Search Results (258)

The present research enhances the efficiency of an airfoil section from the DTU-10MW Horizontal Axis Wind Turbine (HAWT) via Active Flow Control (AFC) implementation and when using synthetic jets (SJ). The flow around two airfoil sections cut along the wind turbine blade and for a wind speed of 10 m/s is initially simulated using the CFD-2D-RANS-K$\omega$-SST turbulence model, from where the time-averaged boundary layer separation point and the associated vortex shedding frequency are obtained. On a second stage of the paper, and considering one of the two airfoil sections, the boundary layer separation point previously determined is used to locate the SJ groove as well as the groove width; the three remaining AFC parameters, momentum coefficient, jet inclination angle, and jet pulsating frequency, are parametrically optimized. Thanks to the energy assessment presented in the final part of the paper, the study shows that a considerable power increase of the airfoil section can be obtained when attaching the former separated boundary layer. The extension of the optimization process to the rest of the blade sections where the boundary layer is separated would lead to an efficiency increase of the HAWT. The Reynolds numbers associated to the respective airfoil sections analyzed in the present manuscript are $Re$ = $14.088\times {10}^6$ and $Re$ = $14.877\times {10}^6$, the characteristic length being the corresponding chord length for each airfoil. Full article

To achieve a near-zero magnetic field environment, the use of permalloy sheets with high-performance magnetic properties is essential. However, mainstream welding processes for magnetically shielded rooms (MSRs), such as argon arc welding and laser welding, can degrade the magnetic properties of the material. Additionally, neglecting the anisotropy of permalloy sheets can introduce unpredictable errors in the evaluation of MSR performance. To address this issue, this paper proposes a modified model for calculating the shielding factor (SF) of MSRs that incorporates the anisotropic magnetic characteristics of permalloy sheets. These characteristics were measured using a two-dimensional single sheet tester (2D-SST). A high-precision measurement system was developed, comprising a 2D-SST (to generate two-dimensional magnetic fields and sense the induced B and H signals) and a control system (to apply in-phase 2D excitation signals and amplify, filter, and record the B and H data). Hysteresis loops were tested at low frequencies (0.1–9 Hz) and under different magnetization states (0.1–0.6 T) in two orientations—parallel and perpendicular to the annealing magnetic field—to verify anisotropy under varying conditions. Initial permeability, near-saturation magnetization, and basic magnetization curves (BM curves) were measured across different directions to provide parameters for simulations and theoretical calculations. Based on these measurements and finite element simulations, a mathematical model was developed to adjust the empirical coefficient λ used in theoretical SF calculations. The results revealed that the ratio of empirical coefficients in different directions is inversely proportional to the ratio of magnetic permeability in the corresponding directions. A verification group was established to compare the original model and the modified model. The mean squared error (MSE) between the original model and the finite element simulation was 49.97, while the MSE between the improved model and the finite element simulation was reduced to 0.13. This indicates a substantial improvement in the computational accuracy of the modified model. Full article

Understanding and monitoring nearshore environments is essential, given that these fine-scaled ecosystems are integral to human well-being. While satellites offer an opportunity to gain synchronous and spatially extensive data of coastal areas, off-the-shelf calibrated satellite sea surface temperature (SST) measurements have only been available at coarse resolutions of 1 km or larger. In this study, we develop a novel methodology to create a simple linear equation to calibrate fine-scale Landsat thermal infrared radiation brightness temperatures (calibrated for land sensing) to derive SST at a resolution of 100 m. The constants of this equation are derived from correlations of coincident MODIS SST and Landsat data, which we filter to find optimal pairs. Validation against in situ sensor data at varying distances from the shore in Northern California shows that our SST estimates are more accurate than prior off-the-shelf Landsat data calibrated for land surfaces. These fine-scale SST estimates also demonstrate superior accuracy compared with coincident MODIS SST estimates. The root mean square error for our minimally filtered dataset (n = 557 images) ranges from 0.76 to 1.20 °C with correlation coefficients from r = 0.73 to 0.92, and for our optimal dataset (n = 229 images), the error is from 0.62 to 0.98 °C with correlations from r = 0.83 to 0.92. Potential error sources related to stratification and seasonality are examined and we conclude that Landsat data represent skin temperatures with an error between 0.62 and 0.73 °C. We discuss the utility of our methodology for enhancing coastal monitoring efforts and capturing previously unseen spatial complexity. Testing the calibration methodology on Landsat images before and after the temporal bounds of accurate MODIS SST measurements shows successful calibration with lower errors than the off-the-shelf, land-calibrated Landsat product, extending the applicability of our approach. This new approach for obtaining high-resolution SST data in nearshore waters may be applied to other upwelling regions globally, contributing to improved coastal monitoring, management, and research. Full article

The Magdalena department, influenced by southern trade winds and ocean currents from the Atlantic and Pacific, is a climatically vulnerable region. This study assesses the Magdalena Department’s precipitation trends and stationary patterns by analyzing multi-year monthly records from 55 monitoring stations from 1990 to 2022. To achieve this, the following methods were used: (i) homogeneous regions were established by an unsupervised clustering approach, (ii) temporal trends were quantified using non-parametric tests, (iii) stationarity was identified through Morlet wavelet decomposition, and (iv) Sea Surface Temperature (SST) in four Niño regions was correlated with stationarity cycles. Silhouette’s results yielded five homogeneous regions, consistent with the National Meteorological Institute (IDEAM) proposal. The Department displayed decreasing annual trends (−32–−100 mm/decade) but exhibited increasing monthly trends (>20 mm/decade) during the wettest season. The wavelet decomposition analysis revealed quasi-bimodal stationarity, with significant semiannual cycles (~4.1 to 5.6 months) observed only in the eastern region. Other regions showed mixed behavior: non-stationary in the year’s first half and stationary in the latter half. Correlation analysis showed a significant relationship between SST in the El Niño 3 region (which accounted for 50.5% of the coefficients), indicating that strong phases of El Niño anticipated precipitation responses for up to six months. This confirms distinct rainfall patterns and precipitation trends influenced by the El Niño–Southern Oscillation (ENSO), highlighting the need for further hydrometeorological research in the area. Full article

The work presents an analysis of the Gorlov helical turbine (GHT) design using both computational fluid dynamics (CFD) simulations and response surface methodology (RSM). The RSM method was applied to investigate the impact of three geometric factors on the turbine’s power coefficient (C_P): the number of blades (N), helix angle ($\gamma$), and aspect ratio (AR). Central composite design (CCD) was used for the design of experiments (DOE). For the CFD simulations, a three-dimensional computational domain was established in the Ansys Fluent software, version 2021R1 utilizing the k-$\omega$ SST turbulence model and the sliding mesh method to perform unsteady flow simulations. The objective function was to achieve the maximum C_P, which was obtained using a high-correlation quadratic mathematical model. Under the optimum conditions, where N, $\gamma$, and AR were 5, 78°, and 0.6, respectively, a C_P value of 0.3072 was achieved. The optimal turbine geometry was validated through experimental testing, and the C_P curve versus tip speed ratio (TSR) was determined and compared with the numerical results, which showed a strong correlation between the two sets of data. Full article

With the continuous development of the shipping market, bow thrusters have become more important for ship maneuvering. Therefore, the performance of bow thrusters is studied in this paper. In order to obtain an unsteady performance of the bow thruster under different ship speed conditions, the SST k-ω turbulence model is adopted to predict the hydrodynamics of the bow thruster. With the ship’s speed increasing gradually, the variation characteristics of hydrodynamic coefficients and the flow field distribution at key positions are analyzed. The results show that with an increase in ship speed to three knots, the thrust coefficient and torque coefficient of the bow thruster decrease by 2.69~4.07% and 2.34~3.08%. In addition, the blade vibration amplitude intensifies. In the departure direction, the propeller load is more susceptible to being influenced and decreases by an additional 2.34~4.16% compared with that in the berthing direction. Meanwhile, it is found that the velocity distribution is asymmetrical. The inlet velocity at the bow side is faster, which results in the maximum peak pressure being about three times the minimum peak pressure. In addition, the pressure’s nonuniformity in the tunnel increases gradually with the increase in ship speed. Compared with the pressure distribution in the berthing direction, the pressure distribution before and after the propeller is more uniform, which is consistent with the results of hydrodynamic change and velocity distribution. The research in this paper has a certain reference significance for understanding the hydrodynamic performance of bow thrust operation. Full article

Understanding the aerodynamic performance of scaled-down models is vital for providing crucial insights into wind energy optimization. In this study, the aerodynamic performance of a scaled-down model (12%) was investigated. This validates the findings of the unsteady aerodynamic experiment (UAE) test sequence H. UAE tests provide information on the configuration and conditions of wind tunnel testing to measure the pressure coefficient distribution on the blade surface and the aerodynamic performance of the wind turbine. The computational simulations used shear stress transport and kinetic energy (SST K-Omega) and transitional shear stress transport (SST) turbulence models, with wind speeds ranging from 5 m/s to 25 m/s for the National Renewable Energy Laboratory (NREL) Phase VI and 4 m/s to 14 m/s for the 12% scaled-down model. The aerodynamic performance of both cases was assessed at representative wind speeds of 7 m/s for low, 10 m/s for medium, and 20 m/s for high flow speeds for NREL Phase VI and 7 m/s for low, 9 m/s medium, and 12 m/s for the scaled-down model. The results of the SST K-Omega and transitional SST models were aligned with experimental test measurement data at low wind speeds. However, the SST K-Omega torque values exhibited a slight deviation. The transitional SST and SST K-Omega models yielded aerodynamic properties that were comparable to those of the 12% scaled-down model. The torque values obtained from the simulation for the full-scale NREL Phase VI and the scaled-down model were 1686.5 Nm and 0.8349 Nm, respectively. Both turbulence models reliably predicted torque and pressure coefficient values that were consistent with the experimental data, considering specific flow regimes. The pressure coefficient was maximum at the leading edge of the wind turbine blade on the windward side and minimum on the leeward side. For the 12% scaled-down model, the flow simulation results bordering the low-pressure region of the blade varied slightly. Full article

In this study, we fabricated Ti-Cu-based friction composites containing waste-metal (Ti, CuZn, stainless steel (SSt), MgAl), Al₂O₃ due to improving properties and its good compatibility with copper and graphene nanoplatelets as reinforcement and lubricant component, using planetary ball mill and technique based on Spark Plasma Sintering (SPS). Understanding the wear behaviour of such engineered friction composites is essential to improve their material design and safety, as these materials could have the potential for use in public and industrial transportation, such as high-speed rail trains and aircraft or cars. This is why our study is focused on wear behaviour during friction between function parts of devices. We investigated the composite materials designed by us in order to clarify their microstructural state and mechanical properties. Using different loading conditions, we determined the Coefficient of Friction (COF) using a ball-on-disc tribological test. We analysed the state of the samples after the mentioned test using a Scanning Electron Microscope (SEM), then Energy-Dispersive X-ray Spectroscopy (EDS), and confocal microscopy. Also, a comparative analysis of friction properties with previously studied materials was performed. The results showed that friction composites with different compositions, despite the same conditions of their compaction during sintering, can be defined by different wear characteristics. Our study can potentially have a significant contribution to the understanding of wear mechanisms of Ti-Cu-based composites with incorporated metal-waste and to improving their material design and performance. Also, it can give us information about the possibilities of reusing metal-waste from different machining operations. Full article

Surface air temperature (SAT) variability is investigated for advancing our understanding of the climate patterns over the Kingdom of Saudi Arabia (KSA). SAT variability reveals significant warming trends, particularly from 1994 onward, as demonstrated by nonlinear and linear trend analysis. This warming is linked to global climate patterns, which serve as significant indicators for studying the effects of climate change on surface air temperature patterns across the KSA. The empirical orthogonal function (EOF) method is employed for analyzing SAT due to its effectiveness in extracting dominant patterns of variability during the winter (DJF) and summer (JJA) seasons. The first mode (EOF1) for both seasons shows positive variability across the KSA, explaining more than 45% of the variance. The second mode (EOF2) indicates negative variability in central and northern regions. The third mode (EOF3) describes positive variability but with lower variance over time. PC1 is used to describe the physical mechanism of SAT variability and correlations with global sea surface temperature (SST). The physical mechanism shows that the variability in Mediterranean troughs during the winter season and high pressure over the Indian Ocean and central Asia controls SAT variability over the KSA. The correlation coefficients (CCs) were calculated during the winter and summer season between the SAT of the KSA and six teleconnection indices, El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Atlantic Meridional Mode (AMM), Pacific Warm Pool (PWP), North Atlantic Oscillation (NAO), and Tropical North Atlantic (TNA) SST for the period from 1994 to 2022. ENSO shifts from positive to negative correlations with SAT from winter to summer. IOD shows a diminished correlation with SAT due to the absence of upper air dynamics. PWP consistently enhances surface warming in both seasons through upper air convergence during both seasons. AMM and NAO have a non-significant impact on SAT; however, TNA contributes warming over central and northern parts during winter and summer seasons. The seasonal SAT variations emphasize the significant role of ENSO, PWP, and TNA across the seasons. The findings of this study can be helpful for seasonal predictability in the KSA. Full article

A seasonal–diurnal analysis of land-sea contrast in lightning activity over Bulgaria and the Black Sea over 10 years is presented here. The maximum number of flashes over both surface types is registered during the summer (with a peak over Bulgaria in June and over the Black Sea in July) and a minimum number in winter (December/February, respectively). During spring, the maximum flash density is observed over Bulgaria (in May), while in autumn, it is over the Black Sea (in September). The results show that only in autumn lightning activity dominates over the Black Sea compared to over land (Bulgaria), while in winter, spring, and summer is vice versa. For this reason, an additional investigation was conducted to determine whether there is a relationship between lightning activity and the sea surface temperature (SST) of the Black Sea in autumn. The analysis reveals that the influence of SST on the formation of thunderstorms over the Black Sea varies depending on the diurnal time interval, with the effect being more significant at night. At nighttime intervals, there is a clear trend of increasing mean flash frequency per case with rising SST (linear correlation coefficients range from R = 0.92 to 0.98), while during the daytime, this trend is not as evident. This indicates that, during the day, other favorable atmospheric processes have a greater influence on the formation of thunderstorms than sea-surface temperature, while in the autumn night hours, the higher SST values probably play a more significant role in thunderstorms formation, in combination with the corresponding orographic conditions. Full article

Numerical weather prediction of sea surface temperature (SST) is crucial for regional operational forecasts. Deep learning offers an alternative approach to traditional numerical general circulation models for numerical weather prediction. In our previous work, we developed a sophisticated deep learning model known as the Attention-based Context Fusion Network (ACFN). This model integrates an attention mechanism with a convolutional neural network framework. In this study, we applied the ACFN model to the South China Sea to evaluate its performance in predicting SST. The results indicate that for a 1-day lead time, the ACFN model achieves a Mean Absolute Error of 0.215 °C and a coefficient of determination ($R^2$) of 0.972. In addition, in situ buoy data were utilized to validate the forecast results. The Mean Absolute Error for forecasts using these data increased to 0.500 °C for a 1-day lead time, with a corresponding $R^2$ of 0.590. Comparative analyses show that the ACFN model surpasses traditional models such as ConvLSTM and PredRNN in terms of accuracy and reliability. Full article

Background: In the geriatric population, the risk of cardiometabolic diseases is strongly influenced by comorbidities. The aim of the study was to estimate the prevalence of depression among hospitalized patients with heart failure (HF) and to assess the relation between physical performance and depression in this population. Methods: We included consecutive hospitalized patients with HF aged >65 years. The depression symptoms were evaluated using the Geriatric Depression Scale (GDS), the physical performance was assessed using the grip strength measurements, the Back Scratch Test, the Timed Up and Go Test (TUGT), the Five Times Sit to Stand Test (5 × SST), and the 6 min walk test. Results: We included 206 patients (134 females and 72 males, median age 82 years (77–86) years). Altogether, 33% of participants had signs of depression. The association was found between depression severity and economic status (p = 0.001), stressful events (p = 0.005), self-reported general health status (p = 0.001), and heart failure severity assessed by the New York Heart Association class (NYHA), p = 0.003. The Back Scratch Test, the TUGT, and the 5xSST were associated with depression severity in a univariable regression analysis (β coefficient 0.04 [95% CI 0.00–0.08], 0.20 [95% CI 0.12–0.27], 0.18 [95% CI 0.07–0.27], respectively); however, when adjusted for co-factors, the TUGT and the 5xSST (0.17 [95% CI 0.08–0.26] and 0.14 [95% CI 0.02–0.26], respectively) were significantly related to the GDS score. Grip strength and the 6 min walk test were not related to the GDS score in the univariable nor multivariable analysis. These findings were confirmed in the logistic analyses. Conclusions: Our study indicated a high incidence of depression among elderly hospitalized patients with heart failure. Depression severity in older patients with HF is related to physical performance decline as assessed by the Timed Up and Go Test and the Five Times Sit to Stand Test. Grip strength and the 6 min walk test are not related to the GDS score in this population. Full article

The mesoscale interaction between sea surface temperature (SST) and wind is a crucial factor influencing oceanic and atmospheric conditions. To investigate the mesoscale coupling characteristics of the Yellow Sea and East China Sea, we applied a locally weighted regression filtering method to extract mesoscale signals from Quik-SCAT wind field data and AMSR-E SST data and found that the mesoscale coupling intensity is stronger in the Yellow Sea during the spring and winter seasons. We calculated the mesoscale coupling coefficient to be approximately 0.009 N·m⁻²/°C. Subsequently, the Tikhonov regularization method was used to establish a mesoscale empirical coupling model, and the feedback effect of mesoscale coupling on the ocean was studied. The results show that the mesoscale SST–wind field coupling can lead to the enhancement of upwelling in the offshore area of the East China Sea, a decrease in the upper ocean temperature, and an increase in the eddy kinetic energy in the Yellow Sea. Diagnostic analyses suggested that mesoscale coupling-induced variations in horizontal advection and surface heat flux contribute most to the variation in SST. Moreover, the increase in the wind energy input to the eddy is the main factor explaining the increase in the eddy kinetic energy. Full article

A Zonal Detached Eddy Simulation (ZDES) based on the SST turbulence model is implemented to the numerical investigation of the trailing edge stall of a LS-0417 airfoil, which includes multiple DES modes for different classifications of flow separation and adopts the subgrid scale definition of ${\Delta }_{\omega }$. The entire stall process under a series of AOA is simulated according to the experiment condition. The performance of URANS and ZDES in the prediction of the stall flow field are compared. The results reveal that the stall point obtained through ZDES is consistent with the experiment; the deviation of the predicted maximum lift coefficient from the measured result is only 0.8%, while the maximum lift is overpredicted by both RANS and URANS. The high frequency fluctuations are observed in the time history of the lift in ZDES result during stall. With the increase in the AOA, a mild development of separation and a gradual decrease in leading edge peak suction are manifested in the ZDES result. The alternate shedding of shear layers and the interference between the leading edge and trailing edge vortices are illustrated through ZDES near the stall point; the corresponding turbulent fluctuations with high intensity are captured in the separation region, which indicates the essential difference in the prediction of stall process between URANS and ZDES. Full article

The Xfinity Racing Series is an American stock car racing series organized by NASCAR. For the 2017 racing season, NASCAR introduced new regulations with the objective of creating a level playing field by reducing aerodynamic influence on vehicle performance. In this context, the primary objective of this work is to explore the differences in the aerodynamic performance between the 2016 and 2017 Toyota Camry Xfinity racecars using only open-source Computational Fluid Dynamics (CFD) and CAE tools. During the CFD validation process, it was observed that none of the standard turbulence models, with default turbulence model closure coefficients, were able to provide racecar aerodynamic characteristics predictions with acceptable accuracy compared to experiments. This necessitated a fine-tuning of the closure coefficient numeric values. This work also demonstrates that it is possible to generate CFD predictions that are highly correlated with experimental measurements by modifying the closure coefficients of the standard $k-\omega$ SST turbulence model. Full article