Wavelet Research Articles

Effects of multi-scale turbulent motions on aerodynamic performance of wind turbine under sand-laden conditions

Wind turbine installation in the desert and Gobi regions offers a promising approach for meeting long-term energy demands. However, the effect of multi-scale characteristics in sand-laden atmosphere flows on wind turbine aerodynamic performance has not been evaluated. In this study, wind velocity data collected from the Qingtu Lake Observation Array (QLOA) were employed to address this gap. Results show that up to 58% of the total turbulent kinetic energy (TKE) is accounted for by very large-scale motions (VLSMs), which make up a considerable portion of the TKE. The contributions of the large-scale motions (LSMs) and the small-scale motions (SSMs) to TKE are 36% and 6%, respectively. The contribution of multi-scale turbulent motions to the aerodynamic loads of wind turbine under sand-laden conditions has been quantified for the first time. The comparison demonstrates that while LSMs and SSMs exhibit a rapid drop in their contributions to wind turbine loads with height, VLSMs show a rapid increase. Wavelet analysis revealed a strong correlation between VLSMs and power, thrust, and blade root flapwise moment at periods ranging from 256 to 1024 s. This correlation weakens as the streamwise length scale of the turbulent coherent structure decreases. This study provides essential insights for optimizing wind turbine design and site selection in sand-laden environments.

Evaluation Method of Implementation Effect of Rural Revitalization Strategy based on Wavelet Analysis Algorithm

This study presents an innovative approach to evaluate the implementation effects of the Rural Revitalization Strategy R2S, utilising the robust capabilities of the wavelet analysis algorithm. The proposed methodology integrates the strength of wavelet transform, an advanced mathematical tool for signal processing, with the entropy weighting method and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) model. This integration creates a comprehensive framework for assessing the multifaceted impacts of rural revitalisation initiatives. The wavelet analysis algorithm is the cornerstone of this approach, enabling the decomposition of complex rural development data into different frequency components, thus facilitating a more nuanced analysis. The entropy weighting method contributes by objectively determining the weights of various evaluation indicators, ensuring that the most relevant factors in rural revitalisation are emphasised in the assessment process. The TOPSIS model complements this by clearly ranking the analysed strategies based on their proximity to an ideal solution, thereby enabling decision-makers to identify the most effective strategies for rural development. Together, these techniques form a powerful tool for evaluating the effectiveness of rural revitalisation strategies, offering critical insights for policy formulation and implementation in rural areas. This study’s methodology not only enhances the accuracy of evaluation but also provides a replicable model for similar assessments in other contexts, contributing significantly to rural development and policy analysis.

Demystifying the dynamic relationship between news sentiment index and ESG stocks: Evidence from time-frequency wavelet analysis

Using the Time-Frequency Wavelet Analysis, this study investigates the dynamic connectedness between the News Sentiment Index (NSI) and ESG leader indices of developed countries. The empirical findings clearly show nontrivial dynamic comovements between NSI and the ESG indices and the existence of the ESG market volatility in different frequency scales. We also report some discrepancies in cross-linkage patterns among different countries. In particular, such associations for some countries (e.g., Australia and the United Kingdom) are negative, with the leading role of the ESG index against market risk due to high fluctuations in sentiments. These findings suggest that ESG stocks possess potential hedging and diversifying features and safe-haven attributes against market risk driven by negative sentiment during the outbreak of Black Swan events. Our empirical results have insightful implications for investors, portfolio managers, and policymakers.

Investigating high-frequency quasi-periodic oscillations in the 6374 Å red coronal line observed during the total solar eclipse on August 21, 2017

Context. We present the results of the search for high-frequency quasi-periodic oscillations (HFQPOs) in the 6374-Å red line emission from the solar corona during the 2017 total solar eclipse (TSE). Aims. The existence of HFQPOs of coronal intensity remains unclear. Motivated by previous observations, this paper focuses on searching for the HFQPOs (> 0.1 Hz) during the 2017 TSE. Methods. Wavelet analysis tools were used to analyze the radiation intensity oscillations in different height layers, local regions of interest (such as the east coronal loop, west cavity, etc.), and coronal feature points as well as changes in oscillations along coronal feature structures and the kinematic wobble of the coronal magnetic features. Results. We have observed oscillatory signals at multiple spatial locations, which reveal the existence of quasi-periodic slow magnetoacoustic waves. These signals display periods ranging from approximately 3 to 9 seconds, with a notable focus on periods around 4 seconds. Conclusions. High-frequency quasi-periodic slow magnetoacoustic oscillations (with periods less than 9.63 seconds) have been identified in the inner corona (at heights less than 1.4 R⊙) in the 6374 Å red coronal line radiation.

Variations in the Inferred Cosmic-Ray Spectral Index as Measured by Neutron Monitors in Antarctica

A technique has recently been developed for tracking short-term spectral variations in Galactic cosmic rays (GCRs) using data from a single neutron monitor (NM), by collecting histograms of the time delay between successive neutron counts and extracting the leader fraction L as a proxy of the spectral index. Here we analyze L from four Antarctic NMs from 2015 March to 2023 September. We have calibrated L from the South Pole NM with respect to a daily spectral index determined from published data of GCR proton fluxes during 2015–2019 from the Alpha Magnetic Spectrometer (AMS-02) on board the International Space Station. Our results demonstrate a robust correlation between the leader fraction and the spectral index fit over the rigidity range 2.97–16.6 GV for AMS-02 data, with uncertainty of 0.018 in the daily spectral index as inferred from L. In addition to the 11 yr solar activity cycle, a wavelet analysis confirms a 27 day periodicity in the GCR flux and spectral index corresponding to solar rotation, especially near sunspot minimum, while the flux occasionally exhibits a strong harmonic at 13.5 days. The magnetic field component along a nominal Parker spiral (i.e., the magnetic sector structure) is a strong determinant of such spectral and flux variations, with the solar wind speed exerting an additional, nearly rigidity-independent influence on flux variations. Our investigation affirms the capability of ground-based NM stations to accurately and continuously monitor cosmic-ray spectral variations over the long-term future.

An unsupervised learning framework for health diagnosis by incorporating multiscale data

Integrating real-time evaluations of health states with Structural Health Monitoring (SHM) data and environmental observations is crucial, particularly under large datasets. Not only is the volume of real-time data analysis substantial, but traditional algorithms like wavelet analysis often require extensive prior work and significant manual input, limiting the application of SHM and big data technologies in structural analysis. To overcome this limitation, this study introduces an unsupervised analytical framework coupling a Denoising Sparse Wavelet Network (DeSpaWN) and a dynamic-inner Principal Component Analysis (DiPCA) algorithm. The DeSpaWN is designed to extract sparse representation from multiscale sensor signals, and the features are subsequently analyzed using the DiPCA to assess health diagnoses. The DeSpaWN is specifically tailored for monitoring high-frequency time series dynamically and adaptively, enabling reconstruction and extraction of signals across various frequency domains. Additionally, the DiPCA effectively handles large-scale time-series data, providing precise evaluations by accounting for dynamic latent correlations. To validate the method, a 5-Degree of Freedom (DoF) structure model is established, equipped with five virtual sensors at each DoF and an anemometer at the top DoF. The sensor signals, obtained under different health states and subjected to artificial excitations, are input into the proposed framework. Validation results demonstrate the effectiveness in synthesizing multi-sensor data and assessing structural integrity. The framework's practical utility is showcased through a real-world case study that assesses the structural integrity of a wind turbine. By integrating real and virtual sensor data collected over a year and cross-referencing this with the fault log, the study validates the algorithm's efficacy in processing multisource heterogeneous data and its precision in structural health diagnosis. The study substantiates real-time tracking of health states for SHM-equipped structures, underscoring its potential to enhance safety and reliability in civil infrastructure.

Volatility of Volatility and VIX Forecasting: New Evidence Based on Jumps, the Short‐Term and Long‐Term Volatility

ABSTRACTThis study explores VIX forecasting by proposing a novel model to characterize the volatility of volatility based on high‐frequency VIX. Specifically, the decomposed jumps, the short‐ and long‐term volatility of VIX realized volatility obtained through wavelet analysis are considered by integrating the HAR‐DJI‐GARCH with GARCH‐MIDAS model. Empirical results show superior performance over competing models, with enhanced predictive accuracy under four non‐parametric jumps. The model's effectiveness is further validated by adjusting prediction windows, wavelet levels, examining VIX term structure, varying the significance level of jump test, and through the assessment of its economic significance.

Study on Self-Learning System for Milling Chatter Feature Based on Hybrid Preprocessing Model and Improved Convolutional Clustering

Milling chatter is a self-excited vibration phenomenon during the cutting process, typically occurring in machining operations with lower stiffness. It significantly impacts the machining precision and surface morphology of the workpiece. To summarize the vibration signal features under operating conditions and enhance the precision of the intelligent recognition model for milling chatter features, this paper proposes an adaptive learning model for chatter feature recognition based on the improved convolutional clustering algorithm. This study uses wavelet analysis to extract chatter features and improved convolutional clustering to identify chatter and stable cutting signals. This method selected appropriate mapping techniques based on differences in chatter signals under various conditions and uses convolution for feature extraction. Improve convolutional clustering evaluates sample similarity through energy transfer, less influenced by signal space structure compared to Euclidean or Mahalanobis distances. This method provides a unified criterion for evaluating different signal feature representation spaces, thereby improving the accuracy and efficiency of chatter sample recognition. The recognition accuracy of chatter phenomena at different spindle speed ranges reaches $95$ % and $96.3$ % respectively. The results show that this method can effectively distinguish between chatter signals and stable cutting signals.

Revisiting the Nexus Between Oil Prices and Economic Activity for Asian Economies Using MIDAS

The objective of this paper is to re-examine the causal relationship between oil prices and economic activity for five Asian economies. We apply the unrestricted MIDAS (U-MIDAS) model using monthly data for real oil prices and quarterly data for real GDP for the period from 1998Q1 to 2019Q4. The causal nexus between oil prices and economic activity is also studied by means of wavelet analysis to investigate whether the relationship between the variables changes over different time scales. The key empirical results under the MIDAS approach show that there is a clear significant causal link from oil prices to economic activity, which is not as clearly found under the standard VAR approach. In addition, our results using impulse response functions suggest that the five Asian economies respond, in general, positively in economic activity to an oil price shock at shorter time horizons (less than two years), while the positive responses switch to mostly negative ones at longer time horizons (from two to four years). Our results, in general, support that the (theoretically) expected negative causal nexus between oil price fluctuations and economic activity for oil importers is dominant over longer time horizons for our dataset. An exception to this pattern is China, which has a dissimilar country profile in terms of its oil market and production structure from the profiles of the other Asian economies in the study. JEL Classification: C32, E32, O53, Q43

Numerical analysis of flow and heat transfer characteristics in Taylor-Couette-Poiseuille flow utilizing Large Eddy Simulation method

The flow and heat transfer characteristics within the Taylor-Couette-Poiseuille configuration induced by supercritical carbon dioxide flowing through the gap between the turbine shaft and the stationary casing were studied using Large Eddy Simulation. Wavelet analysis was used to calculate pressure fluctuations across various frequencies. Computational assessments were carried out to evaluate changes in the field synergy angle and inertial perturbations within the annular gap. A new theory was introduced to seamlessly integrate temperature, pressure, and velocity fields. Additionally, entropy generation in the Taylor-Couette-Poiseuille flow regime was thoroughly examined. The study revealed the emergence of vortices at the rotating wall, indicating a dynamic process in which large-scale vortices dissolve and more minor ones form. As frequency increases, pressure fluctuations grow. Increasing the mass flow rate can reduce the twisting vortex at the rotating wall and delay the onset of temperature fluctuations. Additionally, increasing the mass flow rate enhances the cooling effect on the rotating shaft. In the radial direction, as r increases, the field synergy angle and the inertial disturbance both show a trend of first decreasing and then increasing. The heat transfer performance is best when r = 0.6 and decreases significantly when r = 0.95. The synergy between the temperature gradient field, pressure gradient field, and velocity field shows a trend opposite to the field synergy angle and the inertial disturbance. As one moves from the rotating wall toward the stationary wall, temperature and velocity entropy production rates decrease, with noticeable fluctuations near the rotating wall.

Damage identification in composite structures using high-speed 3D digital image correlation and wavelet analysis of mode shapes

ABSTRACT In this study, the damage identification procedure combining the advantages of high-speed 3D digital image correlation (DIC) that is used for modal analysis and wavelet-based processing algorithm is presented. The proposed procedure has been successfully validated on sandwich composite structures with internal damage of a core and impact damage, and its performance was demonstrated by detection, localisation, and identification of shapes of damage in tested structures, comparable to the results from scanning laser Doppler vibrometry (SLDV). The effectiveness in damage identification was achieved by the development of processing algorithm of acquired mode shapes, which uses a 2D double-density directional wavelet transform with increased sensitivity with entropic weights allowing to automatically select those wavelet coefficients and mode shapes, which are most sensitive to damage. The procedure presented in this paper demonstrates the performance of 3D DIC for damage identification as a cost-effective alternative to the procedures based on SLDV measurements.

Real-time prediction of river ice breakup phenomena: A jittered genetic programming model and wavelet analysis integrating remotely sensed imagery and machine learning

Forecasting the timing of breakup ice jams in rivers is crucial for early flood warning and effective management in cold regions where rising river flows can lead to significant damage. However, this task is hindered by insufficient data and the complex dynamics of river ice. These obstacles pose challenges in developing precise forecasting models. This study aimed to address the data scarcity issue by introducing innovative machine learning methods, focusing on classification and jittering (J), binary genetic programming (BGP), and wavelet transform (WT) for river ice forecasting (WT-JBGP) in the Tornio River, situated between Finland and Sweden. By considering time scales ranging from 2 to 32 days and time lags of 1 to 3 days, this method was applied to enhance the predictive capabilities of predictors. The findings reveal that certain predictors, with specific time scales and time lags, significantly influence the timing of breakup events. These include the 8-day temperature and 32-day discharge, both with a 2-day lag time, as well as the 4-day precipitation, approximation of albedo, and 16-day atmospheric pressure at ground level, all with a 1-day lag obtained from ERA5 and recorded data. Additionally, we conducted a quantitative evaluation of the effectiveness of the proposed model and contrasted its efficacy with that of BGP, WT-BGP, and advanced J-BGP techniques. The WT-JBGP model attains the highest overall classification accuracy of approximately 0.91, alongside a Heidke Skill Score exceeding 0.78, and a Positive Predictive Value surpassing 0.85, thereby demonstrating its superiority over competing methodologies. In summation, this study offers a promising approach to overcoming observed data scarcity in breakup date prediction, providing valuable insights into river ice dynamics.

MORLET’S WAVELET ANALYSIS ON EL NIÑO SOUTHERN OSCILLATION (ENSO) AND THE INDIAN OCEAN DIPOLE (IOD) FOR 84 YEARS: 1940-2023

As is known, the impact caused by El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) can reach extreme levels, especially rainfall in Indonesia. So, updating information on events and cycles of these phenomena is essential. Using Sea Surface Temperature (SST) data spanning the previous 84 years (1940–2023) from ERA5, we examined Sea Surface Temperature Anomalies (SSTA), which serve as a predictive tool for ENSO and IOD events. Apart from that, in this research, SSTA variance analysis was also carried out using Wavelet. The analysis results show several Positive IOD-Like events (1943, 1944, 1977, 1996) and Negative IOD-Like (1985, 1992, 2016). Apart from that, the results of this research also show that El Niño in 2002/03 coincided with Negative IOD in 2002. The results of Wavelet analysis show that the SSTA DMI variance experienced increased activity in the periods 1940-1968, 1969-1991, and 1992-2023. The Wavelet analysis also shows that ENSO activity increased in 1970-2000 and decreased again in 2000-2023.

Investigating Hydrological Drought Characteristics in Northeastern Thailand in CMIP5 Climate Change Scenarios

In this study, we analyzed the predictions of hydrological droughts in the Lam Chiang Kri Watershed (LCKW) by using the Soil and Water Assessment Tool (SWAT) and streamflow data for 2010–2021. The objective was to assess the streamflow drought index (SDI) for 5-, 10-, 25-, and 50-year return periods (RPs) in 2029 and 2039 in two representative concentration pathway (RCP) scenarios: the moderate climate change scenario (RCP 4.5) and the high-emission scenario (RCP 8.5). The SWAT model showed high accuracy (R2 = 0.82, NSE = 0.78). In RCP4.5, streamflow is projected to increase by 34.74% for 2029 and 18.74% for 2039, while in RCP8.5, a 37.06% decrease is expected for 2029 and 55.84% for 2039. A historical analysis indicated that there were frequent short-term droughts according to SDI-3 (3-month-period index), particularly from 2014 to 2015 and 2020 to 2021, and severe droughts according to SDI-6 (6-month-period index) in 2015 and 2020. The RCP8.5 projections indicate worsening drought conditions, with critical periods from April to June. A wavelet analysis showed that there is a significant risk of severe hydrological drought in the LCKW. Drought characteristic analysis indicated that high-intensity events occur with low frequency in the 50-year RP. Conversely, high-frequency droughts with lower intensity are observed in RPs of less than 50 years. The results of this study highlight an increase in severe drought risk in high emission scenarios, emphasizing the need for water management.

Schwabe Solar Cycle in 1000–1700: Variations in the Length and Amplitude

One of the most significant features of solar activity is its variability over a wide range of periods, with the dominance of the 11-year cycle or the Schwabe cycle. In this work, a wavelet analysis of data on solar activity in 1000–1700 was carried out, obtained using the number of auroras, taking into account the contribution of the geomagnetic field. The obtained results demonstrate the stable presence of an 11-year cycle during the entire time interval of 1000–1700 A. D. It was found that in 1000–1350 there was a systematic increase in the length of the Schwabe cycle, after which its decline was traced. At the same time, the length of the solar cycle increases during the grand minima of Oort (13 years), Wolf (14 years) and Spörer (14–15 years). It was found that the correlation between the amplitude and the length of the solar cycle was maintained throughout the entire period of time 1000–1700, but its sign changed. In addition, it was obtained that the correlation between the amplitude of the cycle and the length of the previous cycle is stronger than the correlation between the amplitude and length of the same cycle. This result is similar to that previously known for instrumental series. However, we have shown that this pattern persists over a much longer time interval, and it does not depend on the sign of the correlation. The paper also provides indications of the existence of a variation with a period of 30–40 years in solar activity in 1000–1550.

Financial Contagion between German and BRICS Stock Markets under Multiscale Scrutiny

We employ wavelet analysis using the maximum overlap discrete wavelet transform (MODWT) to examine the return and volatility interconnectedness between the German equity market (a prominent representative of the Eurozone market) and the BRICS countries over the period 2005–2017. Specifically, we investigate the presence of the pure form of financial contagion in the stock markets of Brazil, Russia, India, China, and South Africa subsequent to the Eurozone Sovereign Debt Crisis (EZDC). Our results indicate the presence of financial contagion between the Eurozone equity market and its counterparts in South Africa and Russia, characterised by co-movement and volatility spillover effects. This contagion is particularly evident at higher frequencies, suggesting that the transmission of shocks occurs rapidly across these markets in the short term. No financial contagion is observed in the Brazilian, Chinese, and Indian stock markets during the European Sovereign Debt Crisis. The absence of financial contagion observed in these three BRICS countries during the European Sovereign Debt Crisis suggests that policymakers in these countries should prioritise addressing idiosyncratic shock channels.

Characterization of transient movements within the Joshimath hillslope complex: Results from multi-sensor InSAR observations

This paper investigates the spatiotemporal characteristics and life-cycle of movements within the Joshimath landslide-prone slope over the period from 2015 to 2024, utilizing multi-sensor interferometric data from Sentinel‑1, ALOS‑2, and TerraSAR‑X satellites. Multi-temporal InSAR analysis before the 2023 slope destabilization crisis, when the region experienced significant ground deformation acceleration, revealed two distinct deformation clusters within the eastern and middle parts of the slope. These active deformation regions have been creeping up to −200 mm/yr. Slope deformation analysis indicates that the entire Joshimath landslide-prone slope can be categorized kinematically as either Extremely-Slow (ES) or Very-Slow (VS) moving slope, with the eastern cluster mainly exhibiting ES movements, while the middle cluster showing VS movements. Two episodes of significant acceleration occurred on August 21, 2019 and November 2, 2021, with the rate of slope deformation increasing by 20% (from −50 to −60 mm/yr) and around threefold (from −60 to −249 mm/yr), respectively. Following the 2023 destabilization crisis, the rate of ground deformation notably increased across all datasets for both clusters, except for the Sentinel‑1 ascending data in the eastern cluster. Pre-crisis, horizontal deformation was dominant both in the eastern and middle clusters. Horizontal deformation remained dominant and increased significantly in the eastern cluster post-crisis phase, whereas vertical deformation became predominant in the middle cluster. Wavelet analysis reveals a strong correlation between two acceleration episodes and extreme precipitation in 2019 and 2021, but no similar correlation was detected in other years. This indicates that while extreme rainfall significantly influenced the dynamics of slope movements during these episodes, less strong precipitation had a minimal impact on slope movements during other periods.

Respiratory sinus arrhythmia: physiological mechanisms and relationship with systemic blood pressure fluctuations

Respiratory sinus arrhythmia (RSA) reflects the functioning of the nervous heart control, predominantly of a parasympathetic nature. The study of RSA mechanisms helps to reveal the physiological patterns of regulation of cardiac activity, and the development of new approaches to its assessment is an urgent medical task. This review will examine experimental approaches that have contributed to the development of modern ideas about autonomic nervous system role in the formation of RSA, as well as the connection between RSA and frequency-matched fluctuations in systemic blood pressure. In addition, we will consider new data on the phase relationships of fluctuations in heart rate and blood pressure in the frequency range of respiratory waves, obtained using wavelet analysis of these physiological signals.

Impact of the October 28, 2021 Solar Flare and the November 4, 2021 Geomagnetic Storm on the Low, Middle, and High-Latitude Ionosphere

This study examines the ionospheric Total Electron Content (TEC) responses to a solar flare on October 28, 2021, and a geomagnetic storm on November 4, 2021, across low, middle, and high latitude regions. We utilized GPS-TEC data from the University NAVSTAR Consortium’s dual-frequency GPS devices at the IFR1, IISC, YIBL, YKRO, KERG, and SVTL stations. While the solar flare on October 28, 2021, triggered the geomagnetic storm on November 4, 2021, our analysis revealed notable TEC changes during the latter event. TEC fluctuations were observed across all stations during the geomagnetic storm, with significant disruptions and variable depletion rates. However, distinct TEC variations were noted at KERG and YIBL stations before the storm, likely due to the preceding solar flare. Continuous wavelet analysis (CWT) showed higher periodicity during the storm compared to the flare, proving CWT to be an effective tool for analyzing TEC variability by revealing periodicity fluctuations at all stations. In conclusion, we found that both solar flares and geomagnetic storms can cause significant positive TEC changes.

Characterizing the local and global climatic factors associated with vegetation dynamics in the karst region of southwest China

Understanding the relationship between vegetation and climatic drivers is essential for assessing terrestrial ecosystem patterns and managing future vegetation dynamics. This study examines the effects of local climatic factors and remote large-scale ocean–atmosphere circulations from the Pacific, Atlantic, and Arctic Oceans, as well as the East Asian and Indian summer monsoons, on the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) in the karst region of southwest China (KRSC) using Mann-Kendall test, Sen’s slope, cross-correlation, and wavelet analysis. We observed a significant increase in NDVI over karst and non-karst regions from 1981 to 2019, with a notable abrupt shift from 2001 onwards, underscoring the importance of understanding the underlying drivers. The significant correlation and coherence of surface air (TMP) and soil temperatures (ST) with NDVI, especially when analyzed using wavelet methods, indicate their crucial role in vegetation dynamics. Additionally, the broad coherence patterns of AMO and WHWP with NDVI at annual and decadal cycles suggest that ocean–atmosphere interactions also play a significant part. At interannual periodicities, most large-scale indices displayed significant coherence with NDVI. These findings highlight the complexity of NDVI variability, which is better explained by the integration of multiple local and global factors rather than by single variables. The integrated local–global drivers, particularly TMP-ST-AMO-NP-WHWP and PCP-SM-AMO-NP-WHWP with mean coherence of 0.90 and 0.89, respectively, showed the highest mean coherence, emphasizing the need for a multifaceted approach in understanding vegetation changes rather than a single local variable or atmospheric circulation index. These findings have significant implications for policymakers, aiding in better planning and policy formulations considering climate change and atmospheric variability.