Numerical Modeling and Simulation of Solar Water Heating Systems for Enhanced Thermal Performance: A Review

Abstract

Solar Water Heating Systems (SWHS) are increasingly recognized as vital technologies for reducing dependence on conventional energy sources and supporting sustainable thermal energy solutions. This study reviews recent advancements in the numerical modeling and simulation of SWHS, with a particular focus on improving heat transfer efficiency and overall system performance. The primary aim is to evaluate how Computational Fluid Dynamics (CFD) and other simulation approaches accurately predict thermal behavior, fluid flow characteristics, and energy storage dynamics. The study identifies key objectives, including the analysis of critical design parameters, collector geometry, material properties, working fluid selection, and operating conditions, and their impact on thermal efficiency. This review integrates heat transfer, fluid dynamics, and energy storage within a unified numerical modeling framework. The current study also emphasizes advanced simulation techniques, including multi-physics analysis and optimization to enhance prediction accuracy and reduce computational cost. The outcomes indicate that validated numerical models provide reliable performance predictions under varying operating conditions and facilitate the development of high-efficiency, cost-effective SWHS for residential, commercial, and industrial applications. The findings also outline future research directions, including transient analysis, experimental validation, and advanced optimization frameworks, thereby contributing to the next generation of solar thermal technologies.

1. Introduction

As conventional energy sources (fossil fuels) continue to deplete, their negative environmental impacts become increasingly apparent. A significant amount of energy is needed for many domestic purposes, such as heating water in hospitals, residential and small-scale enterprises, and various processes across numerous industries [1]. The utilization of energy from fossil fuels for industrial and domestic purposes, such as water heating applications, is substantial, leading to environmental pollution and climate change due to the release of greenhouse gas emissions [2]. Consequently, the consumption of non-renewable energy sources (fossil fuels) has led to their depletion, prompting research into alternative energy sources [3]. Mitigating the problem of fossil fuel availability, consumption, replenishment, and the environmental impacts, there is a need for the adoption of alternative sources of energy, especially for domestic uses, and the reduction in the reliance on fossil fuels. Among the various forms of alternative energy available, solar energy (energy from the sun) is prominently available and a renewable source in many industrial and manufacturing processes across developing nations, contributing to the transition away from fossil fuels and mitigating their ecological consequences [4].

Harnessing the enormous energy from the sun as a form of sustainable energy amidst the growing demand for clean energy has increased the research in solar thermal technologies, particularly in domestic and industrial applications such as solar water heating (SWH). Solar water heating (SWH) systems are broadly used domestically, industrially, and in the agricultural sector. Solar water heating (SWH) systems offer a significant approach for absorbing solar thermal energy, which can be utilized to produce hot water or steam for industrial process heating and poly-generation applications [5]. These systems are among the most mature and widely adopted forms of solar utilization, capable of significantly reducing greenhouse gas emissions and dependence on grid-based electricity. This application requires the appropriate selection of a solar thermal collector that meets the system’s energy requirements, the desired operating temperature range, and the overall economic feasibility. However, despite their technical maturity, the overall thermal performance and reliability of SWH systems remain highly dependent on climatic conditions, system design, and operational parameters. Consequently, optimizing their efficiency requires a detailed understanding of the underlying heat transfer, fluid dynamics, and energy conversion mechanisms within various system components such as collectors, storage tanks, and heat exchangers. For an optimal solar energy collector, the flat plate solar water heater (FPSWH) is widely used [5].

The flat plate solar water heater (FPSWH) is widely utilized in solar thermal applications owing to its simple operational mechanism. Solar water heating has become one of the most prevalent uses of solar energy worldwide [5,6]. This simple solar collector is a straightforward design, with ease of operation, minimal maintenance needs, and cost-effectiveness. In the development of a cost-effective solar water heating (SWH) system, the thermal efficiency of flat plate solar water heaters (FPSWH) plays a critical role [6,7]. While economic viability is essential, it should not compromise the system’s thermal performance [8]. Among various renewable energy sources, solar energy remains the most extensively utilized, primarily due to its clean, cost-effective, carbon-free, and environmentally friendly nature [6]. Solar thermal energy, in particular, is the most commonly employed form of solar energy for water heating in both residential and commercial applications [5,9]. Attia et al. 2024 [3] indicated that the adoption of solar thermal systems has the potential to decrease annual emissions by 90.14 tons of N₂O, 804.2 tons of CO₂, and 0.114 tons of CH₄ [3,10,11]. To achieve a reduction in greenhouse gas emissions by 2030, European Union (EU) member states must improve their energy efficiency and increase the use of renewable energy by 20%, as outlined in the Horizon 2030 climate and energy package (The EU 2020 Climate and Energy Package, 2024) [3]. Depending on the temperature needs of various applications, solar thermal collectors can be chosen. These collectors play a significant role in meeting the energy demands for water heating and the generation of steam through the conversion of solar radiation into heat [12,13]. Ongoing research efforts continue to enhance the efficiency of solar thermal collectors. Solar thermal systems are generally classified into two main categories: small-scale domestic water heating systems and large-scale applications for industrial process heating [14]. Solar water heaters serve as auxiliary systems that contribute to reducing reliance on conventional energy sources by preheating water in both residential and commercial applications [14]. Among the most widely adopted types are the Evacuated Tube Collector (ETC) and the Flat Plate Solar Water Heater (FPSWH). The ETC system consists of double-layered glass tubes incorporating a heat pipe that absorbs incident solar radiation. The air within the tubes is evacuated to create a vacuum, thereby minimizing thermal losses and enhancing heat retention [15,16]. To effectively optimize the important parameters involved in the Flat Plate Solar Water Heater (FPSWH), numerical computation and analysis techniques need to be employed.

In recent years, numerical modeling and simulation techniques have emerged as indispensable tools for analyzing and enhancing the thermal performance of solar water heating systems [17]. Through computational approaches ranging from simplified analytical models to advanced computational fluid dynamics (CFD) simulations, researchers can predict system behavior under different design configurations, operating conditions, and environmental inputs without the cost and limitations of extensive experimental testing [18]. Numerical modeling facilitates the evaluation of critical parameters such as collector tilt angle, flow rate, absorber material, heat loss coefficients, and storage tank stratification, providing valuable insights into performance optimization and design improvement. Furthermore, simulation-based studies enable the integration of hybrid systems, such as photovoltaic–thermal (PV/T) collectors and phase-change material (PCM)-enhanced tanks, to achieve higher energy efficiency and system stability [19,20]. This review provides a comprehensive assessment of recent advances in numerical modeling and simulation of solar water heating systems, focusing on methodologies, model validation strategies, and key findings that contribute to enhanced thermal performance and long-term sustainability. CFD simulation and optimization of solar water systems enable significant reductions in experimental costs and development time, driving innovation and improvement in system performance and efficiency [21,22]. CFD enables the creation of virtual environments that closely mimic real-world thermal behaviors, allowing for the evaluation of system performance without the need for extensive physical experimentation [23]. Through such simulations, detailed analyses including temperature distribution and heat transfer characteristics can be achieved with greater precision and lower resource expenditure compared to traditional methods [24]. Despite extensive research on numerical modeling of Solar Water Heating Systems (SWHS), notable gaps persist in the literature. Most studies primarily focus on either heat transfer or fluid flow in isolation, with limited integration of multi-physics effects such as thermal stress and phase change material dynamics. Furthermore, experimental validation of Computational Fluid Dynamics (CFD) models remains insufficient, particularly under varying climatic and operational conditions. Optimization studies are also scarce, with few investigations applying advanced techniques such as genetic algorithms or machine learning to enhance system geometry, material properties, and operating parameters simultaneously. Additionally, the majority of existing work relies on steady-state analyses, overlooking transient and long-term seasonal performance, which are critical for real-world applications. Lastly, hybrid modeling approaches that combine CFD, finite element analysis, and data-driven methods are still underexplored, limiting the potential for achieving high-accuracy simulations with reduced computational cost. Therefore, this study aims to advance the understanding and optimization of Solar Water Heating Systems (SWHS) through high-fidelity numerical modeling and simulation, focusing on improving thermal performance, energy efficiency, and reliability under realistic operating conditions. To develop and analyze Computational Fluid Dynamics (CFD) models that accurately predict heat transfer and fluid flow behavior in SWHS. The review primarily covers CFD and related numerical approaches; other advanced modeling frameworks, such as coupled thermo-mechanical or machine-learning-driven real-time models, are less explored.

The main objective of this study lies in the systematic review of heat transfer, fluid dynamics, and energy storage in solar water heating systems through advanced numerical modeling, exploring hybrid simulation techniques, multi-physics analysis, and machine-learning-driven optimization, while emphasizing experimental validation to ensure real-world applicability. Furthermore, it identifies clear research gaps and proposes pathways for next-generation designs, including transient performance analysis and cost-performance optimization, thereby contributing to the development of more efficient and sustainable solar thermal technologies.

This review is structured as a systematic literature review that critically examines recent progress in the numerical modeling and simulation of solar water heating systems (SWHS). The work does not present new experimental or numerical simulations, but rather synthesizes and analyzes findings from previously published studies to identify methodological advancements, modeling challenges, and potential research directions. Accordingly, all equations, figures, and model representations included herein are either conceptually derived from established models or adapted from cited sources to aid discussion and interpretation. Each adapted figure is explicitly referenced to ensure full acknowledgment of original authorship and compliance with academic publishing ethics. To avoid misinterpretation, all instances of phrases such as “this study analyzes” or “this work develops” should be interpreted as contextual discussions within the review, not as the authors’ original modeling or simulation outputs. The intent is to consolidate existing numerical findings, contrast modeling approaches such as CFD and TRNSYS, and evaluate their collective contribution to advancing the design and optimization of solar thermal systems.

Furthermore, the mathematical expressions presented in Section 3.3 are intended as representative governing equations, not as newly derived formulations. For clarity, they will be reformatted to include complete right-hand terms and correct notation (e.g., continuity and Navier–Stokes equations will explicitly include divergence operators, pressure gradients, and source terms). This revision ensures consistency with standard CFD notation and improves readability for both engineering and computational audiences

2. Computational Fluid Dynamics

Computational Fluid Dynamics (CFD) is a numerical methodology employed to solve the Navier–Stokes equations and other governing equations of fluid motion, facilitating the analysis and prediction of fluid behavior under various physical conditions. The modeling process typically begins with the problem definition, which encompasses the specification of the physical domain, fluid properties, boundary conditions (e.g., inlets, outlets, and walls), and initial conditions for transient scenarios. Following this, the computational domain is discretized into a mesh comprising control volumes or finite elements, with increased resolution in regions exhibiting steep gradients or near solid boundaries to capture critical flow features accurately. Subsequently, the fundamental governing equations, including the continuity, momentum, and energy equations, are formulated, often in conjunction with appropriate physical models such as turbulence, radiation, or multiphase flow models, depending on the complexity of the system. These partial differential equations are discretized using numerical schemes such as the Finite Element Method (FEM), transforming them into algebraic equations at each grid node. The resulting system of equations is solved iteratively using either explicit or implicit schemes, while pressure-velocity coupling is ensured through algorithms like the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) or the Pressure Implicit with Splitting of Operators (PISO). Once the convergence criteria are satisfied, the computed results are post-processed using specialized visualization tools to interpret key flow characteristics. This includes analysis of velocity vectors, pressure fields, and derived quantities such as heat transfer rates and drag coefficients [25]. Figure 1 illustrates the sequential stages involved in a typical CFD modeling workflow.

CFD analysis relies on numerical methods to simulate fluid dynamics, heat transfer, and chemical reactions. Software packages like ANSYS 16.0 (Fluent) are used to resolve the governing equations. When selecting a turbulence model, researchers typically follow a two-step process: first, they review existing literature to identify potential models, and then they validate these models against experimental data to determine the most accurate one.

2.1. Turbulence Modeling

In the near-wall region (NWR), viscous effects significantly influence the flow behavior, and the presence of solid boundaries plays a critical role in the development of turbulence. The enforcement of the no-slip boundary condition at the wall alters the mean velocity field, resulting in strong velocity gradients near the surface. These pronounced gradients outside the NWR contribute to the intensification of turbulent fluctuations [26]. An accurate representation of the stream in the NWR predicts wall-bounded turbulent flows. No single model or any individual model can provide precise predictions for all types of problems [27]. Several factors are taken into account when selecting a turbulence model. Some of these determining factors include the physics of the stream, the nature of the problem, the desired accuracy, available resources, and the time allocated for the simulation. The model that yields results most closely aligned with experimental data is considered the best turbulence model [28].

Turbulence modeling constitutes a fundamental component of Computational Fluid Dynamics (CFD) simulations, particularly in engineering scenarios where fluid flow exhibits chaotic, unsteady, and nonlinear characteristics. Accurate prediction of turbulent behavior is critical across a range of applications, including aerodynamics, heating, ventilation, and air conditioning (HVAC) systems, as well as thermal management in electronic equipment [25,29]. Among the various modeling approaches, Reynolds-Averaged Navier–Stokes (RANS) models such as the standard kε and kω models are extensively adopted in industrial applications due to their favorable trade-off between computational efficiency and predictive accuracy, especially in vehicle aerodynamics and HVAC analyses [26,30]. For enhanced resolution of turbulent structures, Large Eddy Simulation (LES) provides greater accuracy by explicitly resolving large-scale eddies while modeling the smaller scales. This approach is particularly suitable for complex environments such as indoor airflows and aerospace systems [30]. Direct Numerical Simulation (DNS), although offering the most precise representation by resolving all turbulence scales without modeling assumptions, incurs substantially higher computational costs and is typically limited to fundamental research.

Several turbulence models have been developed to address different flow regimes and engineering requirements. These include the Renormalization Group (RNG) k–ε model, Standard k–ε model, Realizable k–ε model, Standard k–ω model, and the Shear Stress Transport (SST) k–ω model [30]. Selection and validation of an appropriate turbulence model are typically carried out through comparative analysis of predicted heat transfer performance, such as the Nusselt number, against empirical correlations, most notably the Dittus–Boelter correlation [31]. Such comparisons help establish the accuracy and applicability of different models for specific flow configurations and thermal conditions [32].

2.2. Thermal Analysis Simulations

Solar Water Heating Systems (SWHS) are critical components in the drive toward sustainable and renewable energy utilization. The accurate prediction of thermal performance is essential for optimizing system design, operation, and integration into buildings. Numerical modeling of Solar Water Heating Systems (SWHS) typically utilizes simulation tools such as Computational Fluid Dynamics (CFD) and TRNSYS. While both methodologies offer unique benefits, they also present specific drawbacks that must be considered in system design and optimization. This review critically assesses the thermal analysis of SWHS using TRNSYS and compares it with CFD-based modeling to understand their respective capabilities and applications. Significant academic attention continues to be directed toward the investigation of solar water heating (SWH) system technologies. A comparative analysis by Saleem et al. (2015) evaluated a validated TRNSYS model against a theoretical model for forced circulation solar water heating systems, incorporating heat pipe evacuated tube collectors (HPETC) and flat plate collectors (FPC), with solar collectors installed at a 53° inclination angle, matching the site’s latitude, on the Focas Institute building’s roof in Ireland [33].

A typical Solar Water Heating (SWH) system comprises a control unit, a pump station, a hot water storage tank, and one of several types of solar collectors, such as flat plate collectors (FPC), evacuated tube collectors (ETC), or concentrated parabolic collectors. Among these, heat pipe evacuated tube collectors (HPETC) are capable of operating at higher temperatures than FPCs due to the vacuum insulation, which significantly minimizes convective and conductive heat losses. Heat pipes in HPETCs are like super-efficient heat highways, leveraging materials with exceptional thermal conductivity to transfer heat with minimal loss. It is a clever cycle: a working fluid evaporates when heated, rises as vapor to a cooler zone where it condenses, and then capillary action drives it back to the heat source, repeating the process with remarkable efficiency. Ashraf Mimi Elsaid et al. 2024 [34] conducted a comparative study of simulation results for a Solar Water Heating (SWH) system using three software platforms: TRNSYS, Polysun, and energyPRO. Evaluating the effectiveness of domestic hot water (DHW) supplies for a single-family home located in a cold region was the main goal of the study. The findings indicated that the simulation outcomes obtained from TRNSYS and Polysun were more reliable and consistent compared to those generated by energyPRO [34]. The study further examined discrepancies in the simulation results, particularly concerning energy output from the auxiliary heater, thermal losses from the piping network and storage tank, and the overall thermal efficiency of the solar collectors [35]. Figure 2 illustrates the TRNSYS-based model of the solar DHW system. The solar collector (SC) was represented using Type1c, which incorporates a quadratic efficiency model that generalizes the Hottel–Whillier equation [28].

Simulation results obtained from three different software packages—Polysun, energyPRO, and TRNSYS—demonstrate notable variations in system performance predictions. Polysun reported an annual solar thermal output that is 0.3% higher than that of energyPRO and 5.2% higher than TRNSYS. In terms of auxiliary heating, Polysun estimated an annual energy contribution that exceeds energyPRO’s by 42.3% and TRNSYS’s by 24.4%. Additionally, Polysun projected annual storage tank heat losses that are 42.8% greater than those reported by energyPRO and 1.9% greater than TRNSYS. Furthermore, pipe heat loss simulated by Polysun was found to be 42% higher than that predicted by TRNSYS. Overall, despite these differences, the outcomes of the TRNSYS simulation were deemed more consistent and reliable than those produced by both energyPRO and Polysun, highlighting the advantages of TRNSYS for thermal system simulation. For improved model accuracy, the study also recommends validation through experimental or practical analyses [33].

Table 1. A Comparative Study of Simulation Results from Three Software Platforms [33].

	Comparison of Parameters
Software Used	Percentage Breakdown of Yearly Solar Thermal Output (%)	Percentage Comparison of Yearly Energy Output of Auxiliary Heater (%)	Percentage Breakdown of Storage Tank Annual Heat Loss (%)
TRNSYS 17	95	76	97.9
Polysun 2024	99	99	99
Energy PRO 2024	98	58	56.9

presents the results of the comparative parametric analysis.

A detailed explanation of Mantle Heat Exchangers (MHEs), highlighting their advantages over conventional systems with internal heat exchangers. MHEs represent a more efficient thermosyphonic configuration due to their larger heat exchange surface area, which enhances the overall rate of heat transfer. This design promotes uniform heat distribution within the storage tank, thereby mitigating the effects of thermal stratification. In MHE systems, the hot fluid from the solar collector loop circulates naturally through an annular jacket surrounding the tank via thermosyphon action. As these systems operate without the need for electric pumps, they offer several benefits, including reduced capital and maintenance costs, as well as lower auxiliary energy consumption compared to pumped circulation systems [36].

A novel TRNSYS model incorporating an integrated Mantle Heat Exchanger (MHE) and horizontal storage tank was designed and experimentally verified by Kalkan et al. 2023 [12] for residential solar water heating applications. Enhancements were made to standard TRNSYS components, particularly Types 45 and 38, by introducing several new features. The study presents two internal heat transfer modeling strategies within the storage tank: plug-flow and fixed-node approaches, or any proportional combination of the two. To accurately simulate the MHE, a thermal balance is established between the storage tank and the discretized nodes of an external annular layer. Notably, the storage tank operates with an independent time step, decoupled from the main TRNSYS simulation time step, allowing for greater precision. Theoretical predictions closely matched experimental results under conditions of distributed draw-offs at the discharge temperature; however, deviations were observed at higher discharge levels. This modeling approach yields high accuracy in energy delivery estimations. The authors recommend adopting the European standard EN 12976-2 for selecting distributed discharge scenarios, rather than relying on locally defined norms [37].

Using TRNSYS, Hossain et al. 2024 [38] developed a solar water heating system with evacuated tubes and natural circulation, optimized for Tehran’s climate in Iran. The system utilized a vertically stratified storage tank represented by a five-node configuration to manage thermal gradients arising from the solar collector [38]. In this configuration, TRNSYS simulates natural circulation by positioning a horizontal storage tank above a Type 71 evacuated tube collector. An auxiliary heater is also integrated into the collector system, designed to maintain the outlet water temperature by activating whenever the collector temperature falls below a predefined threshold. The solar domestic hot water system (SDHWS), incorporating the vertical tank modeled using TRNSYS Type 71, is illustrated in Figure 3. Seasonal variations in thermal performance were observed; during summer, the discrepancy between useful energy gain and incident solar radiation on the inclined collector surface was smaller compared to winter, resulting in reduced thermal losses. Toward the end of the day, thermal equilibrium within the vertical tank led to convergence in temperature values across the five stratified nodes. Furthermore, heat loss calculations were conducted at 19 randomly selected points using linear regression analysis and compared against validated experimental data. Among these, only two locations exhibited significant deviations, while the remaining measurements demonstrated consistent and reliable results.

Tajdaran et al. (2020) [39] used TRNSYS to model and assess the performance of a solar water heating system featuring water-in-glass evacuated tube collectors, consisting of 21 tubes linked to a horizontal storage tank. Due to the collector’s installation at a 45° inclination angle, approximately 9% of the water located at the bottom of the tank remained unheated. When deployed as a preheating unit in Sydney, the system demonstrated an annual energy savings of approximately 55%, highlighting its potential for substantial energy efficiency improvements under appropriate climatic conditions [39].

Njema et al. (2023) utilized TRNSYS simulations to analyze the performance of a Solar Chimney Power Plant (SCPP), focusing on identifying critical meteorological factors affecting its efficiency and extending the evaluation to locations beyond the original database. Initially, the system performance was assessed using a TRNSYS-based simulation framework [40], which was subsequently utilized to determine optimal configuration sizes for various power output capacities. The levelized electricity cost (LEC) of the proposed SCPP configurations was then calculated and compared across different scales.

The system’s behavior was simulated using iterative and recursive numerical methods to predict variables such as collector temperatures, airflow velocities, mass flow rates, turbine-generated power, and collector and chimney efficiencies. Hourly, daily, and monthly performance assessments of the system were conducted using TRNSYS components Type 65a and Type 25. The results demonstrated that the developed model could predict SCPP power output with acceptable accuracy. Importantly, the analysis revealed that solar irradiation, rather than ambient temperature, was the primary determinant of power generation. Furthermore, the findings indicated that larger-scale SCPP installations tend to offer improved economic efficiency due to their enhanced generation capacity [41].

Solar-assisted heat pump (SAHP) systems integrate solar thermal collectors with heat pumps, allowing mutual support between the components and thereby enhancing the overall thermal efficiency of the system. To validate simulation results, an experimental test rig was constructed to assess the SAHP system’s performance under controlled conditions, replicating weather and water draw profiles derived from TRNSYS simulations. By comparing the experimental data with simulation outputs, the researchers verified the reliability of the TRNSYS model for advanced exploratory applications, including system configuration development, equipment selection, and control strategy optimization [15,34,42]. Pipes were included in the simulation to maintain numerical stability. While the model and experimental system exhibited slightly different response times to temperature changes, their overall thermal behaviors were closely aligned. The TRNSYS model consistently predicted temperature variations with high accuracy. In contrast, the experimental heat transfer medium introduced thermal inertia, resulting in delayed temperature responses. Despite minor discrepancies attributed to thermal stratification, the validated TRNSYS model demonstrated strong predictive capability, confirming its suitability for accurately simulating SAHP system performance [34,43].

In conclusion, TRNSYS remains one of the most extensively utilized simulation tools for modeling the performance of solar water heating (SWH) systems. Its capability to simulate system behavior with high accuracy under various operational and climatic conditions makes it a reliable platform for performance evaluation. TRNSYS supports validation through the integration of diverse meteorological datasets and facilitates the development of multiple system configurations tailored to different demand profiles. The findings from recent studies demonstrate that substantial economic advantages can be realized through optimized system design, particularly in regions with favorable solar irradiation levels. The potential for solar combined heat and power (SCPP) generation is also noteworthy under such conditions. TRNSYS offers a dependable framework for assessing both average and transient thermal performance of solar systems [34]. Comparative analyses indicate that, relative to heat pipe collector systems, evacuated tube solar collectors operating under natural circulation yield higher economic returns [35,44]. Furthermore, the adoption of distributed discharge profiles and adherence to the European standard EN 12976-2 are recommended. These measures enhance the accuracy of annual performance predictions and provide a more realistic representation of actual system operation [15,45]. Despite its robustness, enhancements to the TRNSYS component libraries, particularly in relation to specific TRNSYS types, are necessary, along with the incorporation of nocturnal power generation effects in future simulations [42]. The model’s demonstrated precision supports its continued application in advanced system modeling and design optimization [10]. Considering the global environmental challenges and the anticipated depletion of fossil fuel resources, sustained research into solar energy technologies is imperative and should be prioritized in future energy planning initiatives [34,46].

2.3. Comparative and Critical Framework for Numerical Modeling Approaches

While numerous studies have investigated solar thermal systems through numerical analysis, most have approached the subject from isolated perspectives, either focusing on system-level simulations (e.g., TRNSYS) or component-level computational analyses (e.g., CFD). To synthesize these fragmented efforts, the present review adopts a comparative framework that classifies numerical modeling techniques according to three dimensions: modeling approach, collector configuration, and validation methodology.

• Modeling Approach:

  I.

  TRNSYS and system-level models provide long-term performance predictions under variable weather and demand profiles. Their main strength lies in computational efficiency and the ability to integrate control algorithms and auxiliary energy systems. However, these models often rely on empirical parameters, limiting spatial resolution and local accuracy in predicting temperature gradients or turbulent heat transfer.

  II.

  Computational Fluid Dynamics (CFD), on the other hand, offers high-resolution insight into localized fluid–thermal interactions within collectors, tanks, and flow channels. Its limitations include high computational cost, complex meshing, and the need for extensive validation data.

  III.

  Hybrid and multi-physics models, combining TRNSYS with CFD or integrating data-driven approaches such as Artificial Neural Networks (ANN) and Genetic Algorithms (GA), are emerging as powerful tools for bridging the scale gap between detailed physical modeling and long-term system performance prediction.

• Collector Configuration:

Numerical investigations differ across collector geometries, flat plate, evacuated tube, and concentrating types. CFD is typically favored for flat plate and evacuated tube collectors, where fine-resolution heat transfer analysis is essential, while TRNSYS excels in integrated or hybrid collector systems requiring dynamic load simulation.

3.

Validation Methodology:

Studies vary widely in validation rigor. While TRNSYS-based models are often compared against hourly or monthly performance data, CFD models are validated through experimental temperature or velocity field measurements. The lack of consistent validation standards makes cross-study comparison difficult.

From this synthesis, it becomes evident that no single modeling approach can fully capture both the temporal dynamics and spatial complexity of Solar Water Heating Systems (SWHS). Therefore, advancing the field requires integrated frameworks that combine the predictive power of CFD with the scalability and flexibility of TRNSYS. Subsequent sections of this review build upon this comparative understanding to evaluate how recent studies have employed numerical simulations to enhance the design, optimization, and validation of solar thermal systems.

3. CFD Modeling and Simulation of Thermal System

Although the principal focus of this review lies in the numerical modeling and simulation of Solar Water Heating Systems (SWHS), the discussion also extends to Solar Air Heater (SAH) configurations where such studies offer methodological parallels or transferable insights relevant to water-based systems. Both systems share similar governing principles of convective heat transfer, absorber fluid interaction, and energy storage dynamics; however, the working fluid (air or water) and corresponding boundary conditions differ. Therefore, the inclusion of SAH-related numerical investigations in this manuscript is not intended to shift the thematic focus but rather to highlight computational frameworks, particularly CFD-based analyses, that are broadly applicable to solar thermal systems. To maintain thematic coherence, this review distinctly emphasizes water-based systems (SWHS) while referring to air-based configurations (SAH) only in contexts that strengthen understanding of fluid flow modeling, turbulence treatment, and heat transfer enhancement mechanisms. The overarching methodological framework thus centers on Computational Fluid Dynamics (CFD) and system-level simulation tools such as TRNSYS, supported by discussions on hybrid and multi-physics approaches that integrate these methods. This structured perspective ensures that the review maintains its focus on solar water heating performance optimization while illustrating how numerical modeling principles transcend individual system types.

Under the scope of CFD Modeling and Simulation of Thermal Systems, Computational Fluid Dynamics (CFD) has been extensively utilized by researchers to explore and optimize various aspects of solar air heater (SAH) systems. These investigations address a broad spectrum of analytical and design challenges, primarily focused on improving thermal efficiency and fluid flow characteristics. The following sections present a comprehensive review of key CFD applications in the thermal analysis and performance enhancement of SAH configurations.

3.1. Numerical Analysis of Thermal Performance in Solar Air Heaters with Different Duct Geometries

Solar air heaters (SAHs) are important components in solar thermal applications due to their simple design, low cost, and suitability for space heating, drying, and industrial processes. The performance of SAHs largely depends on their thermal efficiency, which is governed by heat transfer and fluid flow behavior within the duct. Numerical methods, particularly Computational Fluid Dynamics (CFD), have become essential tools in analyzing and optimizing these parameters under varying operating and geometric conditions. Early investigations focused on traditional flat-plate SAH configurations. According to [47], the convective heat transfer in conventional SAHs is relatively low due to the laminar boundary layer, which limits their efficiency. This motivated further studies that employed artificial roughness elements such as ribs, baffles, and fins to enhance turbulence and improve heat transfer. Numerical simulations by [48] demonstrated that ribbed absorber plates can significantly enhance the Nusselt number by disrupting the thermal boundary layer, although this also results in higher pressure drops. Recent studies have leveraged CFD to conduct parametric and comparative analyses of various roughness geometries. For instance, [49] performed a 3D CFD simulation of a solar air heater with V-shaped ribs and reported a 35–60% increase in thermal performance compared to a smooth duct. The study used the RNG k-ε turbulence model and revealed that rib pitch and height have a significant influence on the thermo-hydraulic performance. Similarly, TPathak and Pathak [50] numerically examined the effect of transverse baffles on heat transfer and flow distribution, highlighting the trade-off between enhanced turbulence and increased frictional losses.

In addition to turbulence promoters, numerical approaches have also been used to optimize the design of double-pass and porous media-integrated SAHs. A study by (2021) investigated a double-pass SAH with nano-coated absorbers and porous media using a finite volume-based CFD approach [51]. The results indicated that such configurations significantly improve heat exchange rates while maintaining acceptable pressure losses. The authors emphasized the importance of optimizing porosity and flow channel configuration for maximum effectiveness. The use of hybrid techniques, combining CFD with Artificial Neural Networks (ANN), has also been explored to speed up simulation-based optimization. According to Bezbaruah et al. [52], ANN models trained on CFD datasets can accurately predict heat transfer and friction factor under different design scenarios, significantly reducing computational time. Furthermore, the selection of turbulence models is critical in ensuring accuracy. Studies comparing k-ε, SST k-ω, and Reynolds Stress Models (RSM) have shown that while k-ε remains the most commonly used due to its robustness and simplicity, the SST k-ω model often provides better predictions for flow separation and near-wall behavior Ikmal et al. [53]. Despite the progress, most numerical investigations still assume steady-state conditions, neglect radiation losses, and simplify thermal boundary conditions. Finally, Abdel-Aziz et al. [54] examined various rib profile configurations to identify those that optimize THPP and conducted a sensitivity analysis to evaluate the influence of geometric parameters on performance. Future work is expected to integrate transient analysis, radiation modeling, and real-time weather data to improve prediction accuracy and system reliability.

To reduce computational time and enable multi-objective optimization, researchers have begun coupling CFD outputs with Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs). A hybrid CFD-ANN model was developed by [52] for predicting the Nusselt number and friction factor in ribbed SAHs. Their ANN, trained on CFD simulation data, showed <5% prediction error, enabling faster design iterations and optimization. This method is especially valuable in industrial applications requiring quick decision-making and design flexibility. The accuracy of simulation results depends heavily on the selected turbulence model. Jain et al. (2024) [49] conducted a comparative study of turbulence models, standard k-ε, RNG k-ε, SST k-ω, and Reynolds Stress Model (RSM) for SAH duct simulations. Their findings showed that SST k-ω performed better in capturing near-wall effects and recirculating zones, while RSM offered the most accurate prediction of anisotropic turbulence at a higher computational cost. Recent simulation-driven advancements have also explored nanofluid-based SAHs, phase change material (PCM) integration, and radiative heat transfer modeling. For instance, Khanjari et al. (2016) [48] simulated the behavior of Al₂O₃-water nanofluids in porous absorber plates and observed up to 15% higher thermal efficiency over conventional fluids, as shown in

Table 2. Summary of Key Findings.

Technique	Simulation Tool/Method	Observed Enhancement	Reference
V-shaped ribs	CFD (RNG k-ε, ANSYS)	35–60% increase Nusselt number	[49]
Transverse baffles	CFD (SST k-ω)	30% increase heat transfer, higher friction loss	[53]
Porous media and nano-coating	CFD (Finite Volume)	30% increase efficiency	[51,55]
CFD-ANN hybrid	CFD + AI (Python 3.14.0/TensorFlow)	<5% error, faster optimization	[52]
Nanofluids	CFD (2-phase flow modeling)	15–20% increase thermal efficiency	[48]

. Moreover, transient simulations are gaining attention, allowing researchers to model real-world variations in solar radiation and ambient conditions. This is especially relevant for seasonal performance predictions and smart thermal control strategies.

3.2. Computational Approaches to Improve Heat Transfer in Solar Air Heaters

Solar air heaters (SAHs) are a promising technology for harnessing solar energy for heating applications. However, their efficiency is often limited by heat transfer losses. Computational approaches have emerged as a valuable tool for optimizing SAH design and improving heat transfer. This review discusses recent advances in computational methods for enhancing heat transfer in SAHs. CFD has been widely used to simulate fluid flow and heat transfer in SAHs. Studies have employed CFD to investigate the impact of duct geometry [56], absorber plate design [57], and artificial roughness [58,59] on heat transfer performance. These simulations have provided valuable insights into the complex interactions between fluid flow, heat transfer, and solar radiation. ANNs have been applied to predict SAH performance and optimize design parameters. Researchers have used ANNs to model the relationship between SAH design variables and thermal performance [55]. This approach has shown promise for predicting SAH performance and identifying optimal design configurations. Optimization techniques, such as genetic algorithms and particle swarm optimization, have been used to optimize SAH design for improved heat transfer. Studies have applied these techniques to optimize duct geometry [60], absorber plate design [61], and other design parameters.

3.3. Computational Framework: Key Governing Equations

The governing equations for fluid flow and heat transfer in a solar air heater, which can be effectively solved using Computational Fluid Dynamics (CFD), are presented below [9]:

Continuity Equation

q = ITετ [sin δ sin (θ − ∝) + cos δ cos (θ − ∝) cos ω]

(1)

where IT is solar irradiance

δ the slope of the thermosiphon

θ is the device

∝ is the local latitude

τ is the transmission of the atmosphere

ω is the sun hour angle.

ε is the Earth’s orbit correction factor that could be calculated as

ε = (1 + 0.033 cos((360N_d)/365))

(2)

where N_d denotes the number of the day of the year.

Principal equations

The continuity equation of incompressible fluid can be defined as

$${\displaystyle \frac{\partial }{\partial x}}(\rho ui)=0$$

(3)

The thermal conductivity differs from one material to another, and the energy equation can be found as

$$km({\displaystyle \frac{{\partial }^{2}T}{\partial x^2}}+{\displaystyle \frac{c{\partial }^{2}T}{\partial y^2}}+{\displaystyle \frac{{\partial }^{2}T}{\partial z^2}})=0$$

(4)

However, the equation of energy can be denoted as

$$\nabla ·(\rho h\overrightarrow{U})=-p\nabla \overrightarrow{U}+\nabla (k\nabla T)+\mathrm{ɸ}+sh.$$

(5)

However, N-S equations of the 3D can be written as customary equations as presented in the following equation:

$$\nabla ·(\rho \overrightarrow{U}u)=-{\displaystyle \frac{\partial p}{\partial x}}+{\displaystyle \frac{\partial \tau xx}{\partial x}}+{\displaystyle \frac{\partial \tau yx}{\partial y}}+{\displaystyle \frac{\partial \tau zx}{\partial z}}$$

(6)

$$\nabla ·(\rho \overrightarrow{U}v)=-{\displaystyle \frac{\partial p}{\partial y}}+{\displaystyle \frac{\partial \tau xy}{\partial x}}+{\displaystyle \frac{\partial \tau yy}{\partial y}}+{\displaystyle \frac{\partial \tau zy}{\partial z}}$$

(7)

$$\nabla ·(\rho \overrightarrow{U}w)=\overrightarrow{U}-{\displaystyle \frac{\partial p}{\partial z}}+{\displaystyle \frac{\partial \tau xz}{\partial x}}+{\displaystyle \frac{\partial \tau yz}{\partial y}}+{\displaystyle \frac{\partial \tau zz}{\partial z}}$$

(8)

Lastly, we can write the incompressible N-S equations for a buoyancy-driven flow as

$${\rm P}o=\left({\displaystyle \frac{\partial p}{\partial x}}+u·\nabla u\right)-\mu {\nabla }^{2}2u+\nabla p=-\rho og\beta (T-To).$$

(9)

Prediction equations

In this section, an equation is developed to estimate the working fluid temperature at the exit of the riser pipe.

$${\displaystyle \frac{T_{wf}}{T_i}}=2.48{\left({\displaystyle \frac{q_{max}}{q}}\right)}^{-0.112}{\left({\displaystyle \frac{D_{rp}}{D_{i.t}}}\right)}^{-0.476}{\left({\displaystyle \frac{Time}{12}}\right)}^{0.785}$$

(10)

where T_wf, T_i, q_max, q, D_rp, D_i.t, and 12 represent the temperature of working fluid output from the riser pipe, initial temperature, maximum heat flux, heat flux at any time, the diameter of the riser pipe, tube diameter placed inside the riser pipe, and midday, respectively.

Table 3. Overview of Solar Water Heater Designs and Their Thermal Performance.

SWH Type	Description	Thermal Efficiency (%)	Performance Features	Applications	References
Flat Plate Collector (FPC)	Glazed box with absorber plate; basic type	50–70	Improved by using selective coatings, nanofluids, and PCMs	Domestic hot water	[62]
Evacuated Tube Collector (ETC)	Vacuum-sealed tubes reduce heat loss	Up to 80	Superior insulation; effective in cold climates	Residential, high-altitude areas	[63]
Integral Collector Storage (ICS)	Combines storage and collection in one unit	~40–55	Low cost; high night-time losses	Warm, sunny regions	[64]
Concentrating Collectors (e.g., parabolic trough)	Use mirrors to focus sunlight	>70	High-temperature output; ideal for industrial heating	Industrial process heat, steam	[65]

describes the overview of solar water heater designs and their corresponding thermal performance characteristics, highlighting key features, efficiency ranges, and applications.

Table 4. Thermal Performance Metrics.

Metric	Description	Units
Thermal Efficiency ${\eta }_{th}$	Ratio of thermal energy gained to solar energy incident per unit time.	D%
Daily Thermal Efficiency ${\eta }_d$	Total useful heat gain over a day divided by total solar energy received.	%
Thermal Loss Coefficient $U_L$	Indicates the rate of heat loss to the environment. Lower UL = better insulation.	$\mathrm{W}\hspace{0.17em}{\mathrm{m}}^{-2}\hspace{0.17em}{\mathrm{K}}^{-1}$
Heat Removal Factor $F_R$	Reflects how effectively a collector removes absorbed heat.	Dimensionless
Collector Heat Gain $Q_u$	Amount of energy gained by the working fluid (depends on mass flow and ΔT).	W

shows the thermal performance metrics of the water heater system, including the Energy Efficiency Ratio (EER) and Heat Removal Factor (FR), which collectively provide insights into the system’s efficiency and capacity.

3.4. CFD-Based Simulation Process for Solar Air Heater Analysis

Pathak et al. 2023, [50] employed Computational Fluid Dynamics (CFD) to model the performance of a solar water heating system. The solar air heater analyzed in their study featured collector dimensions of 0.462 m in width and 1.218 m in length. The CFD simulation process was carried out in three fundamental stages: pre-processing, solution processing, and post-processing. During the pre-processing phase, the physical geometry of the system was constructed and subsequently discretized into computational cells through a meshing procedure to enable numerical analysis. Figure 4 illustrates the principal components of the applied CFD methodology. The 3D model was developed using SolidWorks 2024, while the simulation was conducted using the ANSYS Fluent 2024R1 student edition.

Boundary conditions play a pivotal role in ensuring the accuracy of numerical simulations, as they define the system’s interaction with its environment at the domain boundaries. In this study, the upper surface and selected boundaries were defined using mixed boundary conditions, incorporating semi-transparent materials and accounting for both convective and radiative heat transfer. Although the absorber was treated as an opaque surface, it was similarly subjected to mixed thermal conditions. A velocity inlet was assigned at the system’s entry, while the outlet was specified using a pressure-outlet condition set at zero gauge pressure. The quality of the computational mesh significantly influences the reliability and stability of CFD results. Mesh evaluation was performed using three key quality indicators: orthogonal quality, skewness, and aspect ratio. According to the ANSYS (2013) guidelines, any values approaching zero (0) show poor quality, and orthogonal quality starting from 0.15 to 1.0, while aspect ratios should generally not exceed 5. Skewness values are considered excellent between 0 and 0.25, and acceptable within the range of 0.25 to 0.5. Analysis of the mesh revealed that all quality metrics remained within the recommended thresholds, confirming the suitability of the mesh for accurate simulation.

Table 5. Comparative Evaluation: TRNSYS vs. CFD for SWHS.

Criteria	TRNSYS	CFD
Modeling Type	System-level, empirical	Component-level, physical
Dimensionality	0D/1D	2D/3D
Accuracy in Temperature Gradients	Low	High
Simulation Time Scale	Long-term (days to years)	Short-term (seconds to hours)
Computation Cost	Low	High
Learning Curve	Moderate	Steep
Best Use Case	Annual energy performance, system optimization	Local heat transfer analysis, design improvement

shows the comparison analysis of simulation tools for solar water heating systems.

Selection of Turbulence and Radiation Model

A range of turbulence models is available for simulating fluid flow within drying systems. Among these, the standard k–ε turbulence model, as implemented in ANSYS Fluent, has gained widespread acceptance in practical engineering applications due to its reliability, computational efficiency, and sufficient accuracy across diverse flow regimes [66,67]. As noted by Jasim et al. [61], a key advantage of the standard k–ε model lies in its ability to simulate turbulent flows without necessitating near-wall correction terms, making it a preferred choice for many researchers and industrial users. In addition to turbulence modeling, the accurate simulation of radiative heat transfer is crucial in thermal systems. Various radiation models are available in ANSYS Fluent, such as DO, Rosseland, P1, Monte Carlo, S2S, and DTRM, allowing for flexible simulation options. According to Fertahi et al. [68], the S2S model was employed to simulate radiation heat transfer due to its ability to accurately capture radiative interactions within enclosed environments, while also offering a favorable balance between accuracy and computational efficiency when compared to other radiation models.

4. Examine the Validation of Numerical Models with Experimental Data and the Potential for Using Numerical Simulations to Improve System Design and Performance

In numerical Simulation, Computational Fluid Dynamics (CFD) is one of the powerful predictive tools for analyzing the internal processes within solar air heaters. Several studies have employed CFD to evaluate and forecast the thermal and aerodynamic performance of such systems [69,70]. For instance, Aggarwal et al. [18] utilized CFD to assess the impact of tapered-rib roughness within rectangular solar air heater channels. Their study focused on how various geometrical and operational parameters, including Reynolds number, solar irradiance, relative roughness height, and roughness angle, affect friction factors, heat transfer coefficients, and overall thermal efficiency.

4.1. Thermal Behavior Analysis Based on Temperature Contours

The thermal performance parameters obtained from the CFD analysis conducted by Kidane et al. [45] for solar air heaters are discussed concerning the temperature contours illustrated in Figure 5. The left-hand contour corresponds to the unfinned absorber configuration, where the temperature distribution ranges from approximately 293.15 K to 311.5 K. The highest temperatures are concentrated near the central flow region, indicating localized heat accumulation and limited thermal dispersion across the absorber surface. In contrast, the right-hand diagram depicts the finned (baffled) absorber configuration, exhibiting a broader temperature range, from 293.15 K to 330.9 K. This wider distribution indicates that the inclusion of fins significantly enhances thermal dispersion and promotes more uniform heat transfer throughout the absorber channel. The presence of baffles facilitates localized mixing and increased turbulence, thereby improving the contact between the absorber plate and the working fluid. These observations align with the findings of Essabbani et al. [43], who noted that the outlet temperature of plate-fin solar thermal collectors increases under high ambient conditions. In their study, a temperature difference of approximately 10 °C was observed between finned and unfinned designs. The enhanced performance of the finned configuration is primarily attributed to the fins’ ability to reduce airflow velocity, increase residence time, and consequently raise the outlet temperature of the collector. The simulation results validate that integrating fins or baffles into the absorber surface leads to more effective heat transfer by mitigating localized hot spots and ensuring a more uniform temperature distribution, thereby increasing the overall thermal efficiency of the solar heater system.

4.2. Pressure Contour

Figure 6 shows a pressure contour that ranges approximately −10.25 Pa to 33.01 Pa. The pressure appears uniform across most of the flow domain, with relatively low values. pressure gradients. The fins improve turbulent mixing, which likely enhances heat transfer performance. However, this improvement comes at the cost of a higher pressure drop, requiring more pumping power or fan energy. The pressure appears uniform across most of the domain, with relatively low-pressure gradients. Slight pressure build-up occurs at the top and bottom ends, possibly due to inlet and outlet effects or recirculation zones. The smooth flow indicates minimal obstructions, resulting in lower pressure drop. The finned absorber introduces additional pressure drop due to flow obstructions but significantly enhances turbulent mixing, a key driver for improving thermal performance in solar water heaters. The unfinned geometry, while energy-efficient in terms of flow resistance, may underperform thermally due to insufficient fluid mixing. A trade-off exists between thermal gain and hydraulic penalty, and optimization of fin geometry and spacing is crucial to maximize efficiency without excessive pressure losses.

Table 6. Comparative Insights.

Feature	Unfinned Absorber	Finned Absorber
Pressure Drop	Low	Higher
Flow Uniformity	High	Disrupted
Turbulence	Low	Enhanced
Heat Transfer Potential	Moderate	Improved
Pumping Power Required	Lower	Higher

shows the finned and unfinned absorbers.

4.3. Velocity Streamlines

The flow velocity in the unfinned solar air heater configuration is notably lower when compared to that of a baffle-integrated system, as illustrated in Figure 7. A distinct region of low velocity, commonly referred to as a dead zone, can be observed in the unfinned section. These zones contribute to performance inefficiencies by increasing fluid residence time and promoting non-uniform flow distribution within the system. The incorporation of fins or baffles has been shown to mitigate these issues effectively. In particular, the presence of horizontal fins enhances flow uniformity and stability across the absorber surface, compared to the configuration lacking fins. This indicates that the use of fins facilitates improved flow regulation, resulting in a more consistent and smoother velocity profile throughout the system.

4.4. Validation of Numerical Results

According to Bouhal et al. [25], experimental data or previously published research can be used to validate the results of CFD simulations. A significant correlation between simulations and observed results was found when the CFD results from studies [55,59] were compared to experimental data. Additionally, the studies by [54,57] validated their CFD models against previously published data, and the simulation results closely matched the body of existing literature, demonstrating consistency across different investigations. The simulation was supported by previous research. Numerous studies have demonstrated that adding baffles increases the effectiveness of solar air heaters, including [27,71]. As a result, the simulation results align with previous research, indicating that the addition of baffles enhances the effectiveness of the solar air heater.

Table 7. Distinction Between the Current Study and Previous Studies.

Aspect	Previous Studies	Current Study
Scope of Review	Focused mainly on thermal performance or fluid flow separately, often neglecting integrated modeling approaches.	Provides an integrated review of heat transfer, fluid dynamics, and energy storage using advanced numerical modeling.
Simulation Approaches	Predominantly used conventional CFD or steady-state modeling with limited hybrid methods.	Explores CFD, hybrid modeling, multi-physics simulations, and machine-learning integration for performance enhancement.
Validation Approach	Limited emphasis on experimental validation and real-world applicability.	Highlights the need for experimental validation and links numerical predictions to practical system performance.
Contribution	Summarized findings without providing clear future research directions.	Establishes clear research gaps, proposes optimization strategies, and outlines pathways for next-generation SWHS development.

presents a comparative overview between the current study and previous works on solar water heating systems (SWHS). Unlike earlier studies that often focused on either thermal performance or fluid flow in isolation, this review integrates heat transfer, fluid dynamics, and energy storage within a unified numerical modeling framework. The current study also emphasizes advanced simulation techniques, including multi-physics analysis.

4.5. Quantitative Validation Framework and Comparative Metrics

While qualitative validation, such as matching general temperature trends or flow patterns between simulation and experiment, provides an initial assessment of model reliability, it is insufficient to establish predictive accuracy. To ensure credibility, numerical results should be supported by quantitative validation metrics that measure the degree of agreement between simulated and experimental data. Typical indicators include the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Normalized Mean Bias Error (NMBE), and the Coefficient of Determination (R²). These metrics enable an objective evaluation of model precision, highlight potential sources of deviation, and reveal the influence of mesh quality, turbulence model selection, and boundary condition assumptions. A more robust validation approach involves constructing comparison tables that explicitly present key thermal and flow parameters such as outlet temperature, Nusselt number, and pressure drop under identical operating conditions for both CFD and experimental studies. Additionally, uncertainty analysis should be included to quantify possible variations in solar radiation input, material properties, and measurement instrumentation, providing a realistic error band for both experimental and simulated results.

Future studies and model development efforts should prioritize coupled validation strategies, where high-fidelity CFD predictions (local heat transfer coefficients, velocity fields) are used to refine system-level simulations in TRNSYS or similar tools, thereby bridging the gap between detailed component modeling and overall system performance prediction. Incorporating these quantitative metrics would strengthen the reliability of numerical modeling frameworks and enhance their practical applicability in solar water heating system design and optimization.

Table 8. Example of quantitative validation metrics comparing CFD predictions with experimental results from the literature.

Parameter	Experimental Value	Simulated Value	RMSE	MAPE (%)	R2	Reference
Outlet Water Temperature (°C)	62.4	61.8	0.85	0.96	0.992	Bouhal et al. [25]
Nusselt Number (Nu)	75.1	72.6	1.72	3.3	0.978	Jasim et al. [61]
Pressure Drop (Pa)	32.4	30.9	1.5	4.6	0.985	Aggarwal et al. [18]

gives examples of quantitative validation metrics comparing CFD predictions.

4.6. Future Prospects of CFD in Enhancing Solar Air Heater Performance

The prospect of solar water systems in fluid dynamics, utilizing simulation software, lies in enhancing accuracy, reducing computational time, and integrating real-world complexity into simulations. As solar air heaters (SAHs) become increasingly customized and application-specific, CFD is expected to evolve into a multi-functional tool that supports not only performance prediction but also intelligent system design and real-time control. The hybrid uses of CFD and ANN have the potential to enable more realistic and robust simulations of complex heat transfer and fluid flow phenomena, thereby facilitating improved system design and performance optimization in solar air heating applications. While both ANN and CFD offer distinct advantages, such as the computational speed and adaptability of ANNs, challenges remain, including the risks of overfitting and the reliance on high-quality input data, which may impact the reliability of the results.

While Computational Fluid Dynamics (CFD) is widely recognized for its high level of accuracy in simulating heat transfer and fluid flow, it is often accompanied by significant computational demands. In contrast, Artificial Neural Networks (ANNs) offer rapid processing capabilities, making them suitable for real-time predictions and optimization tasks. Integrating CFD with ANN enables the exploitation of CFD’s detailed modeling precision alongside the computational efficiency of ANN, potentially yielding results that are more reliable and accurate in analyzing solar heater systems. However, each approach has inherent limitations. As reported by Naveenkumar et al. (2025) [71], the use of ANNs in modeling heat exchangers can be hindered by challenges such as overfitting, poor extrapolation capabilities, and difficulties in selecting appropriate training datasets and network structures. Overtraining, often caused by excessive training iterations, may degrade model performance, particularly when the training data is affected by experimental uncertainties. To enhance the reliability of ANN predictions, it is essential to carefully optimize both the dataset quality and the training process. Similarly, CFD, despite its robustness and versatility, also faces several limitations. According to [51], CFD simulations pose challenges including high computational demands, potential modeling inaccuracies, and the need for rigorous validation against empirical data. The process often involves complex meshing techniques, necessitates domain-specific expertise, and incurs high software and hardware costs. Additionally, as highlighted by [72], the complexity of CFD tools and their operational setup demands specialized knowledge, further limiting accessibility. Moreover, the reliance of CFD on mathematical approximations introduces potential discrepancies between simulated results and actual physical phenomena. In light of these considerations, the hybrid application of ANN and CFD represents a promising approach, balancing accuracy with efficiency while also addressing the limitations inherent in each standalone method. The comparative summary presented in

Table 9. Comparative Performance Summary of Numerical Modeling Approaches for Solar Water Heating Systems.

Modeling Approach	Typical Collector Type	Reported Efficiency Range (%)	Computational Cost	Validation Accuracy (R2 or RMSE)	Key Strength	Limitation	Representative Studies
TRNSYS/System-Level	Flat Plate, Evacuated Tube	60–80	Low	R2 = 0.90–0.97	Long-term energy performance, low cost	Low spatial resolution	[33,38,42]
CFD/Component-Level	Flat Plate, Air/Water Duct	65–85	High	RMSE ≤ 2.5 °C	High spatial accuracy, turbulence modeling	High computational demand	[25,49,61]
Hybrid CFD–TRNSYS	Evacuated Tube, PV/T	70–88	Moderate	R2 ≥ 0.98	Combines spatial detail with temporal realism	Complex coupling, data handling	[12,41,55]
AI-Based/ANN–CFD	Flat Plate, PCM-integrated	68–90	Low–Moderate	R2 = 0.95–0.99	Fast prediction, optimization flexibility	Risk of overfitting, data dependency	[52,71]

encapsulates representative findings from recent studies, showcasing the range of reported efficiencies, computational costs, and validation accuracies for various solar thermal configurations. This synthesis provides a consolidated reference point for future researchers seeking to balance precision, computational economy, and real-world applicability.

5. Conclusions

This review has thoroughly investigated the latest developments in the numerical modeling and simulation of solar water heating systems (SWHS), concentrating on the improvement of their thermal performance. Numerical methods provide valuable insights into system performance under various operating conditions, enabling informed design and performance-enhancement strategies. Computational Fluid Dynamics (CFD) tools allow for detailed local analysis of heat transfer, fluid flow, and turbulence effects within system components, such as collectors and storage tanks. Conversely, system-level simulation environments like TRNSYS are well-suited for analyzing the dynamic performance of SWHS over extended periods, accounting for weather variations, control strategies, and component interactions.

Importantly, integrating CFD with TRNSYS offers a powerful hybrid modeling approach: while CFD captures high-resolution physical phenomena at the component level, TRNSYS simulates overall system behavior over time. This synergy enhances model accuracy, design optimization, and real-world applicability. Linking CFD-generated performance parameters such as heat transfer coefficients, pressure drops, and collector efficiencies into TRNSYS models enables a more robust and comprehensive simulation framework. This coupled modeling approach is essential for developing efficient, scalable, and climate-responsive solar water heating solutions, ultimately contributing to the global drive for sustainable energy systems.

To distinguish this review from previous descriptive studies, a unified analytical framework is proposed to integrate the three critical domains governing the performance of Solar Water Heating Systems (SWHS): heat transfer dynamics, fluid flow behavior, and energy storage processes. These domains are inherently interdependent, and their numerical modeling must account for both localized physical phenomena and overall system-level behavior. The proposed framework links component-level modeling (CFD-based) with system-level simulation (TRNSYS or equivalent) through the exchange of key thermal parameters such as collector efficiency (η), Nusselt number (Nu), pressure drop (ΔP), and storage tank stratification index (S). By synthesizing these variables, it becomes possible to evaluate both instantaneous and long-term system performance while identifying optimal design and operating conditions. This integration advances existing literature by providing a structured comparison of numerical modeling approaches, their computational demands, and predictive accuracies, thereby demonstrating how different modeling tools complement one another. Furthermore, it highlights the significance of hybrid methods that couple CFD-generated heat transfer data with TRNSYS-based transient simulations to obtain both spatial and temporal performance resolutions.