Multipoint Method Research Articles

Effective strategy of non-axisymmetric endwall contouring in a linear compressor cascade

The corner separation and the related secondary flow have great impact on the compressor performance, and non-axisymmetric endwall contouring is proved effective in improving compressor efficiency. The aim of the study is to improve the compressor performance by two local endwall contouring strategies at the design and off-design conditions. The endwall is parameterized and the Bezier curve is used to loft the endwall surface. The design of the contoured endwall is based on a multi-point optimization method to minimize the aerodynamic pressure loss. In order to identify the influence of the contoured endwall, a detailed flow analysis is conducted on four effective contoured endwall designs. The selected endwall geometries exhibit great control ability on the corner separation and significantly reduce the pressure loss at the two operating conditions. The directional concave near the leading edge can induce strong streamwise pressure gradient and accelerate the endwall flow, greatly reducing the cross-passage pressure gradient. The convex structures near the concave edge and at the outlet can block the cross-flow and prevent the interaction between the cross-flow and the suction corner flow. The benefit of the contoured endwall is mainly due to the re-distributed endwall static pressure and blocking of the cross-flow movement. In terms of the control effect, the shape of the concave also matters and better control effect is observed on the deep and wide concave. The flow will be guided by the concave, and the best suppression on corner separation is observed on the concave which follows the suction side. The results also indicate that the relief of the hub corner separation slightly increases the shroud pressure loss.

AQI multi-point spatiotemporal prediction based on K-mean clustering and RNN-LSTM model

The short term air quality index can usually be predicted by statistical and numerical methods, but for the multi-point prediction of AQI, the traditional methods are often inaccurate. In this paper, a new hybrid multi-point prediction method was proposed by combining K-means clustering with the circulating neural network long and short time memory (RNN-LSTM) model. Based on this prediction method, the air quality index in Dezhou was predicted 1-5 days in advance by using 28 multi-point pollution monitoring sensor data from January 1, 2018 solstice to August 31. The prediction results show that the model not only improves the accuracy and effectiveness of the prediction, but also reveals the relationship between land use patterns and air quality index (AQI), which provides important information for land use planning, air pollution mitigation and urban intelligent governance.

A two-zone sound field reproduction based on the region energy control

Multizone sound field reproduction aims to create different acoustical environments in regions without physical isolation. For a real reproduction system, it is always expected to improve system performance and reduce measurement effort. In this paper, a two-zone sound field reproduction is investigated with a proposed region control method. Conventional multipoint method only controls sound field at limited number of measurement points. However, the proposed method tries to control the sound field energy over the whole region. Considering the system's diverse work environment, different interpolation methods are applied in the proposed method. Simulations are conducted under free field and reverberation condition in order to deeply compare with conventional method and another harmonic domain method. Simulation results show that the proposed method achieves better performance than the conventional multipoint method in free field and reverberant environment. On the other hand, the region control method proposed in this paper is free from microphone array geometry requirement, which means the method is more convenient for the practical application.

Hemodynamic factor evaluation using computational fluid dynamics analysis for de novo bleb formation in unruptured intracranial aneurysms

BackgroundAlthough bleb formation increases the risk of rupture of intracranial aneurysms, previous computational fluid dynamic (CFD) studies have been unable to identify robust causative hemodynamic factors, due to the morphological differences of prebleb aneurysm models and a small number of aneurysms with de novo bleb formation. This study investigated the influences of differences in the aneurysm-models and identify causative hemodynamic factors for de novo bleb formation.Materials and methodsCFD analysis was conducted on three aneurysm models, actual prebleb, postbleb, and virtual prebleb models of two unruptured aneurysms with de novo bleb formation. A new multipoint method was introduced in this study. We evenly distributed points with a 0.5-mm distance on the aneurysm surface of the actual prebleb models (146 and 152 points in the individual aneurysm, respectively), and we statistically compared hemodynamics at the points in the areas with and without bleb formation (19 and 279 points, respectively).ResultsVisually, blebs formed on an aneurysm surface area with similar hemodynamic characteristics in the actual and virtual prebleb models. Statistical analysis using the multipoint method revealed that the de novo bleb formation area was significantly correlated with high pressure (p < 0.001), low wall shear stress (WSS) (p < 0.001), and the center of divergent WSS vectors (p = 0.025).ConclusionsDe novo bleb formation in intracranial aneurysms may occur in areas associated with the combination of high pressure, low WSS, and the center of divergent WSS vectors. The multipoint method is useful for statistical analysis of hemodynamics in a limited number of aneurysms.

Training Image Optimization Method Based on Convolutional Neural Network and Its Application in Discrete Fracture Network Model Selection

Abstract Training image (TI) is important for multipoint statistics simulation method (MPS), since it captures the spatial geological pattern of target reservoir to be modeled. Generally, one optimal TI is selected before applying MPS by evaluating the similarities between many TIs and the well interpretations of target reservoir. In this paper, we propose a new training image optimization approach based on the convolutional neural network (CNN). First, candidate TIs were randomly sampled several times to obtain the sample dataset. Then, the CNN was used to conduct transfer learning for all samples, and finally, the optimal TI of the conditioning well data is selected through the trained CNN model. By taking advantage of the strong learning ability of CNN in image feature recognition, the proposed method can automatically identify differences in spatial features between the conditioning well data and the samples of the training image. Hence, it effectively resolves the difficulty of spatial matching between discrete datapoints and grid structures. We demonstrated the applicability of our model via 2D and 3D training image selection examples. The proposed methods effectively selected the appropriate TI, and then the pretreatment techniques for improving the accuracy of continuous TI selection were achieved. Moreover, the proposed method was successfully applied to training image selection of a discrete fracture network model. Finally, sensitivity analysis was carried out to show that sufficient conditioning data volume can reduce the uncertainty of the optimization results. By comparing with the improved MDevD method, the advantages of the new method are verified in terms of efficiency and reliability.

On the Convergence of a New Family of Multi-Point Ehrlich-Type Iterative Methods for Polynomial Zeros

In this paper, we construct and study a new family of multi-point Ehrlich-type iterative methods for approximating all the zeros of a uni-variate polynomial simultaneously. The first member of this family is the two-point Ehrlich-type iterative method introduced and studied by Trićković and Petković in 1999. The main purpose of the paper is to provide local and semilocal convergence analysis of the multi-point Ehrlich-type methods. Our local convergence theorem is obtained by an approach that was introduced by the authors in 2020. Two numerical examples are presented to show the applicability of our semilocal convergence theorem.

Research of Multi-Point Temperature Control Method in the Heating System of 3D Glass Hot Bending Machine

3D glass hot bending machine is widely used to produce 3D glass. The heating system is its one of key components. The uniformity of surface temperature distribution of the heating plate in the heating system decides the quality of 3D glass products. At present, the heating system of 3D glass hot bending machine mainly uses single-point temperature control method to adjust the system temperature. However, the single-point temperature control method cannot guarantee the uniformity of surface temperature distribution of the heating plate. In this research, multi-point temperature control method was proposed and the simulation model was established by ABAQUS. The control effect of single-point control method and multi-point temperature control method were compared though simulations and experiments. The results show that the multi-point temperature control method can reduce the surface temperature difference of the heating plate and achieve temperature uniformity so as to improve the quality of 3D glass products.

Multi-Point Substructure Coupling Method to Compensate Multi-Accelerometer Masses in Measuring Rotation-Related Frequency Response Functions

AbstractTool-tip frequency response functions (FRFs) are often required in milling vibration analysis. Receptance coupling substructures analysis (RCSA) affords an efficient analytical way for different tool-tip FRFs prediction with only one modal test. The coupling theory includes both translational and rotational degrees-of-freedom, so rotation-related FRFs are essential to know in the test. The finite-differential technique is generally used to measure these special FRFs due to the avoidance of specialist equipment. The technique uses several translational accelerometers spatially placed close to each other to approximate the rotational vibration. However, the added sensor masses lead to a frequency shift of the test structure, and the phenomenon would aggravate as the sensors increase. The polluted measurement data would subsequently decrease the tool-tip FRFs prediction accuracy. Addressing this problem, this paper introduces a multi-point substructure coupling method to simultaneously compensate the multi-accelerometer masses in a single experimental setup. The proposed method considers the installed accelerometers as multiple point masses and then uses inverse coupling calculation to isolate their effect. The compensation procedure is first effectively validated in simulation and experiment, and then, it is integrated into an RCSA-based application of predicting different tool-tip dynamics. Experimental results show that the compensated FRF data can improve prediction accuracy, especially when predicting tools shorter than the tested tool.

High-Order Data-Driven Spatial Simulation of Categorical Variables

Modern approaches for the spatial simulation of categorical variables are largely based on multi-point statistical methods, where a training image is used to derive complex spatial relationships using relevant patterns. In these approaches, simulated realizations are driven by the training image utilized, while the spatial statistics of the actual sample data are ignored. This paper presents a data-driven, high-order simulation approach based on the approximation of high-order spatial indicator moments. The high-order spatial statistics are expressed as functions of spatial distances that are similar to variogram models for two-point methods, while higher-order statistics are connected with lower-orders via boundary conditions. Using an advanced recursive B-spline approximation algorithm, the high-order statistics are reconstructed from the available data and are subsequently used for the construction of conditional distributions using Bayes’ rule. Random values are subsequently simulated for all unsampled grid nodes. The main advantages of the proposed technique are its ability to (a) simulate without a training image to reproduce the high-order statistics of the data, and (b) adapt the model’s complexity to the information available in the data. The practical intricacies and effectiveness of the proposed approach are demonstrated through applications at two copper deposits.

Strain gauge experimental study on mode I rock fracture characteristics under impact loading

Based on the analysis of the strain field of the dynamic crack tip under impact loading, a specific acute (obtuse) angle multi-point strain gauge method is proposed to study the mode I rock crack dynamic fracture. Compared with the dynamic caustics method, the feasibility of the strain gauge method for calculating the crack propagation velocity and dynamic stress intensity factor (SIF) is verified. The rock specimen mode I crack dynamic fracture behaviour under impact loading was studied by the strain gauge method and assisted by high-speed photography and scanning electron microscopy. The results showed that the mode I crack propagation behaviour calculated by two strain gauge methods under impact loading was close to the caustics experimental results. Compared with the dynamic caustics method, the multi-point strain gauge method could roughly record the crack propagation behaviour of rock under impact loading, which provides an experimental basis for studying non-transparent impact fracture. The rock section mainly experienced tensile stress failure under impact loading, manifested as mode I cracking, and the granite specimen fracture damage occurred in white spot micro-crack propagation. Under impact loading, cracks in both polymethyl methacrylate (PMMA) and granite specimens propagated stably in the middle stage of propagation, the average granite specimen crack propagation velocity was 647.9 m/s, and the average SIF value of was 5.37 MN/m3/2, which were higher than the PMMA specimen experimental results.

Micromechanical modeling of nacre-mimetic Ti3C2-MXene nanocomposites with viscoelastic polymer matrix

A new two-dimensional nanomaterial—Titanium Carbide MXene (Ti3C2-MXene)—was reported in 2011. In this work, the microscale models of Ti3C2-MXene nanomaterial are considered with polymer matrix. The nanocomposites are modeled using nacre-mimetic brick-and-mortar assembly configurations due to enhanced mechanical properties and interlocking mechanism between the Ti3C2-MXene (brick) and polymer matrices (mortar). The polymer matrix material (Epoxy-resin) is modeled with elastic and viscoelastic behavior (Kelvin–Voigt Model). The Finite Element Method is used for numerical analysis of the microscale models with the multi-point constraint method to include Ti3C2-MXene fillers in the polymer matrix. Ti3C2-MXenes are considered as thick plate elements with transverse shear effects. The response of elastic and viscoelastic models of polymer matrix are studied. Finally, a tensile and compressive load is applied at the microscale and the effective load transfer due to nacre-mimetic configuration is discussed. This paper provides nacre-mimetic models to pre-design the nanocomposite for optimal performance with damage resistance and enhanced strength.

SCMOS real-time nonuniformity correction based on adaptive multipoint method

In order to improve the nonuniformity caused by the process bias of sCMOS readout circuit, an adaptive multipoint nonuniformity correction method is presented. The algorithm first determines the location of the optimal segment point and the optimal number of segments by searching for the minimum norm point and threshold comparison, then corrects two points in each interval segment according to the segment information. This adaptive method can effectively improve the correction performance of traditional multipoint methods, which is caused by improper selection of segment parameters. At the same time, in order to achieve real-time non-uniformity correction, a matching embedded data series correction scheme is proposed based on the algorithm characteristics of adaptive multipoint method, which can achieve non-uniformity correction without affecting the existing camera acquisition structure and acquisition rate.

Motor unit number estimation versus compound muscle action potential in the evaluation of motor unit loss in amyotrophic lateral sclerosis

IntroductionMotor neuron loss is the primary pathologic feature of amyotrophic lateral sclerosis (ALS). An estimate of the number of surviving motor units (MUs) represents a direct measure of the disease state in ALS. The objective of the study is to compare MU number estimation (MUNE) using the multipoint stimulation method (MPS) and compound muscle action potentials (CMAP) amplitude in patients with ALS.MethodsTwenty-eight patients with ALS with a disease duration of 3–48 months were studied. Nerve conduction study of the median, ulnar, tibial, common peroneal, and sural nerves were done. Besides, electromyography (EMG) of cranio-bulbar, cervical, thoracic and lumbosacral muscles, and MPS-MUNE.ResultsMUNE is decreased in patients with ALS. MUNE was positively correlated with CMAP amplitude, medical research council (MRC) scale, and ALS functional rating scale (ALS-FRS). On the contrary, MUNE was negatively correlated with MUAP duration. Case detection by the MUNE methods was high as compared to that of CMAP amplitude.ConclusionsMUNE is highly specific and more sensitive than CMAP amplitude in detecting the neurophysiologic abnormalities in patients with ALS. Case detection by MUNE is three times more than CMAP amplitudes. The rate of decline of motor units using the MPS-MUNE is more sensitive than the MRC score and ALSFRS-R when expressed as the percentage change from baseline.

Computationally Efficient Approximations Using Adaptive Weighting Coefficients for Solving Structural Optimization Problems

With rapid development of advanced manufacturing technologies and high demands for innovative lightweight constructions to mitigate the environmental and economic impacts, design optimization has attracted increasing attention in many engineering subjects, such as civil, structural, aerospace, automotive, and energy engineering. For nonconvex nonlinear constrained optimization problems with continuous variables, evaluations of the fitness and constraint functions by means of finite element simulations can be extremely expensive. To address this problem by algorithms with sufficient accuracy as well as less computational cost, an extended multipoint approximation method (EMAM) and an adaptive weighting-coefficient strategy are proposed to efficiently seek the optimum by the integration of metamodels with sequential quadratic programming (SQP). The developed EMAM stems from the principle of the polynomial approximation and assimilates the advantages of Taylor’s expansion for improving the suboptimal continuous solution. Results demonstrate the superiority of the proposed EMAM over other evolutionary algorithms (e.g., particle swarm optimization technique, firefly algorithm, genetic algorithm, metaheuristic methods, and other metamodeling techniques) in terms of the computational efficiency and accuracy by four well-established engineering problems. The developed EMAM reduces the number of simulations during the design phase and provides wealth of information for designers to effectively tailor the parameters for optimal solutions with computational efficiency in the simulation-based engineering optimization problems.

A fast edge-based two-stage direct sampling method

Direct sampling is an efficient information theory-based multipoint simulation method. To reduce the computational cost, many approaches have been proposed to speed up this simulation method. In this paper, an edge-based two-stage strategy is proposed to achieve speed-up. The proposed method first performs a simulation on a coarse grid and then detects edge cells in the simulation grid. Next, only edge cells are simulated, and the remaining cells are assigned the average value of the neighbouring simulated and hard data cells. The proposed method needs only one easily-interpreted parameter and can be combined with other existing acceleration methods for direct sampling, such as parallelization. Comparison experiments are performed on categorical, continuous and multivariate variables, including two-dimensional and three dimensional examples. The experimental results show that the proposed method can reduce the simulation time by almost half with minimal loss in terms of pattern reproduction.

Adaptive Multi-Level Search for Global Optimization: An Integrated Swarm Intelligence-Metamodelling Technique

Over the last decade, metaheuristic algorithms have emerged as a powerful paradigm for global optimization of multimodal functions formulated by nonlinear problems arising from various engineering subjects. However, numerical analyses of many complex engineering design problems may be performed using finite element method (FEM) or computational fluid dynamics (CFD), by which function evaluations of population-based algorithms are repetitively computed to seek a global optimum. It is noted that these simulations become computationally prohibitive for design optimization of complex structures. To efficiently and effectively address this class of problems, an adaptively integrated swarm intelligence-metamodelling (ASIM) technique enabling multi-level search and model management for the optimal solution is proposed in this paper. The developed technique comprises two steps: in the first step, a global-level exploration for near optimal solution is performed by adaptive swarm-intelligence algorithm, and in the second step, a local-level exploitation for the fine optimal solution is studied on adaptive metamodels, which are constructed by the multipoint approximation method (MAM). To demonstrate the superiority of the proposed technique over other methods, such as conventional MAM, particle swarm optimization, hybrid cuckoo search, and water cycle algorithm in terms of computational expense associated with solving complex optimization problems, one benchmark mathematical example and two real-world complex design problems are examined. In particular, the key factors responsible for the balance between exploration and exploitation are discussed as well.

Establishment and validation of the quantitative analysis of multi-components by single marker for the quality control of Qishen Yiqi dripping pills by high-performance liquid chromatography with charged aerosol detection.

Charged aerosol detection (CAD) has the merits of high sensitivity, high universality and response uniformity. The strategy that combines the quantitative analysis of multi-components by single marker (QAMS) with CAD has certain advantages for the quantification of multi-components. However, relevant research was limited. To comprehensively investigate the crucial factors that affect the performance of the HPLC-CAD-QAMS approach and validate the credibility and feasibility of the method. Multiple components of Qishen Yiqi dripping pills (QSYQ) were assayed using the high-performance liquid chromatography (HPLC)-CAD-QAMS approach. Some factors that affect the sensitivity and accuracy of the approach were sufficiently studied. After the method verification, principal component analysis (PCA) was applied to evaluate the quality consistency of three types of samples: normal samples, expired samples and negative samples. A HPLC-CAD-QAMS method was successfully developed for the multi-component determination of QSYQ. First, chromatographic conditions were optimised by a definitive screening design, and the optimised ranges of operating parameters were obtained with a Monte Carlo simulation method. Next, a new method to select the internal reference standards was successfully introduced based on the heatmap of Pearson correlation coefficients of the response factors. Then, the multi-point method was selected to calculate the relative correction factors, and a robustness test was conducted with Plackett-Burman design. Finally, the PCA was proved to be effective for the quality consistency evaluation of different samples. The developed HPLC-CAD-QAMS method can be a reliable and superior means for the multi-component quantitative analysis of QSYQ.

Robust design optimization under dependent random variables by a generalized polynomial chaos expansion

New computational methods are proposed for robust design optimization (RDO) of complex engineering systems subject to input random variables with arbitrary, dependent probability distributions. The methods are built on a generalized polynomial chaos expansion (GPCE) for determining the second-moment statistics of a general output function of dependent input random variables, an innovative coupling between GPCE and score functions for calculating the second-moment sensitivities with respect to the design variables, and a standard gradient-based optimization algorithm, establishing direct GPCE, single-step GPCE, and multi-point single-step GPCE design processes. New analytical formulae are unveiled for design sensitivity analysis that is synchronously performed with statistical moment analysis. Numerical results confirm that the proposed methods yield not only accurate but also computationally efficient optimal solutions of several mathematical and simple RDO problems. Finally, the success of conducting stochastic shape optimization of a steering knuckle demonstrates the power of the multi-point single-step GPCE method in solving industrial-scale engineering problems.

Generalization of the Multipoint meshless FDM application to the nonlinear analysis

The paper focuses on the new Multipoint meshless finite difference method, following the original Collatz higher order multipoint concept and the essential ideas of the Meshless FDM. The method was formulated, developed, and tested for various boundary value problems. Generalization of the multipoint method application to nonlinear analysis is the purpose of this research.The first attempt of the multipoint technique application to the geometrically nonlinear problems was successfully done recently. The case of physically nonlinear problem is considered in this paper. Several benefits of the proposed approach are highlighted, numerical algorithm and selected results are presented, and application of the multipoint method to nonlinear analysis is summarized.

Development of multipoint gas-liquid phase detection method by using a film-based optical waveguide

Development of multipoint gas-liquid phase detection method by using a film-based optical waveguide