Equivalent Frequency Research Articles

Large quarterly Bayesian vector autoregression model for the modern Russian economy

It is often difficult to choose the necessary variables when we are forecasting the economic dynamics. On the one hand, vector autoregression (VAR) models can solve this problem by taking into account a sufficiently large number of variables. However, on the other hand, excessive parametrization of such models is not always justified, because it is often possible to choose such a small combination of variables the predictive power of which will be no worse than unlimited VARs with a large number of variables and lags. Bayesian methods help to solve this problem by introducing a priori restrictions on the VAR coefficients. The main goal of this work is to build a Large quarterly Bayesian vector autoregression (QBVAR) for the modern Russian economy. The hypotheses of the study: 1) BVAR(p) models reveal their maximum potential with a small number of variables, but with a large number of lags, 2) The Minnesota prior distribution is not always the best option for the modern Russian economy in the BVAR(p) model. As a result of the analysis, it turned out that for a number of the most important macroeconomic variables (GDP, inflation, key rate, unemployment, wages), Normal-Flat / Wishart-type priors turned out to be the optimal. In case of 29 input variables and two lags, the predictive power of the QBVAR(2) model is worse than the equivalent frequency VAR(2) or VECM(2). However, when it was possible to find the optimal combinations of variables, QBVAR(p) turned to be several times more accurate than its analogous frequency VAR(p) for all important macro indicators. In addition, the stiffness parameters of the considered a priori distributions decreases with increasing lags entering the model, i. e. the higher the order of the model with the minimum optimal set of variables, the tighter the distribution is necessary to obtain more accurate predictions. For the Russian realities, it is necessary to use very tight priors, 6–8 variables and 9–12 lags in a quarterly representation.

Non-Degenerate Two-Photon Absorption of Fluorescent Protein Chromophores.

Two-photon absorption (2PA), where a pair of photons are absorbed simultaneously, is recognized as a potent bioimaging technique, which depends on the quantified 2PA probability, defined as cross-section (σ2PA). The absorbed photons either have equivalent (ω1 = ω2) or different frequencies (ω1 ≠ ω2), where the former is degenerate 2PA (D-2PA) and the latter is nondegenerate 2PA (ND-2PA). ND-2PA is of particular interest since it is a promising imaging technology with flexibility of photon frequencies and enhanced cross sections, however, it remains a relatively unexplored area compared to D-2PA. This work utilizes time-dependent density functional theory (TD-DFT) and second-order approximate coupled-cluster with the resolution-of-identity approximation (RI-CC2), for the excitation from S0 to S1, to investigate σD-2PA and σND-2PA of FP chromophore models. Interestingly, comparing CAM-B3LYP with the RI-CC2 computations shows qualitative and, in fact, near quantitative agreement in the computed improvements of σND-2PA for comparable (relative) frequency detunings, despite the known underestimations of 2PA cross sections, for TD-DFT results relative to RI-CC2 values. As expected from the 2-state model, the computed values of σND-2PA are quantitatively larger than σD-2PA, where chromophores with the largest values of σD-2PA show greater potential for σND-2PA improvement. Anionic chromophores demonstrated improvements up to 14%, while substantial enhancements were observed in neutral chromophores with some achieving a 30% increase. This work investigates the ND-2PA photophysical characteristics of FP chromophores and identifies qualitative patterns in the computed properties of ND-2PA relative to D-2PA.

Data-driven prediction of rail neutral temperature for continuously welded rails using impulse-based vibration frequencies

Continuously welded rails (CWR) are prone to the development of high thermal-induced load along the axial direction. Excessive levels of load lead to risk of rail buckling and potential for derailment. Knowledge of the in situ rail axial load in CWRs is therefore important to ensure safe rail management. Field-deployable, nondestructive evaluation techniques for measuring the rail load, or a widely adopted alternative called rail neutral temperature (RNT), are desired. This study uses a data-driven approach to investigate if rail dynamic response data, collected in a non-destructive fashion, can be used to predict RNT. The study is based on a data set comprising rail equivalent strain, temperature and vibration resonance frequencies that was collected from a revenue-service rail over a period of nearly two years. All excited vibration resonance peaks are identified from other peaks caused by noise using spectral amplitude variance. Among these resonance peaks, potentially useful resonances are identified with respect to stacked spectra collected across a testing day using an assumed temperature-frequency relation. A subset of the identified useful resonances is then identified based on their consistent appearance across both testing locations and all testing days, strong correlation to effective strain, and strong correlation to each other. Three particular vibration resonances (or vibration modes -- these terms will be used interchangeably throughout this paper unless specified otherwise. The term mode does not necessarily indicate mode shapes or mode families.) emerge from this process as best candidates. A classic feature selection technique, Lasso linear regression, is then employed to identify critical power combinations of the three resonant mode frequencies. Two power combinations exhibit unique correlation to the measured equivalent axial strain at both test locations across all testing days, and thus show particular ability to predict RNT. The RNT is predicted at one test location using different models based on the power combination data from the other location, and vice versa, where the predictions satisfy standard RNT measurement accuracy expectations.

Research on transformer based online condition monitoring method for IPM

Abstract The Intelligent Power Module (IPM) has the integrated packaging, leading to the advantages of convenient use, safety and reliability. However, once it fails, it will cause the whole power supply system to be inoperative, and it is necessary to perform online Condition Monitoring (CM) of the IPM. In this paper, we extract the aging characteristics such as dynamic equivalent resistance, peak-to-peak value, switching frequency, and turn-off time only from the voltage and current of IPM drain–source port, and then propose a non-intrusive online CM method for the IPM based on the transformer neural network (TNN). We analyse the internal aging mechanism of the IPM, the changing law of aging features, and construct multi-dimensional aging fusion features, and then the TNN model is used to monitor early parameters drift of multi-dimensional fusion feature vectors and realize the accurate online prediction of IPM health condition. The experimental analysis results show that the fault prediction accuracy reaches 96%, and can realize the health CM under the condition of noise interference, weak aging features and few external observable points.

Development of an equivalent system frequency response model based on aggregation of distributed energy storage systems

Energy storage systems (ESSs) installed in distribution networks have been widely adopted for frequency regulation services due to their rapid response and flexibility. Unlike existing ESS design methods which focus on control strategies, this paper proposes a new method based on an ESS equivalent aggregated model (EAM) for calculating the capacity and the droop of an ESS to maintain the system frequency nadir and quasi-steady state frequency using low-order functions. The proposed method 1) uses first-order functions to describe the frequency response (FR) of synchronous generators (SGs); 2) ignores the control strategies of SGs, making the method systematic and allowing it to avoid analyzing complex high-order functions; and 3) is suitable for low inertia systems. The applicability and accuracy of the method is demonstrated using a modified four-generator two-area (4G2A) system.

Influence of earthquake duration on structural collapse capacity: Application to self-centering shear walls

This study investigates the influence of earthquake duration on the structural collapse capacity of self-centering shear walls (SCSW). The assessment process employing different response indicators is examined when considering the duration effect, including the inter-story drift ratio (IDR) and damage index (DI) quantified through a modified damage model. SCSWs with varying self-centering parameters λ are designed and modeled, and incremental dynamic analyses are performed using two record sets with distinct effective durations and equivalent frequency contents. The assessment criteria outlined in FEMA P695 are utilized to quantify the collapse capacity of SCSWs considering the duration effect. Meanwhile, the duration effect on structural performance corresponding to multiple damage states are evaluated. Results show a minimal correlation between the duration effect and collapse capacity when employing IDR during evaluations. Conversely, earthquakes with longer durations may impose higher collapse risks to SCSWs when assessing collapse capacity using DI. Meanwhile, enlarging λ can effectively mitigate damage development and reduce collapse risks for SCSWs. The results further demonstrate that damage may develop more rapidly within SCSWs under long-duration earthquakes, potentially leading to inadequate redundancy during intense seismic events. Additionally, the result suggests that the current performance assessment process only employing deformation-based response indicators may lead to inadequate evaluation of collapse risks for SCSWs, especially when accounting for the effect of earthquake duration.

Minor Faults Diagnosis for Under-Sampled Lithium-Ion Batteries Based on Static-Dynamic Compensation

With the rapid proliferation of electric vehicles, the safety concerns related to lithium-ion batteries are gaining more and more attention. Fault diagnosis is a key approach to reducing the risk of battery failure. However, existing battery management systems (BMS) apply under-sampled voltage signal acquisition, which leads to misdiagnosis and omission of faults. To address this issue, a minor fault early diagnosis method based on static-dynamic compensation voltage data is proposed. First, the voltages of the series-connected cells are asynchronously collected. Then, the collected voltage sequences from various modules are mapped to the voltage sequence of the target battery using the static-dynamic compensating method, which can obtain a new sequence with a significantly higher equivalent sampling frequency. Finally, the sample entropy method is employed to detect minor faults based on the new sequence after compensation. Experimental results reveal that the presented method can increase the sampling frequency by about 8 times. The proposed method can successfully detect minor short circuits and poor connection faults in the battery under different ambient temperatures.

#2824 Use of proton pump inhibitors is not associated with post-dialysis fatigue and time of recovery after dialysis in patients on chronic hemodialysis

Abstract Background and Aims Chronic hemodialysis is often associated with post-dialysis fatigue (PDF), a condition characterized by extreme tiredness and lack of energy. PDF is a debilitating symptom that significantly impairs quality of life of hemodialysis patients. Furthermore, PDF is independently associated with the time to recover after dialysis (TIRD), which can be used as an indirect measure of PDF. Recent studies have shown that the use of proton pump inhibitors (PPI) is independently associated with the prevalence and severity of fatigue in kidney transplant recipients. The aim of this study is to determine whether the use of PPIs in patients undergoing chronic hemodialysis is associated with PDF prevalence and characteristics, as well as the TIRD. Method For this retrospective study, we recruited patients undergoing chronic hemodialysis and asked open-ended questions about their post-dialysis fatigue and time of recovery. Each patient was asked to rate the intensity, duration and frequency of their PDF on a scale of 1 to 5. We defined whether patients used a PPI (no PPI use or PPI use), the type of PPI used, the dose of PPI used, and the duration of the PPI use (<1 year or ≥1year). Results The study analysed 346 patients, of whom 259 used PPIs (55 used omeprazole, 63 esomeprazole, 54 pantoprazole, 87 lansoprazole, and 7 rabeprazole) and 87 did not. Among the patients, 232 reported suffering from PDF, while 114 did not. The median [min-max] TIRD was 210 minutes [0-1440]. The prevalence of PDF in PPI users and non-users was 67% and 68%, respectively (p= 0.878). There was no significant difference in the median [min-max] TIRD between PPI users and non-users (180 [0-1440] and 240 [0-1440], respectively; p=0.871). Additionally, there was no significant difference in the median PDF intensity, duration, frequency, and severity PPI users and non-users. The prevalence of PDF was similar among the different types of PPI used and did not differ with respect to PPI non-users. Duration of PPI exposure was <1 year in 42 patients and ≥1 year in 224 patients. There was no difference in prevalence of PDF between the two exposures. The correlation matrix showed no statistical significance between PPI equivalent dose, treatment duration, PDF frequency, PDF characteristics, and TIRD. Conclusion There is no association between the use of PPIs and PDF or time of recovery after dialysis in patients on chronic hemodialysis.

Multi-timescale analysis and quantification of dynamic regulation characteristics of DFIM-based variable-speed pumped storage units in alleviating wind power fluctuations

To comprehensively clarify the regulating role of variable-speed pumped storage system (VSPS) in alleviating wind power fluctuations, this paper quantitatively evaluates the multi-timescale frequency regulation performance of VSPS in a typical wind-hydro-pumped storage hybrid system (WHPHS). Firstly, the flexible equivalent frequency response models of VSPS and WHPHS are established. Then, the effectiveness of fixed-speed pumped storage system (FSPS) and VSPS in enhancing the stability of dominant hydropower system (HS) in WHPHS is compared, and the dynamic regulation behaviors between VSPS and FSPS are quantitatively evaluated. Based on the operational characteristics, the differences in the allocation of regulation responsibilities between VSPS/FSPS and HS for frequency regulation are clarified. The results indicate that VSPS contributes to an exhaustive improvement in HS stability, reduces the power fluctuation and frequency deviation of WHPSHS. Specifically, in the established simulation scenarios, WHPSHS with VSPS outperforms an equivalent FSPS in reducing frequency deviation frms by 20 %, power fluctuation Is by 40 %, and complementarity Ic by 50 %. Particularly effective in suppressing power fluctuations in range of 0.01 Hz to 0.8 Hz, which decreases the resonant peak of frequency deviation by 61.3 % compared to FSPS. It further highlights the regulatory advantages of VSPS in responding to low-amplitude high-frequency power variations. These findings can provide novel insights into the role of VSPS in mitigating RES fluctuations and energy management strategies in multi-type hybrid systems.

Preterm birth, preeclampsia, gestational hypertension and offspring birth weight in women with active juvenile idiopathic arthritis and healthy controls

ObjectivesThere is insufficient knowledge about pregnancy outcomes in women with juvenile idiopathic arthritis (JIA). Our objective was to explore a possible association of inflammatory active JIA and pregnancy outcomes, including preterm birth, preeclampsia, gestational hypertension, and offspring gestational weight.MethodsWe linked data from the Norwegian nationwide observational register RevNatus with data from the Medical Birth Registry of Norway (MBRN) for the period 2010 to 2019. Singleton births in women with JIA (n = 181) included in RevNatus were cases. After excluding births in mothers with rheumatic inflammatory diseases, the remaining singleton births registered in MBRN, served as population controls (n = 575 798).ResultsPreterm birth was more frequent in women with active JIA (17.6%) and of equivalent frequency in women with inactive JIA (3.1%), compared to population controls (4.9%). Preeclampsia had similar rates in women with JIA and population controls while gestational hypertension was more frequent in women with active JIA (7.2%) and inactive JIA (6.9%) compared to population controls (1.7%). Abnormal fetal growth occurred in similar rates in women with JIA and population controls.ConclusionHaving active JIA in pregnancy increased the risk for preterm birth (risk difference 12.7, 95% CI 4.7 to 25.3) and gestational hypertension (risk difference 6.2, 95% CI 1.4 to 16.8). There was no increased risk for preeclampsia or abnormal fetal growth compared to population controls.

Hydrogen reduction-based energy management strategy of hybrid fuel cell/PV/battery/supercapacitor renewable energy system

Fuel cells can be used in sustainable cities for a variety of purposes, including emergency backup power systems, electric vehicle recharging, and the production of clean, efficient energy for buildings. Additionally, fuel cells can support distributed energy systems, improving energy management, and lowering dependency on conventional energy sources. However, the slow time response of proton exchange membrane fuel cell (PEMFC) during high-level load variation is an issue that needs addressing. To solve this, battery storage and supercapacitor can be integrated with a hybrid generation system including fuel cell, photovoltaic (PV). It's important to have effective energy management strategies (EMSs) to ensure the photovoltaic (PV) array, PEMFC, batteries, and supercapacitors function optimally. An EMS distributes the load demand among these components while maintaining high efficiency and low hydrogen consumption. This paper proposes an effective EMS for a hybrid generating system DC microgrid (MG) having PV, FC, battery, and SC based on recent Harris hawks optimizer (HHO) to manage the energy between the equipment while minimizing the total hydrogen consumption and enhancing the system efficiency. The research work compared several algorithms, including external energy maximization strategy (EEMS), equivalent consumption minimization strategy (ECMS), frequency decoupling and state machine control (FDSMC), proportional integral (PI), Cuckoo search (CS), and grey wolf optimizer (GWO), with the proposed HHO. The EEMS-based HHO algorithm outperformed the others resulting in 18.95 %, 38.77 %, 47.34 %, 34.43 %, 33.9 %, and 20.38 % hydrogen consumption reduction compared to PI, FDSMC, ECMS, EEMS, CS, and GWO, respectively. Moreover, efficiency improved by 7.62 %, 7.77 %, 17.15 %, 11.21 %, 2.17 %, and 5.12 % compared to PI, FDSMC, ECMS, EEMS, CS, and GWO, respectively. The obtained findings demonstrated the robustness and efficacy of the suggested HHO-EMS in obtaining the lowest hydrogen consumption of the DC microgrid that was provided.

Effects of Plyometric-Based Hydro-Kinesiotherapy on Pain, Muscle Strength, Postural Stability, and Functional Performance in Children with Hemophilic Knee Arthropathy: A Randomized Trial

Aim To explore how plyometric-based hydro-kinesiotherapy (Plyo-HKT) would affect pain, muscle strength, postural stability, and functional performance in a convenience sample of children with hemophilic knee arthropathy (HKA). Methods Forty-eight children with HKA (age: 8–16 years) were randomly allocated to the Plyo-HKT group (n = 24; underwent the Plyo-HKT for 45 min, twice/week over 12 wk in succession) or the comparison group (n = 24; performed the standard exercise rehabilitation at an equivalent frequency and duration). Pain, peak concentric torque of quadriceps and hamstring (produced at two angular velocities: 120 and 180 o/sec), dynamic limits of postural stability (DLPS), and functional performance [Functional Independence Score in Hemophilia (FISH) and 6-Minute Walk Test (6-MWT)] were assessed pre- and post-intervention. Results In contrast with the comparison group, the Plyo-HKT group achieved more favorable pre-to-post changes in pain (p = .028, η 2 p = 0.10), peak torque of quadriceps [120°/sec (p = .007, η2 P = 0.15); 180°/sec (p = .011, η2 P = 0.13)] and hamstring [120°/sec (p = .024, η2 P = 0.11); 180°/sec (p = .036, η2 P = 0.09)], DLPSdirectional [forward (p = .007, η2 P = 0.15); backward (p = .013, η2 P = 0.12); affected side (p = .008, η2 P = 0.14); non-affected side (p = .002, η2 P = 0.20)], DLPSoverall (p < .001, η2 P = 0.32), and functional performance [FISH (p < .001, η 2 p = 0.26); 6-MWT (p = .002, η 2 p = 0.19)]. Conclusion Plyo-HKT is likely helpful for reducing pain, improving strength, enhancing postural stability, and boosting functional capabilities in children with HKA. Physical rehabilitation practitioners should, therefore, consider this intervention strategy.

Evaluation of the Accuracy of Pavement ME Methodology in Calculating Equivalent Loading Frequency and Its Effect on Strain Response Predictions in Flexible Pavements

Evaluation of the Accuracy of Pavement ME Methodology in Calculating Equivalent Loading Frequency and Its Effect on Strain Response Predictions in Flexible Pavements

A hybrid constitutive model of high-damping viscoelastic materials used for vibration control of civil structures

The dynamic mechanical properties of high-damping viscoelastic (VE) materials exhibit significant frequency and temperature dependences in the glass transition zone. However, the current state of VE constitutive models face a challenge in balancing the accuracy, simplicity, and ease of structural dynamic analysis. In this study, a power-law and exponent-law hybrid model is proposed to model the frequency-dependent and temperature-dependent complex modulus of VE materials. The equivalence of hybrid model and existing VE constitutive models is presented. A total of 12 groups of experimental data are collected to validate the accuracy of hybrid model. An efficient numerical method for the hybrid model is proposed and then validated through a viscoelastically damped frame. Results show that the hybrid model is equivalent to two rheological models or fractional derivative models owning to the unshared model coefficients. The hybrid model is superior to the eight-parameter fractional derivative model in predicting the complex modulus over a wide equivalent frequency range. The proposed numerical method performs quite well in calculating seismic responses of proportional and non-proportional damped frames.

Research on Nonlinear Vibration of Dual Mass Flywheel Considering Piecewise Linear Stiffness and Damping

Nonlinear torsional vibration differential equation of the nested arc-shaped short spring dual mass flywheel (DMF) is established, considering the piecewise linear stiffness and damping of the spring. The first-order approximate analytical solution under sinusoidal excitation and the amplitude–frequency characteristic function are obtained by means of the average method which verified by the Runge–Kutta (R–K) method. The effects of the parameters of input excitation, inertia, and piecewise linear stiffness and damping of DMF on the resonant amplitude, resonant frequency band, and equivalent linear natural frequency of the system are analyzed. The results show that the amplitude–frequency characteristic curve bending and jumping with the changes of excitation frequency and the peak of resonant amplitude can be obviously reduced by increasing the inertia of the primary flywheel and decreasing the inertia of the secondary flywheel. The complex nonlinear dynamic phenomena such as Period 1, quasi-periodic, and chaos are obtained by analyzing the forced vibration response under the different excitation frequencies.

Enhanced 3-D asynchronous correlation data preprocessing method for Raman spectroscopy of Chinese handmade paper

We have developed a novel 3D asynchronous correlation method (3D-ACM) designed for the classification and identification of Chinese handmade paper samples using Raman spectra and machine learning. The 3D-ACM approach involves two rounds of tensor product and Hilbert transform operations. In the tensor product process, the outer product of the spectral data from different samples within the same category is computed, establishing inner connections among all samples within that category. The Hilbert transform introduces a 90-degree phase shift, resulting in a true three-dimensional spectral data structure. This expansion significantly increases the number of equivalent frequency points and samples within each category. This enhancement substantially boosts spectral resolution and reveals more hidden information within the spectral data. To maximize the potential of 3D-ACM, we employed six machine learning models: principal component analysis (PCA) with linear regression (LR), support vector machine (SVM) with LR, k-Nearest Neighbors (KNN), random forest (RF), and convolutional neural network (CNN). When applied to the 3D-ACM data preprocessing method, R-squared values of PLS-LR, KNN, RF and CNN supervised models, approached or equaled 1. This indicates exceptional performance comparable to unsupervised models like PCA. 3D-ACM stands as a versatile mathematical technique not confined to spectral data. It also eliminates the necessity for additional experimental setups or external control conditions, distinct from traditional two-dimensional correlation spectroscopy. Moreover, it preserves the original experimental data, setting it apart from conventional data preprocessing methods. This positions 3D-ACM as a promising tool for future material classification and identification in conjunction with machine learning.

Transient Response Analysis of Nonlinear Oscillators With Fractional Derivative Elements Under Gaussian White Noise Using Complex Fractional Moments

Abstract Complex fractional moment (CFM), which is defined as the Mellin transform of a probability density function (PDF), has been successfully employed to find the response PDF of a wide variety of integer-order nonlinear oscillators. In this paper, a CFM-based analysis is performed to determine the transient response PDF of nonlinear oscillators with fractional derivative elements under Gaussian white noise. First, an equivalent linear system is introduced for the purpose of deriving the Fokker–Planck (FP) equation for response amplitude. The equivalent natural frequency and equivalent damping coefficient of the system need to be determined, taking into account both the nonlinear and fractional derivative elements of the original oscillator. Moreover, to convert the FP equation into the governing equation of CFMs, these equivalent coefficients must be given in polynomial form of amplitude. This paper proposes formulas for appropriately determining the equivalent coefficients, based on an equivalent linearization technique. Then, applying stochastic averaging, the FP equation is derived from the equivalent linear system. Next, the Mellin transform converts the FP equation into coupled linear ordinary differential equations for amplitude CFMs, which are solved with a constraint corresponding to the normalization condition for a PDF. Finally, the inverse Mellin transform of the CFMs yields the amplitude PDF. The joint PDF of displacement and velocity is also obtained from the amplitude PDF. Three linear and nonlinear fractional oscillators are considered in numerical examples. For all cases, the analytical results are in good agreement with the pertinent Monte Carlo simulation results.

ИСПОЛЬЗОВАНИЕ ШТАТНОЙ ОСНАСТКИ ТОКАРНОГО СТАНКА ДЛЯ АНАЛИЗА ВИБРАЦИОННОГО ФОНА ПРОЦЕССА РЕЗАНИЯ

Predictive maintenance is a proactive maintenance strategy that uses condition monitoring tools to detect signs of damage, abnormalities, and equipment performance. The most commonly used method for rotating machines is vibration analysis. The most commonly used method for condition analysis of rotating parts is vibration analysis. Most signal analysis tools today use the fast Fourier transform (FFT), which is a special case of the generalized discrete Fourier transform and converts a vibration signal from its time domain representation to an equivalent frequency domain representation. This article focuses on the use of discrete Fourier transform for machining signal analysis.

Inertial Estimation in Power Grids Considering Load Contribution Based on FF-UKF

Abstract In modern power systems, the extensive integration of renewable energy sources leads to a reduction in system inertia, thereby affecting grid stability. This study proposes a novel method for estimating grid inertia, which, under quasi-steady state conditions, effectively incorporates load contribution by combining class noise signals collected by PMUs (Phasor Measurement Units) with the Forgetting Factor Unscented Kalman Filter algorithm (FF-UKF). The research initially analyzes the characteristics of various inertia sources in power systems dominated by renewable energy. Building on this, we propose a grid node equivalent inertia estimation model based on node equivalent frequency response, developing an FF-UKF-based algorithm that allows for the continuous dynamic estimation of inertia at various grid nodes. Simulation experiments conducted within the IEEE 39 bus system validate the method’s effectiveness and demonstrate the spatiotemporal distribution of grid inertia under varying levels of renewable energy penetration. This study is significant for understanding and enhancing frequency control and stability in power systems, providing key technical support for the modernization of the power system and the efficient integration of renewable energy.

Equivalent Near-Field Corner Frequency Analysis of 3D Dynamic Rupture Simulations Reveals Dynamic Source Effects

Abstract Dynamic rupture simulations generate synthetic waveforms that account for nonlinear source and path complexity. Here, we analyze millions of spatially dense waveforms from 3D dynamic rupture simulations in a novel way to illuminate the spectral fingerprints of earthquake physics. We define a Brune-type equivalent near-field corner frequency (fc) to analyze the spatial variability of ground-motion spectra and unravel their link to source complexity. We first investigate a simple 3D strike-slip setup, including an asperity and a barrier, and illustrate basic relations between source properties and fc variations. Next, we analyze &amp;gt;13,000,000 synthetic near-field strong-motion waveforms generated in three high-resolution dynamic rupture simulations of real earthquakes, the 2019 Mw 7.1 Ridgecrest mainshock, the Mw 6.4 Searles Valley foreshock, and the 1992 Mw 7.3 Landers earthquake. All scenarios consider 3D fault geometries, topography, off-fault plasticity, viscoelastic attenuation, and 3D velocity structure and resolve frequencies up to 1–2 Hz. Our analysis reveals pronounced and localized patterns of elevated fc, specifically in the vertical components. We validate such fc variability with observed near-fault spectra. Using isochrone analysis, we identify the complex dynamic mechanisms that explain rays of elevated fc and cause unexpectedly impulsive, localized, vertical ground motions. Although the high vertical frequencies are also associated with path effects, rupture directivity, and coalescence of multiple rupture fronts, we show that they are dominantly caused by rake-rotated surface-breaking rupture fronts that decelerate due to fault heterogeneities or geometric complexity. Our findings highlight the potential of spatially dense ground-motion observations to further our understanding of earthquake physics directly from near-field data. Observed near-field fc variability may inform on directivity, surface rupture, and slip segmentation. Physics-based models can identify “what to look for,” for example, in the potentially vast amount of near-field large array or distributed acoustic sensing data.