Recalculation of power costs for the CANDU reactor

"NYO-9716."AT(30-1)-207

Lossless Digitally-Assisted Windowed Current Sensing for Solar Photovoltaic Applications

Sensing current across a sense resistor adds to the total power loss and decreases efficiency. This work demonstrates the use of a current sense amplifier that measures current across a length of wire so as to minimize power loss. Current Sense Amplifiers for solar photovoltaic applications require measuring a wide range of current (e.g. 1-10A), yet a high resolution is needed for certain applications such as maximum power point tracking (MPPT) and differential power processing (DPP). The windowing technique has been adopted in this work, so as to measure the current with high resolution and yet to be able to cover the entire range of current as well. The use of PSOC 4 by Cypress offers a cost effective one chip solution to all current sensing requirements for solar photovoltaic applications. This is achieved by having a nearly $4 cost reduction from previous methods, and yet attaining under 1% error in current measurements.Ope

Hybrid-inspired coolant additive for radiator application

Due to the increasing demand in the industrial application, nanofluids has attracted a considerable attention of researchers in the last few decades. Nanocellulose with water (W) and Ethylene Glycol (EG) addition to coolant for car radiator application exhibits beneficial properties to improve the efficiency of the radiator. Improved efficiency leads to more compact design of the radiator and increase the durability of the engine. The focus of the present work is to investigate the performance of mono or hybrid metal oxide such as Al2O3 and TiO2 with or without plant base extracted nanocellulose (CNC) with varying concentration as a better heat transfer nanofluid as compared to distilled water as radiator coolant. Therefore, the objective of the present work is to improve and create a new radiator coolant based on aluminium oxide and CNC with readily available coolants (EG) and to investigate the erosion of CNC coolant on automotive radiator. The scope of the present work is CNC dispersed in base fluid of EG and W with 60:40 ratio. The volume concentrations such as 0.1, 0.5, and 0.9% of tested samples have been used for further investigation. Comparative heat transfer performance of prepared nanofluids and convection thermal transport fluid has been investigated in the automotive radiator test rig under two different circumstances i.e., with and without the influence of draft fan. Obtained result reveals that heat transfer coefficient, convective heat transfer, Reynolds number, Nusselt number has proportional relation with volumetric flow rate. The highest absorption peak have been noticed in 0.9% volume concentration of TiO2, Al2O3, CNC, Al2O3/TiO2, and Al2O3/CNC nanofluids which indicate the better stability of nanofluids suspension. Better thermal conductivity improvement have been observed for Al2O3 nanofluids in all mono nanofluids followed by CNC and TiO2 nanofluids respectively. Thermal conductivity of Al2O3/CNC hybrid nanofluids with 0.9% volume concentration has been found to be superior to Al2O3/TiO2 hybrid nanofluids. Al2O3/CNC hybrid nanofluid dominates over other mono and hybrid nanofluids in terms of viscosity at all volume concentrations. CNC nanofluids (all volume concentrations) exhibited the highest specific heat capacity than other mono nanofluids. Additionally, in both the hybrid nanofluids, Al2O3/CNC showed the lowest specific heat capacity. The optimized volume concentration from statistical analytical tool was found to be 0.5%. Nanofluid volume concentration with 0.5% (CNC/Al2O3 and CNC) was selected as thermal transport fluid to be compared with convectional EG-W mixture. The experiment result shows that experimental heat transfer coefficient, convective heat transfer, Reynolds number, Nusselt number has proportional relation with volumetric flow rate

A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions

Recent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients. DL needs more data and diversity to build a more accurate system. To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges. To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed. FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy. This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcare; (2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiency; and (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications. An extensive review of the literature on FL from a data-centric perspective was conducted. We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and January 2023. A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers. In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification. We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies

Vibration signal for bearing fault detection using random forest

Based on the chosen properties of an induction motor, a random forest (RF) classifier, a machine learning technique, is examined in this study for bearing failure detection. A time-varying actual dataset with four distinct bearing states was used to evaluate the suggested methodology. The primary objective of this research is to evaluate the bearing defect detection accuracy of the RF classifier. First, run four loops that cycle over each feature of the data frame corresponding to the daytime index to determine the bearing states. There were 465 repetitions of the inner race fault and the roller element fault in test 1, 218 repetitions of the outer race fault in test 2, and 6324 repetitions of the outer race in test 3. Secondly, the task is to find the data for the typical bearing data procedure to differentiate between normal and erroneous data. Out of 3 tests, (22-23) % normal data was obtained since every bearing beginning to degrade usually exhibits some form of a spike in many locations, or the bearing is not operating at its optimum speed. Thirdly, to display and comprehend the data in a 2D and 3D environment, Principal Component Analysis (PCA) is performed. Fourth, the RF algorithm classifier recognized the data frame's actual predictions, which were 99% correct for normal bearings, 97% accurate for outer races, 94% accurate for inner races, and 97% accurate for roller element faults. It is thus concluded that the proposed algorithm is capable to identify the bearing faults

Construction novel highly active photocatalytic H2 evolution over noble-metal-free trifunctional Cu3P/CdS nanosphere decorated g-C3N4 nanosheet

Hydrogen energy possesses immense potential in developing a green renewable energy system. However, a significant problem still exists in improving the photocatalytic H2 production activity of metal-free graphitic carbon nitride (g-C3N4) based photocatalysts. Here is a novel Cu3P/CdS/g-C3N4 ternary nanocomposite for increasing photocatalytic H2 evolution activity. In this study, systematic characterizations have been carried out using techniques like X-ray diffraction (XRD), scanning electron microscopy (SEM), high resolution transmission electron microscopy (HR-TEM), Raman spectra, UV–Vis diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy (XPS), surface area analysis (BET), electrochemical impedance (EIS), and transient photocurrent response measurements. Surprisingly, the improved 3CP/Cd-6.25CN photocatalyst displays a high H2 evolution rate of 125721 μmol h−1 g−1. The value obtained exceeds pristine g-C3N4 and Cu3P/CdS by 339.8 and 7.6 times, respectively. This could be the maximum rate of hydrogen generation for a g–C3N4–based ternary nanocomposite ever seen when exposed to whole solar spectrum and visible light (λ > 420 nm). This research provides fresh perspectives on the rational manufacture of metal-free g-C3N4 based photocatalysts that will increase the conversion of solar energy. By reusing the used 3CP/Cd/g-C3N4 photocatalyst in five consecutive runs, the stability of the catalyst was investigated, and their individual activity in the H2 production activity was assessed. To comprehend the reaction mechanisms and emphasise the value of synergy between the three components, several comparison systems are built

An Approach for the optimization of thermal conductivity and viscosity of hybrid (Graphene Nanoplatelets, GNPs : Cellulose Nanocrystal, CNC) nanofluids using Response Surface Methodology (RSM)

Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 °C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions

Enhancing Heat Transfer in Compact Automotive Engines using Hybrid Nano Coolants

This research aimed to compare the performance of a reduced-scale automotive radiator using single nano coolant (CNC and CuO) and its hybrid nano coolant (CNC and CuO nanoparticles) to enhance heat transmission. Three ratios of 70:30, 80:20, and 90:10 of hybrid nano coolants was tested. UV Vis stability characterization of the nanofluids showed that all samples were highly stable for up to 30 days. A modest concentration (0.01 vol per cent) of the hybrid nano coolant was shown to efficiently increase the heat transfer rate of a reduced-size automobile radiator, demonstrating that the heat transfer behaviour of the nano coolant was reliant on the particle volume percentage. The results show the potential use of hybrid nano coolants in increasing heat transfer efficiency, decreasing cooling system size by up to 71 percent, and thus lowering fuel consumption; these benefits have significant implications for developing more efficient cooling systems in various industrial applications. The experimental findings showed that 80:20 exhibited a significant amount of improvement in thermal properties. The consistency of the low volume concentration of hybrid nano coolants throughout the experiment is further evidence of their promise as a practical substitute for conventional cooling media in the compact size of an automotive engine cooling system

Exploring the Potentials of Copper Oxide and CNC Nanocoolants

The characteristics, stability, kinematic viscosity, viscosity index, thermal conductivity, and specific heat changes of Copper Oxide (CuO) and Cellulose Nanocrystal (CNC) hybrid nanocoolants at low concentrations are investigated in this work. The hybrid nanocoolants were created using different ratios of CNC and CuO nanoparticles and compared to single nanoparticle coolants. The existence of Cu-O and other similar formations was verified using Fourier Transform Infrared Spectroscopy (FTIR). Visual examination and UV Spectrophotometry stability study revealed that the nanocoolants were stable for up to 8 weeks, with little precipitation seen for single nanoparticle coolants after 12 weeks. When tested against temperature, kinematic viscosity decreased with increasing temperature, with very minor differences amongst coolants. The results of the Viscosity Index (VI) indicated that the hybrid nanocoolant performed similarly to the basic fluid, Ethylene Glycol (EG), even at high temperatures. Thermal conductivity rose as temperature increased, with a single CuO nanocoolant and a CNC:CuO (80:20) hybrid having the maximum conductivity. Specific heat capacity measurements revealed a declining trend as temperature rose. Overall, the CNC:CuO (80:20) hybrid nanocoolant and the CuO single nanocoolant displayed improved characteristics and stability, suggesting their potential for increased heat transfer applications

Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model

Medium voltage (MV) switchgear is a vital part of modern power systems, responsible for regulating the flow of electrical power and ensuring the safety of equipment and personnel. However, switchgear can experience various types of faults that can compromise its reliability and safety. Common faults in switchgear include arcing, tracking, corona, normal cases, and mechanical faults. Accurate detection of these faults is essential for maintaining the safety of MV switchgear. In this paper, we propose a novel approach for fault detection using a hybrid model (1D-CNN-LSTM) in both the time domain (TD) and frequency domain (FD). The proposed approach involves gathering a dataset of switchgear operation data and pre-processing it to prepare it for training. The hybrid model is then trained on this dataset, and its performance is evaluated in the testing phase. The results of the testing phase demonstrate the effectiveness of the hybrid model in detecting faults. The model achieved 100% accuracy in both the time and frequency domains for classifying faults in Switchgear, including arcing, tracking, and mechanical faults. Additionally, the model achieved 98.4% accuracy in detecting corona faults in the TD. The hybrid model proposed in this study provides an effective and efficient approach for fault detection in MV switchgear. By learning spatial and temporal features simultaneously, this model can accurately classify faults in both the TD and FD. This approach has significant potential to improve the safety of MV switchgear as well as other industrial applications