CFD Simulation and Performance Comparison of Two Distillation Tank Designs with Different Heat Source Geometries

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Realistic environmental conditions such as ambient pressure, temperature, fluid inlet velocity, and temperature of the fluid entering the tank and the heat source (both being water) were considered. Both models were tested under the same conditions: a pressure of 1 atm, an ambient temperature of 27°C, an inlet fluid velocity in the tank of 0.08 m/s, an inlet fluid temperature to the tank of 27°C, an inlet fluid velocity to the heat source of 0.01 m/s, and an inlet fluid temperature to the heat source of 67°C. The tank and heat source were made of aluminum and copper, respectively. The obtained results showed significant differences and will be discussed in detail in the following sections. ANSYS Desalination Heat source simulation thermal efficiency Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Water, a vital resource for human existence, covers approximately 70% of the Earth's surface. However, the distribution of freshwater is highly uneven, with 96.5% being saltwater in oceans and seas. The remaining freshwater is primarily locked up in glaciers (1.7%) and groundwater (1.6%), leaving only 0.015% accessible in rivers and lakes (Tashtoush et al., 2023 ). The estimated volumes of groundwater, rivers, and lakes are 10.53 million cubic meters, 2100 cubic kilometers, and 91,000 cubic kilometers, respectively. According to UNESCO, 2.2 billion people lack access to safe drinking water, and 3.5 billion live without adequate sanitation (Kumar, 2024 ). These water challenges are particularly acute in arid and semi-arid regions. The growing demand for freshwater and dwindling natural resources have spurred the development of innovative solutions, including solar desalination (Farabi et al., 2024 ). Notably, many arid regions with limited freshwater resources receive abundant solar radiation, making solar desalination a promising option. Solar desalination systems can be categorized as either direct or indirect (Farabi et al., 2024 ). Advanced desalination technologies such as reverse osmosis, membrane distillation, multi-effect distillation, and electrodialysis offer various trade-offs in terms of energy consumption, skilled labor requirements, and capital costs (Kumar, 2024 ). In recent years, research has focused on small-scale, low-pressure desalination units like Single Effect Distillation (SED) and Single Stage Flash (SSF) systems (Siddique et al., 2018 ). For smaller-scale applications, a single-effect configuration can reduce costs and enable deployment in domestic or research settings. Studies have shown that SSF systems require approximately 30% more heat input compared to SED systems, resulting in a 24% decrease in performance (Siddique et al., 2018 ). In SED, saltwater is heated in a vessel by a heat source, typically heated by a solar collector, until it vaporizes. The vapor is then condensed into freshwater using a cooling medium (Saidur et al., 2011 ). Various solar collectors, including evacuated tubes, flat-plate collectors, and parabolic trough solar collectors (PTSCs), can be employed to harness solar energy. PTSCs, in particular, use parabolic mirrors to concentrate solar radiation onto an absorber tube, achieving high temperatures (up to 400°C) (Marrakchi et al., 2018 ). Their high efficiency and simple design have made PTSCs increasingly popular. To enhance research accuracy and reduce time and costs, Computational Fluid Dynamics (CFD) simulations are employed for thermal analysis (Khosravi et al., 2019 ). While existing research explores various desalination technologies, a comprehensive CFD-based comparison of different heat source geometries within a single-effect distillation tank is lacking. This research aims to contribute to the development of more efficient and cost-effective small-scale desalination systems by investigating the impact of heat source geometry on system performance through detailed CFD simulations. Geometric Model Two geometric models were developed for the water desalination system. Both models share a common tank with a diameter of 30 cm and a height of 60 cm, equipped with 12 inlet ports (1 cm diameter) and one outlet port (6 cm diameter). Model 1 Features a hollow cylindrical heat source with a diameter of 15 cm and a height of 30 cm. The inlet and outlet pipes of the heat source have a diameter of 2 cm. The width of the system is equal to the diameter of the pipe plus 2 cm. The heat source is made of copper. Tank dimensions balance application suitability, heat transfer efficiency, and computational cost, while heat source dimensions ensure efficient heat exchange and proper water circulation. Aluminum was chosen for the tank due to its thermal conductivity, affordability, and ease of fabrication, while copper was selected for the heat source for its superior thermal conductivity. Model 2 The heat source in this model is a solid cylinder with a diameter of 15 cm and a height of 30 cm. The inlet and outlet pipe diameters remain the same as in Model 1 (2 cm), and the material is also copper. Simulation Setup and Parameters: This simulation was performed using ANSYS Fluent 2021 on a laptop with the following specifications: 16 GB RAM, AMD Vega10 GPU, AMD Ryzen 7 3700 Pro CPU, and 1 TB NVMe SSD. Problem Statement Given the irregular flow paths of the fluid (water), a turbulent flow regime was assumed rather than a laminar one. An inlet velocity of 0.08 m/s was set in the tank, and the k-epsilon turbulence model was employed. This two-equation model uses k to represent turbulent kinetic energy and epsilon to represent the dissipation rate of turbulent kinetic energy. The inlet water temperature was set to 27°C, matching the ambient temperature. The inlet water to the heat source also utilized the k-epsilon model with a velocity of 0.01 m/s. To account for real-world conditions, the influence of velocity on heat transfer was activated for both water inlets. Due to copper's excellent thermal conductivity, it was chosen as the material for the heat source to enhance efficiency and performance. To improve computational accuracy, the tank and heat source were meshed using different patterns: an unstructured hexagonal mesh for the tank and a triangular mesh for the heat source. Manual meshing was employed in the region where the tank and heat source interface to ensure accurate representation. The inlet velocity of 0.08 m/s in the tank ensures a suitable flow rate for small-scale desalination, balancing production rate and system design, while the lower velocity of 0.01 m/s at the heat source allows efficient heat transfer and reduces pressure drop. The heat source inlet temperature of 340 K (67°C) provides a strong temperature gradient to drive heat transfer and promote evaporation. Computational Fluid Dynamics (CFD) Modeling: CFD is a numerical method used to analyze fluid flow, heat transfer, and mass transfer. Meshing is a critical aspect of CFD simulations as it significantly influences the convergence of the solution (Sharifi et al., 2018 ). While the Navier-Stokes equations can be solved for laminar flow, resolving the small-scale fluctuations in turbulent flow is computationally challenging. Therefore, selecting an appropriate turbulence model is essential (Malekjani and Jafari, 2018 ). The accuracy and reliability of simulation results are significantly influenced by the mesh quality and size. In ANSYS, meshing follows a predefined function based on the geometry, enabling the generation of meshes ranging from simple to complex patterns (Malekjani and Jafari, 2018 ). Meshing and Validation To validate the model, mesh density, time step, and number of iterations were varied. The outlet temperature of the heat source was compared to experimental data to evaluate the mesh quality and ensure it did not adversely affect the results. Initially, simulations were conducted with a coarse mesh and various time steps and iterations. However, the results did not converge to a stable value. By refining the mesh and increasing the number of time steps and iterations, the outlet temperature converged, indicating a valid simulation. Time steps of 0.09, 0.01, 0.007, and 0.001 seconds were tested with 200, 500, 1000, and 2000 iterations, respectively. The results obtained with a time step of 0.09 seconds and 200 iterations provided the necessary accuracy. A time step of 0.09 s and 200 iterations were chosen to ensure accuracy, though further validation could involve comparison with experimental data, grid independence studies, and convergence analysis. Without detailed accuracy metrics, definitive conclusions remain challenging. Due to the interaction between the tank and heat source, different mesh patterns were used for each component. The heat source was meshed using an unstructured hexagonal pattern, while the tank was meshed using a triangular pattern. To ensure accurate representation of the interface between the tank and heat source, manual meshing was employed in this region. Two mesh sizes, coarse and fine, were considered. The coarse mesh resulted in a significant discrepancy between the simulated and experimental temperatures of the heat source. By refining the mesh to a minimum size of 0.008 m and a maximum size of 0.012 m, the accuracy of the simulation results was improved. Figure 2 illustrates the mesh pattern and element sizes. Analysis of Results for the Tank with a Solid Cylindrical Heat Source As previously mentioned, this simulation employed water as the working fluid within both the tank and the heat source. The fluid velocity in the tank was set at 0.08 m/s with a temperature of 300 K, while the heat source had a fluid velocity of 0.01 m/s and a temperature of 340 K. The relationship between the contact surface area between the water and the heat source and the steam generation efficiency is noteworthy: a larger contact area leads to increased steam production. Reynolds Number Calculation: The Reynolds number is calculated to confirm the turbulent flow regime. The equation uses density, velocity, and characteristic length (tank diameter in this case). Assuming water at room temperature with a density of 1000 kg/m³ and dynamic viscosity of 0.001 Pa·s, and a velocity of 0.08 m/s within the 30 cm diameter tank, the calculated Reynolds number is 24,000. Since this value significantly exceeds the critical value for turbulent flow (around 4000), the assumption of turbulent flow is justified. If we divide the time steps into two equal parts, it becomes evident that the temperature difference and steam production exhibit an inverse relationship. In the first half of the simulation, the temperature difference reaches its maximum, while steam production is at its minimum. The opposite is true for the second half. Initially, the heat source temperature is set to 340 K. After 100 time steps, the temperature difference decreases by 12 degrees, and the inlet water temperature approaches its boiling point. This smaller temperature difference results in a slower and lower rate of steam generation. Figure 5 illustrates how the temperature of the heat source decreases in the regions that are in direct contact with the 300 K inlet water, as heat is transferred to the water through convection. In contrast, the temperature increases in other regions due to the circulation of hot water and reduced contact with the inlet water, creating a temperature difference of 20 degrees Celsius. As a result of this temperature difference and convective heat transfer, Fig. 6 shows the amount of steam generated over a specific time interval. By using the formula D = ((0.02*3.14)*6)m^2 * 5.4s * 0.08 m/s, we can calculate the volumetric flow rate of water entering the tank. Dividing this value by the amount of steam produced yields the exergy of the system. Boundary Conditions: Inlet (Tank) Velocity (0.08 m/s), Temperature (300 K), Turbulence model (k-epsilon) Inlet (Heat Source) Velocity (0.01 m/s), Temperature (340 K), Turbulence model (k-epsilon) Outlet (Tank) Pressure outlet (atmospheric pressure) Walls (Tank and Heat Source) No-slip condition (velocity = 0) Interface (Tank and Heat Source) Heat transfer between the two surfaces Ambient Temperature (300 K) (for heat loss considerations) Contact Area: Model 1 (Solid Cylinder) : Contact area can be approximated as the lateral surface area of the cylinder: Rectangle Area = 2 * π * r * height = 2 * π * 0.12 m * 0.3 m ≈ 0.226 m² Area of Circle = 2* π*r 2 = 0.09 m 2 Contact Area = 0.226 m 2 + 0.09 m 2 = 0.316 m 2 Since the conditions in both tanks are identical, the volumetric flow rate of water into both tanks is equal, which is calculated to be 0.162 m^3. As shown in Fig. 6 , the simulated steam production within the tank with a full cylindrical heat source is reported by ANSYS Fluent. The average volume of steam within and exiting the tank is calculated to be 0.006 m³ and 7.93 × 10^-6 m³, respectively. Given the negligible amount of steam exiting the tank compared to the volume within, the steam generation efficiency can be approximated by dividing the volume of steam produced by the total volume of water entering the system. This calculation results in an efficiency of approximately 0.01%, indicating that 99.9% of the total energy is wasted. It is important to note that the inlet water temperature is 340 K, and after circulating through the heat source and undergoing convective heat transfer, it exits at 298 K. Cylindrical Sector Heat Source Initially, the heat source temperature in this configuration is also set to 340 K. However, after half of the simulation time, a temperature difference of 100 degrees is observed. This rapid temperature increase accelerates the heating of the water, leading to earlier boiling and steam generation. Moreover, due to the larger contact area between the water and the heat source, a greater amount of water comes into contact with the heat source, resulting in increased evaporation and steam production. Additionally, the temperature difference between the inner surface of the tank and the existing steam may lead to condensation, causing the produced water to return to the tank. This effect can be mitigated by insulating the tank for larger volumes. In this case, the temperature difference and steam production occur more rapidly in the first half of the simulation compared to the second half, allowing for a proportional estimation of steam production over a longer period. Contact Area: Model 2 (Cylindrical Sector) Contact area is more complex to calculate accurately due to the varying radius. However, it can be estimated as the average radius multiplied by the height Average Radius ≈ 2*(πr 1 2 − πr 2 2 ) = 0.026 m 2 Recatngle Area ≈ (2* π*r 1 *0.3 + 2* π*r 2 *0.3) ≈ 0.41448 m² Contact Area = 0.44048 m 2 Figure 7 illustrates the temperature gradient within the tank with a cylindrical sector heat source. This figure shows that as the volume of the cylinder decreases and the contact surface area with the hot inlet water increases, the heat source absorbs more of the heat released by the water. Consequently, the overall temperature of the copper heat source increases to 568 K, resulting in improved efficiency and higher steam production. Notably, the temperature of the portions of the heat source in direct contact with the inlet water decreases to 538 K. In Model 2, where the tank has a cylindrical sector heat source, the inlet water volume is identical to the first model, equaling 0.162 m³. According to Model 1, the amount of steam produced in the second model is 0.019 m³. To calculate the exergy, we have: [Insert your exergy calculation equation here]. This indicates a 10% increase in efficiency compared to the previous model. Conclusion In this simulation, two types of heat sources were compared under identical conditions of material, inlet water temperature, inlet water velocity, and duration, with the sole difference being the contact surface area. The results indicated that increasing the contact surface area with the inlet water led to a higher amount of heat absorbed by the heat source. Additionally, due to the increased contact surface area between the tank and the heat source, the amount of steam produced also increased. Furthermore, the validity of this finding could be further verified by comparing the second type of heat source with a coiled heat source. Declarations Author Contribution A. Omid shariati wrote the main manuscript text,B. Hadi SamimiAkhijahani reviewed the manuscript, References Farabi, S. N., Habib, K., Mim, M., Zaed, M., Ali, S. A., Younas, M., & Saidur, R. (2024). The future of solar-driven interfacial steam generation for sustainable water desalination: drivers, challenges, and opportunities-Review. Results in Engineering , 102649. Khosravi, A., Malekan, M., & Assad, M. E. (2019). Numerical analysis of magnetic field effects on the heat transfer enhancement in ferrofluids for a parabolic trough solar collector. Renewable Energy , 134 , 54-63. Kumar, A. (2024). ANSYS Fluent-CFD analysis of a continuous single-slope single-basin type solar still. Green Technologies and Sustainability , 100105. Malekjani, N., & Jafari, S. M. (2018). Simulation of food drying processes by Computational Fluid Dynamics (CFD); recent advances and approaches. Trends in food science & technology , 78 , 206-223. Marrakchi, S., Leemrani, Z., Asselman, H., Aoukili, A., & Asselman, A. (2018). Temperature distribution analysis of parabolic trough solar collector using CFD. Procedia Manufacturing , 22 , 773-779. Saidur, R., Elcevvadi, E., Mekhilef, S., Safari, A., & Mohammed, H. A. (2011). An overview of different distillation methods for small scale applications. Renewable and sustainable energy reviews , 15 (9), 4756-4764. Sharifi, K., Sabeti, M., Rafiei, M., Mohammadi, A. H., & Shirazi, L. (2018). Computational fluid dynamics (CFD) technique to study the effects of helical wire inserts on heat transfer and pressure drop in a double pipe heat exchanger. Applied Thermal Engineering , 128 , 898-910. Siddique, M., Turkmen, N., Al-Rabghi, O. M., Shabana, E., & Albeirutty, M. H. (2018). Small-scale low pressure ‘single effect distillation’and ‘single stage flash’solar driven barometric desalination units: a comparative analysis. Desalination , 444 , 53-62. Tashtoush, B., Alyahya, W. e., Al Ghadi, M., Al-Omari, J., & Morosuk, T. (2023). Renewable energy integration in water desalination: State-of-the-art review and comparative analysis. Applied Energy , 352 , 121950. Tables Table 1 Mesh properties created in for tank and heat source Parameter Solid Cylinder Cylindrical Sector Units Description Minimum element size 0.0008 m 0.0008 m m Smallest edge length of any element in the mesh Maximum element size 0.008 m 0.008 m m Largest edge length of any element in the mesh Number of nodes 262,997 292,181 - Total number of points in the mesh Number of elements 427,860 406,468 - Total number of cells in the mesh Minimum angle 70.63° 61.09° degrees Smallest angle between any two edges of an element Maximum angle 141.96° 179.56° degrees Largest angle between any two edges of an element Pinch tolerance 0.000072 0.000072 dimensionless Minimum allowable distance between nodes Inflation algorithm Pre Pre - Predefined inflation algorithm used Skewness - - dimensionless Measure of element distortion Standard deviation 0.2345 0.2572 dimensionless Statistical measure of mesh quality Table 2 system specifications in brief and general Component Material Inlet Diameter (cm) Outlet Diameter (cm) Dimensions (cm) Contact Area (cm²) Inlet Temperature (K) Outlet Temperature (K) Tank Aluminum 12x2 6 D=24, h=60 126 300 310 Heat Source 1 Copper 2 2 D=12, h=30 31.6 340 298 Heat Source 2 Copper 2 2 D1=12, D2=10, h=30 44.0 340 400 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Mar, 2025 Reviews received at journal 07 Mar, 2025 Reviews received at journal 08 Feb, 2025 Reviewers agreed at journal 08 Feb, 2025 Reviewers agreed at journal 07 Feb, 2025 Reviewers invited by journal 07 Feb, 2025 Editor assigned by journal 07 Jan, 2025 Submission checks completed at journal 07 Jan, 2025 First submitted to journal 01 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5746439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398882245,"identity":"1cea61b9-a3a1-4ac5-9ed9-095b8c99b6b7","order_by":0,"name":"Omid Shariati","email":"","orcid":"","institution":"University of Kurdistan","correspondingAuthor":false,"prefix":"","firstName":"Omid","middleName":"","lastName":"Shariati","suffix":""},{"id":398882246,"identity":"22530ec9-0774-47a1-8c2a-fdc21a511a6d","order_by":1,"name":"Hadi 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09:01:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239419,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the first example of the test with the heat source of the hollow cylinder\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5746439/v1/78127a94bca8c606eec0f292.png"},{"id":73362514,"identity":"bee11584-3977-4d71-a47c-b206c1df5276","added_by":"auto","created_at":"2025-01-09 09:01:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":219835,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of the second example of the test with a complete cylinder heat 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water\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5746439/v1/586377c4b0967f6b740664af.png"},{"id":73363712,"identity":"149fb247-5819-4c27-ad6f-b5e8709a9eed","added_by":"auto","created_at":"2025-01-09 09:09:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":414922,"visible":true,"origin":"","legend":"\u003cp\u003eThe temperature of the heat source of model 1\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5746439/v1/300d809af47f25801bfba4e9.png"},{"id":73362524,"identity":"47482c95-7142-474e-b1d6-a2e97f424a92","added_by":"auto","created_at":"2025-01-09 09:01:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":368434,"visible":true,"origin":"","legend":"\u003cp\u003eVolume of water vapor produced in the tank with heat source model 1\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5746439/v1/740bea3a302df19be6f73332.png"},{"id":73362529,"identity":"3a97d863-293a-4f75-8256-5d0ec61ce886","added_by":"auto","created_at":"2025-01-09 09:01:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":304632,"visible":true,"origin":"","legend":"\u003cp\u003eThe temperature of the heat source of model 2\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5746439/v1/d137892f826f14bdc1f27989.png"},{"id":73362532,"identity":"b5abdf2e-475f-4faf-adba-1b8b3f6481b7","added_by":"auto","created_at":"2025-01-09 09:01:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":406307,"visible":true,"origin":"","legend":"\u003cp\u003eVolume of water vapor produced in tank model 2\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5746439/v1/31b0f93add1f8bc942988685.png"},{"id":73364147,"identity":"2450b59b-482e-46e8-87ac-1d9e9d3d62c7","added_by":"auto","created_at":"2025-01-09 09:17:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4661322,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5746439/v1/e71a39f1-e73c-4222-8691-1be21275a0ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CFD Simulation and Performance Comparison of Two Distillation Tank Designs with Different Heat Source Geometries","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater, a vital resource for human existence, covers approximately 70% of the Earth's surface. However, the distribution of freshwater is highly uneven, with 96.5% being saltwater in oceans and seas. The remaining freshwater is primarily locked up in glaciers (1.7%) and groundwater (1.6%), leaving only 0.015% accessible in rivers and lakes (Tashtoush et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The estimated volumes of groundwater, rivers, and lakes are 10.53\u0026nbsp;million cubic meters, 2100 cubic kilometers, and 91,000 cubic kilometers, respectively. According to UNESCO, 2.2\u0026nbsp;billion people lack access to safe drinking water, and 3.5\u0026nbsp;billion live without adequate sanitation (Kumar, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These water challenges are particularly acute in arid and semi-arid regions.\u003c/p\u003e \u003cp\u003eThe growing demand for freshwater and dwindling natural resources have spurred the development of innovative solutions, including solar desalination (Farabi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, many arid regions with limited freshwater resources receive abundant solar radiation, making solar desalination a promising option. Solar desalination systems can be categorized as either direct or indirect (Farabi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Advanced desalination technologies such as reverse osmosis, membrane distillation, multi-effect distillation, and electrodialysis offer various trade-offs in terms of energy consumption, skilled labor requirements, and capital costs (Kumar, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, research has focused on small-scale, low-pressure desalination units like Single Effect Distillation (SED) and Single Stage Flash (SSF) systems (Siddique et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For smaller-scale applications, a single-effect configuration can reduce costs and enable deployment in domestic or research settings. Studies have shown that SSF systems require approximately 30% more heat input compared to SED systems, resulting in a 24% decrease in performance (Siddique et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn SED, saltwater is heated in a vessel by a heat source, typically heated by a solar collector, until it vaporizes. The vapor is then condensed into freshwater using a cooling medium (Saidur et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Various solar collectors, including evacuated tubes, flat-plate collectors, and parabolic trough solar collectors (PTSCs), can be employed to harness solar energy. PTSCs, in particular, use parabolic mirrors to concentrate solar radiation onto an absorber tube, achieving high temperatures (up to 400\u0026deg;C) (Marrakchi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Their high efficiency and simple design have made PTSCs increasingly popular.\u003c/p\u003e \u003cp\u003eTo enhance research accuracy and reduce time and costs, Computational Fluid Dynamics (CFD) simulations are employed for thermal analysis (Khosravi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While existing research explores various desalination technologies, a comprehensive CFD-based comparison of different heat source geometries within a single-effect distillation tank is lacking. This research aims to contribute to the development of more efficient and cost-effective small-scale desalination systems by investigating the impact of heat source geometry on system performance through detailed CFD simulations.\u003c/p\u003e"},{"header":"Geometric Model","content":"\u003cp\u003eTwo geometric models were developed for the water desalination system. Both models share a common tank with a diameter of 30 cm and a height of 60 cm, equipped with 12 inlet ports (1 cm diameter) and one outlet port (6 cm diameter).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eModel 1\u003c/strong\u003e \u003cp\u003eFeatures a hollow cylindrical heat source with a diameter of 15 cm and a height of 30 cm. The inlet and outlet pipes of the heat source have a diameter of 2 cm. The width of the system is equal to the diameter of the pipe plus 2 cm. The heat source is made of copper.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTank dimensions balance application suitability, heat transfer efficiency, and computational cost, while heat source dimensions ensure efficient heat exchange and proper water circulation. Aluminum was chosen for the tank due to its thermal conductivity, affordability, and ease of fabrication, while copper was selected for the heat source for its superior thermal conductivity.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eModel 2\u003c/strong\u003e \u003cp\u003eThe heat source in this model is a solid cylinder with a diameter of 15 cm and a height of 30 cm. The inlet and outlet pipe diameters remain the same as in Model 1 (2 cm), and the material is also copper.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSimulation Setup and Parameters:\u003c/h2\u003e \u003cp\u003eThis simulation was performed using ANSYS Fluent 2021 on a laptop with the following specifications: 16 GB RAM, AMD Vega10 GPU, AMD Ryzen 7 3700 Pro CPU, and 1 TB NVMe SSD.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProblem Statement\u003c/h3\u003e\n\u003cp\u003eGiven the irregular flow paths of the fluid (water), a turbulent flow regime was assumed rather than a laminar one. An inlet velocity of 0.08 m/s was set in the tank, and the k-epsilon turbulence model was employed. This two-equation model uses k to represent turbulent kinetic energy and epsilon to represent the dissipation rate of turbulent kinetic energy. The inlet water temperature was set to 27\u0026deg;C, matching the ambient temperature. The inlet water to the heat source also utilized the k-epsilon model with a velocity of 0.01 m/s. To account for real-world conditions, the influence of velocity on heat transfer was activated for both water inlets.\u003c/p\u003e \u003cp\u003eDue to copper's excellent thermal conductivity, it was chosen as the material for the heat source to enhance efficiency and performance. To improve computational accuracy, the tank and heat source were meshed using different patterns: an unstructured hexagonal mesh for the tank and a triangular mesh for the heat source. Manual meshing was employed in the region where the tank and heat source interface to ensure accurate representation.\u003c/p\u003e \u003cp\u003eThe inlet velocity of 0.08 m/s in the tank ensures a suitable flow rate for small-scale desalination, balancing production rate and system design, while the lower velocity of 0.01 m/s at the heat source allows efficient heat transfer and reduces pressure drop. The heat source inlet temperature of 340 K (67\u0026deg;C) provides a strong temperature gradient to drive heat transfer and promote evaporation.\u003c/p\u003e\n\u003ch3\u003eComputational Fluid Dynamics (CFD) Modeling:\u003c/h3\u003e\n\u003cp\u003eCFD is a numerical method used to analyze fluid flow, heat transfer, and mass transfer. Meshing is a critical aspect of CFD simulations as it significantly influences the convergence of the solution (Sharifi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While the Navier-Stokes equations can be solved for laminar flow, resolving the small-scale fluctuations in turbulent flow is computationally challenging. Therefore, selecting an appropriate turbulence model is essential (Malekjani and Jafari, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe accuracy and reliability of simulation results are significantly influenced by the mesh quality and size. In ANSYS, meshing follows a predefined function based on the geometry, enabling the generation of meshes ranging from simple to complex patterns (Malekjani and Jafari, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eMeshing and Validation\u003c/h3\u003e\n\u003cp\u003eTo validate the model, mesh density, time step, and number of iterations were varied. The outlet temperature of the heat source was compared to experimental data to evaluate the mesh quality and ensure it did not adversely affect the results. Initially, simulations were conducted with a coarse mesh and various time steps and iterations. However, the results did not converge to a stable value. By refining the mesh and increasing the number of time steps and iterations, the outlet temperature converged, indicating a valid simulation.\u003c/p\u003e \u003cp\u003eTime steps of 0.09, 0.01, 0.007, and 0.001 seconds were tested with 200, 500, 1000, and 2000 iterations, respectively. The results obtained with a time step of 0.09 seconds and 200 iterations provided the necessary accuracy.\u003c/p\u003e \u003cp\u003eA time step of 0.09 s and 200 iterations were chosen to ensure accuracy, though further validation could involve comparison with experimental data, grid independence studies, and convergence analysis. Without detailed accuracy metrics, definitive conclusions remain challenging.\u003c/p\u003e \u003cp\u003eDue to the interaction between the tank and heat source, different mesh patterns were used for each component. The heat source was meshed using an unstructured hexagonal pattern, while the tank was meshed using a triangular pattern. To ensure accurate representation of the interface between the tank and heat source, manual meshing was employed in this region. Two mesh sizes, coarse and fine, were considered. The coarse mesh resulted in a significant discrepancy between the simulated and experimental temperatures of the heat source. By refining the mesh to a minimum size of 0.008 m and a maximum size of 0.012 m, the accuracy of the simulation results was improved. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the mesh pattern and element sizes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAnalysis of Results for the Tank with a Solid Cylindrical Heat Source\u003c/h3\u003e\n\u003cp\u003eAs previously mentioned, this simulation employed water as the working fluid within both the tank and the heat source. The fluid velocity in the tank was set at 0.08 m/s with a temperature of 300 K, while the heat source had a fluid velocity of 0.01 m/s and a temperature of 340 K. The relationship between the contact surface area between the water and the heat source and the steam generation efficiency is noteworthy: a larger contact area leads to increased steam production.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReynolds Number Calculation:\u003c/h2\u003e \u003cp\u003eThe Reynolds number is calculated to confirm the turbulent flow regime. The equation uses density, velocity, and characteristic length (tank diameter in this case). Assuming water at room temperature with a density of 1000 kg/m\u0026sup3; and dynamic viscosity of 0.001 Pa\u0026middot;s, and a velocity of 0.08 m/s within the 30 cm diameter tank, the calculated Reynolds number is 24,000. Since this value significantly exceeds the critical value for turbulent flow (around 4000), the assumption of turbulent flow is justified.\u003c/p\u003e \u003cp\u003eIf we divide the time steps into two equal parts, it becomes evident that the temperature difference and steam production exhibit an inverse relationship. In the first half of the simulation, the temperature difference reaches its maximum, while steam production is at its minimum. The opposite is true for the second half.\u003c/p\u003e \u003cp\u003eInitially, the heat source temperature is set to 340 K. After 100 time steps, the temperature difference decreases by 12 degrees, and the inlet water temperature approaches its boiling point. This smaller temperature difference results in a slower and lower rate of steam generation. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates how the temperature of the heat source decreases in the regions that are in direct contact with the 300 K inlet water, as heat is transferred to the water through convection. In contrast, the temperature increases in other regions due to the circulation of hot water and reduced contact with the inlet water, creating a temperature difference of 20 degrees Celsius. As a result of this temperature difference and convective heat transfer, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the amount of steam generated over a specific time interval. By using the formula D = ((0.02*3.14)*6)m^2 * 5.4s * 0.08 m/s, we can calculate the volumetric flow rate of water entering the tank. Dividing this value by the amount of steam produced yields the exergy of the system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBoundary Conditions:\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eInlet (Tank)\u003c/strong\u003e \u003cp\u003eVelocity (0.08 m/s), Temperature (300 K), Turbulence model (k-epsilon)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInlet (Heat Source)\u003c/strong\u003e \u003cp\u003eVelocity (0.01 m/s), Temperature (340 K), Turbulence model (k-epsilon)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOutlet (Tank)\u003c/strong\u003e \u003cp\u003ePressure outlet (atmospheric pressure)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eWalls (Tank and Heat Source)\u003c/strong\u003e \u003cp\u003eNo-slip condition (velocity\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInterface (Tank and Heat Source)\u003c/strong\u003e \u003cp\u003eHeat transfer between the two surfaces\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAmbient\u003c/strong\u003e \u003cp\u003eTemperature (300 K) (for heat loss considerations)\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eContact Area:\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eModel 1 (Solid Cylinder)\u003c/b\u003e: Contact area can be approximated as the lateral surface area of the cylinder: Rectangle Area\u0026thinsp;=\u0026thinsp;2 * π * r * height\u0026thinsp;=\u0026thinsp;2 * π * 0.12 m * 0.3 m\u0026thinsp;\u0026asymp;\u0026thinsp;0.226 m\u0026sup2;\u003c/p\u003e \u003cp\u003eArea of Circle\u0026thinsp;=\u0026thinsp;2* π*r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.09 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eContact Area\u0026thinsp;=\u0026thinsp;0.226 m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.09 m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.316 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSince the conditions in both tanks are identical, the volumetric flow rate of water into both tanks is equal, which is calculated to be 0.162 m^3.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the simulated steam production within the tank with a full cylindrical heat source is reported by ANSYS Fluent. The average volume of steam within and exiting the tank is calculated to be 0.006 m\u0026sup3; and 7.93 \u0026times; 10^-6 m\u0026sup3;, respectively. Given the negligible amount of steam exiting the tank compared to the volume within, the steam generation efficiency can be approximated by dividing the volume of steam produced by the total volume of water entering the system. This calculation results in an efficiency of approximately 0.01%, indicating that 99.9% of the total energy is wasted.\u003c/p\u003e \u003cp\u003eIt is important to note that the inlet water temperature is 340 K, and after circulating through the heat source and undergoing convective heat transfer, it exits at 298 K.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCylindrical Sector Heat Source\u003c/b\u003e Initially, the heat source temperature in this configuration is also set to 340 K. However, after half of the simulation time, a temperature difference of 100 degrees is observed. This rapid temperature increase accelerates the heating of the water, leading to earlier boiling and steam generation. Moreover, due to the larger contact area between the water and the heat source, a greater amount of water comes into contact with the heat source, resulting in increased evaporation and steam production. Additionally, the temperature difference between the inner surface of the tank and the existing steam may lead to condensation, causing the produced water to return to the tank. This effect can be mitigated by insulating the tank for larger volumes. In this case, the temperature difference and steam production occur more rapidly in the first half of the simulation compared to the second half, allowing for a proportional estimation of steam production over a longer period.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eContact Area:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eModel 2 (Cylindrical Sector)\u003c/strong\u003e \u003cp\u003eContact area is more complex to calculate accurately due to the varying radius. However, it can be estimated as the average radius multiplied by the height\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAverage Radius\u0026thinsp;\u0026asymp;\u0026thinsp;2*(πr\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e\u0026minus;\u003c/sub\u003e πr\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;0.026 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRecatngle Area \u0026asymp; (2* π*r\u003csub\u003e1\u003c/sub\u003e*0.3\u0026thinsp;+\u0026thinsp;2* π*r\u003csub\u003e2\u003c/sub\u003e*0.3)\u0026thinsp;\u0026asymp;\u0026thinsp;0.41448 m\u0026sup2;\u003c/p\u003e \u003cp\u003eContact Area\u0026thinsp;=\u0026thinsp;0.44048 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the temperature gradient within the tank with a cylindrical sector heat source. This figure shows that as the volume of the cylinder decreases and the contact surface area with the hot inlet water increases, the heat source absorbs more of the heat released by the water. Consequently, the overall temperature of the copper heat source increases to 568 K, resulting in improved efficiency and higher steam production. Notably, the temperature of the portions of the heat source in direct contact with the inlet water decreases to 538 K.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Model 2, where the tank has a cylindrical sector heat source, the inlet water volume is identical to the first model, equaling 0.162 m\u0026sup3;. According to Model 1, the amount of steam produced in the second model is 0.019 m\u0026sup3;. To calculate the exergy, we have: [Insert your exergy calculation equation here]. This indicates a 10% increase in efficiency compared to the previous model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this simulation, two types of heat sources were compared under identical conditions of material, inlet water temperature, inlet water velocity, and duration, with the sole difference being the contact surface area. The results indicated that increasing the contact surface area with the inlet water led to a higher amount of heat absorbed by the heat source. Additionally, due to the increased contact surface area between the tank and the heat source, the amount of steam produced also increased. Furthermore, the validity of this finding could be further verified by comparing the second type of heat source with a coiled heat source.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA. Omid shariati wrote the main manuscript text,B. Hadi SamimiAkhijahani reviewed the manuscript,\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFarabi, S. N., Habib, K., Mim, M., Zaed, M., Ali, S. A., Younas, M., \u0026amp; Saidur, R. (2024). The future of solar-driven interfacial steam generation for sustainable water desalination: drivers, challenges, and opportunities-Review. \u003cem\u003eResults in Engineering\u003c/em\u003e, 102649.\u003c/li\u003e\n \u003cli\u003eKhosravi, A., Malekan, M., \u0026amp; Assad, M. E. (2019). Numerical analysis of magnetic field effects on the heat transfer enhancement in ferrofluids for a parabolic trough solar collector. \u003cem\u003eRenewable Energy\u003c/em\u003e,\u003cem\u003e 134\u003c/em\u003e, 54-63.\u003c/li\u003e\n \u003cli\u003eKumar, A. (2024). ANSYS Fluent-CFD analysis of a continuous single-slope single-basin type solar still. \u003cem\u003eGreen Technologies and Sustainability\u003c/em\u003e, 100105.\u003c/li\u003e\n \u003cli\u003eMalekjani, N., \u0026amp; Jafari, S. M. (2018). Simulation of food drying processes by Computational Fluid Dynamics (CFD); recent advances and approaches. \u003cem\u003eTrends in food science \u0026amp; technology\u003c/em\u003e,\u003cem\u003e 78\u003c/em\u003e, 206-223.\u003c/li\u003e\n \u003cli\u003eMarrakchi, S., Leemrani, Z., Asselman, H., Aoukili, A., \u0026amp; Asselman, A. (2018). Temperature distribution analysis of parabolic trough solar collector using CFD. \u003cem\u003eProcedia Manufacturing\u003c/em\u003e,\u003cem\u003e 22\u003c/em\u003e, 773-779.\u003c/li\u003e\n \u003cli\u003eSaidur, R., Elcevvadi, E., Mekhilef, S., Safari, A., \u0026amp; Mohammed, H. A. (2011). An overview of different distillation methods for small scale applications. \u003cem\u003eRenewable and sustainable energy reviews\u003c/em\u003e,\u003cem\u003e 15\u003c/em\u003e(9), 4756-4764.\u003c/li\u003e\n \u003cli\u003eSharifi, K., Sabeti, M., Rafiei, M., Mohammadi, A. H., \u0026amp; Shirazi, L. (2018). Computational fluid dynamics (CFD) technique to study the effects of helical wire inserts on heat transfer and pressure drop in a double pipe heat exchanger. \u003cem\u003eApplied Thermal Engineering\u003c/em\u003e,\u003cem\u003e 128\u003c/em\u003e, 898-910.\u003c/li\u003e\n \u003cli\u003eSiddique, M., Turkmen, N., Al-Rabghi, O. M., Shabana, E., \u0026amp; Albeirutty, M. H. (2018). Small-scale low pressure ‘single effect distillation’and ‘single stage flash’solar driven barometric desalination units: a comparative analysis. \u003cem\u003eDesalination\u003c/em\u003e,\u003cem\u003e 444\u003c/em\u003e, 53-62.\u003c/li\u003e\n \u003cli\u003eTashtoush, B., Alyahya, W. e., Al Ghadi, M., Al-Omari, J., \u0026amp; Morosuk, T. (2023). Renewable energy integration in water desalination: State-of-the-art review and comparative analysis. \u003cem\u003eApplied Energy\u003c/em\u003e,\u003cem\u003e 352\u003c/em\u003e, 121950.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Mesh properties created in for tank and heat source\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"528\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSolid Cylinder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCylindrical Sector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinimum element size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0008 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0008 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmallest edge length of any element in the mesh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaximum element size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLargest edge length of any element in the mesh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e262,997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e292,181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal number of points in the mesh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of elements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e427,860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e406,468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal number of cells in the mesh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinimum angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70.63\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.09\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edegrees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmallest angle between any two edges of an element\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaximum angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e141.96\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e179.56\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edegrees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLargest angle between any two edges of an element\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePinch tolerance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.000072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edimensionless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinimum allowable distance between nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInflation algorithm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePredefined inflation algorithm used\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edimensionless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMeasure of element distortion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edimensionless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatistical measure of mesh quality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 system specifications in brief and general\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"528\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComponent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaterial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInlet Diameter (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOutlet Diameter (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDimensions (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eContact Area (cm\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInlet Temperature (K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOutlet Temperature (K)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAluminum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12x2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD=24, h=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeat Source 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD=12, h=30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeat Source 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eD1=12, D2=10, h=30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-mechanics-and-materials-in-design","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Mechanics and Materials in Design](https://link.springer.com/journal/10999)","snPcode":"10999","submissionUrl":"https://submission.springernature.com/new-submission/10999/3","title":"International Journal of Mechanics and Materials in Design","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ANSYS, Desalination, Heat source, simulation, thermal efficiency","lastPublishedDoi":"10.21203/rs.3.rs-5746439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5746439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a numerical simulation using ANSYS Fluent to investigate the performance of two water desalination systems under identical conditions. Realistic environmental conditions such as ambient pressure, temperature, fluid inlet velocity, and temperature of the fluid entering the tank and the heat source (both being water) were considered. Both models were tested under the same conditions: a pressure of 1 atm, an ambient temperature of 27\u0026deg;C, an inlet fluid velocity in the tank of 0.08 m/s, an inlet fluid temperature to the tank of 27\u0026deg;C, an inlet fluid velocity to the heat source of 0.01 m/s, and an inlet fluid temperature to the heat source of 67\u0026deg;C. The tank and heat source were made of aluminum and copper, respectively. The obtained results showed significant differences and will be discussed in detail in the following sections.\u003c/p\u003e","manuscriptTitle":"CFD Simulation and Performance Comparison of Two Distillation Tank Designs with Different Heat Source Geometries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 09:01:12","doi":"10.21203/rs.3.rs-5746439/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-09T05:29:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-07T11:22:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-08T09:07:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93588727736616941628792082171450759508","date":"2025-02-08T06:26:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82700583823243284672796804639985124647","date":"2025-02-07T17:46:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-07T17:36:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-07T12:37:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-07T12:36:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Mechanics and Materials in Design","date":"2025-01-01T13:38:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-mechanics-and-materials-in-design","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Mechanics and Materials in Design](https://link.springer.com/journal/10999)","snPcode":"10999","submissionUrl":"https://submission.springernature.com/new-submission/10999/3","title":"International Journal of Mechanics and Materials in Design","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e6a0975b-0f3f-41cb-bc33-2f2cb6c8c6de","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T03:53:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-09 09:01:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5746439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5746439","identity":"rs-5746439","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}