Search Results (3,344)

In the dynamic realm of golf, where every swing can make the difference between victory and defeat, the strategic selection of golf clubs has become a crucial factor in determining the outcome of a game. Advancements in artificial intelligence have opened new avenues for enhancing the decision-making process, empowering golfers to achieve optimal performance on the course. In this paper, we introduce an AI-based game planning system that assists players in selecting the best club for a given scenario. The system considers factors such as distance, terrain, wind strength and direction, and quality of lie. A rule-based model provides the four best club options based on the player’s maximum shot data for each club. The player picks a club, shot, and target and a probabilistic classification model identifies whether the shot represents a birdie opportunity, par zone, bogey zone, or worse. The results of our model show that taking into account factors such as terrain and atmospheric features increases the likelihood of a better shot outcome. Full article

Soaring drones can use updrafts to reduce flight energy consumption like soaring birds. With control surfaces that are similar to those of soaring birds, the soaring drone achieves roll control through asymmetric sweepback of the wing on one side. This will result in asymmetry of the drone. The moment of inertia and the inertial product will change with the sweepback of the wing, causing nonlinearity and coupling in its dynamics, which is difficult to solve through traditional research methods. In addition, unlike general control objectives, the objective of this study was to enable the soaring drone to follow the soaring strategy. The soaring strategy determines the horizontal direction of the drone based on the vertical wind situation without the need for active control of the vertical movement of the drone. In essence, it is a horizontal trajectory tracking task. Therefore, based on the layout and aerodynamic data of the soaring drone, reinforcement learning was adopted in this study to construct a six-degree-of-freedom dynamic model and a control flight training simulation environment for the soaring drone with asymmetric deformation control surfaces. We compared the impact of key factors such as different state spaces and reward functions on the training results. The turning control agent was obtained, and trajectory-tracking simulations were conducted. Full article

This review aims to enhance the understanding of the fundamentals, applications, and future directions in hydrogen production techniques. It highlights that the hydrogen economy depends on abundant non-dispatchable renewable energy from wind and solar to produce green hydrogen using excess electricity. The approach is not limited solely to existing methodologies but also explores the latest innovations in this dynamic field. It explores parameters that influence hydrogen production, highlighting the importance of adequately controlling the temperature and concentration of the electrolytic medium to optimize the chemical reactions involved and ensure more efficient production. Additionally, a synthesis of the means of transport and materials used for the efficient storage of hydrogen is conducted. These factors are essential for the practical feasibility and successful deployment of technologies utilizing this energy resource. Finally, the technological innovations that are shaping the future of sustainable use of this energy resource are emphasized, presenting a more efficient alternative compared to the fossil fuels currently used by society. In this context, concrete examples that illustrate the application of hydrogen in emerging technologies are highlighted, encompassing sectors such as transportation and the harnessing of renewable energy for green hydrogen production. Full article

Aeolian sand hazards are often a threat to culverts, which are important channels and pieces of infrastructure of the desert railway. In addition to wind speed, wind direction, and culvert structure, terrain may also be an important reason for the formation of culvert sand hazards. However, there are few studies on the effect of terrain on the sediment accumulation characteristics of culverts. This paper established computational fluid dynamics (CFD) models of railway culverts (flat and concave culverts) based on Euler’s two-fluid theory. An analysis of the influence of terrain on the distribution law of the flow fields and sand accumulation around railway culverts was carried out. The results show that the horizontal wind speed curves changes in a “W” shape along the centre axis surface from the forecourt to the rearcourt within a range of 30 m~66.8 m. Low-speed backflow is formed at the inlet and outlet of the culvert, and the minimum wind speed reaches −3.6 m/s and −4.2 m/s, respectively, when the height from the bottom of the culvert is 1.0 m and 1.5 m, resulting in intensified sand sedimentation. In concave culverts, the lower the roadbed height, the easier it is for sand to accumulate at the culvert outlet, the rearcourt, and the track; the sand volume fraction is close to 0.63, affecting the normal operation of the trains. On the contrary, the higher the roadbed, the easier it is for sand to accumulate at the culvert inlet, hindering the passage of engineering vehicles and reducing the function of the culverts. These results reveal that terrain plays a pivotal role in the sand accumulation around culverts and that it should be one of the key considerations for the design of new railway culverts. This work can provide a theoretical basis for preventing and managing sand hazards in railway culverts. Full article

The integration of large-scale wind power into power systems has exacerbated the challenges associated with peak load regulation. Concurrently, the ongoing advancement of electricity marketization reforms highlights the need to assess the impact of direct electricity procurement by large consumers on enhancing the flexibility of power systems. In this context, this paper introduces a Distributed Robust Optimal Scheduling (DROS) model, which addresses the uncertainties of wind power generation and direct electricity purchases by large consumers. Firstly, to mitigate the effects of wind power uncertainty on the power system, a first-order Markov chain model with interval characteristics is introduced. This approach effectively captures the temporal and variability aspects of wind power prediction errors. Secondly, building upon the day-ahead scenarios generated by the Markov chain, the model then formulates a data-driven optimization framework that spans from day-ahead to intra-day scheduling. In the day-ahead phase, the model leverages the price elasticity of the demand matrix to guide consumer behavior, with the primary objective of maximizing the total revenue of the wind farm. A robust scheduling strategy is developed, yielding an hourly scheduling plan for the day-ahead phase. This plan dynamically adjusts tariffs in the intra-day phase based on deviations in wind power output, thereby encouraging flexible user responses to the inherent uncertainty in wind power generation. Ultimately, the efficacy of the proposed DROS method is validated through extensive numerical simulations, demonstrating its potential to enhance the robustness and flexibility of power systems in the presence of significant wind power integration and market-driven direct electricity purchases. Full article

Addressing the challenges of significant speed overshoot, stability issues, and system oscillations associated with the sliding mode control (SMC) strategy in maximum power point tracking (MPPT) for permanent magnet synchronous wind power systems, this paper introduces a fuzzy sliding mode control (FSMC) method employing an innovative exponential convergence law. By incorporating a velocity adjustment function into the traditional exponential convergence law, a novel convergence law was designed to mitigate oscillations during the sliding phase and expedite the convergence rate. Additionally, a fuzzy controller was developed to implement a fuzzy adaptive SMC strategy, optimizing the MPPT for permanent magnet synchronous wind power generation systems. Simulation results indicated that this approach offered a faster response and superior interference rejection capabilities, compared to conventional and modified SMC strategies. The improved FSMC strategy demonstrated a swift, dynamic response and excellent steady-state performance, improving the efficiency of MPPT, thus confirming the effectiveness of the proposed method. Full article

An integrated structural optimization strategy was produced in this study for direct-drive electrical generator structures of offshore wind turbines, implementing a design for an additive manufacturing approach, and using generative design techniques. Direct-drive configurations are widely implemented on offshore wind energy systems due to their high efficiency, reliability, and structural simplicity. However, the greatest challenge associated with these types of machines is the structural optimization of the electrical generator due to the demanding operating conditions. An integrated structural optimization strategy was developed to assess a 100-kW permanent magnet direct-drive generator structure. Generated topologies were evaluated by performing finite element analyses and a metal additive manufacturing process simulation. This novel approach assembles a vast amount of structural information to produce a fit-for-purpose, adaptative, optimization strategy, combining data from static structural analyses, modal analyses, and manufacturing analyses to automatically generate an efficient model through a generative iterative process. The results obtained in this study demonstrate the importance of developing an integrated structural optimization strategy at an early phase of a large-scale project. By considering the typical working condition loads and the machine’s dynamic behavior through the structure’s natural frequencies during the optimization process coupled with a design for an additive manufacturing approach, the operational range of the wind turbine was maximized, the overall costs were reduced, and production times were significantly diminished. Integrating the constraints associated with the additive manufacturing process into the design stage produced high-efficiency results with over 23% in weight reduction when compared with conventional structural optimization techniques. Full article

The stratospheric airship, as a near-space vehicle, is increasingly utilized in scientific exploration and Earth observation due to its long endurance and regional observation capabilities. However, due to the complex characteristics of the stratospheric wind field environment, trajectory planning for stratospheric airships is a significant challenge. Unlike lower atmospheric levels, the stratosphere presents a wind field characterized by significant variability in wind speed and direction, which can drastically affect the stability of the airship’s trajectory. Recent advances in deep reinforcement learning (DRL) have presented promising avenues for trajectory planning. DRL algorithms have demonstrated the ability to learn complex control strategies autonomously by interacting with the environment. In particular, the proximal policy optimization (PPO) algorithm has shown effectiveness in continuous control tasks and is well suited to the non-linear, high-dimensional problem of trajectory planning in dynamic environments. This paper proposes a trajectory planning method for stratospheric airships based on the PPO algorithm. The primary contributions of this paper include establishing a continuous action space model for stratospheric airship motion; enabling more precise control and adjustments across a broader range of actions; integrating time-varying wind field data into the reinforcement learning environment; enhancing the policy network’s adaptability and generalization to various environmental conditions; and enabling the algorithm to automatically adjust and optimize flight paths in real time using wind speed information, reducing the need for human intervention. Experimental results show that, within its wind resistance capability, the airship can achieve long-duration regional station-keeping, with a maximum station-keeping time ratio (STR) of up to 0.997. Full article

This study utilizes six years of hourly meteorological data from seven observation stations in the Beibu Gulf—Qinzhou (QZ), Fangcheng (FC), Beihai (BH), Fangchenggang (FCG), Dongxing (DX), Weizhou Island (WZ), and Hepu (HP)—over the period from 2016 to 2021. It examines the diurnal variations of sea fog occurrence and compares the performance of three machine learning (ML) models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—in predicting visibility associated with sea fog in the Beibu Gulf. The results show that sea fog occurs more frequently during the nighttime than during the daytime, primarily due to day-night differences in air temperature, specific humidity, wind speed, and wind direction. To predict visibility associated with sea fog, these variables, along with temperature-dew point differences ($T_{\mathrm{a}}-T_{\mathrm{d}}$), pressure (p), month, day, hour, and wind components, were used as feature variables in the three ML models. Although all the models performed satisfactorily in predicting visibility, XGBoost demonstrated the best performance among them, with its predicted visibility values closely matching the observed low visibility in the Beibu Gulf. However, the performance of these models varies by station, suggesting that additional feature variables, such as geographical or topographical variables, may be needed for training the models and improving their accuracy. Full article

Due to the ability to improve the low-inertia characteristics of power systems and offer reliable voltage and frequency support, grid-forming permanent-magnet synchronous-generator wind turbines (PMSG-WTs) based on virtual synchronous-generator (VSG) technology are emerging se the direction for future developments. Previous studies on the small-signal stability of grid-forming PMSG-WTs that connect to the grid usually simplify them into grid-connected grid-side converters (GSC), potentially leading to errors in stability analyses. Therefore, this paper considers the machine-side converter (MSC) control and establishes impedance models for grid-forming PMSG-WTs. Based on the sensitivity calculation of controller parameters using symmetric difference computation based on zero-order optimization, the impact of the internal controller on outside impedance characteristics is quantitatively analyzed. Additionally, an optimization method to enhance the stability of a hybrid wind farm by adjusting the ratio of grid-forming and grid-following wind turbines is proposed. Full article

The temperature inside broiler houses is crucial to broiler health, welfare, and productivity. High stocking density in broiler houses can easily lead to nonuniform temperature conditions, which would cause broilers to suffer cold and heat stress. It is essential to assess the temperature distribution inside broiler houses and investigate the factors that affect temperature uniformity. Therefore, in this study, temperature, wind velocity, and differential pressure were monitored in the aisle, at the sidewall inlet, and outside the sidewalls of a commercial stacked-deck cage broiler house in Northeast China aiming to continuously monitor the temperature throughout the entire fattening period. Results show that the maximum temperature difference increased from 1.85 °C to 6.43 °C, while the daily fluctuation increased from 2.27 °C to 5.80 °C. The highest temperature was consistently recorded at the side of the exhaust fans (p < 0.001) in the longitudinal direction. In the lateral direction, the temperature difference varies periodically with solar radiation. The average temperature at the southern location (23.58 ± 1.97 °C), which faces the sun, was higher than that at the northern location (23.35 ± 1.38 °C), which is in the shade, during periods of solar radiation (p < 0.001) at the last testing period. However, without solar radiation, the northern location recorded a warmer temperature (23.19 ± 1.41 °C) compared to the southern location (22.30 ± 1.67 °C) (p < 0.001). The lateral temperature differences are strongly positively correlated with solar radiation and wind speed (p < 0.001). In conclusion, the inside temperature nonuniformity and fluctuation increased as the broiler age increased, which affected the production performance of broilers. Nonuniform solar radiation and wind speed can lead to differences in the inlet temperature and air volume between both sidewalls, thereby affecting the uniformity of the lateral temperature inside the house. Full article

As air traffic intensity increases and stochastic uncertainties, such as wind direction and speed, continue to impact air traffic controllers’ workload significantly, airlines are increasingly pressured to reduce costs by flying via straighter/more direct trajectories. Due to these changes, it is important to search for new means/solutions for aircraft conflict resolution to ensure the required level of safety and rational flight trajectory. Such a solution could be the implementation of Dubin’s method of flight trajectories. This paper aims to propose and deeply analyze a new mathematical model for two-aircraft conflict resolution where the Dubins method is applied in a dynamic conflict scenario. In this model, at a certain moment, the flight trajectory of one aircraft follows a path similar to a moving circle’s tangential line. Upon that, the conflict detection and resolution (CDR) model considers wind uncertainty. The proposed CDR method could be applied when uncertainty such as wind direction and speed are inconstant (stochastic) throughout the simulation. Full article

Increasing wildfire activity has led to complex ecosystem consequences, with direct effects on the subsystems that affect the presence and movement of water. Although studies have investigated the cascading effects of wildfires on the water balance, our understanding of broad-scale groundwater modifications post fire remains unclear. This review aims to elucidate fire-induced shifts in the water balance, their causal factors, and their potential effects on groundwater recharge. By scrutinizing prior research examples that modeled post-fire recharge scenarios, the review highlights persistent knowledge gaps. The challenge of quantifying and integrating fire-induced alterations in precipitation, wind, and land temperature patterns into recharge projection models is specifically addressed. Despite these gaps, post-fire values of hydrologically meaningful parameters such as leaf area index (LAI), curve number (CN), and near-surface saturated hydraulic conductivity (K_ST) have been identified. Simulating post-fire recharge via the extrapolation of these values requires the consideration of site-specific conditions, vegetation recovery, and ash removal. It frequently results in a reduced interception and increased surface runoff, while evapotranspiration remains dependent on site-specific factors and often dictates groundwater recharge estimates. Although post-fire recharge simulations are inherently complex and imprecise, their growing application can guide land-use alterations and support policy implementation that considers fire-induced water availability changes. Full article

The given research employs high-resolution air quality monitoring and contemporary statistical methods to address gaps in understanding the urban air pollution in Pavlodar, a city with a significant industrial presence and promising touristic potential. Using mobile air quality sensors for detailed spatial data collection, the research aims to quantify concentrations of particulate matter (PM_2.5, PM₁₀), carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ground-level ozone (O₃); assess their distribution; and identify key influencing factors. In this study, we employed Geographic Information Systems (GISs) for spatial analysis, integrating multi-level B-spline interpolation to model spatial variability. Correlation analysis and structural equation modeling were utilized to explore the relationships between variables, while regression analysis was conducted to quantify these relationships. These techniques were crucial for accurately mapping and interpreting spatial patterns and their underlying factors. The study identifies PM_2.5 and NO₂ as the primary contributors to air pollution in Pavlodar, with NO₂ exceeding the 24 h threshold in 87.38% of locations and PM_2.5 showing the highest individual air quality index (AQI) in 75.7% of cases. Correlation analysis reveals a positive association between PM_2.5 and AQI and a negative correlation between NO₂ and AQI, likely due to the dominant influence of PM_2.5 in AQI calculations. Structural equation modeling (SEM) further underscores PM_2.5 as the most significant impactor on AQI, while NO₂ shows no significant direct impact. Humidity is positively correlated with AQI, though this relationship is context-specific to seasonal patterns observed in May. The sectoral analysis of landscape indices reveals weak correlations between the green space ratio (GSR) and air quality, indicating that while vegetation reduces pollutants, its impact is minimal due to urban planting density. The road ratio (RR) lacks sufficient statistical evidence to draw conclusions about its effect on air quality, possibly due to the methodology used. Spatial variability in pollutant concentrations is evident, with increasing PM_2.5, PM₁₀, and AQI towards the east-northeast, likely influenced by industrial activities and prevailing wind patterns. In contrast, NO₂ pollution does not show a clear geographic pattern, indicating vehicular emissions as its primary source. Spatial interpolation highlights pollution hotspots near industrial zones, posing health risks to vulnerable populations. While the city’s overall AQI is considered “moderate”, the study highlights the necessity of implementing measures to improve air quality in Pavlodar. This will not only enhance the city’s attractiveness to tourists but also support its sustainable development as an industrial center. Full article

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms. Full article