 Subjects -> STATISTICS (Total: 130 journals)
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- Morphodynamic shallow layer equations featuring bed load and suspended
sediment with lattice Boltzmann method-
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Abstract: Different coupled systems for the shallow water equation, bed elevation, and suspended load equation are proposed until this day. The main differences come from the physical viewpoints, which caused some distinctions in the models. Recently, a coupled shallow water system of equations over an erodible bed has been proposed, in which the water layer, bed morphodynamics, and suspended sediments are interacting with each other. This system possesses a term in the mass conservation equation that couples the water depth and the bed level in the equilibrium distribution function required by lattice Boltzmann method (LBM). In this paper, the main goal is to utilize an advanced LBM to solve this system of equations. Besides solving the bed morphological equation by LBM, another simple and explicit scheme (like LBM) is proposed to investigate the ability of LBM. As the second goal, a practical approach is developed for applying so-called open boundary condition that relaxes the solution onto a prescribed equilibrium flow.
PubDate: 2023-06-07
- CFD-FEA based model to predict leak-points in a 90-degree pipe elbow
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Abstract: The aim of this paper is to numerically investigate Vibration-Based Leak Detection (VBLD) method in pipeline systems based on Fluid–Structure Interaction (FSI) analysis to predict leakages. In previous investigations, laboratory tests were widely used to study the VBLD technique in small-diameter water loop system pipes. The current project uses Ansys Workbench to extend these findings by integrating Computational Fluid Dynamics (CFD) with Finite Element Analysis (FEA). The study outlines a numerical method for VBLD to identify leakages in a 90-degree pipe elbow by predicting variations in vibration signals, with applications in the oil and gas industry. Firstly, changes in fluid behaviour (centrifugal force, pressure drop, secondary flow, and frictional force) experienced in the internal pipe wall resulting from a probable leakage (modelled as an additional outlet) are determined using CFD. Subsequently, the CFD results are coupled with FEA to model structural responses of the pipe walls subjected to different forces. This in turn allows the variations in vibration signals to be measured. The numerical approach presented in this paper based on FSI and incorporating the VBLD method provides a practical and convenient early detection tool that can complement physical vibration monitoring equipment in the field.
PubDate: 2023-06-03
- Damage identification of structural systems by modal strain energy and an
optimization-based iterative regularization method-
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Abstract: Sensitivity-based methods using modal data are effective and reliable tools for damage localization and quantification. However, those may fail in obtaining reasonable and accurate results due to low damage detectability of sensitivity functions and the ill-posedness problem caused by noisy modal data. To address these major challenges, this article proposes a new method for locating and quantifying damage by developing a new sensitivity function of modal strain energy and solving an ill-posed inverse problem via an optimization-based iterative regularization method called Iteratively Reweighted Norm-Basis Pursuit Denoising (IRN-BPD). A stopping condition based on the residual of the solution and an improved generalized cross-validation function are proposed to terminate the iterative algorithm of IRN-BPD and determine an optimal regularization value. The major contributions of this article include getting an idea from the first-order necessary condition of the optimization problem for deriving a sensitivity formulation and proposing a new regularized solution. The great advantages of these methods are increasing damage detectability, determining an optimal regularization value, and obtaining an accurate solution. A simple mass–spring system and a full-scale bridge structure are considered to verify the accuracy and effectiveness of the proposed methods in numerical studies. Results demonstrate that the methods presented in this article succeed in locating and quantifying damage under incomplete noisy modal data.
PubDate: 2023-06-01
- FPGA-orthopoly: a hardware implementation of orthogonal polynomials
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Abstract: There are many algorithms based on orthogonal functions that can be applied to real-world problems. For example, many of them can be reduced to approximate the solution of a dynamical system, and the approximation can be done with orthogonal functions. But calculating the orthogonal functions is very time-consuming, there are many difficulties in implementation of them and because of these drawbacks, they are not utilized in real applications. For the purpose of solving this issue and filling the gap between the theory and real applications, in this paper, an FPGA implementation of some classical orthogonal polynomials families is presented. Here, hardware architectures of the first and second kinds of Chebyshev, Jacobi, Legendre, Gegenbauer, Laguerre, and Hermit polynomials are presented. The experiments show that the presented architectures are low power, fast, and with a small circuit area. The obtained results show a 10.5 \(\times\) speed-up in the best case, 1.5 \(\times\) speed-up in the worst case, and at least 47% reduction in power consumption in comparison with the state-of-the-art hardware implementations. All implementations and codes are available at https://github.com/sampp098/forthopoly.
PubDate: 2023-06-01
- Inverse design optimization framework via a two-step deep learning
approach: application to a wind turbine airfoil-
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Abstract: The inverse approach is computationally efficient in aerodynamic design as the desired target performance distribution is prespecified. However, it has some significant limitations that prevent it from achieving full efficiency. First, the iterative procedure should be repeated whenever the specified target distribution changes. Target distribution optimization can be performed to clarify the ambiguity in specifying this distribution, but several additional problems arise in this process such as loss of the representation capacity due to parameterization of the distribution, excessive constraints for a realistic distribution, inaccuracy of quantities of interest due to theoretical/empirical predictions, and the impossibility of explicitly imposing geometric constraints. To deal with these issues, a novel inverse design optimization framework with a two-step deep learning approach is proposed. A variational autoencoder and multi-layer perceptron are used to generate a realistic target distribution and predict the quantities of interest and shape parameters from the generated distribution, respectively. Then, target distribution optimization is performed as the inverse design optimization. The proposed framework applies active learning and transfer learning techniques to improve accuracy and efficiency. Finally, the framework is validated through aerodynamic shape optimizations of the wind turbine airfoil. Their results show that this framework is accurate, efficient, and flexible to be applied to other inverse design engineering applications.
PubDate: 2023-06-01
- The novel learning solutions to nonlinear differential models on a
semi-infinite domain-
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Abstract: The aim of this paper is to introduce a new numerical approach named least-squares support vector machines based on generalized Laguerre functions collocation quasilinearization method (LS-SVM-GLQ). LS-SVM-GLQ combines collocation Quasilinearization methods based on generalized Laguerre functions and least-squares support vector machines to solve nonlinear differential equations (NDEs) on a semi-infinite domain. Applying LS-SVM-GLQ leads to solve a system of nonlinear/linear equations instead of solving the NDEs. The different types of Lane–Emden, Emden–Fowler and White-dwarf equations are investigated. Comparing numerical results with other numerical techniques declares that the present approach is more accurate and efficient.
PubDate: 2023-06-01
- A novel design of a sixth-order nonlinear modeling for solving engineering
phenomena based on neuro intelligence algorithm-
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Abstract: The current study aims to present a novel design of a sixth-order (SO) nonlinear Emden–Fowler nonlinear system (SO-NSEFM) along with its five types. The novel design of SO-NSEFM is achieved using the typical second-order Emden–Fowler system. The detail of the singularity and shape factors is presented for each type of the SO-NSEFM. Three different examples of each type of the designed SO-NSEFM will be solved using the supervised neural network (SNN) Levenberg–Marquardt backpropagation approach (LMBA), i.e., SNN–LMBA. A reference dataset using the spectral collocation scheme with the proposed SNN–LMBA will be established for the designed SO-NSEFM. The achieved approximate outcomes of the designed SO-NSEFM are accessible using the procedures of testing, verification, and training of the proposed neural networks to reduce the MSE. For the efficiency, correctness, and effectiveness of the proposed SNN-LMBA, the investigations are presented through the proportional performances of regression, MSE results, correlation and error histograms (EHs), and regression.
PubDate: 2023-06-01
- A novel conjoined space–time formulation for explicit analyses of
dynamic models-
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Abstract: In this paper, a novel explicit time-marching procedure is proposed, which adapts to the properties of the spatially discretized model. In this context, the time integrators of the method are locally defined, following the physical/geometrical features of the elements of the adopted spatial discretization, in a way that reduced dissipative and dispersive errors are provided, as well as extended stability limits are enabled. As it is discussed along this manuscript, the proposed novel conjoined space–time solution procedure is very simple to implement and to apply and it allows enhanced performances, providing better accuracy and more efficient analyses than standard time integration techniques. At the end of the paper, numerical results are presented, illustrating the versatility and effectiveness of the proposed new methodology.
PubDate: 2023-06-01
- A novel RBF-based meshless method for solving time-fractional transport
equations in 2D and 3D arbitrary domains-
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Abstract: In this paper, we develop a new meshless method for solving a wide class of time-fractional partial differential equations with general space operators in 2D and 3D regular and irregular domains. These equations are usually used to model transport processes in anisotropic media with sub-diffusive phenomena. In this method, the spatial approximation is given in the form of the truncated series over a set of linearly independent functions. Then the system is solved by the use of an efficient backward substitution method which is based on the collocation procedure using modified basis functions. The main aim of the research is to show the accuracy and efficiency of the proposed algorithm over some of the existing methods. The numerical results of ten examples on 2D and 3D domains demonstrate the advantages of the presented approach.
PubDate: 2023-06-01
- A framework of structural damage detection for civil structures using a
combined multi-scale convolutional neural network and echo state network-
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Abstract: Structural health monitoring (SHM) has become a notable method to ensure structural safety, yet the ability of existing damage detection techniques need improvements on extracting structural information from SHM data. Echo state networks (ESN) and multi-scale convolutional neural networks (MSCNN) proved effective in analyzing time and frequency domain data for civil structures. However, these models cannot identify structural information in the time–frequency domain. This study proposes a novel ESN-MSCNN combined model to effectively extract the time–frequency features of civil structures for damage detection. Firstly, vibration signal data is transformed into continuous time and Fourier spaces via data augmentation operation. Secondly, the ESN and MSCNN structures extract time and frequency domain features from preprocessed data, respectively. Finally, two combined features are fed into two fully connected layers to evaluate the degree of structural damage. Experiments on a scaled bridge and an IASC-ASCE benchmark building indicated that the proposed ESN-MSCNN model outperforms the state-of-the-art models for structural damage detection.
PubDate: 2023-06-01
- An adaptive bivariate decomposition method for interval optimization
problems with multiple uncertain parameters-
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Abstract: A novel interval optimization method based on an adaptive bivariate decomposition algorithm is developed to solve the engineering uncertain optimization problems with multiple interval parameters. Unlike the traditional highly time-consuming nested optimization approach, the interval perturbation method-based interval optimization avoids tedious inner optimization, nonetheless, it faces huge computational challenges in the optimization issues with large uncertainties and requires derivative information that may be unavailable for complex engineering systems. To overcome these shortcomings, an adaptive bivariate decomposition method (ABDM) is proposed to compute the interval ranges of the uncertain function. In the optimization, the objective function and constraints are decomposed by ABDM into a sum of several one- and two-dimensional subsystems. The extrema of the subsystems are approximately calculated through subinterval analysis, and an adaptive convergence strategy is applied to guarantee the accuracy of the obtained bounds. Based on the interval order relation and the reliability-based possibility degree of interval, the interval uncertain optimization model is converted into a deterministic optimization one. To effectively solve the deterministic optimization model, a robust optimization solver known as lightning attachment procedure optimization is employed in the optimization algorithm. Finally, a numerical example and two engineering applications illustrate the accuracy and effectiveness of the method for uncertain optimization issues with multiple interval parameters. Moreover, the new method does not need to determine the derivative information of the uncertain objective and constraints, and thus applies to different engineering optimization issues.
PubDate: 2023-06-01
- A data-driven approach for linear and nonlinear damage detection using
variational mode decomposition and GARCH model-
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Abstract: In this article, an original data-driven approach is proposed to detect both linear and nonlinear damage in structures using output-only responses. The method deploys variational mode decomposition (VMD) and generalized autoregressive conditional heteroscedasticity (GARCH) model for signal processing and feature extraction. To this end, VMD decomposes the response signals that are first decomposed to intrinsic mode functions (IMFs), and then, GARCH model is utilized to represent the statistics of IMFs. The model coefficients’ of IMFs construct the primary feature vector. Kernel-based principal component analysis (PCA) and linear discriminant analysis (LDA) are utilized to reduce the redundancy from the primary features by mapping them to the new feature space. The informative features are then fed separately into three supervised classifiers: support vector machine (SVM), k-nearest neighbor (kNN), and fine tree. The performance of the proposed method is evaluated on two experimental scaled models in terms of linear and nonlinear damage assessment. Kurtosis and ARCH tests proved the compatibility of GARCH model. The results demonstrate that the proposed technique reaches the accuracy of 100% and 98.82% in classifying linear and nonlinear damage, respectively. Also, its accuracy is higher than 80% in the presence of noise with a signal-to-noise ratio (SNR) of more than 10 dB.
PubDate: 2023-06-01
- An improved matrix split-iteration method for analyzing underground water
flow-
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Abstract: The Hermitian and skew-Hermitian splitting iteration method (HSS) is commonly an effective linear iterative method for solving sparse non-Hermite positive definite equations. However, it is time-consuming to solve linear equations. Hence, inexact Hermitian and skew-Hermitian splitting iteration approaches with multistep preconditioner (PIHSS(m)) are proposed for analyzing underground water flow. For unsaturated porous media, an exponential model is adopted to linearize the Richards equation. The governing equations are discretized using the finite element method to produce a system of linear equations. Furthermore, the inexact Hermitian and skew-Hermitian splitting iteration methods (IHSS) and PIHSS(m) are used to solve the linear equations. The results show that PIHSS(m) can effectively solve the 1D unsaturated flow problem and 2D transient drainage problem in partially and completely saturated soils. The IHSS has higher numerical accuracy than the classical methods such as Picard method and Gauss–Seidel iterative method. Compared with IHSS, PIHSS(m) achieves faster convergence rate and higher computational efficiency, particularly for solving groundwater flow problems with high grid density. Additionally, the numerical results reveal that PIHSS(m) has excellent acceleration, that is, at least 50% acceleration compared with the IHSS.
PubDate: 2023-06-01
- Boosting whale optimization with evolution strategy and Gaussian random
walks: an image segmentation method-
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Abstract: Stochastic optimization has been found in many applications, especially for several local optima problems, because of their ability to explore and exploit various zones of the feature space regardless of their disadvantage of immature convergence and stagnation. Whale optimization algorithm (WOA) is a recent algorithm from the swarm-intelligence family developed in 2016 that attempts to inspire the humpback whale foraging activities. However, the original WOA suffers from getting trapped in the suboptimal regions and slow convergence rate. In this study, we try to overcome these limitations by revisiting the components of the WOA with the evolutionary cores of Gaussian walk, CMA-ES, and evolution strategy that appeared in Virus colony search (VCS). In the proposed algorithm VCSWOA, cores of the VCS are utilized as an exploitation engine, whereas the cores of WOA are devoted to the exploratory phases. To evaluate the resulted framework, 30 benchmark functions from IEEE CEC2017 are used in addition to four different constrained engineering problems. Furthermore, the enhanced variant has been applied in image segmentation, where eight images are utilized, and they are compared with various WOA variants. The comprehensive test and the detailed results show that the new structure has alleviated the central shortcomings of WOA, and we witnessed a significant performance for the proposed VCSWOA compared to other peers.
PubDate: 2023-06-01
- Time-dependent reliability analysis method based on ARBIS and Kriging
surrogate model-
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Abstract: Based on the existed idea of adaptive radial-based important sampling (ARBIS) method, a new method solving time-dependent reliability problems is proposed in this paper. This method is more widely used than the existed method combining importance sampling (IS) with time-dependent adaptive Kriging surrogate (AK) model, which is not only suitable for time-dependent reliability problems with single design point, but also for multiple design points, high nonlinearity, and multiple failure modes, especially for small failure probability problems. This method combines ARBIS with time-dependent AK model. First, at each sample point, the AK model of the performance function with regard to time t is established in the inner layer, and its minimum value is calculated as the performance function value of the outer layer to established time-independent AK model. Then, the optimal radius of the β-sphere is obtained with an efficient adaptive scheme. Excluding a β-sphere from the sample pool, there is no need to calculate the performance function value of the samples inside the β-sphere, which greatly improves the estimation efficiency of structural reliability analysis. Finally, three numerical examples are given to show the estimation efficiency, accuracy, and robustness of this method.
PubDate: 2023-06-01
- Modified strain gradient-based nonlinear building sustainability of porous
functionally graded composite microplates with and without cutouts using
IGA-
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Abstract: The current investigation scrutinizes the microstructural-dependent geometrical nonlinear building sustainability characteristics of porous functionally graded (PFG) composite rectangular microplates in the presence and absence of cutouts with different geometries. To this end, for the first type, the effects of various microstructural gradient tensors on the nonlinear flexural of PFG microplates are studied separately using the modified strain gradient elasticity. By taking the isogeometric analysis (IGA) into account, the discretization procedure for the established differential equations including different orders of derivatives is carried out with the aid of non-uniform B-spline functions. The small scale-dependent linear and nonlinear flexural behaviors associated with the building sustainability of PFG microplates are achieved corresponding to various microstructural gradient tensors. It is reached to this point that the strengthening role of the deviatoric stretch type of gradient tensor is more than the symmetric rotation and dilatation ones in the nonlinear flexural manner of a PFG microplate. Also, it is demonstrated that existence of a central cutout leads to change the tendency of the traced load–deflection paths causing various slopes. Accordingly, a specific value of uniform distributed load is created, corresponding to which an identical deflection is induced in microplates having cutouts with different sizes. It is observed that by taking the microstructural strain gradient tensors into account, the value of this specific uniform distributed load enhances.
PubDate: 2023-06-01
- Vectorial surrogate modeling approach for multi-failure correlated
probabilistic evaluation of turbine rotor-
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Abstract: For complex structures like aeroengine turbine rotor, its reliability performance is jointly determined by multiple correlated failure modes. Probabilistic evaluation is an effective way to reveal the output response traits and quantify the structural reliability performance. However, for the requirement of evaluating the multivariate output responses and considering the correlation relationships, the multi-failure correlated probabilistic evaluation often shows the complex characteristics of high-nonlinearity and strong-coupling, leading to the conventional evaluation methods are hard to meet the requirements of accuracy and efficiency. To address this problem, a vectorial surrogate model (VSM) method is proposed by fusing the linkage sampling technique and model updating strategy. First, the linkage sampling technique is developed to build the vectorial sample set and the initial VSM by collaboratively extracting multidimensional input variables and multivariate output responses; moreover, the model updating strategy (MU) is presented to find the optimal undetermined parameters and construct the final VSM by addressing the issues of premature convergence and over-fitting problems. Regarding a typical high-pressure turbine rotor with multiple correlated failure modes (i.e., deformation failure, stress failure, strain failure) as engineering application case, the response distributions, reliability degree, sensitivity degree, correlation relationships for each/all failure modes of turbine rotor are obtained by the proposed method. Through the comparison of methods (direct Monte Carlo simulation, polynomial response surface, random forest, support vector regression, artificial neural network, VSM-I (without MU strategy), VSM-II (with MU strategy)), it is verified that the proposed VSM method can efficiently and accurately accomplish the multi-failure correlated probabilistic evaluation.
PubDate: 2023-06-01
- A comparison of manufacturing constraints in 3D topologically optimized
heat sinks for forced air cooling-
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Abstract: In this paper, the manufacturability of 3D topologically optimized (TO) heat sinks for forced-air cooling is studied for both additive manufacturing and subtractive numerical control machining. To mitigate the manufacturing difficulties which are frequently encountered when fabricating TO designs, we adopt two approaches. First, a constraint on the projected undercut perimeter is added to the standard optimization formulation to limit the number and severity of costly non-self supporting features in additive manufacturing. Second, a multi-axis machining (MAM) constraint is adopted to serve as the basis for enforcing manufacturability in a TO design for an alternative, highly-popular manufacturing modality. Locally refined nonuniform unstructured meshing and unit-cell assembly techniques for increasing the mesh and design resolution were pursued during optimization. Post-analyses were conducted in OpenFOAM for all TO and corresponding parallel-fin designs. Simulation results showed that topologically optimized heat sinks offer a 35–40% advantage in temperature performance over conventional parallel fins. A limited or non-existent compromising of thermal-hydraulic performance was necessary to enforce manufacturability. Finally, to prove the practical efficacy of the overhang angle control constraint, the AM-constrained heat sinks designs were fabricated through direct metal laser sintering in both aluminum and stainless steel.
PubDate: 2023-06-01
- Sharp phase-field modeling of isotropic solidification with a super
efficient spatial resolution-
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Abstract: The phase-field method provides a powerful framework for microstructure evolution modeling in complex systems, as often required within the framework of integrated computational materials engineering. However, spurious grid friction, pinning and grid anisotropy seriously limit the resolution efficiency and accuracy of these models. The energetic resolution limit is determined by the maximum dimensionless driving force at which reasonable model operation is still ensured. This limit turns out to be on the order of 1 for conventional phase-field models. In 1D, grid friction and pinning can be eliminated by a global restoration of Translational Invariance (TI) in the discretized phase-field equation. This is called the sharp phase-field method, which allows to choose substantially coarser numerical resolutions of the diffuse interface without the appearance of pinning. In 3D, global TI restricts the beneficial properties to a few specific interface orientations. We propose an accurate scheme to restore TI locally in the local interface normal direction. The new sharp phase-field model overcomes grid friction and pinning in three-dimensional simulations, and can accurately operate at dimensionless driving forces up to the order of \(10^{4}\) . At one-grid-point interface resolutions, exceptional degrees of isotropy can be achieved, if further the largely inhomogeneous latent heat release at the advancing solid-liquid interface is mitigated. Imposing a newly proposed source term regularization, the new model captures the formation of isotropic seaweed structures without spurious dendritic selection by grid anisotropy, even at one-grid-point interface resolutions.
PubDate: 2023-06-01
- A probabilistic simplified sine cosine crow search algorithm for global
optimization problems-
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Abstract: Crow Search Algorithm (CSA) is a novel meta-heuristic optimizer that is based on the intelligent behavior of crows. There is rather simple with two adjustable parameters only, which in turn makes it very attractive for applications in different engineering areas. To compensate for the blindness of the location update perceived in CSA when being tracked, this paper introduces a probability simplified sine cosine algorithm to form a new hybrid algorithm called PSCCSA (Probabilistic Simplified Sine Cosine Crow Search Algorithm). In 16 well-known standard test functions, the proposed algorithm was compared with 5 meta-heuristic algorithms for evaluating the effectiveness of the algorithms (Crow Search Algorithm, standard Sine Cosine Algorithm, Probability Simplified Sine Cosine Algorithm, Multi-Verse Optimizer and Particle Swarm Optimization). In addition, PSCCSA has also been used to solve four classic engineering problems (pressure vessel design, speed reducer design, welded beam design and tension/compression spring design problem). The results show that the proposed algorithm is feasible and effective.
PubDate: 2023-06-01
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