Journal of Computational Science最新文献

A new decision-making approach based on distance measures is established in this study to effectively reduce unnecessary structural analyses in performing truss optimization by metaheuristic algorithms. This approach termed distance comparison (DiC) judges a new design candidate as worth evaluating by using its distance from the best solution. The new candidate solution will be omitted without evaluating it if it is not closer to the best solution than the one being compared. The DiC method is integrated with a novel hybrid metaheuristic based on differential evolution (DE) and the Rao algorithm. In the proposed hybrid strategy, a modified Rao algorithm and an enhanced DE are applied adaptively based on the population diversity to utilize the advantage of each one for a specific stage of the optimization process. Six truss sizing examples with continuous variables, including the 10-bar and 200-bar planar trusses and the 25-bar, 72-bar, 120-bar, and 942-bar spatial trusses, are examined to evaluate the effectiveness of the proposed method. Numerical results demonstrate that DiC significantly reduces the number of structural analyses. Moreover, the performance of the proposed hybrid metaheuristic algorithm conducted on the examples is better than that of some state-of-the-art metaheuristic algorithms.

This paper studies the computational complexity of two voting problems where the goal is deciding how a given voter should vote to favour their personal stances. In the first problem, given (a) the voter stance towards each law that will be voted by the parliament and (b) the political stance of each party towards each law (all party members are assumed to vote according to it), the goal is finding the parliamentary seats distribution maximizing the number of laws that will be approved/rejected as desired by the voter. In the second problem no parliament is involved, but a single issue with several possible answers is voted by citizens in a presidential election with several candidates. The problem consists in deciding how a group of voters, split in different electoral districts, all of them supporting the same candidate, should vote to make their candidate president. It is assumed that (a) all delegates of each electoral district are assigned to the candidate winning in the district, (b) after the election day, candidates may ask their assigned delegates to support other candidates receiving more votes than them, and these post-electoral supporting stances are known in advance by the electorate, and (c) the group of voters that is coordinated knows the votes that will be cast by the rest of the electorate. For each problem, its NP-hardness as well as its inapproximability are proved. This implies that something as essential as exercising the democratic right to vote, in such a way that the voting choice will be the best for the voter’s political stances, is at least NP-hard. It is also shown how genetic algorithms can be used to obtain reasonable solutions in practice despite the limitations of theoretical approximation hardness.

Improving road safety is hugely important with the number of deaths on the world’s roads remaining unacceptably high; an estimated 1.35 million people die each year (WHO, 2020). Current practice for treating collision hotspots is almost always reactive: once a threshold level of collisions has been exceeded during some predetermined observation period, treatment is applied (e.g. road safety cameras). However, more recently, methodology has been developed to predict collision counts at potential hotspots in future time periods, with a view to a more proactive treatment of road safety hotspots. Dynamic linear models provide a flexible framework for predicting collisions and thus enabling such a proactive treatment. In this paper, we demonstrate how such models can be used to capture both seasonal variability and spatial dependence in time dependent collision rates at several locations. The model allows for within- and out-of-sample forecasting for locations which are fully observed and for locations where some data are missing. We illustrate our approach using collision rate data from 8 Traffic Administration Zones in the US, and find that the model provides a good description of the underlying process and reasonable forecast accuracy.

In this work, a novel control strategy is proposed to obtain the stability boundary in addition to reduce the transients around the equilibrium points. To encounter the described problem, a new approach of combining the bifurcation analysis with the state feedback controller is proposed. A bifurcation analysis at different equilibrium points is performed to obtain the stable region of operation. In addition to this, the transients behavior of the system is also obtained simultaneously in the form of eigenvalues plots. The objective of the proposed controller is to generate a control law and state variables to reduce the transients keeping the system within the stability boundary by tuning the reference input matrix. From the obtained simulation results, it is seen that, by combining the bifurcation analysis with state feedback controller, the transients and the steady state error are reduced by selecting the purely negative real eigenvalues and reference input matrix respectively. The obtained closed loop control law and the state variables utilizing Ackerman’s Formula are found within the stability limit. A sensitivity index obtained from local sensitivity analysis verifies the relationship between the stability boundary at different longitudinal velocity on a low-friction road obtained from bifurcation analysis.

Bubble columns play a crucial role in a wide range of industries, including chemical and biochemical processes, petrochemicals, and environmental engineering. Understanding the dynamics of bubble columns is essential for optimizing their performance in various applications. This study proposes a data-driven approach for analyzing the dynamics of a two-dimensional bubble column system. We conducted simulations with varying superficial velocities and generated a comprehensive training dataset encompassing the entire velocity and pressure fields to achieve this. We then compared the performance of two approaches, the Fast Fourier Transformation (FFT) and the High-Order Dynamic Mode Decomposition (HODMD), in representing the system’s dynamics. Our findings demonstrate that the conventional FFT approach fails to adequately capture the complex dynamics of the dispersed multiphase flow system. This limitation arises due to the distribution of frequencies along the domain. Conversely, our work highlights the success of the HODMD method in accurately representing the system’s dynamics using only a few arbitrary sampling points within the domain. The implications of this study are significant, as it sheds light on the potential benefits of employing HODMD for analyzing bubble column dynamics. By utilizing this approach, industrial processes can be optimized more effectively across various applications.

The nodal integral methods (NIMs) are very efficient and accurate coarse-mesh methods for solving partial differential equations. The cell-centered NIM (CCNIM) is a simplified variant of the NIMs that has recently shown its efficiency in solving fluid flow problems but has been hampered by issues such as inapplicability to one-dimensional problems, complexities in handling Neumann boundary conditions and the formulation of a system of differential-algebraic equations (DAEs) for discrete unknowns. Here, we present a modified version of the CCNIM designed to overcome the challenges associated with its previous version. Our novel development retains the essence of CCNIM while resolving these issues. The proposed scheme, grounded in the nodal framework, achieves second-order accuracy in both spatial and temporal dimensions. Unlike its precursor, the proposed method formulates algebraic equations for discrete variables per node, eliminating the cumbersome DAE system. Neumann boundary conditions are seamlessly incorporated through a straightforward flux definition, and applicability to one-dimensional problems is now feasible. We successfully apply our approach to one and two-dimensional convection-diffusion problems with known analytical solutions to validate our approach. The simplicity and robustness of the approach lay the foundation for its seamless extension to more complex fluid flow problems.

The planning and construction of urban ecological corridors play a role in restoring and improving the ecological environment of cities, promoting the movement of other species and biological factors living in urban environments and wider regions. It also profoundly influences and enriches the spiritual and cultural experiences of people living in cities and nearby spaces. The reason for the emphasis on green corridors is that many cities and towns are constantly expanding, and the urban green space system has not yet formed an effective network. At the same time, factors such as the loss of the urban natural environment, biodiversity reduction, and environmental degradation have led to the need to build urban green corridors to deal with risks. By improving the neural network model, this paper predicted the construction land scale of the urban green corridor network, which was used to adjust the land use structure of the green corridor and optimize the land use layout. This paper aims to use the upgraded neural network method to predict the scale of urban green corridor network building land, which helps to evaluate the ecological security status. It can solve the dynamic solution problem of multi-indicator variable weight problems, overcoming the influence of subjective factors in the weight-setting process. The experiment adopted the improved neural network model for prediction. The results showed that its accuracy was much higher than the gray prediction model, which has improved by about 14.81%. This paper fully proved that the improved neural network model had a high degree of fit and feasibility for predicting the land scale of urban green corridor networks. It is directly related to the rationality and practicability of the urban green corridor network planning scheme, which plays a role in guaranteeing the ecological security of the landscape.

A new pressure-projection pre-conditioning multi-fractional-step (PPP-MFS) method for incompressible Navier–Stokes flow in porous media is presented. This fractional step method is applied to decouple the pressure and the velocity; thereby, overcoming the computational costs and difficulty associated with the resolution of the nonlinear term in the Navier–Stokes equation for fine geometric models. Specifically, time evolution is decomposed into a sequence of multi-fractional solution steps. In the first step, an elliptic problem is solved for pressure ($p$) with a no-slip boundary condition. This gives the Stokes pressure and velocity fields. In the second step, the obtained pressure $p$ is then projected onto the field $p\ast$ and used to solve for velocity field ($u$) required for the pre-conditioning of the solution to the Navier–Stokes equation. The pressure and velocity fields are obtained from the solution of the Navier–Stokes equation in the third step. Numerical and geometric discretisation of porous samples were carried out using finite-element method. For flow in simple channel models represented by two- and three-dimensions and in systems with high conductivity, the Stokes and Navier–Stokes numerical solutions produced close pressure and velocity field approximations. For flow around a cylinder, computation time is consistently higher in the Navier–Stokes equation by a factor of 2 with a pronounced non-symmetric pressure field at high mesh refinements. This computation time is desirable given that Navier–Stokes computation without preconditioning can be orders of magnitude more expensive.

Purpose

In case of necessity of removing a bone fragment, the process of restoring bone continuity relies on the use of bone grafts or bone plates as osteosynthesis methods. These approaches are characterised by several disadvantages, such as significant changes in load transfer method through the bone. Recently, scaffolds emerged as potentially more efficient method in restoring bone continuity. In this paper, an attempt was made to validate the correctness of this statement.

Materials and methods

Three lengths of bone defects were selected for the analysis: 35, 45 and 55 mm, located in the lower, middle and upper section of femur diaphysis. The following methods of restoring bone continuity were evaluated: 1 plate, 2 parallel plates, 1 plate and scaffold, 2 parallel plates and scaffold. Simulations of the forces generated during human gait cycle were performed. The evaluated parameters obtained were: maximal and average stresses, strain energy density as well as percentage changes in values of these parameters in relation to the values obtained for intact bone in its selected zones.

Results

Studies have shown that the best method of restoring bone continuity is to use a single plate with a scaffold. The stress distribution obtained by this method had the highest similarities to the one obtained for intact bone model in terms of load transfer as well as maximal stresses values obtained.

Conclusions

The study validated the statement that the use of a scaffold to restore bone continuity is potentially more efficient method than conventional approaches.

We propose a new class of physics-informed neural networks, called Physics-Informed Generator-Encoder Adversarial Networks, to effectively address the challenges posed by forward, inverse, and mixed problems in stochastic differential equations (SDEs). In these scenarios, while governing equations are known, the available data consist of only a limited set of snapshots for system parameters. Our model consists of two key components: the generator and the encoder, both updated alternately by gradient descent. In contrast to previous approaches that directly match approximated solutions with real snapshots, we employ an indirect matching operating within the lower-dimensional latent feature space. This method circumvents challenges associated with high-dimensional inputs and complex data distributions in solving SDEs. Numerical experiments indicate that, compared to existing deep learning solvers, our proposed approach not only demonstrates superior accuracy but also exhibits advantages in both computational efficiency and model complexity.