Frequency-domain Model Research Articles

Spectral modelling approach for crowd-rhythmic activities performed on steel-concrete composite floors

During the last decades, building floors have become sensitive to vibrations caused by human activities, especially when a group of individuals perform rhythmic activities in a coordinated manner. Resulting effects on floor occupants can range from a mere perception, can pass through discomfort and can even go so far to cause panic. The present paper proposes a spectral modelling approach for crowd-rhythmic activities applied on floor structures. The proposed model comprises a frequency domain model for a single person, combined with coordination factors to account for the lack of synchronization between individuals. The identification of this model was based on vibration experiments carried out on a floor specimen specifically designed for that purpose. Two particular types of rhythmic activity were investigated: jumping and skipping, and performed by up to 16 individuals, where realistic coordination stimulus was adopted. Using least-squares identification procedures, the single person load parameters were determined and predicted load models are in good agreement with their measured counterparts. Resulting parameters are dependent on the motion style of each investigated activity. Coordination factors are also derived from crowd forces and present a hyperbolic decrease by crowd size for jumping activities against an exponential decrease for skipping activities. Based on a closed-form expression using the established crowd model, a design-oriented method is proposed for a simplified evaluation of floor response due to crowd-rhythmic activities and verified against floor response measurements. Slight differences were found between measured and numerical responses due to the variabilities either encountered during experiments or resulting from the load identification process.

Optical properties reconstruction in nonhomogeneous participating medium based on an improved sequential quadratic programming

The reconstruction of optical parameters achieves functional imaging by providing characteristics closely related to the physiological state of biological tissues. With considerable research significance, this imaging technology has received increasing attention in the field of medical diagnosis and treatment. However, owing to the limited number of known measurement signals far less than the number of target parameters to be estimated, reconstruction requires a long ill-conditioned process, which is easily polluted by background noise so as to difficulties for achieving high imaging accuracy and efficiency. To conquer these deficiencies, an improved sequential quadratic programming (ISQP) is proposed in this work. In the ISQP, dual constraints of equality and inequality are added in the process of minimizing the objective functional, which avoids the time-consuming forward problem. Moreover, duality method, reduced Hessian matrix, and second-order correction are introduced in the subproblem. In addition, prior information of diagonal contiguous points is introduced to overcome the ill-posedness. The reconstruction performance of ISQP in different modes is discussed in depth. A time-frequency coupling model based on ISQP is constructed subsequently, of which the practical significance is demonstrated in the biological tissue-like medium model. The results show that in the established frequency domain model, ISQP significantly shortens the calculation time of the traditional method and improves the reconstruction quality of absorption parameters. The developed time-frequency coupling method effectively eliminates the influence of the background disturbance, and in the meantime improves the accuracy and efficiency when simultaneously estimating multi-parameters related to optics, with good robustness. The effectiveness of the ISQP combined with hybrid time-frequency model provides new referable ideas and application potential for the development and progress of medical, biology, photonics and other fields.

Hydroelastic analysis of offshore floating photovoltaic based on frequency-domain model

This paper presents a frequency-domain model for analyzing the hydroelastic response and the vulnerable area of floating photovoltaic structures under wave action. The structure is discretized into an array of modules connected by equivalent elastic beams, while the boundary element method is employed to solve the scattering and radiation problems of the modules. The structural stiffness matrix is assembled by employing coordinate transformations and matrix reorganization techniques. The hydrodynamic matrix and the structural stiffness matrix are combined to establish the frequency-domain motion equation for describing the flexibility of the structure. The maximum gradient index is introduced to evaluate the dynamics issues of floating photovoltaic structures. The vulnerable area of the structure is assessed using the equivalent stress based on the von Mises stress theory. The proposed numerical model is validated by comparing the results with benchmark experiment as well as classical numerical model results. With this method, the hydroelastic response of a 300m×300m offshore floating photovoltaic is investigated. The displacement and internal force of the structure under various wave conditions is analyzed. This work can deepen the understanding of the hydrodynamics of floating photovoltaic structures in marine environment.

WECs microarray effect on the coupled dynamic response and power performance of a floating combined wind and wave energy system

With numerous floating wind-wave hybrid system concepts proposed and studied on a large scale, multiple wave energy converters (WECs) are usually integrated in a hybrid system to spread the cost and obtain more power. The internal interactions between WECs and a platform are complex and difficult to assess. The effect of WECs microarray on the performance and interaction of hybrid systems has not been adequately studied. To bridge this gap, the frequency-domain and time-domain modelling considering the effect of WECs microarray is first established using the coupled AQWA-FAST simulation system. On this basis, this work studies the internal connection between the layout of the WECs, and interaction factors for hydrodynamic coupling between WECs microarrays and the platform. The relationship between the WECs microarray and the aerodynamic response of the wind turbine is analysed, as well as the relationship between the microarray layout and the disturbed wave field of the devices. Results show that the WECs outer microarray performs better in terms of pitch motion suppression and power output, while the inner microarray exerts a more positive influence on the dynamic response of the tower.

An experimental investigation on the effect of in-flow distortions of propeller noise

Propellers usually operate in non-uniform flow conditions, leading to significant levels of noise generation due to interaction with the leading edges of the propeller blades. Under these flow conditions, the noise generated can be substantially different from the rotor-alone self-noise generated under uniform mean flow conditions. This paper presents an experimental investigation aimed at studying the far-field noise due to a two-bladed propeller immersed in a controlled non-uniform flow with known characteristics. This paper presents the results of a parametric study with different degrees of flow non-uniformity generated from different geometric obstructions located in the jet nozzle, while maintaining constant thrust. Measurements of the radiated noise show that the incoming mean flow non-uniformities under investigation in this study cause a significant increase in tonal amplitudes of the blade passing frequencies of harmonic orders greater than 2. At the first two blade passing frequencies the tones are dominated by the rotor-alone ‘Gutin-type’ tones resulting from the steady blade loading.The measured noise spectra are compared with predictions from a simple frequency-domain flat plate model. The analytical model will be shown to capture the main features of the interaction tonal noise spectrum and directivity.

A line loss reduction optimization for renewable energy‐based distribution networks using a probabilistic approach

SummaryThe large integration of distributed generation (DG) and electric vehicles (EVs) facilitates daily life and reduces carbon emissions. However, it brings problems such as increased uncertainty and power electronic permeability to the distribution network. In this context, it is important to solve the line loss optimization problem as closely as possible to the real situation. Therefore, this paper solves the line loss optimization problem based on a probabilistic model of three‐phase unbalanced modern power electrical distribution network. Firstly, the paper proposes a frequency domain model of the distribution network and the optimization problem model. Then, to deal with the uncertainty of the system, the point estimation method (PEM) and Monte Carlo Simulation (MCS) are used. Finally, this paper proposes two optimization schemes considering the power quality when single and multiple DGs are integrated. The simulation results demonstrate that the PEM using the Gram–Charlier series expansion of the fourth‐order statistical moments to estimate the probability density function (PDF) is significantly less time consuming than the traditional MCS method while maintaining accuracy. The comparative analysis reveals that under the optimization scheme with single DG integration, the expected value of system line loss and expected line loss rate are reduced from 283.95 kW to 218.96 kW (22.89% reduction) and from 13.44% to 10.68%, respectively. Under the dual DG integrated optimization scheme, the expected value of line losses and expected line loss rate are reduced from 283.95 kW to 208.88 kW (26.4% reduction) and from 13.44% to 10.25%, respectively. However, after taking the uncertainty into account, the operational reliability of the system has been enhanced under different scenarios. In addition, it is found that the optimization approach incorporating multiple DG units can effectively mitigate the system line loss under diverse operational scenarios while enhancing the system's DG integration capability.

A robust poly-reference frequency-domain identification method to extract dynamic properties from vibration data

When performing vibration tests on structural systems, engineers face the challenge of extracting the dynamic properties from the measured data in an accurate and robust manner. Though several methods exist for this purpose, in some circumstances, they fail to provide clear estimates for these properties, particularly when applied to noise-contaminated data. Here we propose a robust and accurate method formulated in frequency-domain modal model for extracting dynamic properties from vibration data. The method is applied to three application examples, namely the vibrations simulated with the aid of a finite element model and the real-life vibration measurements of a platform specimen and of a full-scale concrete heritage court building. Its performance is thereafter assessed by the so-called stabilization diagrams, the relative error between estimated and exact properties, the modal assurance criterion, and by comparing the synthesized frequency functions to their measured counterparts. This assessment shows that the proposed approach tends to provide clearer and more accurate identification results than those from the state-of-the-art identification methods.

Design Optimization of Floating Offshore Wind Turbine Substructure Using Frequency Domain Modelling and Genetic Algorithm

Floating offshore wind turbines are becoming increasingly popular as a promising technology for producing cleaner energy. However, in order to be competitive with fixed offshore wind projects or even onshore wind projects, the costs associated with floating offshore wind turbines must be significantly reduced. To tackle this challenge, this paper presents a comprehensive framework for optimizing the design of floating offshore wind turbine substructures and their components. The innovative open-source frequency-domain dynamic model RAFT is used to take into account the aerodynamic, hydrodynamic and mooring forces that impact the stability and dynamics of the floating system. The numerical model is coupled with an efficient genetic algorithm to minimize the structural mass of the floating platform while maintaining its stability and dynamic performance. The geometrical parametrization of the substructure, the implementation of the numerical model, and the overall optimization process are all thoroughly detailed in this paper, along with preliminary results that demonstrate the potential cost reductions that can be achieved through this framework.

Analysis of a cusped helicon plasma thruster discharge

Experiments and simulations are used to analyze a compact helicon plasma thruster with a cusp in its internal magnetic field. The former rely on a compensated Langmuir probe and a Faraday cup, while the latter employ a hybrid PIC/fluid transport model combined with a frequency-domain electromagnetic field model. Measurements serve to tune the anomalous transport parameters of the model and overall show the same trends as the numerical results, including a secondary peak of electron temperature downstream in the magnetic nozzle, where electron cyclotron resonance conditions for the 13.56 MHz excitation frequency are met. The cusp plays a central role in determining the plasma losses to the walls and the profile of electron temperature, which in turn defines the excitation and ionization losses. While losses to the rear wall are reduced, losses to the lateral wall are increased, which, together with the low production efficiency, limit the performance of the device.

Objective Image Quality Optimization in Augmented Reality Using Spatial Frequency Domain Models.

Augmented reality (AR) blends the digital and physical worlds by overlapping a virtual image onto the see-through physical environment. However, contrast reduction and noise superposition in an AR head-mounted display (HMD) can substantially limit image quality and human perceptual performance in both the digital and physical spaces. To assess image quality in AR, we performed human and model observer studies for various imaging tasks with targets placed in the digital and physical worlds. A target detection model was developed for the complete AR system including the optical see-through. Target detection performance using different observer models developed in the spatial frequency domain was compared with the human observer results. The non-prewhitening model with eye filter and internal noise results closely track human perception performance as measured by the area under the receiver operating characteristic curve (AUC), especially for tasks with high image noise. The AR HMD non-uniformity limits the low-contrast target (less than 0.02) observer performance for low image noise. In augmented reality conditions, the detectability of a target in the physical world is reduced due to the contrast reduction by the overlaid AR display image (AUC less than 0.87 for all the contrast levels evaluated). We propose an image quality optimization scheme to optimize the AR display configurations to match observer detection performance for targets in both the digital and physical worlds. The image quality optimization procedure is validated using both simulation and bench measurements of a chest radiography image with digital and physical targets for various imaging configurations.

Stiffness profile spectral composition & geometry deterioration of railway tracks

This work addresses the contribution of the wavelength composition of the spectrum of the rail support stiffness profile to the expected long-term settlement. To that aim, purely harmonic stiffness variations of different wavelength are studied. The frequency-domain model with a double periodicity level previously developed by the first and last authors is adopted to embed the stiffness profile in one of the periodicity layers. Additional resonance velocities at which the resonance frequency of the track system coincides with the support-passing frequency or its multiples are found. The susceptibility to degradation is assessed both by quantifying the mechanical energy dissipated in the substructure under a moving train axle within one wavelength of the support stiffness variation, and the work performed by the wheel-rail contact force. It is shown that shorter wavelengths and larger standard deviations of varying ballast/subgrade stiffness result in an increasing energy dissipation in the substructure, and increase the work performed by the wheel-rail contact force, therefore leading to a reduced lifetime of the track. The energetic quantities increase for lower mean values of the stiffness profile, confirming the proneness of tracks on soft soils to degradation. The influence of varying stiffness vanishes for wavelengths of approximately 16 times the sleeper span, which is equivalent to a track length of about 10 m. High railpad stiffness values result in increased energy dissipation but the influence is limited. In general, an increasing train velocity amplifies the rate of track degradation, with no stabilizing trend in the high-speed regime (300 km/h).

Multi-center atrial fibrillation electrocardiogram (ECG) classification using Fourier space convolutional neural networks (FD-CNN) and transfer learning

There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching >85% accuracy for various cardiac pathologies. Despite the high accuracy at individual institutions, challenges remain when it comes to multi-center deployment. Transfer learning (TL) is a technique in which a model trained for a specific task is repurposed for another related task, in this case ECG ML model trained at one institution is fine-tuned to be utilized to classify ECGs at another institution. Models trained at one institution, however, might not be generalizable for accurate classification when deployed broadly due to differences in type, time, and sampling rate of traditional ECG acquisition. In this study, we evaluate the performance of time domain (TD) and frequency domain (FD) convolutional neural network (CNN) classification models in an inter-institutional scenario leveraging three different publicly available datasets. The larger PTB-XL ECG dataset was used to initially train TD and FD CNN models for atrial fibrillation (AFIB) classification. The models were then tested on two different data sets, Lobachevsky University Electrocardiography Database (LUDB) and Korea University Medical Center database (KURIAS). The FD model was able to retain most of its performance (>0.81 F1-score), whereas TD was highly affected (<0.53 F1-score) by the dataset variations, even with TL applied. The FD CNN showed superior robustness to cross-institutional variability and has potential for widespread application with no compromise to ECG classification performance.

Wavefront coding image reconstruction via physical prior and frequency attention.

Wavefront coding (WFC) is an effective technique for extending the depth-of-field of imaging systems, including optical encoding and digital decoding. We applied physical prior information and frequency domain model to the wavefront decoding, proposing a reconstruction method by a generative model. Specifically, we rebuild the baseline inspired by the transformer and propose three modules, including the point spread function (PSF) attention layer, multi-feature fusion block, and frequency domain self-attention block. These models are used for end-to-end learning to extract PSF feature information, fuse it into the image features, and further re-normalize the image feature information, respectively. To verify the validity, in the encoding part, we use the genetic algorithm to design a phase mask in a large field-of-view fluorescence microscope system to generate the encoded images. And the experimental results after wavefront decoding show that our method effectively reduces noise, artifacts, and blur. Therefore, we provide a deep-learning wavefront decoding model, which improves reconstruction image quality while considering the large depth-of-field (DOF) of a large field-of-view system, with good potential in detecting digital polymerase chain reaction (dPCR) and biological images.

An improved frequency-domain model for the supercritical flow instability analysis in vertical tubes

In a supercritical water reactor, the density wave oscillation (DWO) may occur due to the dramatic changes in the density of the working fluid in the pseudo-critical region. Based on the frequency-domain method, this study proposes an improved model to study DWO. First, the latest partition method is used in this study. Second, in the derivation of the transfer function in the light fluid region, the density change of the fluid is considered. Moreover, the different states of the outlet fluid are handled differently. We use the Nyquist criterion to determine the flow stability and obtain the boundary heat flux (BHF) of different working conditions. After model verification, it is proved that the model accuracy is improved. We analyze the influence of pressure, inlet enthalpy, mass flow rate, inlet throttling coefficient, and tube parameter on the instability boundary. At a certain inlet enthalpy, a minimum BHF of flow instability exists.

Hydrodynamic analysis of a floating platform coupled with an array of oscillating bodies

In this paper, based on multi-body dynamics and beam bending theory, a frequency-domain model is developed to investigate the hydrodynamic performance of a hybrid floating platform (HFP). The HFP investigated here is characterized by hydroelasticity and complex interconnection conditions. The constraint matrix is derived to handle the couple response of an elastic platform and an array of rigid bodies, representing wave energy converters. The hydroelasticity and coupled dynamics of the floating platform in waves and the wave power extraction performance is analyzed. The numerical results reveal that the platform deformations have a significant impact on the relative pitch response between the buoy array and the platform. Neglecting hydroelasticity leads to an overestimation of the hydrodynamic efficiency of the buoy array when 0.4 < λ/L < 1.0, as demonstrated in our calculations. Influence of key parameters such as spacing and incident wave angle is examined numerically. As a result of these investigations, suggestions are identified for an improved design of such kind of multi-use floating platform.

Linear and nonlinear frequency-domain modelling of oscillatory flow over submerged canopies

An analytical and experimental study of flow velocities within submerged canopies of rigid cylinders under oscillatory flows is presented, providing insights into the momentum transfer mechanisms between the different flow harmonics. The experimental dataset covers an unprecedented wide range of flow amplitudes with in-canopy velocity reductions ranging between 0.2 and 0.8 of the free stream velocity (from inertia- to drag-dominated in-canopy flow). Results from the analytical model with nonlinear drag compare favourably to the experimental data. Having application of theories for free surface waves over canopies in mind, the effects of linearization of the drag are analysed by comparing sinusoidal and nonlinear model predictions. Finally, a unified prediction formula for in-canopy velocities for sinusoidal, velocity-skewed, and velocity-asymmetric free stream velocities is presented. The formula depends on two non-dimensional parameters related to inertia and drag forces, and the unified formula allows for easy assessment of the maximum in-canopy velocity.

Modeling and stability prediction for the static-power-converters interfaced flexible AC traction power supply system with power sharing scheme

The flexible traction power supply system (FTPSS) is a promising solution to solve the electric phase break and power quality problems existing in traditional traction power supply systems. To promote the balance of train power distribution among multiple substations, power sharing control is proposed by combining average power control and P-f and Q-V droops based on virtual power decoupling method. The active and reactive power is decoupled through introducing the impedance angle without knowing specific values of the line inductance and resistance. This system level control poses new challenges to the stability of the electric railway due to complex interactions between power electronic-based substations of the FTPSS. To address this issue, the frequency-domain model of the FTPSS with power sharing control is established. Then, the eigenvalue-based stability criterion is introduced to predict the stability of the interconnected system. In particular, to obtain how the power sharing control is sensitive to some controller/circuit parameters, a parameter-oriented stability analysis method is developed through assessing the influence of the key influential factors, including the time delay of wide-area communication, the controller parameters of the power sharing scheme, the circuit parameters, the length of the traction network and the train power requirement. The time-domain simulation and the downscaled prototype experiment are conducted to validate the effectiveness of the established model and the theoretical analysis. This lays the foundation for the design and stability analysis of the FTPSS.

Influence of seabed profile on the seismic response of monopile-supported offshore wind turbines including dynamic soil-structure interaction

In this article, the seismic response of four different Offshore Wind Turbines (OWTs) from 5 to 15 MW, founded on monopiles embedded in homogeneous and non-homogeneous soil profiles with equivalent shear-wave velocities from 100 to 300 m/s is analysed. Two types of variable soil profiles with a semi-parabolic variation of the shear-wave velocity as depth increases are considered. The system seismic response under ten different accelerograms is computed through a finite element substructuring model in frequency domain. The foundation behaviour is obtained by a continuum model including kinematic and inertial interaction. Several models, in addition to that of rigid base condition, are considered to determine the influence of soil-structure interaction (SSI), as well as the contribution of each kinematic interaction (KI) factor. The seismic response of the OWTs is obtained in terms of maximum shear forces and bending moments at mudline level, as well as the acceleration amplification factor at hub level. It is found that the maximum response is produced when SSI is considered, including both the inertial and kinematic interaction. The differences arising due to the soil profile definition are shown to be mainly related to the rotational KI factor, which significantly affects the second vibration mode of the system. The largest responses are obtained for the homogeneous equivalent profile.

Conversion of Induced Polarization Data and Their Uncertainty from Time Domain to Frequency Domain Using Debye Decomposition

The time-domain (TD) induced polarization (IP) method is used as an extension of direct current (DC) resistivity measurements to capture information on the ability of the subsurface to develop electrical polarization. In the TD, the transient voltage decay is measured after the termination of the current injection. To invert tomographic TD IP data sets into frequency-domain (FD) models of complex electrical resistivity, a suitable approach for converting TD IP transients and their corresponding uncertainties into the FD is essential. To apply existing FD inversion algorithms to TD IP measurements, a conversion scheme must transform the measured decay curves into FD impedances and also propagate the corresponding measurement uncertainty from the TD to the FD. Here, we present such an approach based on a Debye decomposition (DD) of the decay curve into a relaxation-time distribution and the calculation of the equivalent spectrum. The corresponding FD data error can be obtained by applying error propagation through all of these steps. To accomplish the DD we implement a non-linear Gauss–Newton inversion scheme. We test the conversion scheme in a synthetic study and demonstrate its application to field data on a tomographic TD IP data set measured on the Maletoyvaemskoie ore field (Kamchatka, Russia). The proposed conversion scheme yields accurate impedance data for relaxation processes, which are resolved by the TD measurements. The error propagation scheme provides a reasonable FD uncertainty estimate, as confirmed by a Monte Carlo analysis of the underlying parameter distributions.

Multiscale model reduction of finite-difference frequency-domain wave modelling in acoustic media

SUMMARY Frequency-domain wave modelling can easily describe the visco-acoustic behaviour of wave propagation using frequency-dependent velocities. Conventional finite-difference (FD) modelling in the frequency domain is computationally prohibitive for solving the acoustic Helmholtz equation in complicated and large geological models. To reduce the computational cost of traditional FD Helmholtz solvers, we develop a multiscale FD frequency-domain method that uses multiscale basis functions to significantly reduce the dimension of system matrices associated with the Helmholtz equation. Due to the insufficient accuracy of the first-order multiscale basis functions in the case of strongly heterogeneous models, we introduce the multinode coarse-element scheme into the scalar Helmholtz equation, a scheme previously developed in the extended multiscale finite-element method for vector problems. This multinode scheme enables multiscale basis functions to capture accurate fine-scale medium property variations. We use one homogeneous model and two heterogeneous models to validate our multiscale method for accuracy and computational cost. Numerical results demonstrate that our new approach can significantly reduce the time and memory costs of acoustic wave modelling while maintaining accuracy, indicating the great potential of our multiscale method in large-scale modelling applications.