Performance Of Panels Research Articles

Thermal management enhancement of photovoltaic panels using phase change material heat sinks with various T-shaped fins

A numerical simulation of the heat dissipation performance in photovoltaic (PV) cells with phase change material (PCM) for cooling is performed by COMSOL Multiphysics. A comparative analysis of two T-shaped fin designs in PCM heat sinks is conducted to evaluate their impact on the cooling performance of PV panels. The findings indicate that a 180° T-shaped fin design substantially outperforms a cavity without fin design, reducing the PV cell average temperature by up to 15.1 K and enhancing electrical efficiency by 1.36 %. Further optimization of distal fin distance and secondary length reveals that temperature uniformity and photovoltaic efficiency can be improved, achieving a maximum electrical efficiency increase of 1.40 % and a temperature uniformity enhancement of 71.0 %. Detailed analysis underscores the critical role of the distal fin distance (Scheme 1) in temperature control and PCM convection, highlighting its importance in the design process. In contrast, numerical results of the alternative fin layout (Scheme 2) reveal its unsuitability for PV cooling applications.

Design and implementation of a dual-axis sun tracker for an Arduino-based micro-controller photovoltaic system

This research investigates the optimization of solar panel performance by designing and implementing a low-cost Dual-Axis Sun Tracker (DAST) for an Arduino-based Microcontroller Photovoltaic System. The primary aim is to enhance solar energy extraction by precisely aligning the panel perpendicular to the sun's position, maximizing output voltage and current efficiency compared to fixed systems. The DAST employs two micro servo motors, SG90, controlled by a specialized chronological algorithm in offline mode, ensuring strategic alignment and scheduled adjustments. A comprehensive evaluation of the DAST's performance is conducted, contrasting it with a Fixed System to underscore the advantages of solar tracking. As a result, a DAST produces higher output than a fixed system by 0.896W during sunny days and 0.206W during cloudy days. Besides, the efficiency of PV panels in the DAST is 71.65%, and fixed is 49.66% during sunny days, while DAST is 22.96% and fixed is 17.91% during cloudy days.

Investigating the influence of nanofluid on photovoltaic-thermal systems concerning photovoltaic panel performance

Solar energy has much promise to replace the planet's finite supply of fossil fuels as it is sustainable and eco-friendly. Photovoltaic (PV) panels are one example of the proper technology that may transform solar energy into electrical energy. On the other hand, high temperatures may reduce photovoltaic cells' ability to produce power efficiently. As a result, a study was carried out to use the PV/T (Photovoltaic Thermal) collector system and evaluate the effects of CuO, TiO2, and Al2O3 nanofluids as cooling agents on the performance and operating temperature of PV panels. In Surakarta, Indonesia, an experimental study was conducted with nanoparticle concentrations of 0.2 vol% and a flow rate of 3 L/m at an intensity of radiation of 1000 W/m2. The results showed that, in comparison to uncooled PV panels, employing CuO, TiO2, and Al2O3 nanofluids in the PV/T system resulted in temperature reductions of 18.84 °C, 18.12 °C, and 17.62 °C, respectively. Moreover, measurements of the electrical efficiency produced by PV and PV/T panels using CuO, TiO2, and Al2O3 nanofluids showed that the respective values were 10.98%, 13.92%, 13.65%, and 13.39%. This study contributes to the development of solar energy technology by demonstrating the potential of nanofluid-based cooling devices to improve solar panel performance in hot conditions.

ANALISIS PERBANDINGAN KINERJA SISTEM PENDINGIN PV DENGAN CAIRAN AQUADES, AIR SUMUR, DAN TANPA PENDINGIN DENGAN MONITORING IOT

Solar Power Plants are an environmentally friendly energy source that utilizes sunlight energy which is converted into electricity. However, solar panels must maintain their performance. One of the impacts is that solar panels cannot work optimally if the temperature of the solar panels exceeds the safe temperature. This research examines the factors that cause the performance of solar panels to not work optimally, when the surface temperature of the solar panels is too hot and exceeds the safe temperature limit, then tools are needed to help the solar panels so that the working temperature is not too high. The technology used to control the temperature of solar panels is by utilizing IoT by maximizing the DS18B20 sensor and voltage sensor, to read the surface temperature of the panel, which will then wet the surface of the solar panel with distilled water and well water automatically if the surface temperature of the panel exceeds the safe temperature on the solar panel. And later you can monitor the temperature of the panel with a smartphone and also observe whether distilled water and well water can reduce the temperature on the surface of the panel. When collecting data, it can be compared to when without using cooling, the temperature on the panel is above 33 degrees. Meanwhile, when using a cooler the temperature obtained decreases. The well water cooler produces a temperature of 32 degrees, while the distilled water cooler produces a temperature of 30.5 degrees. It can be concluded that distilled water can reduce the temperature of the panel, so it can make the panel work optimally. So, in order to maximize the performance of the panels to utilize solar power, in this research, distilled water is very good for lowering the temperature on the surface of the panels.

Investigation on the operation performance of solar panels cooled by a natural draft cooling system

This paper is to investigate the feasibility of a natural draft cooling system for PV panel cooling in a typical hot and dry region (Hami, Xinjiang, China). A 3-D model of PV panels cooled by a natural draft cooling system is established using Fluent 2020R2 software, and the cooling effect of the natural draft cooling system on PV panels is simulated and analyzed based on the meteorological data of Hami, China. Simulation finds that the natural draft cooling system can reduce the average temperature of the PV panels. The minimum temperature drops of the PV panels in January (represents winter season), April (represents spring season), July (represents summer season) and October (represents autumn season) of the year 2020 are 1.7 °C, 3.1 °C, 6.1 °C and 2.5 °C, respectively; while the maximum temperature drops of the PV panels are 7.0 °C, 11.2 °C, 15.5 °C and 6.3 °C, respectively. Besides, the natural draft cooling system is also compared with the PV panels without cooling system in six typical days, and finds that the PV panel with a natural draft cooling system increases the photoelectric conversion efficiency by up to 1.41 % (i.e., from 13.71 % to 15.12 %) when compared with the PV panel without cooling. Therefore, the proposed natural draft cooling system can effectively reduce the temperature of PV panels in Hami, Xinjiang.

An Experimental Assessment of the Performance of Solar Panels Using Efficiency Enhancement Techniques in India's Tropical Region

<p style="text-align:justify"><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="color:black">Due to its abundance and lack of pollution, solar energy will soon be the most popular source of electricity production. Numerous factors, including the temperature effect, solar irradiance effect, shadow effect, incident angle effect, dust accumulation on the solar cell, shunt resistance, types of solar cell materials, mechanical defects in the solar panel, spectral mismatch loss, cable loss, maintenance and cleaning cycle, load mismatching effect, etc., harm the efficiency of solar P.V. power generation systems. This study aims to quantify the impact of these factors and explore efficiency enhancement techniques to mitigate their effects. Among the various factors, solar irradiation and temperature significantly influence solar P.V. panel efficiency. This research investigates the effects of solar irradiation, incident angle, and temperature through simulations and experimental methods using two 100W solar panels. Efficiency enhancement techniques like sensor-based sun tracking and temperature cooling systems are incorporated with conventional solar P.V. power generation systems. The experimental results prove that the efficiency enhancement techniques improved the efficiency of solar panels by up to 12% compared to conventional solar panels.</span></span></span></span></span></p>

Multi-Objectives Optimization of Plastic Injection Molding Process Parameters Based on Numerical DNN-GA-MCS Strategy.

An intelligent optimization technique has been presented to enhance the multiple structural performance of PA6-20CF carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) products. This approach integrates a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS), collectively referred to as the DNN-GA-MCS strategy. The main objective is to ascertain complex process parameters while elucidating the intrinsic relationships between processing methods and material properties. To realize this, a numerical study on the PIM structural performance of an automotive front engine hood panel was conducted, considering fiber orientation tensor (FOT), warpage, and equivalent plastic strain (PEEQ). The mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time were employed as design variables. Subsequently, multiple objective optimizations of the molding process parameters were employed by GA. The utilization of Z-score normalization metrics provided a robust framework for evaluating the comprehensive objective function. The numerical target response in PIM is extremely intricate, but the stability offered by the DNN-GA-MCS strategy ensures precision for accurate results. The enhancement effect of global and local multi-objectives on the molded polymer-metal hybrid (PMH) front hood panel was verified, and the numerical results showed that this strategy can quickly and accurately select the optimal process parameter settings. Compared with the training set mean value, the objectives were increased by 8.63%, 6.61%, and 9.75%, respectively. Compared to the full AA 5083 hood panel scenario, our design reduces weight by 16.67%, and achievements of 92.54%, 93.75%, and 106.85% were obtained in lateral, longitudinal, and torsional strain energy, respectively. In summary, our proposed methodology demonstrates considerable potential in improving the, highlighting its significant impact on the optimization of structural performance.

Clinical Performance of the BD Respiratory Viral Panel for BD MAX™ System in Detecting SARS-CoV-2, Influenza A and B, and Respiratory Syncytial Virus

Using a nasopharyngeal (NP) or anterior nasal (NS) swab from prospectively collected or retrospective specimens, we assessed the clinical performance of the BD Respiratory Viral Panel (BD RVP) for BD MAX System against FDA-cleared or authorized comparators. Across prospective and retrospective specimens, positive percent agreement (PPA) was ≥ 98.4% for SARS-CoV-2, ≥ 96.7% for influenza (flu) A, ≥ 91.7% for respiratory syncytial virus (RSV), and 100% for flu B (retrospective only) while negative percent agreement (NPA) was ≥ 97.7% across all targets, leading to the assay FDA clearance. A head-to-head comparison of NS versus NP results with BD RVP was also performed; PPA was ≥ 90% and NPA ≥ 98.2% for SARS-CoV-2, flu A and RSV. These findings confirm that the BD MAX RVP assay performs well for detection and differentiation of the three viruses in NP and NS specimens, with strong interrater agreements for NS versus NP comparisons.

Real-Time Monitoring of Photovoltaic Panel Using Node-RED

This research aims to design and implement an Internet of Things (IoT)-based monitoring system for Photovoltaic (PV) panels using Node-RED. The system can monitor critical parameters such as voltage, current, power, and the electrical energy produced by the PV panels in real-time. The data obtained from the PV panels is sent to a Node-RED server and visualized in the form of indicators and graphs on a dashboard. Statistical analysis calculates the daily average power and total energy produced. The results show that the proposed system can enhance monitoring efficiency and significantly benefit PV system maintenance and management. Users can quickly identify and address issues that may arise, such as panel performance degradation or system disruptions. Energy analysis and maintenance planning can be carried out by collecting historical data. This research supports the broader renewable energy development and provides an effective real-time PV system monitoring solution.

Enhancing the performance of honeycomb core sandwich panels through friction stir spot welding strategies and fracture of lap shear specimen

The manufacturing of honeycomb core sandwich structures using friction stir spot welding (FSSW) methods as alternatives for adhesive bonded (AB) structures is examined. AB, FSSW with disk insert (FSSW_D), FSSW_D with AB (FSSW_D_AB) are adopted for the purpose. The performance during the lap shear test and the cohesive zone modeling (CZM) in the finite element (FE) simulation served as the foundation for the comparison. A homogenized core and equivalent cohesive layer were used during the numerical simulations of lap-shearing of all joints. Maximum load-carrying capacity improved significantly when the FSSW_D and FSSW_D_AB were used; the improvement ranged from 592% to 878% over AB joints. Peak load was shown to be around 24% higher in FSSW_D_AB in comparison to FSSW_D joints. The peak load-to-weight ratio of the FSSW_D_AB joints was remarkable (542% to 747%). Stiffness measurements were approximately 0.43 kN/mm for FSSW_D and 0.63 kN/mm for FSSW_D_AB joints. Microstructural analysis revealed finer grains with increased rotation speed and distinct fracture modes, with SEM showing improved ductility. The good agreement between the numerical predictions and the experimental results for all types of joints showed the usefulness of the proposed numerical model. Moreover, FE simulations-based stress analyses of the three joints provided crucial results on normal and shear stress distributions enabling the sandwich structures to be tailor-made for their intended applications.

Performance of structural insulation panels under eccentric axial and racking loads with varying opening ratios

Structural insulation panels, which are actively applied in the housing market in North America and Europe by utilizing wood, an eco-friendly material, as the main material, are composed of OSB (Oriented Strand Board), EPS (Expanded Polystyrene), and SPF (Spruce-Pine-Fir), which is used as a connection between the panels. Structural insulated panels are used as a load-bearing wall to resist external forces without installing separate columns. The production of structural insulation panels consists of applying adhesive to both sides of EPS, which acts as an insulation material, and pressing wood boards (OSB), which exert strength as a structural material. The OSB on the outside resists compressive and tensile forces, while the EPS on the inside resists shear forces. The joints and openings of the structural insulation panels are reinforced with SPF (Spruce-Pine-Fir) structural wood. When structural insulated panels are used as an exterior wall structure, the installation of openings is essential. Based on the opening size that is popularly used in wooden structures, the performance evaluation of structural insulation panels under eccentric axial load and repeated lateral load due to the reduction of the cross-sectional area by installing the opening was carried out, and the behavior analysis through variable analysis and comparison analysis with the design equation was carried out.

Performance of a Rapid Digital PCR Test for the Detection of Non-Small Cell Lung Cancer (NSCLC) Variants.

Next-generation sequencing is widely used for comprehensive molecular profiling for many cancers including lung cancer. However, the complex workflows and long turnaround times limit its access and utility. ChromaCode's High Definition PCR Non-Small Cell Lung Cancer Panel (HDPCR™ NSCLC Panel) is a low-cost, rapid turnaround, digital polymerase chain reaction assay that is designed to detect variants in nine NSCLC genes listed in National Comprehensive Cancer Network guidelines. This assay uses TaqMan® probe limiting chemistry and proprietary analysis software to enable multi-target detection within a single-color channel. We compared the performance of the HDPCR™ NSCLC Panel against an in-house, laboratory-developed, targeted next-generation sequencing panel used in the Molecular Diagnostics Laboratory at the University of Minnesota Medical Center to detect biomarkers for NSCLC. The overall accuracy of the HDPCR panel was 99.48% (95% confidence interval 99.01-99.76) with a sensitivity of 95.35% (95% confidence interval 88.52-98.72) and a specificity of 99.69% (95% confidence interval 99.29-99.90). The HDPCR wet lab workflow was 4 h, and the time to generate variant calls from raw data using the ChromaCode Cloud was 2 minutes. We demonstrated that the HDPCR™ NSCLC Panel provides timely, comprehensive, and sensitive mutation detection in NSCLC samples with results in less than 24 h.

Advanced particle swarm optimization for efficient and fast global maximum power point tracking under partial shading conditions

Partial shading (PS) is a common issue in photovoltaic systems (PVs), and it can significantly reduce the system's output power. This paper presents the advanced particle swarm optimization (APSO) algorithm. APSO is designed to alleviate the challenges posed by PS in PVs in from where of effectiveness and stability speed so that it works to achieve and maintain the global maximum power point (GMPP) under PS conditions. It leverages persistent variables to store and track system states and iterations; it also includes checks to ensure that the duty cycle remains within specified bounds facilitating more effective optimization. Additionally, APSO optimizes solar panel duty cycles and velocities to converge toward an optimal solution to improve overall power generation efficiency and settling time. The results evaluation involves testing the performance of photovoltaic panels under three different shading scenarios and comparative analysis against recent Heuristic-optimization-based GMPP techniques, this study and comparative analyses demonstrate APSO's effectiveness and superiority in terms of high efficiency that reaches 99.85% and fast settling time of GMPP at less than 0.01 second across all test cases. APSO presents a promising solution for maximizing PV power output in the presence of partial shading.

Research on the influence of core plate joint surface type on the vibration performance of acoustic black hole sandwich panels

Research on the influence of core plate joint surface type on the vibration performance of acoustic black hole sandwich panels

A Droplet Digital Polymerase Chain Reaction-Based Tool to Aid in Melanoma Diagnosis.

Detecting copy number variations (CNVs) at certain loci can aid in the diagnosis of histologically ambiguous melanocytic neoplasms. Droplet digital polymerase chain reaction (ddPCR) is a rapid, automated, and inexpensive method for CNV detection in other cancers, but not yet melanoma. To evaluate the performance of a 4-gene ddPCR panel that simultaneously tests for ras responsive binding element protein 1 (RREB1) gain; cyclin-dependent kinase inhibitor 2A (CDKN2A) loss; MYC proto-oncogene, bHLH transcription factor (MYC) gain; and MYB proto-oncogene, transcription factor (MYB) loss in melanocytic neoplasms. One hundred sixty-four formalin-fixed, paraffin-embedded skin samples were used to develop the assay, of which 65 were used to evaluate its performance. Chromosomal microarray analysis (CMA) data were used as the gold standard. ddPCR demonstrated high concordance with CMA in detecting RREB1 gain (sensitivity, 86.7%; specificity, 88.9%), CDKN2A loss (sensitivity, 80%; specificity, 100%), MYC gain (sensitivity, 70%; specificity, 100%), and MYB loss (sensitivity, 71.4%; specificity, 100%). When one CNV was required to designate the test as positive, the 4-gene ddPCR panel distinguished nevi from melanomas with a sensitivity of 78.4% and a specificity of 71.4%. For reference, CMA had a sensitivity of 86.2% and a specificity of 78.6%. Our data also revealed interesting relationships with histology, namely (1) a positive correlation between RREB1 ddPCR copy number and degree of tumor progression; (2) a statistically significant correlation between MYC gain and nodular growth; and (3) a statistically significant correlation between MYB loss and a sheetlike pattern of growth. With further validation, ddPCR may aid both in our understanding of melanomagenesis and in the diagnosis of challenging melanocytic neoplasms.

Enhancing Photovoltaic Panel Performance through Hybrid Nanoparticle Cooling: A Study on Zinc Oxide and Aluminum Oxide Nanofluids

Enhancing Photovoltaic Panel Performance through Hybrid Nanoparticle Cooling: A Study on Zinc Oxide and Aluminum Oxide Nanofluids

Impact of dust and temperature on photovoltaic panel performance: A model-based approach to determine optimal cleaning frequency

Enhancing the reliability of photovoltaic (PV) systems is of paramount importance, given their expanding role in sustainable energy production, carbon emissions reduction, and supporting industrial growth. However, PV panels commonly encounter issues that significantly impact their performance. Specifically, the accumulation of dust and the rise in internal temperature lead to a drop in energy production efficiency. The primary issue addressed in this paper is using mathematical modeling to determine the optimal cleaning frequency. This paper first focuses on stochastic modeling for dust accumulation and temperature changes in PV panels, considering varying environmental conditions and proposing a model-based approach to determine the optimal cleaning frequency. Dust accumulation is described using a Non-homogeneous compound Poisson process (NHCPP), while temperature evolution is modeled using Markov chains. Within this framework, we consider the impact of wind speed and rainfall on dust accumulation and temperature. These factors, treated as covariates, are modeled using a two-dimensional time-continuous Markov chain with a finite state space. A Condition-based cleaning policy is proposed and assessed based on the degradation model. Optimal preventive cleaning thresholds and cleaning frequency (periodic and non-periodic) are determined to minimize the long-term average maintenance cost. The gain achieved by non-periodic inspections compared to periodic inspections ranges from 3.83% to 9.37%. Numerical experiments demonstrate the performance of the proposed cleaning policy, highlighting its potential to improve PV system efficiency and reliability.

Economic Optimization and Performance Enhancement of Rooftop Solar Power Systems Using Concentrated Solar Technology and Advanced Materials

Abstract: This research investigates the economic optimization and performance enhancement of rooftop solar power systems through the integration of concentrated solar technology and advanced materials. The aim is to assess the viability and effectiveness of these innovations in both residential and commercial settings. By focusing on improving energy efficiency and reducing costs, the study provides a comprehensive economic perspective on the adoption of these advanced solar technologies. Key aspects of the research include the evaluation of lifecycle costs, energy yield, and payback periods, offering insights into the long-term financial benefits and feasibility of implementing such systems. The integration of concentrated solar technology enhances the intensity of solar energy captured, thereby significantly boosting the efficiency of rooftop solar panels. Advanced materials, such as perovskites, multi-junction cells, and nanomaterials, are examined for their potential to improve energy absorption, durability, and overall performance of solar panels. The research employs simulation software to model the performance of these optimized systems under various environmental conditions, providing a detailed analysis of their energy output and efficiency.

Assessment of panel performance in CATA and RATA experiment

AbstractCATATIS is a recently developed method for the analysis of multiple binary datasets such as Check‐All‐That‐Apply (CATA) data. CATATIS yields a homogeneity index reflecting the overall agreement among the panelists and weights associated with the panelists that highlight the extent to which they agree with the general point of view. These weights are used to compute an average group configuration, which is submitted to Correspondence Analysis to derive a perceptual product map. Here, CATATIS is extended to situations where the data are not binary. In particular, we focus on the case of data from a CATA experiment with repetitions and data from a Rate‐All‐That‐Apply (RATA) experiment. Furthermore, a hypothesis‐testing framework based on permutations is set up to assess the significance of the weights associated with the panelists. Finally, the general strategy of analysis is illustrated using data from real case studies pertaining to CATA and RATA experiments.Practical ApplicationsEvaluating panel performance is often important in sensory studies, especially in industrial contexts. While many professionals are familiar with evaluating the outputs of conventional descriptive analysis, the increasing adoption of rapid sensory methods calls for methods and tools tailored to different sensory tasks. Our paper introduces an approach (CATATIS) to enhance the interpretation of the outputs of CATA and RATA tasks, focusing on attribute consistency, overall panel agreement, and individual weights associated with panelists. The approach is demonstrated through concrete case studies. CATATIS, together with its extensions, are available in XLSTAT software and in the R package ClustBlock.

Experimental and numerical investigations on the axial compression performance of precast geopolymer concrete sandwich wall panel

This paper investigated a novel precast concrete sandwich panel (PCSP) which made by the geopolymer concrete and enabled by the glass fiber reinforced polymer (GFRP) hexagonal tube connector. Six precast geopolymer concrete sandwich wall panel (PGCSP) specimens were fabricated and tested under axial compression load. The investigating parameters included the connector spacing, panel height, and panel thickness. The failure mode, axial load capacity, load-axial deflection relationship, and load-concrete strain relationship were studied and discussed. Moreover, two-dimensional (2D) finite-element (FE) analysis was conducted to reproduce the test. Parameter analysis was then performed to modify the axial load capacity formula in the Chinese code GB50010-2010. The results indicated that: all specimens exhibited large eccentric compression failure due to the second-order effect, which was caused by concrete crushing and horizontal cracking in the two wythes, respectively; with the decrease of the connector spacing, the crushing area of the specimens would be enlarged, the out-of-plane lateral deformation would be decreased, and the axial load capacity decreased by 17 % and 42 % when the connector spacing changed from 300 mm to 600 mm and 900 mm, respectively; with the decrease of the panel height and the increase of concrete wythes thickness, the axial load capacity of the specimens almost presented a linear increase; the established 2D FE model and the modified formula effectively reflected the axial load capacity of the specimens.