Propagation Model Research Articles

Fan noise radial mode detection based on [formula omitted] regularization method

Radial mode detection has been widely employed in fan noise research. Many radial mode detection algorithms by conducting the convex L1 norm optimization have been proposed by taking advantage of the fact that the fan noise sound field is usually dominated by a limited set of radial modes. Nevertheless, L1 norm optimization has been shown to be suboptimal and cannot enforce further sparsity. A fan noise radial mode detection method based on L1/2 regularization algorithm is proposed in this paper to improve its robustness and dynamic range. This method employs the L1/2 regularization to directly solve the in-duct sound propagation model. Synthesized simulations indicates that the L1/2 regularization method can detect the correct radial mode order and amplitude under low signal-to-noise ratio. Moreover, the new method is insensitive to regularization parameter. An experimental system of radial rake is implemented to validate the new method. It is shown that the L-curve method, which do not require any prior information of the acoustic mode distribution or contaminating measurement noise, is an appropriate choice of the regularization parameter determination method for the L1/2 regularizer. It is demonstrated that the new L1/2 regularization method can enhance the mode sparsity, especially under low signal-to-noise ratio. Moreover, the resolution and dynamic range can be improved by the L1/2 regularization method, which makes it an effective and robust fan noise radial mode detection technique in practical applications.

Simulation and analysis of proppant transportation and distribution rules under multi-cluster perforation in horizontal wellbore

Unconventional reservoirs require segmented multi-cluster fracturing technology for commercial development. Due to the settling and inertial effects of proppant particle flow, even if the fracturing fluid is evenly distributed among each perforation, the proppant may still be unevenly distributed. However, the uniform distribution of proppant in multi-fracture is a favorable condition for high-yield gas wells. Based on the proppant dynamics model in the wellbore, coupled with the non-uniform fracture propagation model, a mathematical model of proppant wellbore transportation flow under multi-fracture is established. Through numerical calculations, the proppant transport and distribution mechanism are studied. The study shows that due to gravity, distribution in the upstream is more uniform, and the proppant in the downstream gradually settles and most particles flow toward the bottom. Proppant distributed in each cluster is affected by the flow rate of slurry to a certain extent, and is also controlled by its own properties. The turning efficiency determines the inflow of proppant in perforations. Increasing the pump rate can alleviate gravity but reduce turning efficiency. The perforation azimuth primarily regulates the flow of proppant into downstream perforations. The research results can provide theoretical guidance for the design of proppant transport and cluster perforation in hydraulic fracturing.

Existence of solutions to k‐Wave models of nonlinear ultrasound propagation in biological tissue

AbstractWe investigate models for nonlinear ultrasound propagation in soft biological tissue based on the one that serves as the core for the software package k‐Wave. The systems are solved for the acoustic particle velocity, mass density, and acoustic pressure and involve a fractional absorption operator. We first consider a system that incorporates additional viscosity in the equation for momentum conservation. By constructing a Galerkin approximation procedure, we prove the local existence of its solutions. In view of inverse problems arising from imaging tasks, the theory allows for the variable background mass density, speed of sound, and the nonlinearity parameter in the systems. Second, under stronger conditions on the data, we take the vanishing viscosity limit of the problem, thereby rigorously establishing the existence of solutions for the limiting system as well.

Dynamics of the plasma ejection structure in laboratory modeling of young star jets at plasma focus facilities

The use of “plasma focus” type facilities, such as PF-3 (Kurchatov Institute), makes it possible to carry out well-controlled and diagnosable laboratory experiments to study laboratory jets with scale parameters close to the jets of young stars. In this paper, we present the results of numerical modeling of plasma outburst propagation in PF-3. A self-consistent configuration was chosen as the initial conditions, which correctly takes into account the internal structure of the jet. This allowed us to obtain a detailed structure of the interaction between the magnetized emission and the ambient gas. Due to the scalability of such a structure, one should expect such a structure from the head shock waves of jets of young stars.

Kalman-based estimation of loading conditions from ultrasonic guided wave measurements

Abstract Ultrasonic guided wave-based structural health monitoring (SHM) of structures can be perturbed by environmental and operations conditions (EOCs) that alter wave propagation. In this work, we present an estimation procedure to reconstruct an EOC-free baseline of the structure from the only available Ultrasonic guided wave measurements. This procedure could typically be used as a prior step to increase the robustness of a more general ultrasonic imaging algorithm or SHM process dedicated to flaw detection. Our approach is model-based, i.e. we use a precise modeling of the wave propagation altered by structure loading conditions. This model is coupled with the acquired data through a data assimilation procedure to estimate the deformation caused by the unknown loading conditions. From a methodological point of view, our approach is original since we have proposed an iterated reduced-order unscented Kalman strategy, which we justify as an alternative to a Levenberg–Marquardt strategy for minimizing the non quadratic least-squares estimation criteria. Therefore, from a data assimilation perspective, we provide a quasi-sequential strategy that can valuably replace more classical variational approaches. Indeed, our resulting algorithm proves to be computationally very effective, allowing us to successfully apply our strategy to realistic 3D industrial SHM configurations.

Performance Evaluation of Radio Frequency Visualisation Tool for Wireless Communications: A UNIUYO Campus Case Study

This study was motivated by the challenge of evaluating radio frequency (RF) signal behaviour concerning transmitter and receiver positioning prior to deployment and during troubleshooting. This paper presented the development and testing of a MATLAB-based signal visualisation application to enhance RF signal analysis across various frequencies and terrains, specifically focusing on the University of Uyo (UNIUYO) permanent site and selected locations in Uyo. The application featured three distinct tabs with specialised functions. The first tab integrated campus radio broadcasts at 144.5 MHz and visualised signals for mobile communication standards (2G, 3G, and 4G). The second tab offered a computation interface for selected radio propagation parameters and a tool for converting geographic coordinates from degrees, minutes, and seconds (DMS) to decimal degrees (DD). The third tab was dedicated to 5G testing and deployment. To determine the signal travel extent for each transmitter location, the Longley-Rice and Freespace propagation models were used to represent worst-case and best-case scenarios, respectively. The results, illustrated graphically, demonstrated the application’s effectiveness and versatility in RF signal analysis and 5G deployment planning. The study explored two primary 5G deployment strategies: the standalone (SA) approach and the non-standalone (NSA) technique. The SA strategy employed mid-band frequency (3.5 GHz) point-to-multipoint stations within clusters. The application utilised up to 19 transmitters (one main transmitter and 18 repeaters) per cluster. Conversely, the NSA technique integrated 5G technology with the existing 4G infrastructure to enhance coverage. The findings offer valuable insights into optimising 5G deployment in terrains similar to those at the UNIUYO permanent site and Uyo in general.

Comparison of Different Numerical Methods in Modeling of Debris Flows—Case Study in Selanac (Serbia)

Flow-type landslides are not typical in this region of the Balkans. However, after the Tamara cyclone event in 2014, numerous such occurrences have been observed in Serbia. This paper presents the initial results of a detailed investigation into debris flows in Serbia, comparing findings from two programs: RAMMS DBF and Geoflow SPH. Located in Western Serbia, the Selanac debris flow is a complex event characterized by significant depths in the initial block and entrainment zone. Previous field investigations utilized ERT surveys, supplemented by laboratory tests, to characterize material behavior. Approximately 450,000 m3 of material began to flow following an extreme precipitation period, ultimately traveling 1.2 km to the deposition zone. For validation purposes, ERT profiles from both the deposition zone and the source area were utilized, with particular attention given to areas where entrainment was substantial, as this had a significant impact on the final models. The first objective of this research is to conduct a detailed investigation of debris flow using field investigations: geophysical (ERT) and aerial photogrammetry. The second objective is to evaluate the capacity of two debris flow propagation models to simulate the reality of these phenomena. The GeoFlow-SPH code overestimated the maximum propagation thickness in comparison to the RAMMS model. The numerical results regarding final depths closely align, especially when considering the estimated average depth in the deposition zone. The results confirm the necessity of using multiple simulation codes to more accurately predict specific events.

Assessing the value of combined use of distributed acoustic sensing (DAS) and multicomponent vertical seismic profiling (VSP) data with network-based full waveform inversion and uncertainty quantification: A case study in Alberta, Canada

Distributed acoustic sensing (DAS) technology, deployed in a vertical seismic profiling (VSP) experimental configuration, has emerged as a candidate for non-disruptive and low-cost seismic monitoring of CO2 geostorage and plume evolution. Full waveform inversion (FWI) has likewise received significant attention because it uses relatively complete physical models of wave propagation and because of its sample-by-sample incorporation of data information. Recent artificial neural network-based FWI algorithms (built with, for instance, recursive neural networks, or RNNs) have added to FWI a range of flexible and efficient tools for gradient computation and options for uncertainty assessment and initial model proxies. An important current research area for the use of DAS data is to better understand how they change our confidence levels in models selected through FWI. In particular, we seek to understand whether either DAS data or conventional geophone data alone are optimal for FWI model selection in the CO2 problem, and if not, to what degree they complement each other. The Snowflake 4D VSP dataset, which includes multi-offset and multi-azimuth broadband sources illuminating both fiberoptic cable and densely sampled accelerometer phones in the borehole, was acquired by our group to directly address these questions. In this study, we quantify uncertainty (UQ) by sampling the posterior model covariance matrix from the inverse Hessian matrix at the end of RNN-FWI runs on the Snowflake baseline data, invoking a velocity-density parameterization, and involving mixtures of accelerometer and DAS data. In this UQ context, the complementary effect of combining accelerometer and DAS data is evident in the Vp and Vs models. Our confidence in the approach is bolstered by its independent prediction of a region of known high uncertainty. In the overall pursuit of reliable and low-cost monitoring tools, this supports continued consideration of a multicomponent sensors supported by DAS approach.

Dictionary Learning Method Based on K-Sparse Approximation and Orthogonal Procrustes Analysis for Reconstruction in Bioluminescence Tomography.

Bioluminescence tomography (BLT) is one kind of noninvasive optical molecular imaging technology, widely used to study molecular activities and disease progression inside live animals. By combining the optical propagation model and inversion algorithm, BLT enables three-dimensional imaging and quantitative analysis of light sources within organisms. However, challenges like light scattering and absorption in tissues, and the complexity of biological structures, significantly impact the accuracy of BLT reconstructions. Here, we propose a dictionary learning method based on K-sparse approximation and Orthogonal Procrustes analysis (KSAOPA). KSAOPA uses an iterative alternating optimization strategy, enhancing solution sparsity with k-coefficients Lipschitzian mappings for sparsity(K-LIMAPS) in the sparse coding stage, and reducing errors with Orthogonal Procrustes analysis in the dictionary update stage, leading to stable and precise reconstructions. We assessed the method performance through simulations and in vivo experiments, which showed that KSAOPA excels in localization accuracy, morphological recovery, and in vivo applicability compared to other methods.

Fatigue crack closure assessment by wavelet transform of infrared thermography signals

The occurrence of crack closure significantly impacts the fatigue life of materials and structural components. Whether it is induced by the nature of the loading, the fabrication process or the geometry of the structure, its magnitude and effect should be considered to further improve predictive models of fatigue crack propagation. However, the definition of reliable experimental methods for the observation and assessment of fatigue crack closure, and in particular suited to structure testing, remains a challenge. The present study aims to provide a novel approach for the assessment of fatigue crack closure via the continuous wavelet transform of infrared thermography data. The processing of the temperature signal close to the crack in a coherent time–frequency space allows for the identification of crack closing and opening instants associated with high-frequency components. The method is meant to be suited to any testing configuration (conventional compact tension specimen or full-scale structures) with minimum operator-dependent parameters.

Higher-order network information propagation model based on social impact theory

User relationships are becoming increasingly diverse in current online social networks. This paper utilizes simplicial complexes in higher-order networks to depict multiple user relationships and characterize the topology of social networks. By employing social impact theory and factors such as user interest and information aging to define state transition rules among users, we establish a UAPR information propagation model based on simplicial complexes to simulate the information propagation process in higher-order social networks. The proposed model is simulated on three types of synthetic networks and four real-world simplicial complexes, demonstrating its ability to accurately describe the dynamics and influences of information propagation. Furthermore, our results indicate that different network structures, user interest values, information aging speeds, and intimacy levels significantly influence the information transmission process.

Time-evolving traffic resilience performance forecasting during hazardous weather toward proactive intervention

Transportation systems experience significant disruptions and loss during hazardous weather events, exhibiting great needs of timely intervention to effectively improve the resilience of the affected traffic systems. An informed and science-based proactive intervention strategy depends on accurate forecasting of the resilience performance of traffic systems with essential lead time during hazards. A new resilience performance forecasting methodology at both global and local scales is proposed for traffic networks under natural hazards by addressing unique challenges such as scarcity and time-evolving nature of hazard-specific data. The proposed methodology consists of two modules: the local traffic resilience performance short-term forecasting module based on the modified diffusion convolutional recurrent neural network (DCRNN) and transfer learning techniques, and the global traffic resilience performance forecasting module integrating percolation-based robustness assessment and SIR-based congestion propagation modeling. A case study of an urban traffic network during a major snowstorm hazard is conducted as a demonstration, followed by the feasibility investigation to guide proactive intervention during hazards. It is found the proposed methodology can forecast the time-evolving traffic resilience performance with good accuracy at both global and local scales. With sufficient lead time for the forecast, it bears promising potential to assist the stakeholders to make informed and timely decision about possible proactive intervention by providing key information to help identify the optimal moments and individual strategic links for possible intervention.

Development of a Simulator for Submarine Path Estimation and Optimization of Sonobuoy Deployment Patterns

Due to specificity in the underwater environment, the difficulty of detecting submarine and the threat of submarine are increasing. The probability of detecting a submarine can be increased by estimation submarine path and optimizing sonobuoy deployment. In this paper, marine data collection, dynamics of submarine, submarine tracking path modeling, acoustic wave propagation modeling, detection probability modeling are applied in the simulator as similar to reality as possible. A simulator is developed to design submarine path estimation and sonobuoy deployment optimization scenario and to check result according to the scenario.

Influence of the sound propagation models to predict far field wind turbine noise

During the development process of a wind farm, the noise impact on neighboring residents is of crucial importance to limit annoyance and comply with local regulations. In France, this assessment is carried out by modelling the propagation of sound from each wind turbine to residents. For wind farm owners, this phase will in fine define the operation of the wind farm and therefore its electricity production. However, there are major concerns and uncertainties during this modelling phase. A concrete example of the influence of the choice of propagation model on the production of a wind farm is presented, giving an idea of its economic impact. Comparisons of the impact of wind farm noise modelled by engineering models with measurements taken on wind farms in operation provide a better understanding of the choice of certain methods or parameters. Taken together, these observations suggest areas for further research and development, with the aim of perhaps defining a more restricted framework for the sound propagation modelling stage, in order to help operators predict wind farm noise more accurately.

Passive defect localization on a vibrating plate using Matched-Field Processing

Passive defect localization on a vibrating plate using Matched-Field Processing (MFP). The aim of this study is to investigate the use of MFP for passive localization of a local change in parameter of a vibrating shell in the modal regime. Matched-Field Processing is a beamforming method that uses the correlation between the recorded data and a controllable digital model of wave propagation (replica) to locate a source. Vibration measurements were conducted on an aluminum plate using a laser vibrometer. The plate was tested both with and without damage (added mass) while being excited by a shaker. Replicas were created by performing modal analysis on measurements taken from the undamaged structure. Matched-Field Processing is applied directly to the measured data to locate the active source (shaker) and to the residues (field difference between undamaged and damaged states) to locate a local variation of the parameter (defect). The method's feasibility is also being explored for passive measurements in modal analysis and residue computation.

Empirical validation and engineering model comparison of outdoor long-range 3D GPU FDTD acoustic simulations with turbulence

We build upon previous work which introduced an acoustic GPU-accelerated 3D Finite-Difference Time-Domain solver, Empapu, equipped with a dynamic moving frame and turbulence modeled as a position-dependent refractive index for long-range outdoor sound propagation simulations. This physics-based model would serve as a benchmark to assess different engineering models, e.g., ISO 9613-2, CNOSSOS-EU and sonX, in the presence of uneven terrain with different ground properties and an inhomogeneous atmosphere. The solver now integrates an improved model of turbulence reproducing the kinetic spectral energy of velocity fluctuations, derived from wind velocity fields, with an eddy (quasiwavelet) approach, relevant in long-range propagations. This study validates our model by comparing level differences in third-octave band spectra at receiver points up to 300 m to analytical methods and measurements in a mostly flat topography comprised of asphalt and grassland. Further analysis is done by comparing results in different locations in Switzerland in the presence of uneven ground and barriers. This work will provide a physics-based basis for evaluating and comparing engineering models for outdoor sound propagation in lieu of measurements.

Semi-empirical model for amplitude fluctuations by atmospheric turbulence in aircraft flyover sound

Atmospheric turbulence leads to well-audible fluctuations in the amplitude of sound propagating through the atmospheric boundary layer. However, the best currently available theoretical model is valid only for short propagation distances and low turbulence regimes. A new model for predicting the standard deviation of amplitude fluctuations was developed based on the existing theoretical model of Ostashev and Wilson and the analysis of empirical data. The dataset includes over 5000 aircraft flyover events in different meteorological conditions from March to August. The model considers an effective propagation length based on the boundary layer height, frequency-dependent saturation of amplitude fluctuations, turbulence decay time during periods of decreasing solar radiation, and turbulence production by nocturnal low-level-jets. The model adaptations have significantly reduced the mean squared error from over 10 dB in the pure theoretical model to 0.3 dB in the new model. The findings will help improving the modelling of outdoor sound propagation for elevated sound sources like aircraft and wind turbines. Modelling these fluctuations can considerably improve the realism of the listening experience in aircraft flyover auralisation. Further, it helps estimating the measurement uncertainty in measured aircraft noise, especially for maximum levels.

Identification of ultrasound characteristics in viscoelastic architectured materials by angular measurements in double through-transmission

Recent advances in additive manufacturing of viscoelastic materials have paved the way towards the design of increasingly complex multi-material structures with heterogeneities from the micro- to the macroscale. However, the measurement of ultrasound characteristics in such viscoelastic structures is a challenging problem, due to the strong attenuation related to both the constituent materials and the architecture. We propose here a methodological framework to identify the phase velocity and attenuation in viscoelastic multi-material quasi-heterogeneous samples. The proposed approach relies on measurements achieved in double through-transmission at various incidence angles, referred as theta-scan. The forward modeling of the wave propagation in such media allows then to identify the ultrasound characteristics based on an inverse approach. Experiments were performed on multi-material samples made of a random pattern of inclusions of a soft elastomer inside a rigid glassy polymer. The effects of the volume fraction of the constituent materials on the ultrasound characteristics was evaluated. This methodology open the path towards ultrasonic measurements in highly attenuating architectures

Efficient modeling of atmospheric infrasound propagation with the Semi-Analytical-Finite-Element (SAFE) method

Downward refraction in the lower atmosphere, attributed to winds and temperature inversion, can enable infrasound to efficiently propagate over long distances inside acoustic waveguides. In this study, we use the semi-analytic finite element (SAFE) method to predict this effect in stratified, inhomogeneous, moving air for range-independent noise propagation over reflective half-planes. We present solutions for different temperature and velocity profiles and compare them to direct numerical solutions of the two-dimensional linearized Euler equations. The SAFE method separates the exact two-dimensional wave equation for a stratified, inhomogeneous, moving medium by expressing the pressure field as a sum of vertical eigenmodes propagating in the range direction. This simplifies the acoustic problem into a one-dimensional eigenvalue problem. As this problem is addressed with the finite-element method, the method can accommodate arbitrary wind and temperature profiles. For large, range-independent domains, the proposed procedure proves to be much more efficient than the tested direct numerical computations. Additionally, it converges against the exact solution of the acoustic problem if enough modes are included in the calculation. Therefore, the method offers a viable procedure for benchmarking other pressure field prediction techniques.

Numerical modeling of infrasounds radiation and propagation in hurricanes

Infrasound can be useful for monitoring natural or human-induced energetic events in the atmosphere. For high-energy sources, the infrasonic signals can propagate over large distances around the globe, given the low rate of atmospheric absorption. This study focuses on the numerical simulation of infrasound propagation in realistic large-scale three-dimensional (3D) environments. More precisely, we are interested in the simulation of infrasound, called microbaroms, which are radiated from extreme weather conditions such as hurricanes. The simulation of hurricane-induced microbaroms relies on three main components: i) a hurricane model describing the atmospheric wind and pressure fields, ii) a microbarom source model, and iii) an infrasound propagation model. 3D simulations have been performed on a rectangular mesh. Azimuthal deviation caused by the hurricane and the stratified atmosphere can be seen in 3D. Further studies are needed to improve each model, and the correlation between them. The ultimate goal is to be able to simulate radiated infrasound propagation for any meteorological conditions.