Search Results (59)

This study presents a detailed experimental comparison of the rotordynamic and thermal performance of automotive turbochargers supported by two distinct hydrodynamic bearing configurations: fully floating ring bearings (FFRBs) and semi-floating ring bearings (SFRBs). While both designs are widely used in commercial turbochargers, they exhibit significantly different dynamic behaviors due to differences in ring motion and fluid film interaction. A cold air-driven test rig was employed to assess vibration and temperature characteristics across a range of controlled lubricant conditions. The test matrix included oil supply pressures from 2 bar (g) to 4 bar (g) and temperatures between 30 °C and 70 °C. Rotor speeds reached up to 200 krpm (thousands of revolutions per minute), and data were collected using a high-speed data acquisition system, triaxial accelerometers, and infrared (IR) thermal imaging. Rotor vibration was characterized through waterfall and Bode plots, while jump speeds and thermal profiles were analyzed to evaluate the onset and severity of instability. The results demonstrate that the FFRB configuration is highly sensitive to oil supply parameters, exhibiting strong subsynchronous instabilities and hysteresis during acceleration–deceleration cycles. In contrast, the SFRB configuration consistently provided superior vibrational stability and reduced sensitivity to lubricant conditions. Changes in lubricant supply conditions induced a jump speed variation in floating ring bearing (FRB) turbochargers that was approximately 3.47 times larger than that experienced by semi-floating ring bearing (SFRB) turbochargers. Furthermore, IR images and oil outlet temperature data confirm that the FFRB system experiences greater heat generation and thermal gradients, consistent with higher energy dissipation through viscous shear. This study provides a comprehensive assessment of both bearing types under realistic high-speed conditions and highlights the advantages of the SFRB configuration in improving turbocharger reliability, thermal performance, and noise suppression. The findings support the application of SFRBs in high-performance automotive systems where mechanical stability and reduced frictional losses are critical. Full article

With the increasing integration of renewable energy, the secure operation of distribution networks faces significant challenges, such as voltage limit violations and increased power losses. To address the issue of reactive power and voltage security under renewable generation uncertainty, this paper proposes a graph-based security reinforcement learning method. First, a graph-enhanced neural network is designed, to extract both topological and node-level features from the distribution network. Then, a primal-dual approach is introduced to incorporate voltage security constraints into the agent’s critic network, by constructing a cost critic to guide safe policy learning. Finally, a dual-critic framework is adopted to train the actor network and derive an optimal policy. Experiments conducted on real load profiles demonstrated that the proposed method reduced the voltage violation rate to 0%, compared to 4.92% with the Deep Deterministic Policy Gradient (DDPG) algorithm and 5.14% with the Twin Delayed DDPG (TD3) algorithm. Moreover, the average node voltage deviation was effectively controlled within 0.0073 per unit. Full article

Effective vertical seismic profiling (VSP) of upgoing and downgoing wave separation is essential for high-quality imaging. However, VSP wavefield separation is particularly challenging under complex geological conditions. Existing solutions encompass one derived from the mathematical characteristics of upgoing and downgoing waves, employing signal decomposition methodologies, and another that utilizes data-driven machine learning techniques, achieving wavefield separation by training sample data to identify the distinct characteristics of upgoing and downgoing waves. This study introduces a VSP wave-separation method using signal knowledge representation, primarily by constructing knowledge representations of upgoing and downgoing waves. Physics-informed recurrent neural network FWI and Poynting vector physical knowledge representation yielded accurate velocity models. Axial gradient information was utilized to construct morphological knowledge representations of upgoing and downgoing waves. Directional differentiation knowledge representations were established based on kinematic characteristic disparities between upgoing and downgoing waves in the time-depth domain. These wave knowledge representations (KRs) built a dual convolutional autoencoder. Its distinct branches extracted up/down wave information, while the KRs, transformed into loss functions, enabled knowledge-driven unsupervised VSP wave separation. The proposed methodology was validated using a homogeneous layer and Marmousi models, demonstrating the effective separation of upgoing and downgoing waves from the VSP seismic records. Full article

Structural characterization of natural products in complex herbal extracts remains a major challenge in phytochemical analysis. In this study, we present a novel post-acquisition data-processing strategy—key ion diagnostics–neutral loss filtering (KID-NLF)—combined with ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) for systematic profiling of the medicinal plant Terminalia chebula. The strategy consists of four main steps. First, untargeted data are acquired in negative electrospray ionization (ESI⁻) mode. Second, a genus-specific diagnostic ion database is constructed by leveraging characteristic fragment ions (e.g., gallic acid, chebuloyl, and HHDP groups) and conserved substructures. Third, MS/MS data are high-resolution filtered using key ion diagnostics and neutral loss patterns (302 Da for HHDP; 320 Da for chebuloyl). Finally, structures are elucidated via detailed spectral analysis. The methanol extract of T. chebula was separated on a C18 column using a gradient of acetonitrile and 0.1% aqueous formic acid within 33 min. This separation enabled detection of 164 compounds, of which 47 were reported for the first time. Based on fragmentation pathways and diagnostic ions (e.g., m/z 169 for gallic acid, m/z 301 for ellagic acid, and neutral losses of 152, 302, and 320 Da), the compounds were classified into three major groups: gallic acid derivatives, ellagitannins (containing HHDP, chebuloyl, or neochebuloyl moieties), and triterpenoid glycosides. KID-NLF overcomes key limitations of conventional workflows—namely, isomer discrimination and detection of low-abundance compounds—by exploiting genus-specific structural signatures. This strategy demonstrates high efficiency in resolving complex polyphenolic and triterpenoid profiles and enables rapid annotation of both known and novel metabolites. This study highlights KID-NLF as a robust framework for phytochemical analysis in species with high chemical complexity. It also paves the way for applications in quality control, drug discovery, and mechanistic studies of medicinal plants. Full article

Shockwaves induce considerable flow separation loss; it is essential to reduce this using the flow control method. In this manuscript, a method for suppressing flow separation in turbomachinery through a constant adverse-pressure gradient was investigated. The first-passage shock was split into a compression wave system of the vane suction surface. The aim of this was to reduce loss from shockwave/boundary layer interactions (SWBLIs). This method promotes the performance parameters of the supersonic compressor cascade. The investigation targets were a baseline cascade and the improved system. Both cascades were numerically studied with the aid of the Reynolds-averaged Navier–Stokes (RANS) method. The simulation results of the baseline cascade were also validated through experimentation, and a further physical flow analysis of the two cascades was conducted. The results show that the first-passage shockwave was a foot above the initial suction surface, with a weaker incident shock along with a clustering of the compression wave corresponding to the modified cascade. It was also concluded that the first-passage shockwave foot of the baseline cascade was replaced with a weak incident shock, and a series of compression waves emanated from the adopted negative-curvature profile. The shock-induced boundary layer separation bubble disappeared, and much smaller boundary layer shape factors over the SWBLI region were obtained for the improved cascade compared to the baseline cascade. This improvement led to a high level of stability in the boundary layer state. Sensitivity analyses were performed through different simulations on both cascades, unveiling that the loss in total pressure was lower in the case of the updated cascade as compared to the baseline. Full article

The purpose of infrared and visible image fusion is to generate a composite image that can contain both the thermal radiation profile information of the infrared image and the texture details of the visible image. This kind of composite image can be used to detect targets under various lighting conditions and offer high scene spatial resolution. However, the existing image fusion algorithms rarely consider light factor in the modeling process. The study presents a novel image fusion approach (AIF) that can adaptively fuse infrared and visible images under various lighting conditions. Specifically, the infrared image and the visible image are extracted by the AdC feature extractor, respectively, and both of them are adaptively fused under the guidance of the illumination perception subnetwork. The image fusion model is trained in an unsupervised manner with a customized loss function. The AdC feature extractor adopts an ascending–descending feature extraction mechanism to organize convolutional layers and combines these convolutional layers with cross-modal interactive differential modules to achieve the effective extraction of hierarchical complementary and differential information. The illumination perception subnetwork obtains the scene lighting condition based on the visible image, which determines the contribution weights of the visible image and the infrared image in the composite image. The customized loss function consists of illumination loss, gradient loss, and intensity loss. It is more targeted and can effectively improve the fusion effect of visible images and infrared images under different lighting conditions. Ablation experiments demonstrate the effectiveness of the loss function. We compare our method with nine other methods on public datasets, including four traditional methods and five deep-learning-based methods. Qualitative and quantitative experiments show that our method performs better in terms of indicators such as SD, and the fused image has more prominent contour information and richer detail information. Full article

Three-dimensional ocean observation is the foundation for accurately predicting ocean information. Although ocean observation sensor arrays can obtain internal data, their deployment is difficult, costly, and prone to component failures and environmental noise, resulting in discontinuous data. To address the severe missing data problem in three-dimensional ocean flow fields, this paper proposes an unsupervised model: Three-dimensional Spatio-Temporal Slim Weighted Generative Adversarial Imputation Network (3D-STA-SWGAIN). This method integrates spatio-temporal attention mechanisms and Wasserstein constraints. The generator captures the three-dimensional spatial distribution and vertical profile dynamic patterns through the spatio-temporal attention module, while the discriminator introduces gradient penalty constraints to prevent gradient vanishing. The generator strives to generate data that conforms to the real ocean flow field, and the discriminator attempts to identify pseudo-ocean current data samples. Through the adversarial training of the generator and the discriminator, high-quality completed data are generated. Additionally, a spatio-temporal continuity loss function is designed to ensure the physical rationality of the data. Experiments show that on the three-dimensional flow field dataset of the South China Sea, compared with methods such as GAIN, under a 50% random missing rate, this method reduces the error by 37.2%. It effectively solves the problem that traditional interpolation methods have difficulty handling non-uniform missing and spatio-temporal correlations and maintains the spatio-temporal continuity of the current field’s three-dimensional structure. Full article

The ongoing research in Environmental Scanning Electron Microscopy (ESEM) is contributed to in this paper. Specifically, this study investigates supersonic flow in a nozzle aperture under low-pressure conditions at the continuum mechanics boundary. This phenomenon is prevalent in the differentially pumped chamber of an ESEM, which separates two regions with a significant pressure gradient using an aperture with a pressure ratio of approximately 10:1 in the range of 10,000 to 100 Pa. The influence of nozzle wall roughness on the boundary layer characteristics and its subsequent impact on the oblique shock wave behavior, and consequently, on the static pressure distribution along the flow axis, is solved in this paper. It demonstrates the significant effect of varying inertial-to-viscous force ratios at low pressures on the resulting impact of roughness on the oblique shock wave characteristics. The resulting oblique shock wave distribution significantly affects the static pressure profile along the axis, which can substantially influence the scattering and loss of the primary electron beam traversing the differential pumping stage. This, in turn, affects the sharpness of the resulting image. The boundary layer within the nozzle plays a crucial role in determining the overall flow characteristics and indirectly affects beam scattering. This study examines the influence of surface roughness and quality of the manufactured nozzle on the resulting flow behavior. The initial results obtained from experimental measurements using pressure sensors, when compared to CFD simulation results, demonstrate the necessity of accurately setting roughness values in CFD calculations to ensure accurate results. The CFD simulation has been validated against experimental data, enabling further simulations. The research combines physical theory, CFD simulations, advanced experimental sensing techniques, and precision manufacturing technologies for the critical components of the experimental setup. Full article

This study examines the distribution of pore pressure (PP) and fracture gradient (FG) within intervals of lost circulation encountered during drilling operations in the Ordovician reservoir (IV-3 unit) of the Tin Fouye Tabankort (TFT) field, located in the Illizi Basin, Algeria. The research further aims to determine an optimized drilling mud weight to mitigate mud losses and enhance overall operational efficiency. PP and FG models for the Ordovician reservoir were developed based on data collected from five vertical development wells. The analysis incorporated multiple datasets, including well logs, mud logging reports, downhole measurements, and Leak-Off Tests (LOTs). The findings revealed an average overburden gradient of 1.03 psi/ft for the TFT field. The generated pore pressure and fracture gradient (PPFG) models indicated a sub-normal pressure regime in the Ordovician sandstone IV-3 reservoir, with PP values ranging from 5.61 to 6.24 ppg and FG values between 7.40 and 9.14 ppg. The analysis identified reservoir depletion due to prolonged hydrocarbon production as the primary factor contributing to the reduction in fracture gradient, which significantly narrowed the mud weight window and increased the likelihood of lost circulation. Further examination of pump on/off cycles over time, coupled with shallow and deep resistivity variations with depth, confirmed that the observed mud losses were predominantly associated with induced fractures resulting from the application of excessive mud weight during drilling operations. Based on the established PP and FG profiles, a narrow mud weight window of 6.24–7.40 ppg was recommended to ensure the safe and efficient drilling of future wells in the TFT field and support the sustainability of drilling operations in the context of a depleted reservoir. Full article

High-standing islands, such as Taiwan, offer unique opportunities to study soil organic carbon (SOC) dynamics due to their steep terrains, rapid erosion, and strong climatic gradients. In this study, we investigated 54 forest soil profiles across northern, central, and southern Taiwan to assess SOC inventories and turnover using stable carbon isotope (δ¹³C) analyses. We applied Rayleigh fractionation modeling to vertical δ¹³C enrichment patterns and derived the parameter β, which serves as a proxy for SOC turnover rates. Our findings reveal that SOC stocks increase notably with elevation, aligning with lower temperatures and reduced decomposition rates at higher altitudes. Conversely, mean annual precipitation (MAP) did not show a straightforward relationship with SOC stocks or β, highlighting the moderating effects of soil drainage, topography, and local hydrological conditions. Intriguingly, higher soil nitrogen levels were associated with a negative correlation to ln(β), underscoring the complex interplay between nutrient availability and SOC decomposition. Overall, temperature emerges as the dominant factor governing SOC turnover, indicating that ongoing and future warming could accelerate SOC losses, especially in cooler, high-elevation zones currently acting as stable carbon reservoirs. These insights underscore the need for models and management practices that account for intricate temperature, moisture, and nutrient controls on SOC stability, as well as the value of stable isotopic tools for evaluating soil carbon dynamics in mountainous environments. Full article

The present study is concerned with the power transfer efficiency enhancement using a combination of the multi-hop node (MHN) and the Intelligent Reflecting Surface (IRS)-based passive beamforming technologies. The primary objective is to ensure a high RF-DC converter power conversion efficiency (PCE) used at the receiving end, which is difficult to achieve due to path loss and multi-path propagation. An electronically tunable reconfigurable reflectarray (RRA) designed to operate at the sub-GHz ISM band (865.5 MHz) is utilized to implement the IRS concept. Both the MHN and RRA were developed and studied in our earlier research. The RRA redirects the reflected power-carrying wave amplified by the MHN toward the intended receiver. It comprises two layers: the RF layer containing tunable phase shifters and the ground plane. Each phase shifter comprises two identical eight-shaped metal patches coupled by a pair of varactor diodes used to achieve the reflection phase tuning. The phase gradient method is used to synthesize the RRA phase profiles, ensuring different desired reflection angles. The RRA prototype, composed of 36 phase shifters, is employed in conjunction with the MHN equipped with two antennas and an amplifier. The RRA parameter optimization is accomplished by randomly varying the varactor diode voltages and measuring the corresponding received power levels until the power reflected in the desired direction is maximized. Two measurement scenarios are examined: power transmission without and with the MHN. In the first scenario, the received power is calculated and measured at several distinct beam steering angles for different distances between the Tx antenna and RRA. The same procedure is applied to different distances between the RRA and MHN in the second scenario. The effect of slight deviations in the operating frequency from the designed one (865.5 MHz) on the RRA performance is also examined. Additionally, the received power levels for both scenarios are estimated via full-wave analysis performed using the full-wave simulation software Ansys HFSS 2023 R1. A Huygens’ surface equivalence principle-based model decomposition method was developed and employed to reduce the CPU time. The calculated results are consistent with the measured ones. However, some discrepancies attributed to the adverse effect of RRA diode biasing lines, manufacturing tolerances, and imperfection of the indoor environment model are observed. Full article

Cross-flow turbines are widely used in microhydropower systems because of their cost-effectiveness, environmental sustainability, adaptability, and robust design. However, their relatively lower efficiency than other turbine types limit their application in large-scale projects. Previous studies have identified poor flow profiles as a significant factor contributing to inefficiency, with the number of blades playing a critical role in the flow behavior, efficiency, and structural stability. This study employed numerical simulations to analyze how varying the number of blades affects the internal flow characteristics and performance of the turbine at, and off, its best operating points. Configurations with 16, 20, 24, 28, 32, 36, 40, and 44 blades were investigated under constant low-head conditions, fully open valve settings, and varying runner speeds. Simulations were performed using ANSYS CFX, incorporating steady-state conditions, a two-phase flow model with a movable free surface, and a shear stress turbulence model. The results indicate that the 28-blade configuration achieved a maximum hydraulic efficiency of 83%, outperforming the preset 24-blade setup by 6%. Flow profiles were examined using pressure and velocity gradients to identify regions of adverse pressure. Due to the impulse nature of the turbine, the flow profile is more sensitive to changes in the flow speed than to pressure. The flow trajectory showed stability in the first stage but exhibited discrepancies in the second stage, which were attributed to turbulence, recirculation, and shaft flow impingement. The observed performance improvements were linked to reduced hydraulic losses due to flow separation and friction, emphasizing the significance of the number of blades and the regions of optimal efficiency under low-head conditions. Full article

Sustainable energy consumption in cement production involves practises and strategies aimed at reducing energy use and minimising environmental impact. The efficiency of a cement kiln is dependent on the kiln design, fuel type, and operating temperature. In this study, a dynamic simulation analysis is used to investigate heat losses and distribution within kilns with the aim of improving energy efficiency in cement production. This study used Computational Fluid Dynamics (CFD) with Conjugate Heat Transfer, Turbulent Flow, and the Realisable k−ϵ turbulence model to simulate heat transfer within the refractory and wall systems of the kiln, evaluate the effectiveness of these systems in managing heat losses, and establish the relationship between the heat transfer coefficient (HTC) and the velocities of solid and gas phases. The simulation results indicate that a temperature gradient from the kiln’s interior to its exterior is highly dependent on the effectiveness of refractory lining in absorbing and reducing heat transfer to the outer walls. The results also confirm that different thermal profiles exist for clinker and fuel gases, with clinker temperatures consistently peaking at approximately 1450 °C, an essential condition for optimal cement-phase formation. The results also indicate that phase velocities significantly influence heat absorption and transfer. Lower velocities, such as 0.2 m/s, lead to increased heat absorption, but also elevate heat losses due to prolonged exposure. The relationship between the heat transfer coefficient (HTC) and the velocities of solid and gas phases also indicates that higher velocities improve HTC and enhance overall heat transfer efficiency, reducing energy demand. Full article

Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit risk. Data are vital at the core of the credit decision-making processes. Decision-making depends heavily on accurate, complete data, and failure to harness high-quality data would impact credit lenders when assessing the loan applicants’ risk profiles. In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. This task uses seven performance measures, including the F1 Score (recall, accuracy, and precision), ROC-AUC, and HL and MCC metrics. Then, the harnessing of generative adversarial networks (GANs) simulation to enhance the robustness of the single machine learning classifiers for predicting credit risk is proposed. The results show that when GANs imputation is incorporated, the decision tree is the best-performing classifier with an accuracy rate of 93.01%, followed by random forest (92.92%), gradient boosting (92.33%), support vector machine (90.83%), logistic regression (90.76%), and naïve Bayes (89.29%), respectively. The classifier is the worst-performing method with a k-NN (88.68%) accuracy rate. Subsequently, when GANs are optimised, the accuracy rate of the naïve Bayes classifier improves significantly to (90%) accuracy rate. Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. In summary, most individual classifiers are more robust to missing data when GANs are used as an imputation technique. The differences in performance of all seven machine learning algorithms are significant at the 95% level. Full article

Stabilized Li_6.25La₃Al_0.25 Zr₂O₁₂ (cubic LLZO or c-LLZO) is a Li⁺-conducting ceramic with ionic conductivities approaching 1 mS-cm. Processing c-LLZO so that it is suitable for use as a solid state electrolyte in all solid state batteries, however, is challenging due to the formation of secondary phases at elevated temperatures. The work described in this manuscript examines the formation of one such secondary phase La₂Zr₂O₇ (LZO) formed during sintering c-LLZO at 1000 °C. Specifically, spatially resolved Raman spectroscopy and X-ray Diffraction (XRD) measurements have identified gradients in Li distributions in the Li ion (Li⁺)-conducting ceramic Li_6.25La₃Al_0.25 Zr₂O₁₂ (cubic LLZO or c-LLZO) created by thermal processing. Sintering c-LLZO under conditions relevant to solid state Li⁺ electrolyte fabrication conditions lead to Li⁺ loss and the formation of new phases. Specifically, sintering for 1 h at 1000 °C leads to Li⁺ depletion and the formation of the pyrochlore lanthanum zirconate (La₂Zr₂O₇ or LZO), a material known to be both electronically and ionically insulating. Circular c-LLZO samples are covered on the top and bottom surfaces, exposing only the 1.6 mm-thick sample perimeter to the furnace’s ambient air. Sintered samples show a radially symmetric LZO gradient, with more LZO at the center of the pellet and considerably less LZO at the edges. This profile implies that Li⁺ diffusion through the material is faster than Li⁺ loss through volatilization, and that Li⁺ migration from the center of the sample to the edges is not completely reversible. These conditions lead to a net depletion of Li⁺ at the sample center. Findings presented in this work suggest new strategies for LLZO processing that will minimize Li⁺ loss during sintering, leading to a more homogeneous material with more reproducible electrochemical behavior. Full article