Number Of Measurement Points Research Articles

Study on blade zoning measurement point planning method

Due to the complex surface profiles of blades, the current measurement point planning does not adequately capture the distinct zones, leading to low measurement efficiency and challenges in meeting online detection requirements. This study proposes a novel approach to blade zoning measurement point planning. First, it utilizes an improved centroid Voronoi extreme model and smoothness indicators to select seed points during the zoning process. Following that, feature tensor-driven regional clustering is carried out based on these seed points. Finally, measurement points are determined through adaptive sampling of profile deviations along the feature stacking axis within each zone. Simulation and real experiments demonstrate that the measurement points generated by this method effectively represent the blade surface's distinctive features, reducing the number of measurement points by 91.7% and significantly enhancing efficiency.

Validation of a Simplified Method for Estimating the Harmonic Response of Rogowski Coils with the Monte Carlo Method.

The need to monitor the power network is leading to a significant increase in the number of measurement points. These points consist of intelligent electronic devices and instrument transformers (or more in general sensors). However, as the number of devices increases, so does the demand for their characterization and testing. To this end, the authors formulated a new characterization procedure that offers numerous benefits for manufacturers and system operators. These benefits include: (i) reducing testing time (thus lowering costs), (ii) simplifying the existing procedures, and (iii) increasing the number of tested devices. In this study, to complete the validation of the proposed characterization procedure, the authors performed a comprehensive uncertainty evaluation. This included the identification and analysis of the uncertainty sources, the implementation of the Monte Carlo method to obtain the statistical parameters of the quantities of interest, and the final method assessment according to the obtained results. Each step is described in detail, and the results allow one to (i) replicate the uncertainty analysis on other types of instrument transformers and (ii) implement the proposed harmonic characterization procedure with the confidence that the method is accurate, flexible, and scalable.

Optimising laser-induced phased-arrays for defect detection in continuous inspections

This paper proposes a novel optimisation approach for laser induced phased-arrays for detection purposes in large area inspection. The optimisation aims to maximise the detection capability of a phased-array while minimising the inspection time. This is done by using a simulation ray-tracing model that evaluates multiple candidate configurations. The metrics that measure the detection performance for a target defect are the receiver operating characteristic curve and the signal-to-noise ratio. The inspection time is indirectly computed through the number of measurement points per unit length in a continuous inspection. This depends on the physical aperture and pitch of the phased-array and they are the quantities optimised. The optimisation and modelling approach are validated in an experimental case study for a side-drilled hole as target defect. Further recommendations about directional defects (e.g. cracks at different angles) are provided.

Visualization of slit defect by scanning nonlinear airborne ultrasound source technique using compressed sensing

Abstract A high-speed, non-contact non-destructive testing method using a scanning airborne ultrasound source and a nonlinear harmonic method has been developed for plate-like structures. The testing time depends on the scanning speed and the number of measurement points. To solve this problem, we propose using compressed sensing with this method. In this paper, we investigated visualizing Lamb wave propagation using the proposed compressed sensing method. In addition, we detected a slit defect by using the Lamb wave propagation image. Our results demonstrated that compressed sensing could be used to reduce the testing time.

Non-contact impact load identification based on intelligent visual sensing technology

Accurate identification of impact loads is vital for structural assessment and design. Traditional methods rely on complex equipment, such as accelerometers or strain gauge, which can be expensive and prone to failure. This study introduces a non-contact intelligent identification approach incorporating visual sensing technology, providing a convenient means to identify impact loads. Numerical simulations explore the differences in identifying impact forces through acceleration and displacement responses, particularly by considering such variables as measurement noise and number of measurement points. A meticulously designed experiment verified the feasibility of the proposed method for measuring the displacement and velocity of rapidly moving targets, and evaluated its performance in terms of accuracy. A series of impact loading experiments were conducted on a simply supported girder bridge model to validate the effectiveness of the proposed impact force identification method. Results indicate strong agreement between displacement response measurements and percentile meters. The proposed non-contact method accurately identifies single or continuous impact loads, with a minimum peak relative error of 0.2%. This study represents a pioneering application of intelligent visual sensing technology in the field of impact load identification. Moreover, the current research introduces a novel approach to address the challenges faced by conventional methods in identifying impact loads. Future research can leverage the groundwork laid by this study to further optimize and expand the proposed method, enhancing its capabilities and fully harnessing its potential to offer advanced solutions in structural health monitoring.

Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by Multi-Objective Genetic Optimization of Sensor Placement and Strain Pre-Extrapolation.

The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as "shape sensing". The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.

Study on the regional ASF prediction method based on the ordinary kriging interpolation

The eLoran system functions as a robust backup to the Global Navigation Satellite System (GNSS), providing substantial signal power, robust anti-jamming capabilities, and an easily maintainable ground system. Nonetheless, the propagation time delay of the system, primarily driven by the Additional Secondary Phase Factor (ASF), significantly influences the positioning accuracy. Compensating for ASF can effectively enhance the positioning performance. Traditional propagation delay theories frequently yield substantial discrepancies between the predicted and measured values in regions characterized by extended propagation distances and varying topographic features. To address this issue, we conducted ASF measurements within a selected test area using a limited number of measurement points. We employed the ordinary Kriging interpolation method to predict ASF values across the entire test area, and used cross-validation to validate our predictions. The results confirmed the accuracy and effectiveness of the Kriging interpolation algorithm in predicting ASF values within specific regions. The cross-validation demonstrated that the errors remained within acceptable ranges. Furthermore, we applied Ordinary Kriging, Inverse Distance Weighting, and Radial Basis Function Interpolation methods to evaluate the positioning accuracy of the test area before and after ASF correction. Compared to other methods, using the Ordinary Kriging interpolation algorithm for predicting ASF values resulted in a corrected positioning accuracy of up to 68.8 m at various test locations. This approach effectively resolves the challenge of low accuracy in theoretical calculations in complex environments. By utilizing ordinary Kriging interpolation, we required measurements of only a few ASF values within a specific region to create an ASF correction map, addressing the challenges related to inaccurate theoretical calculations in complex pathways and avoiding time-consuming and labor-intensive large-scale measurements. The results of this study offer valuable theoretical support for improving the accuracy of land-based navigation systems.

Effect of combined side-wall air supply on ventilation and respiratory pollutant dispersion characteristics of high-speed trains

The ventilation system significantly affects the energy saving, thermal comfort, and pollutant dispersion characteristics of high-speed trains. Using Computational Fluid Dynamics (CFD), the effects of combining side-wall air supply with side roof or ceiling air supply, on the performance of a high-speed train ventilation system is studied. The results show that the addition of side-wall air supply aids energy saving and emission reduction, resulting in an increase in energy utilisation coefficient of between 5% and 30%. The number of measurement points with wind speed less than 0.3 m/s increases by 3%∼9%, which can improve passenger comfort. Also, the concentration of pollutants can be reduced by 6%∼29%, and the diffusion distance along the length of the train can be reduced by 18%∼45%, which means more efficient ventilation and a shorter diffusion range of pollutants. However, it reduces temperature uniformity. The results of this study can be used for ventilation optimisation of high-speed trains, to reduce ventilation energy consumption, improve thermal comfort, and increase ventilation efficiency.

2.5 dimensional sound field reproduction based on mode matching and equivalent sources considering primary reflections

Many sound field reproduction methods using many loudspeakers (secondary sources) have been studied to reproduce a physically-accurate sound field. In our previous research, we proposed a pressure matching method using the modeled transfer functions of the secondary sources based on the sparse equivalent source method (ESM). By modeling the sound field emitted from the loudspeaker, the number of measurement points required for pressure matching can be significantly reduced. Moreover, another well-known method of sound field reproduction is Mode Matching (MM). In the MM method, the driving functions are derived such that the weight coefficients of the spherical harmonics are matched between the desired and reproduced sound fields. In this study, we proposed an implementation of the MM method based on the ESM considering the sound reflections and loudspeakers' directivity. We estimate the spherical harmonic coefficients of the loudspeakers and sound reflections based on the superposition of equivalent sources. In the simulation experiments, we evaluate the reproduction accuracy achieved with the first-order sound reflections.

The Inversion Method Applied to the Stress Field around a Deeply Buried Tunnel Based on Surface Strain

To identify the magnitude and direction of in situ stress in deeply buried tunnels, an inversion method for the stress field was proposed based on a finite number of measurement points of surface strain. Firstly, elastic strain data of finite points on the surface of tunnel surrounding rock were acquired using the borehole stress relief method at the engineering site. Secondly, a finite element model of the tunnel surrounding rock with plastic damage was established, and the parameters of the finite element model were substituted using the SIGINI subroutine. Then, an improved Surrogate Model Accelerated Random Search (SMARS) was developed using genetic algorithm programming on the MATLAB™ platform to invert and attain the globally optimal boundary conditions. Finally, the obtained optimal boundary conditions were applied to the numerical model to calculate the stress distribution in the engineering site. The reliability of this method was validated through a three-dimensional example. The method has been successfully applied to the stress-field analysis of deep tunnels in Macheng Iron Mine, Hebei Province, China. The research results show that this method is a low-cost, reliable approach for stress-field inversion in the rock around a tunnel.

Industrial units modeling using self-attention network based on feature selection and pattern classification

Data-driven modeling method is an effective method for large-scale refinery industrial units. However, due to the long operational cycle, large number of measurement points, complex spatial and temporal correlations and dynamic switching of operation modes of refinery industrial units, utilizing a single data-driven model for process modeling may not be optimal. To tackle with the issues above, a self-attention model based on feature selection and pattern classification (FS-PC-SA) is develop. This approach uses Random Forest algorithm for feature selection and extract different operating modes from industrial data through K-means clustering algorithm. To capture complex spatial and temporal correlations in refinery units, sub-models based on self-attention for each mode are built for prediction. Finally, the method is used to predict the gasoline product yield in fluid catalytic cracking process and wax oil product yield in ebullated bed residue hydrocracking process, which demonstrates that the proposed method is better than other traditional methods.

An Integrated Kinematic Calibration and Dynamic Identification Method With Only Static Measurements for Serial Robot

In this article, we propose an integrated calibration and identification method that can identify kinematic and dynamic parameters at the same time. Only a series of static experiments are required in this method, which can significantly simplify the experimental operation and improve the accuracy of identification. A novel calibration model based on axis configuration space and adjoint error model considering joint compliance with only position measurements is developed and both joint compliance and joint zero-offset errors are included in the proposed model. Identifiability analysis shows which joint compliance can be identified and the relationships between the identifiability of joint compliance and the number of measurement points. A recursive algorithm considering physical consistency is newly derived to obtain the derivatives of estimated inertial matrix and gravity vector with respect to joint twists. In this way, the coupling between kinematic and dynamic model is solved and the optimal kinematic and dynamic parameters can be derived using gradient-based optimizer. Comparative study with experiments are reported to show the effectiveness of our algorithm.

Simulation-based inference for the in situ estimation of the acoustic boundary admittance

Achieving accurate results in interior acoustic simulations relies on precise knowledge of the boundary properties of all interacting surfaces. Typically, the boundary admittance, which fully characterizes the acoustic properties of a surface, is determined under laboratory conditions such as the impedance tube. Yet, this approach has limitations, motivating the exploration of in situ methods to characterize materials in real-world conditions. In this work, we present a Bayesian approach to determine the acoustic boundary admittance in situ based on a limited number of measurement points. The method utilizes simulation-based inference, where a neural network is trained to approximate the posterior probability distributions of the unknown boundary admittances. The core of the approach is a finite element model used to generate sound pressure data, which also acts as the forward model during the inference process. Consequently, this technique is especially well-suited for applications involving pre-existing geometrical models, such as digital twin applications or model updating. By adopting simulation-based inference, we gain advantages over sampling-based Bayesian approaches, as it effectively handles complex and computationally expensive forward models.

Temperature Estimation in Lithium-Ion Cells Assembled in Series-Parallel Circuits Using an Artificial Neural Network Based on Impedance Data

Lithium-ion cells are widely used in various applications. For optimal performance and safety, it is crucial to have accurate knowledge of the temperature of each cell. However, determining the temperature for individual cells is challenging as the core temperature may significantly differ from the surface temperature, leading to the need for further research in this field. This study presents the first sensorless temperature estimation method for determining the core temperature of each cell within a battery module. The accuracy of temperature estimation is in the range of ΔT≈1 K. The cell temperature is determined using an artificial neural network (ANN) based on electrochemical impedance spectroscopy (EIS) data. Additionally, by optimizing the frequency range, the number of measurement points, input neurons, measurement time, and computational effort are significantly reduced, while maintaining or even improving the accuracy of temperature estimation. The required time for the EIS measurement can be reduced to 0.5 s, and the temperature calculation takes place within a few milliseconds. The setup consists of cylindrical 18,650 lithium-ion cells assembled into modules with a 3s2p configuration. The core temperature of the cells was measured using sensors placed inside each cell. For the EIS measurement, alternating current excitation was applied across the entire module, and voltage was measured individually for each cell. Various State of Charge (SoC), ambient temperatures, and DC loads were investigated. Compared to other methods for temperature determination, the advantages of the presented study lie in the simplicity of the approach. Only one impedance chip per module is required as additional hardware to apply the AC current. The ANN consists of a simple feedforward network with only one layer in the hidden layer, resulting in minimal computational effort, making this approach attractive for real-world applications.

Reconstruction of transient convective heat transfer coefficients in heat transfer of gun barrels with variable coefficients

On the basis of the conjugate gradient method, an inversion model of the transient convective heat transfer coefficient between the bore surface and the gunpowder gas is presented. In this study, the measured temperature at a certain depth from the bore surface is employed as input. The variation of the thermophysical properties of gun steel with respect to temperature is taken into consideration. After verifying the feasibility of the model with constant convective heat transfer coefficients, it is applied to the reconstruction of transient convective heat transfer coefficients that consistent with the classical interior ballistic laws. Results indicate that decreasing the measurement error and increasing the number of measurement points can effectively improve the performance of the model. As well, measurement points that placed further away from the inlet boundary offer superior accuracy. When studying the inverse heat transfer problem of the gun barrel, it is found that setting the monitoring time to be twice the duration of the interior ballistic period may guarantee sufficient input data. The inversion model may offer a promising theoretical framework for indirect measurement manner of the bore surface temperature of gun barrel.

Efficient sampling method based on co-kriging for free-form surface measurement

Numerous studies on sampling methods have been conducted to balance the inspection efficiency and accuracy of coordinate measuring machines (CMMs). However, almost all existing methods only take into account the information from the processed surface during the reconstruction process. To address this limitation, an adaptive sampling method based on co-kriging method is proposed, according to the characteristics of the interpolation reconstruction method, we treat certain feature points on the surface as anchor points and determine the distribution of other measurement points through iteration. In the reconstruction process, we employ the co-kriging interpolation method and combine extra information of the model surface to enhance the reconstruction accuracy. Through simulations and actual measurements of processed surfaces, we compare the proposed method with other algorithms. The results indicate that our method is particularly suited for complex surfaces with a limited number of measurement points, and can substantially reduce the reconstruction error when the number of measurement points is the same.

Application of equivalent source intensity density interpolation in near-field acoustic holography

The spatial resolution of near-field acoustic holography based on the equivalent source method (ESM) is closely related to the number of measurement points, the higher the number of measurement points, the higher the resolution. However, the number of measurement points in the actual measurement cannot be increased infinitely. To solve the contradiction between the resolution and the number of measurement points, this paper proposes an equivalent source density interpolation method (ESDIM). First, the equivalent source intensity is obtained using the sound pressure measured by the array element and the Green function, and the equivalent source intensity density is obtained based on the equivalent source intensity and grid area. Second, the Hermite interpolation function was used to obtain the interpolated equivalent source intensity density. However, as the number of interpolated grids increased, the resolution, computation, and running time of ESDIM increased, and the number of subdivided grids per unit grid was 9–25 in this study. Finally, the sound field was reconstructed based on the obtained interpolated equivalent source intensity and Green transfer function, and the reconstruction accuracies of ESDIM and ESM were compared and analyzed. The simulation and experimental data processing results showed that the resolution of the equivalent source intensity density interpolation method was higher than that of the ESM.

Error Analysis of Sound Source Directivity Interpolation Based on Spherical Harmonics

Precise measurement of the sound source directivity not only requires special equipment, but also is time-consuming. Alternatively, one can reduce the number of measurement points and apply spatial interpolation to retrieve a high-resolution approximation of directivity function. This paper discusses the interpolation error for different algorithms with emphasis on the one based on spherical harmonics. The analysis is performed on raw directivity data for two loudspeaker systems. The directivity was measured using sampling schemes of different densities and point distributions (equiangular and equiareal). Then, the results were interpolated and compared with these obtained on the standard 5° regular grid. The application of the spherical harmonic approximation to sparse measurement data yields a mean error of less than 1 dB with the number of measurement points being reduced by 89%. The impact of the sparse grid type on the retrieval error is also discussed. The presented results facilitate optimal sampling grid choice for low-resolution directivity measurements.

Reconstruction of the heat flux input of coated gun barrel with the interfacial thermal resistance

A 2-D inversion model to reconstruct the transient inner wall heat flux of the coated barrel is proposed in this paper. Indirect measurement of the inner wall temperature of the barrel under harsh conditions is realized. Measurement points locate at specified distances from the inner surface. Temperature calculated from a direct problem plus the measurement errors is taken as measured temperature. Variations of the temperature dependent thermo-physical properties of materials are taken into consideration. The total heat input of the firing and thermal resistance between chromium coating and gun steel are determined by depths of gun steel phase transition and chromium recrystallization after firing. The direct problem, sensitivity problem and adjoint problem are solved with the finite difference method (FDM). After verifying the reliability of the inversion model with a special numerical example, it is promoted to reconstruction of general heat flux varying with time and space. Results indicate that decreasing the measurement error and increasing the number of measurement points can effectively improve the performance of the model. As well, measurement points closer to the inner wall offer superior accuracy. The inversion model herein can serve as a promising indirect measurement method of the inner wall temperature.

Reconstructing parameters of the Holocene paleofloods in alluvial lowland river valleys – An example from the Bug valley (East Poland)

One of the ways to improve the accuracy of hydrological forecasts is to extend the data strings with the parameters of the maximum flows in the river from the period preceding their instrumental recording. The reconstruction of such parameters is difficult in the case of alluvial river valleys, mainly due to the difficulties in determining the shape of the channel cross-section. The article proposes a method for reconstructing extreme Holocene paleo-flow in the case of large alluvial rivers in the European Plain. It is based on identifying specific features of the geological structure of the valley, as well as elements of its bottom morphology, which can act as high water marks (HWM). The research was carried out in the section of the Bug valley, a large lowland river flowing through Ukraine, Belarus, and Poland.In the valley reach under analysis, the cross-section of the high water channel is determined by: the surface of the resistant to erosion sub-alluvial bedrock protrusion built of glacial till, and traces of flood erosion on the Pleistocene terraces, including traces of dune washouts. The age of the paleo-flow was determined by means of the C14 dating of sediments filling the flood erosion troughs. Due to the limited number of tests, the reconstructed maximum flow values are subject to uncertainty. Yet, even quite uncertain the reconstructed paleofloods may result in a significant improvement in the estimates of high quantiles using the flood frequency analysis, or at least allow to prepare for the floods not recorded by the monitoring system. In consequence, increasing the number of measurement points, and thus improving the correctness of the determination of paleo channels cross-sections, may contribute to a significant reduction in the uncertainty of the obtained paleo-flow values.