The derivation and validation of the practical operating equation for electromagnetic flowmeters: case of having an electrolytic conductor flowing through

In this paper, the practical operating equation for the electromagnetic flowmeter is derived for the case of having a circular pipe with an electrolytic conductor flowing through. It is assumed further that the magnetic field is uniform and the velocity profile is axisymmetric (assuming nonconducting walls). The derivation was done before by many authors to whom the author refer to in this paper, but the approach provided here is comprehensive and simple. To add to that, the solution for the electromagnetic flowmeter's operating equation illustrated in this paper is new and is provided using very simple mathematical concepts, eliminating the complexity of solutions provided by other authors in the past. In the end, the practical operating equation derived was validated using a new approach based on the finite element analysis and the moving stream method to estimate the error resulting from using this operating equation with the assumptions of having a uniform magnetic field and an axisymmetric velocity profile, which are difficult to achieve in practice. This error can be used in a dry calibration to estimate the error caused by variable flow characteristics.     

Parametric Reconstruction of Glass Fiber-reinforced Polymer Composites from X-ray Projection Data—A Simulation Study

We present a new approach to estimate geometry parameters of glass fibers in glass fiber-reinforced polymers from simulated X-ray micro-computed tomography scans. Traditionally, these parameters are estimated using a multi-step procedure including image reconstruction, pre-processing, segmentation and analysis of features of interest. Each step in this chain introduces errors that propagate through the pipeline and impair the accuracy of the estimated parameters. In the approach presented in this paper, we reconstruct volumes from a low number of projection angles using an iterative reconstruction technique and then estimate position, direction and length of the contained fibers incorporating a priori knowledge about their shape, modeled as a geometric representation, which is then optimized. Using simulation experiments, we show that our method can estimate those representations even in presence of noisy data and only very few projection angles available.     

Application of least square collocation technique in finite element and finite strip formulation

A new approach in the formulation of finite elements using the concepts of least squares in conjunction with collocation is developed. No numerical integration is required in the stiffness formulation and the resulting matrix has the advantage of being always symmetrical. This approach has also been applied to the finite strip method and provides a means for rapid and accurate analysis of high order partial differential equations. The accuracy and versatility of the method are demonstrated by several examples.     

Theoretical prediction of welding distortion in large and complex structures

Welding technology is widely used to assemble large thin plate structures such as ships, automobiles, and passenger trains because of its high productivity. However, it is impossible to avoid welding-induced distortion during the assembly process. Welding distortion not only reduces the fabrication accuracy of a weldment, but also decreases the productivity due to correction work. If welding distortion can be predicted using a practical method beforehand, the prediction will be useful for taking appropriate measures to control the dimensional accuracy to an acceptable limit. In this study, a two-step computational approach, which is a combination of a thermoelastic-plastic finite element method (FEM) and an elastic finite element with consideration for large deformation, is developed to estimate welding distortion for large and complex welded structures. Welding distortions in several representative large complex structures, which are often used in shipbuilding, are simulated using the proposed method. By comparing the predictions and the measurements, the effectiveness of the two-step computational approach is verified.     

Theoretical prediction of welding distortion in large and complex structures

Welding technology is widely used to assemble large thin plate structures such as ships, automobiles, and passenger trains because of its high productivity. However, it is impossible to avoid welding-induced distortion during the assembly process. Welding distortion not only reduces the fabrication accuracy of a weldment, but also decreases the productivity due to correction work. If welding distortion can be predicted using a practical method beforehand, the prediction will be useful for taking appropriate measures to control the dimensional accuracy to an acceptable limit. In this study, a two-step computational approach, which is a combination of a thermo-elastic-plastic finite element method (FEM) and an elastic finite element with consideration for large deformation, is developed to estimate welding distortion for large and complex welded structures. Welding distortions in several representative large complex structures, which are often used in shipbuilding, are simulated using the proposed method. By comparing the predictions and the measurements, the effectiveness of the two-step computational approach is verified.     

A comparison of reliability estimation methods for binary systems

This paper uses a simulation-based approach to compare the predictive accuracy of five different methods for estimating the risk of failure for binary failure/no failure systems such as US strategic missiles, space launch vehicles, and security systems based on the results of a number of tests. This paper tests two Bayesian approaches, two classical (frequentist) approaches, and the method currently used the US Air Force Strategic Command (STRATCOM) to estimate the reliability of strategic nuclear missiles. First, test results are simulated based on an assumed underlying reliability profile. Then the system's reliability is estimated by each of the approaches using the simulated test results, and these estimates are compared with the assumed underlying reliability. Statistical procedures are used to compare the errors from the different methods. The results of this study show that the STRATCOM approach and a classical approach using only the test data from the current period are significantly less accurate than the other three methods and that the accuracy of the Bayesian methods depend on the prior density functions used. The results in this paper provide a quantitative assessment of the accuracy of the tested methods.     

A recursive application of the integral equation in the boundary element method

This paper presents a recursive application of the governing integral equation aimed at improving the accuracy of numerical results of the boundary element method (BEM). Usually, only the results at internal domain points when using BEM are found using this approach, since the nodal boundary values have already been calculated. Here, it is shown that the same idea can be used to obtain better accuracy for the boundary results as well. Instead of locating the new source points inside the domain, they are positioned on the boundary, with different coordinates to the nodal points. The procedure is certainly general, but will be presented using as an example the two dimensional Laplace equation, for the sake of simplicity to point out the main concepts and numerical aspects of the method proposed, especially due to the determination of directional derivatives of the primal variable, which is part in hyper-singular BEM theory.     

Error estimate of point selection in uncertainty quantification of nonlinear structures involving multiple nonuniformly distributed parameters

The error analysis for the selection of representative point sets (RPSs) in multidimensional random-variate space assigned with arbitrary nonuniform distribution with compact support for uncertainty quantification is developed by extending the Koksma-Hlawka inequalities, which bound the worst error with some kind of discrepancy of the RPS and the variation of the integrand. The novel concepts of the EF-discrepancy and the GF-discrepancy are introduced, and the connection and equivalence between them are inquired into. Based on such theoretical basis, the error estimate of selecting RPSs in the standardized space instead of that in the original physical space is studied, showing that the standardization of the input random variables does not increase, but usually reduce, the error bound by GF-discrepancy. The extended Koksma-Hlawka inequality also establishes the theoretical basis for the error estimate of the probability density evolution method. Besides, the closed-form expressions for the EF-discrepancy are given in the Appendix. A numerical example involving a complex nonlinear engineering structure modeled by the finite element method is studied, showing the accuracy of the proposed approach.     

Circular prediction regions for miss distance models under heteroskedasticity

Circular prediction regions are used in ballistic testing to express the uncertainty in shot accuracy. We compare two modeling approaches for estimating circular prediction regions for the miss distance of a ballistic projectile. The miss distance response variable is bivariate normal and has a mean and variance that can change with one or more experimental factors. The first approach fits a heteroskedastic linear model using restricted maximum likelihood, and uses the Kenward-Roger statistic to estimate circular prediction regions. The second approach fits an analogous Bayesian model with unrestricted likelihood modifications, and computes circular prediction regions by sampling from the posterior predictive distribution. The two approaches are applied to an example problem, and are compared using simulation.     

Simulation metamodelling with neural networks: An experimental investigation

Artificial neural networks are often proposed as an alternative approach for formalizing various quantitative and qualitative aspects of complex systems. This paper examines the robustness of using neural networks as a simulation metamodel to estimate manufacturing system performances. Simulation models of a job shop system are developed for various configurations to train neural network metamodels. Extensive computational tests are carried out with the proposed models at various factor levels (study horizon, system load, initial system status, stochasticity, system size and error assessment methods) to see the metamodel accuracy. The results indicate that simulation metamodels with neural networks can be effectively used to estimate the system performances.     

Heat Transfer in Composite Beams using Combined Cellular Automaton and Fibre Model

A simple cellular automaton (CA) scheme is proposed to simulate heat conduction in anisotropic domains. The CA is built on random nodes rather than an irregular grid. The local rule used in the CA is defined by physical concepts instead of differential equations. The accuracy of the proposed approach is verified by classical examples. As an application of the proposed method, the CA approach is incorporated into fibre model which is widely used in finite element analysis to calculate the temperature distribution on the cross-section of composite beams. Numerical examples demonstrate that the proposed scheme can be conveniently applied to finite element analysis.     

Using bilinear and quadratic forms for frequency estimation

This paper introduces an innovative approach for frequency estimation. The proposed algorithm can be used to estimate both small and off-nominal frequency deviations. The computational effort is dramatically reduced relative to conventional methods. In addition, the method provides high measurement accuracy and is relatively insensitive to harmonic distortion     

Higher-order mass defect analysis for mass spectra of complex organic mixtures

Higher-order mass defect analysis is introduced as a unique formula assignment and visualization method for the analysis of complex mass spectra. This approach is an extension of the concepts of Kendrick mass transformation widely used for identification of homologous compounds differing only by a number of base units (e.g., CH(2), H(2), O, CH(2)O, etc.) in complex mixtures. We present an iterative renormalization routine for defining higher-order homologous series and multidimensional clustering of mass spectral features. This approach greatly simplifies visualization of complex mass spectra and increases the number of chemical formulas that can be confidently assigned for given mass accuracy. The potential for using higher-order mass defects for data reduction and visualization is shown. Higher-order mass defect analysis is described and demonstrated through third-order analysis of a deisotoped high-resolution mass spectrum of crude oil containing nearly 13,000 peaks.     

Synthesis of feedback-based design concepts for sensors

We explore an approach to synthesize concepts of a class of sensors, where a quantity is sensed indirectly after nullifying its effect by using negative feedback. These sensors use negative feedback to increase the dynamic range of operation without compromising the sensitivity and resolution. The synthesis technique uses knowledge about existing phenomena to come up with an approach to synthesize concepts of sensors and also study their interactions with their surroundings, so as to generate robust designs. The approach uses a database of building blocks which are based on physical laws and effects that capture the transduction rules underlying the working principles of sensors. A simplified variant of the SAPPhIRE model of causality, which also uses physical laws and effects, has been adapted to represent the building blocks. SAPPhIRE model had been used earlier to understand analysis and synthesis of conceptual designs. We have adapted it here for automated generation of concepts. The novelty of the approach lies in the way and the ease with which it constructs a graph which is a super-set of the concept-space. The individual concepts are extracted out of the graph at a later point in time. The extraction of the concepts is done by using a modified breadth-first search algorithm which detects loops in the graph. The usage of breadth-first search algorithm for loop detection is novel, as we have demonstrated that it performs better than depth-first search algorithm for the specific problem. The technique has been implemented as a web-based application. For the sensor problems attempted, a number of existing patents were found that were based on the concepts that were generated by the synthesis algorithm, thus emphasizing the usefulness of the designs produced. The tool generated 35 concepts for accelerometers, out of which 2 concepts were found in patents. The synthesis approach also proposed new, feasible sensor concepts, thereby indicating its potential as a stimulator for enhancing creativity of designers. Automated generation of feedback-based sensor designs is a novel outcome of this approach.     

Mean of Microaccelerations Estimate in the Small Spacecraft Internal Environment with the Use of Fuzzy Sets

Assessment of parameters of rotary motion of the small spacecraft around its center of mass and of microaccelerations using measurements of current from silicon photocells is carried out. At the same time there is a problem of interpretation of ambiguous telemetric data. Current from two opposite sides of the small spacecraft is significant. The mean of removal of such uncertainty is considered. It is based on an fuzzy set. As membership function it is offered to use a normality condition of the direction cosines. The example of uncertainty removal for a prototype of the Aist small spacecraft is given. The offered approach can significantly increase the accuracy of microaccelerations estimate when using measurements of current from silicon photocells.     

Sizing of multiple cracks using magnetic flux leakage measurements

This study presents an approach to estimate the characteristics of multiple narrow-opening cracks from magnetic flux leakage (MFL) signals. The number, locations, orientations and lengths of the cracks are the objective of the inversion process. The proposed procedure provides a reliable estimation of crack parameters in two separate consecutive steps. In the first step, the Canny edge detection algorithm is used to estimate the number, locations, orientations and lengths of the cracks. Then, an inversion procedure based on space mapping is used in order to estimate the crack depths efficiently. The accuracy of the proposed algorithm is examined via simulations based on the finite element method as well as real experimental MFL data.     

An empirical approach to obtaining bounds on the failure probability through stress/strength interference

This paper proposes a new empirical approach to computing the unreliability bounds based upon the subinterval probabilities of the stress and strength in the interference region. It is demonstrated by the numerical examples that in most cases the unreliability bounds so obtained not only will include the exact value but also are narrower than the existing ones. The average of the lower and the upper bound is shown to serve as a good point estimate of the unreliability. It is also shown that the approach is practical since the number of subintervals required for an acceptable accuracy is small. In some cases a rather acceptable accuracy is achieved when the number of intervals is around ten.     

Quantile POD for nondestructive evaluation with hit--miss data

Probability of detection (POD) is commonly used to measure a nondestructive evaluation (NDE) inspection procedure’s performance. Due to inherent variability in the inspection procedure caused by variability in factors such as crack morphology and operators, it is important, for some purposes, to model POD as a random function. Traditionally, inspection variabilities are pooled and an estimate of the mean POD (averaged over all sources of variability) is reported. In some applications it is important to know how poor typical inspections might be, and this question is answered by estimating a quantile of the POD distribution. This article shows how to fit and compare different models to repeated-measures hit--miss data with multiple inspections with different operators for each crack and shows how to estimate the mean POD as well as quantiles of the POD distribution for binary (hit--miss) NDE data. We also show how to compute credible intervals (quantifying uncertainty due to limited data) for these quantities using a Bayesian estimation approach. We use NDE for the detection of fatigue cracks as the motivating example, but the concepts apply more generally to other NDE applications areas.     

Spectral reflectance estimation from camera responses by support vector regression and a composite model

Regression methods are widely used to estimate the spectral reflectance of object surfaces from camera responses. These methods are under the same problem setting as that to build an estimation function for each sampled wavelength separately, which means that the accuracy of the spectral estimation will be reduced when the training set is small. To improve the spectral estimation accuracy, we propose a novel estimating approach based on the support vector regression method. The proposed approach utilizes a composite modeling scheme, which formulates the RGB values and the sampled wavelength together as the input term to make the most use of the information from the training samples. Experimental results show that the proposed method can improve the recovery accuracy when the training set is small.     

Attribute Selection Based on Rough Set Theory for Electromagnetic Interference (EMI) Fault Diagnosis

Electromagnetic emissions are radiated from every part of a personal computer motherboard, thus producing electromagnetic interference (EMI). EMI has an adverse effect on the surrounding environment because EMI could cause malfunctions or fatal problems in other digital devices. EMI engineers diagnose motherboard EMI problems using the electromagnetic noise data measured by the spectrum analyzer. Finding the sources (e.g., PS2, USB, VGA) of electromagnetic noise is a time-consuming process. The attribute selection and fault diagnosis was developed based on the advantage of rough set theory (RST). RST is a novel data mining approach for dealing with vagueness and uncertainty. It can be used to find hidden patterns in data sets. In this study, the basic rough set theory concepts are introduced. The rough set approach enables one to discover the minimal subsets of condition attributes associated with the motherboard EMI fault diagnosis problem. The operating sequence includes data collection, data preprocessing, discretization, attribute reduction, reduction filtering, rule generation, and classification accuracy. Historical EMI noise data, colleted from a famous motherboard company in Taiwan, were used to generate diagnostic rules. Our research result (average diagnostic accuracy of 80% above) shows that the RST model is a promising approach for EMI diagnostic support systems.