Practical Engineering Problems Research Articles

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.

Three-dimensional forward modeling and quantitative assessment of electrode offset effects in ERT

Resistivity data has important applications in geophysical exploration, but the impact of electrode offsets on resistivity response characteristics remains unclear. This study aims to explore the influence of horizontal electrode offset angles and vertical offsets caused by topographical variations on the forward modeling of resistivity data. By analyzing experimental models with different measurement arrays, the paper revealed their influence laws on the buried depth of the target body and resistivity resolution. Utilizing tools like ZondRes3D, we conducted 3D resistivity forward modeling and analyzed the results in detail. It is found that horizontal electrode offsets lead to pseudo-anomalies in the apparent resistivity response, which is related to the offset angles and the number of electrodes. Under different conditions, the horizontal electrode offsets exhibit a “gradient variation” pattern. In addition, topographical variations can also cause distortions and offsets in the apparent resistivity curves and the locations of the anomaly response. Specifically, the measuring lines near the edge of the target bodies are more susceptible to these effects. Based on the comprehensive experimental results, we have drawn several conclusions regarding the impact of electrode offsets and topographical variations, including the effects of offset angles on the pseudo-anomalies, the anomalous response laws under different topographic conditions, as well as anomalous situations under specific angles. These findings provide crucial insights for interpreting resistivity data in geophysical exploration and addressing practical engineering problems, and offer guidance for optimizing measuring line layouts and post-processing terrain correction algorithms.

Adaptive grey wolf optimizer based on transfer function inertia weight of second-order high-pass filter

This paper proposes an Adaptive Grey Wolf Optimizer based on Transfer function Inertia Weight of Second-order High-pass Filter (SAGWO), aiming to overcome the limitations of Grey Wolf Optimizer (GWO) in terms of convergence speed and solution accuracy. SAGWO enhances the algorithm's dynamic adaptability and greatly speeds up convergence by incorporating the transfer function of the second-order high-pass filter into the modifications of inertia and leader wolves' weight. Furthermore, SAGWO incorporates an adaptive random search component, which significantly enhances the search range and the ability to explore globally. The performance of SAGWO is assessed and compared against three prominent Swarm Intelligence Algorithms (ALO, WOA, and EHO), along with GWO and GWO optimized by PSO. This evaluation is conducted through trials on the CEC2017 standard test set. The experimental results indicate that SAGWO outperforms in terms of both convergence speed and solution correctness. Moreover, SAGWO is utilized to address practical engineering problems such as pressure vessel design and robot gripper design, showcasing its remarkable effectiveness once more. This research enhances the theoretical development of Swarm Intelligence Algorithms and demonstrates the significant utility of SAGWO in practical applications, establishing it as a powerful instrument for scientific investigation and engineering optimization.

Multi-strategy improved seagull optimization algorithm and its application in practical engineering

Metaheuristic algorithms play a crucial role in engineering optimization, as they can find the optimal parameter configuration in engineering systems. This article proposes a multi-strategy improved seagull optimization algorithm (OPSOA) to solve engineering application problems. Aiming to solve the problems of slow search speed and low convergence accuracy of the standard seagull optimization algorithm (SOA), four strategies, including Lévy flight and Cauchy mutation, were introduced to improve its performance. Comparison shows that OPSOA and its incomplete algorithms are better than SOA, indicating that each improvement is effective. By testing the benchmark functions of CEC 2017 and CEC 2022, it is shown that OPSOA has a strong ability to find the optimal solution and is superior to other algorithms in terms of convergence accuracy and search speed. The application of this algorithm in practical engineering problems proves that it has significant advantages in solving complex problems.

A Modified Sand Cat Swarm Optimization Algorithm Based on Multi-Strategy Fusion and Its Application in Engineering Problems

Addressing the issues of the sand cat swarm optimization algorithm (SCSO), such as its weak global search ability and tendency to fall into local optima, this paper proposes an improved strategy called the multi-strategy integrated sand cat swarm optimization algorithm (MSCSO). The MSCSO algorithm improves upon the SCSO in several ways. Firstly, it employs the good point set strategy instead of a random strategy for population initialization, effectively enhancing the uniformity and diversity of the population distribution. Secondly, a nonlinear adjustment strategy is introduced to dynamically adjust the search range of the sand cats during the exploration and exploitation phases, significantly increasing the likelihood of finding more high-quality solutions. Lastly, the algorithm integrates the early warning mechanism of the sparrow search algorithm, enabling the sand cats to escape from their original positions and rapidly move towards the optimal solution, thus avoiding local optima. Using 29 benchmark functions of 30, 50, and 100 dimensions from CEC 2017 as experimental subjects, this paper further evaluates the MSCSO algorithm through Wilcoxon rank-sum tests and Friedman’s test, verifying its global solid search ability and convergence performance. In practical engineering problems such as reducer and welded beam design, MSCSO also demonstrates superior performance compared to five other intelligent algorithms, showing a remarkable ability to approach the optimal solutions for these engineering problems.

Flavoring search algorithm with applications to engineering optimization problems and robot path planning

In this paper, a human-based meta-heuristic algorithm, the Flavoring Search Algorithm, is proposed and mathematically modeled with the aim of providing an alternative optimization method for solving practical engineering problems. Flavoring Search Algorithm is inspired by the human behavior of flavoring in everyday life, including basic flavoring, formal flavoring, and auxiliary flavoring. By introducing a unique taste factor, it not only succeeded in making the Flavoring Search Algorithm corresponds to the real flavoring process but also balanced the exploration and exploitation of the algorithm. With the help of the taste factors, Flavoring Search Algorithm performs basic flavoring (initial flavoring and random flavoring) in the exploration phase and formal flavoring and auxiliary flavoring in the exploitation phase. In addition, theoretical analysis and experiments have led to the conclusion that the taste factor can be used as an effective and practical new threshold conversion mechanism for meta-heuristic algorithms. This study also establishes a Markov model to rigorously analyze the Flavoring Search Algorithm as a globally convergent algorithm from a mathematical point of view. Through experimental and analytical comparisons with other excellent optimizers on 30 test functions, as well as on 3 real-world engineering design problems and 1 path planning problem. The results show that the Flavoring Search Algorithm generally outperforms the tested competitors in solving benchmark functions and engineering problems, validating the utility of the proposed optimizer in solving challenging real-world problems.

Version control of speaker recognition systems

This paper discusses one of the most challenging practical engineering problems in speaker recognition systems — the version control of models and user profiles. A typical speaker recognition system consists of two stages: the enrollment stage, where a profile is generated from user-provided enrollment audio; and the runtime stage, where the voice identity of the runtime audio is compared against the stored profiles. As technology advances, the speaker recognition system needs to be updated for better performance. However, if the stored user profiles are not updated accordingly, version mismatch will result in meaningless recognition results. In this paper, we describe different version control strategies for speaker recognition systems that had been carefully studied at Google from years of engineering practice. These strategies are categorized into three groups according to how they are deployed in the production environment: device-side deployment, server-side deployment, and hybrid deployment. To compare different strategies with quantitative metrics under various network configurations, we present SpeakerVerSim, an easily-extensible Python-based simulation framework for different server-side deployment strategies of speaker recognition systems.

Multi‐strategy Grey Wolf Optimizer for Engineering Problems and Sewage Treatment Prediction

Grey wolf optimizer (GWO) is a highly valued heuristic algorithm in many fields. However, for some complex problems, especially high‐dimensional and multimodal problems, the basic algorithm has limited computational power and cannot get a satisfactory answer. In order to find a better solution, an improved algorithm based on GWO is proposed herein. Gaussian barebone, random selection and chaotic game mechanisms are introduced into the GWO algorithm to enhance the global search ability. The GWO enhanced by three mechanisms is called CBRGWO. To verify the performance of CBRGWO, using IEEE CEC 2017 as a test function, CBRGWO is compared to five GWO variants, five basic algorithms, six advanced algorithms, and four champion algorithms. CBRGWO is evaluated using the Friedman test and Wilcoxon signed‐rank test. Then, the stability of CBRGWO is analyzed. To verify that CBRGWO is still effective in practical application, CBRGWO is applied to five engineering problems and a water quality prediction problem. The experimental findings indicate that CBRGWO maintains excellent optimization ability in practical engineering problems.

Application of Finite Element Simulation Technology in the Teaching of Process Equipment and Control Engineering

Improving the teaching level of the professional courses of Process Equipment and Control Engineering has an important role in promoting the cultivation of compound and applied equipment talents. In this paper, finite element simulation technology is introduced into the teaching of Process Equipment and Control Engineering. Taking the finite element analyses of truss beam in the filled tower and fixed tube-sheet heat exchanger as examples, the complementary role of finite element simulation technology for the theoretical and practical teaching of professional courses was introduced. Teaching practice shows that the visualization of finite element analysis results can effectively stimulate the interest of students in learning, deepen their understanding and mastery of theoretical knowledge, improve the ability of students to use finite element simulation technology to solve practical engineering problems, and achieve the unity of teaching and learning in professional courses.

A New Reduced-Dimension Iteration Two-Grid Crank–Nicolson Finite-Element Method for Unsaturated Soil Water Flow Problem

The main objective of this paper is to reduce the dimensionality of unknown coefficient vectors of finite-element (FE) solutions in two-grid (CN) FE (TGCNFE) format for the nonlinear unsaturated soil water flow problem by using a proper orthogonal decomposition (POD) and to design a new reduced-dimension iteration TGCNFE (RDITGCNFE). For this objective, a new time semi-discrete CN (TSDCN) scheme for the nonlinear unsaturated soil water flow problem is first designed and the existence, stability, and error estimates of TSDCN solutions are demonstrated. Subsequently, a new TGCNFE format for the nonlinear unsaturated soil water flow problem is designed and the existence, unconditional stability, and error estimates of TGCNFE solutions are demonstrated. Next, a new RDITGCNFE format with the same FE basis functions as the TGCNFE format is built by the POD method and the existence, unconditional stability, and error estimates of RDITGCNFE solutions are discussed. Ultimately, the rightness of theory results and the superiority of the RDITGCNFE format are verified by two sets of numerical tests. It is worth noting that the RDITGCNFE format differs completely from all previous reduced-dimension methods, including the authors’ previous works. Therefore, the study of this paper can not only provide a new theoretical method for the dimensionality reduction of numerical models for nonlinear problems but also provide an algorithm implementation technology for the numerical simulation of practical engineering problems.

An Improved Boundary Element Method for Predicting Half-Space Scattered Noise Combined with Permeable Boundaries

The boundary element method is widely used in practical engineering problems, especially in the field of acoustics. For flow-induced noise, the main target of acoustic calculations is to solve the wave equation with the flow field information. However, the sound field distribution of noncompact structures in half-space is especially complex because of the strong scattering effect, while the object surface boundary integration often brings a large workload and generates numerical singularities. In this paper, an improved boundary element method for predicting the aeroacoustic noise of noncompact structures is proposed, which can consider the characteristic distribution of sound field induced by complex structures in half-space. The smooth permeable boundary surrounding the object is used as the integration boundary, while the scattering effect of the ground boundary is investigated by combining the mirror Green’s function method, and the numerical prediction of aeroacoustic noise is carried out for the dipole source and NACA0012 airfoil in half-space. Numerical results show that the far-field noise obtained by using the permeable surface is consistent with that obtained by integrating the direct object boundary under the influence of ground boundary scattering. The mirror image Green’s function method is able to finely capture the ground scattering effect, which has a significant effect on the sound field as the frequency increases.

Review of Research on Volume Shrinkage Regulation Mechanism of Ultra-high Performance Concrete

Ultra-high performance concrete (UHPC) is a cement-based composite building material with excellent durability and mechanical properties, but its autogenous shrinkage and drying shrinkage phenomenon have become a common problem in practical engineering. In order to solve this practical problem, new mineral admixtures and admixtures are usually added to UHPC, and the shrinkage of UHPC is effectively improved.