Optimal Speed Research Articles

Winding process of fibre-reinforced thermoplastic tubes with integrated tape production through in-situ roving impregnation and infrared consolidation

This paper introduces a novel method for producing fibre-reinforced thermoplastic tubes by integrating tape production and consolidation into a single operation. This innovation diverges from conventional methods by combining processes to reduce costs by using raw materials instead of organotapes, allowing customised material combinations and utilising residual heat from tape production. The new process uses carbon roving and molten low-viscosity PA6 granulates, processed through a new direct impregnation setup in siphon design. Key advancements include a high-speed impregnation module capable of up to 1 m/s with high-performance extruders, cost-effective infrared emitters for winding, and a powered consolidation unit with adjustable winding angles between ± 65° and ± 90°. Experiments demonstrate operational speeds of approximately 471 mm/min, with an optimal cross-winding speed of 354 mm/min due to the technical limitations of the laboratory extruder and IR emitter used. Based on the technical limitations of the current system, future improvements and methodological changes will be discussed.

Theoretical Analysis on Active Polarization Control of Fiber Laser Based on Root Mean Square Propagation Algorithm

High-power linearly polarized fiber lasers are widely used in coherent beam combination, nonlinear frequency conversion, and gravitational wave detection. With the increase in output power, it is challenging for fiber lasers to maintain a high polarization extinction ratio (PER). Combined with intelligent techniques, active polarization control is a prospective method to obtain the laser output with high PER and high stability. We demonstrate a comprehensive model of an active polarization control system. The root mean square propagation (RMS-Prop) algorithm is used to control the non-polarization-maintaining (non-PM) fiber laser to generate linearly polarized laser. The parameters of the RMS-Prop algorithm are theoretically analyzed, including cost function, perturbation amplitude, and global learning rate. The simulation results show that PER is the optimal cost function. When the perturbation amplitude is 0.06 and the global learning rate is 0.6, the system can achieve the optimal control speed and accuracy. By comparison with the stochastic parallel gradient descent (SPGD) algorithm, the RMS-Prop algorithm has an advantage in obtaining higher PER.

Experimental Study of an Industrial Data Transmission Network in the Automatic Control System of a Wind Turbine

This article explores and optimizes network technologies for wind energy systems, focusing on the RS-485 interface to ensure reliable data transmission in extreme conditions. The study aims to address the impact of various distortions on data quality and wind turbine management. A system was proposed with two wind turbines, each equipped with a Raspberry Pi 4, connected to sensors measuring temperature, vibration, and wind speed. The research examined how data transmission rates affect signal shape, calculating the distortion coefficient. At 460,800 baud, the signal was almost completely distorted, with significant amplitude loss. The distortion coefficients were 1.84 for logic ‘1’ and 1.92 for logic ‘0’. The optimal speed to minimize distortions was found to be 19,200 baud, providing the most stable signal. Additionally, temperature significantly impacted transmission quality, highlighting the need to consider climatic conditions in system design. The findings and methods can help improve existing data transmission systems and enhance wind turbine performance.

Hybrid wind speed optimization forecasting system based on linear and nonlinear deep neural network structure and data preprocessing fusion

Wind speed time series forecasting has been widely used in wind power generation. However, the nonlinear and non-stationary characteristics of wind speed make accurate wind speed forecasting a difficult task. In recent years, the rapid development of artificial intelligence and machine learning technology provides a new solution to the problem of wind speed forecasting. Combining the advance of artificial intelligence and data analysis strategy, this paper proposes a wind speed forecasting system based on multi-model fusion and integrated learning. Considering the differences in data observation and training principles of various algorithms and exploiting the advantages of each model, a wind speed forecasting system with multiple machine learning algorithms embedded in integrated learning is constructed, which includes three modules: data preprocessing, optimization and forecasting. The data preprocessing module can conduct quantitative analysis through input data decomposition and feature extraction, and the combination of multi-objective intelligent optimization algorithm and combined forecasting method can effectively forecast the wind speed time series. The validity of the algorithm is verified using the data of Shandong wind farm in China. The forecasting results show that compared with the traditional single model forecasting, the proposed integrated wind speed forecasting system based on the fusion of multiple models has higher forecasting accuracy.

A hybrid sparrow optimization Kriging model and its application in geological modeling

With the proposal of intelligent mines, the demand for drilling is increasing daily. Therefore, it is particularly crucial to gather more geological data by interpolation of limited drilling data for subsequent three-dimensional geological modeling. In this paper, a hybrid sparrow optimization Kriging model (HSSA), in which chaos theory and Levy flight are integrated into the initial population update algorithm of the sparrow algorithm and the location update algorithm of the entrants, is proposed. Next, the golden sine optimization algorithm is introduced into the reconnaissance and early warning mechanism of the sparrow algorithm to further improve the accuracy and local escape ability. By the correlation optimization of the original sparrow algorithm, the speed and accuracy of swarm intelligence optimization are further improved. In addition, the model solves the parameters of the variation function of the ordinary Kriging interpolation and reduces the generation error of the formation data interpolation. The results of relevant experiments show that the hybrid sparrow optimization Kriging model improves the accuracy and convergence speed compared with other swarm intelligence algorithms and that the accuracy of this model is improved by 8.4% compared with the original Kriging interpolation algorithm. Based on the hybrid sparrow optimization Kriging model, we propose a three-dimensional stratigraphic model for the Yangchangwan Coal Mine, which provides further support for mining operations and three-dimensional stratigraphic research in this area. The accuracy and applicability of the hybrid sparrow optimization Kriging model are further explained using a case study with the stratigraphic model data in the Yangchangwan Coal Mine. HSSA with significant potential for applications in industries such as coal mining and geological exploration. In these fields, the efficient acquisition, processing, and modeling of stratigraphic data are critical for enhancing geological interpretation and optimizing operational workflows.

A Note on the Optimal Speed of Transition: Aghion and Blanchard Revisited

Abstract This note illustrates, by reconsidering the seminal optimal speed-of-transition model of Aghion, P., and O. J. Blanchard. (1994. “On the Speed of Transition in Central Europe.” NBER Macroeconomics Annual 9: 283–319), that optimal transition paths, in general, exhibit nonlinearities and discontinuities. Aghion and Blanchard consider only an approximate solution with a constant unemployment rate over the transition process. The exact solution features an increasing unemployment rate with a discontinuity when the state sector is closed down at the optimally chosen endpoint of transition. Economic transition problems bear many similarities to scrap value problems with free terminal time, often encountered in resource economics. In relation to the transition to a green economy, the discussion in this note therefore casts doubt on the optimality of a green transition discussed in, e.g., the European Union in terms of politically specified rather than optimally designed milestones for emissions reductions, i.e., by −55 % compared to 1990 levels until 2030 and net zero until 2050.

Aerodynamic study of a leading-edge rotating cylinder in a cambered aerofoil (NACA2412)

Purpose Flow separation over an aircraft’s wing beyond a specific angle of attack is challenging. Flow boundary layer manipulation has been investigated to improve aerofoil lift and mitigate flow separation difficulties including stall and drag. This is solved via active or passive flow control. Active flow control method moving surface boundary (MSB) enhances shear flow momentum, making it effective. MSB is easier than suction and blowing. Asymmetrical airfoils, which generate lift in aircraft wings, have received less MSB research than symmetrical ones. The purpose of this study is to asses the design efficacy of MSB’s NACA 2412 aerofoil. Design/methodology/approach To observe the performance of MSB in NACA 2412, a computational model has been created, and aerodynamical performance has been analyzed. Findings The study results show that the NACA 2412 with MSB has better aerodynamic efficiency than the NACA 2412 base design. It works best when it reaches its optimal speed and the delay in flow separation works well. Research limitations/implications Limitations may include specific aerofoil applicability, external factors and simulation constraints. Implications guide future research for broader insights and applications. Practical implications Improving asymmetrical aerofoil performance, mitigating stall effects, reducing drag and optimizing designs with moving surface boundary. Insights gained can enhance overall aircraft efficiency and flow control techniques. Originality/value The MSB flow control in a chambered aerofoil is less explored and not explored enough in wing-based aerofoils, and the optimal cylinder speed ratio trend has been discussed at each angle of attack studied.

Gorilla algorithm based on double random perturbation and its engineering application

Aiming at the problems of artificial gorilla troop optimization algorithm, such as easy to fall into local optimum, slow convergence speed and low optimization accuracy, an artificial gorilla troop optimization algorithm based on double random perturbation strategy is proposed. Firstly, the Halton sequence is introduced to initialize the population to increase the diversity of the population; secondly, a multi-dimensional random number strategy is used in the algorithm optimization stage and an adaptive position search mechanism is proposed in the exploration stage to improve the convergence speed of the algorithm; thirdly, a double random perturbation strategy is proposed to solve the group effect of gorillas and enhance the ability of the algorithm to jump out of the local optimum; finally, a dimension-by-dimensional update strategy is adopted to update individual positions, which improves the convergence accuracy of the algorithm. Through the comparison of the optimization results of 10 benchmark test functions and the Wilcoxon rank sum test, it can be seen that the improved algorithm has greatly improved the optimization accuracy and convergence speed. In addition, through the experimental comparative analysis of an engineering optimization problem, the superiority of the proposed algorithm in dealing with real engineering problems is further verified.

Wind speed effect on infrared-image-based coal and gangue recognition with liquid intervention in LTCC

The commitment to achieving carbon neutrality by 2060 makes the clean transition of fossil energy production inevitable in China. As the primary fossil energy source, China produced approximately 4.66 billion tons of raw coal in 2023. Among them, in the fields of mining engineering and environmental science, the accurate recognition of gangue (one of the environmental pollutants produced by coal combustion is black-gray solid waste) from coal is crucial because it is a key step in reducing resource waste and environmental pollution. Hence, coal and gangue automatic recognition with high accuracy is very important for intelligent longwall top coal caving (LTCC) mining. A novel approach of “liquid intervention + infrared detection” is introduced to enhance the recognition accuracy when the exterior color of coal and gangue is similar. This study focuses on the infrared image recognition of coal and gangue after liquid intervention under varying wind speed conditions, and optimizes the image processing path. The effect of varying wind speed conditions on the recognition accuracy of coal and gangue after liquid intervention was analyzed, and the optimal wind speed range was determined. The morphological evolution characteristics of droplets under varying wind field conditions were studied by COMSOL two-phase flow interface. The results show that with the increase of wind speed, the surface contour of coal is gradually clear, and the overall recognition accuracy is also improved. When the high wind speed is 1.55 m/s, the recognition accuracy reaches 99.89%, and the recognition accuracy of other wind speed regions is also above 90% (within 7s). In addition, the numerical simulation can maintain the volume fraction of the droplet in a short time under the constant flow wind flow field, thus inhibiting the phenomenon that the recognition accuracy decreases with time. The simulation results are in good agreement with the physical experimental phenomena, which proves the reliability of the numerical simulation. This study is of great significance to the recognition of coal gangue types with low identification characteristics in LTCC, so as to ensure the primary economic benefits, ecological environment safety and sustainable development of coal mines.

Optimal Speed Control of Hybrid Stepper Motors through Integrating PID Tuning with LFD-NM Algorithm

Optimal Speed Control of Hybrid Stepper Motors through Integrating PID Tuning with LFD-NM Algorithm

Research on adaptive hydraulic drive optimization control of concrete mixing tank truck for open-pit mine.

The non-axisymmetric problem caused by the fluid sloshing in the tank of a mining concrete mixing tank truck during driving is affected by the excitation of complex road surfaces. The fluid sloshing is coupled with the dynamics of the vehicle body due to the excitation of the complex road surface. The traditional hydraulic drive proportional integral differential (PID) control method is not effective in dealing with such problems, which can easily lead to accidents such as overturning. To improve the accuracy and stability of the hydraulic drive control system, this paper proposes an optimized particle filter PID adaptive control method based on the elastic firefly (FA) algorithm to accelerate the convergence speed of control parameter optimization, and then analyzes its hydraulic drive control characteristics and structural applications, and discusses step steering and double lane change modes are simulated under filling rates of 1.5 and 2.0, respectively. The experimental results show that compared with traditional PID control, the proposed adaptive control method can significantly reduce the average speed error of hydraulic drive control to 0.03km/h and the maximum speed error to 0.17km/h. It also improves the control tracking performance and stability. The practicality of the adaptive hydraulic drive is verified in the filling rate experiments under step steering and double-lane shifting conditions. It has important reference value for the practical application of hydraulic drive control optimization of mining concrete mixing transport tank trucks.

Overcoming the Strong Sample Solvent Effect for Sustainability Measurement Challenges in Liquid Chromatography. Example for Bisphenol-A.

This paper describes an approach to achieve low parts per billion (ppb) concentration level detection using a reversed-phase ultrahigh-performance liquid chromatographic ultraviolet absorbance detection method with large-volume feed injection (FI) for analytes in dichloromethane (DCM). FI is a novel technology that allows sample injection at a defined speed into the LC mobile phase. We demonstrate this approach for a mixture of bisphenol A and its diglycidyl ether derivatives in DCM. DCM is a very strong injection solvent at reversed-phase LC conditions and is typically immiscible with a starting reversed-phase gradient mobile phase containing a high percentage of water. As a result, reduced separation performance and even peak splitting are seen with classic flow-through injection. The method setup comprised an octyl-bonded core-shell stationary phase (150 × 4.6 mm i.d., 2.7 μm particle size) with a water/acetonitrile mobile phase gradient and an exceptionally high injection volume of 45 μL of DCM solution using FI. Optimization of feed speed (1%) and isocratic hold duration (4 min) was crucial for final separation conditions. FI delivered more than a 20-fold improvement in the limit of detection (LOD) compared to standard flow-through injection while maintaining peak resolution. The LOD of the final method ranged between 1 and 10 ppb in the polymer resin. This methodology has high potential for trace analysis in a large variety of applications that suffer from the "strong solvent effect".

2-Axis controlled spray pyrolysis deposition device for Dye-sensitized Solar Cell (DSSC)

Dye-sensitized solar cells (DSSCs) are a promising alternative to traditional silicon-based photovoltaic systems due to their efficient light-to-electricity conversion. A critical component of DSSCs is titanium dioxide (TiO2), responsible for converting light into electrical energy. Spray pyrolysis was one of the methods for fabricating TiO2 thin films. However, there are several drawbacks, such as challenges in particle size control, maintaining homogeneity of the thin film, and scalability issues during the deposition process. Modifications to the manufacturing process are necessary to achieve optimal performance in DSSCs, particularly with the thickness of the cell. This work focuses on the 2-axis spray pyrolysis process, a cost-effective way to create thin and thick films. In particular, it focuses on TiO2 thin films utilized as working electrodes in DSSC applications. The method was performed at different motor speeds, namely MS80, MS100, and MS120. The X-ray diffraction (XRD) spectrum showed that the dominance of the anatase phase appeared in an MS100. The UV-Vis results depict that the band gap value is 3.02 eV. The surface profiler analysis indicates that sample MS100 has an optimal thickness of 15.17 µm. The DSSC achieved 9.4% efficiency with sample MS100. This finding demonstrates that using 2-axis controlled spray pyrolysis deposition improves DSSC performance with an optimal motor speed.

Optimization of Coreless PCB Coils Based on a Modified Taguchi Tuning Method for WPT of Pedelec

The printed circuit board (PCB) winding coil offers advantages such as small size, high precision, high repeatability, and low cost, making it conducive to the miniaturization of electronic equipment and a popular choice in wireless power transmission systems. This paper aims to clarify the correlation between induction parameters and inductive capabilities using the orthogonal array of the modified Taguchi method for Pedelec applications. The conventional Taguchi method typically achieves only local optimization; however, this paper considers practical application conditions and combines experimental data to establish the initial values of the orthogonal array, thereby achieving global optimization. Additionally, the tuning process of the Taguchi method replaces physical experiments with simulations, enhancing optimization speed and reducing hardware implementation costs. The performance index for the proposed modified Taguchi tuning method is selected as a combination of the quality factor (Q) and coupling coefficient (k) to minimize AC resistance and improve system efficiency. To validate the proposed method, the designed coils were implemented and tested in a WPT system based on S–S compensation with a half-bridge topology. The experimental results demonstrate that the optimized PCB coil parameters derived from the proposed tuning method accurately validate the method’s effectiveness and accuracy. From the measured results with the proposed modified tuning method, the system efficiency is increased by 43.87% and the system transmitting power is increased by 28.51%.

Maritime Inventory Routing with Speed Optimization: A MIQCP Formulation for a Tanker Fleet Servicing FPSO Units

This paper presents a continuous-time formulation for the management of deep-sea floating production storage and offloading (FPSO) units, which process and store crude oil from nearby oil platforms while waiting for a shuttle tanker to arrive and collect it. A heterogeneous fleet of tanker vessels travelling between the FPSO units and a port refinery is available, with the optimization deciding on the number and type of vessels to use, their route and travelling speed. The goal is to achieve an environmentally friendly solution featuring vessels travelling at lower speeds to minimize fuel consumption (a quadratic function of speed), which may require renting additional shuttle tankers to maintain production. The resulting mixed-integer quadratically constrained problems (MIQCP) can be solved by GUROBI, but not to global optimality, whereas a mixed-integer linear programming (MILP) relaxation that underestimates the total operating cost can tackle moderate problem sizes (5 FPSOs and 4 shuttle tankers).

Optimization of eccentric speed during supine medicine ball chest throw

Optimization of eccentric speed during supine medicine ball chest throw

Adaptive Frequency Green Light Optimal Speed Advisory Based on Deep Reinforcement Learning

Adaptive Frequency Green Light Optimal Speed Advisory Based on Deep Reinforcement Learning

Designing of Ecological Model Predictive Control Algorithm for Electric Trucks Platoons with Optimal Energy Consumption

Abstract In response to the problem of motor vehicle exhaust emissions pollution, the development of new energy in China’s truck industry will be an inevitable trend. At present, the penetration rate and market share of pure electric trucks are constantly increasing. The research on electric truck platoons has strong practical significance. This article focuses on the ecological control strategy of pure electric truck platoons. Firstly, a platoon model of four electric trucks is established in Trucksim. The leading vehicle designs an ecological optimal speed dynamic programming algorithm, and in terms of following vehicle tracking control, the battery SOC state motor speed is considered to be improved, Designed an ecological model predictive control algorithm for electric truck platoon with optimal energy consumption. Using real road slope data in the mining area, a Trucksim/Matlab/Simulink joint simulation was conducted. The simulation results showed that compared with traditional constant speed cruise control, the proposed ecological predictive cruise control algorithm for pure electric commercial vehicle platoons saved 9.2577% of the overall battery SOC.

Adaptive Speed Optimization Strategy at Signalized Intersections Based on the Penetration Rate of Connected Automated Vehicles

Adaptive Speed Optimization Strategy at Signalized Intersections Based on the Penetration Rate of Connected Automated Vehicles

Balancing Carrier Dynamics in Oxygen-Vacancy-Tuned Amorphous Ga2O3 Thin-Film Self-Powered Photoelectrochemical-Type Solar-Blind Photodetector Arrays for Underwater Imaging.

Underwater imaging technology plays a pivotal role in marine exploration and reconnaissance, necessitating photodetectors (PDs) with high responsivity, fast response speed, and low preparation costs. This study presents the synergistic optimization of responsivity and response speed in self-powered photoelectrochemical (PEC)-type photodetector arrays based on oxygen-vacancy-tuned amorphous gallium oxide (a-Ga2O3) thin films, specifically designed for solar-blind underwater detection. Utilizing a low-cost one-step sputtering process with controlled oxygen flow, a-Ga2O3 thin films with varying oxygen vacancy (VO) concentrations are fabricated. By balancing the trade-offs among electrocatalytic reactions, charge transfer, carrier recombination, and trapping, both the responsivity and response speed of a-Ga2O3-based self-powered PEC-PDs are simultaneously improved. Consequently, the optimized PEC-PDs demonstrated exceptional performance, achieving a responsivity of 33.75mA W-1 and response times of 12.8ms (rise) and 31.3ms (decay), outperforming the vast majority of similar devices. Furthermore, a pronounced positive correlation between anomalous transient photocurrent spikes and the concentration of VO defects is observed, offering compelling evidence for VO-mediated indirect recombination. Finally, the proof-of-concept solar-blind underwater imaging system, utilizing an array of self-powered PEC-PDs, demonstrated clear imaging capabilities in seawater. This work provides valuable insight into the potential for developing cost-effective, high-performance a-Ga2O3 thin-film-based PEC-PDs for advanced underwater imaging technology.