Panel Surface Research Articles

Characterizing the Movement of Thin Concrete Overlay Panels Subject to Truck Loads

The construction of thin concrete overlays as a pavement preservation option for both asphalt and concrete pavements is gaining popularity in the U.S. To reduce costs, as well as mitigate tendencies toward curling and warping, most thin bonded and unbonded concrete overlays are constructed with either smaller panels, or shorter length panels with undoweled transverse joints. Similar to undoweled concrete pavements on grade, thin undoweled concrete overlay projects have developed faulting of the transverse joints, sometimes at an early age. Toward the efforts to understand the mechanisms causing joint faulting in thin concrete overlays, this study sought to characterize the basic surface movements of thin overlay panels, including bending, translation, and joint load transfer, when subject to heavy vehicle loads. This study utilized digital image correlation (DIC) equipment to measure the dynamic displacement along the edge of thin bonded and unbonded concrete overlay panels loaded by heavy trucks. Testing showed that as the heavy front axle first lands on a thin overlay panel, localized bending is the predominant movement, regardless of panel length. For bonded concrete overlays on asphalt, most transverse joints did not show strong load transfer behavior, even when fiber-reinforced concrete was used. In addition to characterization of the panel surface movements, general correlation to observed joint faulting and panel distress was described. The findings from this study should serve useful to the continuing determination of the mechanisms causing joint faulting in thin concrete overlays

A novel solar panel self-cleaning method based on piezoelectric films

Maintaining clean surfaces on solar panels is critical for maximizing energy efficiency, particularly in regions with high dust accumulation. Conventional cleaning methods, which often rely heavily on water, pose significant sustainability challenges, especially in water-scarce environments. This paper introduces an innovative self-cleaning solution for photovoltaic (PV) panels based on polyvinylidene fluoride (PVDF) piezoelectric films. The technology harnesses the inverse piezoelectric effect, whereby mechanical vibrations are generated when an alternating current (AC) voltage is applied to the PVDF film, effectively dislodging dust and particulate matter from the panel surface. Aluminum foil electrodes are affixed to the PVDF film, and vibrations are propagated across the surface to remove dust particles within a defined timeframe. Experimental results demonstrate the system's efficiency in removing particles while consuming minimal energy, making it particularly suitable for arid regions where water-based cleaning methods are impractical. Additionally, the PVDF films possess favorable mechanical and optical properties, including high transparency, flexibility, and cost-effectiveness, supporting their potential for large-scale deployment. The technology represents an environmentally friendly and water-saving alternative to traditional methods and has significant commercialization potential. This research paves the way for further development of self-cleaning PV technologies, offering a sustainable solution for maintaining solar panel performance in challenging environmental conditions.

Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming.

Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors into the network will be instrumental, offering high-speed and precise 3D mapping capabilities, even in low light or adverse weather conditions. LiDAR data facilitate user localization, enabling the determination of optimal RIS coefficients. Our approach extends a Graph Neural Network (GNN) by integrating LiDAR-captured user locations as inputs. This extension enables the GNN to effectively learn the mapping from received pilots to optimal beamformers and reflection coefficients to maximize the RIS-assisted sumrate among multiple users. The permutation-equivariant and -invariant properties of the GNN proved advantageous in efficiently handling the LiDAR data. Our simulation results demonstrated significant improvements in sum rates compared with conventional methods. Specifically, including locations improved on excluding locations by up to 25% and outperformed the Linear Minimum Mean Squared Error (LMMSE) channel estimation by up to 85% with varying downlink power and 98% with varying pilot lengths, and showed a remarkable 190% increase with varying downlink power compared with scenarios excluding the RIS.

Numerical Analysis of Explosion-Induced Deformations on Steel Panels in Urban Environments

Explosion proof structures are designed to be very heavy and non-functional using conventional systems so that they can be resistant to blast effects. As a result, not only the emergence of non-economic structures, but also their operational performance decreases. To effectively mitigate these challenges, a substantial body of research has focused on the development and application of designs and materials specifically engineered to withstand explosive load impacts. In this study, the strength of steel plates under explosives with different energies was tested. Related tests were performed using Ls-Dyna finite element software. An experimental literature was used to calibrate the numerical model. When the results obtained as a result of the calibration were compared with the experimental data, a high level of agreement was obtained. The calibrated numerical model was subjected to burst loads by varying the panel thicknesses and its dynamic responses were simulated. The displacement values were analyzed by placing the explosives equidistant from the panel centers. By comparing the analysis results, explosive energies were compared. The most effective explosive types could be listed according to the amount of change that the evaluated explosives in the same amount caused on the panel surfaces. In line with these studies, information will be gained about what type of steel materials will be used against which type of explosives in areas that need to be protected in urban areas.

Design and Experimental Research on Composite Bottom Guard Plate for Power Battery Bottom Ball Impact Protection

<div>Safety concerns surrounding new energy vehicles have gained increasing national and social attention. Bottom impacts to power batteries are a leading cause of fires and explosions in new energy vehicles. Focusing on the safety of power battery bottom impacts, this article first proposes applying honeycomb panels to the battery’s bottom guard plate. Through the ball impact test, the effect of honeycomb panel surface material thickness on bottom protection is studied, and the mechanism of the honeycomb panel’s ball impact protection is explored. Second, the honeycomb panel and the aluminum alloy plate are structurally compounded to improve the ball impact protection ability. Finally, the optimized composite bottom guard plate is assembled on the lower box of the power battery, and the whole package ball impact experiment is successfully verified. This study serves as a reference for future research on power battery bottom impact protection and the industrial application of bottom guard plates.</div>

Unified Generative Data Augmentation for Efficient Solar Panel Soiling Localization

As the usage of solar power generation increases, it has become essential to predict power generation accurately. Among the various factors that affect solar power generation, soiling on the panel surface drastically reduces solar power generation. Therefore, accurately identifying the area of soiling on the panel surface helps predict solar power generation. However, most existing studies classify the presence or absence of soiling on the panel or the type of soiling. Additionally, current datasets used for training these models, such as the Solar Panel Soiling Image (SPSI) dataset, suffer from limitations, including a lack of diversity in panel types and a small number of unique soiling shapes. To address these issues, we propose three novel data augmentation techniques—Naïve, Realistic, and Translucent—that generate diverse solar panel images with various soiling patterns. Using Pix2Pix and Copy-Paste methods, we created three corresponding datasets to address the imbalances in the existing SPSI dataset. We trained the DeepLabV3+ model for soiling localization using both the original SPSI dataset and our augmented datasets. Experimental evaluations on real-world solar panels installed at Chungbuk National University demonstrated that models trained on our proposed datasets significantly outperform those trained on SPSI data, with improvements in the Jaccard Index of 3.3%, 2.4%, and 14.6% for the Naïve, Realistic, and Translucent datasets, respectively. These results highlight the effectiveness of our data augmentation techniques for improving soiling localization in solar panels.

DETECTION OF DUST ON SOLAR PANELS WITH DEEP LEARNING

Solar energy is an environmentally friendly, clean, and sustainable alternative. The widespread use of this energy source offers excellent environmental and economic benefits. However, some factors affect the efficiency of solar panels. One of these factors is dust. When dust accumulates on the surface of solar panels, it can significantly reduce the efficiency of energy production. Therefore, detecting and quickly removing dust from solar panels is crucial. Managing this process with unmanned artificial intelligence systems, especially in large areas, will provide significant advantages in terms of time and cost. In recent years, convolutional neural networks have achieved significant success in image classification. In particular, transfer learning methods have proven their success in this field. In this study, we aim to solve a new task with limited data using pre-trained deep learning models (EfficientNetB3, ResNet50, MobileNet, VGG19, Xception, InceptionResNetV2, VGG16, ResNet101, DenseNet201, EfficientNetB7) to classify dirty and clean solar panels. These models were chosen because they each have different strengths and have performed well on various tasks. The models with the best performance among these models are combined to improve classification prediction. The proposed ensemble learning approach achieved 99.31% classification accuracy by considering the prediction results of the models with a voting approach. As a result, this approach aims to optimize the maintenance processes of solar energy systems, improve energy efficiency, and support sustainable energy use in the long term.

Structural optimisation of free-swinging agrivoltaic fences

Agrivoltaic fences enable farmers and ranchers to produce energy while maintaining profitable agricultural activity. They consist of vertical posts between which vertical solar panels are mounted. A significant part of the structural costs of these frames comes from their high wind load, which requires large diameter posts, often with wind bracing, and substantial foundations. This research focuses on free-swinging agrivoltaic fences, a new concept that allows panels to 'float' in the wind, an akin to clothes drying on a clothesline. In other words, the panels are suspended from hinged joints, allowing them to swing in strong winds. This study demonstrates that the internal forces in the posts are more than 8 times lower than those induced by fixed vertical panels and scales various parameters involved such as the height of the system, the wind force acting on the panel, its dimensions and weight. The results highlight the fact that the maximum forces generated in the posts of such a system are independent of the wind model considered and can be determined using only the weight of the panel, the height at which it is fixed and the position of the wind pressure on the panel's surface. Finally the study provides a much more reliable and safer design methodology than that used for vertical fixed panels. Design curves are established to provide designers with guidelines on what type of section to use, whether it is steel or timber.

Enhanced Electrostatic Dust Removal from Solar Panels Using Transparent Conductive Nano-Textured Surfaces.

Dust accumulation on solar panels is a mjor operational challenge faced by the photovoltaic industry. Removing dust using water-based cleaning is expensive and unsustainable. Dust repulsion via charge induction is an efficient way to clean solar panels and recover power output without consuming any water. However, it is still challenging to remove particles of ≈30µm and smaller because Van der Waals force of adhesion dominates electrostatic force of repulsion. Here, the study proposes nano-textured, transparent, electrically conductive glass surfaces to significantly enhance electrostatic dust removal for particles smaller than ≈30µm. We perform atomic force microscopy pull-off force experiments and demonstrates that nano-textured surfaces reduce the force of adhesion of silica micro-particles by up to 2 orders of magnitude compared to un-textured surfaces from 460 to 8.6 nN. We show that reduced adhesion on nano-textured surfaces results in significantly better dust removal of small particles compared to non-textured or micro-textured surfaces, reducing the surface coverage from 35% to 10%. We fabricate transparent, electrically conductive, nano-textured glass that can be retrofitted on solar panel surfaces using copper nano-mask based scalable nano-fabrication technique and shows that 90% of lost power output for particles smaller than ≈10µm can be recovered.

Estimation of the Optimum Tilt Angle of Solar PV Panels to Maximize Incident Solar Radiation in Libya

The most significant factor affecting the performance of a solar photovoltaic (PV) system is its tilt angle. It determines the amount of incident solar energy at the panel surface. In this paper, the optimum tilt angle of solar PV panels is estimated based on measured data recorded in twelve major cities in Libya by changing the panel’s tilt angle from 0∘ up to 90∘ in steps of 1∘ and searching for the corresponding maximum daily total solar radiation. A non-linear regression technique was applied to establish six empirical models to determine the optimum tilt angle in Libya. The accuracy of the models was evaluated using statistical criteria such as Taylor diagrams, root mean square error, mean bias error, and correlation coefficient. The results demonstrated that the monthly optimum tilt angle increased during the winter and decreased during the summer varying from 0∘ to 59∘. In addition, both third-order polynomial and Fourier models presented the best efficiency in estimating the optimum tilt angle with a correlation coefficient of 0.9943. The percent gain in average yearly solar energy received at the monthly optimum tilt angle varies from 12.43% to 17.24% for all studied sites compared to the horizontal surface.

Evaluation study of solar powered public street lighting as battery charging at Islamic Center based on PSIM

Electrical energy is essential for the growth and advancement of technological knowledge that continues to increase. Solar cells are devices that convert solar energy through photovoltaic devices. Solar cell public street lighting (PJUBS) uses unlimited and free solar energy, is a feasible and cheap alternative to providing electric lighting. The Islamic center mosque is one of the places of worship for the majority of Muslims in the city of Lhokseumawe using public street lighting to support public security and safety. Installation of lamps for lighting is installed at several points, namely right, left or in the middle of the road, as needed, including flyovers, underpasses, and bridges. Public street lighting that is efficient and environmentally friendly is in accordance with SNI 7391: 2008. In this study, the lamps used in this evaluation study are solar types with a power of 50 Watts. The calculation results show that the illumination produced at the end of the road when using a 50 Watt solar LED lamp is 0.29 lux so that this does not comply with the provisions of BSN SNI 7391: 2008. This condition is influenced by several factors, namely surface temperature, shadows, inclination angle, light intensity, irradiation, and the condition of the panel surface in order to maximize the conversion value of sunlight into electrical energy

Machine learning and FPGA implementation for predicting luminance decay and temperature distribution in micro-LED displays

AbstractA machine learning-based method predicts the luminance decay of micro light emitting diode (micro-LED) displays, utilizing temperature distribution and degradation experiments alongside implementation on field-programmable gate array (FPGA). Micro-LEDs, used in outdoor and indoor billboards, experience degradation due to harsh environmental conditions. To model temperature distribution in indoor advertising displays using minimal data, a temperature model is constructed based on sensor from the panel and thermal images captured by a camera, in relation to the display pattern. The input data is processed using an FPGA, which transmits the sensed temperatures and display patterns to the micro-LED panel. Multilayer Perceptron (MLP), a type of neural network, predicts the temperature distribution over the panel surface, achieving an error of less than 1.1 °C. Separate degradation models forecast luminance decay, factoring in enclosure temperature, input current, and usage time, with distinct models for red, green, and blue LEDs. Exponential curve-fitting and interpolation, following TM-21 standards, ensure long-term accuracy. The luminance decay predictions have an average error below 1.05% (approximately 9 nits). The FPGA implementation minimizes resource consumption while maintaining prediction accuracy, making it suitable for real-time applications. The degradation model accurately predicts performance over tens or even hundreds of thousands of hours, aligning with the exponential decay trends defined by TM-21.

Self-cleaning of photovoltaic modules: The role of liquid droplets on hydrophobic surfaces in surface dust removal

Dust accumulation on photovoltaic (PV) panels reduces their energy efficiency. Although droplets play a crucial role in the self-cleaning of dust on the surface of PV panels, the underlying mechanism of surface dust self-cleaning in the presence of droplets has not been fully understood. This study aims to investigate the dust removal mechanisms on the surface of blank and coated PV panels and analyze the effects of factors such as dust particle size, PV panel tilt angle, and ash density on droplet self-cleaning efficiency. Our experiments show that superhydrophobic coatings can significantly improve the droplet self-cleaning efficiency and output power of PV panels. The maximum relative power recovery rate of the coated sample is 78.53%, which is much higher than 27.83% of the blank sample. To better understand the droplet self-cleaning mechanism, we analyze the differences in droplet self-cleaning between blank and coated PV panels from a mechanical perspective. Our model explains the main forces and motion modes of dust particles in the presence of droplets, and we find that smaller dust particles are easier to remove than larger particles. Additionally, we find that a smaller inclination angle of the PV panel surface inhibits the dust particle removal process. Finally, we conduct a comparative study between droplet self-cleaning and other self-cleaning methods to evaluate their effectiveness. Our results show that droplet self-cleaning is a more efficient and effective method for removing dust from PV panels.

Analysis of the Effect of Delay Time and Type of Coolant Media with the Spray Method on the Back Surface of PV Panel

Sunlight can be harnessed as a clean and renewable energy source using solar cells and the photovoltaic process. However, relying on direct sunlight exposure can increase solar cell temperatures and negatively impact performance. This research aims to maintain cell efficiency by exploring the effectiveness of spraying coolant media on the bottom surface of panels through three timed intervals (10, 20, and 30 minutes) using three different media (A, B, and C). Each spray application lasts for 1 minute. Analyzing the test results with Minitab18 software with full factorial design will identify the most effective treatment for maintaining performance. Based on the results of the experimental test, coolant A with a 10-minute delay spraying time has a maximum power of 52.89 Watts, a temperature of 48oC, and an efficiency of 5.69%. Response Optimization using Design of Experiment (DOE) Full Factorial shows an optimal response with coolant A and a 10-minute delay spraying time with the lowest temperature at 49.3oC, maximum output power at 46.87 Watts, and efficiency at 5.61%. Moreover, Tukey Krammer test result provides an information about 10-minute delay spraying time has a better performance to reduce the PV panel temperature compared to 30-minute delay spraying time by 3.94 oC.

Effect of the Type and Amount of Sedimented Sand and Clay Dust on the Energy of the Solar Panel

Background: The application of solar energy utilization in the world to generate electricity is established. The use of solar panels to generate electricity has witnessed steady scientific progress and plays a significant role in confronting environmental pollution. For solar panels, the efficiency depends to a large extent on the relationship between the solar radiation reaching the Earth’s surface and the dust layer covering the panels.In the case of a solar panel collecting a large amount of dust in its working place, the solar radiation is first diluted through the dust layer before it reaches the panel. This is the main reason for the decrease in the panel’s efficiency. Objectives: It is of utmost importance to study the effects of dust on the design of photovoltaic devices. The potential power reduction of panels due to dust is a pressing issue, with immediate consequences for the operation and number of panels in large solar power plants. This work aims to urgently study the effect of dust accumulation on the front surface of solar panels. Methods: The experiment is being conducted on two different solar panels: the first is monocrystalline with a power of 200 watts, and the second is polycrystalline with a power of 280 watts. Two types of dust – sand and clay – are being used in quantities ranging from 5 g to 25 g. Results: The introduction of sand dust led to a substantial decrease in the energy output of the monocrystalline solar panel, ranging from 3.3% to 7.5 %. Similarly, the use of clay dust resulted in a significant energy drop of 4% to 8%.While the energy of the polycrystalline plate decreased between 2.5% to 6.7% when using sand dust and between 3% to 7.1% when using clay dust. Conclusion: Our research uncovers the negative impact of dust on the efficiency of solar panels, particularly in the case of monocrystalline panels and those exposed to clay dust. This opens up avenues for further research and exploration in the field of solar technology.

Recycled PET foam core sandwich panels with reinforced hybrid composite facesheets: A sustainable approach for enhanced impact resistance

This research explores the Low-Velocity Impact behavior of thermoplastic composite sandwich panels with 100% recycled Polyethylene Terephthalate (PET) foam sourced from post-consumer plastic water bottles. Being recognized as a reliable technique, hybridization using stainless-steel mesh layers was employed to reinforce the panels’ composite facesheets of sandwich panels accessible for modular housing, cold storage rooms and cargo trucks. Adequate impregnation of the reinforcement metallic mesh layer alongside proper skin-to-core adhesion was accomplished by optimizing a two-phase compression molding method. The effect of hybridization on impact response of sandwich panels with two different PET foam core thicknesses, and stacking sequence were evaluated. It was revealed that reinforcing the impacted surface of the composite sandwich panels significantly increased the perforation threshold. Moreover, analyzing the post-impact section view of the samples indicated that hybridization modified the damage propagation response of the PET foam core sandwich composites, through which the energy absorption capacity was improved.

Evaluation of self-cleaning mechanisms for improving performance of roof-mounted solar PV panels: A comparative study.

Solar panel installation is generally exposed to dust. Therefore, soiling on the surface of the solar panels significantly reduces the effectiveness of solar panels. Accumulation of dust also shortens their lifespan and reduces efficiency by about 15% to 20%. A significant reduction in the efficiency of solar photovoltaic panels has been observed due to inadequate insulation and dust deposition or shading. To harness maximum solar energy from solar panels up to their rated capacity, they need to be cleaned periodically. Therefore, the current study focuses on the comparative performance analysis of two distinct types of self-cleaning mechanisms, namely self-cleaning wiper (SCW) and nano-coating method. These methods are economical and sustainable for the standard atmospheric conditions of Pakistan. Solar panels (reference, nano-coated, and self-cleaning wiper mechanism) were placed on the roof of the Mechanical Engineering Department MUST Mirpur AJK for five weeks. Solar irradiance, dust density & performance parameters of these three panels were recorded on weekly basis. It was observed that an increase in the rate of dust deposition negatively affects the conversion efficiency of solar panels. When dust density was increased from 7.5 to 18.15 (g/m2), the percentile drops in rated power (50W) for reference, nano-coated, and self-cleaning wiper mechanisms are 37%, 33% and 23%, respectively. Moreover, the payback period of nano-coated and SCW is 1.07 years and 2.79 years, respectively.

Thermal Analysis of Photovoltaic Panel Cooled by Electrospray Using Different Fluids

In this study, the cooling performance of the photovoltaic (PV) panel was examined by the electrospray cooling method. The experiments were carried out under 1000 W/m² irradiation, 25 G nozzle diameter and 70 mm nozzle-to-PV panel distance and 20 kV voltage. Water, ethanol and water - ethanol (50%- 50%) mixture were atomized and sprayed on the panel surface at flow rates of 50-80-110 ml/h. The results showed that electrical power output decreased with increasing PV panel surface temperature. Ethanol and water - ethanol mixture showed a more effective cooling performance than water, especially at flow rates of 80 and 110 ml/h. At the highest flow rate, ethanol reduced the panel temperature by 59%, providing 6,8% more electrical power output than the uncooled condition. These findings show that the electrospray cooling method is effective in increasing the electrical efficiency of PV panels and that better cooling performance is achieved with ethanol, water - ethanol mixture compared to water.

Improving solar panel performance using MPPT optimization algorithms

One of the primary challenges faced by solar energy systems is the issue of inconsistent lighting and uneven distribution of sunlight across the surface of solar panels. These conditions can result in a substantial reduction in the power output of the panels and, in some cases, the complete failure of certain solar cells. This paper examines the effects of shading on solar panel performance, focusing on how partial shading impacts energy production and efficiency through both experimental analysis and simulation. The findings emphasize the critical influence of shading patterns and configurations on the overall efficiency of photovoltaic systems. The study highlights the necessity of implementing shading mitigation strategies and optimization algorithms to enhance energy production and ensure the reliability of solar systems operating in environments with varying light conditions. This study showed that the use of optimization algorithms in photovoltaic systems exposed to shading increases their efficiency in terms of energy saving as well as performance stability. The simulation results shows that the GWO algorithm gives a higher performance than the other algorithms in the same partial shading conditions.

Comprehensive evaluation of solar floating photovoltaic prospective in Saudi Arabia: Comparative experimental investigation and thermal performance analysis

Recently, floating photovoltaic systems have been regarded as a promising technology for producing clean energy by utilizing the surface of water bodies, such as lakes, rivers and oceans. This study introduces a comparative experimental study and energy performance evaluation of a 1.0 kW offshore floating photovoltaic (FPV) system and a nearby traditional ground-based PV system (GPV) installed in the eastern province of Saudi Arabia. The FPV system was deployed in the Arabian Gulf, 25 m off the coast, at an average depth of 1 to 1.5 m depending on tide, wave, and current intensity. The FPV system employs a strong novel eco-friendly platform structure made of recycled buoyant materials such as engineered plastic drums and wood. This system is anchored with tension cables and concrete blocks that can withstand the changing sea water conditions. The GPV system, on the other hand, was installed 150 m inland from the shore. Real-time monthly data monitoring and assessment indicated that FPV systems outperformed GPV systems in terms of lower PV panel surface temperatures, higher power output, and panel efficiency. Based on daily average back surface temperatures, FPV system temperature was decreased by 7.5 % to 21.34 % when compared to GPV. Moreover, the FPV system efficiency was also increased by 12.2 % when compared to the GPV system. This study aims to assist in promoting the applicability of solar floating photovoltaic systems to synergistically fulfill the requirements of sustainable electricity production for the arid costal community in Saudi Arabia and similar areas.