Practical Engineering Problems Research Articles

A closed-form expression of a ductile Fracture Limit Surface (FLS) for general plane stress deformation paths

Ductile fracture in metals is a complex phenomenon caused by the nucleation, growth, and aggregation of micron-sized voids. Existing theoretical models for fracture use experimentally determined Fracture Limit Curves (FLCs) in two-dimensional strain space, which require multiple curves to account for anisotropic effects. This paper proposes a practical closed-form analytical function for a Fracture Limit Surface (FLS) which has a conical shape in the Elastic Distortional Deformation Space (EDDS). The EDDS is a space generated by the components of the elastic distortional metric of the microstructural vectors that characterize elastic distortional deformations and the orientations of anisotropy. The FLS is determined by simulating only four experimental FLCs using a large deformation Eulerian formulation of the microstructural vectors and it depends only on the current state of elastic distortional deformation. It is shown that the failure limit points predicted by the FLS agreed very well with those predicted using a popular evolution equation for damage-based ductile fracture. Path-independence of the FLS is also examined using experimental data. In addition, the example of a B-pillar forming process is presented to show that the FLS can be applied to practical engineering problems with material points that experience general loading paths.

APSO-SL: An Adaptive Particle Swarm Optimization with State-Based Learning Strategy

Particle swarm optimization (PSO) has been extensively used to solve practical engineering problems, due to its efficient performance. Although PSO is simple and efficient, it still has the problem of premature convergence. In order to address this shortcoming, an adaptive particle swarm optimization with state-based learning strategy (APSO-SL) is put forward. In APSO-SL, the population distribution evaluation mechanism (PDEM) is used to evaluate the state of the whole population. In contrast to using iterations to just the population state, using the population spatial distribution is more intuitive and accurate. In PDEM, the population center position and best position for calculation are used for calculation, greatly reducing the algorithm’s computational complexity. In addition, an adaptive learning strategy (ALS) has been proposed to avoid the whole population’s premature convergence. In ALS, different learning strategies are adopted according to the population state to ensure the population diversity. The performance of APSO-SL is evaluated on the CEC2013 and CEC2017 test suites, and one engineering problem. Experimental results show that APSO-SL has the best performance compared with other competitive PSO variants.

Theory and practice of BIM skills of construction management professional based on conceive–design–implement–operate engineering teaching mode

AbstractBuilding information modeling (BIM), as the core technology for digital transformation and development of the construction industry, is in vigorous development. This study analyzes the current situation of teaching BIM to construction management professionals in universities. Based on the CDIO (conceive–design–implement–operate) engineering education concept, a new teaching system of BIM technology for construction management professionals is proposed. It is composed of a diversified teaching mode of implanting BIM technology in professional courses, establishing a school–enterprise cooperation platform, expert linkage, BIM team construction, skill competition, and transformation of scientific research achievements, and provides a reference for the reform of practical teaching. It is proved that this education mode improves students' learning enthusiasm, engineering practice ability, solidarity and ability to solve practical engineering problems, cultivates outstanding talents in BIM technology, drives students' employment, and provides innovative talents in BIM technology needed for the development of the construction industry.

DGS-SCSO: Enhancing Sand Cat Swarm Optimization with Dynamic Pinhole Imaging and Golden Sine Algorithm for improved numerical optimization performance

This paper introduces DGS-SCSO, a novel optimizer derived from Sand Cat Swarm Optimization (SCSO), aiming to overcome inherent limitations in the original SCSO algorithm. The proposed optimizer integrates Dynamic Pinhole Imaging and Golden Sine Algorithm to mitigate issues like local optima entrapment, premature convergence, and delayed convergence. By leveraging the Dynamic Pinhole Imaging technique, DGS-SCSO enhances the optimizer's global exploration capability, while the Golden Sine Algorithm strategy improves exploitation, facilitating convergence towards optimal solutions. The algorithm's performance is systematically assessed across 20 standard benchmark functions, CEC2019 test functions, and two practical engineering problems. The outcome proves DGS-SCSO's superiority over the original SCSO algorithm, achieving an overall efficiency of 59.66% in 30 dimensions and 76.92% in 50 and 100 dimensions for optimization functions. It also demonstrated competitive results on engineering problems. Statistical analysis, including the Wilcoxon Rank Sum Test and Friedman Test, validate DGS-SCSO efficiency and significant improvement to the compared algorithms.

P–S–N curves fitting method of small samples obeying Weibull distribution

AbstractA three‐parameter Weibull small sample aggregation method is proposed to address the problem of small sample fatigue data in practical engineering. First, based on the empirical assumption that the shape parameter is a constant and data equivalent conversion and aggregation, the small sample fatigue life data are converted into “equivalent large samples.” Second, through the linear relationship between logarithmic scale parameter and stress, an iterative procedure is established to estimate the scale parameters of small sample data. At the same time, the moment and rank estimation methods are used to solve the location parameters quickly. Finally, P–S–N curves with different survival rates are given, and a 7‐series aluminum alloy is selected to verify the correctness and reliability of the method proposed in this study. The results show that the predicted life obtained by the method still has high accuracy under a high survival rate, which can provide a theoretical basis for solving practical engineering problems.

Distributed control of a plate platform by NES-cells

Nonlinear energy sink (NES) has become a good passive vibration control technique with great potential. For a huge structure, it is unpractical to control the vibration by just one NES, let alone the structure has multi-modes. In this work, a technique who uses distributed nonlinear energy sink cells (NES-cells) to control the multi-modal resonance of the floating raft is proposed. The floating raft is simplified as a corner-point supported plate. Natural modes of it guide the distribution of NES-cells to make NESs locate on the position with strong vibration, which enhances the coupling ship between the plate platform and NES-cells. An example of installing NES-cells guiding by the first-two modes of the corner-point supported plate is presented in this paper. It is compared with the vibration reduction effect by a single NES. To make a fair comparison, total mass of NES-cells is the same with that of the single NES. The results show that NES-cells can deal with multi-modal vibration reduction better. The experimental results also fully illustrate this view. At the same time, the reaction of each NES-cell to the plate is effectively dispersed while comparing with the single NES, which decreases the stress at the attachment points and is more suitable for engineering applications. The number of NES-cells also has an effect on the vibration reduction efficiency. When the design parameters are fixed, there exists an optimal number to make the vibration reduction efficiency tend toward saturation. The multimodal vibration control of the plate platform is effectively solved by the proposed NES-cells technique. It also provides guidance for other practical engineering problems.

A two-level surrogate framework for demand-objective time-variant reliability-based design optimization

Complex engineering problems in the real world often involve uncertainties and require time-consuming simulations and experiments, hindering the efficiency of constraints processing. Additionally, practical engineering problems may have varying demands that pose new challenges for dealing with dynamic environments. However, most existing methods focus on immediate demands, making it inevitable to undergo tedious procedures to find feasible solutions. To address these issues, this paper proposes a demand-objective time-variant reliability-based design optimization framework to meet different demands in varying environments. Meanwhile, a corresponding two-level surrogate-based solving strategy is developed to reduce the computational resources required. The framework consists of two stages: time-variant reliability-based constraint handling and demand-objective optimization. An adaptive two-level surrogate method is proposed for time-variant reliability-based constraint handling by combining Kriging to reduce computational costs associated with evaluating constraints. This paper introduces moderate, conservative, and radical models for demand-objective optimization, combining the two-level surrogate method to deal with dynamic cost functions with different demands. Also, a new constrained minimax optimization method is developed for the radical model, which is the trickiest but very useful in practical engineering problems so that the algorithm can converge quickly. Finally, some examples are demonstrated to specify the proposed framework in applications.

Experimental and numerical studies of impact loading on single-layer reticulated shells with aluminium alloy gusset joints

Aluminium alloy materials have found extensive application in large-span space grid structures. While research on the mechanical properties of aluminium alloy materials and components is generally well-established, there remains an incomplete understanding of the overall structure. Therefore, further investigation into the overall performance of aluminium alloy reticulated shells is crucial to promote their use in spatial structures, guiding structural design and addressing practical engineering challenges. This study focuses on the design of a single-layer aluminium alloy reticulated shell with a 2 m span, which was subjected to an impact test to examine its dynamic response and failure mode. By comparing and analysing stress, acceleration, and displacement time-history curves obtained from these different test conditions, the dynamic response behavior of the reticulated shell was determined. To simulate the impact on reticulated shells, explicit dynamic analysis was conducted using non-linear finite element software LS-DYNA. Based on the deformation characteristics and failure phenomena observed during the impact test, three distinct failure modes were defined for the single-layer reticulated shell. Through parameter analysis using the validated finite element model, it was determined that increasing the thickness (tp) of the aluminium alloy gusset plate or reducing the radius (R) of the gusset plate effectively enhances the overall stiffness and impact resistance of the single-layer reticulated shell. Overall, this research contributes to a deeper understanding of the performance of aluminium alloy reticulated shells under impact loads. The findings provide valuable insights for guiding structural design and addressing practical engineering problems in the field of spatial structures.

Implementation and Validation of Explicit Immersed Boundary Method and Lattice Boltzmann Flux Solver in OpenFOAM

In this work, the explicit boundary-condition-enforced immersed boundary method (EIBM) and the lattice Boltzmann flux solver (LBFS) are integrated into OpenFOAM to efficiently solve incompressible flows with complex geometries and moving boundaries. The EIBM applies the explicit technique to greatly improve the computational efficiency of the original boundary-condition-enforced immersed boundary method. In addition, the improved EIBM inherits the accurate interpretation of the no-slip boundary condition and the simple implementation from the original one. The LBFS uses the finite volume method to discretize the recovered macroscopic governing equations from the lattice Boltzmann equation. It enjoys the explicit relationship between the pressure and density, which avoids solving the pressure Poisson equation and thus saves much computational cost. Another attractive feature of the LBFS lies in its simultaneous evaluation of the inviscid and viscous fluxes. OpenFOAM, as an open-source CFD platform, has drawn increasing attention from the CFD community and has been proven to be a powerful tool for various problems. Thus, implementing the EIBM and LBFS into such a popular platform can advance the practical application of these two methods and may provide an effective alternative for complicated incompressible flow problems. The performance of the integrated solver in OpenFOAM is comprehensively assessed by comparing it with the widely used numerical solver in OpenFOAM, namely, the Pressure-Implicit with Splitting of Operators (PISO) algorithm with the IBM. A series of representative test cases with stationary and moving boundaries are simulated. Numerical results confirm that the present method does not have any streamline penetration and achieves the second-order accuracy in space. Therefore, the present method implemented in the open-source platform OpenFOAM may have good potential and can serve as a powerful tool for practical engineering problems.

State Estimation and Detectability of Networked Discrete Event Systems With Multi-Channel Communication Networks

In this paper, we extend existing results on detectability of Discrete Event Systems (DES) to Networked Discrete Event Systems (NDES), a class of DES whose communication between the plant and the agent (supervisor, diagnoser, prognoser, etc) is carried out through a network that can have several channels. We assume that each channel is subject to different communication delays, which may cause changes in the order of the event observations with respect to their actual occurrences in the system automaton, and also that event transmission may be lost in the communication channels causing loss of observations. We follow a previously presented approach to construct an equivalent nondeterministic untimed automaton, and, based on this model, we present a method for the state estimation of NDES. We extend existing D-detectability definitions of DES to NDES subject to delays and loss of observation, referred here to as strong networked D-detectability, weak networked D-detectability, strong periodic networked D-detectability, weak periodic networked D-detectability. Finally, necessary and sufficient conditions for the networked D-detectability notions proposed here are proposed and algorithms for their verification are developed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —State estimation is an important problem that appear in several practical applications, e.g. industrial systems, aircraft control and business management. Of current interest is the problem of distinguishing in real time if the system is not in pairs of states, the so-called D-detectability. In practical terms, it is not necessary to actually know the current state of the system but if the system may not be in two or more conflicting states. With the use of communication networks, additional features such as communication delay must also be taken into account in the process of performing D-detectability. In this paper we consider discrete event systems, a class of systems used to model a wide class of practical engineering problems whose dynamic evolution is driven by event occurrences, that are embedded in a communication network; we will refer to these systems as networked discrete event systems. In this regard, we generalize existing notions of D-detectability, and we call them networked D-detectability. Algorithms for networked D-detectability verification and a method to perform online state estimation are given in the paper.

Effects of Plateau Climate Environment on Concrete Performance

Transportation construction on the Qinghai-Tibet Plateau has always attracted much attention, and the completion of the Qinghai-Tibet Plateau Railway, one of the four major projects in the new century, is bound to accelerate the economic development of the plateau region and the completion of more infrastructure. Therefore, concrete materials are widely used in the construction of the plateau region, and the long-term performance of its materials and structures in this environment is worthy of attention. The plateau climate has three main characteristics: large temperature difference, low air pressure, and low humidity, which makes the concrete withstand different degrees of damage from preparation to use, thereby reducing the overall carrying capacity and safety of the building structure, reducing its service life, and increasing the risk of building erosion by the environment. This paper mainly summarizes and analyzes the factors affecting the performance of concrete in the plateau environment, that is, temperature, humidity and air pressure, and on this basis, explores their comprehensive influence. The comprehensive study shows that temperature could cause a freeze-thaw cycle of concrete, resulting in continuous accumulation of internal damage of concrete; Low temperature could cause water migration in concrete, easily causing concrete shrinkage and carbonization; Low air pressure could reduce the shrinkage and durability of concrete structures. Based on a series of theoretical studies, this paper further discusses the relevant solutions applicable to various practical engineering problems.

Numerical modeling of stress corrosion cracking in steel structures with phase field method

This study uses the phase field method to present a coupled mechano-electro-chemical formulation for predicting stress corrosion cracking (SCC) phenomena in steel structures. SCC constitutes an intricate damage process stemming from the interplay between mechanical loading and corrosion within an aggressive environment. The formulation presented herein introduces a phase-field parameter that aggregates the mechanical and electrochemical influences. To achieve this goal, the internal energies governing the SCC phenomenon are separated into elastic-damage strain energy, interfacial reaction energy, and energy resulting from changes in corrosion ion concentration. The Allen-Cahn equation is modified to include all energy contributions and calculate the phase-field parameter. Furthermore, a specific interfacial kinetic coefficient is introduced to the mechanical energy to consider corrosion current effects on mechanical properties. The Cahn-Hilliard equation is applied to model the corrosion ion concentration in the domain, and the mechanical state of the body is obtained by solving the equilibrium equations. Several numerical examples are presented to indicate the robustness and accuracy of the proposed formulation. In this respect, the method is applied to predict crack propagation resulting from SCC in two practical engineering problems, yielding promising results.

Reform and Reconstitution of Core Courses in Mechanical and Electronic Engineering Under the Background of New Engineering

With the arrival of the intelligence era of Industry 4.0, social development has shown complex and diversified characteristics, gradually forming an innovation ecological environment constructed by vertical interaction of multi-layer innovation systems and horizontal collaboration of multiple organizations and departments, which has posed extremely challenging requirements for higher engineering education to cultivate engineering talents with comprehensive engineering qualities who can solve complex engineering problems. However, practical engineering problems may involve a complex knowledge chain of interdisciplinary and multi-disciplinary cross-coupling. Therefore, higher engineering education needs to form a new interdisciplinary and multi-disciplinary integrated engineering innovation talent training system. Based on the characteristics of the mechanical and electronic engineering major, we will reshape and reconstruct the core courses of the mechanical and electronic engineering major. Two core courses are formed: Drive and Measurement and Control I and Drive and Measurement and Control II, with information flow and energy flow as the main lines, following up with the comprehensive practical curriculum system based on the unity of knowledge and practice and ability-oriented thinking, supporting teaching objectives, promoting students’ individual development, and providing guidelines for relevant curriculum reforms.

An adaptive hybrid surrogate model for FEA of telescopic boom of rock drilling jumbo

The rapid collaborative optimization (CO) has an increasing demand for high-fidelity surrogate models. However, the traditional surrogate model cannot be applied to all working conditions due to the limitations of model applicability. A hybrid surrogate model is proposed, which uses the attention mechanism to automatically decide the weights of the sub models according to the working conditions to ensure its approximation ability under all working conditions. First, according to the characteristics of the finite element analysis (FEA) parameters, a comprehensive design of experiment (DOE) is proposed, which ensures the space-filling property of the samples. Secondly, a Self-attention artificial neural network (ANN) is proposed to automatically adjust the weights of sub-surrogate models, improving the attention to the working condition-related features. The proposed Self-attention ANN is a general framework that can provide support for the adaptive weight decision in other equipment simulation hybrid surrogate models. The experiment on the database shows that the error of the two hybrid surrogate models established by the proposed method is 36.04% and 33.31% lower than that of the advanced model, respectively, and is significantly superior to other methods. This achievement not only combines the spatial approximation ability of sub models to establish nonlinear model, achieving the purpose of high-fidelity simulation of FEA systems, but also enables surrogate model covering complete space using limited samples, making the model suitable for various practical engineering problems. In summary, the proposed method has obvious advantages in solving existing problems, provides strong support for research and practical applications.

Research on a Grading Evaluation System for Water Inflow in Three-Hole Parallel Subsea Tunnels Considering Inter-Tunnel Influence

Water inflow analysis is critical for subsea tunnel construction. However, existing studies largely concentrate on the inflow issues pertaining to single-hole tunnels. To address current practical engineering problems, a three-hole parallel configuration is common for subsea tunnels, which may alter water inflow patterns due to the influence of their seepage fields. Herein, numerical simulations are conducted to investigate the water inflow characteristics of a three-hole parallel subsea tunnel. Specifically, the impact of various factors on the water inflow phenomenon, including the permeability coefficient of the surrounding rock, the depth of the seawater, the depth of the tunnel, the spacing between tunnels, and the relative size of the tunnels, are comprehensively studied. Furthermore, based on the principles of the analytic hierarchy process and fuzzy mathematics, an exhaustive assessment framework is developed to evaluate the water inflow of three-hole parallel subsea tunnels. The results indicate that there is a mutual influence between the three parallel tunnels, differing from the predicted water inflow, which is overestimated in a single-hole tunnel model. Therefore, the water inflow assessment for a three-hole parallel subsea tunnel system should account for the inter-tunnel influences. The findings of this study offer valuable insights for the design of waterproofing and drainage systems in three-hole subsea tunnels.

Application of high-credible statistical results calculation scheme based on least squares Quasi-Monte Carlo method in multimodal stochastic problems

Solving stochastic problems based on multimodal distributions with high accuracy and efficiency is the focus of current research in the stochastic field. However, since exact statistical results in practical engineering problems are unknown, how to efficiently obtain statistical results with high credibility is of great importance. Therefore, a high-credible statistical results calculation scheme is proposed in this paper. First, the multimodal model is transformed into a weighted sum form of multiple unimodal models by Gaussian mixture model. Then, a high-credible statistical results calculation scheme is designed based on the reusability of low-discrepancy sequences in the quasi-Monte Carlo (QMC) method. The scheme calculates the coefficient of variation for the random response statistical results of the unimodal model across various sample ranges. When the coefficient of variation is less than the tolerance error, which indicates low fluctuation, the statistical results are deemed to have converged to the exact solution. In order to further accelerate the calculation efficiency of the proposed scheme, this paper proposes a least squares quasi-Monte Carlo (LSQMC) method to yield high accuracy statistical moments. Compared with the averageness weights in the QMC method, the non-averageness weights obtained by LSQMC method can more effectively reflect the numerical Characteristics of the random variables and the location distribution of the sampling points in the probability space. Finally, numerical examples have demonstrated that the high-credible statistical results calculation scheme based on the LSQMC method can obtain the high accuracy statistical moments with high efficiency in multimodal stochastic problems.

Exploring safety capacity from a risk and safety information integration perspective: Connotation, dimension mining and dimensionality reduction

With the development of safety science, the concept of safety capacity (SC) has been proposed. SC has been applied in many fields. Concurrently, there has been an active and robust development of research pertaining to it. However, SC research mainly focuses on practical engineering problems, and SC research is only regarded as the establishment of a quantitative model for system safety. Hence, the theoretical study of SC is ignored. Indeed, the integrated theoretical system and connotation of SC are lacking. In this study, an attempt is made to enrich the theoretical connotation of SC. The following aspects of theoretical exploration are included: (a) The theoretical connotation of SC is redefined so that the lack of theory with respect to SC can be made up. Moreover, research directions of SC are indicated as dimension mining (DM) and dimensionality reduction (DR). (b) The application value of safety similarity theory in the DM of SC is expounded according to the principle of analogism. Core principles of DM are proposed and explained. (c) Common methods of DR are summarized, and the one-sided cognition of DR for the current SC research is corrected. A case study of an explosion accident caused by a boiler water shortage is employed for further discussion. For the investigation of the explosion accident using fault tree analysis (FTA), DR of SC is utilized to provide a more comprehensive explanation. Therefore, the proposed SC is proven to be practical for the analysis of system safety. The results obtained from this study have important implications for research and practice of SC.

Wall-modeled large-eddy simulation of turbulent non-Newtonian power-law fluid flows

For high-fidelity predictions of turbulent flows in complex practical engineering problems, the Wall-Modeled (WM) Large-Eddy Simulation (LES) has aroused great interest. In the present study, we prove that the conventional Wall-Stress Models (WSMs) developed for WMLES of Newtonian fluids fail to predict the shear-thinning-induced drag reduction in power-law fluids. Therefore, we propose novel algebraic, integrated, and Ordinary-Differential-Equation (ODE) WSMs, for the first time, for WMLES of power-law Non-Newtonian (NN) fluids and assess their performance against reference Wall-Resolved (WR) LES solutions. In addition, the effects of the key model parameters, including the WSM type, sampling height, sampling cell, and axial grid resolution are explored, and it is revealed that turbulent NN flow predictions have a much higher sensitivity to the choice of WSM, compared to their Newtonian counterparts. It is manifested that, in contrast to WRLES, accurate modeling of the mean apparent and subgrid-scale NN viscosities in the Non-Newtonian ODE (NNODE) model can improve the predictions considerably. Therefore, closures with lower uncertainties on coarse WMLES grids are sought for these terms. Finally, the best performance for the present test cases is obtained via the integrated NN WSM, sampling at the lower edge of the log layer within the third off-the-wall grid cell. Nevertheless, the new NNODE WSM can have an advantage in the presence of non-equilibrium effects in more complex problems.

Improved Dual-Population Genetic Algorithm: A Straightforward Optimizer Applied to Engineering Optimization

Aiming at the current limitations of the dual-population genetic algorithm, an improved dual-population genetic algorithm (IDPGA) for solving multi-constrained optimization problems is proposed by introducing a series of strategies, such as remaining elite individuals, a dynamic immigration operator, separating the objective and constraints, normalized constraints, etc. We selected 14 standard mathematical benchmarks to check the performance of IDPGA, and the results were compared with the theoretical value of CEC 2006. The results show that IDPGA with the current parameters obtains good solutions for most problems. Then 6 well-known engineering optimization problems were solved and compared with other algorithms. The results show that all of the solutions are feasible, the solution precision of IDPGA is better than other algorithms, and IDPGA performs with good efficiency and robustness. Meanwhile, no parameters need to be ignored when IDPGA is applied to solving engineering problems, which is enough to prove that IDPGA is suitable for solving engineering optimization. A Friedman test showed no significant difference between IDPGA and six algorithms, but significant differences between IDPGA and seven other algorithms; thus, a larger number of evaluators will be needed in the future. In addition, further research is still needed about the performance of IDPGA for solving practical large-scale engineering problems.

Implementation of Chaotic Reverse Slime Mould Algorithm Based on the Dandelion Optimizer.

This paper presents a hybrid algorithm based on the slime mould algorithm (SMA) and the mixed dandelion optimizer. The hybrid algorithm improves the convergence speed and prevents the algorithm from falling into the local optimal. (1) The Bernoulli chaotic mapping is added in the initialization phase to enrich the population diversity. (2) The Brownian motion and Lévy flight strategy are added to further enhance the global search ability and local exploitation performance of the slime mould. (3) The specular reflection learning is added in the late iteration to improve the population search ability and avoid falling into local optimality. The experimental results show that the convergence speed and precision of the improved algorithm are improved in the standard test functions. At last, this paper optimizes the parameters of the Extreme Learning Machine (ELM) model with the improved method and applies it to the power load forecasting problem. The effectiveness of the improved method in solving practical engineering problems is further verified.