Numerical Noise Research Articles

Simulating cold shear flows on a moving mesh

ABSTRACT Rotationally supported, cold, gaseous discs are ubiquitous in astrophysics and appear in a diverse set of systems, such as protoplanetary discs, accretion discs around black holes, or large spiral galaxies. Capturing the gas dynamics accurately in these systems is challenging in numerical simulations due to the low sound speed compared to the bulk velocity of the gas, the resolution limitations of full disc models, and the fact that numerical noise can easily source spurious growth of fluid instabilities if not suppressed sufficiently well, negatively interfering with real physical instabilities present in such discs (like the magnetorotational instability). Here, we implement the so-called shearing-box approximation in the moving-mesh code arepo in order to facilitate achieving high resolution in local regions of differentially rotating discs and to address these problems. While our new approach offers manifest translational invariance across the shearing-box boundaries and offers continuous local adaptivity, we demonstrate that the unstructured mesh of arepo introduces unwanted levels of ‘grid-noise’ in the default version of the code. We show that this can be rectified by high-order integrations of the flux over mesh boundaries. With our new techniques we obtain highly accurate results for shearing-box calculations of the magnetorotational instability that are superior to other Lagrangian techniques. These improvements are also of value for other applications of the code that feature strong shear flows.

A coupled CAD‐free parameterization‐morphing method for adjoint‐based shape optimization

AbstractThis article presents a fully differentiated CAD‐free shape parameterization coupled with a grid displacement method for performing adjoint‐based aerodynamic shape optimization efficiently. Both tools are integrated into an adjoint‐based aerodynamic shape optimization process, where the shape changes and the corresponding grid adaptation take place simultaneously, in a single step. The role of the proposed shape parameterization technique is to control the shape changes of the aerodynamic body under study during shape optimization, and to modify it according to the spatial distribution of the gradient. The latter frequently contains numerical noise, due to the limited resolution of spatial discretization schemes, which can result in irregular surfaces, if the raw gradient is used directly. The proposed parameterization undertakes the elimination of this noise, thus ensuring smooth surfaces during the shape optimization. More specifically, a subset of the nodes belonging to the design surface is selected as the design vector (handles) and is responsible for controlling the surface displacements. The analytical differentiation of the parameterization, considering the adjoint morphing technique for the computation of the grid sensitivities, allows for its integration within a gradient‐based optimization process, where the adjoint method is used to compute the gradient of the objective w.r.t. all nodal positions. The propagation of this gradient information to the handles is efficiently and accurately achieved through the inclusion of the differentiated parameterization expression. The developed CAD‐free process chain is successfully demonstrated in an automotive S‐section cooling duct and a serpentine‐like one, used for internal turbomachinery blade cooling.

Continuous denoising level adjustment of seismic data through filter modification

Noise reduction is an important step in seismic data processing. Noise levels display a significant degree of spatial and temporal variability owing to complicated geologic conditions and acquisition environments, making noise reduction extremely challenging. Existing denoising models for seismic data based on deep learning are typically developed using a training set with a single constant noise level or with numerous discrete noise levels within a given range. These models cannot be applied for the recovery of seismic data with continuous noise levels. If the noise level of the seismic data to be processed does not match the denoising model, noise reduction is likely to be incomplete or signal details may be lost. Notably, the filters (convolution kernels) in the models trained by data sets with different noise levels are very similar in terms of visual patterns; only the statistics of their weights differ, such as the mean and variance. Based on this principle, we have designed a generic denoising network to artificially adjust the denoising level. The filter modification layer (FML) in this generic denoising network modifies the filter channel-by-channel. A continuous change in the denoising level between the beginning and final levels can be conducted by altering the FML, thereby preventing over- or underdenoising.

Research Progress of Noise in High-Speed Cutting Machining.

High-speed cutting technology has become a development trend in the material processing industry. However, high-intensity noise generated during high-speed cutting exerts a potential effect on the processing efficiency, processing accuracy, and product quality of the workpiece; it may even cause hidden safety hazards. To conduct an in-depth study of noise in high-speed cutting machining, this work reviews noise sources, noise collection and numerical recognition, noise control, and condition monitoring based on acoustic signals. First, this article introduces noise sources, noise signal acquisition equipment, and analysis software. It is pointed out that how to accurately classify and recognize the target signal in the complex high-speed machining environment is one of the focuses of scholars’ research. Then, it points out that a computer achieves high accuracy and practicability in signal analysis, processing, and result display. Second, in the aspect of noise signal processing, the characteristics of noise signals are analyzed. It is pointed out that accurately analyzing the characteristics of different noise source signals and adopting appropriate methods for identification and processing are the necessary conditions for effectively controlling and reducing the noise in the process of high-speed cutting. The advantages and applicable fields of artificial intelligence algorithms in processing mixed noise source signals with different frequency characteristics are compared, providing ideas for studying the mechanism of noise generation and the identification of noise sources. Third, in terms of noise control, a detailed overview is provided from the aspects of the treatment of the noise source that contributes the most to the overall noise, the improvement of the tool structure, the optimization of cutting parameters, and the analysis of contact factors between the tool and the workpiece. It provides an effective way for noise control in the process of high-speed cutting. In addition, the application of acoustic signals to condition monitoring is also thoroughly analyzed. The practical application value of condition monitoring based on acoustic signals in high-speed machining is highlighted. Finally, this paper summarizes the positive significance of noise research in high-speed machining and identifies key problems and possible research methods that require further study in the future.

Machine learning methods for identification and classification of events in ϕ-OTDR systems: a review.

The phase sensitive optical time-domain reflectometer (φ-OTDR), or in some applications called distributed acoustic sensing (DAS), has been a popularly used technology for long-distance monitoring of vibrational signals in recent years. Since φ-OTDR systems usually operate in complicated and dynamic environments, there have been multiple intrusion event signals and also numerous noise interferences, which have been a major stumbling block toward the system's efficiency and effectiveness. Many studies have proposed different techniques to mitigate this problem mainly in φ-OTDR setup upgrades and improvements in data processing techniques. Most recently, machine learning methods for event classifications in order to help identify and categorize intrusion events have become the heated spot. In this paper, we provide a review of recent technologies from conventional machine learning algorithms to deep neural networks for event classifications aimed at increasing the recognition/classification accuracy and reducing nuisance alarm rates (NARs) in φ-OTDR systems. We present a comparative analysis of the current classification methods and then evaluate their performance in terms of classification accuracy, NAR, precision, recall, identification time, and other parameters.

Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance

This paper presents a comparison of two multi-fidelity methods for the forward uncertainty quantification of a naval engineering problem. Specifically, we consider the problem of quantifying the uncertainty of the hydrodynamic resistance of a roll-on/roll-off passenger ferry advancing in calm water and subject to two operational uncertainties (ship speed and payload). The first four statistical moments (mean, variance, skewness, and kurtosis), and the probability density function for such quantity of interest (QoI) are computed with two multi-fidelity methods, i.e., the Multi-Index Stochastic Collocation (MISC) and an adaptive multi-fidelity Stochastic Radial Basis Functions (SRBF). The QoI is evaluated via computational fluid dynamics simulations, which are performed with the in-house unsteady Reynolds-Averaged Navier–Stokes (RANS) multi-grid solver chinavis. The different fidelities employed by both methods are obtained by stopping the RANS solver at different grid levels of the multi-grid cycle. The performance of both methods are presented and discussed: in a nutshell, the findings suggest that, at least for the current implementation of both methods, MISC could be preferred whenever a limited computational budget is available, whereas for a larger computational budget SRBF seems to be preferable, thanks to its robustness to the numerical noise in the evaluations of the QoI.

Numerical simulation and flow noise computation during transient launching process of torpedo in deep-sea simulator

The transient launching process of a torpedo in a deep-sea simulator is a complex nonlinear fluid–solid coupling problem. Complex problems, such as projectile movement and gap flow, must be dealt with in the numerical computation. The launching channel is usually simplified to reduce computational cost, which affects the accuracy of the simulated flow field and acoustic computation results. The complete inner structure of the launching channel was retained in the numerical computations in this study to make the computation results much realistic. The spring mesh method and the radial basis function method were applied to update the mesh for the projectile movement, and a fine simulation was conducted on the launching flow field. The computation results related to torpedo movement were validated based on the test results, and the evolution of each flow structure in the deep-sea simulator was analysed. Based on the simulated flow field, acoustic computations were completed using the acoustic analogy method, and the acoustic characteristics, e.g. the propagation direction and sound source contribution of the launching noise, were analysed.

Accuracy and Precision in Electronic Structure Computation: Wien2k and FPLO

Electronic structure calculations in the framework of density functional theory are based on complex numerical codes which are used in a multitude of applications. Frequently, existing experimental information is used as a gauge for the reliability of such codes. However, their results depend both on the chosen exchange-correlation energy functional and on the specific numerical implementation of the Kohn-Sham equations. The only way to disentangle these two items is a direct comparison of two or more electronic structure codes. Here, we address the achievable numerical accuracy and numerical precision in the total energy computation of the two all-electron density-functional codes Wien2k and FPLO. Both codes are based on almost independent numerical implementations and largely differ in the representation of the Bloch wave function. Thus, it is a highly encouraging result that the total energy data obtained with both codes agree within less than 10−6. We here relate the term numerical accuracy to the value of the total energy E, while the term numerical precision is related to the numerical noise of E as observed in total energy derivatives. We find that Wien2k achieves a slightly higher accuracy than FPLO at the price of a larger numerical effort. Further, we demonstrate that the FPLO code shows somewhat higher precision, i.e., less numerical noise in E than Wien2k, which is useful for the evaluation of physical properties based on derivatives of E.

On the numerical solution of the iterative parabolic equations by ETDRK pseudospectral methods in linear and nonlinear media

Iterative parabolic equations (IPEs) were recently introduced as a promising tool for solving wave propagation problems in complex linear and non-linear media. In this study a general approach to the numerical solution of iterative parabolic equations on the basis of powerful ETD pseudospectral method is developed. The solution of nth IPE requires the computation of the input term that is obtained by differentiation of the solution of n−1th IPE. The numerical noise resulting from such differentiation spoils the solutions of higher-order IPEs. It is shown that this noise can be suppressed via a filtering procedure involving Chebyshev polynomials of a discrete variable. In this study Padé type iterative parabolic approximations are introduced both for the case of linear and nonlinear media. It is shown that Padé-type approximations are more suitable for solving the problems of wave propagation.

Phase-field implicit material point method with the convected particle domain interpolation for brittle–ductile failure transition in geomaterials involving finite deformation

A phase-field implicit material point method with the convected particle domain interpolation (PF-ICPDI) is proposed to model the brittle–ductile failure transition in pressure-sensitive geomaterials involving finite deformation. In this method, the phase-field fracture formulation relying on the microforce balance law and the second law of thermodynamics is adopted as a nonlocal damage function for the elastoplastic fracture analysis. A smooth two-yield-surface plasticity constitutive model is utilized to evaluate the brittle–ductile failure transition behavior of pressure-sensitive geomaterials. The coupling effect of phase-field fracture model and cap plasticity model is established by introducing an effective phase-field stress and splitting the total stored energy into elastic and plastic parts. The implicit material point method that can avoid severe mesh distortion and improve numerical stability is then developed to solve the quasi-static elastoplastic fracture finite deformation problem. Furthermore, the convected particle domain interpolation technique is adopted to eliminate numerical noises and improve computational accuracy while material points crossing cell boundaries in modeling the large deformation brittle–ductile failure transition process. The staggered incremental iterative scheme is carried out to solve the coupled discrete governing equations. The accuracy and capability of the proposed PF-ICPDI method are demonstrated through several representative numerical examples, as well as the effects of main material parameters on the phase-field brittle–ductile failure transition modeling of geomaterials are discussed.

An Adaptive ECG Noise Removal Process Based on Empirical Mode Decomposition (EMD).

The electrocardiogram (ECG) is a generally used instrument for examining cardiac disorders. For proper interpretation of cardiac illnesses, a noise-free ECG is often preferred. ECG signals, on the other hand, are suffering from numerous noises throughout gathering and programme. This article suggests an empirical mode decomposition-based adaptive ECG noise removal technique (EMD). The benefits of the proposed methods are used to dip noise in ECG signals with the least amount of distortion. For decreasing high-frequency noises, traditional EMD-based approaches either cast off the preliminary fundamental functions or use a window-based methodology. The signal quality is then improved via an adaptive process. The simulation study uses ECG data from the universal MIT-BIH database as well as the Brno University of Technology ECG Quality Database (BUT QDB). The proposed method's efficiency is measured using three typical evaluation metrics: mean square error, output SNR change, and ratio root mean square alteration at various SNR levels (signal to noise ratio). The suggested noise removal approach is compatible with other commonly used ECG noise removal techniques. A detailed examination reveals that the proposed method could be served as an effective means of noise removal ECG signals, resulting in enhanced diagnostic functions in automated medical systems.

Multiscale Contrastive Learning Networks for Automatic Denoising of Geological Sedimentary Model Images

The well-described river courses in geological sedimentary models are essential for identifying oil and gas reservoirs. However, numerous noises are generated around the river course, including irregular noises and regions due to errors in the geological data, as well as traces left over from the printed model. The cluttered distributions of noises make the complete river course unobtainable, which interferes with reservoir prediction. To the best of our knowledge, deep learning-based methods for automatic noise detection and removal have not been explored in the field of processing geological sedimentary model images. In this paper, we present Multi-scale Contrastive Learning Networks (CLGAN) for detecting noise in geological sedimentary model images. A multi-scale contrastive training strategy is proposed to capture noise locations and river discontinuities by comparing the distributions with and without noise in image space and feature space. A paired dataset that consists of images with noise and images without noise is constructed for contrastive training. Moreover, cyclic denoising is proposed to completely denoise by constantly modifying the pixel values at the noise, which prevents missed detections by calling the model multiple times. Subsequently, pixel-level denoising is designed to initially remove a portion of the noise by contouring non-river regions, which reduces the pressure on the cyclic denoising method. The detection results demonstrate the effectiveness of CLGAN. The denoising results with excellent river connectivity and integrity highlight the superiority of the proposed denoising methods.

Povezanost između komunalne buke i arterijske hipertenzije kod stanovnika grada Niša

Aim: The aim of the study was to examine the relationship between noise exposure and the incidence of arterial hypertension as well as the relationship of noise annoyance and noise sensitivity with hypertension. Methods: A cross-sectional questionnaire survey was performed on 911 adult residents of Niš, Serbia, of whom 388 were men (42.6%) and 523 women (57.4%). The criterium for the diagnosis of arterial hypertension was that it was diagnosed by a physician. Based on measurement results of noise, residents, streets were marked as noisy [daily equivalent noise level (Leq) ≥ 55 dB (A) and night Leq ≥ 45 dB (A)] and as quiet [daily equivalent noise level (Leq) ≤ 55 dB (A) and night Leq ≤ 45 dB (A)]. The numerical and verbal noise annoyance scales were used to assess the degree of noise annoyance, while noise sensitivity was measured with the Weinstein's Noise Sensitivity Scale. Results: Arterial hypertension was statistically significantly more frequent in residents in the noisy area compared to residents in the quiet area. After the inclusion of independent variable factors in the logistic regression models, no significant diference was found (p = 0.66, OR = 1.11; 95% CI: 0.69-1.78). A correlation analysis between noise annoyance wich was measured by verbal and numeric scales and arterial hypertension showed a highly significant positive relation. The same case was between the relationship between subjective sensitivity and arterial hipertension. Conclusion: The introduction of the limit for the community noise level that corresponds to the requirements of highly sensitive persons is a significant measure in preventing the harmful effects of noise on the exposed population.

A Constrained Block-Term Tensor Decomposition Framework for Spectrum Cartography

Joint spectrum cartography and disaggregation from sparse spatial observations has been proven to be theoretically feasible based on block-term tensor decomposition (BTD) model. However, the existing BTD framework suffers from inherent drawbacks in terms of numerical stability, complexity and noise robustness. To combat with these drawbacks, we propose a new Constrained-BTD (CBTD) framework in this letter by fully utilizing practical traits of geographical power density spectrum (PSD) and spatial loss field (SLF). The cornerstone of CBTD framework is formulating the joint PSD and SLF estimation as a constrained matrix factorization problem, instead of addressing the factors of BTD. Further, a projection gradient-based (PG) algorithm, which has sublinear convergence, is proposed to handle the restrictions of PSD and SLF by projection on manifolds. Compared with the baseline methods, simulations verify that the proposed approach obtains better performances in terms of stability, complexity and noise robustness.

Broadband reduction of phase noise in a spatially extended tunnel‐diode oscillator through multiple self‐injection locking

SummaryA self‐injection locking scheme is proposed to reduce phase noise in a spatially extended oscillator that uses a tunnel‐diode (TD) transmission line. When a linear transmission line is connected to each cell of the TD line, a part of the oscillating edge flows through the linear line. This wave on the linear line is reflected at its far end and flows back to the point of connection of the TD and linear transmission lines. By matching the time taken by the reflected wave on the linear transmission line to reach the point of connection with the time taken by the forward‐moving oscillating edge to return to the point, the edge can be synchronized with the wave that flows back through each linear line. This multiple self‐injection locking significantly reduces the phase noise of the oscillating edge. In this work, the technique of phase noise reduction is explained through numerical phase noise analysis using the perturbation projection vector of the system. Several experimental observations were made to validate the reduction of the phase noise in the oscillating edge traveling on the TD line.

EEG/ERP signal enhancement through an optimally tuned adaptive filter based on marine predators algorithm

Electro-encephalogram (EEG) signals are corrupted with numerous noises while recording itself which changes the attributes of physiological data. In this research, a swarm intelligence-based method for the enhancement of adaptive filters/noise cancellers (AFs/ANCs) is proposed and designed for denoising of electro-encephalogram/event-related potential (EEG/ERP) signals. The results have been evaluated both qualitatively and quantitatively using marine predators algorithm (MPA), teaching learning-based optimization (TLBO) and firefly algorithm (FA) used to compute and accommodate the coefficients of the adaptive filter, which is very much suitable to monitor unconstrained changes of uneven waveforms. The performance of the proposed method is tested on simulated visual evoked potentials (SVEP) and real sensorimotor evoked potentials (SEP). A comprehensive comparative study and analysis of performance have been performed among the previously reported gradient techniques and evolutionary-based techniques, which reveals that the proposed ANC based on MPA is an enhanced competitor compared to other futuristic techniques with fewer evaluation parameters. The performance superiority of the proposed method has been justified through the Wilcoxon signed-rank test. The proposed ANC provides a lesser mean of normalized root mean square error (NRMSE), lesser mean square error (MSE) and an improved mean SNR in contrast to different state-of-the-art techniques as well as considered benchmark algorithms-based ANCs. Furthermore, the ANC based on MPA advances the resemblance between the clean and filtered EEG/ERP signals.

Data Filtering in Vehicle–Bridge Impact Simulations: Evaluation of Different Force Filtering Methods and Recommendations

Abstract The impact force time history obtained from a crash test or high-fidelity simulation is usually contaminated by numerical noise. The noise signal usually appears as a series of short durat...

Numerical investigation of non-Newtonian power law flows using B-spline material point method

• A weakly compressible BSMPM method for simulating non-Newtonian flow is proposed. • Complex free surface flow and fluid structure interaction problems for power law fluid are studied. • Shear thickening/thinning, jet buckling/splashing, and vortex whirling are well represented. • Results demonstrate the efficacy of the proposed method for non-Newtonian power law flow problems. Simulating the complex flow with free surface and large deformation of non-Newtonian fluid is a challenging problem and plays a significant role in many industrial areas. In this paper, a weakly compressible B-spline material point method (WC-BSMPM) is presented and extended to solve the complex free surface flow of non-Newtonian fluid with a power law rheology model. Numerical examples including two-dimensional problems of plane Poiseuille, dam break, and container filling flow as well as three-dimensional sphere water entry problems are adopted to demonstrate the effectiveness and capabilities of the proposed method. The simulated results show good agreements with analytical solutions or published experimental data. Moreover, significant flow features for non-Newtonian power law fluid, including shear thickening, shear thinning, jet buckling, jet splashing, vortex whirling, and fluid-structure interactions can be well captured. Meanwhile, the numerical noise of the traditional MPM can be eliminated by the proposed method, which confirms that the WC-BSMPM method is a promising alternative to simulate the complex free surface flow and fluid-structure interactions for the non-Newtonian power law fluid.

Toward emulating nuclear reactions using eigenvector continuation

We construct an efficient emulator for two-body scattering observables using the general (complex) Kohn variational principle and trial wave functions derived from eigenvector continuation. The emulator simultaneously evaluates an array of Kohn variational principles associated with different boundary conditions, which allows for the detection and removal of spurious singularities known as Kohn anomalies. When applied to the K-matrix only, our emulator resembles the one constructed by Furnstahl et al. (2020) [29] although with reduced numerical noise. After a few applications to real potentials, we emulate differential cross sections for 40Ca(n,n) scattering based on a realistic optical potential and quantify the model uncertainties using Bayesian methods. These calculations serve as a proof of principle for future studies aimed at improving optical models.

Stochastic Crashworthiness Optimization Accounting for Simulation Noise

Abstract Finite element-based crashworthiness optimization is extensively used to improve the safety of motor vehicles. However, the responses of crash simulations are characterized by a high level of numerical noise, which can hamper the blind use of surrogate-based design optimization methods. It is therefore essential to account for the noise-induced uncertainty when performing optimization. For this purpose, a surrogate, referred to as Non-Deterministic Kriging (NDK), can be used. It models the noise as a non-stationary stochastic process, which is added to a traditional deterministic kriging surrogate. Based on the NDK surrogate, this study proposes an optimization algorithm tailored to account for both epistemic uncertainty, due to the lack of data, and irreducible aleatory uncertainty, due to the simulation noise. The variances are included within an extension of the well-known expected improvement infill criterion referred to as Modified Augmented Expected Improvement (MAEI). Because the proposed optimization scheme requires an estimate of the aleatory variance, it is approximated through a regression kriging, which is iteratively refined. The proposed algorithm is tested on a set of analytical functions and applied to the optimization of an Occupant Restraint System (ORS) during a crash.