Noise Prediction Research Articles

GAN augmented phase noise modeling of PLL for use in automated testing

ABSTRACT Phase noise measurement of Phase Locked Loop (PLL) in automated test equipment (ATE) is expensive and time-consuming. Indirect phase noise measurement relies on a few correlated low-cost measurements called signatures obtained from specific nodes of the PLL. A regression model using these signatures is learned during the design phase for phase noise estimation. Extraction of these signatures and phase noise performance in PLL requires significant simulation time, raising non-recurring design costs and computer aided design (CAD) tool usage costs. In this work, a new methodology for indirect phase noise measurement is developed where Generative Adversarial Network (GAN), a deep learning framework, has been used for generating a substantial amount of model-driven synthetic signatures that are strongly correlated to simulation-driven real signatures. These model-driven signatures are then used to construct a regression model for phase noise prediction. The GAN model efficacy is verified by comparing the prediction accuracy of the simulation-driven signature-based model with the GAN-augmented signature-based regression model.

An End-to-End Ocean Environmental Noise Anomaly Detection Framework Combining Time–Frequency Information and Expert Gating

The detection and optimization of ocean environmental noise anomalies play a crucial role in enhancing the safety of marine engineering applications and ecological protection. Current anomaly detection methods for ocean environmental noise often suffer from issues of accuracy and robustness. To address these challenges, this paper first proposes an end-to-end framework that combines time–frequency information and expert gating, significantly improving the precision of noise sequence generation. Secondly, a Gamma distribution-based residual analysis method for anomaly detection is designed, enhancing the robustness of anomaly detection. Finally, an anomaly optimization module is developed to improve data quality, enabling efficient noise anomaly detection and optimization. Our experimental results demonstrate that the proposed model significantly outperforms traditional models in multi-frequency noise prediction, with strong robustness in anomaly detection and high generalization performance. The proposed framework offers a novel approach for analyzing the causes of noise anomalies and optimizing models. Additionally, the research outcomes provide efficient technical support for deep-sea exploration, equipment optimization, and environmental protection.

Impact of slip velocity-dependent friction coefficient on surface traction, wear, RCF and curve squeal noise prediction in wheel-rail contact

Wheel-rail contact friction coefficient is often assumed to be constant through the entire contact patch for the calculation of surface traction. In reality, however, the friction value in a certain point decreases when transitioning from adhesion to slip regimes. Including this friction coefficient behaviour in the estimations of surface traction on the contact patch can potentially provide more accurate calculations of wear and rolling contact fatigue (RCF). In the present work, a slip velocity-dependent friction coefficient is implemented in the tangential contact solver using the concept of ‘Friction Memory’. The effect of this implementation on traction estimations and on the prediction of wear and RCF is analysed by comparing the results with a case with constant friction coefficient in the contact patch. Furthermore, the slip velocity-dependent friction coefficient provides a creep curve with a maximum creep forces value, and a decreasing creep force for higher creepages. This is commonly known as one of the possible mechanisms of curve squeal noise generation. The results provide insights into the likelihood of curve squeal generation, and an on-set curve squeal noise detection technique is proposed that also accounts for the influence of profile changes due to wear.

Prediction of Aerodynamic Noise of a Traction Induction Motor Using Methods of Computational Fluid Dynamics and Lighthill Acoustic Analogy

Prediction of Aerodynamic Noise of a Traction Induction Motor Using Methods of Computational Fluid Dynamics and Lighthill Acoustic Analogy

A regional road network traffic noise limit prediction method based on design elements.

Since traffic flow has not been generated, a traffic noise prediction model based on actual traffic state data cannot be directly applied to the planned road network. Therefore, a regional traffic noise prediction method is proposed to find the upper limit of network noise emission based on design elements. The model is developed with noise predictions of the basic road section, interrupted/continuous intersections, and regional network. Meanwhile, ranges of traffic flow speed and volume are inferred by design elements and constraints between road units are obeyed. A four-scenes experiment to verify the method's accuracy is organized and the average noise difference between the upper limit calculated value and maximum measurement value is 1.53 dBA. All noise differences are positive as the measured noise values may not reach the upper limit of network emission in the experimental state. The method is applied to a network under design elements, and the results show that the model is suitable for the predicting upper limits of noise under design constraints; under the same design elements, noise emission at interrupted intersections is higher than that at continuous intersections. The method can provide a theoretical and data basis for planning network noise protection.

Multi-Layer Ceramic Capacitor Source Model Application in Acoustic Noise Prediction

Multi-Layer Ceramic Capacitor Source Model Application in Acoustic Noise Prediction

Effect of model scale and airflow velocity on aerodynamic noise prediction for high-speed train leading car and bogie

Predicting noise from the actual train model by numerical simulation and wind tunnel testing requires appropriate corrections based on reduced model scale and airflow velocity. This paper utilises the Improved Delayed Detached Eddy Simulation method (IDDES) in conjunction with the Ffowcs Williams-Hawkings equation (FW-H) for simulating flow fields and predicting noise. The subdomain models of high-speed train head and leading bogie were used to investigate the effects of the model scale ratio (λ) and the airflow velocity ratio (γ) on the aerodynamic noise. The results show that compared with the baseline condition (case0), only reducing the model scale (case1) will lead to an increase in local viscous flow, thereby weakening the flow velocity and surface pressure fluctuation intensity in the bogie region, and the overall noise level is slightly reduced, with a difference of less than 1 dB. However, as expected, there is a significant frequency shift in the noise spectra. For case2, the model scale and the airflow velocity are reduced simultaneously to ensure the same Strouhal number, and the vortex structure and sound source distribution characteristics are closer to case0. The noise spectra have no frequency shift, but the noise level is reduced by 60 log10(γ).

A Parameterized Prediction Method for Turbulent Jet Noise based on Physics-informed Neural Networks

Abstract Turbulent noise prediction is integral to fluid equipment design, and multiple simulations or experiments are often required for noise distribution under varying operating conditions during the design optimization process, which could be expensive. Recently, the Physics Informed Neural Networks (PINNs) method has emerged as an efficient machine learning method for solving parameterized partial differential equations with geometric shapes, boundary conditions, and equation parameters as variable parameters through a single training session without data. In this study, a parameterized prediction method is developed to predict turbulent jet noise based on PINNs without any training datasets. Both two-dimensional (2D) and three-dimensional (3D) jet flow problems are solved. The 2D problem is solved with the Reynolds number as a variable parameter, and the 3D problem is solved with the Reynolds number and nozzle eccentricity as variable parameters. The predicted results are in good agreement with those from conventional computational fluid dynamics (CFD), with average errors of 3% and 6% for the 2D and 3D flow and acoustic power fields, respectively. In terms of computational efficiency, the time required by the method for the three-dimensional problem with two variable parameters is only one-seventh of that of the traditional CFD method. This study demonstrates that for engineering noise scenarios with varying parameters, the method based on PINNs offers a more efficient parameterized predicting approach and is promising for future applications.

Characteristics of Noise Caused by Trains Passing on Urban Rail Transit Viaducts

With the large-scale construction of urban rail transit viaducts in China, the noise problem caused by trains traversing these sections has become increasingly prominent and a key technical challenge that restricts the sustainable development of rail transit. There are two main noise sources when trains pass on rail transit viaducts, namely, wheel-rail noise (WRN) and bridge-borne noise (BBN). However, most of the existing rail transit viaduct noise prediction models consider only a single noise source. In this study, a total noise prediction model incorporating both WRN and BBN was established using the finite element method (FEM), the boundary element method (BEM), and statistical energy analysis (SEA). The viaducts of Wuhan Metro Line 2 were selected as the research object, and noise tests of trains passing on the viaducts were carried out to validate the total noise prediction model. Based on the validated model, the spatial distribution characteristics and attenuation laws of the total noise were investigated, along with the influence of train speed on the total noise. The results show that the prediction model accurately simulated the total noise caused by trains passing on viaducts. When a train passed on the viaduct at a speed of 60 km/h, the total noise near the viaduct reached 88 dB(A) and decreased with the increase in the distance; at 120 m from the track centerline, the total noise decreased to less than 57 dB(A). As the distance increased, the total noise diminished across the entire frequency spectrum. Notably, low-frequency noise decayed at a slower rate than high-frequency noise. As the distance from the track centerline doubled, the total noise decreased by about 4.23 dB(A). The total noise increased with train speed. When the train speed doubled, the total noise at 30 m and 120 m from the track centerline increased by 6.32 dB(A) and 5.96 dB(A), respectively. The reason for this phenomenon is that the wheel-rail forces increase with the increase in train speed. This study will have important guiding significance and scientific value for the sustainable development of urban rail transit.

Gaussian–Student’s t Mixture Distribution-Based Robust Kalman Filter for Global Navigation Satellite System/Inertial Navigation System/Odometer Data Fusion

Multi-source heterogeneous information fusion based on the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/odometer is an important technical means to solve the problem of navigation and positioning in complex environments. The measurement noise of the GNSS/INS/odometer integrated navigation system is complex and non-stationary; it approximates a Gaussian distribution in an open-sky environment, and it has heavy-tailed properties in the GNSS challenging environment. This work models the measurement noise and one-step prediction as the Gaussian and Student’s t mixture distribution to adjust to different scenarios. The mixture distribution is formulated as the hierarchical Gaussian form by introducing Bernoulli random variables, and the corresponding hierarchical Gaussian state-space model is constructed. Then, the mixing probability of Gaussian and Student’s t distributions could adjust adaptively according to the real-time kinematic solution state. Based on the novel distribution, a robust variational Bayesian Kalman filter is proposed. Finally, two vehicle test cases conducted in GNSS-friendly and challenging environments demonstrate that the proposed robust Kalman filter with the Gaussian–Student’s t mixture distribution can better model heavy-tailed non-Gaussian noise. In challenging environments, the proposed algorithm has position root mean square (RMS) errors of 0.80 m, 0.62 m, and 0.65 m in the north, east, and down directions, respectively. With the assistance of inertial sensors, the positioning gap caused by GNSS outages has been compensated. During seven periods of 60 s simulated GNSS data outages, the RMS position errors in the north, east, and down directions were 0.75 m, 0.30 m, and 0.20 m, respectively.

Analysis of hydrodynamic performance and acoustic characteristics of loop propellers

To improve the efficiency and stealthiness of marine operations of autonomous underwater vehicles (AUVs), 12 loop propellers with different structural parameters are selected as research subjects. The Reynolds-averaged Navier–Stokes equations method is used to simulate the flow field result around the loop propellers and is combined with detached eddy simulation and the acoustic analogy method for hydrodynamic analysis and noise prediction. The influence of three structural parameters, namely the number of propeller blades, the thickness of propeller blades, and the pitch of propeller blades, on the hydrodynamic and noise performance of the propeller is investigated, and the loop propeller structure with the optimal hydrodynamic and noise performance is selected. The research results indicate that increasing the number of propeller blades and increasing the pitch of the loop propeller can significantly improve the thrust and torque of the propeller and effectively reduce the noise. However, increasing the thickness of the propeller blades can also increase the thrust and torque of the propeller, but it will sacrifice the noise performance of the loop propeller to a certain extent. The sensitivity of the noise performance with respect to the blade thickness is significantly higher than that of the two parameters of blade number and pitch. To verify the accuracy of the hydrodynamic and noise performance simulation results, this study conducted hydrodynamic and noise performance tests on the preferred loop propeller structure in the towing pool and anechoic pool and successfully verifies the reliability of the numerical simulation method used in this study for the prediction of the propeller performance by comparing the test data with the simulation results. This study provides theoretical support for the design optimization of loop propellers and helps to promote the design of high-efficiency and low-noise propellers in complex marine environments.

Efficient prediction of propeller noise in non-axial uniform inflow conditions

Efficient prediction of propeller noise in non-axial uniform inflow conditions

Turbulence distortion and leading-edge noise

The distortion of turbulence interacting with thick airfoils is analyzed with scale-resolved numerical simulations to elucidate its impact on leading-edge-noise generation and prediction. The effect of the leading-edge geometry is investigated by considering two airfoils with different leading-edge radii subjected to grid-generated turbulence. The velocity field is shown to be altered near the stagnation point, in a region whose extension does not depend on the leading-edge radius. Here, the deformation of large-scale turbulence causes the amplitude of the upwash velocity fluctuations to increase in the low-frequency range of the spectrum because of the blockage exerted by the surface. Conversely, the distortion of small-scale structures leads to an exponential decay of the spectrum at high frequencies due to the alteration of the vorticity field. The prevalence of a distortion mechanism over the other is found to depend on the size of the turbulent structures with respect to the curvilinear length from the stagnation point to the location where surface-pressure fluctuations and pressure gradient peak. This occurs at the curvilinear abscissa where the curvature changes the most. The same high-frequency exponential-decay slope observed for the upwash velocity is retrieved for surface-pressure spectra in the leading-edge region, suggesting that the airfoil unsteady response is induced by the distorted velocity field. This physical mechanism can be accounted for in Amiet's model by using a distorted turbulence spectrum as input and accounting for the increased amplitude of the distorted gust in the aeroacoustic transfer function, retrieving an accurate noise prediction for both airfoils.

Multi-Sensor Fusion for Wheel-Inertial-Visual Systems Using a Fuzzification-Assisted Iterated Error State Kalman Filter.

This paper investigates the odometry drift problem in differential-drive indoor mobile robots and proposes a multi-sensor fusion approach utilizing a Fuzzy Inference System (FIS) within a Wheel-Inertial-Visual Odometry (WIVO) framework to optimize the 6-DoF localization of the robot in unstructured scenes. The structure and principles of the multi-sensor fusion system are developed, incorporating an Iterated Error State Kalman Filter (IESKF) for enhanced accuracy. An FIS is integrated with the IESKF to address the limitations of traditional fixed covariance matrices in process and observation noise, which fail to adapt effectively to complex kinematic characteristics and visual observation challenges such as varying lighting conditions and unstructured scenes in dynamic environments. The fusion filter gains in FIS-IESKF are adaptively adjusted for noise predictions, optimizing the rule parameters of the fuzzy inference process. Experimental results demonstrate that the proposed method effectively enhances the localization accuracy and system robustness of differential-drive indoor mobile robots in dynamically changing movements and environments.

Applications of Denoising Diffusion Probability Model in Image Processing

Abstract. In 2020, Jonathan Ho et al. from the University of Berkeley proposed the Denoising Diffusion Probabilistic Models (DDPM), which improved on the shortcomings of the previous-generation Deformable Part Models (DPM). At the same time, they also surpassed previous generative models in image synthesis effects by using noise prediction, such as Generative Adversarial Networks (GAN), Variational Auto-Encoders (VAE), flow-based models and energy-based models, etc. After that, the denoising diffusion probabilistic models gradually received more discussions and research. In 2022, OpenAI launched the DALL-E 2, realizing Text-to-Image generation combined with the denoising diffusion probabilistic model. Two years later, with the release of Sora, the first Text-to-Video large models based on DALL-E, the denoising diffusion probabilistic models have received unprecedented attention in the multi-modal field. This article first provides the background and development process of the denoising diffusion probabilistic models. Secondly, it introduces the algorithmic principles of the diffusion models. Following that, it lists the applications of the models in the area of images. In the last section, it expounds on the advantages, disadvantages and future trends of the denoising diffusion probabilistic models.

Improved model for overhead line audible noise prediction

Improved model for overhead line audible noise prediction

Integration of cost-effective datasets to improve predictability of strategic noise mapping in transport corridors in Delhi city, India.

Assessing exposure to environmental noise levels at transport corridors remains complex in conditions where no standardized noise prediction model is available. In planning and policy implementation for noise control, noise mapping is an important step. In the present study, land use regression model has been developed to predict the environmental noise levels in Delhi city, India, using previously developed approaches along with machine learning techniques, however improved using new datasets. Lday, Lnight, LAeq,24h, and Ldn were modeled at daily resolution by utilizing an annual noise levels dataset from 31 locations in Delhi city. The noise-monitored data was integrated with travel time data, nighttime light data along with common parameters including land use, road networks, and meteorological parameters. The developed LUR models showed good fit with R2 of 0.72 for Lday, 0.55 for Lnight, 0.71 for LAeq,24h, and 0.61 for Ldn, which was further improved up to 0.88 for Lday, 0.79 for Lnight, 0.86 for LAeq,24h, and 0.81 for Ldn by integrating machine learning approaches. The developed models were validated through tenfold cross validation and by comparison to a separate noise level dataset. The average travel time variable was observed to be the most influential predictor of LUR models for Lday and LAeq,24h, which signifies the crucial impact of road traffic congestion on environmental noise levels. The study also analyzed the parametric sensitivity of various infrastructural factors reported in the study, which shall be helpful for planning for smart cities.

A flame image soft sensor for oxygen content prediction based on denoising diffusion probabilistic model

High-precision oxygen content measurement is crucial for statistical analysis of combustion chemical reaction. Deep learning based soft sensor is a new class of intelligent tools for monitoring combustion oxygen content. But in the actual production, data for sensors are often insufficient. A new soft sensing model is proposed to display the excellent performance of denoising diffusion probabilistic model (DDPM) in data generation. Firstly, a UNet based soft sensor is designed by integrating self-attention mechanism into the convolution layers. Then, a denoising loss function is designed to link the feature extraction process of soft sensor model with the reverse denoising process of DDPM, and the noise prediction neural network of DDPM is used to improve the feature extractability of the soft sensor model. Finally, the proposed model is compared with common models. The effectiveness and superiority of the proposed soft sensing model for oxygen content prediction, especially in the case with a small sample size, are both confirmed by the results.

Prediction of splash noise in a rectangular tank under longitudinal periodic excitation

Sloshing phenomena within fuel tanks have emerged as a significant contributor to noise in hybrid and high-end vehicles, particularly as other sources of noise, such as those from the engine and transmission system, minimized. The manifestation of noise during sloshing results from the dynamic interplay between the fluid within the tank and both its surrounding structures and the fluid itself. “Splash noise” is an acoustic phenomenon which arises due to pressure fluctuations induced by fluid-fluid interactions during sloshing. Thus, the generation of splash noise encompasses a multi-physics process involving fluid flow and acoustic radiation. Accurate prediction of splash noise is crucial for implementing measures to mitigate it during the design phase. The current paper proposes a hybrid approach for predicting splash noise within a rectangular tank. The first step in the methodology is to predict the flow field within the tank, from which acoustic sources are computed. These sources are then utilized to calculate the sound propagation based on the acoustic analogy. To emulate a regime dominated by fluid-fluid interactions, periodic longitudinal excitations are imposed on the rectangular tank, both with and without baffles. Parameters such as fluid free-surface profile, tank wall pressures, and radiated sound pressure levels are systematically calculated and subsequently validated against experimental results available in the existing literature.

Experimental and numerical analysis of rubber isolator dynamic stiffness under hydrostatic pressure

Due to their function in damping and attenuating vibrations, as well as their relatively low cost, rubber isolators have widespread applications in industries such as aerospace, automotive and maritime. As components with distinct nonlinear behavior, accurately predicting the dynamic characteristics of isolators is essential for overall structural design and vibration noise prediction. Although extensive research has been conducted on the static and dynamic performance of rubber isolators, there has been limited investigation into performance under certain specialized application scenarios, such as hydrostatic pressure environments. These environments are indeed real, for instance, isolators employed in the bow ballast tank of underwater vehicle to mitigate vibrations during weapon launch processes. In such instances, isolators are subjected not only to the influence of added mass due to the water medium during vibration but also to increasing hydrostatic pressure on the isolator surface with increasing depth. This study introduces an original experimental apparatus capable of measuring the dynamic stiffness of isolators while simulating hydrostatic pressure conditions. The constitutive model parameters governing the hyperelastic and viscoelastic properties of rubber were identified via nonlinear tests, serving as input parameters for numerically predicting the dynamic stiffness within a water medium environment. Furthermore, a comprehensive analysis was conducted to evaluate the impacts of preload, water medium, boundary conditions, and hydrostatic pressure on the dynamic stiffness of the isolator. Results indicate that preload tends to reduce peak dynamic stiffness in the shear directions, while the water medium significantly increases high-frequency dynamic stiffness in the shear directions. Within the range of 1 MPa, the impact of increasing hydrostatic pressure on dynamic stiffness can be largely disregarded. When calculating the dynamic stiffness of the isolator, it is crucial to consider the actual installation environment and set the acoustic boundary conditions of the surrounding water domain accordingly.