1. Introduction and Research Motivation
The global energy landscape has witnessed significant transformation over the past decade. According to the *BP Statistical Review of World Energy 2024*, global primary energy consumption rose by 2% in 2023 compared to 2022, with renewable energy accounting for 14.6% of the total primary energy mix. China, as the world’s largest energy consumer, has committed to achieving carbon peak and carbon neutrality goals, necessitating rapid development of renewable energy technologies. Solar energy, being abundant and clean, offers a promising solution to meet the growing energy demand while addressing environmental concerns. However, a critical challenge in photovoltaic power generation lies in the temperature-dependent efficiency of solar panels. Research indicates that crystalline silicon photovoltaic efficiency decreases by approximately 0.5% for every 1°C rise in panel temperature, while amorphous silicon panels suffer a 0.25% efficiency loss per degree Celsius increase.
The concept of photovoltaic/thermal (PV/T) systems was initially introduced by Wolf in 1976, proposing the integration of photovoltaic cells with solar thermal collectors. The primary objective of PV/T systems is twofold: first, to recover waste heat generated during photovoltaic conversion for useful thermal applications; second, to maintain solar panels at lower operating temperatures, thereby improving electrical efficiency. Through such synergistic integration, overall solar energy utilization can reach 50-70%, substantially higher than conventional photovoltaic systems.
Given this context, my research focuses on optimizing solar panel cooling techniques through innovative cold plate design and advanced heat transfer fluids. Specifically, I designed a spiral coil vortex generator cooling plate and combined it with hybrid nanofluids to achieve efficient thermal management of solar panels. This chapter describes the construction of the experimental platform, followed by systematic investigations of structural, operational, and fluid-related parameters affecting cooling performance of solar panels.
2. Experimental System Construction
2.1 System Architecture of PV/T Platform
I developed a liquid-cooled photovoltaic/thermal experimental system consisting of a solar simulator, photovoltaic module, liquid cooling plate, heat storage tank, circulation pump, flow meter, and data acquisition equipment. Table 1 summarizes the key parameters of the photovoltaic module used in my experiments.
Table 1: Structural parameters of photovoltaic module
| Component | Specification |
|---|---|
| Glass cover | Thickness: 2.5mm; Size: 670mm×430mm |
| PV cell layer | Maximum power: 50W; Voltage: 18V; Current: 2.77A |
| Thermal paste | Thickness: 0.5mm; Thermal conductivity: 13 W/(m·K) |
| Heat preservation layer | Thickness: 10mm; Thermal conductivity: 0.036 W/(m·K) |
| Effective cell area | 0.2358 m² |
2.2 Solar Simulator Design
I constructed a solar radiation simulation device using three 1000W iodine-tungsten lamps. The spectral range of 0.2-2.7 μm closely approximates natural sunlight. Table 2 shows the relationship between radiation intensity and distance from the light source.
Table 2: Radiation intensity measurements at different positions
| Distance from light source (cm) | Radiation intensity (W/m²) |
|---|---|
| 100 | 684 |
| 90 | 806 |
| 80 | 921 |
| 70 | 1003 |
| 60 | 1129 |
| 50 | 1356 |
The solar simulator achieves stable irradiance at approximately 1000W/m² with a tunable range of 0-1200W/m², meeting IEC standards for photovoltaic performance testing.
2.3 Design of Spiral Coil Cooling Plate
A key innovation in my research involves the spiral coil vortex generator cooling plate, fabricated from aluminum. Table 3 presents the design specifications of the cooling plate.
Table 3: Parameters of spiral coil cooling plate
| Parameter | Value | Parameter | Value |
|---|---|---|---|
| Dimensions (L×W×H) | 260×160×13 mm | Flow channel shape | Circular |
| Inlet/outlet diameter | 11 mm | Channel inner diameter | 7 mm |
| Thermal conductivity | 15 W/(m·K) | Number of channels | 4 |
The spiral coil inside the channel acts as a turbulence promoter, effectively increasing fluid mixing and enhancing heat transfer between the coolant and the cooling plate surface.
2.4 Performance Evaluation Method
For comprehensive evaluation of the PV/T system, I adopted the following performance metrics:
The system thermal efficiency can be expressed as:
$$ \eta_{th} = \frac{Q}{H A_c} = \frac{m c_p (T_f – T_i)}{H A_c} \tag{1} $$
The instantaneous electrical efficiency is given by:
$$ \eta_{PV} = \frac{U I}{G A_{PV}} \tag{2} $$
The total solar utilization efficiency combining both thermal and electrical outputs:
$$ \eta_{total} = \eta_{th} + \zeta \eta_{PV} \tag{3} $$
For considering the exergy difference between thermal and electrical energy, the photovoltaic-thermal comprehensive efficiency is:
$$ \eta_f = \eta_{th} + \frac{\zeta \eta_{PV}}{\eta_{power}} \tag{4} $$
where η_power typically takes a value of 0.38.
I also conducted uncertainty analysis to verify the reliability of my measurements. Table 4 summarizes the uncertainty calculations.
Table 4: Uncertainty analysis results
| Parameter | Instrument accuracy | Uncertainty |
|---|---|---|
| Radiation intensity | ±5 W/m² | 2.9 W/m² |
| Ambient temperature | ±0.2°C | 0.11°C |
| Flow rate | ±3.5×10⁻⁴ kg/s | 2×10⁻⁴ kg/s |
| Output current | ≤±0.1 A | 0.05 mA |
| Output voltage | ≤±1 V | 0.58 V |
| Heat collection efficiency | — | 2.88% |
| Power generation efficiency | — | 2.9% |
3. Effects of Cooling Plate Structure on Solar Panel Performance
3.1 Experimental Conditions
I conducted comparative experiments under three configurations: without cooling plate, with conventional cooling plate, and with spiral coil cooling plate. The experimental parameters were: radiation intensity of 950±25W/m², ambient temperature of 24.5°C, deionized water as the working fluid, flow rate of 1.1L/min, and wind speed of 0.5m/s. Each experiment lasted 540 minutes.
3.2 Temperature Control Performance
The experimental results demonstrated significant temperature differences among the three configurations. In the steady-state phase, the solar panel temperature without cooling plate stabilized at approximately 70°C, while with conventional cooling plate and spiral coil cooling plate, temperatures were maintained at approximately 61°C and 57°C, respectively.
The spiral coil cooling plate achieved a maximum temperature reduction of 13.8°C compared to the no-cooling-plate configuration, with an average temperature drop of 10.74°C. Furthermore, during 74.07% of the experimental period, the temperature reduction remained consistently above 10°C. When compared to the conventional cooling plate, the spiral coil design demonstrated a maximum temperature reduction of 3.6°C and an average reduction of 2.16°C, maintaining a temperature drop exceeding 2°C for 66.67% of the experimental duration. These results confirm the enhanced cooling capability of solar panels achieved through the spiral coil structure.
3.3 Thermal and Electrical Performance Comparison
Table 5 presents a comprehensive comparison of system performance across different cold plate configurations.
Table 5: Performance comparison under different cooling plate configurations
| Performance metric | Without cold plate | Conventional cold plate | Spiral coil cold plate |
|---|---|---|---|
| Total heat collection (kJ) | ~0 | 3386.25 | 3901.80 |
| Heat collection efficiency | ~0% | 40.04% | 46.60% |
| Total power generation (kJ) | 455.90 | 500.76 | 564.05 |
| Power generation efficiency | 6.82% | 7.44% | 8.46% |
| Total solar utilization efficiency | 5.45% | 45.99% | 53.36% |
| PV/T comprehensive efficiency | 14.35% | 55.70% | 64.41% |
The spiral coil cooling plate improved heat collection efficiency by approximately 16.38% compared to the conventional design and increased power generation by 12.64%. The comprehensive PV/T efficiency reached 64.41%, representing a 348.85% improvement over the no-cooling-plate condition.
4. Nanofluid Preparation and Thermophysical Characterization
4.1 Material Selection and Synthesis
I selected multi-walled carbon nanotubes (MWCNT) and aluminum oxide (Al₂O₃) nanoparticles as the dispersed phase based on their superior thermal conductivity. The base fluids included deionized water, thermal fluid (50% water + 50% ethylene glycol by volume), and dimethyl silicone oil. I used the two-step method to prepare nanofluids, which allows better control over particle concentration and mass fraction.
Table 6 lists the specifications of nanoparticles used in my experiments.
Table 6: Nanoparticle specifications
| Nanoparticle | Purity | Dimensions |
|---|---|---|
| MWCNT | 98% | Diameter: 3-15nm; Length: 15-30μm |
| Al₂O₃ | 99.9% | Average diameter: 30nm |
The preparation process is shown in Figure 4.4, where the dispersed phase and surfactant were mixed through magnetic stirring followed by ultrasonic dispersion. I tested three surfactants: CTAB, SDBS, and PVP.
4.2 Stability and Dispersion Optimization
Through systematic evaluation, I investigated the effect of surfactant type and ratio on the dispersion stability of various nanofluids, measured by optical transmittance. Table 7 summarizes the optimal surfactant configurations.
Table 7: Optimal surfactant configurations for different nanofluids
| Nanofluid type | Optimal surfactant | Optimal ratio (nanoparticle:surfactant) | Lowest transmittance (%) |
|---|---|---|---|
| MWCNT | PVP | 1:2 | 46.2 |
| Al₂O₃ | SDBS | 1:3 | 51.6 |
| Al₂O₃-MWCNT hybrid | CTAB | 1:2 | 45.9 |
4.3 Thermal Conductivity Analysis
I employed the transient hot-wire method to measure the thermal conductivity of nanofluids. The mathematical basis for this technique is expressed as:
$$ \lambda = \frac{q}{4\pi} \bigg/ \frac{d\theta}{d\ln t} \tag{5} $$
Table 8 presents thermal conductivity measurements for Al₂O₃-MWCNT/deionized water hybrid nanofluids at different mass fractions under 24°C.
Table 8: Thermal conductivity at different mass fractions
| Mass fraction (%) | Thermal conductivity (W/(m·K)) |
|---|---|
| 0 (pure water) | 0.605 |
| 0.05 | 0.635 |
| 0.10 | 0.670 |
| 0.15 | 0.674 |
| 0.20 | 0.702 |
| 0.25 | 0.712 |
The data revealed a nonlinear enhancement trend: adding a small number of nanoparticles significantly increases thermal conductivity at low concentrations, while further concentration increases yield diminishing improvements. Temperature also plays an important role, as shown in Table 9.
Table 9: Thermal conductivity of different nanofluids at various temperatures
| Temperature (°C) | Al₂O₃-MWCNT/water (W/(m·K)) | Al₂O₃-MWCNT/thermal fluid (W/(m·K)) | Al₂O₃-MWCNT/silicone oil (W/(m·K)) |
|---|---|---|---|
| 10 | 0.611 | 0.393 | 0.152 |
| 20 | 0.656 | 0.420 | 0.153 |
| 30 | 0.713 | 0.487 | 0.155 |
| 40 | 0.735 | 0.523 | 0.156 |
| 50 | 0.764 | 0.571 | 0.158 |
The temperature effect was more pronounced for water-based and thermal fluid-based nanofluids than for silicone oil-based formulations, primarily due to the higher viscosity of silicone oil, which promotes nanoparticle agglomeration.
4.4 Density Characteristics
The density of nanofluids can be calculated theoretically as:
$$ \rho_{nf} = (1 – \phi)\rho_{bf} + \phi\rho_{np} \tag{6} $$
My experimental measurements confirmed that nanofluid density increases with increasing particle concentration but decreases with increasing temperature. Table 10 shows density values at various temperatures and concentrations.
Table 10: Density of Al₂O₃-MWCNT/deionized water nanofluids (kg/m³)
| Temperature (°C) | 0.10% mass fraction | 0.20% mass fraction | 0.25% mass fraction |
|---|---|---|---|
| 10 | 0.998 | 1.001 | 1.005 |
| 20 | 0.996 | 0.998 | 0.999 |
| 30 | 0.995 | 0.996 | 0.997 |
| 40 | 0.994 | 0.995 | 0.995 |
| 50 | 0.994 | 0.993 | 0.990 |
5. Cooling Performance of Al₂O₃-MWCNT Hybrid Nanofluids for Solar Panels
5.1 Experimental Design
I conducted systematic experiments using deionized water, thermal fluid, and Al₂O₃-MWCNT/thermal fluid nanofluids at mass concentrations of 0.1%, 1%, and 2%. The experiments covered three radiation intensity scenarios: 700W/m², 1100W/m², and 200W/m², with flow rates of 0.9L/min and 1.8L/min. The performance evaluation focused on two aspects: cooling from 40°C to 25°C at low radiation intensity, and steady-state temperature control at high radiation intensities.
5.2 Results at 700W/m² Radiation Intensity
At 700W/m² and a flow rate of 0.9L/min, the photovoltaic module temperatures reached 38.25°C, 37.19°C, and 37.62°C for nanofluid concentrations of 0.1%, 1%, and 2%, respectively. The cooling performance improvements compared to the base thermal fluid were 2.69%, 10.85%, and 7.54%, respectively. Similar trends were observed at 1.8L/min flow rate, although the improvements were less pronounced.
5.3 Results at 1100W/m² Radiation Intensity
The high-intensity experiments revealed more significant improvements. Table 11 summarizes the cooling performance at 1100W/m².
Table 11: Cooling performance at 1100W/m² radiation intensity
| Nanofluid concentration | Flow rate (L/min) | Final temperature (°C) | Cooling improvement (%) |
|---|---|---|---|
| Base fluid (thermal fluid) | 0.9 | 52.30 | — |
| 0.1% | 0.9 | 52.15 | 2.46 |
| 1% | 0.9 | 51.59 | 11.64 |
| 2% | 0.9 | 51.77 | 8.69 |
| Base fluid | 1.8 | 51.20 | — |
| 0.1% | 1.8 | 51.08 | 1.72 |
| 1% | 1.8 | 50.72 | 6.66 |
| 2% | 1.8 | 51.08 | 1.67 |
At the high radiation intensity, the 1% concentration consistently demonstrated the best cooling performance, confirming the existence of an optimal nanoparticle concentration. However, the cooling improvement at 1.8L/min was generally lower than at 0.9L/min for all concentrations, indicating that excessive flow rates may cause unfavorable effects on nanofluid stability.
5.4 Cooling Performance at 200W/m²
For the low-radiation cooling experiments, I evaluated the time required to cool solar panels from 40°C to 25°C. Table 12 presents the results.
Table 12: Cooling time from 40°C to 25°C at 200W/m²
| Working fluid | Flow rate (L/min) | Cooling time (min:s) | Performance improvement (%) |
|---|---|---|---|
| Deionized water | 0.9 | 32:24 | — |
| Thermal fluid | 0.9 | 37:54 | — |
| Nanofluid 0.1% | 0.9 | 36:33 | 6.45 |
| Nanofluid 1% | 0.9 | 35:53 | 9.65 |
| Nanofluid 2% | 0.9 | 37:46 | 0.01 |
| Thermal fluid | 1.8 | 32:35 | — |
| Nanofluid 0.1% | 1.8 | 31:45 | 3.43 |
| Nanofluid 1% | 1.8 | 30:46 | 6.89 |
| Nanofluid 2% | 1.8 | 35:53 | -1.14 |
The results at 200W/m² radiation intensity were consistent with the patterns observed at higher radiation intensities, with 1% concentration providing the best cooling performance. However, the 2% concentrated nanofluid occasionally performed worse than the base fluid. This performance degradation at high particle concentrations is primarily attributed to nanoparticle agglomeration, which diminishes the effective thermal conductivity enhancement and may increase the fluid viscosity, thus reducing the heat transfer capability when the nanoparticles cluster and lose their nanostructure characteristics.
5.5 Temperature Differential Analysis
I also analyzed the temperature differential between inlet and outlet of the cooling system. The outlet-inlet temperature difference (ΔT) provides valuable insight into the heat absorption capability of the working fluids. Figure 5.19 through 5.22 illustrated these variations under different radiation intensities.
For the nanofluid at 1% concentration, the temperature differential at 1100W/m² reached 6.26°C, compared with 5.6°C for pure thermal fluid. This 11.8% increase in temperature differential indicates enhanced heat absorption by the nanofluid per unit mass flow. The temperature differential at 200W/m² showed a decreasing trend over time as the temperature gradient between the solar panel surface and the cooling fluid diminished during the cooling process.
5.6 Performance Evaluation Formula for Nanofluid Cooling
I defined a quantitative metric to evaluate the relative cooling performance of nanofluids compared to their base fluids. The cooling performance enhancement factor B is calculated as:
$$ B = \left( \frac{\Delta T”}{\Delta T’} – 1 \right) \times 100\% = \frac{T_2” – T_2′}{T_2′ – T_2} \times 100\% \tag{7} $$
Alternatively, when using cooling time as the evaluation metric:
$$ B = \frac{\tau_2” – \tau_2′}{\tau_2′ – \tau_2} \times 100\% \tag{8} $$
These equations enabled quantitative comparison of the cooling performance enhancement provided by nanofluids relative to conventional coolants in solar panel applications.
6. Discussion and Analysis
6.1 Optimal Concentration Behavior
My experimental results consistently demonstrated that 1% mass concentration of Al₂O₃-MWCNT hybrid nanoparticles provides the optimal cooling performance. This behavior arises from the competition between two opposing effects. On one hand, increasing nanoparticle concentration enhances the effective thermal conductivity and heat transfer coefficient of the fluid. On the other hand, higher particle concentrations increase fluid viscosity and promote particle aggregation, which reduces the contact area between the nanoparticles and the base fluid, compromises the micro-scale thermal enhancement effects, and increases pumping losses.
6.2 Flow Rate Effect
Higher flow rates generally improve cooling of solar panels by increasing the convective heat transfer coefficient. However, my experiments revealed that the enhancement from increasing the flow rate from 0.9L/min to 1.8L/min was less significant at higher nanofluid concentrations. Under high-flow conditions, nanoparticles experience greater shear forces and hydrodynamic disturbances, making them more likely to overcome van der Waals forces of repulsion and aggregate. Although continuous flow prevents obvious sedimentation of agglomerated particles, severely aggregated nanofluids essentially lose their nanoscale characteristics and behave similarly to the base fluid.
6.3 Radiation Intensity Effects
The effectiveness of nanofluid cooling varies with radiation intensity. At 1000W/m², the nanofluid cooling enhancement reaches 11.64%, while at 200W/m², the improvement is only 9.65%. At lower radiation intensities, the thermal load is smaller, and the temperature difference between solar panels and the cooling fluid provides sufficient driving force for heat transfer even with the base fluid alone, thus diminishing the relative benefit of nanofluids. Conversely, under high intensity conditions, the enhanced thermal properties of nanofluids become more critical to achieve optimal cooling performance of solar panels.
6.4 Mechanism of Heat Transfer Enhancement
The micro-scale effects of nanoparticles dispersed in the base liquid account for the enhanced heat transfer and increased temperature differentials observed in the nanofluid cooling systems. Nanoparticles possess significantly larger specific surface areas, resulting in more surface atoms and higher free energy. When dispersed in the liquid, nanoparticle surface atoms transition from free to non-free states, forming ordered liquid molecular layers around the particles. These interfacial phenomena substantially affect the overall thermal properties of the system.
7. Conclusions
Based on my systematic experimental investigation of cooling characteristics of solar panels using nanofluids, I draw the following conclusions:
(1) The spiral coil vortex generator cooling plate significantly enhances heat dissipation of solar panels. Compared to the no-cooling-plate configuration, the maximum temperature reduction reached 13.8°C, with an average reduction of 10.74°C. The PV/T comprehensive efficiency achieved with the spiral coil design was 64.41%, representing a 15.64% improvement over the conventional cooling plate.
(2) I successfully prepared stable hybrid nanofluids using the two-step method. The Al₂O₃-MWCNT hybrid nanofluid exhibited optimal stability with CTAB surfactant at a mass ratio of 1:2. Thermal conductivity of nanofluids increases nonlinearly with particle concentration, showing diminishing returns at higher concentrations, while increasing temperature consistently enhances thermal conductivity.
(3) The optimal cooling performance for solar panels is achieved with 1% mass concentration of Al₂O₃-MWCNT nanoparticles under all tested radiation intensities. At 1100W/m² and 0.9L/min, the 1% nanofluid improved cooling efficiency by 11.64% compared to the base thermal fluid. However, at 2% concentration, the cooling performance diminished and occasionally fell below that of the base fluid due to particle agglomeration and increased viscosity.
(4) The cooling performance enhancement from nanofluids is more pronounced at higher radiation intensities, where the enhanced thermal properties play a more critical role. High flow rates may reduce the effectiveness of nanofluid cooling for solar panels by promoting nanoparticle aggregation, suggesting that an optimal flow range exists for practical applications.
These findings provide experimental evidence and a theoretical foundation for the practical application of hybrid nanofluids in thermal management of solar panels, contributing to improved energy conversion efficiency and promoting the sustainable development of solar energy technologies.