Engineering Computations

doi: 10.1108/ec-05-2025-995pmid: N/A

Najibi, Amir; Huang, Bingfeng; Shojaeefard, Mohammad Hassan

doi: 10.1108/ec-06-2024-0537pmid: N/A

We conducted transient thermal stress assessment of a 2D-FG short axisymmetric cylinder.Design/methodology/approachThe higher-order finite element analysis of nonlinear thermo-elastic response on a two-dimensional functionally graded (2D-FG) short axisymmetric cylinder, incorporating temperature-dependent (TD) material properties and the Mori–Tanaka scheme has been utilized.FindingsWhen taking into account TD and TID, the coefficient of thermal expansion (CTE) combined with temperature differences on the inner surface plays an important role in formative thermal stresses. Neglecting temperature dependency results in substantial discrepancies in thermal stress levels.Originality/valueOur research focuses on temperature, displacements, thermal stresses and Drucker–Prager (D-P) stress to determine the effect of material gradation, temperature dependency and cylinder strength under severe thermal loading.

Xiao, Zhenyu; Liao, Yulei; Zhang, Qiang

doi: 10.1108/ec-06-2024-0542pmid: N/A

Utilizing USV Swarm for escort operations represents a maritime security model that can provide comprehensive, all-weather, multidimensional protection for vessels at sea.Design/methodology/approach(1) Addressing the problem of intercepting maritime threat targets, this paper proposes a method for calculating interception points based on DCPA (Distance to Closest Point of Approach). (2) Furthermore, to address the clustering of interception points, this paper introduces a TK-means algorithm based on the threat degree. This algorithm, grounded in the threat degree calculation formula designed in this paper, improves the initialization strategy and allocation rules of the traditional K-means algorithm, enabling interception points with similar threat levels to be grouped together.Findings1) Simulation comparisons demonstrate that by considering the relative velocity and position between the maritime threat target and the escort object, the proposed method yields more reasonable interception points, thereby effectively enhancing the navigational safety. (2) Simulation comparison experiments indicate that the improved algorithm more effectively clusters the interception points.Originality/valueIn summary, the proposed calculation method and the improved algorithm better meet security decision-making needs and further enhance the navigational safety of the escorted vessels.

Qian, Cheng; He, Yiqian; Wang, Xiaoteng; Yang, Haitian

doi: 10.1108/ec-08-2024-0752pmid: N/A

This research aims to advance the Isogeometric Scaled Boundary Finite Element Method (IG-SBFEM) by introducing a partitioning approach for solving elastic and viscoelastic problems with cyclic symmetry. The study seeks to mitigate the computational burden associated with eigenvalue problems by proving the block-circulant nature of the system matrices. Through partitioning, the solution scale is reduced, and the study further explores the integration of the Lagrange multiplier scheme and temporally adaptive algorithms (TPAA) to handle complex displacement constraints and viscoelastic properties, ensuring efficient computation even in cyclically symmetric structures.Design/methodology/approachThe methodology centers on the development of a partitioning algorithm integrated into the Isogeometric Scaled Boundary Finite Element Method (IG-SBFEM). By leveraging the block-circulant nature of matrices under cyclic symmetry, the study reduces the solution scale of both eigenvalue and system equations. Displacement constraints are addressed through a Lagrange multiplier scheme. The approach further applies a temporally piecewise adaptive algorithm (TPAA) to convert viscoelastic problems into elastic problems, allowing efficient numerical analysis and computation for cyclically symmetric structures.FindingsThis study finds that the partitioning IG-SBFEM efficiently addresses elastic and viscoelastic problems with cyclic symmetry, reducing both the solution scale and computational cost. The block-circulant property of the matrices enables the decomposition of complex equations into smaller sub-problems, improving performance. Additionally, the Lagrange multiplier scheme successfully handles displacement constraints. The temporally piecewise adaptive algorithm (TPAA) further enhances efficiency by transforming viscoelastic problems into elastic equivalents. Numerical results confirm that this approach achieves accurate solutions with reduced computational effort.Originality/valueThe originality of this research stems from the innovative partitioning algorithm that reduces the computational burden of IG-SBFEM in elastic and viscoelastic problems with cyclic symmetry. By proving the block-circulant nature of the matrices and integrating the Lagrange multiplier scheme and TPAA, the study offers a unique approach to efficiently solve complex problems. The value of this work lies in its ability to provide accurate results with reduced computational effort, making it a valuable contribution to advanced numerical analysis techniques.Highlights(1)The first time to utilize cyclic symmetry in reduced order modelling of IG-SBFEM for elastic and viscoelastic problems.(2)Block-circulant eigenvalue and stiffness Matrices under a symmetry-adapted reference co-ordinate system.(3)Partitioning algorithms to solve eigenvalue and system equations with smaller solution scale and less computational expense.(4)No restriction on distribution of displacement constraints, cyclically symmetric or not.(5)A steady temporal solution accuracy provided by TPAA for viscoelastic problems.

Karimi-Asrami, Ali; Jafari-Talookolaei, Ramazan-Ali

doi: 10.1108/ec-10-2024-0981pmid: N/A

The purpose of this study is to analyze the free and forced vibration behavior of frames constructed from functionally graded porous materials (FGPMs). By utilizing Timoshenko beam theory, this research aims to provide a comprehensive understanding of how varying porosity affects the dynamic response of the frame, contributing valuable insights into the design and optimization of FGPM structures in engineering applications.Design/methodology/approach This research employs Timoshenko beam theory to calculate the kinetic and potential energies of frame members with different porosity types. A specific finite element approach is used to derive mass and stiffness matrices and the force vector. The assembled global matrices facilitate the formulation of equations that account for continuity at joints. The Newmark method is applied to determine the time response of the system, allowing for a detailed analysis of the vibration characteristics influenced by various parameters.Findings The study demonstrates that the vibration characteristics of FGPM frames are significantly influenced by porosity and applied forces. The results reveal a strong correlation with previous studies for simpler configurations, validating the model’s accuracy. Additionally, the analysis highlights how varying material properties and loading conditions affect the dynamic behavior of the structure, providing crucial data for engineering applications in vibration control and structural optimization.Originality/value This paper offers a novel contribution to the field of structural dynamics by focusing on FGPMs, which have been less explored in vibration analysis. The integration of Timoshenko beam theory with detailed finite element methods enhances the understanding of FGPM behavior under dynamic loading. The findings provide valuable guidelines for engineers and researchers seeking to design advanced materials with tailored vibration properties, thereby expanding the practical applications of FGPMs in modern engineering challenges.

Jiang, Hao; Lin, Sicheng; Chen, Jing; Miao, Xiren

doi: 10.1108/ec-12-2024-1120pmid: N/A

Multi-unmanned aerial vehicle (UAV) missions aim to optimize the execution of multiple missions using limited resources, making it possible to balance the objectives of each mission while minimizing the time to completion.Design/methodology/approachAn algorithm combining cluster analysis and differential evolution particle swarm optimization (DE-PSO) is proposed to solve this problem.FindingsThe investigative study is based on the homogenization of multi-UAV missions in multi-objective task distribution to reduce the total elapsed time.Practical implicationsThis method effectively reduces task time and provides a solution for multi-UAV operations in transmission line cooperation.Originality/valueA novel heuristic algorithm is proposed, and the algorithm fully considers the clustering characteristics under multi-region and the positional relationship characteristics of scene target distribution. It also fully considers the physical characteristics of airport location and UAV power to uniformly optimize the time.

Khalid, Fatima; Khan, Asad-ur-Rehman; Fareed, Shamsoon

doi: 10.1108/ec-07-2024-0679pmid: N/A

This study aims to improve the performance of the existing constitutive model of recycled aggregate concrete (RAC) by estimating the parameters used in the model using artificial neural networks (ANN).Design/methodology/approachThe use of RAC as structural concrete is gaining importance as it contributes towards several sustainable development goals (SDGs) defined by the United Nations. Several parameters are required to model the complete behaviour of concrete under multiaxial loading. The existing RAC model proposed by the authors requires four parameters to define the complete stress–strain curve. Concrete compressive strength (fc/) and Modulus of elasticity are the most important properties of concrete used in the design of concrete structures, whereas α and β are the other two parameters to control the damage growth rate and to capture the behaviour of RAC at respective peak stress levels. ANNs were trained to estimate these parameters by using the data available in the literature. Proposed ANN models were first validated and then used in the constitutive model to estimate these four parameters.FindingsProposed ANN models accurately estimated the parameters needed to improve the predicting capabilities of existing RAC constitutive models. The overall performance of the constitutive model in terms of peak stresses improved with the use of parameters predicted by ANN models.Originality/valueANN has been used for the estimation of parameters that directly influence the behaviour of RAC under multiaxial states of stress instead of conventional regression techniques.

Wang, Min; Liu, Weixia; Ning, Jida; Yu, Shihang; Tang, Shikai; Li, Jiaqi

doi: 10.1108/ec-10-2024-0937pmid: N/A

The purpose of this study is to improve the accuracy and generalization ability of intelligent fault diagnosis models for rolling bearings under varying operating conditions. By integrating multidimensional features through multi-view learning (MVL) and utilizing Mamba feature fusion, the method aims to address the challenge of data distribution differences that reduce diagnostic accuracy when working conditions change. The approach also incorporates domain adaptation techniques to align source and target domain data, ensuring robust and accurate fault detection. This work seeks to enhance fault diagnosis performance, reduce maintenance costs and ensure operational continuity in industrial environments.Design/methodology/approachThis paper proposes an integrating multidimensional feature method based on multi-view learning (IMDF-MVL) for intelligent fault diagnosis of rolling bearings. MVL is used to capture multidimensional fault features, while Mamba feature fusion combines features from different views to enhance the model’s generalization ability. Domain adaptation is applied to align data distributions between source and target domains. Experimental validation is conducted by comparing IMDF-MVL with state-of-the-art methods, demonstrating its superior diagnostic accuracy and robustness under varying conditions. The proposed approach aims to provide an effective solution for real-world industrial fault detection applications.FindingsThe findings of this study demonstrate that the proposed IMDF-MVL method significantly outperforms existing fault diagnosis models, such as DCTLN, NCNN, InDo-DDM, GMVTDA and RTDGN, in both source and target domain datasets. On the source domain, IMDF-MVL achieves an average diagnostic accuracy of 99.98 and 99.89%, highlighting its high efficiency and stability. In target domain transfer experiments, even without target domain fine-tuning, the method achieves diagnostic accuracies of 93.71 and 63.40%, indicating its robustness under changing operating conditions. These results confirm the method’s ability to maintain diagnostic performance and improve generalization across diverse scenarios.Originality/valueThe originality of this study lies in the integration of multidimensional feature extraction through multi-view learning (MVL) and Mamba feature fusion, addressing the challenge of fault diagnosis under varying operating conditions. By leveraging domain adaptation techniques, the proposed IMDF-MVL method aligns data distributions between source and target domains, enhancing model generalization. This work contributes to the advancement of intelligent fault diagnosis by providing a robust and effective approach for rolling bearings, with potential applications in other rotating machinery. The method’s ability to maintain high diagnostic accuracy across diverse conditions offers significant value in industrial operation and maintenance.

Wang, Hui; Zhao, Li; Peng, Qihui

doi: 10.1108/ec-08-2024-0709pmid: N/A

This paper aims to contribute primarily in two areas: using multiple new strategies to devise an improved sand cat swarm optimization (ISCSO) algorithm with superior performance and exploring its applicability to the path planning issue that requires finding a safe route with the shortest length for an agricultural robot.Design/methodology/approachThis paper designs and introduces multiple new strategies to modify the sand cat swarm optimization (SCSO) algorithm from different perspectives. Subsequently, 23 well-known standard benchmark function experiments and CEC2021 function experiments are performed using the ISCSO algorithm and another five approaches, encompassing the SCSO algorithm, the Harris Hawks optimization (HHO) algorithm, the GWO, the Snake Optimizer (SO) and the Zebra Optimization Algorithm (ZOA). Then, the results are analyzed to showcase the efficacy and superiority of the ISCSO algorithm. On this basis, we also explore the effect of applying the ISCSO algorithm to puzzle out the agricultural robot path planning issue.FindingsAll experimental results manifest that, except for a few functions among the 23 standard benchmark function experiments and CEC2021 function experiments, the ISCSO algorithm performs better overall than the other five algorithms with regard to optimization ability, convergence rate and stability. Moreover, the ISCSO algorithm is better suited for addressing the path planning issue encountered by the agricultural robot and exhibits stronger optimization ability in comparison to the SCSO algorithm.Originality/valueThis paper devised a novel improved SCSO algorithm with better performance and explored its applicability to the path planning issue that requires finding a safe route with the shortest length for an agricultural robot.

Bala Sai Sankar, Bellagubbala; Karunakar, Perumandla

doi: 10.1108/ec-09-2024-0834pmid: N/A

The study aims to obtain a semi-analytical solution for the fuzzy time-fractional Fisher equation (FtfFE) considering uncertain coefficients involved in initial condition as a triangular fuzzy number (TFN).Design/methodology/approachFractional reduced differential transform method (FRDTM) has been used to find the convergent series solution for the time-fractional Fisher equation in both crisp and fuzzy environment.FindingsThe convergent series solution has been found using FRDTM and the obtained fuzzy FRDTM solution is compared with exact for integer case α = 1. The lower and upper bound solutions of FtfFE for different spatial and temporal values at α = 0.75 are provided. The relation between both bounds of the solution and fractional order α is evaluated.Originality/valueApplication of FRDTM to the fuzzy time-fractional Fisher equation to study the behavior and other characteristics of the solution.