Large Speed Research Articles

Numerical study of wind characteristics at a long-span bridge site in mountain valley

Wind loads play an important role in the design and construction of long-span bridges, especially for that built in mountain valley, and wind characteristics in mountain terrain are still unclear. To further understand the wind characteristics at a bridge site in a mountain valley, large eddy simulations were employed to comprehensively study the wind characteristics at the bridge site in an L-shaped canyon. Different wind directions consisting of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315° were considered, and the wind characteristics on the main girder and towers, as well as the flow field, were comprehensively investigated. The results indicate that the mean and fluctuating wind characteristics are significantly different from those specified in the relevant specifications, and it is inappropriate to apply the specifications directly to mountainous terrain. When wind flows along a channel, it leads to a large wind speed amplification factor, strong non-Gaussianity, and large kurtosis and skewness. The steeper the slope of the mountain, the stronger the non-Gaussianity of the fluctuating wind. The coherence function remains nearly unchanged when the distance between the two points is constant, regardless of the location. The wind characteristics at the main girder and towers are so different that they should be considered separately and taken into account in engineering practice. This study provides a reference for the wind-resistant design of bridges and structures built on mountainous terrains.

A fast method for robust estimation of gearbox instantaneous speed

Under non-stationary condition, estimating the instantaneous frequency (IF) of shaft from vibration signal is an ideal way for gearbox fault monitoring without tachometer. Among them, the method based on harmonic phase demodulation has always occupied a dominant position. In the low signal-to-noise ratio (SNR) condition, the noisy harmonic components may cause greater IF estimation error. In this work, an improved approach for estimating the IF of gearbox reference shaft is proposed based on the short time Fourier transform spectrogram. The theoretical derivation proves that the high-order meshing harmonic can reduce the IF estimation error of gearbox reference shaft. By constructing the spectrogram weight factor, an improved maximum tracking algorithm is proposed to redefine the IF estimation range. On this basis, the robust IF estimation of high-order meshing harmonic with low SNR is realized. Finally, it is equivalent to the estimated IF of gearbox reference shaft. The proposed strategy is compared with the two advanced IF estimation methods. The civil aircraft engine dataset and the wind turbine gearbox dataset provided in the Safran contest demonstrate that the proposed strategy works. In the case of heavy load and large speed change, the proposed method has extremely low consumption time while maintains good estimation accuracy.

Field-Oriented Control of a Nine-Phase Cage Induction Generator with Large Speed Changes and Variable Load

This paper presents a voltage control system for multiphase squirrel-cage induction generators operating at a high variability of speed and variable load. Field-oriented vector control was used with a change in the sequence of the stator phase currents what changes the number of poles of the magnetic field produced by nine-phase stator winding. At low speeds, the current sequence is changed so that the number of poles increases allowing for the desired voltage to be obtained with greater efficiency. The task of the automatic control system was to control the DC voltage to a desired value at the output of the multiphase PWM converter. This is an alternative control method to the scalar control of voltage and frequency presented in a previous work. The control method and parameters of the automatic control system result from the mathematical model of the multiphase induction machine. The results of the laboratory tests were compared with the effects of the operation of the same nine-phase scalar controlled generator.

Efficient selection of gravitationally lensed OH megamasers with MeerKAT and the Square Kilometre Array

ABSTRACT There has been a recent resurgence in hydroxyl (OH) megamaser research driven by Square Kilometre Array (SKA) precursor/pathfinder telescopes. This will continue in the lead-up to the SKA mid-frequency array, which will greatly expand our view of OH megamasers and their cosmic evolution over ≳80 per cent of the age of the Universe. This is expected to yield large scientific returns as OH megamasers trace galaxy mergers, extreme star formation, high molecular gas densities, and potentially binary/dual supermassive black hole systems. In this paper, we predict the distortion to the OH luminosity function that a magnification bias will inflict, and in turn, predict the distortion on the OH megamaser number counts as a function of redshift. We identify spectral flux density thresholds that will enable efficient lensed OH megamaser selection in large spectral line surveys with MeerKAT and SKA. The surface density of lensed galaxies that could be discovered in this way is a strong function of the redshift evolution of the OH megamaser luminosity function, with predictions as high as ∼1 lensed OH source per square degree at high redshifts (z ≳ 1) for anticipated SKA spectral line survey designs. This could enable efficient selection of some of the most highly obscured galaxies in the Universe. This high-redshift selection efficiency, in combination with the large survey speed of the SKA at ≲1 GHz frequencies and the high magnifications possible with compact OH emission regions (μOH ≫ 10), will enable a transformational view of OH in the Universe.

Repeatedly Programmable Liquid Crystal Dielectric Elastomer with Multimodal Actuation.

Dielectric elastomers (DEs) are actuatable under an electric field, whose large strain and fast response speed compare favorably with natural muscles. However, the actuation of DE-based devices is generally limited to a single mode and cannot be reconfigured after fabrication, which pales in comparison to biological counterparts given the ability to alter actuation modes according to external conditions. To address this, liquid crystal dielectric elastomers (LC-DEs) that can alter the dielectric actuation modes based on the thermally triggered shape-changing are prepared. Specifically, the two shapes through the LC phase transition possess different bending stiffness, which leads to distinct actuation modes after an electric field is applied. Moreover, the two shapes can be individually programmed/reprogrammed, that is, the one before the transition is regulated through force-directed solvent evaporation and the one after the transition is via bond exchange-enabled stress relaxation. As such, the multimodal dielectric actuation behaviors upon temperature change can be readily diversified. Meanwhile, the space charge mechanism endows LC-DEs with the significantly reduced driving e-field (8 V µm-1) and bidirectional actuation manners. It is believed this unique adaptivity in the actuation modes under a low electric field shall offer versatile designs for practical soft robots.

Experimental investigation of wind turbine stress and power characteristics under dynamic changes in wind direction

ABSTRACT The dynamic inlet conditions of variable wind direction had an obvious influence on the response of wind turbine stress and power, and accurately analyzing their changing characteristics is of great significance to enhancing wind turbines’ stability and efficient operation. Experiments were performed in a wind tunnel, where the wind turbine was mounted on a rotating platform to simulate dynamic changes in wind direction at prescribed speeds and angles. The wind turbine’s stress and power were measured. Results show that the stress was minimally reduced by 4.4% (tower) and 1.7% (blade) when the wind direction change angle was small. Maximum stress reduction of 92% (tower) and 93% (blade) with increasing wind direction change angle. As the wind direction change speed increased, the magnitude of the drop in blade stress decreased by 6% ~32%. The decrease in power coefficient and rotational speed at small wind direction change angles and large wind direction change speeds were not significant, and the fluctuations were more obvious. The coupling of aerodynamic deterioration and inertial effects during dynamic changes in wind direction was one of the main reasons for the variations in the wind turbine power and stress response.

MARKET BASKET ANALYSIS MENGGUNAKAN ALGORITMA HASH BASED PADA DATA PENJUALAN DI O2 SWALAYAN

O2 Supermarkets have stock of goods available in the warehouse. During this time there is a buildup of goods in the warehouse because there is no system to analyze transaction data so that losses occur. Therefore, a system is needed to manage these goods in order to reduce stocks of goods that are not selling well and make stocks of goods that sell well in warehouses through consumer spending patterns. To analyze data on consumer spending patterns, the Market Basket Analysis Method can be used. This method can be used to analyze consumer spending patterns. The algorithm used is the Hash Based and Association Rule Algorithm because it can overcome the determination of frequent itemsets from k-itemset candidates with a large number and speed up information on consumer spending patterns. With existing data and using this algorithm it can produce a lot of information. The results of this study get 6 Rules with a minimum Confidence of 50% and minimum Support>= 4. From consumer spending patterns that can be used as a strategy to support sales, knowing which items are selling best, placing goods so that they are more efficient in picking up the goods desired by consumers, not only shopping patterns but also more controlled stock of goods.

How the air slows a closing book

When two flat surfaces approach each other, the fluid in between is accelerated and ejected from the sides at large speeds. This situation occurs often in everyday life, such as when you step in a puddle and create splashes of water, or when you clap your hands or close a book and create jets of air. For these systems, the inertia of the fluid resists the acceleration, creating large nonlinear forces on the flat surfaces. In this work, we study the case of a closing book. The fluid motion in this case is relatively easy to model, using the conservation of mechanical energy, and to measure using a MEMS gyroscopic sensor. This study reveals the unusual forces that occur when two plates collide in an experiment that can be performed by students at home.

A study of identity authentication using blockchain technology in a 5G multi-type network environment

The 5G technology, known for its large bandwidth, high speed, low latency, and multi-connection capabilities, significantly accelerates digital transformation in enterprises, especially in addressing factory automation challenges. It facilitates efficient machine-to-machine (M2M) and device-to-device (D2D) connectivity, ensuring rapid data transfer and seamless process convergence under 5G standards. Although 5G offers substantial communication and low latency benefits, its limited indoor coverage requires the deployment of decentralized antennas or small base stations. In contrast, Wi-Fi 6 seamlessly complements 5G, providing superior indoor mobile connectivity. This integration is crucial for businesses looking to accelerate digital transformation. To optimize 5G, the deployment of devices such as bypass switches, SDN switches, and MEC in the 5G Local Breakout network enables user access control and fast authentication. Real-world validation confirms the effectiveness of these measures, which are expected to lead to the future of 5G mobile networks.

Steering droplets on substrates with plane-wave wettability patterns and deformations.

Motivated by strategies for targeted microfluidic transport of droplets, we investigate how sessile droplets can be steered toward a preferred direction using travelling waves in substrate wettability or deformations of the substrate. To perform our numerical study, we implement the boundary-element method to solve the governing Stokes equations for the fluid flow field inside the moving droplet. In both cases we find two distinct modes of droplet motion. For small wave speed the droplet surfs with a constant velocity on the wave, while beyond a critical wave speed a periodic wobbling motion occurs, the period of which diverges at the transition. These observation can be rationalized by the nonuniform oscillator model and the transition described by a SNIPER bifurcation. For the travelling waves in wettability the mean droplet velocity in the wobbling state decays with the inverse wave speed. In contrast, for travelling-wave deformations of the substrate it is proportional to the wave speed at large speed values since the droplet always has to move up and down. To rationalize this behavior, the nonuniform oscillator model has to be extended. Since the critical wave speed of the bifurcation depends on the droplet radius, this dependence can be used to sort droplets by size.

Effectiveness of Field Oriented Control and Direct Torque Control Methods for Induction Motor Speed Regulation

Induction motors are used extensively in many sectors, including manufacturing, power generation, healthcare, and transportation. Robust speed regulation of inductor motors is a key requirement for ensuring their intended usage and increasing their effectiveness. This study analyzes two advanced and popular methods for regulating the torque and speed of induction motors, namely, field-oriented control (FOC) and direct torque control (DTC). Detailed mathematical modeling of both methods is described, providing the in-depth perspective of the control algorithms and problem variables. This is followed by a thorough analysis of the performance of the control methods. Different control scenarios have been analyzed using simulation with MATLAB Simulink under different speed and loading conditions. The outcomes show that FOC provides better performance in terms of reduced torque jitter, smooth torque, and speed tracking response for highly variable load and reference speed profiles. Further, the FOC is found to generate better current waveforms. This leads to improving the power factor, decreasing electrical noise, and enhancing the motor performance. Meanwhile, the DTC demonstrates a strong capability to handle large speed changes and provide faster torque response, but it suffers from considerable torque variation. The simulation outcomes also suggest that the method selected to tune PI controllers is effective. The findings contribute to enhancing the efficient operation of induction motors and fostering their applications in diverse sectors for improved productivity and service, and superior economic gain.

Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder.

ASD (autism spectrum disorder) is a complex developmental and neurological disorder that impacts the social life of the affected person by disturbing their capability for interaction and communication. As it is a behavioural disorder, early treatment will improve the quality of life of ASD patients. Traditional screening is carried out with behavioural assessment through trained physicians, which is expensive and time-consuming. To resolve the issue, several conventional methods strive to achieve an effective ASD identification system, but are limited by handling large data sets, accuracy, and speed. Therefore, the proposed identification system employed the MBA (modified bat) algorithm based on ANN (artificial neural networks), modified ANN (modified artificial neural networks), DT (decision tree), and KNN (k-nearest neighbours) for the classification of ASD in children and adolescents. A BA (bat algorithm) is utilised for the automatic zooming capability, which improves the system's efficacy by excellently finding the solutions in the identification system. Conversely, BA is effective in the identification, it still has certain drawbacks like speed, accuracy, and falls into local extremum. Therefore, the proposed identification system modifies the BA optimisation with random perturbation of trends and optimal orientation. The dataset utilised in the respective model is the Q-chat-10 dataset. This dataset contains data of four stages of age groups such as toddlers, children, adolescents, and adults. To analyse the quality of the dataset, dataset evaluation mechanism, such as the Chi-Squared Statistic and p-value, are used in the respective research. The evaluation signifies the relation of the dataset with respect to the proposed model. Further, the performance of the proposed detection system is examined with certain performance metrics to calculate its efficiency. The outcome revealed that the modified ANN classifier model attained an accuracy of 1.00, ensuring improved performance when compared with other state-of-the-art methods. Thus, the proposed model was intended to assist physicians and researchers in enhancing the diagnosis of ASD to improve the standard of life of ASD patients.

A new framework for evaluating dust emission model development using dichotomous satellite observations of dust emission

Dust models are essential for understanding the impact of mineral dust on Earth's systems, human health, and global economies, but dust emission modelling has large uncertainties. Satellite observations of dust emission point sources (DPS) provide a valuable dichotomous inventory of regional dust emissions. We develop a framework for evaluating dust emission model performance using existing DPS data before routine calibration of dust models. To illustrate this framework's utility and arising insights, we evaluated the albedo-based dust emission model (AEM) with its areal (MODIS 500 m) estimates of soil surface wind friction velocity (us∗) and common, poorly constrained grain-scale entrainment threshold (u∗ts) adjusted by a function of soil moisture (H). The AEM simulations are reduced to its frequency of occurrence, P(us∗>u∗tsH). The spatio-temporal variability in observed dust emission frequency is described by the collation of nine existing DPS datasets. Observed dust emission occurs rarely, even in North Africa and the Middle East, where DPS frequency averages 1.8 %, (~7 days y−1), indicating extreme, large wind speed events. The AEM coincided with observed dust emission ~71.4 %, but simulated dust emission ~27.4 % when no dust emission was observed, while dust emission occurrence was over-estimated by up to 2 orders of magnitude. For estimates to match observations, results showed that grain-scale u∗ts needed restricted sediment supply and compatibility with areal us∗. Failure to predict dust emission during observed events, was due to us∗ being too small because reanalysis winds (ERA5-Land) were averaged across 11 km pixels, and inconsistent with us∗ across 0.5 km pixels representing local maxima. Assumed infinite sediment supply caused the AEM to simulate dust emission whenever P(us∗>u∗tsH), producing false positives when wind speeds were large. The dust emission model scales of existing parameterisations need harmonising and a new parameterisation for u∗ts is required to restrict sediment supply over space and time.

Stochastic motion under nonlinear friction representing shear thinning.

We study stochastic motion under a nonlinear frictional force that levels off with increasing velocity. Specifically, our frictional force is of the so-called Coulomb-tanh type. At small speed, it increases approximately linearly with velocity, while at large speed, it approaches a constant magnitude, similarly to solid (dry, Coulomb) friction. In one spatial dimension, a formal analogy between the associated Fokker-Planck equationand the Schrödinger equationfor a quantum mechanical oscillator in a nonharmonic Pöschl-Teller potential is revealed. Then, the stationary velocity statistics can be treated analytically. From such analytical considerations, we determine associated diffusion coefficients, which we confirm by agent-based simulations. Moreover, from such simulations and from numerically solving the associated Fokker-Planck equation, we find that the spatial distribution function, starting from an initial Gaussian shape, develops tails that appear exponential at intermediate timescales. At small magnitudes of stochastic driving, the velocity distribution resembles the case of linear friction, while at large magnitudes, it rather approaches the case of solid (dry, Coulomb) friction. The same is true for diffusion coefficients. In a certain sense thus interpolating between the two extreme cases of linear friction and solid (dry, Coulomb) friction, our approach should be useful to describe several cases of practical relevance. For instance, a reduced increase in friction with increasing relative speed is typical of shear-thinning behavior. Therefore, driven motion in shear-thinning environments is one specific example to which our description may be applied.

Short‐term prediction of wind power based on temporal convolutional network and the informer model

AbstractIn this study, a new short‐term wind power prediction model based on a temporal convolutional network (TCN) and the Informer model is proposed to solve the problem of low prediction accuracy caused by large wind speed fluctuations in short‐term prediction. First, an input feature selection method based on the maximum information coefficient is proposed after considering the problem of information interference caused by excessively large input features. A dynamic time planning method is used to select the optimal input step of historical power. Then, the combined forecasting model composed of TCN and the Informer is constructed in accordance with the numerical weather forecast and historical power data. Lastly, the pinball loss function is used to expand the prediction model into a quantile regression model, measure the effect of volatility, quantify the volatility range of prediction, and finally, obtain a deterministic prediction result. The actual measured data of wind farms in the Bohai Sea area are selected for analysis and calculation. The results show that the prediction model proposed in this study achieves better accuracy in deterministic prediction and interval prediction than the traditional model.

Quantified the influence of different synoptic weather patterns on the transport and local production processes of O3 events in Pearl River Delta, China

Regional ozone (O3) pollution in the Pearl River Delta (PRD) region has become a topic of discussion in recent years. The occurrence of regional O3 pollution are influenced by local emissions and cross-regional transportation. In this study, we identified the predominant synoptic patterns that were associated with regional O3 pollution from August to November in 2015–2021 using the Lamb-Jenkinson classification technique. All synoptic types were divided into four major categories of NE-type, C-type, S-type and A-type, which accounted for 42 %, 25 %, 18 % and 15 % of the total number of regional O3 pollution days, respectively. The weather conditions for each synoptic pattern were described by using MERRA-2 datasets. Then a rapidly method was established to quantify the contribution of cross-regional processes to high O3 concentration in different synoptic patterns over the PRD through the WRF-Flexpart model. The NE-type weather condition was characterized by a relatively large wind speed with a significant cross-regional transport contribution of 35.8 %. The A-type weather condition had moderate surface wind speed with the stable weather condition, resulting in a lower cross-region transport contribution of 27.7 %. Under controlled by C-type, the stagnant weather condition caused by low-pressure systems on its periphery, would suppress diffusion of O3. As a result, the regional O3 pollution in the PRD were mostly attributed to locally (87.9 %) with minimal cross-regional transport (12.1 %). The S-type weather condition was mainly associated with the West Pacific Subtropical High and the surface equalization pressure field, accompanied by low wind speed. Therefore, the considerable (minor) contribution of local production (cross-regional transport) of 83.3 % (16.7 %) to O3 pollution in the PRD is a consequence of the stagnation weather condition.

Observed near-inertial waves generated by tropical and extratropical cyclones in the East China Sea

Both tropical and extratropical cyclones can affect the East China Sea (ECS) shelf in summer. Based on a current mooring deployed in 2014, near-inertial waves (NIWs) triggered by both extratropical and tropical cyclones were examined. During the observation period, three tropical cyclones (i.e., NEOGURI, MATMO, and NAKRI) passed by the mooring site. MATMO and NAKRI led to obvious near-inertial responses. The weakest tropical cyclone (MATMO) caused the strongest NIWs due to its large translation speed and favorable position relative to the mooring site. Mode 1 dominates the vertical structure of the NIWs caused by tropical cyclones. Due to the modulation of the background vorticity, the NIWs show a significant blueshift. However, the NIWs caused by extratropical cyclones are mainly concentrated near the surface layer with a higher proportion of mode-2 NIWs. The highest value of the near-inertial kinetic energy generated by extratropical cyclones is about half of that generated by tropical cyclones. In addition, both two kinds of cyclones can lead to an enhancement of sub-inertial current in the ECS shelf. The nonlinear interactions between NIWs and semidiurnal internal tides (D2) are also evidenced, with the NIWs being a potential trigger for D2-f waves. This study highlights the important role of NIW on the nonlinear interactions and energy transfer within the internal wave spectral continuum in the continental shelf region.

GAMP-Based Low-Complexity Sparse Bayesian Learning Channel Estimation for OTFS Systems in V2X Scenarios

Vehicle to everything (V2X) is widely regarded as a critical application for future wireless communication networks. In V2X, large relative speeds between vehicles may severely deteriorate the performance of communication between vehicles. Orthogonal time frequency space (OTFS) modulation, which converts time- and frequency-selective channels into non-selective channels in the delay-Doppler (DD) domain, provides a solution for establishing reliable wireless communications in V2X scenarios. However, in the complex multi-scattering scenarios, the channel also suffers from a serious inter-Doppler interference (IDI) problem, which poses a great challenge to the accurate demodulation of OTFS receiver signals. To address the above problems, this paper considers the variation of Doppler sampling points within one symbol when deriving the channel model, which effectively overcomes the IDI problem, and employs a basis expansion model (BEM) to convert the channel estimation into a sparse recovery problem for the basis coefficients. In addition, to better utilize the sparse nature of the OTFS channel, a generalized approximate message passing-sparse Bayesian learning (GAMP-SBL)-based algorithm is employed to estimate the basis coefficients of the channel. The complexity of this algorithm is greatly reduced compared to the conventional SBL algorithm. Finally, system simulation results are reported to verify the superiority of the proposed scheme.

Modeling the Drying Process of Porous Catalysts: Impact of the Pore Size Distribution.

The distribution of catalytically active species in heterogeneous porous catalysts strongly influences their performance and durability in industrial reactors. A drying model for investigating this redistribution was developed and implemented using the finite volume method. This model embeds an analytical approach regarding the permeability and capillary pressure from arbitrary pore size distributions. Subsequently, a set of varying pore size distributions are investigated, and their impact on the species redistribution during drying is quantified. It was found that small amounts of large pores speed up the drying process and reduce internal pressure build up significantly while having a negligible impact on the final distribution of the catalytically active species. By further increasing the amount of large pores, the accumulation of species at the drying surface is facilitated.

Wide-angle high-precision electronically controlled continuous beam scanning implementation based on MLAS cascaded AFOC

Microlens array scanner (MLAS), as a rapidly growing branch of the semisolid micro-mechanical beam scanning system, can achieve large-scale beam scanning through small-scale (μm) physical motion, which is an effective beam scanning scheme that balances aperture, field of view (FOV), inertia, and other vital indicators. However, the discrete addressing caused by the periodic structure of microlens array (MLA) will affect the scanning accuracy in applications. We design and develop a continuous laser beam scanning device with an effective aperture of 20 mm based on MLAS and adaptive fiber collimator (AFOC), and the scanning beam quality is close to the diffraction limit. In addition, the electrical control mechanism of the device is explained, which achieves a scan FOV of 20°, a scan angular velocity of 10,000°/s, and two-dimensional (2D) scan. This approach effectively realizes a beam scanning system with excellent comprehensive performance, including large aperture, large scan angle, high precision, high speed, tunable wavelength, and high power.