Search Results (395)

The time series prediction of visibility in terms of various meteorological variables, such as relative humidity, temperature, atmospheric pressure, and wind speed, is presented in this paper using Single-Variable Regression Analysis (SVRA), Artificial Neural Network (ANN), and Hybrid Adaptive Neuro-fuzzy Inference System (ANFIS) techniques for several sub-tropical locations. The initial method used for the prediction of visibility in this study was the SVRA, and the results were enhanced using the ANN and ANFIS techniques. Throughout the study, neural networks with various algorithms and functions were trained with different atmospheric parameters to establish a relationship function between inputs and visibility for all locations. The trained neural models were tested and validated by comparing actual and predicted data to enhance visibility prediction accuracy. Results were compared to assess the efficiency of the proposed systems, measuring the root mean square error (RMSE), coefficient of determination (R²), and mean bias error (MBE) to validate the models. The standard statistical technique, particularly SVRA, revealed that the strongest functional relationship was between visibility and RH, followed by WS, T, and P, in that order. However, to improve accuracy, this study utilized back propagation and hybrid learning algorithms for visibility prediction. Error analysis from the ANN technique showed increased prediction accuracy when all the atmospheric variables were considered together. After testing various neural network models, it was found that the ANFIS model provided the most accurate predicted results, with improvements of 31.59%, 32.70%, 30.53%, 28.95%, 31.82%, and 22.34% over the ANN for Durban, Cape Town, Mthatha, Bloemfontein, Johannesburg, and Mahikeng, respectively. The neuro-fuzzy model demonstrated better accuracy and efficiency by yielding the finest results with the lowest RMSE and highest R² for all cities involved compared to the ANN model and standard statistical techniques. However, the statistical performance analysis between measured and estimated visibility indicated that the ANN produced satisfactory results. The results will find applications in Optical Wireless Communication (OWC), flight operations, and climate change analysis. Full article

This paper presents a preliminary investigation into the aeroelastic behavior of a tailless flying wing equipped with a rotating wingtip. Based on the configuration of Innovative Control Effectors (ICE) aircraft, an aeroelastic model of the tailless flying wing with a rotating wingtip has been developed. Both numerical simulation and wind tunnel tests (WTTs) are employed to study the aeroelastic characteristics of this unique design. The numerical simulation involves the coupling of computational fluid dynamics (CFD) and implicit dynamic approaches (IDAs). Using the CFD/IDA coupling method, aeroelastic response results are obtained under different flow dynamic pressures. The critical flutter dynamic pressure is identified by analyzing the trend of the damping coefficient, with a focus on its transition from negative to positive values. Additionally, the critical flutter velocity and flutter frequency are obtained from the WTT results. The critical flutter parameters, including dynamic pressure, velocity, and flutter frequency, are examined under different wingtip rotation frequencies and angles. These parameters are derived using both the CFD/IDA coupling method and WTT. The results indicate that the rotating wingtip plays a significant role in influencing the flutter behavior of aircraft with such a configuration. Research has shown that the rotation characteristics of the rotating wingtip are the primary factor affecting its aeroelastic behavior, and increasing both the rotation frequency and rotation angle can raise the flutter boundary and effectively suppress flutter onset. Full article

The development in coastal engineering and maritime transport demands accurate wave height prediction. In this study, hybrid deep learning models, including CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU, are employed to develop regional multivariate wave prediction models that incorporate multiple features, such as wave height, wind stress, water depth, pressure, and sea surface temperature (SST), for the entire Bay of Bengal area. Sensitivity analysis is performed to evaluate the accuracy using statistical metrics, such as the correlation coefficient, RMSE, and MAE. The findings demonstrate that regional multivariate models offer satisfactory results for the entire Bay of Bengal region. The multivariate model performs better compared to the univariate model as the forecast horizon increases. Performance assessment of each environmental factor, employing the integrated gradient method, reveals that sea surface temperature has the most significant influence, while wind stress is the least dominant factor in the wave prediction model. Among the tested models, the CNN-BiGRU has superior performance with a correlation of 0.9872, an RMSE of 0.1547, and an MAE of 0.1005 for the 3 h prediction and is proposed as the optimal model. This study contributes to assessing the contribution of each environmental feature and improving the accuracy of regional wave prediction. Full article

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development. Full article

Aircraft performance metrics such as range and endurance are highly dependent on induced and vortex drag. There is a close relationship between wake structure and aerodynamic performance. In the present paper, the velocity field behind the model of a wing with winglet is studied. The methodology and equipment for study in a low-speed wind tunnel ULAK–1 are presented. The pressure field was obtained using a five-hole pressure probe, which was positioned in a cross plane at 300 mm behind the wing trailing edge. The acquired experimental data are used to calculate the cross flow velocity and vorticity fields at an angle of attack of 6 degrees—around the maximum lift-to-drag ratio. The results are compared to the data of a model with planar wing. During the subsequent processing, coefficients of lift and induced drag can be obtained. Full article

Canopy temperature is an important indicator of plants’ water status. The so-called PeriodiCT model was developed to forecast canopy temperature using ambient weather variables, providing a powerful tool for planning crop irrigation scheduling. As this model requires observed data in its parameter training before implementing the forecast, it is important to understand the data requirements in the model training such that accurate forecasts are attained. In this work, we conduct a comprehensive assessment of the PeriodiCT model in terms of sample size requirement and predictabilities across sensors in a field and across seasons for the full model and sub-models. The results show that (1) 5 days’ observations are sufficient for the full model and sub-models to achieve very high predictability, with a minimum coefficient of efficiency of 0.844 for the full model and 0.840 for the sub-model using only air temperature. The predictability decreases in the following order: full model, sub-model without radiation S, with air temperature Ta and vapor pressure VP, and with only Ta. The predictions perform reasonably well even when only one day’s observations are used. (2) The predictability into the future is very stable as the prediction steps increase. (3) The predictabilities of the full and sub-models when using a trained model from one sensor for another sensor perform comparatively well, with a minimum coefficient of efficiency of 0.719 for the full model and 0.635 for the sub-model using only air temperature. (4) The predictabilities of the sub-models without solar radiation when using trained models from one season for another season perform comparatively well, with a minimum coefficient of efficiency of 0.866 for the full model and 0.764 for the sub-model using only air temperature, although the cross-season performances are not as good as the cross-sensor performances. The importance of the predictors is in the order of air temperature, vapor pressure, wind speed, and solar radiation, while vapor pressure and wind speed have similar contributions, and solar radiation has only a marginal contribution. Full article

This paper analyzes the influence of the number of ignition coils and spark discharge energy on the Coefficient of Variation of Indicated Mean Effective Pressure (COV_IMEP) of an SI internal combustion piston engine. A modern electronically controlled induction ignition system is used during the test. Two fuels are used in the experiment. The reference fuel is gasoline and the tested fuel is ammonia. For the traditional fuel, using an additional ignition coil does not improve COV_IMEP. This parameter for gasoline has an almost constant value for different ignition system charging times. The situation is different for ammonia. This fuel requires high ignition energy. The use of one ignition coil demands a long charging time. For short charging times, unrepeatability of the engine cycles is unacceptable. The use of an additional ignition coil allowed to the charging coil timing to be shortened and the unrepeatable engine cycles to be reduced. This paper determined the maximum charging time of the used ignition coil, above which the spark parameters are worse. In addition, the influence of charging time and number of ignition coils on total spark energy, spark discharge duration, maximum spark power, and voltage during spark discharge for ammonia is presented. The data presented in this paper are developed based on measurements of current and voltage in the secondary winding of the ignition coil. A self-developed electronic device enabling the change in spark energy is used to control the ignition system. This paper also presents the construction of modern ignition systems, describes the functions of selected components, and briefly discusses their diagnostics. Full article

In this paper, the aerodynamic performance of an improved hybrid vertical-axis wind turbine is investigated, and the performance of the hybrid turbine at high tip–speed ratios is significantly enhanced by adding a spoiler at the end of the inner rotor. The improved design increases the average torque coefficient by 7.4% and the peak power coefficient by 32.4%, which effectively solves the problem of power loss due to the negative torque of the inner rotor in the conventional hybrid turbine at high TSR; the spoiler improves the performance of the outer rotor in the wake region by optimizing the airflow distribution, reducing the counter-pressure differential, lowering the inner rotor drag and at the same time attenuating the wake turbulence intensity. The study verifies the validity of the design through 2D CFD simulation, and provides a new idea for the optimization of hybrid wind turbines, which is especially suitable for low wind speed and complex terrain environments, and is of great significance for the promotion of renewable energy technology development. Full article

Due to its complex mechanism of action, the wind-resistant design of square cross-section structures against vortex-induced vibration (VIV) still presents significant challenges. The angle of the wind direction is an important factor affecting the VIV characteristics of square cylinders. A series of stationary model pressure tests were performed and an elastic supporting model was used in the present study. The effects of the wind direction angle on parameters corresponding to fluid–structure interaction were analyzed with reference to the Strouhal number, range of “lock-in”, amplitude, and aerodynamic forces. The Strouhal number of the square cylinder was greatest at a 16° wind direction angle. When the wind direction angle was 10°, the wind speed range of vortex-induced vibration (VIV) of the square cylinder was the greatest, and the corresponding value was the smallest when the wind direction angle ranged from 20° to 45°. Within the vibration interval, the extreme value of the amplitude was smallest when the wind direction angle was 10°, and the extreme value of the amplitude was greatest when the wind direction angle was 30°. The vibration state had a minimal influence on the mean lift coefficient and a relatively large influence on the mean drag coefficient. Full article

The aerodynamic loading of four as well as of six tilted flat plates-panels arranged in tandem and in close proximity to the ground is examined through force and pressure measurements. In the four-plate set up, conducted in an open-circuit wind tunnel, a movable floor is used to vary the ground clearance, and a one-component force balance is employed to measure the drag coefficient Cd of each plate for tilt angles 10° to 90° and for two head-on wind directions, 0° and 180°. An increase in the ground clearance from 20% to 60% of the plates’ chord length, results in a Cd increase of over 40% in the downstream plates, and up to 20% in the leading one. For tilt angles below 40°, the drag on the first plate is up to 25% higher under the 180° wind direction compared to the opposite direction. Pressure distributions are also presented on a series of six much larger plates, examined in a closed-circuit wind tunnel at tilt angles ±30°. While the windward surfaces exhibit relatively uniform pressure distributions, regions of low pressure develop on their suction side, near the plates’ tips leading edge, tending to become uniform streamwise. Full article

The aerodynamic optimization of the airfoil of vertical-axis wind turbines (VAWTs) is limited by the time-consuming nature of computational fluid dynamics (CFD), resulting in difficulty in the efficient implementation of multi-parameter optimization. In response to this challenge, this study constructed a collaborative optimization framework based on the Kriging surrogate model and the multi-island genetic algorithm (MIGA). Based on the NACA 0015 airfoil, 13 geometric variables (including 12 Bernstein polynomial coefficients and 1 installation angle) were defined through the Classification and Shape Transformation (CST) parameterization method. Through sensitivity analysis, seven key parameters were screened as design variables. Seventy training samples and ten validation samples were generated via Latin hypercube sampling to construct a high-precision Kriging surrogate model (R² = 0.91368). The optimized results show that the power coefficient of the new airfoil increases by 14.2% under the condition of the tip velocity ratio (TSR > 1.5), and the average efficiency of the entire working condition increases by 9.8%. The drag reduction mechanism is revealed through pressure cloud maps and velocity field analysis. The area of the high-pressure zone at the leading edge decreases by 23%, and the flow separation phenomenon at the trailing edge is significantly weakened. This research provides an engineering solution that takes into account both computational efficiency and optimization accuracy for the VAWT airfoil design. Full article

Understanding optical turbulence within the atmospheric boundary layer (ABL) is essential for refining atmospheric motion analyses, enhancing numerical weather prediction models, and improving light propagation assessments. This study develops an optical turbulence model for the boundary layer over the South China Sea (SCS) by integrating multiple observational and reanalysis datasets, including ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF), radiosonde observations, coherent Doppler wind lidar (CDWL), and ultrasonic anemometer (CSAT3) measurements. Utilizing Monin–Obukhov Similarity Theory (MOST) as the theoretical foundation, the model’s performance is evaluated by comparing its outputs with the observed diurnal cycle of near-surface optical turbulence. Error analysis indicates a root mean square error (RMSE) of less than 1 and a correlation coefficient exceeding 0.6, validating the model’s predictive capability. Moreover, this study demonstrates the feasibility of employing ERA5-derived temperature and pressure profiles as alternative inputs for optical turbulence modeling while leveraging CDWL’s high-resolution observational capacity for all-weather turbulence characterization. A comprehensive statistical analysis of the atmospheric refractive index structure constant ($C_n^2$) from November 2019 to September 2020 highlights its critical implications for optoelectronic system optimization and astronomical observatory site selection in the SCS region. Full article

Anti-insect nets are characterized by a very low porosity that determines a variation in the microclimate below the protection in terms of an increase in the relative humidity, a reduction in air ventilation, and a temperature rise. The air permeability of the textile depends on numerous factors such as the thickness of the wires, the size of the holes, the porosity, and the air velocity. The knowledge of this relationship would make it possible to optimize the size of the holes in order to maintain the anti-insect function with the increase in air velocity. The air permeability coefficients of 10 anti-insect nets were evaluated by means of a micro wind tunnel. The results showed that the loss coefficient is linked to the porosity (ε) of the nets: as the porosity increases, the loss coefficient decreases. The parameter that demonstrated the strongest correlation with the loss coefficient was the function of porosity h(ε) = (1 − ε²)/ε². In the interval of porosity $0.10<\epsilon <0.60$, the linear regression correlation is quite high ($R^2=0.87$). Finally, the reduction factor RF(ε)—an estimation of the reduction in wind pressure acting perpendicularly on the surface of a textile due to its porosity—was calculated and compared with that proposed by the Australian standard, which, currently, is the only international standard that explicitly considers the effect of porosity on wind action. Full article

Understanding wind pressure distribution on structures is crucial for evaluating design wind loads, especially for complex designs. This study investigated the wind pressure distribution on a windmill shape building with intricate geometries, i.e., the Chengdu Future Science and Technology City Exhibition Centre. Both wind tunnel test and CFD simulations are conducted to analyze the wind pressure distribution on building surface. Since the research object has intricate geometries, featuring sharp corners, curved surfaces, and ridges, the Reynolds Average Navier-Stokes (RANS) method adopting k-ε turbulence models is employed in the CFD simulations. Furthermore, scalable wall functions and non-structured grids with appropriate refinement on both turbulent regions and structural surfaces are also adopted in the RANS method. A comparison between the simulation results and wind tunnel tests demonstrated that the numerical simulations based on RANS method effectively capture surface wind pressure distribution on complex structures. This study reveals the occurrence of complicated flow phenomena that lead to a very complex wind pressure distribution on the surface of the structure, and drastic variance of the wind pressure coefficient is observed. Moreover, it is found that wind pressure distribution on the surface of the structure is highly sensitive to wind angle, exhibiting extreme negative pressure coefficients of −1.1, −1.0, and −1.8 at angles of 0°, 30°, and 60°, respectively. The analysis of the flow field around the structure at various wind angles reveals that its complex shape significantly alters the flow dynamics, creating distinct vortices and wake patterns at different angles. Consequently, CFD simulations help to understand wind loads on structures and improve wind resistance design. Full article

As global energy grows short and environmental governance pressure increases, the automotive industry, a major energy consumer and pollution emitter, must enhance vehicle aerodynamics to cut energy use and emissions. This study creates an open-domain and virtual wind tunnel dual-computational-domain setup. It optimizes mesh refinement and boundary conditions, and evaluates the k-ε, k-ω, and Detached Eddy Simulation (DES) turbulence models. These models predict vehicle aerodynamic resistance, lift, and wake flow structure. The k-ε model best predicts the steady-state drag coefficient (Cd) (error 0.0009). DES excels in transient conditions (Cd error −0.4%, lift coefficient Cl matching experiments). The k-ω model, with its near-wall flow capture ability, has the lowest lift prediction error (−2.7%). Moreover, open-domain simulations align more closely with real free-flow environments and experimental data than virtual wind tunnel simulations. Overall, the study clarifies the varying applicability of turbulence models in complex flows, and offers a basis for model selection and technical support for vehicle aerodynamic optimization. It is highly significant for reducing fuel consumption, boosting the range of new-energy vehicles, and promoting sustainable industry development. Full article