Search Results (586)

The use of pressure-sensitive paints (PSPs), an optical oxygen sensing technique, to visualise and measure the surface pressure on vehicle models in wind tunnel testing is becoming increasingly prevalent. Porphyrins have long been the standard luminophore for PSP formulations, with the majority employing the red-emitting platinum(II)-5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorphenyl)-porphyrin. nIR-emitting luminophores, such as Pt(II) tetraphenyl tetrabenzoporphyrins, possess distinct advantages over visible emitting luminophores. In particular, they have wider spectrally useful ‘windows’, facilitating the insertion of a secondary visible emitting temperature-sensitive luminophore to be used for internal calibration without spectral crosstalk that detrimentally impacts PSP performance. In this work, we explore the effect of changing the loading quantity of an nIR-emitting para-CF₃ Pt(II) benzoporphyrin luminophore on the performance of PSP formulations. An optimal luminophore loading of 1.28% wt/wt benzoporphyrin luminophore to polystyrene binder was identified, resulting in a low temperature sensitivity at 100 kPa of 0.61%/K and a large pressure sensitivity at 293 K of 0.740%/kPa. These strong performance metrics, for a polystyrene-based PSP, demonstrate the efficacy of benzoporphyrin luminophores as an attractive luminophore option for the development of a new generation of high-performance PSP formulations that outperform current commercially available ones. Full article

To overcome the complexity of yarn color measurement using spectrophotometry with yarn winding techniques and to enhance consistency with human visual perception, a yarn color measurement method based on digital photography is proposed. This study employs a photographic colorimetry system to capture digital images of single yarns. The yarn and background are segmented using the K-means clustering algorithm, and the centerline of the yarn is extracted using a skeletonization algorithm. Spectral reconstruction and colorimetric principles are then applied to calculate the color values of pixels along the centerline. Considering the nonlinear characteristics of human brightness perception, the final yarn color is obtained through a nonlinear texture-adaptive weighted computation. The method is validated through psychophysical experiments using six yarns of different colors and compared with spectrophotometry and five other photographic measurement methods. Results indicate that among the seven yarn color measurement methods, including spectrophotometry, the proposed method—based on centerline extraction and nonlinear texture-adaptive weighting—yields results that more closely align with actual visual perception. Furthermore, among the six photographic measurement methods, the proposed method produces most similar to those obtained using spectrophotometry. This study demonstrates the inconsistency between spectrophotometric measurements and human visual perception of yarn color and provides methodological support for developing visually consistent color measurement methods for textured textiles. Full article

This study introduces a predictive model for estimating indoor room temperatures in vernacular building using external environmental factors such as air temperature, humidity, sunshine duration, and wind speed. The dataset was sourced from the Meteorology, Climatology, and Geophysics Agency and supplemented with direct measurements collected from four rooms within a vernacular building in Aceh Province, Indonesia. A Generative Adversarial Network (GAN)-based imputation technique was implemented to address missing data during preprocessing. The prediction model adopts a hybrid framework that integrates Multiple Linear Regression (MLR) and Artificial Neural Networks (ANNs), with both models optimized using Support Vector Regression (SVR) to better capture the nonlinear dynamics between inputs and outputs. The evaluation results show that the ANN-SVR model achieved the lowest average $\overline{MAE}$ and $\overline{RMSE}$ values, at 0.164 and 0.218, respectively, and the highest average $\overline{R}$ and $\overline{R^2}$ values, at 0.785 and 0.618. Evaluation results indicate that the ANN-SVR model consistently achieved the lowest error rates and the highest correlation coefficients across all four rooms, identifying it as the most effective model for forecasting indoor thermal conditions. These results validate the combined use of ANN-SVR for prediction and GAN for preprocessing as a powerful strategy to enhance data quality and model performance. The findings offer a scientific basis for architectural planning to improve thermal comfort in vernacular buildings such as the Rumoh Aceh. Full article

This paper addresses the problem of controlling quadcopters operating in an environment characterized by unpredictable disturbances such as wind gusts. From a control point of view, this is a nonstandard, highly challenging problem. Fundamentally, these quadcopters are high-order dynamical systems characterized by an under-actuated and highly nonlinear model with coupling between several state variables. The main objective of this work is to achieve a trajectory by tracking desired altitude and attitude. The problem was tackled using a robust control approach with a multi-loop nonlinear controller combined with extended Kalman filtering (EKF). Specifically, the flight control system consists of two regulation loops. The first one is an outer loop based on the backstepping approach and allows for control of the elevation as well as the yaw of the quadcopter, while the second one is the inner loop, which allows the maintenance of the desired attitude by adjusting the roll and pitch, whose references are generated by the outer loop through a standard PID, to limit the 2D trajectory to a desired set path. The investigation integrates EKF technique for sensor signal processing to increase measurements accuracy, hence improving robustness of the flight. The proposed control system was formally developed and experimentally validated through indoor tests using the well-known Parrot Mambo unmanned aerial vehicle (UAV). The obtained results show that the proposed flight control system is efficient and robust, making it suitable for advanced UAV navigation in dynamic scenarios with disturbances. Full article

An innovative method is proposed to analyze the coupled vibration between random traffic and large-span bridges under the combined action of wind loads. The dynamic behavior of bridges subjected to these multifactorial influences is investigated through a comprehensive bridge dynamics model. Specifically, a refined full-bridge finite element model is developed to simulate the traffic–bridge coupled vibration, with wind forces applied as external dynamic loads. The effects of wind speed and vehicle speed on the coupled system are systematically evaluated using the finite element software ABAQUS 2023. To ensure computational accuracy and efficiency, the large-span nonlinear dynamic solution method is employed, integrating the Newmark-β time integration method with the Newton–Raphson iterative technique. The proposed method is validated through experimental measurements, demonstrating its effectiveness in capturing the synergistic impacts of wind and traffic on bridge dynamics. By incorporating the stochastic nature of traffic flow and combined wind forces, this approach provides a detailed analysis of bridge responses under complex loading conditions. The study establishes a theoretical foundation and practical reference for the safety assessment of large-span bridges. Full article

Wind shear at the Hong Kong International Airport (HKIA) poses a significant safety risk due to terrain-induced airflow disruptions near the runways. Accurate assessment is essential for safeguarding aircraft during take-off and landing, as abrupt changes in wind speed or direction can compromise flight stability. This study introduces a hybrid framework for short-term wind shear prediction based on data collected from Doppler LiDAR systems positioned near the central and south runways of the HKIA. These systems provide high-resolution measurements of wind shear magnitude along critical flight paths. To predict wind shear more effectively, the proposed framework integrates a signal processing technique with a deep learning strategy. It begins with optimized variational mode decomposition (OVMD), which decomposes the wind shear time series into intrinsic mode functions (IMFs), each capturing distinct temporal characteristics. These IMFs are then modeled using bidirectional gated recurrent units (BiGRU), with hyperparameters optimized via the Tree-structured Parzen Estimator (TPE). To further enhance prediction accuracy, residual errors are corrected using Extreme Gradient Boosting (XGBoost), which captures discrepancies between the reconstructed signal and actual observations. The resulting OVMD–BiGRU–XGBoost framework exhibits strong predictive performance on testing data, achieving R² values of 0.729 and 0.926, RMSE values of 0.931 and 0.709, and MAE values of 0.624 and 0.521 for the central and south runways, respectively. Compared with GRUs, LSTM, BiLSTM, and ResNet-based baselines, the proposed framework achieves higher accuracy and a more effective representation of multi-scale temporal dynamics. It contributes to improving short-term wind shear prediction and supports operational planning and safety management in airport environments. Full article

The aerial measurement of wind speed and direction is important for the development of the low-altitude economy, meteorology, climate research, and renewable energy systems. Existing UAV-based wind measurements, whether instrument-based or flight-dynamic-based, consistently exhibit bias and significant errors, limiting their reliability for precise wind estimation. This study introduces a machine learning (ML) approach to improve the accuracy of the wind speed and direction estimation using UAVs. The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. The experiments reveal that incorporating a 10 s smoothing window yields a root mean square error (RMSE) value of 0.39 m/s for the wind speed (horizontal) and an even lower bias (≤0.069 m/s) when using a 60 s smoothing window, representing a marked improvement over traditional techniques. These results are particularly promising at longer smoothing windows (>50 s), where the ML-based approach achieves superior accuracy compared to LiDAR measurements. The findings underscore the potential of integrating machine learning with UAV-based wind measurement systems to achieve higher precision and reliability in wind characterization. Full article

Robust dynamic equivalents of large power networks are essential for fast and reliable stability analysis of bulk power systems. This is because the dimensionality of modern power systems raises convergence issues in modern stability-analysis programs. However, even with modern computational power, it is challenging to find reduced-order models for power systems due to the following factors: the tedious mathematical analysis involved in the classical reduction techniques requires large amounts of computational power; inadequate information sharing between geographical areas prohibits the execution of model-dependent reduction techniques; and frequent fluctuations in the operating conditions (OPs) of power systems necessitate updates to reduced models. This paper focuses on a measurement-based approach that uses a deep artificial neural network (DNN) to estimate the dynamics of an external system (ES) of a power system, enabling stability analysis of a study system (SS). This DNN technique requires boundary measurements only between the SS and the ES. However, machine learning-based techniques like this DNN are known for their extensive training requirements. In particular, for power systems that undergo continuous fluctuations in operating conditions due to the use of renewable energy sources, the applications of this DNN technique are limited. To address this issue, a Deep Transfer Learning (DTL)-based technique is proposed in this paper. This approach accounts for variations in the OPs such as time-to-time variations in loads and intermittent power generation from wind and solar energy sources. The proposed technique adjusts the parameters of a pretrained DNN model to a new OP, leveraging symmetry in the balanced adaptation of model layers to maintain consistent dynamics across operating conditions. The experimental results were obtained by representing the Queensland (QLD) system in the simplified Australian 14 generator (AU14G) model as the SS and the rest of AU14G as the ES in five scenarios that represent changes to the OP caused by variations in loads and power generation. Full article

Quantifying methane (CH₄) emissions is essential for climate change mitigation; however, current estimation methods often suffer from substantial uncertainties, particularly at the site level. This study introduces a drone-based approach for measuring CH₄ emissions using an open-path Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor mounted parallel to the ground, rather than in the traditional nadir-pointing configuration. Controlled CH₄ release experiments were conducted to evaluate the method’s accuracy, employing a modified mass-balance technique to estimate emission rates. Two wind data processing strategies were compared: a logarithmic wind profile (LW) and a constant scalar wind speed (SW). The LW approach yielded highly accurate results, with an average recovery rate of 98%, while the SW approach showed greater variability with increasing distance from the source, although it remained reliable in close proximity. The method demonstrated the ability to quantify emissions as low as 0.08 g s⁻¹ with approximately 4% error, given sufficient sampling. These findings suggest that the proposed UAV-based system is a promising, cost-effective tool for accurate CH₄ emission quantification in sectors, such as agriculture, energy, and waste management, where traditional monitoring techniques may be impractical or limited. Full article

Power systems need to meet the ever-increasing demand for higher quality and reliability of electricity in distribution systems while remaining sustainable, secure, and economical. The globe is moving toward using renewable energy sources to provide electricity. An evaluation of the influence of artificial intelligence (AI) on the accomplishment of SDG7 (affordable and clean energy) is necessary in light of AI’s development and expanding impact across numerous sectors. Microgrids are gaining popularity due to their ability to facilitate distributed energy resources (DERs) and form critical client-centered integrated energy coordination. However, it is a difficult task to integrate, coordinate, and control multiple DERs while also managing the energy transition in this environment. To achieve low operational costs and high reliability, inverter control is critical in distributed generation (DG) microgrids, and the application of artificial neural networks (ANNs) is vital. In this paper, a power management strategy (PMS) based on Inverter Control and Artificial Neural Network (ICANN) technique is proposed for the control of DC–AC microgrids with PV-Wind hybrid systems. The proposed combined control strategy aims to improve power quality enhancement. ensuring access to affordable, reliable, sustainable, and modern energy for all. Additionally, a review of the rising role and application of AI in the use of renewable energy to achieve the SDGs is performed. MATLAB/SIMULINK is used for simulations in this study. The results from the measures of the inverter control, m, VL-L, and Vph_rms, reveal that the power generated from the hybrid microgrid is reliable and its performance is capable of providing power quality enhancement in microgrids through controlling the inverter side of the system. The technique produced satisfactory results and the PV/wind hybrid microgrid system revealed stability and outstanding performance. Full article

Diagnostic systems for drive with AC motors of key importance for machine safety require the use of limitations related to the processing of measurement information. These limitations result in significant difficulties in assessing the technical condition of the object’s components. The article proposes the use of a combination of artificial intelligence techniques in the form of shallow and convolutional structures in the diagnostics of stator winding damage from an induction motor. The proposed approach ensures a high level of defect detection efficiency while using information preserved in samples from three periods of current signals. The research presents the possibility of combining the data classification capabilities of self-organizing maps (SOMs) with the automatic feature extraction of a convolutional neural network (CNN). The system was verified in steady and transient operating states on a test stand with a 1.5 kW motor. Remarkably, this approach achieves a high detection precision of 97.92% using only 600 samples, demonstrating that this reduced data acquisition does not compromise performance. On the contrary, this efficiency facilitates effective fault detection even in transient operating states, a challenge for traditional methods, and surpasses the 97.22% effectiveness of a reference system utilizing a full 6 s signal. Full article

This study presents a wind speed prediction model using monthly average wind speed data, employing the Extreme Gradient Boosting (XGBoost) algorithm to enhance forecasting accuracy for wind farm operations. Accurate wind speed forecasting is crucial for optimizing energy production, ensuring grid stability, and improving operational planning. Existing studies on enhancing wind speed prediction using ML algorithms have some drawbacks based on accuracy, efficient prediction, and stuck-in-local-optima parameters. The dataset comprises monthly average wind speed measurements, and extensive preprocessing is conducted to prepare the data for machine learning. Various hyperparameter tuning techniques, including Randomized Search, Grid Search, and Bayesian Optimization, are applied to improve prediction accuracy. The performance of the model is evaluated utilizing key metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Continuous Ranked Probability Score (CRPS), and Maximum Error. The results indicate that hyperparameter tuning significantly improves model accuracy. Specifically, Grid Search demonstrates superior performance for short-term (one-month) forecasting, while Randomized Search is more effective for long-term (six-month) forecasting. The findings emphasize the critical importance of hyperparameter tuning strategies in the development of reliable wind speed forecasting models, which have significant implications for the efficient management of wind energy resources. Full article

The effective management of landfills requires advancements in techniques for rapid data collection and analysis of gas emissions. This work aims to refine methane (CH₄) emission data acquired from landfills by applying a robust geostatistical method to drone-collected measurements. Specifically, we use UAV-mounted laser spectrophotometer technology (TDLAS-UAV) to gather rapid, high-resolution data, which can sometimes be noisy due to atmospheric variations and sensor drift. For data handling, the key innovation is the application of the local indicator of spatial association (LISA), a technique that typically provides p-values to assess the statistical significance of observed spatial clusters. This approach was applied both on an areal basis and on a linear basis, following the order of data acquisition, and it produced comparable results. Very low p-values are considered indicative of non-random clustering, suggesting the influence of an underlying spatial control factor. These results were subsequently validated through independent flux chamber surveys. This validation confirms the reliability and objectivity of our geostatistical method in improving drone-based methane emission assessments. The research highlights the need to optimize drone flight paths to ensure a uniform spatial distribution of data and reduce edge effects. It notes that many CH₄ flux measurements often yield non-detectable results, suggesting a review of detection limits. Future work should refine UAV flight patterns and data processing with semi-controlled experiments—using known methane sources—to determine optimal acquisition parameters, such as flight height, sampling frequency, grid resolution, and wind influence. Full article

This paper presents a vibration analysis method for detecting typical faults in gears of the drive train of a 2 MW wind turbine. The data were collected over a one-year period from an operating wind turbine with a gearbox composed of one planetary stage and two helical gear stages. Failures in two pairs of helical gears were identified: one involving pitting and wear in the gears connecting the intermediate-speed shaft to the low-speed shaft, and another one involving significant material detachment in the gears connecting the intermediate-speed shaft to the high-speed shaft. The continuous evaluation of time signals, frequency spectra, and amplitude modulations allowed the most sensitive sensors and frequencies for predicting surface damage on gear teeth in this type of turbine to be determined. A steady-state frequency analysis was performed, enabling the detection of the aforementioned surface faults. This approach is simpler compared with more complex transient-state techniques. By tracking vibration signals over time, the importance of analyzing gear mesh frequencies and their harmonics was highlighted. Additionally, it was found that the progression of gear damage was dependent on the power output of the wind turbine. As a result, the most appropriate ranges of power were identified, within which the evolution of the vibration measurement was associated with the damage evolution. Since many turbines currently in operation have similar designs and power output levels, the present findings can serve as a guideline for monitoring an extensive number of units. Full article

Atmospheric pollution due to an increased particulate matter (PM) concentration imposes a threat for human health. This is particularly true for regions with intensive industrial activity and nature-based solutions, such as tree plantations, are adopted to mitigate the phenomenon. Here, we report on the case of the lignite complex of western Macedonia (LCWM), the largest in Greece, where extensive Robinia pseudoacacia L. plantations have been established during the last 40 years for post-mining reclamation, but their PM retention capacity and the controlling parameters have not been assessed to date. Thus, during the 2021 growth season (May to October), we determined the PM10 capture by leaves sampled twice per month, across four 10-m long transects, each consisting of five trees, and at three different heights along the tree canopy. During the same period, we also measured the leaf area index (LAI) of the plantations and collected climatic data, as well as data on PM10 production by the belt conveyors system, the main polluting source at the site. We estimated that the plantations’ foliage captures on average c. 42.85 μg cm⁻² PM10 and we developed a robust linear model that describes PM10 retention on a leaf area basis, as a function of PM10 production, LAI (a proxy of seasonal changes in leaf area), distance from the emitting source, and wind speed and foliage height within the crown. The accuracy of the estimates and the performance of the model were tested with the bootstrap cross-validate resampling technique. PM10 retention increased in spring and early summer following the increase in LAI, but its peak in August and October was controlled by the highest PM10 production, due to elevated energy demands. Moreover, PM10 retention was facilitated by wind speed, and it was higher at the lower part of the trees’ canopy. On the contrary, the PM10 load on the trees’ foliage decreased with an increasing distance from the conveyor belt system and the frontline of the plantations. Our findings support the positive role of R. pseudoacacia plantations for PM10 retention at heavily polluted areas, such as the lignite mines in Greece, and provide a model for the estimation of PM10 retention by their foliage based on basic environmental drivers and characteristics of the plantations, which could be helpful for planning their future management. Full article