Complicated Engineering Problems Research Articles

Exponential-trigonometric optimization algorithm for solving complicated engineering problems

This article presents an innovative optimization algorithm called the Exponential-Trigonometric Optimization (ETO) algorithm, which is based on a sophisticated combination of exponential and trigonometric functions. The ETO algorithm is structured to strike a balance between two important stages: exploration and exploitation, a challenge that many other optimization algorithms have also sought to address. ETO incorporates several additional random and adaptive variables, resulting in better performance compared to existing algorithms. The algorithm's performance and potential were validated through three phases of testing. First, the ETO algorithm is evaluated against seven other meta-heuristic algorithms, including SCHO, SCA, AOA, GWO, HHO, HGS, and GJO, using 23 classical benchmark functions of varying sizes. These benchmark functions are categorized into three main groups: "unimodal" (F1 to F7), "multi-modal" (F8 to F13), and "fixed-dimension" (F14 to F23). Next, the ETO algorithm was tested on CEC2019 and CEC2020 functions and compared with 7 algorithms mentioned above to evaluate performance. In addition, the ETO algorithm is tested on the CEC2017 benchmark functions with dimensions of 10-D, 30-D, and 50-D. It is then compared and evaluated against other potential algorithms, such as SHADE, LSHADE, and JADE, using the Wilcoxon rank-sum test. Finally, the ETO algorithm is utilized to tackle five intricate engineering problems to show its robustness, including optimizing the shape of aircraft wings to increase lift. These results have demonstrated the effectiveness and potential of the ETO algorithm, yielding outstanding results and ranking first among other algorithms. Notably, the optimization of the improved aircraft wing increased the lift force from 0.797055 to 0.951817, signaling promising prospects for advancing optimization problems in the future. Source codes of ETO is publicly available at https://ceats.ou.edu.vn/us/exponential-trigonometric-optimization-algorithm-.html.

Reducing heat gain in buildings by artificially lowering the outside air temperature through evaporative cooling

Introduction. Systems for microclimate regulation in buildings consume a large amount of natural energy resources. Statistics show that 40 % of energy is used for heating, ventilation and air conditioning. This problem is directly related to the heating and cooling load of the building, i.e. it depends on the thermal engineering characteristics of building envelopes and the thermal protection of the building.
 
 Materials and methods. To reduce the consumption of energy resources, the most common method is the thermal insulation of enclosing structures, increasing the thermal resistance of enclosing structures, which is a complicated thermal engineering problem. An alternative new method of reducing heat gain into buildings through evaporative cooling of outside air which leads to reduction of cooling load of building and subsequently influences the reduction of greenhouse gas emissions is considered. For this purpose, a spray irrigation system is installed on the southern wall of the building to lower the conditional boundary layer temperature which is formed as a result of solar radiation.
 
 Results. The suggested method passively reduces the boundary layer temperature, which tends to the temperature of the wet-bulb thermometer, as a result, the heat influx into the building is reduced, which leads to a lower cooling load and increases the energy efficiency of the system.
 
 Conclusions. Green architecture and zero-energy building design are relevant and important at the present time. World statistics show that 14 % of all potable water is used in buildings, in order to use potable water economically the proposed system installs a rainwater accumulation tank for outdoor irrigation. A comparative analysis showed that by cooling evaporation and artificially lowering the outside air temperature, the cooling load of the building is reduced by 20 %, which results in a reduction of greenhouse gas emissions.

On methodology and application of smoothed particle hydrodynamics in fluid, solid and biomechanics.

Smoothed particle hydrodynamics (SPH), as one of the earliest meshfree methods, has broad prospects in modeling a wide range of problems in engineering and science, including extremely large deformation problems such as explosion and high velocity impact. This paper aims to provide a comprehensive overview on the recent advances of SPH method in the fields of fluid, solid, and biomechanics. First, the theory of SPH is described, and improved algorithms of SPH with high accuracy are summarized, such as the finite particle method (FPM). Techniques used in SPH method for simulating fluid, solid and biomechanics problems are discussed. The δ-SPH method and Godunov SPH (GSPH) based on the Riemann model are described for handling instability issues in fluid dynamics. Next, the interface contact algorithm for fluid-structure interaction is also discussed. The common algorithms for improving the tensile instability and the framework of total Lagrangian SPH are examined for challenging tasks in solid mechanics. In terms of biomechanics, the governing equations and the coupling forces based on SPH method are exemplified. Then, various typical engineering applications and recent advances are elaborated. The application of fluid mainly depicts the interaction between fluid and rigid body as well as elastomer, while some complicated fluid-structure interaction ocean engineering problems are also presented. In the aspect of solid dynamics, galaxy, geotechnical mechanics, explosion and impact, and additive manufacturing are summarized. Furthermore, the recent advancements of SPH method in biomechanics, such as hemodynamically and gut health, are discussed in general. In addition, to overcome the limitations of computational efficiency and computational scale, the multiscale adaptive resolution, the parallel algorithm and the automated mesh generation are addressed. The development of SPH software in China and abroad is also summarized. Finally, the challenging task of SPH method in the future is summarized. In future research work, the establishment of multi-scale coupled SPH model and deep learning technology in solid and biodynamics will be the focus of expanding the engineering applications of SPH methods.

Optimal Tuning of Power System Stabilizers for a Multi-Machine Power Systems Using Hybrid Gorilla Troops and Gradient-Based Optimizers

This work discusses the production of a novel hybrid algorithm by combining the gorilla troops optimizer (GTO) with the gradient-based optimizers (GBO) approach. The novel approach is called GTO-GBO, it is offered as a useful tool for optimizing the power system stabilizer (PSS) used in the IEEE four-generator, two-area multi-machine power system subjected to a three-phase short-circuit fault. MATLAB/Simulink software was utilized to carry out the assessments. The suggested approach is initially evaluated using multiple benchmark functions with unimodal and multimodal properties. The results are then compared to other competing algorithms (artificial ecosystem optimizer, artificial rabbits optimizer, Coati Optimization Algorithm, and northern goshawk optimization). The comparisons with various algorithms reveal the developed hybrid GTO-GBO algorithm’s considerable promise. This demonstrates the GTO-GBO algorithm’s improved balance of global and local search stages. The proposed GTO-GBO algorithm’s performance is also evaluated by developing an optimum performing PSS for further examination, allowing observation of its capabilities for difficult real-world engineering challenges. To illustrate the applicability and superior performance of the suggested hybrid algorithm for such a complicated real-world engineering problem, the PSS damping controller is formulated as an optimization problem, and the developed GTO-GBO algorithm is used to search for optimal controller parameters. The latter case’s findings are compared to the competitive optimization algorithms where the GTO-GBO demonstrates the efficiency and robustness of this suggested optimization algorithm to enhance power system stability.

Optimal design of dampers in seismic applications utilizing the MOPSO algorithm

New technological developments in engineering present an opportunity for improved efficiency in structural design through optimization. High-performance computing resources reduce the time needed for computational calculations. Concurrently, optimization algorithms have greatly evolved to provide the opportunity to solve complicated nonlinear engineering problems that typically include several interrelated, and often conflicting, objectives under a set of constraints. This research proposes a method for the optimal design of viscous dampers in seismic applications utilizing the multi-objective particle swarm optimization (MOPSO) algorithm. The MOPSO, with its inherent metaheuristic approach and geographically-based adaptive grids, effectively discovers global and diverse non-convex solutions. To further improve the efficiency and quality of the search in the milieu of an engineering application, we have extended MOPSO by introducing constraints on objective functions and implementing parallel computing. Additionally, this research provides recommendations on how to efficiently generate reliable solution sets by proper selection of objective (cost) functions and adequate set-up of MOPSO input parameters. These recommendations are derived from a series of sensitivity studies. The proposed method is verified by utilizing an engineered solution of a viscously damped moment frame. It was found that under the same set of constraints and performance objectives, MOPSO produces a solution set that contains outcomes that are superior to the engineered solutions. For example, the MOPSO solution set contains outcomes that reduce demands on dampers (force and stroke) while maintaining engineering demand parameters, generating construction savings as a result of the reduced manufacturing costs of dampers.

Isoparametric numerical integration on enriched 4D simplicial elements

The enriched finite element method is a natural improvement of the classical finite element method for solving complicated engineering problems. It is an essential tool for successful treating difficulties, singularities, non-Lipschitz internal boundaries, curved boundaries with complex geometry, crack propagations, etc. This paper deals with an enrichment of the four-dimensional isoparametric quadratic Lagrangian finite element. The paper discusses a new approach in which bubble functions are replaced with specific cubic ones in the enrichment process. The new method is applied to simplicial elements in four-dimensional Euclidean space, where lumping of the mass matrix is obtained. Error estimates in numerical integration and the interpolation by the new elements are proved. Applications of the quadrature rules for solving elliptic eigenvalue problems are demonstrated. A new operator that maps tesseract onto a four-dimensional ball is defined. The new quadrature rules are tested by integrating Bessel functions and by solving a second-order elliptic eigenvalue problem with Dirichlet boundary conditions.

Distribution network reconfiguration using time-varying acceleration coefficient assisted binary particle swarm optimization

The particle swarm optimization (PSO) algorithm is widely used to solve a variety of complicated engineering problems. However, PSO may suffer from an effective balance between local and global search ability in the solution search process. This study proposes a new acceleration coefficient for the PSO algorithm to overcome this issue. The proposed coefficient is implemented on the distribution network reconfiguration (DNR) problem to reduce power loss. The lowest power loss is obtained while problem constraints (maintain radial structure, voltage limits, and power flow balance) are satisfied with the proposed method. The validity of the proposed acceleration coefficient-based binary particle swarm optimization (BPSO) in handling the DNR problem is examined through simulation studies on IEEE 33-bus, P&G 69-bus, and 84-bus Taiwan Power Company (TPC) practical distribution networks. Furthermore, the DNR problem is evaluated regarding energy cost and environmental issues. Finally, the average computational times of the different acceleration coefficient-based PSO methods are compared. The solution speed of the proposed acceleration coefficient-based method is faster than the other methods in the DNR problem.

Solving the Eigenfrequencies Problem of Waveguides by Localized Method of Fundamental Solutions with External Source

The localized method of fundamental solutions (LMFS) is a domain-type, meshless numerical method. Compared with numerical methods that have a high grid dependence, it does not require grid generation and numerical integration, so it can effectively improve computational efficiency and avoid complex integration processes. Moreover, it is formed using the traditional method of fundamental solutions (MFS) and the localization approach. Previous studies have shown that the MFS may produce a dense and ill-conditioned matrix. However, the proposed LMFS can yield a sparse system of linear algebraic equations, so it is more suitable and effective in solving complicated engineering problems. In this article, LMFS was used to solve eigenfrequency problems in electromagnetic waves, which were controlled using two-dimensional Helmholtz equations. Additionally, the resonant frequencies of the eigenproblem were determined by the response amplitudes. In order to determine the eigenfrequencies, LMFS was applied for solving a sequence of inhomogeneous problems by introducing an external source. Waveguides with different shapes were analyzed to prove the stability of the present LMFS in this paper.

A Novel Root-Finding Algorithm With Engineering Applications and its Dynamics via Computer Technology

Root-finding of non-linear equations is one of the most appearing problems in engineering sciences. Most of the complicated engineering problems can be modeled easily by means of non-linear functions. The role of iterative algorithms via computers for solving such functions is much important and cannot be denied in the modern age. In an iterative algorithm, the convergence order and the computational cost per iteration are the main characteristics that depict its efficiency and performance i.e., a method with higher-order and lower computational cost will be more efficient and vice versa. Keeping these facts into consideration, the main goal of this paper is to introduce a new derivative-free iterative method that performs better. We develop this algorithm by utilizing the forward- and finite-difference schemes on well-known Househölder’s method, resulting in an efficient and derivative-free algorithm with a low per iteration computing cost. We also look at the developed algorithm’s convergence criterion and show that it is quartic-order convergent. We investigate nine test-examples and solve them to demonstrate its correctness, validity, and efficiency numerically. Some real-world engineering problems in civil and chemical engineering are also included in these examples. The numerical results of the test-examples reveal that the newly constructed method outperforms the existing similar algorithms found in the literature. We consider various different-degrees complex polynomials for the graphical analysis and used a computer tool to create the polynomiographs of the proposed quartic-order algorithm and compare it to other comparable existing approaches. The graphical findings show that the developed method has a faster convergence speed than the other comparable algorithms.

Corroded Subsea Pipelines Burst Pressure Prediction Utilizing Finite Element Data Using ANN

The Engineering industry is constantly exploring an effective and fast-solving method for complicated engineering problems. The adaptation of artificial intelligent technology can diminish the time-consuming of conventional analysis methods, especially in offshore engineering. For that reason, this study is pursued to build a prediction model to predict the residual strength of API 5L X42 subsea pipelines. An artificial neural network is used as an analytical medium in developing the prediction model. Three (3) physical shapes of corrosion data with diverse corrosion level are designed as input data based on the corroded subsea pipelines of true 2009 historical inspection data of South China Sea. The output data are obtained from the finite element analysis to produce the burst pressure data. The performance model is evaluated using mean squared error (MSE) and mean absolute error (MAE) which results in 9.13 x 10-5 and 0.005499 respectively for the optimum model. The predicted output shows significant similarity in line with the finite element output for validation purposes. This model is expected to provide quick prediction reliability of subsea pipelines to the engineers and reduce or eliminate massive analysis work.

Topology optimization of irregular flow domain by parametric level set method in unstructured mesh

ABSTRACT In this study, we present a novel method for the topology optimization of the irregular flow domain using a parametric level set method (PLSM). Some improvement was applied on the CS-RBFs (radial basis functions with compact support)-based PLSM to make it suitable for nonuniform mesh, expanding the range field of engineering application of the PLSM. The optimization problem is solved by a gradient-based algorithm with Stokes equations as state constraints, and the objective is set to minimize the power dissipation subject to the volume constraint of flow channels. A PLSM is introduced to avoid the direct solving of the Hamilton–Jacobi partial differential equation, which can have the potential to break through the restriction of relying on structured meshes because no finite difference scheme is required. Then, a self-adaption support radius approach is presented to allow the parametric level set to be evolved on the nonuniformed mesh, which can expand the application of the PLSM to more complicated engineering problems with irregular geometric shapes. A volume integration scheme is applied during the design sensitivity analysis to calculate the shape derivatives, allowing the nucleation of new holes. Numerical examples in two and three dimensions are provided to demonstrate the effectiveness of the proposed method.

Shear Slip of Fracture Surface: A New Mechanism for Increased Drilling Fluid Loss in Deep Formation

During drilling and completion of fractured reservoirs, drilling fluid loss is the most serious form of reservoir damage, and it is one of the complicated engineering problems that affect safe and efficient drilling in the long term. This paper aims at the problem of repeated leakage of deep fractured reservoir with more leakage after the failure of the initial plugging. In order to analyze the causes of the increased drilling fluid loss, simulation experiment of fracture sample sealing before and after shear slip of fracture surface was designed and carried out, combining with drilling fluid under the environment of rock plate and block sliding friction experiment, rock mechanics parameter test based on micro-indentation and surface 3D scanning. Then we analyze the induced effect of drilling fluid invasion into natural fracture on the friction sliding of fracture surface and discuss the influence of fracture surface friction on drilling fluid leakage in fractured reservoir. The experimental results show that drilling fluid intrudes into natural fractures, both oil-based drilling fluid and water-based drilling fluid can weaken the friction coefficient of rock blocks, and the weakening effect of oil-based drilling fluid is stronger, and it is easy to induce friction slip of the fracture surface to form dislocation fractures, which provides passage and space for intensified drilling fluid leakage. Friction slip of the fracture surface can strengthen the permeability of the fracture, leading to the decrease of drilling fluid plugging ability and the increase of drilling fluid leakage. After friction sliding, the fracture surface is smoother, which may further expand the dislocation degree, increase the fracture length and width greatly, and promote a larger lost circulation. The results show that shear slip of fracture surface in deep fractured reservoir is one of the reasons for drilling fluid leakage increase, strengthening the friction strength between the natural fracture surfaces and effectively controlling the friction slipping is one of the important ways to control the drilling fluid leakage in deep fractured oil and gas reservoirs.

Development of a 3D fluid-saturated element for dynamic analysis of two-phase media in ABAQUS based on u-U formed equations

The commonly used finite element software does not provide a three-dimensional (3D) fluid-saturated element available for dynamic analysis of two-phase media, which extremely limits their advantages in solving complicated engineering problems. This paper develops a user element for 3D dynamic analysis of fluid-saturated porous media in ABAQUS based on the u-U formed equations. The dynamic governing formulations are firstly revisited, and an implicit time integration algorithm is adopted to solve the dynamic equations. Then, the major development steps, the structure of the subroutine file, and the keywords directly related to the user element in the input file are carefully presented for the application of the user element. Moreover, the equivalent linear approach is introduced with a detailed realization procedure to consider the soil nonlinearity. Finally, the effectiveness and accuracy of the developed user element are validated by comparing the results with those from four different analytical and numerical solutions. The proposed numerical method with user element has a great potential in modeling practical engineering problems due to the prominent advantages of ABAQUS in simulating complicated materials and irregular geometrics.

Integrating model tree and modified stepwise regression in concrete slump prediction and steel fabrication estimating

The model tree algorithm of M5 is integrated with the multiple linear regression technique called modified stepwise regression (MSR), resulting in a new method for modeling complex civil engineering problems. We purposefully chose artificial neural networks (ANN) for comparison against the proposed “M5+MSR” because they fall at the two ends of the model interpretability spectrum in machine learning. This research addresses the critical question of how to balance the trade-off between bias, variance and model complexity in machine learning through contrasting “M5+MSR” against other commonly applied methods. In two application cases (Case 1: concrete workability and Case 2: steel fabrication estimating), the proposed “M5+MSR” gave rise to explainable regression tree models featuring substantially reduced complexities against ANN and model prediction errors comparable to ANN. The resulting “M5+MSR” models consistently outperformed ANN in terms of model overfitting metrics by 19% in Case 1 and 21% in Case 2, thus boasting better learning performances. The proposed new method will potentially find applications in tackling a wide range of complicated engineering problems that entail fitting prediction models based on laboratory or field data.

Implementation of Gray Wolf Optimization algorithm to recycled gas centrifuge cascades

Since the separative power of a single centrifuge is relatively small, to achieve adequate enrichment and throughput, a large number of centrifuges are interconnected to form a cascade. An enrichment plant usually holds thousands of centrifuges. The pattern of connections is determined by the properties of the individual machines, the required quantity, and the concentration of the final product. Because the most separation costs of a cascade are proportional to the number of machines, it is required to determine the optimal operating parameters of the cascade to reduce the number of centrifuge machines. In a problem in which the number of variables is large, the amount of possible arrangement is truly astronomical, and a direct exhaustive search for the best pattern would be prohibitive. In recent years, nature-inspired meta-heuristic optimization algorithms have been developed to treat complicated engineering problems. In this paper, the Gray Wolf Optimization (GWO) algorithm based on the behavior of gray wolves for hunting is implemented to multi-objective optimization of gas centrifuge cascades. In this analysis, the performance of the algorithm is examined for challenging test functions, primarily; and then several real cascades are evaluated and the results are compared with other methods. The GWO is used to minimize the number of machines and optimum operational parameters of cascades for binary mixture separations. It is found from an evolutionary point of view, the performance of the GWO as an efficient method is quite adequate.

Investigating effect of particle shape on suffusion by CFD-DEM modeling

Suffusion is one of the most common causes of hydraulic geological structure failure. During the suffusion process, particle shape is a significant factor to affect the micro-structure and macro-mechanical behaviour of soils. This study applies polyhedron as coarse particles and different aspect ratio values to investigate the effect of particle shape on suffusion by adopting the coupled CFD-DEM method. The loss mass of fines during erosion, the fines distribution in the central section, the GSD curves in different layers of specimen from macroscopic perspective are studied. The micro contact and force fabric information are obtained to further explore the influence of particle shape on suffusion. Additionally, an in-house CFD-DEM code with GPU parallel computing technique is developed. The simulation results indicate that particle shape has an inhibitory effect on erosion rate. This work would provide evidence for further development of phenomenological model and micro mechanical constitutive model to solve complicated engineering problems involving multi-field and multi-phase media.

Optimization of a segmented mirror with a global radius of curvature actuation system based on multi-fidelity surrogates

Separately-polished segmented mirrors hardly meet the co-phasing surface shape error due to the fabrication difficulties. Therefore, some space-based segmented telescope system such as the James Webb Space Telescope (JWST) uses the global radius of curvature (GRoC) actuation system as an effective solution. A segmented mirror with a GRoC actuation system was optimized in this paper. The high-precision finite element model (FEM) usually has low computational efficiency, and the low-precision finite element model cannot guarantee calculation accuracy. In order to alleviate the conflict between computational cost and calculation accuracy in the optimization of a segmented mirror, multi-fidelity surrogates (MFS) based on sensitivity analysis were proposed. The surrogates were then optimized through the multi-island genetic algorithm (MIGA), and the segmented mirror produces 0.0623λ (λ = 632.8) surface shape error RMS per 1 mm of GRoC is corrected, which met the design requirement. Besides, it can also be deployed to solve other complicated engineering problems.

An improved artificial ecosystem optimization algorithm for optimal configuration of a hybrid PV/WT/FC energy system

This paper mainly focuses on the optimal design of a grid-dependent and off-grid hybrid renewable energy system (RES). This system consists of Photovoltaic (PV), Wind Turbine (WT) as well as Fuel Cell (FC) with hydrogen gas tank for storing the energy in the chemical form. The optimal components sizes of the proposed hybrid generating system are achieved using a novel metaheuristic optimization technique. This optimization technique, called Improved Artificial Ecosystem Optimization (IAEO), is proposed for enhancing the performance of the conventional Artificial Ecosystem Optimization (AEO) algorithm. The IAEO improves the convergence trends of the original AEO, gives the best minimum objective function, reaches the optimal solution after a few iterations numbers as well as reduces the falling into the local optima. The proposed IAEO algorithm for solving the multiobjective optimization problem of minimizing the Cost of Energy (COE), the reliability index presented by the Loss of Power Supply Probability (LPSP), and excess energy under the constraints are considered. The hybrid system is suggested to be located in Ataka region, Suez Gulf (latitude 30.0, longitude 32.5), Egypt, and the whole lifetime of the suggested case study is 25 years. To ensure the accurateness, stability, and robustness of the proposed optimization algorithm, it is examined on six different configurations, representing on-grid and off-grid hybrid RES. For all the studied cases the proposed IAEO algorithm outperforms the original AEO and generates the minimum value of the fitness function in less execution time. Furthermore, comprehensive statistical measurements are demonstrated to prove the effectiveness of the proposed algorithm. Also, the results obtained by the conventional AEO and IAEO are compared with those obtained by several well-known optimization algorithms, Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO). Based on the obtained simulation results, the proposed IAEO has the best performance among other algorithms and it has successfully positioned itself as a competitor to novel algorithms for tackling the most complicated engineering problems.

Genetic programming in civil engineering: advent, applications and future trends

Over the past two decades, machine learning has been gaining significant attention for solving complex engineering problems. Genetic programing (GP) is an advanced framework that can be used for a variety of machine learning tasks. GP searches a program space instead of a data space without a need to pre-defined models. This method generates transparent solutions that can be easily deployed for practical civil engineering applications. GP is establishing itself as a robust intelligent technique to solve complicated civil engineering problems. This paper provides a review of the GP technique and its applications in the civil engineering arena over the last decade. We discuss the features of GP and its variants followed by their potential for solving various civil engineering problems. We finally envision the potential research avenues and emerging trends for the application of GP in civil engineering.

A user-defined element for dynamic analysis of saturated porous media in ABAQUS

Abstract General finite element software has unique advantages in solving complicated engineering problems, which has been widely applied in the dynamic analysis of single-phase media. However, the commonly used software does not provide a saturated porous two-phase element that can be adopted for dynamic analysis. In this paper, a user-defined element for dynamic analysis of saturated porous media is developed in the general finite element software ABAQUS. The user-defined element takes the displacements of solid skeleton and pore fluid as the basic unknown-variables, and adopts the implicit time integration scheme. By combining it with the equivalent linear method, the nonlinear dynamic analysis of saturated soil can also be realized. The correctness of the user-defined element is verified by solving four numerical examples.