. [Google Scholar] [CrossRef]
  20. Kabeel, A.E.; Harby, K.; Abdelgaied, M.; Eisa, A. A comprehensive review of tubular solar still designs, performance, and economic analysis. J. Clean. Prod. 2020, 246, 119030. [Google Scholar] [CrossRef]
  21. Ayoub, G.M.; Malaeb, L. Economic feasibility of a solar still desalination system with enhanced productivity. Desalination 2014, 335, 27–32. [Google Scholar] [CrossRef]
  22. Manchanda, H.; Kumar, M. A comprehensive decade review and analysis on designs and performance parameters of passive solar still. Renew. Wind. Water Sol. 2015, 2, 17. [Google Scholar] [CrossRef]
  23. Asadi, R.Z.; Suja, F.; Ruslan, M.H.; Jalil, N.A. The application of a solar still in domestic and industrial wastewater treatment. Sol. Energy 2013, 93, 63–71. [Google Scholar] [CrossRef]
  24. Sathyamurthy, R.; El-Agouz, S.; Dharmaraj, V. Experimental analysis of a portable solar still with evaporation and condensation chambers. Desalination 2015, 367, 180–185. [Google Scholar] [CrossRef]
  25. Kwatra, H.S. Performance of a solar still: Predicted effect of enhanced evaporation area on yield and evaporation temperature. Sol. Energy 1996, 56, 261–266. [Google Scholar] [CrossRef]
  26. Kumar, K.V.; Bai, R.K. Performance study on solar still with enhanced condensation. Desalination 2008, 230, 51–61. [Google Scholar] [CrossRef]
  27. Kalogirou, S. Seawater desalination using renewable energy sources. Prog. Energy Combust. Sci. 2005, 31, 242–281. [Google Scholar] [CrossRef]
  28. Mevada, D.; Panchal, H.; Sadasivuni, K.K.; Israr, M.; Suresh, M.; Dharaskar, S.; Thakkar, H. Effect of fin configuration parameters on performance of solar still: A review. Groundw. Sustain. Dev. 2020, 10, 100289. [Google Scholar] [CrossRef]
  29. Sharshir, S.W.; Elsheikh, A.H.; Ellakany, Y.M.; Kandeal, A.W.; Edreis, E.M.A.; Sathyamurthy, R.; Thakur, A.K.; Eltawil, M.A.; Hamed, M.H.; Kabeel, A.E. Improving the performance of solar still using different heat localization materials. Environ. Sci. Pollut. Res. 2020, 27, 12332–12344. [Google Scholar] [CrossRef]
  30. Ayoub, G.M.; Malaeb, L.; Saikaly, P.E. Critical variables in the performance of a productivity-enhanced solar still. Sol. Energy 2013, 98, 472–484. [Google Scholar] [CrossRef]
  31. Kumar, P.V.; Kumar, A.; Prakash, O.; Kaviti, A.K. Solar stills system design: A review. Renew. Sustain. Energy Rev. 2015, 51, 153–181. [Google Scholar] [CrossRef]
  32. Manokar, A.M.; Winston, D.P.; Kabeel, A.; El-Agouz, S.; Sathyamurthy, R.; Arunkumar, T.; Madhu, B.; Ahsan, A. Integrated PV/T solar still- A mini-review. Desalination 2018, 435, 259–267. [Google Scholar] [CrossRef]
  33. Abimbola, T.O.; Takaijudin, H.; Singh, B.S.M.; Yusof, K.W.; Abdurrasheed, A.S.; Al-Qadami, E.H.H.; Isah, A.S.; Wong, K.X.; Nadzri, N.F.A.; Ishola, S.A.; et al. Comprehensive passive-mode performance analysis on a new multiple-mode solar still for sustainable clean water processing. J. Clean. Prod. 2021, 334, 130214. [Google Scholar] [CrossRef]
  34. Sharshir, S.; Kandeal, A.; Ismail, M.; Abdelaziz, G.; Kabeel, A.; Yang, N. Augmentation of a pyramid solar still performance using evacuated tubes and nanofluid: Experimental approach. Appl. Therm. Eng. 2019, 160, 113997. [Google Scholar] [CrossRef]
  35. Kabeel, A.; El-Said, E.M.; Abdulaziz, M. Computational fluid dynamic as a tool for solar still performance analysis and design development: A review. Desalination Water Treat. 2019, 159, 200–213. [Google Scholar] [CrossRef]
  36. Briner, R.B.; Denyer, D. Systematic review and evidence synthesis as a practice and scholarship tool. In Oxford Handbook of Evidence-Based Management; Oxford University Press: Oxford, UK, 2012; pp. 112–129. [Google Scholar]
  37. De Oliveira, O.J.; Da Silva, F.F.; Juliani, F.; Barbosa, L.C.F.M.; Nunhes, T.V. Bibliometric method for mapping the state-of-the-art and identifying research gaps and trends in literature: An essential instrument to support the development of scientific projects. In Scientometrics Recent Advances; IntechOpen: London, UK, 2019. [Google Scholar]
  38. Al-Qadami, E.H.H.; Mustaffa, Z.; Al-Atroush, M.E. Evaluation of the Pavement Geothermal Energy Harvesting Technologies towards Sustainability and Renewable Energy. Energies 2022, 15, 1201. [Google Scholar] [CrossRef]
  39. Alaloul, W.S.; Qureshi, A.H.; Musarat, M.A.; Saad, S. Evolution of close-range detection and data acquisition technologies towards automation in construction progress monitoring. J. Build. Eng. 2021, 43, 102877. [Google Scholar] [CrossRef]
  40. Baarimah, A.O.; Alaloul, W.S.; Liew, M.S.; Kartika, W.; Al-Sharafi, M.A.; Musarat, M.A.; Alawag, A.M.; Qureshi, A.H. A Bibliometric analysis and review of building information modelling for post-disaster reconstruction. Sustainability 2021, 14, 393. [Google Scholar] [CrossRef]
  41. Johnson, R.B.; Onwuegbuzie, A.J. Mixed methods research: A research paradigm whose time has come. Educ. Res. 2004, 33, 14–26. [Google Scholar] [CrossRef]
  42. Su, H.-N.; Lee, P.-C. Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology Foresight. Scientometrics 2010, 85, 65–79. [Google Scholar] [CrossRef]
  43. He, Q.; Wang, G.; Luo, L.; Shi, Q.; Xie, J.; Meng, X. Mapping the managerial areas of Building Information Modeling (BIM) using scientometric analysis. Int. J. Proj. Manag. 2017, 35, 670–685. [Google Scholar] [CrossRef]
  44. Van Eck, N.J.; Waltman, L. Visualizing bibliometric networks. In Measuring Scholarly Impact; Springer: Cham, Switzerland, 2014; pp. 285–320. [Google Scholar]
  45. Van Eck, N.J.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2009, 84, 523–538. [Google Scholar] [CrossRef]
  46. Rahbar, N.; Esfahani, J. Productivity estimation of a single-slope solar still: Theoretical and numerical analysis. Energy 2013, 49, 289–297. [Google Scholar] [CrossRef]
  47. Abdurrasheed, A.S.; Yusof, K.W.; Takaijudin, H.; Al-Qadami, E.H.H.; A Ghani, A.; Muhammad, M.M.; Sholagberu, A.T.; Kumar, V.; Patel, S.M. An integrated technique for assessing flow parameters through subsurface drainage module systems. IOP Conf. Ser. Earth Environ. Sci. 2020, 476, 12112. [Google Scholar] [CrossRef]
  48. Sa’id Abdurrasheed, A.; Wan Yusof, K.; Hamid Hussein Alqadami, E.; Takaijudin, H.; Ab Ghani, A.; Muhammad, M.M.; Sholagberu, A.T.; Zainalfikry, M.K.; Osman, M.; Shihab Patel, M. Modelling of flow parameters through subsurface drainage modules for application in BIOECODS. Water 2019, 11, 1823. [Google Scholar] [CrossRef]
  49. Al-Qadami, E.H.H.; Mustaffa, Z.; Abdurrasheed, A.S.; Khamaruzaman, W.Y.; Shah, S.M.H.; Malek, M. Numerical assessment on floating stability limits for static vehicle under partial submergence. J. Eng. Sci. Technol. 2020, 15, 1384–1398. [Google Scholar]
  50. Al-Qadami, E.H.H.; Abdurrasheed, A.S.; Mustaffa, Z.; Yusof, K.W.; Malek, M.; Ab Ghani, A. Numerical modelling of flow characteristics over sharp crested triangular hump. Results Eng. 2019, 4, 100052. [Google Scholar] [CrossRef]
  51. Al-Qadami, E.H.H.; Mustaffa, Z.; Martínez-Gomariz, E.; Yusof, K.W.; Abdurrasheed, A.S.; Shah, S.M.H. Numerical Simulation to Assess Floating Instability of Small Passenger Vehicle Under Sub-Critical Flow. In Proceedings of the International Conference on Civil, Offshore and Environmental Engineering, Kuching, Malaysia, 13–15 July 2021; pp. 258–265. [Google Scholar]
  52. Rahbar, N.; Esfahani, J.A. Estimation of convective heat transfer coefficient in a single-slope solar still: A numerical study. Desalination Water Treat. 2012, 50, 387–396. [Google Scholar] [CrossRef]
  53. Alvarado-Juárez, R.; Álvarez, G.; Xamán, J.; Hernández-López, I. Numerical study of conjugate heat and mass transfer in a solar still device. Desalination 2013, 325, 84–94. [Google Scholar] [CrossRef]
  54. Rahbar, N.; Esfahani, J.A.; Fotouhi-Bafghi, E. Estimation of convective heat transfer coefficient and water-productivity in a tubular solar still–CFD simulation and theoretical analysis. Sol. Energy 2015, 113, 313–323. [Google Scholar] [CrossRef]
  55. Rashidi, S.; Bovand, M.; Esfahani, J.A. Optimization of partitioning inside a single slope solar still for performance improvement. Desalination 2016, 395, 79–91. [Google Scholar] [CrossRef]
  56. Malaiyappan, P.; Elumalai, N. Numerical investigations: Basin materials of a single-basin and single-slope solar still. Desalination Water Treat. 2015, 57, 21211–21233. [Google Scholar] [CrossRef]
  57. Taamneh, Y. Influence of Jordanian zeolite on the performance of a solar still: Experiments and CFD simulation studies. Water Supply 2016, 16, 1700–1709. [Google Scholar] [CrossRef]
  58. Bait, O.; Si–Ameur, M. Numerical investigation of a multi-stage solar still under Batna climatic conditions: Effect of radiation term on mass and heat energy balances. Energy 2016, 98, 308–323. [Google Scholar] [CrossRef]
  59. Rashidi, S.; Bovand, M.; Rahbar, N.; Esfahani, J.A. Steps optimization and productivity enhancement in a nanofluid cascade solar still. Renew. Energy 2018, 118, 536–545. [Google Scholar] [CrossRef]
  60. Panchal, H.N.; Patel, N. ANSYS CFD and experimental comparison of various parameters of a solar still. Int. J. Ambient Energy 2017, 39, 551–557. [Google Scholar] [CrossRef]
  61. Arunkumar, T.; Kabeel, A.; Raj, K.; Denkenberger, D.; Sathyamurthy, R.; Ragupathy, P.; Velraj, R. Productivity enhancement of solar still by using porous absorber with bubble-wrap insulation. J. Clean. Prod. 2018, 195, 1149–1161. [Google Scholar] [CrossRef]
  62. Rahbar, N.; Asadi, A.; Fotouhi-Bafghi, E. Performance evaluation of two solar stills of different geometries: Tubular versus triangular: Experimental study, numerical simulation, and second law analysis. Desalination 2018, 443, 44–55. [Google Scholar] [CrossRef]
  63. Maheswari, C.U.; Reddy, R.M. CFD Analysis of different types of single basin solar stills. IOP Conf. Ser. Mater. Sci. Eng. 2018, 330, 012097. [Google Scholar] [CrossRef]
  64. Yan, T.; Xie, G.; Liu, H.; Wu, Z.; Sun, L. CFD investigation of vapor transportation in a tubular solar still operating under vacuum. Int. J. Heat Mass Transf. 2020, 156, 119917. [Google Scholar] [CrossRef]
  65. Mouhsin, N.; Bouzaid, M.; Taha-Janan, M.; Oubrek, M. Modeling and experimental study of cascade solar still. SN Appl. Sci. 2020, 2, 708. [Google Scholar] [CrossRef]
  66. Khare, V.R.; Singh, A.P.; Kumar, H.; Khatri, R. Modelling and Performance Enhancement of Single Slope Solar Still Using CFD. Energy Procedia 2017, 109, 447–455. [Google Scholar] [CrossRef]
  67. García-Chávez, R.; De México, U.N.A.; Chávez-Ramirez, A.; Villafán-Vidales, H.; Velázquez-Fernández, J.B.; Rosales, I.H. Thermal study of a solar distiller using computational fluid dynamics (CFD). Rev. Mex. Ing. Quim. 2019, 19, 677–689. [Google Scholar] [CrossRef]
  68. Hussain, M.; Shoukat, B.; A Wahid, A.; Shukrullah, S.; Anwar, M.; Ashraf, M.Z.; Naz, M.Y. Computational fluid dynamics study of a single stage solar still for water treatment. IOP Conf. Ser. Mater. Sci. Eng. 2020, 863, 012018. [Google Scholar] [CrossRef]
  69. Subhani, S.; Kumar, R.S. Numerical performance evaluation of tubular solar still with different geometries of water basin. Desalination Water Treat. 2020, 196, 360–369. [Google Scholar] [CrossRef]
  70. Keshtkar, M.; Eslami, M.; Jafarpur, K. Effect of design parameters on performance of passive basin solar stills considering instantaneous ambient conditions: A transient CFD modeling. Sol. Energy 2020, 201, 884–907. [Google Scholar] [CrossRef]
  71. Sankaran, N.V.S.; Sridharan, M. Experimental research and performance study of double slope single basin solar distillation still using CFD techniques. Int. J. Ambient Energy 2020, 41, 1–17. [Google Scholar] [CrossRef]
  72. Zuo, L.; Li, Z.; Liu, W.; Yuan, Y. Numerical analysis of flow heat transfer characteristics in solar chimneys integrated with seawater desalination. In Proceedings of the International Conference on Sustainable Power Generation and Supply, Hangzhou, China, 8–9 September 2012. [Google Scholar] [CrossRef]
  73. Esfe, M.H.; Toghraie, D. Numerical study on the effect of solar radiation intensity on the fresh water productivity of solar still equipped with Thermoelectric Cooling System (TEC) for hot and dry areas of Semnan. Case Stud. Therm. Eng. 2022, 32, 101848. [Google Scholar] [CrossRef]
Figure 1. Study flowchart.
Figure 1. Study flowchart.
Sustainability 14 10700 g001
Figure 2. Yearly distribution of the raw collected publications.
Figure 2. Yearly distribution of the raw collected publications.
Sustainability 14 10700 g002
Figure 3. Yearly publication distribution (refined).
Figure 3. Yearly publication distribution (refined).
Sustainability 14 10700 g003
Figure 4. Percentage of articles published per journal.
Figure 4. Percentage of articles published per journal.
Sustainability 14 10700 g004
Figure 5. Percentage of articles published per region.
Figure 5. Percentage of articles published per region.
Sustainability 14 10700 g005
Figure 6. Co-occurrence mapping of author keywords.
Figure 6. Co-occurrence mapping of author keywords.
Sustainability 14 10700 g006
Figure 7. Flowchart of the typical numerical modelling [35].
Figure 7. Flowchart of the typical numerical modelling [35].
Sustainability 14 10700 g007
Figure 8. The temperature contours of (a) internal mixture, (b) glass cover, and (c) absorber at 14:00 [13].
Figure 8. The temperature contours of (a) internal mixture, (b) glass cover, and (c) absorber at 14:00 [13].
Sustainability 14 10700 g008
Figure 9. Geometry and coordinate system of the solar still [52].
Figure 9. Geometry and coordinate system of the solar still [52].
Sustainability 14 10700 g009
Figure 10. Relation between Rayleigh number and Nusselt number [52].
Figure 10. Relation between Rayleigh number and Nusselt number [52].
Sustainability 14 10700 g010
Figure 11. Variation of hourly yield and convective heat transfer vs. length [46].
Figure 11. Variation of hourly yield and convective heat transfer vs. length [46].
Sustainability 14 10700 g011
Figure 12. Sketch of the geometry, coordinate system, and boundary conditions of the tubular solar still [54].
Figure 12. Sketch of the geometry, coordinate system, and boundary conditions of the tubular solar still [54].
Sustainability 14 10700 g012
Figure 13. Effects of (a) glass temperature and (b) water temperature on hourly mass productivity [54].
Figure 13. Effects of (a) glass temperature and (b) water temperature on hourly mass productivity [54].
Sustainability 14 10700 g013
Figure 14. Effect of buoyancy ratio and Rayleigh number on vapor mass fraction [54].
Figure 14. Effect of buoyancy ratio and Rayleigh number on vapor mass fraction [54].
Sustainability 14 10700 g014
Figure 15. The schematic view of the solar still [55].
Figure 15. The schematic view of the solar still [55].
Sustainability 14 10700 g015
Figure 16. The mesh distribution inside the solar still [55].
Figure 16. The mesh distribution inside the solar still [55].
Sustainability 14 10700 g016
Figure 17. Streamlines, stream function, isotherm, and vapor mass fraction contours [55]. (a) still partition at the bottom surface, optimized; (b) still partition at the glass cover, optimized.
Figure 17. Streamlines, stream function, isotherm, and vapor mass fraction contours [55]. (a) still partition at the bottom surface, optimized; (b) still partition at the glass cover, optimized.
Sustainability 14 10700 g017
Figure 18. CFD results of liquid water fraction for different basin materials: (a) galvanized iron, (b) glass, and (c) aluminum [56].
Figure 18. CFD results of liquid water fraction for different basin materials: (a) galvanized iron, (b) glass, and (c) aluminum [56].
Sustainability 14 10700 g018
Figure 19. Comparison of water productivity for experimental and CFD results [56].
Figure 19. Comparison of water productivity for experimental and CFD results [56].
Sustainability 14 10700 g019
Figure 20. Computational domain and the grid generation using Gambit [57].
Figure 20. Computational domain and the grid generation using Gambit [57].
Sustainability 14 10700 g020
Figure 21. Hourly variation of basin water temperature (Tw), glass cover temperature (Tg), and ambient temperature (Ta) during one day at φ = 0.05 [57].
Figure 21. Hourly variation of basin water temperature (Tw), glass cover temperature (Tg), and ambient temperature (Ta) during one day at φ = 0.05 [57].
Sustainability 14 10700 g021
Figure 22. Hourly variation of freshwater production for a solar still filled with and without zeolite at φ ¼ 0.05 [57].
Figure 22. Hourly variation of freshwater production for a solar still filled with and without zeolite at φ ¼ 0.05 [57].
Sustainability 14 10700 g022
Figure 23. 3D modeling of the multi-stage solar distillation system: (a) Major components inside the distillation unit. (b) View of a one angled stage [58].
Figure 23. 3D modeling of the multi-stage solar distillation system: (a) Major components inside the distillation unit. (b) View of a one angled stage [58].
Sustainability 14 10700 g023
Figure 24. Schematic drawing of the stepped solar still model [59].
Figure 24. Schematic drawing of the stepped solar still model [59].
Sustainability 14 10700 g024
Figure 25. Adopted mesh and solar still geometry overview [60].
Figure 25. Adopted mesh and solar still geometry overview [60].
Sustainability 14 10700 g025
Figure 26. Temperature contours [60].
Figure 26. Temperature contours [60].
Sustainability 14 10700 g026
Figure 27. Comparison between experimental and simulation results of the hourly distillate output of the solar still [60].
Figure 27. Comparison between experimental and simulation results of the hourly distillate output of the solar still [60].
Sustainability 14 10700 g027
Figure 28. (a) SSSS without insulation, (b) SSSS with BW insulation, (c) SSSS-CIF with BW insulation, and (d) Conventional solar still [61].
Figure 28. (a) SSSS without insulation, (b) SSSS with BW insulation, (c) SSSS-CIF with BW insulation, and (d) Conventional solar still [61].
Sustainability 14 10700 g028
Figure 29. Temperature distribution in Kelvin of the floating absorber in the solar still [61].
Figure 29. Temperature distribution in Kelvin of the floating absorber in the solar still [61].
Sustainability 14 10700 g029
Figure 30. Schematic drawing of (a) the tubular solar still and (b) the triangular solar still [62].
Figure 30. Schematic drawing of (a) the tubular solar still and (b) the triangular solar still [62].
Sustainability 14 10700 g030
Figure 31. Mass fraction in tubular and triangular solar stills (Tw = 325 K, Tg = 318.5 K) [62].
Figure 31. Mass fraction in tubular and triangular solar stills (Tw = 325 K, Tg = 318.5 K) [62].
Sustainability 14 10700 g031
Figure 32. Generated mesh for different types of solar stills [63]. (a) generated mesh for 15°,20°, fine, steps, PCM solar stills; (b) generated mesh for double slope solar still.
Figure 32. Generated mesh for different types of solar stills [63]. (a) generated mesh for 15°,20°, fine, steps, PCM solar stills; (b) generated mesh for double slope solar still.
Sustainability 14 10700 g032
Figure 33. The Tubular Solar Still (TSS): (a) Sketch of the geometry, boundary, and operating conditions, and (b) the mesh of the computational domain [64].
Figure 33. The Tubular Solar Still (TSS): (a) Sketch of the geometry, boundary, and operating conditions, and (b) the mesh of the computational domain [64].
Sustainability 14 10700 g033
Figure 34. Contours of vapor mass fraction at Tw = 60 °C and Pop of (a) 101 kPa and (b) 40 kPa [64].
Figure 34. Contours of vapor mass fraction at Tw = 60 °C and Pop of (a) 101 kPa and (b) 40 kPa [64].
Sustainability 14 10700 g034
Figure 35. Effect of the water depth (Dw) and radius ratio (i = r/R) on freshwater yield rate of the TSS [64].
Figure 35. Effect of the water depth (Dw) and radius ratio (i = r/R) on freshwater yield rate of the TSS [64].
Sustainability 14 10700 g035
Table 1. Database, keywords, and the collected publications.
Table 1. Database, keywords, and the collected publications.
DatabaseKeywordsTimespanNo. of Publications
Scopus TITLE-ABS-KEY (solar AND still AND (numerical OR simulation OR CFD) AND (desalination OR seawater))2012–2022148
WoSTS = ((solar) AND (still) AND (numerical OR CFD OR simulation) AND (desalination OR seawater))2012–2022262
SDTitle, abstract, keywords: (solar AND still AND (numerical OR simulation OR CFD) AND (desalination OR seawater))2012–202263
Other sourcesCFD; Numerical simulation, modeling, solar still, computational fluid dynamic, water treatment, desalination2012–202213
Table 2. Summary of components, sub-components, and equipment used at the DWTP.
Table 2. Summary of components, sub-components, and equipment used at the DWTP.
DatabasesCollected DataExclusions
Common PapersOther LanguagesInitial ScreeningReview PapersDeep Screening
WOS262022061022
Scopus 1489813651
SD63580400
Extra papers 1300000
Total Removed015632461523
Remain486330327816643
Table 3. Details of co-occurring keywords.
Table 3. Details of co-occurring keywords.
KeywordTotal Link StrengthOccurrencesAvg. Pub. Year
Solar still27162019
CFD29112019
CFD simulation2182017
Desalination1872018
Solar desalination1052019
Solar energy1152018
Single slope solar still1342018
Productivity1442019
Stepped solar still1032019
Double diffusive natural convection1132017
Heat and mass transfer732015
Tubular solar still632018
Solar chimney732018
Parametric study822019
Transient model822019
Ansys CFD422019
Heat transfer522021
Multiphase model722019
Simulation422020
Aspect ratio522015
Entropy generation722018
Optimization522013
Single-slope solar still322013
Water productivity622017
Computational fluid dynamics422021
Condensation1022018
Evaporation1022018
Numerical study222022
Performance922019
Table 4. Summary of the research work on solar still systems using CFD approach.
Table 4. Summary of the research work on solar still systems using CFD approach.
ApproachesStill TypeSoftware Discretization MethodWater Depth Multiphase ModelValidationStudied ParametersProductivity/PerformanceReference
Experimental and numericalSingle slope (SS)Ansys Fluent FVM2 cmVolume of fluid (VOF); three phases Yes. The error is 8.3%.Temperature’s variation, fluid and vapor fraction, productivity, and mixture velocity contoursDaily simulated and experimental cumulative freshwater productivities were to be 1.982 and 1.785 L/m2 h, respectively.[13]
Experimental and numericalCascade solar stillAnsys Fluent FVMNAVolume of fluid (VOF); three phases YesAbsorber plate temperature, hourly productivity, and solar intensity variations The absorber plate temperature and freshwater productivity can reach more than 60 °C and 1.66 kg/m2 h, respectively.When the absorber plate temperature is over 50 °C, the freshwater productivity may reach 0.6 kg/m2 h.[65]
Theoretical and numerical analysisSingle slope (SS)Ansys FluentFVMNAVolume of fluid (VOF); three phases The theoretical results have a maximum deviation of 12.5% from the numerical results. The convective heat transfer coefficient, Rayleigh number, and still geometry (aspect ratio = length/height)It was reported that the heat transfer coefficient rises as the Rayleigh number increases for a certain aspect ratio (A = L/H).[52]
Theoretical and numerical analysisSingle slope (SS)Ansys Fluent 6.3FVMNAVolume of fluid (VOF); three phases Yes. The maximum deviation of the CFD model from Chiltone Colburn for the hourly productivity is 15%.Variation of thermal conditions (water and glass temperatures) and geometry parameters (height, length, and inclination angle)The freshwater productivity is increasing with decreasing the specific height at fixed length of the solar still.There is an optimum length for the solar still to increase the freshwater productivity at fixed specific height.[46]
Numerical analysisSingle slope (SS)Ansys FluentFVMNAVolume of fluid (VOF); three phases - In case of the buoyancy ratio (N), the maximum error is 2.1% for N = −0.9 compared with literature.- In case of the convective, radiative and total average Nusselt number, the maximum error is 2.8% compared with literature. Streamlines, isotherms, and isolines of vapor and water mass flow rate, as well as average Nusselt (Nu) and Sherwood (Sh) numbers for different inclination anglesAs the slope angle, Rayleigh number, or aspect ratio rises, the freshwater productivity rises as well.[53]
Theoretical and numerical analysisTubular solar still Ansys Fluent 14.0FVMNAVolume of fluid (VOF); three phases At certain value of the convective heat transfer coefficient between water and glass cover, the estimation accuracy is 10% compared with experimental data reported in literature.Water productivity, heat, and mass transfer coefficients at different water and glass temperaturesProductivity increases by more than 250% even with a 5 °C increase in water temperature.While the temperature of the glass just rises by 5 °C, productivity is reduced by almost 200%.[54]
Numerical analysisSingle slope (SS)Ansys FluentFVMNAVolume of fluid (VOF); two phases There is a high level of agreement between the results of this study and the previously published data.Position and height of the partition on heat transfer rate and productivityIt is found that the partition height has a greater impact on heat transfer rate and freshwater productivity rather than the partition position parameter.[55]
Numerical and experimental analysisSingle slope (SS)Ansys CFX solverFVM2 cmVolume of fluid (VOF); three phasesThe average errors in CFD for the same boundary conditions as the experiment are 5.26%.Production using three different basin materials (galvanized iron, glass, and aluminium)It was found that the solar still with aluminum basin material achieves the highest freshwater productivity, followed by the galvanized iron basin material, and then the glass basin material, which is the least of them in productivity.[56]
Experimental and numericalPyramid shaped solar stillAnsys Fluent FVM4 cmVolume of fluid (VOF); two phases With increasing zeolite particle volume fraction, the deviation of CFD model from experimental data rises (0.05% for φ = 0.0 and 12.3% for φ = 0.05).Basin water temperature, freshwater productivity, water vapor fraction, and velocity counters The freshwater productivity can be enhanced by adding zeolite particles.[57]
Design and numerical analysisMulti-stage solar still (four stages)Solidworks and Ansys FluentFVMNAVolume of fluid (VOF); three phases NATemperature and freshwater productivity as a result of thermal radiation term exchangeThe four-stage distillation system can operate at high temperatures of roughly 80.96 °C and achieve high freshwater productivity (8.88 kg/day for all stacked stages).[58]
Numerical analysisStepped solar stillAnsys FluentFVMNAVolume of fluid (VOF); two phases In compared to the CFD simulation, RSM accurately predicts freshwater hourly productivities with a maximum error of 2.4%.Percentage of nanoparticles, stepped solar still geometry (length and height), and freshwater hourly productivityIt is shown that raising nanoparticle concentration from 0% to 5% leads to a 22% increase in hourly productivity.When the stair length is increased for medium and high values of the stair height, the sensitivity of the hourly productivity to the stair height improves, but when the stair length is increased for small values of the stair height, it decreases.[59]
Numerical and experimental analysisSingle slope (SS)Ansys Fluent 11FVM3 cmVolume of fluid (VOF); two phases The simulation and experimental results are quite close.Freshwater productivity, temperature of the inner glass, outside glass cover temperature, basin water temperature, and vapor temperatureIt is found that the freshwater productivity is affected by the temperature of the inner glass, outside glass cover temperature, basin water temperature, and vapor temperature.[60]
Numerical and experimental analysisSingle slope (SS)Ansys Fluent 15FVM3 cmVolume of fluid (VOF); three phases The simulation results of the CFD show a good agreement with the experimental results.Water temperature, internal air temperature, inner core temperature, carbon impregnated foam temperature, and freshwater productivityThe freshwater productivity of the SSSS without insulation, SSSS with BW insulation, SSSS-CIF with BW insulation, and conventional solar still with sawdust insulation, respectively, was 1.9 L/m2/day, 2.3 L/m2/day, 3.1 L/m2/day, and 2.2 L/m2/day.[61]
Numerical and experimental analysisTriangular and tubular solar stillsAnsys FluentFVMNAVolume of fluid (VOF); three phases The comparison of the CFD results revealed a maximum error of 6.1% from the experimental results.Contours of streamlines, isotherms, mass fraction, and local entropy generation inside the solar stillsIt is found that the tubular had a 20% advantage over the triangular in terms of performance.[62]
Theoretical, numerical and experimental analysisSingle slope (SS), double slope (DS), stepped, finned and PCM solar stillsAnsys CFXFVM5 cmVolume of fluid (VOF); two phases There is a high level of agreement between the simulation and experimental results.Temperature distributions of water and vapor, and freshwater productivity at 15 and 20 inclination angles of solar stillsIt is established that a single slope with a 20° angle and PCM equipment yielded the highest freshwater productivity when compared to other types.[63]
Numerical and experimental analysisTubular Solar Still (TSS)Ansys Fluent 16FVM2 cmVolume of fluid (VOF); three phasesThe average and maximum deviations between the experimental and simulated yield rates are 14.1% and 32.4%, respectively.Operating pressure Pop and geometrical parameters (radius ratio and water depth) on freshwater productivityAt operating pressures less than 60 kPa, the freshwater yield rate was enhanced by more than 50%.The freshwater yield rate reduced by 30% when the water depth was reduced from r to 0.8 r, and by 38% when the radius ratio (r/R) was varied from 0.6 to 0.9 at operating pressure of 20 kPa.[64]
Numerical and experimental analysisSingle slope solar still (SS)Ansys FluentFVM5–15 L water Three phases In case of the convective, radiative, and total average Nusselt number, the maximum error is 2.8% compared with literature.Water depth, materials of basin, and radiation intensities With less water depth, freshwater productivity is increased.By adding a blackened baseliner to the basin and using reflecting glasses to maximize incident solar radiation, the still’s efficiency is further improved.[66]
Numerical analysisSingle slope solar still (SS)ANSYS 17.1, CFXFVMNANAYes, 0.6–14% difference is found between experiments and CFD model’s output.Internal temperature, productivity, and transfer coefficient The radiation is the main factor in high efficiency, although freshwater productivity can also be impacted by wind speed and ambient temperature.[67]
Numerical and experimental analysisSingle slope solar still (SS)Ansys FluentFVMNAThree phases approach Yes. The difference between experimental and numerical approaches is 8.7% in terms of daily productivity. Design parameters on performance of a solar stillThe produced water volume found to be 1.03 L which was collected between 9 AM and 5 PM. [68]
Numerical analysisSingle slope solar still (SS) and tubular solar stillAnsys WorkbenchFVM2 cm VOF -two dimensional The variation ranges from 2.8 to 0.95% between experimental and CFD studies.Analyzing the effectiveness of each design and considering the impact of temperature variations caused on by modifications to the basin’s geometry.The curved water basin should be at 1/2 of the still’s diameter for higher Rayleigh numbers for the solar still to function well. The tubular still with a linear basin at D/2 dissipates 31.4% more heat than conventional stills (SS). This results in a higher rate of solar still fresh water productivity at higher Rayleigh numbers.[69]
Numerical and experimental analysisSingle stepped slope solar stillAnsys FluentFVM1–4 cm VOFVery good agreement between the numerical model and experimental data.Wind speed, glass cover thickness, water depth, water to cover distance, and cover inclination.When the wind speed is increased from 1 m/s to 6 m/s, productivity increases by 14.4%, and when the glass thickness is decreased from 4 mm to 2 mm, productivity improves by 3.5%. While the cover tilt is equal to the angle of the local latitude, the ideal values of water depth and water to cover distance are identified to be 2 and 8 cm, respectively. Moreover, freshwater productivity rises by roughly 17.4% when the basin solar still is changed to the suitable stepped solar still.[70]
Numerical and experimental analysisDouble slope solar still (DSSS)ANSYS FluentFVMN/AThree-dimensionalThe simulation results deviate from 5 to 13%.Glass cover temperature, surface water temperature, basin temperature, ambient temperature, and solar intensityWhile a CFD modeling result shows 0.50 L/m2/day, experimental investigation indicates that the total amount of distilled water is around 0.46 L/m2/day. Therefore, there is good agreement between the simulated findings and the experimental distillate yield data.[71]
Numerical analysisAxisymmetric shape column solar stillFLUENTPRESTON/AThree-phase three-dimensional modelNAPressure, velocity, and temperature of the integrated systemAfter the geometric dimensions are optimized, the airflow characteristics in the collector minimally change, thus the freshwater productivity in the still is not much impacted.[72]
Numerical analysisDouble slope solar still (DSSS)CFDSIMPLENAThree dimensional Yes. The difference in results for both models is less than 25%. The error of the results is 15.33% compared to the filed data.Intensity and direction of solar radiation and temperature gradient The rate of freshwater productivity rises as solar radiation increases in intensity, and vice versa.The freshwater productivity may be increased by 15% to 62% by installing a thermoelectric coolng system (TEC) over a portion of the glass, according to research.[73]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

AlSaleem, S.S.; Al-Qadami, E.; Korany, H.Z.; Shafiquzzaman, M.; Haider, H.; Ahsan, A.; Alresheedi, M.; AlGhafis, A.; AlHarbi, A. Computational Fluid Dynamic Applications for Solar Stills Efficiency Assessment: A Review. Sustainability 2022, 14, 10700. https://doi.org/10.3390/su141710700

AMA Style

AlSaleem SS, Al-Qadami E, Korany HZ, Shafiquzzaman M, Haider H, Ahsan A, Alresheedi M, AlGhafis A, AlHarbi A. Computational Fluid Dynamic Applications for Solar Stills Efficiency Assessment: A Review. Sustainability. 2022; 14(17):10700. https://doi.org/10.3390/su141710700

Chicago/Turabian Style

AlSaleem, Saleem S., Ebrahim Al-Qadami, Hussein Zein Korany, Md. Shafiquzzaman, Husnain Haider, Amimul Ahsan, Mohammad Alresheedi, Abdullah AlGhafis, and Abdulaziz AlHarbi. 2022. "Computational Fluid Dynamic Applications for Solar Stills Efficiency Assessment: A Review" Sustainability 14, no. 17: 10700. https://doi.org/10.3390/su141710700

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop