Periodic Fluctuations Research Articles

Ensuring Reliable Network Communication and Data Processing in Internet of Things Systems with Prediction-Based Resource Allocation

The distributed nature of IoT systems and new trends focusing on fog computing enforce the need for reliable communication that ensures the required quality of service for various scenarios. Due to the direct interaction with the real world, failure to deliver the required QoS level can introduce system failures and lead to further negative consequences for users. This paper introduces a prediction-based resource allocation method for Multi-Access Edge Computing-capable networks, aimed at assurance of the required QoS and optimization of resource utilization for various types of IoT use cases featuring adaptability to changes in users’ requests. The method considers the current resource load and predicted changes in resource utilization based on historical request data, which are then utilized to adjust the resource allocation optimization criteria for upcoming requests. The proposed method was developed for scenarios utilizing edge computing, e.g., autonomous vehicle data exchange, which can be susceptible to periodic resource demand fluctuations related to typical rush hours, predictable with the proposed approach. The results indicate that the proposed approach can increase the reliability of processes conducted in IoT systems.

The depth of the thermal influence zone of the surface in the snow cover

The subject of the study is the snow cover, which determines the formation of the thermal regime of soils in winter. The purpose of the work is to determine the depth of the zone of thermal influence of the surface in the snow cover. That is, the determination of the zone of temperature fluctuations (daily, decadal) in the snow cover when the temperature of the atmospheric air changes. Determining the depth of this zone is important both for taking into account the formation of the properties of the snow cover itself, and for choosing a method for modeling the process of thermal interaction of the atmosphere with the ground in the presence of snow cover. In particular, the possibility of taking into account snow cover as thermal resistance in modeling thermal processes. To assess the depth of thermal influence, the well-known Goodman formula was used, obtained by solving the corresponding problem of thermal conductivity by the integral method and representing the dependence of the depth of the zone of temperature change in a solid with an abrupt change in surface temperature on time and thermal conductivity of the material (in this case, snow of a certain density). To determine the thermal conductivity, the formulas of Abels and Osokin were used to determine the thermal conductivity coefficient of snow depending on density. At the same time, it was taken into account that the density of snow cover is a variable in depth, determined by the linearized Abe formula. Alternatively, a snow cover with a density equal to the average integral density in depth is considered. Dependences are obtained to determine the duration of the attenuation period of surface temperature fluctuations at a certain depth of snow cover. An indicator of the change in the depth of vibration attenuation (the depth of thermal influence) is proposed. To assess the effect of snow reclamation, a formula is proposed that allows us to determine the degree of change in the duration of the period of complete attenuation of temperature in depth during compaction of snow cover, depending on the compaction coefficient. A dependence has been obtained linking the depth of the zone of thermal influence with the duration of the period of daily temperature fluctuations on the surface of the snow cover and its density. Comparison of the calculated data according to the obtained formulas with the data on the depth of attenuation of daily temperature fluctuations in snow cover with different snow densities, given in the literature, showed good convergence. This allows us to recommend the obtained formulas for practical use in assessing the process of formation of the thermal regime of snow cover.

Tracing the source and behaviour of sulphate in karst reservoirs, using stable isotopes and Bayesian isotopic-mixing models.

Increases in sulphate concentrations in natural water bodies can lead to the deterioration of water quality. Human activities, such as coal mining and agricultural fertilisation, can generate sulphate, which can enter water bodies through surface runoff or underground pipelines. Owing to the widespread distribution of coal-bearing strata and an intensification of industrial and agricultural activities, the Pingzhai Reservoir is increasingly at risk of sulphate pollution. In this study, 42 water samples were collected from the Pingzhai Reservoir in April (normal season), July (wet season), and December (dry season) of 2022. Additionally, two precipitation samples, two sewage samples, and two acidic mine drainage samples were collected. Using hydrochemistry, multiple isotopes (δ34SSO4, δ18OSO4, δ13CDIC, and δ18OH2O) and Bayesian isotopic-mixing model methods, we qualitatively and quantitatively determined the source, contribution proportion, and behaviour of SO42- in the Pingzhai Reservoir watershed and evaluated the uncertainty of the estimated results. Isotope analysis and the Bayesian isotope-mixing model results indicated that the sources of SO42- in the Pingzhai Reservoir were coal sulphides and organic sulphur oxidation (64.2%), soil organic sulphur (18.7%), sewage (9.9%), and agricultural sulphur fertiliser (7.2%). The characteristics of karst landforms (thin soil that is easily eroded), combined with periodic fluctuations in water level (hydrofluctuation belts) in reservoirs, resulted in the release of organic matter from soil to water. The proportion of SO42- sources of coal sulphide and organic sulphur oxidation in the river was lower than that in the reservoir area, whereas the proportion of the SO42- sources of soil organic sulphur, sewage, and agricultural sulphur fertiliser was greater than that in the reservoir area. Isotope evidence and the aerobic conditions in water indicated that bacterial sulphate reduction processes did not play a major role. The uncertainty index (UI90) indicated that the contributions of agricultural sulphur fertiliser and sewage manure to SO42- were relatively constant. This study provides a reference for the protection of water environments and for the development of water pollution control strategies in the karst areas of southwestern China.

Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics

Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics

Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling.

Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as "wiggling". Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance.

Spatial extension of soil water regime variables derived from soil moisture values using geomorphological variables in Hungary

Accurate measurement and spatial extension of soil properties are essential in geoinformatics and precision agriculture for effective resource management, particularly irrigation planning. This study addresses the challenge of extending soil moisture data and related soil water regime variables in heterogeneous agricultural landscapes by integrating geomorphological variables (GVs) derived from high-resolution digital elevation models (DEM). In digital soil mapping, machine learning and geostatistical models often struggle with validation due to data scarcity and variability across space through many geographical regions that come from the point readings of soil properties. A different approach was developed in the form of a new methodology combining two hourly Sentek soil moisture measurements from the topsoil with DEM-derived GVs to model and extend soil water regime variables. The research was conducted on an agricultural field in a hilly area with diverse geomorphological variability. The model’s performance was validated using cross-validation techniques. The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. We observed that certain GVs, such as the slope, both type of curvatures and the convergence, were strong predictors of soil moisture variation, enabling the model to produce more accurate irrigation recommendations for agricultural areas with similar geomorphological areas. One of the soil water regime variables was validated during the preliminary validation with mixed results. The main issue was coming from the field use and spatial scarcity of the measurements. Our approach not only provides a different method for spatially extending the current soil water regime data but also offers a framework for improving irrigation decision-making with the help of other value rates and limit related soil regime variables derived from the time series readings from the soil moisture sensors. With its variables, the model allows for forecasts of soil moisture changes, which can inform better irrigation scheduling and water resource management, all based on data from the soil monitoring sensor system.

Synthesis of Porous Anodic Alumina Featuring a Periodic Pore Structure by Regulating the Anode Temperature

Abstract Porous anodic alumina (PAA) with a periodic pore structure has been synthesized by using an innovative preparation method. The morphology of PAA pores can be modulated within the same electrolyte by adjusting the temperature of the aluminum anode, enabling periodic variations in pore size (Dp). The formation mechanism of PAA has been elucidated through analyses of micromorphology, anodization current density (ia), interpore distance (Dint), and Dp of the samples. Results indicate that the average Dint for the synthesized PAA is approximately 260–340 nm, while the average Dp ranges from 90 to 260 nm. Both ia and Dp exhibit periodic fluctuations corresponding to changes in anode temperature under consistent electrolyte conditions and anodization voltage (Ua). Lateral pores are generated via a phosphoric acid etching process, resulting in PAA with a distinctive three-dimensional interconnected pore architecture. Furthermore, ridges with an arc-like shape on the outer walls of PAA pores have been observed; their formation mechanism can be effectively explained by the convection model and the viscous flow model. These findings contribute significantly to achieving precise control over the pore structure of PAA.

Distribution of the creepex-magnitude correlation parameter for big-depth seismicity in the context of global tectonics

In a number of the author’s papers, the successful application of creepex-parameter of seismicity in the problems of geodynamic research of preparation areas of large events by time-changing both the creepex value in the vicinity of their sources and the pair correlation coefficient of creepex Cr(t) and magnitude MS(t) graphs (the creepex-magnitude correlation KCOR) in accompanying earthquakes swarms or during the preparation of a large shock was demonstrated. With the transition to regional and global levels of research on the KCOR change, the influence of big-depth seismicity (H≥50 km), distributed along the regional and global deep faults closest to the focal zone on the processes of source preparation was discovered. In this paper, the dynamics of KCOR parameter for the entire period covered by the CMT global catalog along the main Earth’s seismic belts is considered. The “main belts” are the global lineaments detected by the GIS-ENDDB system, in the 10% confidence band of which the 100% of all Globe earthquakes with MS≥7.5 are located. In KCOR dynamics a pattern of high correlation of MS(t) and Cr(t) sets was revealed on the eve of several major earthquakes (called “key ones”). This can indicate the creation of a special organized environment state during their preparation, possibly associated with the environment consolidation along the seismic belts (in the case of inverse correlation of MS(t) and Cr(t)), or with increased environmental variability (in the case of direct correlation). The periodic nature of KCOR time-change on a global scale is shown (in the case of its calculation with a fixed size of a time-sliding series), possibly associated with periodic fluctuations in the Earth’s rotation speed.

Fifty years of land use and land cover mapping in the United Arab Emirates: a machine learning approach using Landsat satellite data

This study analyses the spatiotemporal distribution of land use and land cover (LULC) in the United Arab Emirates (UAE) over the past 50 years (1972–2021) using 72 multi-temporal Landsat satellite images. Three machine learning (ML) classifiers, Classification and Regression Tree (CART), Support Vector Machine (SVM) and Random Forest (RF), were tested, with RF finally chosen for its higher performance. Spectral, spatial, topographic, and object aspect attributes were extracted and used as input for the RF algorithm to enhance the classification accuracy. A dataset comprising 46,146 polygons representing four LULC classes was created, with 80% allocated for training and 20% for testing, ensuring robust model validation. The algorithm was trained to develop a machine learning model that classified the data into four LULC classes namely: built areas, vegetation, water, and desert and mountainous regions, producing eight thematic maps for the years 1972, 1986, 1992, 1997, 2002, 2013, 2017, and 2021. The results reveal the dominance of desert and mountainous regions, with their coverage gradually declining from over 97% in 1972 to nearly 91% in 2021. In contrast, built areas grew from less than 1% to nearly 6%, while vegetation cover increased from 0.71% to 2.85%. Water bodies have exhibited periodic fluctuations between 0.4% and 0.35%. These changes are attributed to extensive urbanization, agricultural expansion, forest plantation programs, land reclamation, and megaprojects. Accuracy assessment of the classified maps showed high overall accuracy, ranging from 85.11% to 98.4%. The study provides a unique long-term analysis of the UAE over 50 years, capturing key developments from the 1970s oil boom through subsequent megaprojects at the onset of the new millennium, leading to reduced reliance on oil. These findings underscore the role of machine learning and geospatial technologies in monitoring LULC distribution in challenging environments, and the results serve as a vital tool for policymakers to manage land resources, urban planning, and environmental conservation.

Utilizing Wastewater Surveillance to Model Behavioral Responses and Prevent Healthcare Overload During ‘Disease X’ Outbreaks

During the COVID-19 pandemic, healthcare systems worldwide faced severe strain. This study, utilizing wastewater virus surveillance, identified that periodic spontaneous avoidance behaviors significantly impacted infectious disease transmission during rapid and intense outbreaks. To incorporate these behaviors into disease transmission analysis, we introduced the Su-SEIQR model and validated it using COVID-19 wastewater data from Beijing and Hong Kong. The results demonstrated that the Su-SEIQR model accurately reflected trends in susceptible populations and confirmed cases during the COVID-19 pandemic, highlighting the role of spontaneous collective avoidance behaviors in generating periodic fluctuations. These fluctuations helped reduce infection peaks, thereby alleviating pressure on healthcare systems. However, the effect of these spontaneous behaviors on mitigating healthcare overload was limited. Consequently, we incorporated healthcare capacity constraints into the model, adjusting parameters to further guide population behaviors during the pandemic, aiming to keep the outbreak within manageable limits and reduce strain on healthcare resources. This study provides robust support for the development of environmental and public health policies during pandemics by constructing an innovative transmission model, which effectively prevents healthcare overload. Additionally, this approach can be applied to managing future outbreaks of unknown viruses or ‘Disease X’.

Observations on the structure of turbulent boundary layers interacting with embedded propeller tip vortices

The study presents observations on the interaction of double-blade propeller tip vortices with a smooth-wall turbulent boundary layer (TBL). The wall-bounded helicoidal vortices from the propeller modify the velocity profiles and turbulence statistics. The effects of two different tip clearances, $\epsilon = 0.1\delta _0$ and $0.5\delta _0$ , at a matched thrust, are explored with particle image velocimetry to understand the dynamics of tip-vortex formation within the logarithmic and wake regions of the boundary layer. The measurements are performed with $\lambda =U_{tip}/U_{\infty }$ in the range 5.3–5.9, and a blade passing frequency ( $\,f_{prop}$ ) of the same order of the boundary-layer time scale ( $\,f_{TBL}$ ). Observations indicate a reduction in the extent of the log region and an enhancement of the wake parameter $\varPi$ , mirroring the behaviour seen in TBLs under adverse pressure gradient conditions. Notably, the slipstream most contracted region exhibits a significant reduction in the skin friction coefficient $C_f$ and an amplification of the velocity fluctuation statistics across the entire boundary layer. At a clearance of $\epsilon = 0.1\delta _0$ , there is evidence of the formation of paired coherent wall-bounded structures. The presence of the wall decreases the amplitude of both periodic and stochastic fluctuations obtained with a phase-locked triple decomposition. An exception is observed behind the propeller for the stochastic fluctuations of the wall-normal component of the flow, which become amplified as the blades move away from the wall. This leads to the creation of a more intense phase-locked two-point spatial coherence than that observed in fluctuations aligned with the streamwise direction. Furthermore, results reveal that reduced tip clearances lead to higher viscous dissipation and more active energy exchange between the mean flow and organized motions.

Research on speed fluctuation suppression of PMSM based on SADRC and QRC cascade control

The objective of this research is to reduce the speed fluctuation of Permanent Magnet Synchronous Motor (PMSM) due to periodic torque fluctuation. To achieve this, the authors designed a cascade control system based on Linear/Nonlinear Switching Active Disturbance Rejection Control (SADRC) and Quasi Resonant Controller (QRC). Periodic torque pulsations, mainly the first, second, and fourth harmonics, are analyzed first. The architecture of the SADRC and QRC cascade control systems is then established. Subsequently, the parameters of SADRC and QRC are calibrated by the bandwidth method, and a closed-loop system model of cascade control is developed. Ultimately, periodic load fluctuations with step load fluctuations are applied at the load side to ascertain the efficacy of the SADRC and QRC cascade control systems. The SADRC and QRC cascade control reduce the first, second, and fourth harmonics of speed fluctuations by 74%, 33%, and 47.5%, respectively, through the use of QRC current compensation in the presence of peak 2 N m periodic torque fluctuations. The SADRC and QRC cascade control systems demonstrate excellent performance in the context of cyclic and step load fluctuations.

Impacts of Thunderstorm-Generated Gravity Waves on the Ionosphere-Thermosphere Using TIEGCM-NG/MAGIC Simulations and Comparisons With GNSS TEC, ICON, and COSMIC-2 Observations.

We use the TIEGCM-NG nudged by MAGIC gravity waves to study the impacts of a severe thunderstorm system, with a hundred tornado touchdowns, on the ionospheric and thermospheric disturbances. The generated waves induce a distinct concentric ring pattern on GNSS TIDs with horizontal scales of 150-400km and phase speeds of 150-300m/s, which is well simulated by the model. The waves show substantial vertical evolution in period, initially dominated by 0.5hr at 200km, shifting to 0.25hr and with more higher-frequency waves appearing at higher altitudes (∼400km). The TADs reach amplitudes of 100m/s, 60m/s, 80K, and 10% in horizontal winds, vertical wind, temperature, and relative neutral density, respectively. Significantly perturbations in electron density cause dramatic changes in its nighttime structure around 200km and near the EIA crest. The concentric TIDs are also simulated in ion drifts and mapped from the Tornado region to the conjugate hemisphere likely due to neutral wind-induced electric field perturbations. The waves manage to impact the ionosphere at altitudes of ICON and COSMIC-2, which pass through the region of interest on a total of 8 separate orbits. In situ ion density observations from these spacecrafts reveal periodic fluctuations that frequently show good agreement with the TIEGCM-NG simulation. The O+ fraction observations from ICON indicate that the density fluctuations are the result of vertical transport of the ions in this region, which could result from either direct forcing by neutral winds or electrodynamic coupling.

A novel structure adaptive new information priority grey Bernoulli model and its application in China's renewable energy production

At present, the global energy structure is undergoing major changes. China is in the transition period of energy structure. Accurately anticipating future energy trends is critical for China's energy structure and modernization. Considering the uncertainty and sparsity characteristics of China's energy system sequence, this study examines China's renewable energy scenario using the grey model with limited sample and uncertain system modelling features. Renewable energy is affected by a variety of uncertain factors, exhibiting a range of complicated traits including nonlinearity, periodicity and random volatility. The traditional grey model has been difficult to appropriately predict its future evolution. This paper focuses on the adaptability of the model, optimizes and improves the accumulation operator and model structure, and establishes a fractional-order structural self-adaptation grey Bernoulli model based on new information priority. Firstly, based on the new information priority accumulation operator, it is extended to the fractional order. In terms of model structure, combined with NGBM(1,1) model, SADGM(1,1) model and FPGM(1,1) model, periodic fluctuation term and nonlinear power term are included to improve the model's capacity to capture nonlinear, fluctuating and periodic features, and enhance the adaptability and flexibility of the model. The backward difference technique yields the model's parameter estimation and temporal response sequence. Based on the results of the algorithm comparison experiment, the Improved Grey Wolf Optimization Algorithm is chosen to optimize the structural parameters of the model in an effort to enhance its performance. The performance comparison experiment of the model was designed, and three cases of China's hydropower generation, China's renewable energy power generation installed capacity and China's solar energy quarterly power generation were selected to compare the performance with a variety of grey prediction models. Monte-Carlo simulation and probability density analysis were utilized to confirm the stability and accuracy of the proposed model. The results show that the proposed FSANGBM(1,1) model can handle data series of renewable energy with nonlinear, volatile, and periodic features with high prediction ability. Finally, the model is applied to forecast three cases' future development trends.

Impact of working fluid properties on heat transfer and flow characteristics of two-phase loop thermosyphon with high filling ratios

The two-phase loop thermosyphon (TPLT), known for its excellent heat transfer performance, simple and compact structure, and lack of need for a pump, effectively addresses heat transfer challenges in confined spaces under high heat loads.Compared to TPLT with low filling ratio, high-filling-ratio TPLT not only exhibit a higher maximum heat transfer capacity but also has a more complex heat and mass transfer process, leading to increased sensitivity to the working fluid's properties. Therefore, studying the impact of the working fluid on the operational state of high-filling-ratio TPLTs is crucial for understanding their heat transfer mechanisms. In this paper, comprehensive experiments were conducted on TPLT filled with H2O and R134a as working fluids in a wide filling ratio range (30 % -90 %), and their heat transfer performance and flow characteristics were compared. Heat transfer diagram, two-phase flow pattern diagram, and the distribution of gas-liquid two-phase of the TPLT was established with different filling ratio and heat input. Due to differences in latent heat of vaporization, the maximum heat transfer capacity of the H2O-TPLT (390 W/cm2) is greater than that of the R134a-TPLT (270 W/cm2). In the H2O-TPLT, the predominant large-volume slug flow leads to significant flow resistance. Whereas in the R134a-TPLT, the flow pattern is primarily dominated by small-volume bubbly flow and churn flow, resulting in low resistance. High viscosity and flow pattern in the H2O-TPLT cause oscillation phenomena, leading to significant temperature and pressure fluctuations. Under high filling ratio, both types of TPLT experiences geyser boiling phenomena causing periodic temperature and pressure fluctuations and flow pattern changes. In summary, R134a is the preferred working fluid when heat transfer requirements are met, as it effectively reduces temperature fluctuations while dissipating heat. When exceeding the R134a-TPLT's maximum heat transfer capacity and less stringent temperature control is acceptable, H2O may serve as the working fluid.

Research on Resilience Measurement and Impact Effects of China's High tech Manufacturing Industry

This article defines the connotation of resilience in high-tech manufacturing industry, measures the resilience of China's high-tech manufacturing industry from the perspectives of fracture toughness and impact toughness from 1997 to 2022, constructs indicator obstacle and contribution models, identifies the main influencing factors of industrial resilience from both positive and negative aspects, and conducts empirical analysis on the impact effects. Research has found that since 1997, the resilience index of China's high-tech manufacturing industry has shown periodic fluctuations in growth and transformation, specifically divided into four stages: negative growth, low-speed growth, rapid growth, and impact transformation. There is an interactive effect between innovation and transformation as the main factors affecting the resilience of high-tech manufacturing industry and industrial resilience.

Effect of geometry on homogenised properties of selected auxetic metamaterials

Our study investigates the influence of geometrical parameters of two types of auxetic metamaterials on their effective properties. In particular, we focus on three-dimensional lattice structures, which we represent with discrete beam models of their respective Periodic Unit Cells (PUCs). Limiting the scope of the study to linear elasticity, we compute the effective response of PUCs by plugging the kinematic ansatz of the first-order numerical homogenisation into the strain energy expression arising from the Direct Stiffness Method and minimising the energy with respect to the periodic fluctuation field. The obtained effective stiffness matrices are post-processed to arrive at elastic parameters as Poisson’s ratios coefficients, that are reported in different directions with respect to the key geometrical parameters.

Cavitation erosion characteristics influenced by a microstructure at different scales

The scale effect of vortex generators, as microstructures, influences cavitation erosion remains unclear, posing a key challenge to applying vortex generators in large-scale hydraulic machinery. In this study, the vortex generators (VGs) with heights of 0.25 mm (micro-VG) and 2.5 mm (large-VG), installed at the leading edge of a smooth NACA0015 hydrofoil, were investigated through experimental and simulation methods. The results demonstrate that the vortex generators can induce tubular vortexes that enhance near-wall flow stability. After installing the VGs, the large-scale cloud cavitation is effectively controlled. On the hydrofoil with micro-VGs, this control manifests as localized, small-scale cavitation shedding and collapse, while on the hydrofoil with large-VGs, the cavitation shedding is entirely absent, which shows that larger VGs further mitigate cavitation effects. Pressure signal analysis reveals that the VGs alter the pressure fluctuation period and reduce the main frequency amplitude compared to that on the smooth hydrofoil, with larger VGs providing superior suppression of pressure fluctuations. Additionally, an improved strength function method is proposed and applied, highlighting that the reduction in large-scale cloud cavitation by the VGs contributes to decreased erosion risk on the hydrofoil, with larger VGs showing enhanced effectiveness in preventing cavitation erosion.

Full-operating-condition performance improvement strategy and optimal design of a two-stage ejector with auxiliary secondary flow for MED-TVC systems

Periodic fluctuation of steam-source pressure occurs in the thermal vapor compression multi-effect distillation (MED-TVC) systems. Because ejectors are sensitive to the boundary conditions, as a key component for recovering excess steam in systems, ejectors experience a rapid decline in performance when the steam-source pressure fluctuates and does not satisfy the design condition. This decline negatively impacts the overall system performance and reduces the energy utilization efficiency. Therefore, a two-stage ejector with auxiliary secondary flow (ASF-TSE) and a full-operating-condition performance improvement strategy were developed in this study to enhance the ejector performance under full working conditions and broaden the efficient operation range of ejectors. The flow field characteristics and performance of the ASF-TSE, single ejector (SE), and two-stage ejector (TSE) in the low and design-pressure ranges were compared and investigated. The results showed that within the design-pressure range, the maximum and average increase ratios of the ASF-TSE for entrainment performance compared with the TSE are 17.53 % and 13.43 %. Moreover, the entrainment capacities of the three ejectors were compared and analyzed in the full operating range. Simulation results show that compared with the SE and TSE, the entrainment performance of the ASF-TSE is improved by 87.5 % and 8.0 %, respectively.

Metro short-term section passenger flow inherent patterns extraction and prediction: A novel denoising-based method

Eliminating the random noise is the key issue to be solved to deeply explore the inherent patterns of passenger flow fluctuation and apply accurate prediction. This paper proposes a multi-stage data denoising based short-term section passenger flow patterns extraction and prediction method. Coarse-grained denoising is performed by adaptive soft thresholding of the Empirical Wavelet Transform (EWT) model, and the component attributes are categorized based on the Gramian Angular Summation Field (GASF) images and Determinism (DET) indicator. The periodic inherent patterns of passenger flow fluctuation are analysed by recomposing the deterministic components, and finally fine-grained denoising and trend prediction are achieved by the Gate Recurrent Unit (GRU) algorithm. Numerical experimental results show that the multi-stage denoising model outperforms the noise-balance based time-domain method and accurately identifies periodic fluctuation patterns. Moreover, the proposed EWT-GRU model in this paper significantly improves the accuracy of section passenger flow prediction.