Variable Speed Research Articles

Interpretable machine learning-based analysis of coal fly ash flotation by conditioning process intensification

ABSTRACT Conditioning process intensification is necessary for improving the fly ash flotation effect. Understanding the relationship between conditioning parameters and flotation results is crucial for optimizing the conditioning-flotation process. This study developed a machine learning framework for predicting the yield, loss on ignition, and removal rate of unburned carbon using data from conditioning-flotation experiments. Feature variables of conditioning speed, impeller type, stirred tank diameter, collector dosage, and frother dosage were employed to develop five machine learning models. After tuning the hyper-parameters with GridSearchCV technique, it was found that Random Forest Regression model, eXtreme Gradient Boosting Regression model, and Gradient Boosting Regression model demonstrated good agreements between the predicted data and test data for the yield, loss on ignition, and removal rate of unburned carbon. The maximum R2 values were 0.9184, 0.8339, and 0.9434, respectively, while the minimum MSE values were 3.30, 5.82, and 20.59, respectively. The interpretable analysis using SHapley Additive exPlanations (SHAP) method indicated that conditioning speed was the most significant feature variable affecting the yield, loss on ignition, and removal rate of unburned carbon, while impeller type had a secondary impact. This investigation is expected to reveal the important role of conditioning process intensification in optimizing fly ash flotation.

Improved Power Control Based Variable Speed Wind-Turbine DFIG under Hard Work Conditions: Application of Sliding Mode Theory

Improved Power Control Based Variable Speed Wind-Turbine DFIG under Hard Work Conditions: Application of Sliding Mode Theory

Simulation of ambient temperature and humidity distribution in eight-span greenhouse under different wind conditions and corn height

The greenhouse environment suitable for crop growth usually depends on reasonable ventilation, and outdoor air flow state and indoor crop height are important factors affecting the ventilation effect. This study conducted simulation and experimental research on corn cultivation in an eight-span plastic greenhouse, analyzing the effects of outdoor wind speed, wind direction, and indoor crop height variations on the uniformity of temperature and relative humidity distribution as well as the air flow dynamics within the greenhouse. The study shows that as outdoor wind speed increases from 0.5 m/s to 2.5 m/s, for every 1 m/s increment, temperatures decrease by 2.2 °C and 0.9 °C, respectively. The increase in outdoor wind speed resulted in uniformity of greenhouse temperature and humidity along the vertical height, but exacerbated the degree of inhomogeneity in the east-west direction. The change in outdoor wind direction leads to differences in airflow direction and ventilation volume. On the south side of the greenhouse, while temperature is lower, the relative humidity is higher, with maximum differences in temperature and humidity between the north and south sides being 0.6 °C and 1.5 %, respectively, when the northeast wind blows. The east wind has the best cooling effect. The uneven degree of temperature and humidity distribution in the east and west direction of the greenhouse is positively correlated with crop height. This study can provide valuable insights for adjusting and optimizing natural ventilation strategies in large multi-span greenhouses.

Asynchronous localization of dynamic node through underwater acoustic sensor networks

In most cases, the operation of underwater acoustic sensor networks (UASNs) relies on accurate sensor location information. However, UASNs present more challenges than terrestrial sensor networks, such as the propagation speed variation with depth (i.e., stratification effect), asynchronous clock, node mobility, etc. In this paper, an efficient method of asynchronous localization is proposed by taking into account the stratification effect and node mobility. Firstly, a network is constructed that consists of surface buoys, AUVs, and target sensors. Secondly, an iterative least squares (LS) method is developed to locate AUV by establishing the relationship between propagation delay and location estimation. Thirdly, the passive mobility velocity of AUV is used to develop and solve the mobility model of the target sensors. Then, with the support of AUV, an asynchronous localization method is developed to estimate the target position, which improves the localization accuracy through motion and stratification compensation strategies. Moreover, the proposed method is validated through Cramer Rao lower bound (CRLB) analysis. Finally, simulation is performed to show that the proposed method is more efficient in reducing the estimation errors.

Experimental optimization of the FSW parameters to weld the dissimilar Al- and Cu-alloys by using additives at their joints

Friction stir welding (FSW) stands as a promising technique for joining materials, offering advantages over conventional methods. This study investigates the influence of tool tilt angle, rotational speed and profile on welded joint characteristics using a design of experiment (DOE) approach. The research objective aims to determine optimal parameter combinations for improved mechanical properties. Methodology entails the systematic variation of tilt angle, rotational speed and tool profile in a DOE setup, with subsequent FSW of samples. Mechanical testing, including tensile, bend and hardness tests, is conducted on 24 samples, with scanning electron microscope (SEM) and microstructure analysis on two specimens. Results reveal that higher tilt angles, rotational speeds and threaded profiles yield increased tensile strength but decreased ductility. Similarly, bend tests show higher fracture loads but reduced deflection before fracture under these conditions. Hardness tests indicate elevated hardness in welded areas, with variations based on input parameters. Main findings highlight the importance of parameter optimization for desired mechanical properties. Lower tilt angles and rotational speeds promote smoother material flow and uniform heating, resulting in consistent microstructure and hardness distribution. In conclusion, this study provides valuable insights for optimizing FSW parameters to achieve desired mechanical properties in welded joints. The combination of a 2° tilt angle, threaded tool profile and 1500 rpm rotational speed demonstrated the highest tensile strength, while a 1° tilt angle, cylindrical tool profile and 1000 rpm rotational speed yielded highest ductility.

Optimization of Controllable-Pitch Propeller Operations for Yangtze River Sailing Ships

The Yangtze River’s substantial variation in water depth and current speeds means that inland ships face diverse operational conditions within a single voyage. This paper discusses the adoption of controllable-pitch propellers, which adjust their pitch to adapt to varying navigational environments, thereby optimizing energy efficiency. We developed an optimization framework to determine the ideal pitch angle and rotation speed (RPM) under different sailing conditions. The energy performance model for inland ships was enhanced to account for the open-water efficiency of CPPs across various pitch angles and RPMs, considering the impacts of current and shallow water, among other factors. The optimization approach was refined by incorporating an improved genetic algorithm with an annealing algorithm to enhance the initial population, applying the K-means clustering algorithm for population segmentation, and using multi-parent crossover from diverse clusters. The efficacy of the optimization method for CPP operations was validated by analyzing three operational scenarios of a Yangtze sailing ship. Additionally, key components of the ship performance model were calibrated through experimental tests, demonstrating an anticipated fuel consumption reduction of approximately 5% compared to conventional fixed-pitch propellers.

Assessment of wind power potential and economic viability at Al-Hodeidah in Yemen: Supplying local communities with electricity using wind energy

Wind energy is a promising sector in the power generation industry because it is renewable and globally available. This study assessed the wind power potential and the economic viability of utilizing wind turbines at the chosen location to produce electricity. This work uses wind speed data collected during (2012–2014) at 10 m height. This research presents the maximum and mean values of measured wind speeds. We have analyzed annual, seasonal, and monthly variations of wind speed. The wind characteristics and wind power potential of the study site have been investigated using Weibull and Rayleigh distribution functions. The annual mean wind speed and Weibull parameters (shape and scale) have been determined to be 4.477 m/s, 2.494, and 5.046 m/s, respectively, according to the actual data collected over the three years. The annual mean power density and energy density have been computed to be 86.39 W/m2 and 757.226 kWh/m2 according to the actual data collected over the three years, and the predicted annual mean power density and energy density have been computed to be 86.492 W/m2 and 758.358 kWh/m2 using Weibull parameters and 164.372 W/m2 and 1441.2 kWh/m2 using Rayleigh distribution, respectively. Accordingly, the power density computed using the Weibull parameters was similar to the power density computed using real wind data, whereas the power density calculated by the Rayleigh function was higher. An economic analysis has been done to choose a suitable turbine for the study site. The results indicate that the wind turbine ZEFIR D14-Px-Ty is indeed a viable option for wind power generation in Al-Hodeidah. It has a relatively low cost of energy of $0.03/kWh and a high capacity factor of 0.428, indicating its efficiency in converting wind energy into electricity. Therefore, the selected location is recommended for some small stand-alone systems as well as small wind farms.

Linear Active Disturbance Rejection Control System for the Travel Speed of an Electric Reel Sprinkling Irrigation Machine

The uniformity of the travel speed of electric reel sprinkling irrigation machines is a key factor affecting irrigation quality. However, conventional PID control is susceptible to sudden disturbances under complex farmland conditions, leading to reduced speed uniformity. To enhance the robustness of the control system, it is necessary to investigate new disturbance rejection control algorithms and their effects. Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. The simulation results show that under variable speed conditions, the system exhibits no overshoot, with an adjustment time of 0.064 s; under variable load conditions, the speed vibration amplitude is less than 0.3%. The field test results indicate that at travel speeds of 10 m/h and 30 m/h, the maximum absolute deviation rate under IPSO-LADRC control is reduced by 27.07% and 13.98%, respectively, compared to PID control. The control strategy based on IPSO-LADRC effectively improves the control accuracy and robustness under complex farmland conditions, providing a reference for enhancing the control performance of other electric agricultural machinery.

Comparative analysis of offshore wind turbine blade maintenance: RL-based and classical strategies for sustainable approach

This study compares traditional methods like Corrective Maintenance (CM), Scheduled Maintenance (SM), and Condition-based Maintenance (CbM) with Reinforcement Learning (RL)-based offshore wind turbine (OWT) blade maintenance strategies. In order to address the dual challenge of minimizing carbon output while managing maintenance costs and operational efficiency, the study presents a mathematical model intended to estimate carbon emissions associated with OWT maintenance activities. The ability of the RL-based strategy to reduce the risk of fatigue failure in OWT blades and account for wind speed variability in maintenance schedule optimization is assessed. In order to provide a sustainable maintenance solution this strategy balances the trade-offs between economic profit and environmental effect. The findings demonstrate how RL can provide a balanced approach to maintenance that enhances both operational performance and environmental sustainability.

From Radar Sensor to Floating Car Data: Evaluating Speed Distribution Heterogeneity on Rural Road Segments Using Non-Parametric Similarity Measures

Rural roads, often characterized by winding paths and nearby settlements, feature frequent curvature changes, junctions, and closely spaced private accesses that lead to significant speed variations. These variations are typically represented by average speed or v85 profiles. This paper examines complete speed distributions along rural two-lane roads using Floating Car Data (FCD). The Wasserstein distance, a non-parametric similarity measure, is employed to compare speed distributions recorded by a radar Control Unit (CU) and a selected FCD sample. Initially, FCD speeds were validated against CU speeds. Subsequently, differences in speed distributions between the CU location and specific sections identified by sharp curves, intersections, or accesses have been assessed. The Wasserstein Distance is proposed as the most effective synthetic indicator of speed distribution variability along roadways, attributed to its metric properties. This measure offers a more concise and immediate assessment compared to an extensive array of statistical metrics, such as mean, median, mode, variance, percentiles, v85, interquartile range, kurtosis, and symmetry, as well as qualitative assessments derived from box plot trends.

New Fault Diagnosis Method for Rolling Bearings Based on Improved Residual Shrinkage Network Combined with Transfer Learning.

The fault diagnosis of rolling bearings is faced with the problem of a lack of fault data. Currently, fault diagnosis based on traditional convolutional neural networks decreases the diagnosis rate. In this paper, the developed adaptive residual shrinkage network model is combined with transfer learning to solve the above problems. The model is trained on the Case Western Reserve dataset, and then the trained model is migrated to a small-sample dataset with a scaled-down sample size and the Jiangnan University bearing dataset to conduct the experiments. The experimental results show that the proposed method can efficiently learn from small-sample datasets, improving the accuracy of the fault diagnosis of bearings under variable loads and variable speeds. The adaptive parameter-rectified linear unit is utilized to adapt the nonlinear transformation. When rolling bearings are in operation, noise production is inevitable. In this paper, soft thresholding and an attention mechanism are added to the model, which can effectively process vibration signals with strong noise. In this paper, the real noise is simulated by adding Gaussian white noise in migration task experiments on small-sample datasets. The experimental results show that the algorithm has noise resistance.

Decrease in muscle shortening and effect on strength and speed in adolescent soccer players (u-13)

In soccer, the variables of strength, speed and flexibility are associated with the athletic performance capacity of athletes, being these determinants not only to adapt to the demands of the competition, but at the same time flexibility can help to reduce the risk of injury. The aim of the study was to determine the effect on strength and speed after reducing muscle shortening. This is a quantitative study with an observational-analytical cross-sectional design. The evaluated sample consisted of 22 under-13 category players with an average age of 13.40±0.66 years, a height of 156.95±9.27 cm, and a body mass of 44.54±7.29 kg. They were randomly divided into a control group (n=10) and an experimental group (n=12). The experimental group underwent an 8-week intervention involving static stretches, incorporating the Proprioceptive Neuromuscular Facilitation method in the fourth week to decrease muscle shortening before the post-test. Knee extension and dorsiflexion were assessed using a goniometer, and physical abilities were measured through the My Jump Lab app, including Squat Jump, Counter Movement Jump, Counter Movement Jump with Arms, the Nordics hamstring test, and speed in 10, 20, and 30 meters using the Runmatic app. Significant results (p<0.05) were observed for jump height in Squat Jump, jump height, flight time, and speed in Counter Movement Jump, torque in the Nordics test, and speed in 30 meters. According to the findings of the present study it seems that flexibility influences vertical jumping ability and speed.

Space-Time Graph Path Planner for Unsignalized Intersection Management with a V2V Agent Coordination Architecture

Reducing traffic congestion and increasing passenger safety are important objectives for emerging automated transportation systems. Autonomous intersection management systems (AIMS) enable large scale optimization of vehicle trajectories with connected and autonomous vehicles (CAVs). We propose a novel approach for computing the fastest waypoint trajectory in intersections using graph search in a discretized space-time graph that produces collision-free paths with variable vehicle speeds that comply with traffic rules and vehicle dynamical constraints. To assist our planner algorithm in decentralized scenarios, we also propose a multi-agent protocol architecture for vehicle coordination for trajectory planning using a vehicle-to-vehicle (V2V) network. The trajectories generated allow a much higher evacuation rate and congestion threshold, with lower O(N) algorithm runtime compared to the state of the art conflict detection graph platoon path planning method, even for large scenarios with vehicle arrival rate of 1/s and thousands of vehicles.

Fuel-economy-optimal power regulation for a twin-shaft turboshaft engine power generation unit based on high-pressure shaft power injection and variable shaft speed

Fuel-economy-optimal power regulation for a twin-shaft turboshaft engine power generation unit based on high-pressure shaft power injection and variable shaft speed

Integer PI, Fractional PI and Fractional PI data trained ANFIS Speed Controllers for Indirect Field Oriented Control of Induction Motor

Induction motor drives with variable speed applications that employ vector control are quite popular nowadays because they provide strong dynamic performance and flexible speed control. By decoupling the torque-producing current components of stator current from the rotor flux, Indirect Field Oriented Control is recognized for generating excellent performance in induction motor drives. This investigation is being done to show the effectiveness of the novel FPI input-output data-trained ANFIS controller and compare the three controllers’ performance in terms of load variation capabilities, motor parameter variation, and speed tracking. Consequently, a comparison of the three controllers is important to select which controller performs high in induction motor drive. Indirect Field Oriented Control of induction motor with Fractional Proportional Integral (FPI), Integer Proportional Integral (IPI), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers are all discussed in this work along with their designs and comparative analysis. The square of error was used as a fitness function to genetically optimize the FPI and IPI controller parameters. The suggested Adaptive Neuro-Fuzzy Inference System (ANFIS) controller uses a hybrid learning approach. It is trained by the FPI controller's input-output data. Using the results of MATLAB simulations under various operating situations, the performance of the ANFIS controller was compared with FPI and IPI controllers. Because of FPI controller includes an extra parameter for adjustment, namely integration order, it performed better than IPI controller for speed control of the induction motor. According to the simulation findings, the percentage peak overshoots while employing ANFIS, FPI, and IPI controllers were 0.495%, 12.062%, and 14.699% respectively. As a result, ANFIS exhibits a drastic reduction in overshoot. Additionally, with the ANFIS controlled induction motor drive, the speed achieves the required set value at 0.14 seconds. For no load, constant, and changing loads, the induction motor drive's performance has been examined.

Market Tempo: Decoding Information Speed Across Global Stock Markets

This study explores the speed of information absorption in global stock markets using the Damodaran model. Analysing daily stock returns from 2005 to 2015 across 25 countries and 26 indices, the research categorizes markets into major, OECD, and emerging types. Major markets demonstrate the fastest adjustment, achieving a remarkable 1-day speed, followed by OECD markets with a 2–4-day span and emerging markets showing a 2–6-day duration for full information absorption. The results highlight significant mean speed variations across these market categories, offering practical insights for international businesses, corporations, and mutual funds navigating financial transactions globally. This study contributes valuable insights into information absorption speed, emphasizing the hierarchy of major, OECD, and emerging markets based on their speediness. Limitations include the exclusive focus on daily stock returns and the absence of specific diagnostic statistics for the Damodaran model.

Effect of rotation speed fluctuation on rotor noise generation: A numerical and experimental study

The practical operations of rotors for unmanned aircraft systems can undergo time-varying rotation speeds due to various uncertainty factors. This work investigates the aeroacoustic effect of the rotor’s rotation speed fluctuation using both numerical and experimental approaches. The measured real-time angular speed signal reveals that a hovering rotor experiences highly deterministic rotation speed variations in one revolution, which are closely related to the motor property. Delayed detached eddy simulations are performed, and the fluctuating rotation speed is realized by controlling the mesh displacement at each time step. The results suggest that significant additional tones are produced at the excitation frequency and neighbour frequencies, even though the amplitudes of the rotation speed fluctuations at the dominant frequencies are much smaller than the mean rotation speed. By comparing the full signal to individual harmonic components, it is found that the phase difference between each mode is unimportant. Therefore, the total aeroacoustic effect can be regarded as a linear superposition of different harmonic components. Aeroacoustic source analysis is also performed to isolate the contribution of various source types and correlate the far-field noise with the near-field source distribution. The results show that the extra tonal noise is mostly dominated by the loading noise, with a weaker contribution from the thickness noise. The agreement between the experiments and the simulations indicates that the rotation speed fluctuation is likely responsible for the generation of middle-frequency tonal noise at harmonics of the blade passing frequency.

Murmansk Marine Biological Institute of Kola Scientific Center of the Russian Academy of Sciences

The authors assess the wind potential indicators and the scale of its use for power supply of hard-to-reach off-grid settlements in the eastern Russian Arctic. The primary challenge facing autonomous power plants is fuel delivery due to long distances and underdeveloped transport infrastructure. The use of wind power plants will reduce fuel consumption and cut the cost of energy production. The researchers use ground-based measurement data and the characteristics of existing autonomous power plants as initial data when assessing the rational capacity of wind power plants. They employ authoring research methods. Via Wind-MCA program they estimate wind potential indicators: average wind speed, capacity factor, and coefficient of wind speed variation. The research results are presented in cartographic form. The rational capacities of wind power plants as part of hybrid energy systems are determined using the generalized dependencies method based on the maximum load of a populated area and calculated wind potential indicators, taking into account the characteristic intra-annual distribution of wind speeds. The best conditions for the development of wind energy are observed on the coast of the Arctic seas in the Krasnoyarsk Territory and the Chukot Autonomous Area. The rational capacity of wind power plants in zones of high wind potential in hard-to-reach areas of eastern Russian Arctic is estimated at 12,5 MW.

Modeling of an Adaptive HHO Gas Controller Based on Fuzzy Logic and Polynomial Function Controls to Improve Engine Torque of Gasoline Engine

Typical hydrogen-hydrogen-oxygen gas (HHO) usage to improve gasoline engine performance refers to the fixed HHO flow rate method, providing 0.25‒0.5 liters/minute of HHO for every 1000 cc engine size. However, the arising hypothesis expresses that the fixed HHO flow rate method does not optimally improve engine torque for various loads. The research objective is to propose an adaptive HHO gas controller that manages the HHO generator to produce an appropriate HHO flow rate for engine operation by adapting to load and engine speed variations. Hence, the engine torque improvement optimally occurs for various loads. The adaptive HHO controller combines fuzzy logic and polynomial function controls involving real-time engine data, such as mass airflow (MAF), air-fuel ratio (AFR), and the commanded AFR from the engine control unit (ECU), whose values vary with load and engine speed. A system simulation based on Matlab-Simulink investigates engine performance improvement due to the controller. The results show that the adaptive HHO flow rate due to the proposed adaptive HHO controller improves engine torque during small, medium, and big-loaded engine operations, respectively, by 1.5%‒4.7%, 6.8%‒26.8%, and 21.1%‒72.8% depending on engine speed 2500 rpm‒4000 rpm) and the commanded AFR (12.6‒15.4). Conversely, under the same condition, the fixed HHO flow rate with 0.75 liters/minute HHO for a 1500 cc engine, used for comparison, improves the engine torque by 0%‒0.6%, 0.3%‒14.2%, and 9.3%‒50.1%, respectively. The data show that the adaptive HHO controller provides better improvement. Moreover, the adaptive HHO controller improves engine thermal efficiency and reduces AFR error against the commanded AFR.

Topological prevalence of variable speed of convergence in the deterministic chaos game

Let A be the attractor of a Banach contractive iterated function system (IFS) on a complete space. We prove that the orbit generated by a typical (in the sense of Baire category) driver recovers A with every possible speed. Our result extends the one from the paper: Leśniak et al. (Chaos 32(1):013110, 2022). We also show that our result is optimal from a certain point of view.