Practical Engineering Problems Research Articles

Personalized Indicator Based Evolutionary Algorithm for Uncertain Constrained Many‐Objective Optimization Problem With Interval Functions

ABSTRACTIn practical engineering problems, uncertainties due to prediction errors and fluctuations in equipment efficiency often lead to constrained many‐objective optimization problem with interval parameters (ICMaOPs). These problems pose significant challenges for evolutionary algorithms, particularly in balancing solution convergence, diversity, feasibility, and uncertainty. To address these challenges, a personalized indicator‐based evolutionary algorithm (PI‐ICMaOEA) specifically designed for ICMaOPs is proposed. The PI‐ICMaOEA integrates a comprehensive quality indicator that encapsulates convergence, diversity, uncertainty, and feasibility factors, converting multiple objectives in high‐dimensional search spaces into a single evaluative metric. Each factor's weight is personalized assigned based on individual performance, objective dimension, and the evolving conditions of the population. By prioritizing individuals with excellent indicator values for mating and environmental selection, PI‐ICMaOEA effectively enhances selection pressure in high‐dimensional spaces. Comparative simulations demonstrate that PI‐ICMaOEA is highly competitive, offering a robust solution for balancing convergence, diversity, uncertainty, and feasibility in ICMaOPs.

Using design kits to test the problem-solving abilities of engineering students

Problem Solving; Application of Scientific and Engineering Knowledge; as well as Engineering Design, are core attributes the Accreditation Board for Engineering and Technology (ABET) requires in engineering graduates. The author investigated these three skills in two classes of second year students at the Botswana International University of Science & Technology (BIUST) in the 2019/2020 and the 2020/2021 academic years. Two different practical engineering problems were given to the participants. The 2019/2020 class designed a sanitizer dispenser, while the 2020/2021 designed a vehicle and trailer locking system. In both cases, participants were given problems to solve and corresponding toolkits of components to use. The toolkits were, essentially, illustrated scrambled lists of components, which, if correctly put together would achieve the solutions to the posed problems. The participants were first asked to generate functional steps to be achieved by the designs. Secondly, the participants were asked to use the generated functional steps to arrange the given components in meaningful ways to achieve the functionalities stipulated in design problems. The works of the participants were evaluated against moderated functional and design models generated by the module instructor. The time limit in both investigations was 90 min. The results show poor performance by the participants who did not only fail to deliver meaningfully functional models in both cases but also produced below average schematic designs. This paper presents the two investigations as follows. The vehicle/trailer project of the 2020/2021 class is presented in the main body of this paper as a representative case study given that both investigations used similar approaches and delivered comparable performance levels. The 2019/2020 sanitizer dispenser project, whose results were slightly lower than the former, is presented as an addendum.

Adsorption and hydrophobic action of different surfactants on montmorillonite Na-001 surface: a molecular simulation study

Montmorillonite (Mt) is a typical clay mineral in tailings, and its strong hydrophilicity is easy to lead to the instability of tailings dam structure and the difficulty of tailings wastewater treatment. The influence of different types of surfactants on the Mt Na-001 surface hydrophobicity was analysed from the perspective of adsorption energy, electron transfer and concentration distribution. DFT simulation results showed that the adsorption effect of surfactant was cationic > anionic > non-ionic, and the best surfactant were methyl primary amine, methyl sulfonic acid and methyl formate. The MD simulation results of the adsorption of dodecyl primary amine (DDA) and sodium dodecyl sulfonate (SDS) with different coverage on the surface of Mt Na-001 showed that the adsorption effect of DDA and SDS was significantly improved with the increase of coverage, and DDA was significantly better than SDS, and the optimal coverage of DDA was 1 ML. Finally, the reason why DDA is superior to SDS was explained from the concentration distribution of water and Na+ on Mt surface. This simulation calculation study can provide micro-level insights for solving practical engineering problems.

Chaos Optimization Algorithms: A Survey

Chaos Optimization Algorithms (COAs) have emerged as a potent global optimization technique, exhibiting remarkable capabilities in tackling complex optimization challenges. In recent years, a plethora of COAs and their variants have been developed and employed to address a wide array of practical problems in engineering and science. Despite the proliferation of these algorithms, existing reviews often suffer from being outdated or lacking in robust classification criteria. This deficiency impedes the accurate assessment of the latest advancements and obstructs researchers’ ability to swiftly comprehend the current state of research in chaos optimization. To bridge this gap, this paper offers a review by classifying chaos optimization into five distinct categories: chaos map-based optimization algorithms, chaos metaheuristic optimization algorithms, chaos game optimization algorithms, hybrid optimization algorithms, and others. Each category is meticulously analyzed, with a thorough discussion of the inherent strengths and weaknesses of the respective algorithms. This analysis not only clarifies the unique features of each algorithm but also enhances understanding by contrasting their various applications. The review extends to the practical deployment of chaos algorithms across specific problems, illustrating their versatility and effectiveness. Conclusively, this paper delineates potential future research evolution of COAs, providing a clear and structured guide to forthcoming explorations in this dynamic field. This survey aims to empower researchers within the optimization community with a deeper and more comprehensive understanding of the landscape of COAs, paving the way for innovative research and applications.

Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains

We implement a set of simulation experiments in NetSquid specifically designed to estimate the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations that are not currently available in other tools and are useful for gaining insight into practical and realistic quantum network engineering problems: an arbitrary number of memory registers at the switches, simplicity in including entanglement distillation mechanisms, arbitrary switching topologies, and routing protocols. An illustrative case study is presented, namely a comparison in terms of performance between a repeater chain where repeaters can only swap sequentially and a single switch equipped with multiple memory registers that is able to handle multiple swapping requests.

A multi-strategy boosted bald eagle search algorithm for global optimization and constrained engineering problems: case study on MLP classification problems

The Bald Eagle Search (BES) algorithm is an innovative population-based method inspired by the intelligent hunting behavior of bald eagles. While BES shows promise, it faces challenges such as susceptibility to local optima and imbalances between exploration and exploitation phases. To address these limitations, this paper introduces the Multi-Strategy Boosted Bald Eagle Search (MBBES) algorithm. MBBES enhances the original BES by incorporating an adaptive parameter, two distinct mutation strategies, and replacing the swoop stage with a fall stage. We rigorously evaluate MBBES against classic and improved algorithms using the CEC2014 and CEC2017 test sets. The experimental results demonstrate that MBBES significantly improves the ability to escape local optima and achieves superior convergence accuracy. Moreover, MBBES ranks first according to the Friedman test, outperforming its counterparts in solving five practical engineering problems and three MLP classification problems, underscoring its effectiveness in real-world optimization scenarios. These findings indicate that MBBES not only surpasses BES but also sets a new benchmark in optimization performance.

Implementation and Application of Cell-Based S-FEM Based on the Object-Oriented Programming Framework

The object-oriented programming is widely used in different fields of finite element analysis (FEA). The commonly used object-oriented codes were almost written by C++. Due to the inability to automatically manage memory and the lack of gigantic standard library and a third-party library for C++, building matrix classes are needed and a strong programming ability for program developers is also necessary. The conventional FEM method still has many disadvantages such as poor accuracy, hard stiffness matrix, mesh dependence and lack of upper bound solution. The smoothed FEM (S-FEM) method proposed by Liu et al. can efficiently overcome these disadvantages and is therefore widely used in FEA. In this work, the elastic-plastic CS-FEM method is extended and implemented using Python programming based on the framework proposed [H. Li and Y. Xiao, Implementation and numerical simulation on object-oriented elastic-plastic finite element method based on Python, J. Syst. Simul. 36(5) (2024) 1107–1117] The resulting object-oriented elastic-plastic CS-FEM framework is then applied to FEA of elastic-plastic mechanics problems. The correctness of the resulting framework and the high computational accuracy and good mesh adaptability of CS-FEM models have been verified by comparing the corresponding results with an analytical solution or ABAQUS results. The object-oriented elastic-plastic CS-FEM framework provides a more efficient and adaptable approach to FEA, and can also overcome many limitations of the conventional FEM method and C++ programming. The use of Python and the CS-FEM method offers improved computational accuracy, mesh adaptability and development efficiency, and this will make it a valuable tool for solving practical problems in mechanics and engineering.

Numerical Strategy on the Grid Orientation Effect in the Simulation for Two‐Phase Flow in Porous Media by Using the Adaptive Artificial Viscosity Method

ABSTRACTIn the context of numerical simulators for multiphase flow in porous media, there exists a long‐standing issue known as the grid orientation effect (GOE), wherein different numerical solutions can be obtained when considering grids with different orientations under certain unfavorable conditions. The GOE is relevant to the instability near displacement fronts. If numerical oscillations accompanied by sharp fronts are not adequately suppressed, the GOE occurs. To reduce or even eliminate the GOE, we propose augmenting adaptive artificial viscosity in the process of solving the saturation equation. It has been demonstrated that appropriate artificial viscosity can effectively reduce or even eliminate the GOE. The proposed numerical method can be easily applied in practical engineering problems.

Numerical investigation on effect of non-parallelism on natural transition in boundary layers around underwater axisymmetric bodies

For both fluid mechanics and practical engineering problems, it is important to study the effect of non-parallelism on the natural transition in boundary layers around underwater axisymmetric bodies. This paper develops the method of harmonic linearized Navier–Stokes (HLNS) equations for such boundary layers. The method can be used to study the evolution of small disturbances considering the non-parallelism of the basic flow. Based on the results of the HLNS equations, the transition positions of the boundary layers are predicted by the eN method. Because traditional linear stability theory (LST) neglects non-parallelism, the difference between the results using the HLNS method and those given by LST represents the effects of non-parallelism. Numerical calculations are performed for five classical forebody shapes, and the effect of non-parallelism is identified. (i) At each streamwise location, non-parallelism suppresses low-frequency disturbances while promoting high-frequency ones. The influence on high-frequency disturbances is more obvious. (ii) The effect of non-parallelism on the neutral curve focuses on the region near the critical instability position. Non-parallelism slightly delays the critical instability position for most forebody shapes and pushes the unstable zone toward the high-frequency direction while broadening its frequency bandwidth. (iii) The streamwise range at each growth-rate level is widened, implying that non-parallelism destabilizes the boundary layers. (iv) The transition occurs earlier, indicating that non-parallelism promotes the transition. (v) Non-parallelism shifts the dangerous frequency band toward the high-frequency direction. (vi) Non-parallelism enhances the wall pressure fluctuations (WPF) in the unstable laminar zone.

Competition of tribes and cooperation of members algorithm: An evolutionary computation approach for model free optimization

Metaheuristic algorithms are a series of intelligent algorithms that are inspired by natural phenomenon and achieve the optimal searching via utilizing behaviors from nature. The rising complexity and dimensions of practical engineering problem in reality, a significant number of metaheuristic algorithms are promoted and applied in various of fields. Inspired by ancient tribes competition and members cooperative behavior, this paper proposes the Competition of Tribes and Cooperation of Members Algorithm (CTCM). Subsequent experiments are conducted on 23 benchmark test functions and exhaustively compared with other state-of-the-art algorithms, including particle swarm optimization (PSO), grey wolf optimizer (GWO), sparrow search algorithm (SSA), egret swarm optimization (ESOA), beetle antennae search (BAS) and whale optimization (WOA). The standard deviation and average, as well as statistic test are utilized to compare the performance of each algorithms, which reveal that CTCM outperforms in most kinds of problem. And from the result of Wilcoxon and Friedman rank test, the CTCM achieve the first place in all categories of problems, which indicate that CTCM possesses strong global optimization search capability and stability, and has faster convergence speed. The superiority of CTCM is then proofed on practical engineering optimization problems, in which CTCM achieve all the optimal solution for each engineering problem.

Machine Learning Aided Modeling of Granular Materials: A Review

AbstractArtificial intelligence (AI) has become a buzzy word since Google’s AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.

Local Convergence Study for an Iterative Scheme with a High Order of Convergence

In this paper, we address a key issue in Numerical Functional Analysis: to perform the local convergence analysis of a fourth order of convergence iterative method in Banach spaces, examining conditions on the operator and its derivatives near the solution to ensure convergence. Moreover, this approach provides a local convergence ball, within which initial estimates lead to guaranteed convergence with details about the radii of domain of convergence and estimates on error bounds. Next, we perform a comparative study of the Computational Efficiency Index (CEI) between the analyzed scheme and some known iterative methods of fourth order of convergence. Our ultimate goal is to use these theoretical findings to address practical problems in engineering and technology.

An Experimental Study on the Force Characteristics and Stability of an FRP Pipe Floating in Mud

Based on the occurrence of floating and fracture accidents of FRP pipes on muddy seabeds in practical engineering combined with the water intake and drainage project of the 2 × 660 MW coal-fired power plant in the second phase along the coast of Vietnam, a model test study was carried out. The laboratory simulated the FRP pipe conditions during the construction and operation periods by configuring different densities of silt. The experiment results obtained are as follows: the FRP pipes floated up in the mud during the construction period; the thickness of sediment deposition when the FRP pipes are lifted up, as well as the entire lifting process time and maximum force, are all within 2 h and 70 kN/m; after adopting a cover layer design section during the operation period, the FRP pipes in the silt remain stable while obtaining a maximum force of 15 kN/m; and based on the experimental results, an empirical formula for the stress on FRP pipes and was derived and measures for FRP pipe stability were proposed. The research results not only solve practical problems in water intake and drainage engineering but also provide technical support for the promotion and application of FRP pipes.

Stress Analysis of Radial Compression Disk Based on MATLAB

Elastic mechanics is an important branch of solid mechanics, mainly studies the deformation and internal force of elastic objects under the action of external forces and other external factors, also known as elastic theory. The study object of elasticity is elastomer. The detailed theoretical structure helps us solve more practical engineering problems. We used Matlab software in the data simulation for visualization processing. On the basis of the study of elasticity, this paper explores the stress distribution of the uniform ideal elastic body to the radial compression disk model, and observes the change of the stress of the radial compression disk when the external pressure changes gradually. The expressions of the corresponding stress components , and are obtained by using the theory of normal concentrated force on the boundary of a half plane body. The stress solutions considering the thickness t of the disk are derived. Finally, the drawing software in Matlab software is used to visualize the theoretical derivation results in order to intuitively feel the change of stress. After the data simulation, the data simulation results are compared with the stress analysis results derived from the theoretical deduction, and the conclusion is drawn.

Teaching reform and practice of the postgraduate course "Biocatalysis and Enzyme Engineering" under the "dual-drive and dual-guide" model

Value shaping and innovation capability improvement are two important goals of postgraduate course teaching. Biocatalysis and Enzyme Engineering is a core course for postgraduates majoring in bioengineering. Our teaching team established a "dual-drive and dual-guide" teaching model, that is, "double drive" (value-driven + innovation-driven) as the traction, this model integrated professional quality with industry needs, engineering ethics, and scientist spirit, combined theoretical teaching with research frontiers, industrial cases, and engineering practice, and incorporated thematic lectures, group peer evaluation, and review papers into course assessment. The teaching under this model achieved the teaching objectives of "double guide" (guiding ideology and ability), and improved the postgraduates' value identification, innovation awareness, engineering thinking, and ability to solve practical engineering problems.

Multiobjective enterprise development algorithm for optimizing structural design by weight and displacement

This study presents a novel metaheuristic optimization algorithm for complex multiobjective engineering problems. By integrating advanced population and nondominated sorting techniques into the existing single-objective enterprise development algorithm, this new multiobjective approach effectively identifies Pareto-optimal solutions. The algorithm leverages these techniques to explore engineering solutions within multiobjective search spaces. We evaluated its performance using 29 multiobjective mathematical benchmark problems and conducted a comparative analysis against ten established metaheuristic optimization algorithms. The results demonstrate that the algorithm produces highly accurate approximations of Pareto-optimal fronts while maintaining a uniform distribution of solutions. Additionally, the algorithm was applied to real-world engineering challenges, including the optimization of structures such as the 942-bar tower, the 1536-bar double-layer barrel vault, and the 1410-bar double-layer dome truss, with the primary objectives of minimizing both structural weight and maximum nodal deflection. The findings highlight the algorithm's effectiveness in solving practical engineering problems and consistently achieving optimal Pareto fronts.

Reliability analysis of the solidification cooling of solid rocket motor grain material.

The reliability of solid rocket motor grain structure during solidification cooling is analyzed. First, a three-dimensional parametric modeling of the grain is carried out by ANSYS finite element software. The dangerous point and dangerous moment can be obtained based on the transient and dynamic thermo-structure coupling under the cooling condition. Moreover, the maximum equivalent strain and temperature values are extracted. Second, a dual neural network model is established based on the probability distribution of the copula function and specific parameters. Finally, the instantaneous reliability during the solidification cooling process of the grain is calculated. Then, the dynamic reliability analysis is realized. The proposed method reduces the computational cost of dynamic reliability of grain structure, demonstrating its applicability in practical engineering problems. Furthermore, comparing the results of the proposed method with the MCS method demonstrates that the proposed method has high computational accuracy.

Analytical solution for the kinematic response of end-bearing piles subjected to SH waves in a homogeneous layer by an improved beam-foundation model

This paper presents an analytical solution for evaluating kinematic response in piles subjected to shear waves. The proposed model treats the pile as a Timoshenko beam supported by a two parameter Winkler foundation, capturing both transverse and rotational ground reactions. Validation against both analytical and numerical solutions from the literature, particularly 3D finite element models, shows very good agreement. The proposed model generalizes different solutions currently applied in practice, covering various pile slenderness ratios and ground conditions. This approach addresses limitations in existing Euler-Bernoulli beam resting on Winkler or Vlasov-Pasternak foundation models, by incorporating shear distortions in the structure and ground friction at the interface. Shear stresses induced by the rotational springs are found to significantly influence the kinematic factors, as well as bending and, particularly, shear forces. Finally, simplified expressions for kinematic interaction factors and forces are provided for expedited analyses of practical engineering problems.

A multiobjective optimization framework based on FEA, ANN, and NSGA-II to optimize the process parameters of tube-to-tubesheet joint

This study presents a multiobjective optimization framework that integrates Artificial Neural Network (ANN) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for the optimization of rolling process parameters of tube-to-tubesheet joint (TTT-joint). During the rolling process, both beneficial contact pressure and detrimental tensile residual stress are generated within the joint. The primary objective of this framework is to minimize the tensile residual stress while maximizing the contact pressure in the TTT-joint. To achieve this, a backpropagation ANN model is trained to rapidly estimate the residual stress and contact pressure for various sets of rolling process parameters. For training purposes, a series of nonlinear elastoplastic finite element (FE) simulations are performed to generate the input database for the neural network. A detailed parametric study is performed based on the axisymmetric FE model of the TTT-joint. The trained neural network is then incorporated into the NSGA-II optimization algorithm to find the fitness function and optimized process parameters. The contact pressure and residual stress predicted by the proposed ANN-NSGA-II framework are validated by finite element analysis (FEA) using the optimized parameters. The present analysis established that the proposed methodology can be applied in practical engineering problems to obtain the process parameters that yield the maximum contact pressure and minimum tensile residual stress in the TTT-joint.

An improved grey wolf optimization algorithm based on scale-free network topology

The grey wolf optimizer is a novel intelligent optimization algorithm that has become popular due to its low number of parameters, fast convergence speed, and simplicity. However, the classical algorithm, with its update strategy allowing wolves to learn only from the alpha wolves, often leads to premature convergence and lower convergence accuracy. Therefore, in this paper, an improved grey wolf optimization algorithm based on scale-free network topology (SFGWO) is proposed to address these issues. The improved algorithm first employs a strategy for formulating a population based on a scale-free network topology, where interaction between wolves is limited to topological neighbors, which helps enhance the exploration capabilities of the algorithm. Second, a neighbor learning strategy is introduced to capture individual diversity, facilitating the solution space exploration. Finally, an adaptive individual regeneration strategy is adopted to balance the exploration and exploitation processes and reduce the risk of falling into local optima. The proposed algorithm is evaluated through simulation experiments using 23 classical and the CEC2019 benchmark functions. The experimental results demonstrate that the SFGWO algorithm excels in terms of solution accuracy and exploration capabilities. The applicability and effectiveness of the SFGWO algorithm are further validated through testing on three practical engineering problems.