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Summary This paper proposes a new framework for early hotspot detection in the photovoltaic (PV) panels using color image descriptors and a machine learning algorithm. In the proposed approach, the acquired thermographic images of PV panels are divided into non‐overlapping regions, and then color image descriptors are computed for the regions. The color descriptors are then used as features to train different machine learning algorithms to classify the PV panels into three classes (ie, normal, hotspot, and defective). After extensive testing and comprehensive analysis, the experimental results show that Red‐Green Scale‐Invariant Feature Transform (rgSIFT) descriptor with k‐Nearest Neighbor (k‐NN) outperforms all other images descriptors and machine learning combinations with an accuracy rate of 98.7%. The experimental results also show the effects of the size of non‐overlapping regions on the classification accuracy. It is observed that the classification accuracy decreases as size is increased or decreased around the optimal non‐overlapping region image size of 71 × 71 pixels. The proposed method has a significant role in carbon‐free cities and can easily be implemented to inspect the PV system.

In today’s world, a brain tumor is one of the most serious diseases. If it is detected at an advanced stage, it might lead to a very limited survival rate. Therefore, brain tumor classification is crucial for appropriate therapeutic planning to improve patient life quality. This research investigates a deep-feature-trained brain tumor detection and differentiation model using classical/linear machine learning classifiers (MLCs). In this study, transfer learning is used to obtain deep brain magnetic resonance imaging (MRI) scan features from a constructed convolutional neural network (CNN). First, multiple layers (19, 22, and 25) of isolated CNNs are constructed and trained to evaluate the performance. The developed CNN models are then utilized for training the multiple MLCs by extracting deep features via transfer learning. The available brain MRI datasets are employed to validate the proposed approach. The deep features of pre-trained models are also extracted to evaluate and compare their performance with the proposed approach. The proposed CNN deep-feature-trained support vector machine model yielded higher accuracy than other commonly used pre-trained deep-feature MLC training models. The presented approach detects and distinguishes brain tumors with 98% accuracy. It also has a good classification rate (97.2%) for an unknown dataset not used to train the model. Following extensive testing and analysis, the suggested technique might be helpful in assisting doctors in diagnosing brain tumors.

Jet impingement is considered to be an effective technique to enhance the heat transfer rate, and it finds many applications in the scientific and industrial horizons. The objective of this paper is to summarize heat transfer enhancement through different jet impingement methods and provide a platform for identifying the scope for future work. This study reviews various experimental and numerical studies of jet impingement methods for thermal-hydraulic improvement of heat transfer surfaces. The jet impingement methods considered in the present work include shapes of the target surface, the jet/nozzle–target surface distance, extended jet holes, nanofluids, and the use of phase change materials (PCMs). The present work also includes both single-jet and multiple-jet impingement studies for different industrial applications.

This paper proposes a multi-scale analysis technique based on the micromechanics of failure (MMF) to predict and investigate the damage progression and ultimate strength at failure of laminated composites. A lamina’s representative volume element (RVE) is developed to predict and calculate constituent stresses. Damages that occurred in the constituents are calculated using separate failure criteria for both fiber and matrix. Subsequently, the volume-based damage homogenization technique is utilized to prevent the localization of damage throughout the total matrix zone. The proposed multiscale analysis procedure is then used to investigate the notched and unnotched behavior of three multi-directional composite layups, [30, 60, 90, −60, 30]2s, [0, 45, 90, −45]2s, and [60, 0, −60]3s, subjected to static tension and compression loading. The specimen is fabricated from unidirectionally reinforced composite (IM7/977-3). The prediction of ultimate strength at failure and equivalent stiffness are then benchmarked against the experimental test data. The comparative analysis with various failure models is also carried out to validate the proposed model. MMF demonstrated the capability to correctly predict the ultimate strength at failure for a range of multidirectional composites laminates under tensile and compressive load. The numerically predicted findings revealed a good agreement with the experimental test data. Out of the three investigated composite layups, the simulated results for the quasi-isotropic [0, 45, 90, −45]2S layup agreed extremely well with the experimental results with all the percentage errors within 10% of the measured failure loads.

An ejector is a simple mechanical device that can be integrated with power generation or the refrigeration cycle to enhance their performance. Owing to the complex flow behavior in the ejector, the performance prediction of the ejector is done by numerical simulations. However, to evaluate the performance of an ejector integrated power cycle or refrigeration cycle, the need for simpler and more reliable thermodynamic models to estimate the performance of the ejector persists. This research, therefore, aims at developing a single mathematical correlation that can predict the ejector performance with reasonable accuracy. The proposed correlation relates the entrainment ratio and the pressure rise across the ejector to the area ratio and the mass flow rate of the primary flow. R141b is selected as the ejector refrigerant, and the results obtained through the proposed correlation are validated through numerical solutions. The comparison between the analytical and numerical with experimental results provided an error of less than 8.4% and 4.29%, respectively.

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