Adaptive Analysis Method Research Articles

A high-order isogeometric vibrational method for adaptive analysis of athlete diving equipment using advanced kinematic and geometric modeling

Isogeometric high-order analysis of a sandwich beam is presented in this article. The kinematic and geometric modeling is applied using the higher order modeling based on the stretchable models. The suggested model in this article can be applied for the sport equipment specially for analysis of diving beams boards and beams. The Variational-based approach is extended to derive governing equations. The results are analytically obtained in order to seek impact of significant parameters of the problem on the responses. The results are confirmed through comparison with available ones in the literature.

A damage-related adaptive self-consistent clustering analysis method with localized refinement capability for the damage problem of 3D woven composites

In this work, a Damage-related Adaptive Self-Consistent Clustering Analysis (DASCA) method is developed to achieve higher solving accuracy for 3D damage problems than Self-Consistent Clustering Analysis (SCA) method, which is applied to investigate the mesoscale damage behavior of 3D woven composites (3DWC). The developed clustering adaptivity framework is based on the characteristics of the damage problem, which consists of five fundamental stages: evaluation of the adaptivity conditions, selection criterion of potential damage clusters, online adaptive clustering analysis, update of the cluster database and adaptive rewind of the analysis process. In the second stage, the clusters with the highest potential to enter the damage state are identified and marked as adaptivity target clusters. In the solving process, the distribution of clusters can achieve problem-dependent dynamic adjustments, ensuring that clusters are concentrated on the regions of interest. Though the numerical examples of 3DWC, the DASCA solutions, using fewer clusters and less computational cost, exhibit higher prediction accuracy for macroscopic mechanical performance, first damage initiation moments and damage evolution process than the SCA solutions.

Adaptive multi-patch isogeometric analysis with truncated hierarchical B-splines in isotropic/orthotropic media

We present an adaptive multi-patch isogeometric analysis on the basis of truncated hierarchical B-splines (THB-splines) for solving two-dimensional complex isotropic/orthotropic elasticity. The THB-splines have local refinement property and their basis functions exhibit linear independence, so these features are highly applicable for adaptive isogeometric analysis. Guided by a posterior error estimator based on stress recovery, the adaptive algorithm is utilized in isogeometric analysis. In order to further extend the proposed method to solve complex geometry problems, the multi-patch technique is adopted to achieve exact modeling with Nitsche’s method as a multi-patch coupling approach. An isotropic numerical example with exact analytical solutions and three orthotropic numerical examples are presented to verify the effectiveness and accuracy of the developed method. Numerical solutions show that the developed adaptive isogeometric analysis method has high computational efficiency.

Empirical analysis of dust and noise pollutants produced in the wood-CNC machining process.

In the manufacturing processes, consideration of sustainability is of particular importance. The current study is concerned with the influences of changing the process variables on the reduction of pollutions in the wood-CNC machining operation. Noise and dust are the studied pollutants in the present research work. Process variables include feed rate, spindle speed, step-over, and depth of cut, and the aim is to predict the behavior of aforementioned pollutants variations in the current process. The amounts of these harmful factors are measured based on existing standards. In order to analyze the findings, adaptive neuro-fuzzy inference system (ANFIS) and regression analysis methods have been employed, separately. The effects of process parameters on response variables have been comprehensively studied. The research findings demonstrated that for the present problem, ANFIS outcomes are more accurate. According to the mean absolute error (MAE) criterion, the prediction errors of ANFIS for noise and dust factors were computed to, in turn, 0.50 and 14.89. Meanwhile, the error values for prediction of noise and dust responses using regression analysis were calculated as 1.54 and 34.62, respectively.

The impact of the colonial architecture on the urban sustainability of Laghouat city in Algeria: ksar of Laghouat as a case

Among the risks that cities are exposed to, colonization emerges as a multifaceted concern representing historical and modern dimensions that can significantly impact a city’s urban sustainability. This study explores the diverse implications of colonization on the urban sustainability with a focus on the Ksar of Laghouat in Laghouat city, Algeria. It examines the urban adaptation cycle and the imprint left by the colonial era on the fabric of the city. Utilizing the method of adaptive cycle approach and transfer entropy analysis, our goal is to quantify interaction on the historical and urban development of potential, connectedness, and resilience of Laghouat city. This study enhances the understanding of the Architectural heritage of Algeria and highlights dynamics between traditional and colonial architectural dimensions of Ksar of Laghouat. By assimilating the impact of colonization on the urban development of the city, we aim to improve the resilience and sustainability of Laghouat, through empowering its local community, and providing decision-makers with valuable sustainable strategies and adaptation mechanisms for future urban visions of the city.

An Adaptive Kriging-Based Fourth-Moment Reliability Analysis Method for Engineering Structures

The fourth-moment method can accurately perform a reliability analysis when it is challenging to determine the distribution of the random variable due to limited available samples. This method only utilizes the first four moments of the random variable and constructs the fourth-moment reliability index. However, it cannot be applied in engineering cases where the state function cannot be expressed explicitly, as it becomes difficult to establish a correlation between the first four moments of the random variable and the state function. Simplifying the state function forcefully may result in significant reliability prediction errors. To address this limitation, this study proposes an adaptive Kriging-based fourth-moment method for reliability analysis under complex state equations. The proposed method demonstrates better applicability and efficiency compared to existing methods. Several numerical examples are provided to validate the effectiveness and accuracy of the proposed method.

Probabilistic analysis of dual circular tunnels in rock masses considering rotated anisotropic random fields

Considering the spatial variability of rock masses in tunnelling analysis is a challenge in geotechnical engineering. This paper focuses on predicting collapse loads and probabilities of design failure for dual circular tunnels constructed in anisotropic rock masses. This study employs the random adaptive finite element limit analysis method, which incorporates rotated anisotropic random fields, to model uncertainties in rock mass properties. The generalised Hoek–Brown constitutive model is employed to define the yield failure criteria of a rock mass. The adaptive finite element limit analysis is first validated against a previous study, demonstrating good agreement. Then, the probability of design failure for dual circular tunnels is comprehensively investigated. This study emphasises the importance of considering both geometric parameters and rotated anisotropic random fields in comprehensive probabilistic analyses. The results provide insights into the mean and coefficient of variation of the stability numbers, as well as the probability of design failure for different geometric configurations and rotated anisotropic random fields. In conclusion, the variation in failure probability for practical applications is presented, highlighting the significance of accounting for spatial variability, rotation angle and anisotropy in rock mass properties in tunnelling.

An alternative 2D ICEEMDAN-based denoising method and its application in processing magnetic anomaly data

Due to environmental noise and human factors, magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation. Empirical Mode Decomposition (EMD), an adaptive multiscale analysis method for nonlinear and non-stationary signals, is widely used in geophysical and geodetic data processing. Compared with traditional EMD, Improved Complete Ensemble EMD with Adaptive Noise (ICEEMDAN) is more effective in addressing the problem of mode mixing. Based on the principles of 1D ICEEMDAN, this paper presents an alternative algorithm for 2D ICEEMDAN, extending its application to two-dimensional scenarios. The effectiveness of the proposed approach is demonstrated through synthetic signal experiments, which show that the 2D ICEEMDAN exhibits a weaker mode mixing effect compared to the traditional bidimensional EMD (BEMD) method. Furthermore, to improve the performance of the denoising method based on 2D ICEEMDAN and preserve useful signals in high-frequency components, an improved soft thresholding technique is introduced. Synthetic magnetic anomaly data testing indicates that our denoising method effectively preserves signal continuity and outperforms traditional soft thresholding methods. To validate the practical application of this improved threshold denoising method based on 2D ICEEMDAN, it is applied to ground magnetic survey data in the Yandun area of Xinjiang. The results demonstrate the effectiveness of the method in removing noise while retaining essential information from practical magnetic anomaly data. In particular, practical applications suggest that 2D ICEEMDAN can extract trend signals more accurately than the BEMD. In conclusion, as a potential tool for multi-scale decomposition, the 2D ICEEMDAN is versatile in processing and analyzing 2D geophysical and geodetic data.

Study on the Mechanism and Suppression of Harmonic Vibration of AMB-Rotor System

The AMB-rotor system is complex and has strong coupling characteristics, which allows multi-harmonic disturbances to enter the system through different ways to produce vibrations with rich spectrum components, which has a great influence on the improvement of micro-vibration accuracy of the rotor system. To further achieve active control of the micro-vibration in the AMB-rotor system, firstly, the mechanism of multi-source disturbance is analyzed according to the working principle of the AMB-rotor system, and the mathematical and physical relationship between the mechanism of disturbance generation and the inducement is deeply studied. Then, the structure of a novel adaptive notch filter, the method of adaptive frequency estimation and analysis of harmonic current suppression in the AMB system are presented. Finally, simulation and experimental research using an MSCMG system demonstrate the feasibility of the proposed method regarding the elimination of harmonic control current.

Shape sensing for thin-shell spaceborne antennas with adaptive isogeometric analysis and inverse finite element method

As the preventive maintenance paradigm transfers to condition-based maintenance, deformation monitoring has become a fundamental system capacity in aerospace engineering. In this study, a novel shape sensing method is proposed for accurate and efficient reconstruction of full-field deformation of thin shell structures from discrete strain measurements. Firstly, a flexible isogeometric approach based on the geometry-independent field approximation is developed for characterizing the geometric and physical domains, which fully unlocks the potential of local refinement while preserving the original exact geometry without re-parameterization. On this basis, a posteriori error estimation algorithm is put forward to automatically drive the adaptive refinement procedure, reducing the discretization error with a fast convergence rate. Subsequently, according to the Kirchhoff-Love theory and the least-squares variational principle, an isogeometric inverse-shell element is created to integrate the inherent advantages of adaptive isogeometric analysis with excellent shape-sensing capabilities of the inverse finite element method. Moreover, a smoothing technique is applied to replenish strain data into each inverse shell element, by which the compatibility between the interpolated and measured strain components is also enforced. Finally, the excellent accuracy and efficiency of the proposed deformation reconstruction framework are verified using both experimental and numerical strain data for two thin-shell spaceborne antennas.

An h-adaptive edge-based smoothed point interpolation method for elasto-plastic analysis of saturated porous media

This study presents the first application of the edge-based smoothed point interpolation method (ESPIM) for adaptive analysis of coupled elasto-plastic problems in saturated porous media. The original ESPIM is enhanced with an adaptive strategy involving an automated h-refinement scheme, a simple, yet effective, recovery-based error indicator, and an efficient data transfer scheme to analyse coupled elasto-plastic problems in geomechanics. The governing equations are presented and discretised in space and time to yield the fully coupled system of equations. The solution strategy is then discussed in terms of handling the material non-linearity, and accommodating the proposed adaptivity procedure in the numerical algorithm. Four numerical examples comprising two different bearing capacity problems, a two-dimensional consolidation problem, and a slope stability analysis are thoroughly investigated. Different adaptive analyses with a range of adaptivity control parameters are investigated in each example and the performances of different techniques are compared in terms of accuracy and the time of the analysis. The results show the ability of the proposed adaptive analyses in yielding accurate and efficient results. Conclusions are drawn in terms of the most efficient numerical set-up to solve non-linear problems in porous media using the proposed numerical scheme.

Adaptive filters based efficient EEG classification for steady state visually evoked potential based BCI system

Brain-computer interfaces (BCIs) system is a link to generate a communication between disable people and physical devices. Thus, steady state visually evoked potential (SSVEP) is analysed to improve performance efficiency of BCIs system using multi-class classification process. Thus, an adaptive filtering-based component analysis (AFCA) method is adopted to examine SSVEP from multiple-channel electroencephalography (EEG) signals for BCIs system efficiency enhancement. Further, flickering at varied frequencies is used in a visual stimulation process to examine user intentions and brain responses. A detailed solution for optimization problem and efficient feature extraction is also presented. Here, a large SSVEP dataset is utilized which contains 256 channel EEG data. Experimental results are evaluated in terms of classification accuracy and information transfer rate to measure efficiency of proposed SSVEP extraction method against varied traditional SSVEP-based BCIs. The average information transfer rate (ITR) results are 308.23 bits per minute and classification accuracy is 93.48% using proposed AFCA method. Thus, proposed AFCA method shows decent performance in comparison with state-of-art-SSVEP extraction methods.

A multi-mode failure boundary exploration and exploitation framework using adaptive kriging model for system reliability assessment

Various adaptive reliability analysis methods based on surrogate models have recently been developed. A multi-mode failure boundary exploration and exploitation framework (MFBEEF) was proposed for system reliability assessment using the adaptive kriging model based on sample space partitioning to reduce computational cost and use the characteristics of the failure boundary in multiple failure mode systems. The efficiency of the adaptive construction of kriging model can be improved by using the characteristics of the center sample of the small space to represent the characteristics of all samples in the small space. This method proposes a failure boundary exploration and exploitation strategy and a convergence criterion based on the maximum failure probability error for a system with multiple failure modes to adaptively approximate the failure boundary of a system with multiple failure modes. A multiple-failure-mode learning function was used to identify the optimal training sample to gradually update the kriging model during the failure boundary exploration and exploitation stages. In addition, a complex failure boundary-oriented adaptive hybrid importance sampling method was developed to improve the applicability of the MFBEEF method to small failure probability assessments. Finally, the MFBEEF method was proven to be effective using five system reliability analysis examples: a series system, a parallel system, a series–parallel hybrid system, a multi-dimensional series system with multiple failure modes, and an engineering problem with multiple implicit performance functions.

Free vibration disturbance and local mesh refinement induced by microcrack damage in circularly curved beams

PurposeThis study aimed to solve the engineering problem of free vibration disturbance and local mesh refinement induced by microcrack damage in circularly curved beams. The accurate identification of the crack damage depth, number and location depends on high-precision frequency and vibration mode solutions; therefore, it is critical to obtain these reliable solutions. The high-precision finite element method for the free vibration of cracked beams needs to be developed to grasp and control error information in the conventional solutions and the non-uniform mesh generation near the cracks. Moreover, the influence of multi-crack damage on the natural frequency and vibration mode of a circularly curved beam needs to be detected.Design/methodology/approachA scheme for cross-sectional damage defects in a circularly curved beam was established to simulate the depth, location and the number of multiple cracks by implementing cross-section reduction induced by microcrack damage. In addition, the h-version finite element mesh adaptive analysis method of the Timoshenko beam was developed. The superconvergent solution of the vibration mode of the cracked curved beam was obtained using the superconvergent patch recovery displacement method to determine the finite element solution. The superconvergent solution of the frequency was obtained by computing the Rayleigh quotient. The superconvergent solution of the eigenfunction was used to estimate the error of the finite element solution in the energy norm. The mesh was then subdivided to generate an improved mesh based on the error. Accordingly, the final optimised meshes and high-precision solution of natural frequency and mode shape satisfying the preset error tolerance can be obtained. Lastly, the disturbance behaviour of multi-crack damage on the vibration mode of a circularly curved beam was also studied.FindingsNumerical results of the free vibration and damage disturbance of cracked curved beams with cracks were obtained. The influences of crack damage depth, crack damage number and crack damage distribution on the natural frequency and mode of vibration of a circularly curved beam were quantitatively analysed. Numerical examples indicate that the vibration mode and frequency of the beam would be disturbed in the region close to the crack damage, and a greater crack depth translates to a larger frequency change. For multi-crack beams, the number and distribution of cracks also affect the vibration mode and natural frequency. The adaptive method can use a relatively dense mesh near the crack to adapt to the change in the vibration mode near the crack, thus verifying the efficacy, accuracy and reliability of the method.Originality/valueThe proposed combination of methodologies provides an extremely robust approach for free vibration of beams with cracks. The non-uniform mesh refinement in the adaptive method can adapt to changes in the vibration mode caused by crack damage. Moreover, the proposed method can adaptively divide a relatively fine mesh at the crack, which is applied to investigating free vibration under various curved beam angles and crack damage distribution conditions. The proposed method can be extended to crack damage detection of 2D plate and shell structures and three-dimensional structures with cracks.

Hardware architecture design for complementary ensemble empirical mode decomposition algorithm

In this paper, we have presented a field-programmable gate array (FPGA) as well as an application-specific integrated circuit (ASIC) based design for the complementary ensemble empirical mode decomposition (CEEMD) algorithm. CEEMD is an adaptive signal analysis method that decomposes an input signal into many signals, each of which is termed an intrinsic mode function (IMF). In this work, the CEEMD architecture is developed in the form of Verilog-HDL code. The ASIC is designed by using a 180 nm technology library. Our proposed ASIC can be operated at a clock rate of 58.82 MHz, and the total power dissipation of the ASIC is 76.20 mW. The core area of the CEEMD ASIC is 1.53 mm2. The FPGA-based design of CEEMD can work with a clock rate of 50 MHz, and Virtex-7 FPGA is used for the implementation purpose. The proposed parallel architecture of CEEMD extensively reduces the computational time of the design by employing parallel processing. FPGA and the ASIC-based design of CEEMD have very less computation time and can be used for real-time applications.

Quality Risk Analysis for Sustainable Smart Water Supply

With the rapid growth of contemporary cities, building sustainable smart water supply systems is facing significant hurdles worldwide. Water quality has a significant impact on every aspect of our lives and is given priority in urban planning. Previous methods of regulating urban water quality have mostly relied on performing routine tests on several physical, chemical, and biological groupings of quality indicators. However, the inevitable lag time for biological indicators has raised the risk of illness, resulting in mishaps like widespread infections in many major cities. In this essay, we examine the issue, the technological difficulties, and the research questions. Next, we offer a potential remedy by creating a methodology for risk analysis specific to the urban water delivery system. To detect risks and detect changes in water quality, we need the indicator data we gathered from industrial activities. We provide an Adaptive Frequency Analysis (Adp- FA) method to resolve the data using indicators' frequency domain information for their internal linkages and individual prediction in order to produce results that are understandable. We also look into this method's scalability in the indicator, geographic, and time domains. We choose data sets of industrial quality for the application that was gathered as part of a Norwegian project from four separate urban water supply systems: Oslo, Bergen, Strmmen, and Lesund. Comparing the new method to the traditional Artificial Neural Network and Random Forest approach, we examine the proposed method's spectrogram, prediction accuracy, and time requirements. The outcomes demonstrate that our strategy performs better in most areas. Supporting early alerts for industrial water quality risks and additional decision support is possible. Key Words: Water Quality, Risk Analysis, Industrial Quality Data, Quality Indicators

Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network.

As living standards improve, people's demand for appreciation and learning of art is growing gradually. Unlike the traditional learning model, art teaching requires a specific understanding of learners' psychology and controlling what they have learned so that they can create new ideas. This article combines the current deep learning technology with heart rate to complete the action recognition of art dance teaching. The video data processing and recognition are conducted through the Openpose network and graph convolution network. The heart rate data recognition is completed through the Long Short-Term Memory (LSTM) network. The optimal recognition model is established through the data fusion of the two decision levels through the adaptive weight analysis method. The experimental results show that the accuracy of the classification fusion model is better than that of the single-mode recognition method, which is improved from 85.0% to 97.5%. The proposed method can evaluate the heart rate while ensuring high accuracy recognition. The proposed research can help analyze dance teaching and provide a new idea for future combined research on teaching interaction.

An Error-Pursuing Adaptive Uncertainty Analysis Method Based on Bayesian Support Vector Regression

The Bayesian support vector regression (BSVR) metamodel is widely used in various engineering fields to analyze the uncertainty arising from uncertain parameters. However, the accuracy of the BSVR metamodel based on the traditional one-shot sampling method fails to meet the requirements of the uncertainty analysis of complex systems. To this end, an error-pursing adaptive uncertainty analysis method based on the BSVR metamodel is presented by combining a new adaptive sampling scheme. This new sampling scheme was improved by a new error-pursuing active learning function that is named, herein, adjusted mean square error (AMSE), which guides the adaptive sampling of the BSVR metamodel’s design of experiments (DoE). During the sampling process, AMSE combines mean square error and leave-one-out cross-validation error to estimate the prediction error of the metamodel in the entire design space. Stepwise refinement of the metamodel was achieved by placing the sampled regions at locations with large prediction errors. Six benchmark analytical functions featuring different dimensions were used to validate the proposed method. The effectiveness of the method was then further illustrated by a more realistic application of an overhung rotor system.

Adaptive mesh refinement for finite element analysis of elastic buckling disturbance of circularly curved beams due to multiple micro-cracks damage

PurposeThis study aimed to overcome the challenging issues involved in providing high-precision eigensolutions. The accurate prediction of the buckling load bearing capacity under different crack damage locations, sizes and numbers, and analysing the influence mechanism of crack damage on buckling instability have become the needs of theoretical research and engineering practice. Accordingly, a finite element method was developed and applied to solve the elastic buckling load and buckling mode of curved beams with crack damage. However, the accuracy of the solution depends on the quality of mesh, and the solution inevitably introduces errors due to mesh. Therefore, the adaptive mesh refinement method can effectively optimise the mesh distribution and obtain high-precision solutions.Design/methodology/approachFor the elastic buckling of circular curved beams with cracks, the section damage defect analogy scheme of a circular arc curved beam crack was established to simulate the crack size (depth), position and number. The h-version finite element mesh adaptive analysis method of the variable section Euler–Bernoulli beam was introduced to solve the elastic buckling problem of circular arc curved beams with crack damage. The optimised mesh and high-precision buckling load and buckling mode solutions satisfying the preset error tolerance were obtained.FindingsThe results of testing typical examples show that (1) the established section damage defect analogy scheme of circular arc curved beam crack can effectively realise the simulation of crack size (depth), position and number. The solution strictly satisfies the preset error tolerance; (2) the non-uniform mesh refinement in the algorithm can be adapted to solve the arbitrary order frequencies and modes of cracked cylindrical shells under the conditions of different ring wave numbers, crack positions and crack depths; and (3) the change in the buckling mode caused by crack damage is applicable to the study of elastic buckling under various curved beam angles and crack damage distribution conditions.Originality/valueThis study can provide a novel strategy for the adaptive mesh refinement for finite element analysis of elastic buckling of circular arc curved beams with crack damage. The adaptive mesh refinement method established in this study is fundamentally different from the conventional finite element method which employs the user experience to densify the meshes near the crack. It can automatically and flexibly generate a set of optimised local meshes by iteratively dividing the fine mesh near the crack, which can ensure the high accuracy of the buckling loads and modes. The micro-crack in curved beams is also characterised by weakening the cross-sectional stiffness to realise the characterisation of locations, depths and distributions of multiple crack damage, which can effectively analyse the disturbance behaviour of different forms of micro-cracks on the dynamic behaviour of beams.

Multi-objective reliability optimization design of high-speed heavy-duty gears based on APCK-SORA model

For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects, but in the gear optimization design and little consideration of thermal transmission errors and thermal resonance and other factors, while the conventional multi-objective optimization design methods are difficult to achieve the optimum of each objective. Based on this, the paper proposes a gear multi-objective reliability optimisation design method based on the APCK-SORA model. The PC-Kriging model and the adaptive k-means clustering method are combined to construct an adaptive reliability analysis method (APCK for short), which is then integrated with the SORA optimisation algorithm. The objective function is the lightweight of gear pair, the maximum overlap degree and the maximum anti-glue strength; the basic parameters of the gear and the sensitivity parameters affecting the thermal deformation and thermal resonance of the gear are used as design variables; the amount of thermal deformation and thermal resonance, as well as the contact strength of the tooth face and the bending strength of the tooth root are used as constraints; the optimisation results show that: the mass of the gear is reduced by 0.13kg, the degree of overlap is increased by 0.016 and the coefficient of safety against galling Compared with other methods, the proposed method is more efficient than the other methods in meeting the multi-objective reliability design requirements of lightweighting, ensuring smoothness and anti-galling capability of high-speed heavy-duty gears.