Number Of Channels Research Articles

Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.

To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications. We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data. DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm. The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration. This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.

Thermal management of lithium-ion batteries based on the coupling of liquid cooling and composite phase change materials

ABSTRACT Effective thermal management techniques for lithium-ion batteries are crucial to ensure their optimal efficiency. This paper proposes a thermal management system that combines liquid cooling with composite phase change materials (PCM) to enhance the cooling performance of these lithium-ion batteries. A numerical study was conducted to examine the impact of various factors on the battery module’s thermal management performance, including the number of flow channels, the distance between these flow channels and the upper and front surfaces, fluid flow rate, coolant inlet temperature, and material’s phase change temperature. The simulation results indicate that at a discharge rate of 6C and a flow channel count of 5, the maximum temperature and the maximum temperature difference of the battery module decrease by 6.44% and 34.35%, respectively, when PCM is coupled with liquid cooling, compared to the pure liquid cooling. However, as the flow rate increases, the rate of temperature reduction slows down, while the pressure drop increases. When the flow rate reaches 0.4 m/s, the maximum temperature only drops by 0.57°C while the pressure drop increases by nearly two times. Furthermore, an increase in the PCM’s melting temperature leads to a larger maximum temperature difference in the battery module. This effect is more pronounced with higher coolant inlet temperatures. Therefore, careful consideration of both flow rate and coolant inlet temperature is essential for designing an effective thermal management system for batteries.

High dynamic range mapping for the evaluation of parallel transmit arrays.

Demonstration of a high dynamic-range and high SNR method for acquiring absolute maps from a combination of gradient echo and actual-flip-angle measurements that is especially useful during the construction of parallel-transmit arrays. Low flip angle gradient echo images, acquired when transmitting with each channel individually, are used to compute relative maps. Instead of computing these in a conventional manner, the equivalence of the problem to the ESPIRiT parallel image reconstruction method is used to compute maps with a higher SNR. Absolute maps are generated by calibration against a single actual flip-angle acquisition when transmitting on all channels simultaneously. Depending on the number of receiver channels and the location of the receive elements with respect to the subject being investigated, moderate to high gains in the SNR of the acquired maps can be achieved. The proposed method is especially suited for the acquisition of maps during the construction of transceiver arrays. Compared to the original method, maps with higher SNR can be computed without the need for additional measurements, and maps can also be generated using previously acquired data. Furthermore, easy adoption and fast estimation of receiver channels is possible because of existing highly optimized open-source implementations of ESPIRiT, such as in the BART toolbox.

SOD-YOLO: A lightweight small object detection framework

Currently, lightweight small object detection algorithms for unmanned aerial vehicles (UAVs) often employ group convolutions, resulting in high Memory Access Cost (MAC) and rendering them unsuitable for edge devices that rely on parallel computing. To address this issue, we propose the SOD-YOLO model based on YOLOv7, which incorporates a DSDM-LFIM backbone network and includes a small object detection branch. The DSDM-LFIM backbone network, which combines Deep-Shallow Downsampling Modules (DSD Modules) and Lightweight Feature Integration Modules (LFI Modules), avoids excessive use of group convolutions and element-wise operations. The DSD Module focuses on extracting both deep and shallow features from feature maps using fewer parameters to obtain richer feature representations. The LFI Module, is a dual-branch feature integration module designed to consolidate feature information. Experimental results demonstrate that the SOD-YOLO model achieves an AP50 of 50.7% and a FPS of 72.5 on the VisDrone validation set. Compared to YOLOv7, our model reduces computational costs by 20.25% and decreases the number of parameters by 17.89%. After scaling the number of channels in the model, it achieves an AP50 of 33.4% with an inference time of 27.3ms on the Atlas 200I DK A2. These experimental results indicate that the SOD-YOLO model can effectively perform small object detection tasks in a large number of aerial images captured by UAVs.

Identifying rice lodging based on semantic segmentation architecture optimization with UAV remote sensing imaging

Lodging, a prevalent issue during rice growth, detrimentally impacts both yield and quality. It also complicates the harvesting process, reducing the efficiency of mechanized collection. Existing monitoring methods, predominantly based on manual observation and satellite remote sensing, fall short in addressing the requirements of contemporary, efficient, and real-time agriculture. This research integrates image analysis techniques with advanced optimization algorithms to develop a semantic segmentation model specifically designed for detecting rice lodging in remote sensing images. The model, named MI-UConvNeXt, employs a ConvNeXt-based feature extraction network utilizing UNet architecture (UConvNeXt) and incorporates an improved multi-objective salp swarm algorithm with Latin hypercube sampling and an elite opposition-based learning strategy (ISSA-LE) to dynamically adjusting the number of UConvNeXt channels. MI-UConvNeXt achieves a balance between accuracy and complexity. Compared to seven other semantic segmentation models from the literature, MI-UConvNeXt exhibits enhanced performance, with a Pixel Accuracy (PA) of 95.59%, mean Pixel Accuracy (mPA) of 95.62%, and mean Intersection over Union (mIoU) of 91.91% on the validation set. This demonstrates the model’s superior accuracy, lower computational resource demands, and enhanced efficiency. By integrating deep learning with intelligent optimization algorithms, this study offers a novel and effective approach for monitoring crop lodging in agricultural production, providing robust technical support for the accurate extraction of crop phenotypic information.

Channel structures of third-party platforms

The growing prominence of third-party (3P) platforms in the online retail sector has made the selection of an appropriate channel structure strategy a critical concern for manufacturers, 3P platforms, and retailers operating within this environment. We construct a Stackelberg game model with the manufacturer as the leader, evaluating four channel structures: (A) reselling and retailer agency selling, (B) agency selling and retailer agency selling, (C) reselling and agency selling, and (D) reselling, agency selling, and retailer agency selling. The supply chain can adopt two pricing strategies: uniform pricing (UP) and differential pricing (DP). Research indicates that expanding the number of channels may not always enhance profitability for suppliers and supply chain participants. Under strategy UP, the 3P platform opts for structure B, whereas the retailer favors structure C. Incorporating agency selling is advantageous for the manufacturer; however, structure A represents the least beneficial option for the supply chain. Under strategy DP, a substantial portion of the pareto optimal region is present. Intense competition and reduced agency fees lead both the supply chain as a whole and individual member to prefer structure A. The addition of a reselling or retailer agency channel boosts the manufacturer's profits, and benefits the 3P platform, while structure D emerges as the least favorable option for the retailer. Furthermore, under strategy DP, structure C is rendered obsolete, signifying the retailer's essential role in the supply chain to the advantage of all involved parties. Our study advances the scholarly understanding of sales models and platform economies by offering valuable insights into the decision-making processes of manufacturers, third-party (3P) platforms, and retailers regarding channel structure choices in a 3P platform environment.

MCI net: mamba- convolutional lightweight self-attention medical image segmentation network.

With the development of deep learning in the field of medical image segmentation, various network segmentation models have been developed. Currently, the most common network models in medical image segmentation can be roughly categorized into pure convolutional networks, Transformer-based networks, and networks combining convolution and Transformer architectures. However, when dealing with complex variations and irregular shapes in medical images, existing networks face issues such as incomplete information extraction, large model parameter sizes, high computational complexity, and long processing times. In contrast, models with lower parameter counts and complexity can efficiently, quickly, and accurately identify lesion areas, significantly reducing diagnosis time and providing valuable time for subsequent treatments. Therefore, this paper proposes a lightweight network named MCI-Net, with only 5.48M parameters, a computational complexity of 4.41, and a time complexity of just 0.263. By performing linear modeling on sequences, MCI-Net permanently marks effective features and filters out irrelevant information. It efficiently captures local-global information with a small number of channels, reduces the number of parameters, and utilizes attention calculations with exchange value mapping. This achieves model lightweighting and enables thorough interaction of local-global information within the computation, establishing an overall semantic relationship of local-global information. To verify the effectiveness of the MCI-Net network, we conducted comparative experiments with other advanced representative networks on five public datasets: X-ray, Lung, ISIC-2016, ISIC-2018, and capsule endoscopy and gastrointestinal segmentation. We also performed ablation experiments on the first four datasets. The experimental results outperformed the other compared networks, confirming the effectiveness of MCI-Net. This research provides a valuable reference for achieving lightweight, accurate, and high-performance medical image segmentation network models.

A Task-driven Adversarial Channel Selection Method for Binary Classification Based on Magnetocardiography.

As the number of sensors in magnetocardiography (MCG) arrays increases to capture detailed cardiac activity, some channels contribute minimally to task performance, resulting in data redundancy and resource consumption. Although existing methods can reduce the number of channels required to meet task demands, they often struggle to balance computational time and the accuracy of the selected channels and overlook the scalability of the selected channels. This limitation means that when environmental conditions change, or when sensors malfunction, redesigning channel configurations becomes necessary, which increases experimental uncertainties. This study introduces a task-driven adversarial channel selection method tailored for binary classification of MCG signals. The optimal channel combination is determined through a group-wise search using a heuristic algorithm, whose objective function is designed to maximize the difference between the classification accuracy and cosine similarity of the selected channel. In evaluations using an MCG dataset from Qilu Hospital of Shandong University, the proposed method successfully reduced the number of channels from 36 to 5 without compromising classification performance. Furthermore, it outperforms existing hybrid sequential forward search method by achieving comparable accuracy with fewer channels, while also demonstrating superior scalability compared to both hybrid sequential forward search and pearson-rank methods. This approach strikes a balance between computational consumption and accuracy, while improving the scalability of the selected channel combinations, enhancing the efficiency and practicality of the MCG system.

Research on X-ray security contraband identification technology based on lightweight YOLOv8

Aiming at the difficulty of identification and localization in the detection of contraband targets in X-ray images, as well as the demand for upgrading and deployment of security equipment during the peak flow stage, a lightweight X-ray security contraband detection algorithm is proposed and optimized for YOLOv8. Firstly, the number of channels in the original network is reduced according to a predetermined ratio, which effectively reduces model parameters and computational complexity and achieves better results than the current mainstream lightweight methods, such as model pruning and detection backbone optimization. Secondly, due to the presence of objects of various scales and sizes in X-ray security inspection images, we introduced the BiFPN feature extraction strategy in the neck. We designed a simplified version of BiFPN based on the YOLOv8 network structure to effectively integrate feature information from different levels and scales. Finally, we incorporated the idea of Focal Loss to address the issue of imbalance between positive and negative samples and proposed a new regression loss function, Focal-GIoU Loss, which combines GIoU Loss. This increases the loss weight for important but difficult-to-detect samples, such as small targets and overlapping targets, making the model more focused on these critical samples. Results show that our proposed YOLO-CBF improves mAP by 1.5%, 0.9%, and 0.5% on three datasets with different levels of occlusion in PIDray, while reducing the parameter count and computational cost from the original 3.0M and 8.1G to 2.1M and 6.3G.Moreover, it performs well in comparative experiments between different models and in generalization experiments across different datasets.

Effect of ultrasound power on moisture status change of apple by contact ultrasound reinforced far-infrared radiation drying

To explore the effect of ultrasound power on internal water change of apple dried by contact ultrasound enhanced far-infrared radiation drying (CUFRD), the drying characteristics, microstructure, moisture migration, and molecular vibration patterns of apple slices subjected to this process at different ultrasound powers were investigated using low-field nuclear magnetic resonance (LF-NMR), Near-infrared (NIR) and two-dimensional(2D) correlation analysis. With the increase of contact ultrasound (CU) power, the drying times of apple were shortened by 8.69% to 17.39%, respectively. Scanning electron microscopy (SEM) observation indicated that higher CU power resulted in an increase in the number of microporous channels and pore diameter within apple’s internal tissue. The findings of LF-NMR clarified that free water content kept decreasing, while the content of semi-bound water initially increased and then reduced. Magnetic resonance imaging (MRI) images illustrated that higher CU power corresponded to a faster decrease in the redness value of the H+ density map of apple during drying. 2D correlation analysis revealed that the changes in the O-H bonds of apple were prioritized over the C-H bonds during drying process. NIR spectroscopy and LF-NMR heterogeneous correlation spectrograms demonstrated that at wavelengths of 950, 1450, and 1680 nm, the order of changes in water-related functional groups was quadruple-frequency O-H > combination of the first overtone of O-H stretching and OH-bending band > double-frequency C-H. Moreover, with increasing CU power, the vibration of hydrogen-containing functional groups intensified, leading to increased water activity. Thus, CU application could significantly enhance the CUFRD process of apple slices.

Integrating 3D technology with the Sampaio classification for enhanced percutaneous nephrolithotomy in complex renal calculi treatment

BackgroundTo investigate the safety and efficacy of percutaneous nephrolithotomy (PCNL) in the treatment of complicated renal calculi by integrating three-dimensional (3D) computed tomography (CT) reconstruction with the Sampaio classification of the renal collecting system.MethodsSixty-four consecutive patients with complex kidney calculi who underwent PCNL between January 2019 and October 2023 were retrospectively analyzed and divided into experimental group (3D printing) and control group (CT imaging) according to their willingness to pay for 3D imaging. Both groups underwent preoperative CT urography. The Digital Imaging and Communications (DICOM) in Medicine data of the experimental group from CT imaging were used for 3D reconstruction and model printing. Then, the Sampaio classification system was used to design the puncture channel and develop a surgical strategy.ResultsThe 3D-printed models of the experimental group successfully displayed the Sampaio classification system. There was no significant difference in the baseline parameters between the groups. Compared with the control group, the experimental group exhibited significant improvements in the puncture time, number of puncture needles, number of puncture channels, target calyx consistency, number of first puncture channels, and stone clearance. There were no significant differences in the total operative time, decrease in the hemoglobin level, length of hospital stay, and postoperative complications between the groups.ConclusionsIntegration of 3D technology with the Sampaio classification of the renal collecting system can enhance the preoperative evaluation and planning of percutaneous renal access. This approach allows a more precise method of PCNL for treating complex renal calculi.

Enhanced Feature Refinement Network Based on Depthwise Separable Convolution for Lightweight Image Super-Resolution

Image super-resolution (SR) techniques aim to enhance the clarity and realism of images. Recently, a wide range of excellent SR algorithms with powerful characterization capabilities have emerged and are widely used. However, there are still challenges and room for improvement in designing a lighter and more edge-friendly SR networks for hardware devices. In this paper, we propose a lightweight enhanced feature refinement network (EFRN) based on depthwise separatable convolution for SR reconstruction. The core network components consist of multiple enhanced feature refinement blocks (EFRB), which fully fuse channel features to extract more accurate low-frequency information based on the attention of different channels. In addition, a lightweight residual block (LRB) and a lightweight dual attention block (LDAB) are designed to enhance network information extraction with minimal parameter cost. We improve the feature refinement by using 1 × 1 convolution instead of a channel selection operation to reduce the dimensionality of the features and extract the refined features more efficiently. Finally, to achieve better reconstruction performance, the depth and number of channels of the network are expanded while keeping the total number of parameters at a low level. Extensive experiments have been conducted to demonstrate the superiority of our EFRN over other mainstream SR algorithms in terms of reconstruction results and the number of parameters.

Graphene Phase Modulators Operating in the Transparency Regime.

Next-generation data networks need to support Tb/s rates. In-phase and quadrature (IQ) modulation combine phase and intensity information to increase the density of encoded data, reduce overall power consumption by minimizing the number of channels, and increase noise tolerance. To reduce errors when decoding the received signal, intersymbol interference must be minimized. This is achieved with pure phase modulation, where the phase of the optical signal is controlled without changing its intensity. Phase modulators are characterized by the voltage required to achieve a π phase shift, Vπ, the device length, L, and their product, VπL. To reduce power consumption, IQ modulators are needed with <1 V drive voltages and compact (sub-cm) dimensions, which translate in VπL < 1Vcm. Si and LiNbO3 (LN) IQ modulators do not currently meet these requirements because VπL > 1Vcm. Here, we report a double single-layer graphene (SLG) Mach-Zehnder modulator (MZM) with pure phase modulation in the transparency regime, where optical losses are minimized and remain constant with increasing voltage. Our device has VπL ∼ 0.3Vcm, matching state-of-the-art SLG-based MZMs and plasmonic LN MZMs, but with pure phase modulation and low insertion loss (∼5 dB), essential for IQ modulation. Our VπL is ∼5 times lower than the lowest thin-film LN MZMs and ∼3 times lower than the lowest Si MZMs. This enables devices with complementary metal-oxide semiconductor compatible VπL (<1Vcm) and smaller footprint than LN or Si MZMs, improving circuit density and reducing power consumption by 1 order of magnitude.

Optimum Design of Cooling Structure for In-Wheel Motor Drive System

The high-efficient cooling structure is one of the key basic problems in the design of the highly integrated in-wheel motor (IWM) drive system. A 15 kW IWM drive system with the spiral cooling structure is taken as the research object in this article. First, the thermal characteristics under different conditions are analyzed based on the modeling and the heat source determination of the IWM drive system. Second, the highest temperature of insulation and permanent magnet and temperature difference of insulation are taken as optimization objectives. The inlet flow rate, the inlet and outlet diameters, the channel numbers, the channel height, and the channel ribs thickness of cooling structure are taken as optimization parameters to optimize based on the designed experiments, the multivariate linear regression equation and PSO method. Finally, the optimization results are verified by comparing the temperature field before and after optimization. The results show that the optimized structure can achieve better effect. The highest temperature of insulation, the highest temperature of permanent magnet and the temperature difference of insulation of IWM decreased by 9.27%, 9.52%, and 20.93%, respectively. This research work can provide some ideas and methods for the optimization design of cooling structure of IWM drive system.

Doubly Optimal Parallel Wire Cutting without Ancilla Qubits

A restriction in the quality and quantity of available qubits presents a substantial obstacle to the application of near-term and early fault-tolerant quantum computers in practical tasks. To confront this challenge, some techniques for effectively augmenting the system size through classical processing have been proposed; one promising approach is quantum circuit cutting. The main idea of quantum circuit cutting is to decompose an original circuit into smaller subcircuits and combine outputs from these subcircuits to recover the original output. Although this approach enables us to simulate larger quantum circuits beyond physically available circuits, it needs classical overheads quantified by two metrics: the sampling overhead in the number of measurements to reconstruct the original output, and the number of channels in the decomposition. Thus, it is crucial to devise a decomposition method that minimizes both of these metrics, thereby reducing the overall execution time. This paper studies the problem of decomposing the n-qubit identity channel, i.e., n-parallel wire cutting, into a set of local operations and classical communication; then we give an optimal wire-cutting method composed of channels based on mutually unbiased bases that achieves minimal overheads in both the sampling overhead and the number of channels, without ancilla qubits. This is in stark contrast to the existing method that achieves the optimal sampling overhead yet with ancilla qubits. Moreover, we derive a tight lower bound on the number of channels in parallel wire cutting without ancilla systems and show that only our method achieves this lower bound among the existing methods. Notably, our method shows an exponential improvement in the number of channels, compared to the aforementioned ancilla-assisted method that achieves optimal sampling overhead. Our work significantly alleviates the additional overheads for the wire-cutting method and may provide essential components for the early success of quantum computing. Published by the American Physical Society 2024

MSIreg: an R package for unsupervised coregistration of mass spectrometry and H&E images.

Joint analysis of mass spectrometry images (MS images) and microscopy images of hematoxylin and eosin (H&E) stained tissues assists pathologists in characterizing the morphological structure of the tissues, and in performing diagnosis. Unfortunately, the analysis is undermined by substantial differences between these modalities in terms of aspect ratios, spatial resolution, number of channels in each image, as well as by large global or small local elastic spatial deformations of one image with respect to the other. Therefore, accurate coregistration of the images is a critical pre-requisite for their joint interpretation. We introduce MSIreg, an open-source R package for coregistration of MSI and H&E images. MSIreg is designed for high-dimensional MSI experiments where each spatial location is represented by thousands of mass features. Unlike most existing coregistration methods, MSIreg implements a landmark free workflow, and quantitative metrics for performance evaluation. We evaluate the performance of MSIreg on six case studies, including coregistration of contiguous tissues with large deformations, as well as simultaneous coregistration of 29 tissue microarray cores. The R package, installation instructions, and fully reproducible vignettes describing methods and Case Studies are are available open-source under the GPL-3.0 license at https://github.com/sslakkimsetty/msireg/.

Quantitative Analysis of Acquisition Speed of High-Precision FLIM Technologies via Simulation and Modeling

In practical applications such as cancer diagnosis and industrial detection, there is a critical demand for fast fluorescence lifetime imaging (Fast-FLIM). The Fast-FLIM systems suitable for complex environments are typically achieved by enhancing the hardware performance of time-correlated single-photon counting (TCSPC), with an acquisition speed of about a few frames per second (fps). However, due to the limitation of single-photon acquisition, the imaging speed is still far from the demand of practical application. The synchroscan streak camera (SC) maps signals from the temporal dimension to the spatial dimension, effectively overcoming the long acquisition time caused by single-photon acquisition. This paper constructs a method to calculate the acquisition time for the TCSPC-FLIM and SC-FLIM systems, and it quantitatively compares the speed. The research demonstrates that the main factors limiting the acquisition speed of the FLIM systems are the photon emission rate, the photon counting rate, the required SNR, the dwell time, and the number of parallel channels. In high-quality and large-scale lifetime imaging, the acquisition speed of the SC-FLIM is at least 104 times faster than that of the TCSPC-FLIM. Therefore, the synchroscan streak camera has more significant potential to promote Fast-FLIM.

Dynamics of converting skyrmion bags with different topological degrees into skyrmions in synthetic antiferromagnetic nanotracks

Skyrmion bags are spin textures with any integer topological degree, which can be driven by spin-polarized currents and generate multiple skyrmions when passing through racetracks with special geometries. We have proposed three nanotrack configurations with different narrow channels on synthetic antiferromagnetic racetracks and investigated the dynamic process of current-induced conversion of skyrmion bags into skyrmions. We have found that when skyrmion bags enter narrow channels, they can be converted into magnetic domains, while when the driving force from spin-transfer torque is strong enough, the magnetic domains can break free from the pinning at the ends of channels and form skyrmions. Both the number of channels and driving current density affect the number of generated skyrmions. As the number of channels rises, magnetic domains split at the junctions of channels, forming more magnetic domains and producing more skyrmions. Furthermore, the number of generated skyrmions is also related to the quantity, arrangement, and interaction forces of inner antiskyrmions. When the number of channels remains constant, the number of antiskyrmions only affects the transition of skyrmion bags to magnetic domains and does not affect the movement of magnetic domains or the transition of magnetic domains to skyrmions. The maximum of generated skyrmions in nanotracks with triple channels reaches 9. Dzyaloshinskii–Moriya interaction and anisotropy may play an important role in the structural stability of skyrmion bags, which can affect the splitting behavior of skyrmion bags. This work is beneficial for the design of artificial synapses and the application of neuromorphic computing based on skyrmion bags.

Temperature Analysis of Waveform Water Channel for High-Power Permanent Magnet Synchronous Motor

Abstract High-power permanent magnet synchronous motors (HPMSM) face extremely harsh cooling conditions due to their high power and complex structure. An efficient cooling system is pivotal to ensuring the safety and operational reliability of HPMSM. To improve the uneven axial temperature distribution in HPMSM and enhance the cooling effect, this paper presents an optimization of the water channels within the motor's cooling system. Initially, targeting the maximum temperature of the motor, the number and width of the traditional water channel (TWC) ribs are parameterized, and the optimal parameters are determined. Subsequently, based on the optimal parameters, three different waveform water channels are designed: circular channel (CC), triangular channel, and square channel. By employing the computational fluid dynamics numerical simulation, the influence of three kinds of water channels on the temperature of an HPMSM is analyzed under rated conditions. When the depth is 36 mm and the span is 40 mm for the CC, the average temperature rise of the motor winding is 9.88% lower than that of the TWC, reaching 48.77 °C. Results indicate that the cooling effect of the CC is better than others, which improves the cooling effect and operation performance of the motor.

Performance evaluation of different new channel box photovoltaic thermal systems

Current research has highlighted three major gaps in the literature: (a) a rarity of studies on photovoltaic thermal systems with channel boxes, (b) a lack of data on pressure drop and surface temperature distribution in these systems, and (c) absence of analysis regarding the impact of materials (metals and polymers) used in photovoltaic thermal systems on performance, cost, payback time and weight. The novelty of this paper lies in addressing these gaps by studying a new channel-box photovoltaic thermal system, varying the number of channels and the height of the collectors that distribute water to these channels, in order to identify the most efficient design in terms of electrical, and thermal efficiency as well as pressure drop. In addition, the impact of different heat exchanger materials (metals and polymers) on the performance, cost, payback time, and weight of photovoltaic thermal systems was investigated. 3D numerical simulations were carried out using COMSOL Multiphysics software, based on the finite element method, and validated by outdoor experimental research. The results show that the photovoltaic thermal system 8 design performs best, with a total efficiency of 89.42%. In addition, the aluminum photovoltaic thermal system stands out as the most cost-effective option, with a payback time of 0.823 y (300 d) and annual savings of $459.262, while being 38% lighter than the copper system. Based on this study, it is recommended that the photovoltaic thermal system 8 be implemented because of its exceptional performance and ease of implementation.