Journal of Computational Science最新文献

In this work, a system of linear partial differential equations with constant and variable coefficients via Cauchy conditions is handled by applying the numerical algorithm based on operational matrices and equally-spaced collocation points. To demonstrate the applicability and efficiency of the method, four illustrative examples are tested along with absolute error, maximum absolute error, RMS error, and CPU times. The approximate solutions are compared with the analytical solutions and other numerical results in literature. The obtained numerical results are scrutinized by means of tables and graphics. These comparisons show accuracy and productivity of our method for the linear systems of partial differential equations. Besides, an algorithm is described that summarizes the formulation of the presented method. This algorithm can be adapted to well-known computer programs.

Detecting Change Points (CPs) in data sequences is a challenging problem that arises in a variety of disciplines, including signal processing and time series analysis. While many methods exist for PieceWise Constant (PWC) signals, relatively fewer address PieceWise Linear (PWL) signals due to the challenge of preserving sharp transitions. This paper introduces a Markov Random Field (MRF) model for detecting changes in slope. The number of CPs and their locations are unknown. The proposed method incorporates PWL prior information using MRF framework with an additional boolean variable called Line Process (LP), describing the presence or absence of CPs. The solution is then estimated in the sense of maximum a posteriori. The LP allows us to define a non-convex non-smooth energy function that is algorithmically hard to minimize. To tackle the optimization challenge, we propose an extension of the combinatorial algorithm DPS, initially designed for CP detection in PWC signals. Also, we present a shared memory implementation to enhance computational efficiency. Numerical studies show that the proposed model produces competitive results compared to the state-of-the-art methods. We further evaluate the performance of our method on three real datasets, demonstrating superior and accurate estimates of the underlying trend compared to competing methods.

Forest fires are a key component of natural ecosystems, but their increased frequency and intensity have devastating social, economic, and environmental implications. Thus, there is a great need for trustworthy digital tools capable of providing real-time estimates of fire evolution and human interventions. This work develops an interpretable, physics-based model that will serve as the core of a broader wildfire prediction tool. The modeling approach involves a simplified description of combustion kinetics and thermal energy transfer (averaged over local plantation height) and leads to a computationally inexpensive system of differential equations that provides the spatiotemporal evolution of the two-dimensional fields of temperature and combustibles. Key aspects of the model include the estimation of mean wind velocity through the plantation and the inclusion of the effect of ground inclination. Predictions are successfully compared to benchmark literature results concerning the effect of flammable bulk density, moisture content, and the combined influence of wind and slope. Simulations appear to provide qualitatively correct descriptions of firefront propagation from a localized ignition site in a homogeneous or heterogeneous canopy, of acceleration resulting from the collision of oblique firelines, and of firefront overshoot or arrest at fuel break zones.

In the cloud, users need to connect to the data server to perform the file transmission via the Internet, and the Server transmits data to many servers. A machine or vehicle that can fly with the assistance of the air is known as an Aircraft. As an alternative to the downward thrust of jet engines, it uses either static lift or an airfoil's dynamic lift to combat gravity's pull. Drawing wall panel measurement points in the model is easy using the Aircraft Wall Panels (AWP) button. Draw wall panels between existing nodes or on the drawing grid using the relevant wall panel specifications. The technique intends to discover and extract information about undesirable defects such as dents, protrusions, or scratches based on local surface attributes gathered from a 3D scanner. Defects from a perfectly smooth surface include indentations and bumps on the surface. An image's features may be extracted by reducing the number of pixels in the picture to a manageable size so that the most exciting sections of the image can be recorded with Surface Feature Extraction (SFE). Some of the problems are the threat of drones and composite materials that do not break easily in oxymoronic. The aircraft's inner structure may have been damaged, although this is impossible to determine. A runway incursion severely threatens aviation safety because of the rise in aircraft movement on the airport surface and other human factors. An electronic moving map of airport runways and taxiways is shown to the pilot through a head-up display in the cockpit's head-down position. A practical feature extraction approach is required to ensure the safety of the airport scene in runway incursion prevention systems. All the drawbacks are rectified by AWP-SFE sensors installed along the runway centerline to detect magnetic signals generated by surface-moving targets, and this information is utilized to compute the target's length. The target length may extract peak features after regularizing the time domain data. Differentiation of target characteristics is used to determine the similarities between distinct targets. The suggested method's signal characteristics are more easily recognized than time domain or frequency domain feature methods. The experimental results show the proposed method AWP-SE to achieve a high-efficiency ratio of 88.2 %, activity ratio of 73.3 %, Analysis of aircraft in wall plane measurement point of 87.8 % and an error rate of 32.3 % compared to other methods.

A large number of research efforts have been made in trying to solve global decision problems with cellular automata by means of their cells reaching a distributed consensus via their local action. Among them, the determination of the most frequent state in configurations with arbitrary size, i.e., the density classification task, has been the most widely approached benchmark problem. The literature abounds with cases demonstrating that, depending on how it is formulated, a solution can be shown to exist or not. Here we address the problem in terms of deterministic, block-sequential asynchronous updates, over cyclic configurations, by which the possibility of a solution remains open. Our main results are negative in terms of the possibility of solving the problem with such formulation, encompassing the cases of any cellular automaton with any sequential update, and any elementary cellular automaton with any block-sequential update; furthermore, we uncover some properties that any potential solution with block-sequential update should have in order for it to be a candidate to solving the problem. Incidentally, we also give a new, very simple proof of the impossibility of solving the problem with any synchronous rule.

Creating valid and trustworthy models is a key issue in biomedical engineering that affects the quality of life of both patients and healthy individuals in various scientific and industrial domains. This however is a difficult task due to the complex nature of biomechanical joints. In this study, a sampling strategy combining Genetic Algorithm and clustering is proposed to investigate biomechanical joints. A computational model of a human ankle joint with 43 input parameters serves as an illustrative case for the procedure. The Genetic Algorithm is used to efficiently search for distinct variants of the model with similar output, while clustering helps to quantify the obtained results. The search is performed in a close vicinity to the original model, mimicking subjective decisions in parameter acquisition. The method reveals twelve distinct clusters in the model parameter set, all resulting in the same angular displacements. These clusters correspond to three unique internal load states for the model, confirming the complex nature of the ankle. The proposed approach is general and could be applied to study other models in mechanical engineering and robotics.

It is known that the solution of the Dirichlet fractional Laplacian in a bounded domain exhibits singular behavior near the boundary. Consequently, numerical discretizations on quasi-uniform meshes lead to low accuracy and nonphysical solutions. We adopt a finite element discretization on locally refined composite meshes, which consist in a combination of graded meshes near the singularity and uniform meshes where the solution is smooth. We also provide a reference strategy on parameter selection of locally refined composite meshes. Numerical tests confirm that finite element method on locally refined composite meshes has higher accuracy than uniform meshes, but the computational cost is less than that of graded meshes. Our method is applied to discrete the fractional-in-space Allen–Cahn equation and the fractional Burgers equation with Dirichlet fractional Laplacian, some new observations are discovered from our numerical results.

Nonlinear diffusion–reaction systems model a multitude of physical phenomena. A common situation is biological development modeling where such systems have been widely used to study spatiotemporal phenomena in cell biology. Systems of coupled diffusion–reaction equations are usually subject to some complicated features directly related to their multiphysics nature. Moreover, the presence of advection is source of numerical instabilities, in general, and adds another challenge to these systems. In this study, we propose a NURBS-based isogeometric analysis (IgA) combined with a second-order Strang operator splitting to deal with the multiphysics nature of the problem. The advection part is treated in a semi-Lagrangian framework and the resulting diffusion–reaction equations are then solved using an efficient time-stepping algorithm based on operator splitting. The accuracy of the method is studied by means of a advection–diffusion–reaction system with analytical solution. To further examine the performance of the new method on geometries more general than rectangles (e.g., L-shaped domains and parts of annuli), the well-known Schnakenberg–Turing problem is considered with and without advection. Finally, a Gray–Scott system on a circular domain is also presented. The results obtained demonstrate the efficiency of our new algorithm to accurately reproduce the solution in the presence of complex patterns on more complicated geometries. Moreover, the new method clarifies the effect of geometry on Turing patterns.

The significance of chemical reaction with activation energy and convective boundary conditions on the fluid flow via an oscillatory stretchy surface in the presence of permeable media and radiation is analyzed in this study. This inspection presents Fourier and Fick's laws-based equations for heat, mass transport, and liquid flow through an oscillating stretchy sheet. Understanding these dynamics aids in the optimisation of catalytic reaction settings, where gradients greatly influence reaction rates in concentration and temperature. The governing differential equations of the current study are modelled and changed into their non-dimensional form by employing suitable similarity variables. The finite difference method (FDM) is also used to numerically solve the obtained dimensionless equations. The influence of many factors on the several profiles is portrayed with graphical representations. The outcome of the unsteadiness and porosity parameters on the velocity profile with time coordinate is depicted. The increase in the radiation parameter and Biot number upsurges the thermal profile. The temperature reduces as the unsteadiness parameter and temperature relaxation time parameter grow. The upsurge in the activation energy parameter intensifies the mass transport. The rise in concentration relaxation time parameter diminishes the concentration profile.