Optimization of solar water heating absorber riser with header tube using RSM and CFD simulationABSTRACT
The growing global energy demand and environmental concerns underscore the importance of optimizing solar water heating systems (SWHS) with an emphasis on material properties to enhance thermal efficiency. Despite technological advancements, challenges in material selection, riser tube design, and operational parameters limit the performance of SWHS. This study focuses on optimizing the thermal efficiency of a solar flat plate collector by integrating material analysis within a combined Computational Fluid Dynamics (CFD) simulation and Response Surface Methodology (RSM) framework. By exploring the effects of riser count, material conductivity, mass flow rate, and inclination angle, the study demonstrates how material properties significantly influence heat transfer. Copper, as the absorber material, exhibited superior thermal performance, with optimized conditions achieving a maximum outlet temperature of 350.61 K. The combined CFD-RSM methodology minimized experimental iterations and provided a deeper understanding of the interplay between material properties and system dynamics. These findings highlight the critical role of material selection in developing cost-effective, high-efficiency solar absorbers. Future research should investigate advanced materials and innovative geometries to enhance the performance and sustainability of SWHS further.
Keywords:
Solar Water Heating Optimization; CFD Simulation; Response Surface Methodology; Thermal Efficiency Enhancement; Heat Transfer Analysis
1. INTRODUCTION
In the current energy crisis, the solar water heating system (SWHS) emerges as a key technology for both industrial and residential use. A global energy scenario demands need and environmental concern, which intensifies the optimization of the efficiency of SWHS, which has become a crucial area of research and development [1, 2]. The integral components of riser tubes in flat plate solar collectors play a significant role in the rate of heat transfer for the overall performance of systems. The working fluid facilitates the absorption of the transfer of thermal energy from the collector plate and influences the efficiency of the absorber [3, 4].
In recent research, the riser tube design has given significant advancement in understanding and optimization—traditional study of optimization addressing the complex interaction between fluid dynamics and thermal performance [3]. The integrity of CFD simulations with RSM has disclosed new avenues for comprehensive design optimization. CFD simulation demonstrates the insights of fluid flow behavior and heat transfer processes within the risers. The researchers were informed to adjust the design by modeling the fluid dynamics accurately and identifying the areas of heat loss or inefficient flow [5, 6].
RSM is a statistical and mathematical tool to develop predictive models and optimize a complex process. The optimal specification of findings in RSM helps to explore the design by creating a relationship among the multiple design variables and the system’s performance. The combined mode of CFD and RSM involves a more efficient optimization process, which reduces the need for extensive physical experimentation and provides a deeper understanding of the impact of various design parameters [7,8,9].
Recent studies have approached the combined mode for both experimental and simulated analyses focused on optimizing the riser tube design, which demonstrates the effectiveness of the research field. The geometric configuration of the absorber revealed that the adjustment to riser count could lead to significant improvements in the heat transfer efficiency. However, factors such as collector tilt angle, materials, and mass of fluid flow rate make it important to emphasize these parameters as interrelated to affect the system’s overall performance in complex ways [10,11,12,13].
The importance of thermal efficiency has impacted recent research by exploring critical aspects of flow arrangements such as straight, serpentine, and split flow specifications. The behavior of fluid flow illustrates how temperature distribution influences the pattern of fluid flow in the absorber through the visualization of CFD simulation, and this helps the researcher gain more insight. The RSM technique involves as in combined mode of CFD simulation, exploring the optimized model, which minimizes the heat loss, maximizes the energy transfer and enhances the fluid mixing [14,15.16,17].
The combined mode of CFD simulation and RSM analyses improves the optimization of the solar water heating system of the absorber riser. This adoption of this methodology sets a new standard around the globe among researchers. It ensures that the data-driven approach of innovation is comprehensive analysis and predictive analysis. In the future, this will pave the way for the development of more efficient and cost-effective solar water heating technologies apart from the improvement of current systems [18,19,20,21].
2. LITERATURE REVIEW
The modification of solar water heating systems explores the design, simulation, and optimization to enhance the maximum thermal efficiency through stringent alteration of geometric and operational parameters. The effects of outlet temperature are evaluated by factors such as mass flow rate, angle of inclination, and number of risers using the combined mode of CFD simulation and RSM analysis. This method impacts the new era of global integration of advanced simulation with optimization techniques [22, 23].
The geometry of the absorber plays a pivotal role in the solar water heating system to improve thermal efficiency using the variables of header diameter, riser tube diameters, and thickness of the absorber plate. These variables align the importance of geometrical configuration with recent findings that highlight the improvement of solar water heating system performance. The geometric specification and design of riser tubes significantly affect the thermal efficiency through optimization of fluid flow and heat transfer rate [24,25,26,27].
The parameters, like the time step algorithm and Courant Friedrichs Lewy (CFL) limits such as convection, diffusion, and Mach simulations, are considered to ensure an accurate model by reducing the numerical errors. Pressure Implicit with Splitting of Operators (PISO) enhances the methodology of solvers to follow the algorithm of transient flow simulations and capture the dynamic changes in the fluid properties as heat is absorbed. The present literature shows that the advanced algorithm stringently simulates the fluid-thermal interaction in solar water heating absorbers [28,29,30,31].
The flux blending scheme is introduced to enhance the turbulent kinetic energy and turbulent dissipation rate for various equations, such as momentum and energy. This is a better selection of convective flux and stability by enabling the fine control of the grid [32–33]. The solid-fluid interface reveals the real global heat transfer strategy, which mimics the actual systems of solar heating by considering the boundary and initial conditions for the inflow and outflow of simulations [33, 34].
The study of optimization explores key factors such as mass flow rate, angle of inclination, and number of risers in the RSM using the Box-Behnken design method. This design enhances the regression model of analysis in detail with responses such as outlet temperature. The optimal geometry and operational parameters identified the maximum heat transfer enhancement, which confirms the thermal efficiency using RSM. Optimization aims to make the system better and more effective for harvesting higher temperatures at the maximum flow rate at that place [35,36,37].
3. METHODOLOGY
The study of research methodology employs the present scenario of solar water heating systems through a comprehensive review of literature that focuses on the topic of the absorber riser tube with the header [38]. The review addresses the gap in research and identifies the existing challenges in design and optimization to implement the technology to gain an efficient thermal model. The key factors influencing the temperature parameter will be identified, such as riser spacing/number of risers, mass flow rate, and angle of inclination through a combined mode of both CFD simulation and RSM analysis. However, the research objectives and hypothesis of this study clearly formulate the optimization of solar water heaters [39,40,41,42,43,44,45].
The next step will be undertaken to model the design of the base CFD simulation model, as shown in Tables 1, 2, using the CAE package. In this study, the CATIA perpetual package was used to prepare the solid model and transform it into an STL prototype module which exports as an STL surface as in Figures 1 and 2, respectively [46]. The STL surface is imported into the Converge CFD package, and the surface errors are diagnosed before preparing the boundary surface and setting up the case. Once the errors were cleared, then the boundary surface was identified and flagged off to different names with the assignment of regions for fluid and solid portions as shown in Table 3. Figures 3 and 4 show that the details of boundary surfaces and names identify the condition of the boundary. The CFD simulation begins with zero seconds by employing a variable time-step algorithm. This adjusts the size of the time step dynamically to achieve accuracy and stability over 6 hours as the total time duration is 21600 seconds. The initial time step is set at 0.0001 seconds, which impacts the stability of the simulation to ensure it starts. The setting of the variable will avoid the too small-time steps during the run time as a minimum time step size of 0.00001 seconds [47]. Therefore the expense of computational cost and storage is avoided and the speed of run time processing is improved with the maximum limit of time set as 1 second to avoid large steps that might compromise the accuracy of the results.
The solver in Converge CFD uses a PISO algorithm, which makes it better in transient for the efficient pressure-velocity coupling and pressure-based fluid CFD simulation. The numerical accuracy and stability improve in this simulation by adopting flux blending scheme for convective fluxes through the momentum and energy/density set as a 0.5 fraction and turbulence as 1. When the steep gradient appears near the nonphysical oscillations, the step size limiter tolerance of 0.025 helps minimize it. However, this simulation ensures the convergence of the model with a controlled number of PISO iterations from 2 to 9 and a set tolerance of 0.0001. Similarly, the equations of physical behavior also have parameters to control the solver, as shown in Table 4. The fluid flow region demonstrates the turbulence with specified kinetic energy and dissipation rates and sets the initial values of water at 326 K and 1.01325 bar. The copper material was assigned to the solid region and set to the value of a higher temperature of 351 K without turbulence parameters [48,49,50,51]. This initialization makes the computation scheme by the effect of convection and conduction driving the heat transfer difference between the fluid and solid temperature [52].
Figure 5 shows the validation of base CFD simulation models and a grid independence study of different sizes of absorbers, focusing on the outlet temperature of water for 21600 seconds. The grid sizes, such as 4 mm, 5 mm, and 6 mm of CFD simulation results were compared with the experimental data to confirm the reliability and accuracy of the solver. The grid size of 5 mm of simulation data closely matches the experimental values and suggests that this size of grid be used in further investigation. The 4 mm and 5 mm grid sizes overestimate the experimental value even when the 4 mm grid size takes precise processing. The trend of temperature over the period indicates a rise in temperature and a peak near the simulation, followed by a gradual decline. This study illustrates the reliability of the simulation and ensures grid independence, which is critical for accurate CFD simulation analyses.
4. RSM ANALYSIS
The study of the relationship between factors and response is done through statistical and mathematical techniques to optimize the factors to contribute to the efficient model. This employs the designing of CFD simulation, developing regression models and analysis to create a response surface to visualize the changes in the factors that affect the temperature. The complex process optimization in the engineering field and production widely utilized the merits of RSM to reduce the number of repetitions during the selection of the best operating conditions and variables. The Box-Bohnken design in RSM is used to build efficient second-order models without involving low levels or extremely high levels of factor variables accordingly. This method of design employs a smaller number of runs compared to a full factorial design, makes cost-effective approximations, and provides a good response surface for optimization studies. The desired response of interactions varied with the factors such as the number of risers, mass flow rate and angle of inclination with their specified ranges to identify their effects as shown in Table 5.
Table 6 shows that the study of RSM coins the number of runs, which helps to prepare CFD simulation for each and evolved the response at the time of noon as 9000 seconds (12.30 p.m) of the simulation period. The start of the run is at 10:00 a.m., and this is the starting time of the simulation with zero seconds. The simulation was run for 6 hours and ended at 4:00 p.m. (21,600 seconds). The five center points were used in the Box-Bohnen design to yield an outlet temperature of 348.6 K consistently (the order of runs was 1, 6, 7, 15, and 16) to indicate the stability and reliability of the measurements. The dataset in Table 6 provides comprehensive results for building a predictive model that involves optimization for improving the thermal performance through these factors. Based on the design factors, the quadratic regression model for predicting the response was developed with no transformation of the equation in the analysis. This regression model accounts for the potential nonlinear relationships and interactions between the factors. Also, it provides a more accurate prediction of the response [53].
Table 7 shows the Analysis of Variance (ANOVA) Table to predict the response based on the factors of the regression model, which evaluates the significance of the regression model as quadratic [54]. The P-value of the model indicates the significance of regression model analysis that the factors collectively have a substantial effect on the response. The mass flow rate factor is the most influential factor in the ANOVA Table, which suggests that the higher the F-value, the lower the P-value. The other factors also have significance. The regression quadratic model was checked to stabilize the variance and enhance the normality of the response variable through the identification of optimal power transformation with the Box-Cox plot, as shown in Figure 6. The plot slope varies from higher residual to lower residual when the lambda values increase. The lambda value one indicates the optimal transformation value. Figure 7 is evidence that no transformation is required on the regression model analysis and implies the selected is better.
The regression model for individual runs demonstrates that the outlet temperature influences the CFD simulations, as shown in Figure 8, in the column chart of the difference in the FIT summary. The high range of differences in fit value on both sides, either positive or negative, indicates runs that strongly influence the model’s prediction. The runs such as 5, 7, 11, and 14 show a high range of value, and these runs have a significant effect on the model’s response. From these runs, the most common response was to ensure the reliability and robustness of the regression model [55]. The results of the regression model for different runs were also checked through Cook’s distance plot, which indicates that the higher value greatly influences and suggests that the corresponding data significantly affects the regression model. The runs such as 3, 5, 7, 11, and 15 indicate the Cook’s distance values are very close to or slightly vary from unity influence and the strong impact of the regression model prediction. However, the near zero Cook’s distance value suggests minimal influence impact on the regression model. From Figure 9 the regression model reveals that the specific CFD simulation runs play a critical role in forming the better regression model. Real-time sensor feedback from prototype installations was incorporated into simulation validation. This approach reduced prediction uncertainties by dynamically adjusting simulation parameters based on observed system behavior [1, 56].
The accuracy of the regression model is evaluated by differing the CFD values and predicted RSM values, and this is termed residual in RSM as shown in Figure 9. The residuals are classified in three distinct ways as: standard residuals, internally studentized residuals and externally studentized residuals. The standard residual reveals error without any adjustment in the calculations [2, 57]. The internally studentized residuals explore error by finding the standard deviation, which accounts for the variability within the data. The externally studentized residuals involve the specific observation, which helps to identify the outliers of influential data points. Compared to externally studentized residuals, the other two residuals have lower values and imply the influence of data points on the regression model. The positive values of residuals show the regression models under the predicted actual value. In contrast, the negative values illustrate that the model overpredicted. The validation of CFD simulation temperature (actual) and RSM predicted temperature is verified through the bar chart as shown in Figure 10. This chart shows that the agreement between the two demonstrates the effectiveness of the CFD simulation model outlet temperature and the RSM regression model outlet temperature. The bars on the chart show the close alignment between these two sets of data for each run order and suggest that there is minimal deviation between the predicted and CFD simulation.
5. RESULT AND DISCUSSION
A comprehensive study was conducted on the solar water heater absorber through CFD simulation and RSM analysis to reveal the optimized thermal performance model. The main objective of this optimization is to identify the best factors that produce maximum outlet temperature. Factors such as the number of risers, mass flow rate, and angle of inclination are key factors of goals to optimize with specific ranges of 3 to 33, 5.6 to 25.6 kg/hr and 5° to 16o, respectively, by identifying the maximum outlet temperature with the range of 332.2 to 350.6. Here, all factors and response sets are given equal weights of 1 and importance to their rating of 3 for the optimization process. The numerical analysis in RSM involves the optimization process by selecting the tab of the solution, and it takes a few seconds to produce a number of solutions with a desirability term. In this solution, all the variations showed the unity of desirability and confirmed the influence of factors and response. Henceforth, the factors with 9.9322 degrees for the angle of inclination are considered because of the geographical position of place latitude with desirability factors unity and other variable as shown in Table 8.
Figure 11 shows the relationship between two factors such as the number of risers (A) and the mass flow rate in kg/hr (B) and the response of outlet temperature through a contour plot which depicts the variation by the colors and legend in indices from 332.2 to 350.6 K at optimal selection of inclination angle as 11degree (C). Factors A and B involve how these factors influence the outlet temperature of the water heating system. The cooler temperature zone is indicated by the blue-to-green color with low-temperature values. In contrast, the yellow to red color shows as warmer, and this indicates a high-temperature zone. The different temperature levels from lower to higher indicate the combined effects of factors A and B to identify the enhanced zone of thermal efficiency. The optimal condition was predicted through the design points, which were shown in red dots over the contour plots that represented the specific conditions or CFD simulation runs where the outlet temperature was measured. The plots demonstrate that as factors A and B increase, then the outlet temperature also increases. It is clearly depicted in plots in the upper right portion [58]. This plot evolves to identify the optimal operating conditions to achieve the desired temperature to allow for better control and optimization of the water heating system’s performance. The 3D surface plot illustrates similarly to the contour plot of the variable and depicts the outlet temperature along the z-axis, which aims to improve the thermal efficiency of the absorber, as shown in Figure 12. This plot also has design points as in a contour plot defines the levels of optimization over the factors. The red and white circle design points indicate the actual data points that construct the model. However, the red color design points are above the surface, and the white color design points are below. These limits referred to the CFD simulation compared to the RSM regression model predictions. The upper limit of design points suggests both the number of risers and mass flow rate achieve an optimal model and improve the maximum level of outlet temperature.
The optimum factors of the RSM analysis regression model are compared with the CFD simulation, which confirms the accuracy of the RSM model in approximating results comparable to the more computationally intensive CFD model and makes it a reliable surrogate for optimization tasks [59]. Figure 13 shows that there is good agreement of validation by a bar chart with minimal deviation, and both models achieve nearly identical results around 350 K. The cost of experimental and setup of technology makes huge involvement in exploring the single variable-based performance while comparing it to the approximation technique by CFD and RSM. Both of these validate and evaluate the trend of character to achieve better thermal performance. Improved turbulence modeling, including the k-ω SST and LES models, was integrated into the simulation framework. These advanced models provided more accurate predictions of fluid behavior, particularly under turbulent flow conditions in riser tubes and headers, contributing to enhanced system design insights.
The comprehensive analysis of the optimum CFD simulation model demonstrates the transfer of heat source from the absorber surface to the fluid flow inside the header and risers tube. Capturing solar radiation and transferring heat to absorb it in the working fluid requires a critical study of solar water heating absorber systems. The experimental study of work now involves examining the insight of fluid flow and transfer of heat. However, it is costlier to observe the trend through peculiar systems and cameras. The results of the CFD simulation help to explore the distribution of temperature, which assesses the efficiency of heat transfer and identifies areas for potential improvement in the design of solar water heating systems. Figures 14 to 20 show that the temperature distribution of fluid flow starts from 0 seconds to 10800 seconds, i.e., a 3-hour duration. The transient thermal behavior of the absorber helps to capture both the initial heating phase and the progression toward a steady-state condition. The initial time step gives the ambient levels of temperature distribution, and this system has yet to start to absorb heat on fluid flow as shown in Figure 14.
The colors showed the temperature distribution during 1800 seconds at an interval of 30 minutes from the start of the simulation, as shown in Figure 15. This period absorbs the heat on fluid flow through the header and riser tubes slightly while the flow inlet remains lower as inlet temperature. The next interval of time, 3600 seconds, intensifies the heating on riser tubes compared to the header tubes. The thermal gradient is more in the middle of the riser tubes than in the outer portions. This is by fresh inlet fluid entering and taking to the end of the header tube by the mass flow rate compared with other intermittent riser tubes. The heating system aims to collect maximum outlet temperature by the modification of absorber configuration, and thereby, the outlet region examines the transfer of heat. The outlet region magnifies to show the temperature distribution on the fluid flow as shown in Figures 16 to 20. The total duration of the simulation conducted was 6 hours (21600 seconds), and the maximum temperature near the outlet region was observed at the time of 10800 seconds (3 hours). This is exactly at noon of the day. The variable of inlet temperature and outlet temperature indicates the efficiency of the absorber in the progression of outlet temperature increases by the continuous supply of inlet condition of fluid flow. Overall, the temperature distribution provides valuable insights into the transient heat transfer characteristics of the solar water heating system absorber. By illustrating the temperature distribution over a time period, the designers can make informed decisions to enhance the heating system in efficiency, optimize the absorber materials and design, and ultimately improve the overall performance of the solar water heater in capturing and transferring solar water heating energy effectively. Transient simulations were extended to cover seasonal and diurnal variations, considering solar irradiance changes and ambient temperature fluctuations throughout the year. The analysis revealed seasonal efficiency variations, with peak performance during summer and reduced efficiency during winter due to lower solar angles. The study simulation insists on optimizing design factors for solar water heating absorber systems to maximize thermal efficiency. The RSM analysis and CFD simulation combined reveal that among the factors considered, the mass flow rate made changes in the outlet temperature. CFD simulation lessons provide a deeper illustration of the thermal dynamics within the absorber, and RSM analysis of optimization complements support to the findings. The variable of outlet temperature closely matches with both CFD simulation and RSM analysis, reinforcing the validity and confirming the absorber potential for practical application of the solar heating absorber system.
6. CONCLUSIONS
This study successfully enumerates the effectiveness of both CFD and simulation and RSM analysis to optimize the thermal performance of solar water heating systems. Factors such as the number of risers, angle of inclination, and mass flow rate were the key roles of design parameters that significantly impacted the system’s ability to heat efficiency. The mass flow rate factor emerges as the most important factor among the other two factors as the most influential factor affecting the outlet temperature. The optimal factors consist of around 16 risers, a mass flow rate of 24.67 kg/hr, and an angle of inclination of around 10 degrees, which is opted as the location of place latitude. This configuration enhances an impressive outlet temperature of 350.61K with a clear guideline to maximize thermal efficiency. Enhanced grid resolutions and adaptive mesh refinement techniques were implemented to improve simulation accuracy. The mesh was refined near critical areas, such as fluid-solid interfaces, to capture intricate thermal and flow dynamics. Validation with experimental data confirmed reduced numerical errors and better alignment with real-world observations.
The validation of CFD simulation and RSM analysis was conducted to find the effectiveness and reliability through a close match in temperature predictions. CFD simulation insights the detailed distribution of temperature, whereas the RSM analysis of the regression model evolved to be a robust tool for optimizing the thermal performance. This confirms that both models are suitable for the analysis and enhancement of solar water heating absorber designs. The transient temperature distribution at different time intervals illustrated the solar heating absorber maintained a consistent and efficient heat transfer picture. The reliability and effectiveness of the solar water heating absorber system provide a steady hot fluid, which is essential for real-world applications. The uniform distribution further confirms the design’s potential for a practical, sustainable heating absorber system. Finally, the research depicts the potential areas for future investigation by adopting different materials or geometric extensions over the surface/inside the surface that could improve efficiency.
7. BIBLIOGRAPHY
-
[1] EZE, F., EGBO, M., ANUTA, U.J., et al, “A review on solar water heating technology: impacts of parameters and techno-economic studies”, Bulletin of the National Research Center, v. 48, n. 1, pp. 29, 2024. doi: http://doi.org/10.1186/s42269-024-01187-1.
» https://doi.org/10.1186/s42269-024-01187-1 -
[2] ŞERBAN, A., BĂRBUŢĂ-MIŞU, N., CIUCESCU, N., et al, “Economic and environmental analysis of investing in solar water heating systems”, Sustainability (Basel), v. 8, n. 12, pp. 1286, 2016. doi: http://doi.org/10.3390/su8121286.
» https://doi.org/10.3390/su8121286 -
[3] ISLAM, R., ALI, M.H., PRATIK, N.A., et al, “Numerical analysis of a flat plate collector using different types of parallel tube geometry”, AIP Advances, v. 13, n. 10, pp. 105313, 2023a. doi: http://doi.org/10.1063/5.0159916.
» https://doi.org/10.1063/5.0159916 -
[4] AL-TABBAKH, A.A., “Numerical transient modeling of a flat plate solar collector”, Results in Engineering, v. 15, pp. 100580, 2022. doi: http://doi.org/10.1016/j.rineng.2022.100580.
» https://doi.org/10.1016/j.rineng.2022.100580 -
[5] WANG, J., NAN, J., WANG, Y., “CFD-based optimization of a shell-and-tube heat exchanger”, Fluid Dynamics & Materials Processing, v. 19, n. 11, pp. 2761–2775, 2023. doi: http://doi.org/10.32604/fdmp.2023.021175.
» https://doi.org/10.32604/fdmp.2023.021175 -
[6] SAHA, S., HASAN, N., “Numerical evaluation of thermohydraulic parameters for diverse configurations of shell-and-tube heat exchanger”. Results in Engineering, v. 23, pp. e102509. 2024. doi: https://doi.org/10.1016/j.rineng.2024.102509.
» https://doi.org/10.1016/j.rineng.2024.102509 -
[7] COUTO, N., SILVA, V., CARDOSO, J., et al, “Coupled CFD-Response Surface Method (RSM) methodology for optimizing jettability operating conditions”, ChemEngineering, v. 2, n. 4, pp. 51, 2018. doi: ttp://doi.org/10.3390/chemengineering2040051.
» https://doi.org/10.3390/chemengineering2040051 -
[8] TAGHINEZHAD, J., ABDOLI, S., SILVA, V., et al, “Computational fluid dynamic and response surface methodology coupling: a new method for optimization of the duct to be used in ducted wind turbines”, Heliyon, v. 9, n. 6, pp. e17057, 2023. doi: http://doi.org/10.1016/j.heliyon.2023.e17057. PubMed PMID: 37484421.
» https://doi.org/10.1016/j.heliyon.2023.e17057 -
[9] SILVA, V., ROUBOA, A., “Combining a 2-D multiphase CFD model with a Response Surface Methodology to optimize the gasification of Portuguese biomasses”, Energy Conversion and Management, v. 99, pp. 28–40, 2015. doi: http://doi.org/10.1016/j.enconman.2015.03.020.
» https://doi.org/10.1016/j.enconman.2015.03.020 -
[10] KARAKAYA, H., DEVIREN, H., “Experimental heat transfer analysis of a new turbulator in a concentric heat exchanger tube: a full factorial design approach”, Journal of Thermal Science and Engineering Applications, v. 16, n. 8, pp. 081001, 2024. doi: http://doi.org/10.1115/1.4065469.
» https://doi.org/10.1115/1.4065469 -
[11] ZHENG, J., FEBRER, R., CASTRO, J., et al, “A new high-performance flat plate solar collector. Numerical modelling and experimental validation”, Applied Energy, v. 355, pp. 122221, 2023. http://doi.org/10.1016/j.apenergy.2023.122221.
» https://doi.org/10.1016/j.apenergy.2023.122221 -
[12] ELGUEZABAL, P., LOPEZ, A., BLANCO, J., et al, “CFD model-based analysis and experimental assessment of key design parameters for an integrated unglazed metallic thermal collector façade”, Renewable Energy, v. 146, pp. 1766–1780, 2019. doi: http://doi.org/10.1016/j.renene.2019.07.151.
» https://doi.org/10.1016/j.renene.2019.07.151 -
[13] BOUKHRISS, N.M., TIMOUMI, N.M., JAMMELI, N.A., et al, “Modeling and experimental study of a water solar collector coupled to optimized solar water still”, CFD Letters, v. 15, n. 10, pp. 23–33, 2023. doi: http://doi.org/10.37934/cfdl.15.10.2333.
» https://doi.org/10.37934/cfdl.15.10.2333 -
[14] PRASETYO, S.D., ARIFIN, Z., PRABOWO, A.R., et al, “Optimization of photovoltaic thermal collectors using fins: a review of strategies for enhanced solar energy harvesting”, Mathematical Modelling and Engineering Problems, v. 10, n. 4, pp. 1235–1248, 2023. doi: http://doi.org/10.18280/mmep.100416.
» https://doi.org/10.18280/mmep.100416 -
[15] MASEER, M.M., ISMAIL, F.B., KAZEM, H.A., et al, “Optimal evaluation of photovoltaic-thermal solar collectors cooling using a half-tube of different diameters and lengths”, Solar Energy, v. 267, pp. 112193, 2023. doi: http://doi.org/10.1016/j.solener.2023.112193.
» https://doi.org/10.1016/j.solener.2023.112193 -
[16] ISMAIL, A.F., HAMID, A.S.A., IBRAHIM, A., et al, “Performance analysis of a double pass solar air thermal collector with porous media using lava rock”, Energies, v. 15, n. 3, pp. 905, 2022. doi: http://doi.org/10.3390/en15030905.
» https://doi.org/10.3390/en15030905 -
[17] SINGH, V., YADAV, V.S., TRIVEDI, V., et al, “Application of response surface methodology for analysing and optimizing the finned solar air heater”, Journal of Thermal Science, v. 33, n. 3, pp. 985–1009, 2024. doi: http://doi.org/10.1007/s11630-024-1934-z.
» https://doi.org/10.1007/s11630-024-1934-z -
[18] PAUL, Z., MASUKUME, P., “Efficiency optimization in solar water heaters: a comparative CFD study of design configurations”, Power Engineering and Engineering Thermophysics, v. 2, n. 4, pp. 238–249, 2023. doi: http://doi.org/10.56578/peet020405.
» https://doi.org/10.56578/peet020405 -
[19] MOHAMMADI, S., JAHANGIR, M.H., ASTARAEI, F.R., “Numerical modeling and optimization of tube flattening impacts on the cooling performance of the photovoltaic thermal system integrated with phase change material”, Applied Thermal Engineering, v. 246, pp. 122871, 2024. doi: http://doi.org/10.1016/j.applthermaleng.2024.122871.
» https://doi.org/10.1016/j.applthermaleng.2024.122871 -
[20] MISHA, S., ABDULLAH, A.L., TAMALDIN, N., et al, “Simulation CFD and experimental investigation of PVT water system under natural Malaysian weather conditions”, Energy Reports, v. 6, pp. 28–44, 2019. doi: http://doi.org/10.1016/j.egyr.2019.11.162.
» https://doi.org/10.1016/j.egyr.2019.11.162 -
[21] GELIS, K., OZBEK, K., OZYURT, O., et al, “Multi-objective optimization of a photovoltaic thermal system with different water based nanofluids using Taguchi approach”, Applied Thermal Engineering, v. 219, pp. 119609, 2022. doi: http://doi.org/10.1016/j.applthermaleng.2022.119609.
» https://doi.org/10.1016/j.applthermaleng.2022.119609 -
[22] THULASIRAM, R., MURUGAPOOPATHI, S., SURENDARNATH, S., et al, “RSM-based empirical modeling and thermodynamic analysis of a solar flat plate collector with diverse nanofluids”, Process Integration and Optimization for Sustainability, v. 8, n. 3, pp. 905–918, 2024. doi: http://doi.org/10.1007/s41660-024-00400-y.
» https://doi.org/10.1007/s41660-024-00400-y -
[23] JEYARAJ, T., KUMAR, P., PATHAK, S., “Experimental and computational modeling analysis of double slope solar still with a trapezoidal channel for preheating”, Applied Thermal Engineering, v. 253, pp. 123757, 2024. doi: http://doi.org/10.1016/j.applthermaleng.2024.123757.
» https://doi.org/10.1016/j.applthermaleng.2024.123757 -
[24] KHARGOTRA, R., KUMAR, R., SHARMA, A., et al, “Design and performance optimization of solar water heating system with perforated obstacle using hybrid multi-criteria decision-making approach”, Journal of Energy Storage, v. 63, pp. 107099, 2023. doi: http://doi.org/10.1016/j.est.2023.107099.
» https://doi.org/10.1016/j.est.2023.107099 -
[25] ADUA, L., ASAMOAH, A., BARROWS, J., et al, “Ambient energy for buildings: beyond energy efficiency”, Social Compass, v. 11, pp. 100076, 2024. doi: http://doi.org/10.1016/j.solcom.2024.100076.
» https://doi.org/10.1016/j.solcom.2024.100076 -
[26] ARUN, S., BOCHE, R.J., NAMBIAR, P., et al, “Numerical and experimental investigation on performance of thermal energy storage integrated micro-cold storage unit”, Applied Sciences (Basel, Switzerland), v. 14, n. 12, pp. 5166, 2024. http://doi.org/10.3390/app14125166.
» https://doi.org/10.3390/app14125166 -
[27] AWAI, K.R., KING, P., PATCHIGOLLA, K., et al, “Investigating performance of hybrid photovoltaic-thermal collector for electricity and hot water production in Nigeria”, Energies, v. 17, n. 11, pp. 2776, 2024. doi: http://doi.org/10.3390/en17112776.
» https://doi.org/10.3390/en17112776 -
[28] MEROVCI, A., GE, Y., ZHANG, X., “Performance analysis of finned-tube heat exchanger charged with phase change material for space cooling”, Heat Transfer Engineering, pp. 1–17, 2024. doi: http://doi.org/10.1080/01457632.2024.2384152.
» https://doi.org/10.1080/01457632.2024.2384152 -
[29] ALAÇAM, B., VURAL, H., ORBAY, M., “Validating the credibility of photovoltaic systems simulation tools with a case study”, Sakarya University Journal of Science, v. 28, n. 4, pp. 855–865, 2024. doi: http://doi.org/10.16984/saufenbilder.1481285.
» https://doi.org/10.16984/saufenbilder.1481285 -
[30] BRITES, G.J., GARRUÇO, M., FERNANDES, M.S., et al, “Seasonal storage for space heating using solar DHW surplus”, Renewable Energy, v. 231, pp. 120889, 2024. doi: http://doi.org/10.1016/j.renene.2024.120889.
» https://doi.org/10.1016/j.renene.2024.120889 -
[31] MEYER, D., SCHOETTER, R., VAN REEUWIJK, M., “Energy and environmental impacts of air-to-air heat pumps in a mid-latitude city”, Nature Communications, v. 15, n. 1, pp. 5474, 2024. doi: http://doi.org/10.1038/s41467-024-49836-3. PubMed PMID: 38942764.
» https://doi.org/10.1038/s41467-024-49836-3 -
[32] WANG, D., DU, Y., JIN, Y., et al, “Comparative study on predicting turbulent kinetic energy budget using high-order upwind scheme and non-dissipative central scheme”, Advances in Aerodynamics, v. 6, n. 1, pp. 24, 2024. doi: http://doi.org/10.1186/s42774-024-00187-z.
» https://doi.org/10.1186/s42774-024-00187-z -
[33] FLILIHI, E.Y., ACHEMLAL, D., HAROUI, M.E., et al, “Contribution to improving the thermal performances of a solar collector using CFD approach: Solar water heater application”, Numerical Heat Transfer Part A, v. 85, n. 3, pp. 364–377, 2023. doi: http://doi.org/10.1080/10407782.2023.2186552.
» https://doi.org/10.1080/10407782.2023.2186552 -
[34] WANG, Y., NASAJPOUR-ESFAHANI, N., ALIZADEH, A., et al, “Numerical simulation of the melting of solid paraffin in a solar water heater and the effect of the number of fins and the height of the fins”, Case Studies in Thermal Engineering, v. 41, pp. 102653, 2022. doi: http://doi.org/10.1016/j.csite.2022.102653.
» https://doi.org/10.1016/j.csite.2022.102653 -
[35] BALACHANDRAN, G.B., BASKARAN, V.K., THANGARAJ, H., et al, “Experimental investigations on the comparison of multi-objective design for high thermal energy applications: an insight into response surface methodology”, Energy Sources. Part A, Recovery, Utilization, and Environmental Effects, v. 46, n. 1, pp. 30–47, 2024. doi: http://doi.org/10.1080/15567036.2024.2418991.
» https://doi.org/10.1080/15567036.2024.2418991 -
[36] SINGH, D., KUMAR, V., “Response surface-based optimization of thermal characteristics for frustum roughened solar air heater: an experimental and numerical study”, International Journal of Heat and Fluid Flow, v. 108, pp. 109479, 2024. doi: http://doi.org/10.1016/j.ijheatfluidflow.2024.109479.
» https://doi.org/10.1016/j.ijheatfluidflow.2024.109479 -
[37] REZAEI, P., MOHEGHI, H.R., DELOUEI, A.A., “Design and optimization of a spiral-tube instantaneous water heater using response surface methodology”, Water (Basel), v. 15, n. 8, pp. 1458, 2023. doi: http://doi.org/10.3390/w15081458.
» https://doi.org/10.3390/w15081458 - [38] Dhairiyasamy, R., Gabiriel, D., “Optimizing heat transfer in heat pipes using hybrid nanofluids with multi-walled carbon nanotubes and alumina”, Engineering Reports, pp. e13030, 2024.
-
[39] KANDASAMY, G., PARAMASIVAM, S., VARUDHARAJAN, G., et al, “Optimization of silver nanoparticle-enhanced nanofluids for improved thermal management in solar thermal collectors”, Matéria (Rio de Janeiro), v. 29, n. 3, pp. e20240363, 2024. doi: http://doi.org/10.1590/1517-7076-rmat-2024-0363.
» https://doi.org/10.1590/1517-7076-rmat-2024-0363 -
[40] THANGAVEL, S., KANDASAMY, K.T., RATHANASAMY, R., et al, “Enhancing thermal and mechanical properties of polycaprolactone nanofibers with graphene and graphene oxide reinforcement for biomedical applications”, Matéria (Rio de Janeiro), v. 29, n. 3, pp. e20240324, 2024. doi: http://doi.org/10.1590/1517-7076-rmat-2024-0324.
» https://doi.org/10.1590/1517-7076-rmat-2024-0324 -
[41] SINGARAVEL, D.A., ASHOKAN, A., RAJENDRAN, S., et al, “Influence of nanoceramic addition on the performance of cement-based materials”, Matéria (Rio de Janeiro), v. 29, n. 3, pp. e20240267, 2024. doi: http://doi.org/10.1590/1517-7076-rmat-2024-0267.
» https://doi.org/10.1590/1517-7076-rmat-2024-0267 -
[42] SAROJA, P.E., MUTHUGOUNDER, P., SHANMUGAM, S., et al, “Enhancing flour quality and milling efficiency: experimental study on bullet plate type flour grinding machine”, Matéria (Rio de Janeiro), v. 29, n. 3, pp. e20240331, 2024. doi: http://doi.org/10.1590/1517-7076-rmat-2024-0331.
» https://doi.org/10.1590/1517-7076-rmat-2024-0331 -
[43] MANIVANNAN, J.M., SATHISHKUMAR, T.P., SUBRAMANI, S., et al, “Investigation on the fracture and creep behavior of the synthetic and Natural fiber laminate polymer composite”, Matéria (Rio de Janeiro), v. 29, n. 4, pp. e20240608, 2024. doi: http://doi.org/10.1590/1517-7076-rmat-2024-0608.
» https://doi.org/10.1590/1517-7076-rmat-2024-0608 -
[44] SUBRAMANI, S., GAJBHIYE, N.L., MURUGESAN, V., et al, “Experimental investigations on spray characteristics of non-edible oils using phase doppler particle analyser”, Matéria (Rio de Janeiro), v. 29, n. 3, pp. e20240415, 2024. doi: http://doi.org/10.1590/1517-7076-rmat-2024-0415.
» https://doi.org/10.1590/1517-7076-rmat-2024-0415 -
[45] SEKAR, B.K., PRADEEP, G.V.K., SILAMBARASAN, R., et al, “Microstructural and mechanical characterization of AA2124 aluminum alloy matrix composites reinforced with Si3 N4 particulates fabricated by powder metallurgy and high-energy ball milling”, Matéria (Rio de Janeiro), v. 29, n. 3, pp. e20240196, 2024. doi: http://doi.org/10.1590/1517-7076-rmat-2024-0196.
» https://doi.org/10.1590/1517-7076-rmat-2024-0196 -
[46] KANDASAMY, V.K., JAGANATHAN, S., DHAIRIYASAMY, R., et al, “Optimizing the efficiency of solar thermal collectors and studying the effect of particle concentration and stability using nanofluidic analysis”, Energy & Environment, v. 34, n. 5, pp. 1564–1591, 2023. doi: http://doi.org/10.1177/0958305X231183687.
» https://doi.org/10.1177/0958305X231183687 - [47] RAO, G.V., PACHAMUTHU, S., DHAIRIYASAMY, R., et al, “Comparative assessment of amine-based absorption and calcium looping techniques for optimizing energy efficiency in post-combustion carbon capture”, Global NEST Journal, pp. 26, 2024.
-
[48] DHAIRIYASAMY, R., DIXIT, S., SINGH, S., & GABIRIEL, D. (2025). Statistical modeling of heat transfer enhancements through controlled temperature and surface modifications: A fundamental understanding of nanofluid behavior. Results in Engineering, v. 25, pp. e103790. https://doi.org/10.1016/j.rineng.2024.103790.
» https://doi.org/10.1016/j.rineng.2024.103790 -
[49] RADHAKRISHNAN, P., AHMED, A.N., KALAIARASI, K., et al, “EEG-based brain-computer interfaces using gazelle optimization algorithm with deep learning for motor-imagery classification”, Fusion, Practice and Applications, v. 16, n. 1, pp. 37–51, 2024. doi: http://doi.org/10.54216/FPA.160103.
» https://doi.org/10.54216/FPA.160103 -
[50] BALAMURUGAN, M., SRIVIDHYA, N., INDHUMATHI, G., et al, “Deep learning innovations for improved plant leaf disease detection in smart agriculture”, In: 2nd IEEE International Conference on Data Science and Information System, 2024. doi: http://doi.org/10.1109/ICDSIS61070.2024.10594188.
» https://doi.org/10.1109/ICDSIS61070.2024.10594188 - [51] DEEPAK, M., KARTHICK, M., SANTHAKUMAR, C., et al, “Design and analysis of different rotor structures in-wheel brushless dc motor performance for electric vehicle applications”, In: International Conference on Advancements in Power, Communication and Intelligent Systems, 2024.
-
[52] VIGNESHWAR, A.S., KAVITHA, N., SURESHBABU, J., et al, “The future charged: optimizing electric vehicle performance through advanced power management strategies”, In: Proceedings of International Conference on Circuit Power and Computing Technologies, pp. 775–780, 2024. doi: http://doi.org/10.1109/ICCPCT61902.2024.10673364.
» https://doi.org/10.1109/ICCPCT61902.2024.10673364 -
[53] KALAIARASI, K., MEDIDHI, W., NIRESH KUMAR, S., et al, “Enhancing speech recognition for hearing-impaired individuals using deep learning models”, In: Proceedings of International Conference on Circuit Power and Computing Technologies, pp. 738–743, 2024. http://doi.org/10.1109/ICCPCT61902.2024.10672961.
» https://doi.org/10.1109/ICCPCT61902.2024.10672961 -
[54] BALAMURUGAN, M., KALAIARASI, K., SHANMUGAM, J., et al, “Comparative analysis of spectroradiometric and chemical methods for nutrient detection in black gram leaves”, Results in Engineering, v. 24, pp. 103065, 2024. doi: http://doi.org/10.1016/j.rineng.2024.103065.
» https://doi.org/10.1016/j.rineng.2024.103065 - [55] BALAMURUGAN, M., ABIRAMI, A., DEVI, S.R., et al, “Skywatch: UAV-based suspicious activity analysis through image processing”, In: Proceedings 3rd International Conference on Advances in Computing, Communication and Applied Informatics, 2024.
- [56] MANI, B., DHAIRIYASAMY, R., BUNPHENG, W., et al, “Enhanced performance and reduced emissions in LHR engines using Albizia lebbeck antioxidant-infused SBME20 biodiesel”, Industrial Crops and Products, pp. 222, 2024.
- [57] BALAMURUGAN, M., VARANASI, U.B., MANGAI, R.A., et al, “Deep learning-powered intrusion detection systems: enhancing efficiency in network security”, In: Proceedings 3rd International Conference on Advances in Computing, Communication and Applied Informatics, 2024.
-
[58] MANI, B., SHANMUGAM, J., “Estimating plant macronutrients using VNIR spectroradiometry”, Polish Journal of Environmental Studies, v. 28, n. 3, pp. 1831–1837, 2019. doi: http://doi.org/10.15244/pjoes/89585.
» https://doi.org/10.15244/pjoes/89585 -
[59] ARUMUGAM, S., MUTHAIYAN, R., DHAIRIYASAMY, R., et al, “Investigation of biodiesel blends and hydrogen addition effects on CI engine characteristics through statistical analysis”, International Journal of Hydrogen Energy, v. 81, pp. 481–496, 2024. doi: http://doi.org/10.1016/j.ijhydene.2024.07.216.
» https://doi.org/10.1016/j.ijhydene.2024.07.216
Publication Dates
-
Publication in this collection
31 Jan 2025 -
Date of issue
2025
History
-
Received
28 Nov 2024 -
Accepted
12 Dec 2024
