Amplitude Errors Research Articles

Dynamic stress analysis of cracked rectangular blade: Simulation and experiment

Blade crack is one of the main causes of aero-engine failure, resulting in catastrophic accidents. At present, most of the research on dynamic characteristics of cracked blades is conducted based on acceleration response and displacement response and rarely based on dynamic stress response. To simulate the evolving stress state of the cracked rectangular blade (CRB) during the crack propagation, a dynamic model of the CRB is established by shell elements considering the crack-induced breathing effect simulated by the pseudo-contact spring, and a new surface stress calculation method is proposed by linear interpolation based on stress at Gauss integral points within elements. Based on the intact blade, the proposed modeling method and stress calculation method are verified by the measured natural frequencies and dynamic stress in resonance, respectively. Finally, a comparative analysis of the dynamic stress at the crack tip and the root of the CRB is conducted between simulation and experiment at different stages of crack propagation. The results show that (1) the maximum error of the dynamic stress amplitude between simulation and experiment is less than 15% at the root and crack tip of the CRB. (2) The root stress first increases slowly and then remains roughly constant as the crack propagates, while the stress at the crack tip exhibits a linear increase.

РАЗРАБОТКА МАТЕМАТИЧЕСКОЙ МОДЕЛИ КОЛЕБАТЕЛЬНЫХ ПРОЦЕССОВ ПРИ ТОКАРНОЙ ОБРАБОТКЕ МАТЕРИАЛОВ С ОБРАТНЫМ РАСПРЕДЕЛЕНИЕМ ТВЕРДОСТИ

For technological guarantee of the required quality indicators of the surface layer of metallic rotation body parts at finishing machining needs the correct assignment of technological modes. During finishing turning, vibrational processes have a significant (and often decisive) effect on the formation of surface roughness. It is necessary to choose such values of technological modes, which provide an acceptable level of vibration (previously it is advisable to solve this problem by modelling). Mathematical model was carried out based on the determination of the turning tool deformations under the influence of cutting forces. To determine the amplitudes of the cutter oscillations, the Mohr integral was used, and the amplitude-frequency characteristics were determined using the Duhamel integral. Cutting forces were determined taking into account the shape and nature of chip formation, the value of technological modes and processing conditions. As the material to be machined, the authors considered the cast iron with a layer hardened by diffusion alloying. Determination of input data for model was carried out directly during machining on the lathe and with the help of measurements of chip parameters. As cutting tools, we used standard cutters with mechanically fastened plates. The evaluation of the modelling results showed high convergence with the experimental results: the error in amplitude was 8.5 percent, in oscillation frequency - 12.3 percent; in general, the error did not exceed 15 percent. Thus, the developed model can be used for preliminary vibration assessment at the design stage of turning operations.

Analysis of Self-Injection-Coupled Quadrature Oscillator: Oscillation Amplitude and Phase Error

Analysis of Self-Injection-Coupled Quadrature Oscillator: Oscillation Amplitude and Phase Error

Suppression of Amplitude and Phase Errors in Optically Pumped Magnetometers Using Dual-PI Closed-Loop Control

Suppression of Amplitude and Phase Errors in Optically Pumped Magnetometers Using Dual-PI Closed-Loop Control

Nonlinear diffusion‐limited two‐dimensional colliding‐plume simulations with very high‐order numerical approximations

AbstractAtmospheric numerical models play a crucial role in operational weather forecasting, as well as in improving our understanding of atmospheric dynamics via research studies. Maximising their accuracy is of paramount importance. Use of advective flux schemes greater than O7 in atmospheric models is largely undocumented, with no studies considering O3–O17 fluxes with formal accuracy‐preserving high‐order interpolation, pressure gradient/divergence, and subgrid‐scale (SGS) turbulent fluxes. Higher‐order numerical approximations can reduce truncation, amplitude and phase errors, and potentially improve model accuracy and effective resolution. Here, simulations are presented using very‐high‐order O3–O17 fluxes, with/without high‐order O2–O18 Lagrangian interpolations, pressure gradient/divergence approximations, and SGS turbulent fluxes for a two‐dimensional, highly‐viscous (Re ∼ 100) diffusion‐limited, nonlinear colliding‐plumes problem using 25–200 m spatial resolutions. The highest‐order flux schemes coupled with higher‐order interpolations, pressure gradient/divergence and SGS flux approximations produced the best solutions, with higher‐order fluxes and interpolations being most important. Overall solution convergence of order about O1–O2 with mode‐split (fast sound/slow advective waves) O3 Runge–Kutta temporal schemes was negatively impacted by order at most O1 temporal convergence with SGS fluxes, divergence damping, and especially spatial filters, compared to about order O3 convergence with these inactivated. While very‐high‐order schemes were shown to improve solution accuracy, few cost‐effective higher‐order highly‐viscous test problem solutions (higher order vs finer resolution) were found using theoretical floating‐point operations (FPO) with Courant‐Friedrichs‐Lewy (CFL)‐limited or constant stable Courant number‐based time steps. However, employing central processing unit (CPU) time, rather than FPOs, demonstrated there was reduced computational burden using higher‐order approximations. We conclude that O9–O17 flux schemes with or without high‐order (≥O4) interpolations, pressure gradient/divergence approximations, and SGS fluxes can improve atmospheric model solution accuracy, without prohibitive computation costs, compared to O3–O7 flux with O2 interpolations, pressure gradient/divergence approximations, and SGS fluxes.

A 4.5–5.5-GHz Compact 6-bit Phase Shifter With Low RMS Phase and Amplitude Errors Based on Bandpass Structure for High-Power Applications

A 4.5–5.5-GHz Compact 6-bit Phase Shifter With Low RMS Phase and Amplitude Errors Based on Bandpass Structure for High-Power Applications

A 10-20 GHz 6-Bit High-Accuracy Digital Step Attenuator with Low Insertion Loss in 0.15 µm GaAs p-HEMT Technology.

In a beamforming circuit for a modern broadband phased-array system, high accuracy and compactness have received sufficient attention as they are directly related to side lobe level and fabrication cost, respectively. In order to meet the low phase error required, this paper proposed an ultra-broadband 6-bit digital step switched-type attenuator (STA) with capacitive/inductive compensation networks. Compared to the conventional methods, the proposed technique employs an improved simplified T-structure with capacitive compensation networks, which simultaneously achieves low insertion loss and high-accuracy amplitude/phase control. In addition, on-chip level shifting circuit is integrated to avoid complex control schemes. The strategy of prioritizing return loss is adopted to alleviate the performance degradation caused by impedance mismatch after cascade. As a proof-of-principle demonstration, a wideband 6-bit STA with core area of only 0.5 mm × 1.8 mm was designed via 0.15-micrometer GaAs pHEMT technology. It exhibits ultra-broadband operation with a 31.5 dB amplitude tuning range and a 0.5 dB tuning step. The insertion loss of the reference state is 4-5.3 dB. The return loss is better than 15 dB for all the 64 states. The RMS amplitude and phase errors are less than 0.2 dB and 2° over the 10 to 20 GHz.

Inversion of Target Magnetic Moments Based on Scalar Magnetic Anomaly Signals

As a key physical property of underwater ferromagnetic targets, magnetic moment can reflect important information such as the mass and heading of the target. However, most of the current magnetic moment estimation methods rely on vector magnetic field sensors or sensor arrays to measure the magnetic field, which limits its application in remote target magnetic moment calculation on mobile platforms to some extent. To solve this problem, a real-time magnetic moment inversion method based on the high-precision scalar magnetic measurement data of a high-speed moving platform is proposed in this paper. The method allows the estimation of the magnetic moment of underwater ferromagnetic targets by the scalar magnetic measurement data of an optical pump magnetic field sensor mounted on a high-speed moving platform. The experimental results show that this method has high precision in estimating magnetic moment; the average error of the magnetic moment amplitude was only 5.85%, while the average errors of the magnetic moment inclination and magnetic moment deflection were 1.58° and 2.79°, respectively. These results provide a new and effective way to estimate the magnetic moment of underwater ferromagnetic targets and are expected to have important practical applications.

Error correction algorithm of array time-varying amplitude and phase based on autoencoder

As array antennas are widely used in various mobile platforms, the time-varying amplitude and phase error has become an important factor affecting the application of array signal processing technology. A deep learning-based algorithm for the correction of time-varying amplitude and phase errors in arrays is proposed in terms of the idea of autoencoder. The algorithm makes full use of the data feature extraction and reconstruction capability of the autoencoder network, designs a deep learning network for the correction of time-varying amplitude phase error of the channel, gives a double-driven learning mechanism without time-varying amplitude phase error data (unperturbed data) and time-varying amplitude phase error data (perturbed data), completes the extraction of the array stream shape hidden features based on the principle of minimising the mean square error of the desired output and the ideal model. The simulated experiments show that the algorithm can effectively correct the time-varying amplitude and phase errors of each channel, and the mean square error of the corrected amplitude and phase errors are within 0.5% and 1.5% respectively when there are ±80% random time-varying amplitude errors and ±5° random time-varying phase errors. The effectiveness of the proposed algorithm is verified.

Semi-Implicit Computation of Fast Modes in a Scheme Integrating Slow Modes by a Leapfrog Method Based on a Selective Implicit Time Filter

Abstract A scheme for integration of atmospheric equations containing terms with differing time scales is developed. The method employs a filtered leapfrog scheme utilizing a fourth-order implicit time filter with one function evaluation per time step to compute slow-propagating phenomena such as advection and rotation. The terms involving fast-propagating modes are handled implicitly with an unconditionally stable method that permits application of larger time steps and faster computations compared to fully explicit treatment. Implementation using explicit and recurrent formulation is provided. Stability analysis demonstrates that the method is conditionally stable for any combination of frequencies involved in the slow and fast terms as they approach the origin. The implicit filter used in the method damps the computational modes without noticeably sacrificing the accuracy of the physical mode. The O[(Δt4)] accuracy for amplitude errors achieved by the implicitly filtered leapfrog is preserved in applications where terms responsible for fast propagation are integrated with a semi-implicit method. Detailed formulation of the method for soundproof nonhydrostatic anelastic equations is provided. Procedures for implementation in global spectral shallow-water models are also given. Examples comparing numerical and analytical solutions for linear gravity waves demonstrate the accuracy of the scheme. The performance is also shown in more practical nonlinear applications, where numerical solutions accomplished by the method are evaluated against those computed from a scheme where the slow terms are handled by the third-order Runge–Kutta scheme. It demonstrates that the method is able to accurately resolve fine-scale dynamics of Kelvin–Helmholtz shear instabilities, the evolution of density current, and nonlinear drifts of twin tropical cyclones.

Single CFOA-based active Negative Group Delay circuits for signal anticipation

The group delay of a signal prior to data monitoring is a crucial consideration in today’s real-time applications, especially for those that involve long sensor arrays or high-order filters. In this article, nine new second-order Negative Group Delay (NGD) circuits based on Current Feedback Operation Amplifier (CFOA) are proposed, and their transfer functions are demonstrated. These circuits have a wide range of applications, from audio to mechanical signals and sensor signal anticipation. An example design procedure is provided for one of the introduced circuits. A time-domain analysis is performed using both a single-tone sinusoidal and a band-limited audio recording in the frequency range of 1 Hz to 500 Hz. The article assesses the change in the signal using Root Mean Square Error (RMSE) and cross-correlation. Furthermore, the relationship between the NGD value and the operation range of the circuit is investigated and verified experimentally. The results show that an NGD value of approximately 100μs can be achieved with an amplitude error of 0.86% for a single-tone input and 1.54% for an audio recording, and an operation range of about 650 Hz.

MJO Influence on Subseasonal-to-Seasonal Prediction in the Northern Hemisphere Extratropics

Abstract The impacts of the Madden–Julian oscillation (MJO) on the subseasonal-to-seasonal (S2S) prediction in the Northern Hemisphere extratropics are examined using the reforecasts from the S2S Project and Subseasonal Experiment Project (SubX). When forecasts are initialized during an active MJO, extratropical prediction skill becomes significantly higher at 3–4-week windows compared to inactive MJO. Such prediction skill improvement is evident in the 500-hPa geopotential height over the Pacific–North America region and the North Atlantic and in surface temperature over North America, especially when the model is initialized during the MJO phases 6–7 and 8–1. However, the extratropical prediction skill is not modulated by the MJO phases 2–3 and 4–5. This phase dependency is likely determined by the arrival time of the MJO at the Maritime Continent (MC) barrier that substantially enhances the MJO amplitude error. This result suggests that only MJO phases whose convection lies east of the MC are a source of wintertime S2S predictability in the extratropics.

Predictions of multi-scale vortex-induced vibrations based on a multi-fidelity data assimilation method

This paper presents a data assimilation method based on the POD-DeepONet structure to fuse two types of fidelity data from vortex-induced vibration (VIV) problems. The data is mainly focused on amplitude response in different oncoming flow cases from cross-flow (CF) and inline (IL) directions for new flow speed predictions. The low-fidelity data in this paper is calculated from a semi-empirical code called DAVIV, while the high-fidelity data is measured from laboratory experiments. For a complex nonlinear correlation in amplitude and phase error between low and high-fidelity data, the POD-DeepONet structure receives better accuracy and more stable predictions than Neural Network with few training cases. It can successfully reconstruct the amplitude response along the marine riser and capture the changing trend with the oncoming flow speed. The POD-DeepONet is then applied to predict the VIV cross-flow and inline responses with different datasets. The prediction of mean square error (MSE) shows an exponential decline trend with the increasing case number for training. With the exponentially fitted MSE formula, the required case number under the expected error can be quickly obtained, which may provide a reference for efficient and high-fidelity VIV response prediction in engineering.

UNet-rootMUSIC: A high accuracy direction of arrival estimation method under array imperfection

In the practical direction-finding systems, the accuracy and resolution of the direction of arrival (DOA) estimation are affected not only by the Gaussian noise and array size but also by hardware configuration imperfections, such as errors in element manufacturing and mounting. These impairments cause phase and amplitude errors in estimating the DOA of signal sources. To address this issue, this paper proposes to combine a U-shape deep neural network (UNet) with the multiple signal classification via the root of the polynomial (rootMUSIC) algorithm (so-called UNet-rootMUSIC) to improve the DOA estimation accuracy. In this approach, the UNet model plays a role in converting a covariance matrix of received signals containing phase and gain errors into a nearly perfect one of the ideal antenna array. The rootMUSIC algorithm is then employed to estimate the signal DOA based on the converted covariance matrix. The DOA estimation performance of the uniform linear array of eight elements with an inter-element distance of λ/2 is analyzed through experimental simulations. The simulation results demonstrate that our method can significantly reduce the root mean square error of DOA estimation compared to the conventional MUSIC, rootMUSIC, ESPRIT methods and two deep neural network-based angular classification methods.

Alteration of body representation in typical and atypical motor development.

Developmental coordination disorder (DCD) impacts the quality of life and ability to perform coordinated actions in 5% of school-aged children. The quality of body representations of individuals with DCD has been questioned, but never assessed. We hypothesize that children with DCD have imprecise body representations in the sensory and motor domains. Twenty neurotypical children, seventeen children with DCD (8-12 years old) and twenty neurotypical adults (25-45 years old) performed both sensory and motor body representation tasks: a limb identification and a limb movement task. We observed lower accuracy in the sensory task but not in the motor task. In both tasks, we observe a larger amplitude of errors, or synkinesis, in children with DCD than in neurotypical children. In neurotypical children, accuracy was lower than in neurotypical adults in the motor and sensory task, and the amplitude of sensory errors and synkinesis was higher than in neurotypical adults. Using a linear regression model, we showed that sensory accuracy is a good predictor of synkinesis production, and that synkinesis production is a good predictor of sensory accuracy, as can be expected by the perception-action loop. Results support the hypothesis of an imprecision of body representation in DCD. We suggest that this imprecision arises from noise in the body representation used at the level of internal models of action. Future studies may assess whether slower plasticity of body representations, initial imprecision, or both may account for this observation. At the clinical level, prevention strategies targeting body representation in early childhood are strategically important to limit such impairments. RESEARCH HIGHLIGHTS: Body representation is impaired in children with DCD and has a significant cost in terms of the accuracy of sensory identification of body parts and associated movements. Inaccuracies in the body representation measured in perception and in action (error amplitude and synkinesis) are related in both NT children and adults. In typical development, we provide evidence of a strong link between body schema and body image.

A general Bayesian framework to account for foreground map errors in global 21-cm experiments

ABSTRACT Measurement of the global 21-cm signal during Cosmic Dawn and the Epoch of Reionization is made difficult by bright foreground emission which is 2–5 orders of magnitude larger than the expected signal. Fitting for a physics-motivated parametric forward model of the data within a Bayesian framework provides a robust means to separate the signal from the foregrounds, given sufficient information about the instrument and sky. It has previously been demonstrated that, within such a modelling framework, a foreground model of sufficient fidelity can be generated by dividing the sky into N regions and scaling a base map assuming a distinct uniform spectral index in each region. Using the Radio Experiment for the Analysis of Cosmic Hydrogen as our fiducial instrument, we show that, if unaccounted-for, amplitude errors in low-frequency radio maps used for our base map model will prevent recovery of the 21-cm signal within this framework, and that the level of bias in the recovered 21-cm signal is proportional to the amplitude and the correlation length of the base-map errors in the region. We introduce an updated foreground model that is capable of accounting for these measurement errors by fitting for a monopole offset and a set of spatially dependent scale factors describing the ratio of the true and model sky temperatures, with the size of the set determined by Bayesian evidence-based model comparison. We show that our model is flexible enough to account for multiple foreground error scenarios allowing the 21-cm sky-averaged signal to be detected without bias from simulated observations with a smooth conical log spiral antenna.

Variable-Gain Phase Shifter with Phase Compensation Using Varactors

This paper demonstrates a phase compensation technique using varactors for variable-gain phase shifters (VGPSs). The VGPS consists of an I/Q generator and I/Q variable gain amplifiers (I/Q VGAs). I/Q VGAs based on common-emitter stages are enabled to control the gain by adjusting the collector current of the transistor. However, the phase control performance degenerates because the input capacitance varies with the collector current. The proposed phase compensation technique reduces the variation in the insertion phase of the I/Q VGA by adjusting the voltage of the varactor provided at its input and maintaining the input capacitance constant in any gain state. As a result, the VGPS can provide a low phase and amplitude error under phase control. A Ka-band VGPS with the proposed phase compensation technique, fabricated in a 130-nm SiGe BiCMOS process, demonstrates a 0.73° and 0.06 dB improvement in the RMS phase and amplitude error compared with the case without the compensation technique. The VGPS achieves measured RMS amplitude and phase errors of less than 0.19 dB and 0.75°, respectively, in an amplitude control range of more than 20 dB with a frequency range of 28 to 32 GHz.

Antenna Pattern Modeling Accuracy for a Very Large Aperture Array Radio Telescope With Strongly Coupled Elements

Modern radio telescopes strongly rely on accurate computational electromagnetic tools for “beam” models. Especially for densely-packed aperture array radio telescopes, the only feasible way to produce accurate models of the individual embedded element patterns is by using electromagnetic codes. In this paper, the accuracy of two models computed by different commercial codes is evaluated for one station of the SKA-Low radio telescope. Except for a couple of critical frequencies, the amplitude and phase errors are low enough to allow a beamformer efficiency higher than 99%.

Seismic performance evaluation of a five-story column-and-beam glulam frame with steel-timber buckling restrained braces

This paper summarizes the results of numerical simulation and fragility analysis of a five-story column-and-beam glulam frame with chevron steel-timber buckling restrained braces (BRBs). Key parameters of the rotational behavior of the bolted beam-to-column connections and column base connections were modeled and calibrated using shaking table test results. Prediction methods of the stiffness and capacity of the steel-timber BRBs were proposed and validated with the test results. An FEM model of the glulam frame with steel-timber BRBs was developed to capture the frame seismic response and validated using shaking table test results. The FEM model which considered damping change and damage accumulation achieved good agreement with shaking table test results, with the modelling errors of displacement amplitude for most conditions less than 15 %. Fragility analysis of the frame was conducted based on the validated FEM model. The probability of exceeding the collapse prevention limit state under the VII-rare earthquake excitations was reduced to close to zero when steel-timber BRBs were used. This study demonstrates the potential for using glulam frames with steel-timber BRBs in Chinese VII seismic regions.

Bayesian deep neural networks for spatio-temporal probabilistic optimal power flow with multi-source renewable energy

Probabilistic optimal power flow (POPF) plays a crucial role in ensuring the economic and secure operation of power systems with multiple fluctuating loads and renewable energy power generations. However, the practical application of POPF faces challenges because of the lack of accurate input scenarios and the heavy computational burden. With the increasing power system uncertainties and frequent changes in topologies, the existing POPF analysis methods struggle to achieve both high precision and efficient training. To address these issues, firstly, this work decomposes the POPF into three stages: 1) the generation of source-load random scenarios; 2) the non-linear mapping derived from the Karush-Kuhn-Tucker conditions; and 3) the non-linear mapping derived from the power flow equations. Secondly, this work proposes two Bayesian deep neural networks to separately solve each stage of the POPF. Multi-collinearity reduction conditional variational autoencoder can generate source-load random scenarios while consistency condition guided Bayesian deep neural network can predict POPF calculation results. The work further conducts numerical simulations on four IEEE benchmark power systems with multiple renewable energy sources, using uncertainty data collected from the Belgian wind power generation, photovoltaic power generation and load dataset. The simulation results demonstrate that: 1) the precision in generating source-load random scenarios has been enhanced, leading to a reduction of up to 128% in the distribution statistical quantities of the accuracy measures; and 2) the mean absolute error of active power injections, and voltage angles as well as the mean absolute percentage error of the voltage amplitudes in the benchmark systems are reduced by at least 0.7% compared to deterministic neural networks.