Solve Complex Engineering Problems Research Articles

Research on Developing Students' Ability to Solve Complex Engineering Problems through Gradual Training

Solving complex engineering problems is the requirement of engineering practice for the training of talents in Higher Engineering education. It requires engineering graduates to master the ability to solve complex engineering problems. The certification of engineering education clearly stipulates the knowledge, ability and quality requirements for solving complex engineering problems. The gradual cultivation of students' ability to solve complex engineering problems has the characteristics of continuity and periodicity. It conforms to the process from mastering basic skills, practical ability, innovative ability to finally forming the ability to solve complex engineering problems. It conforms to the rule of excellent output. Based on the analysis of the quality requirements of graduates, this paper puts forward the gradual cultivation of students' ability to solve complex engineering problems. By optimizing the curriculum system and innovating the teaching content, the cultivation efficiency and quality of college students are improved, which provides strong support for the cultivation of talents with innovative consciousness and ability to solve complex engineering problems.

The Construction of Ecosystem for Cultivating Automobile Innovative Talents Based on the Development Process of Racing Car Products

Focusing on the cultivation of students' engineering innovation ability, the paper puts forward the talent cultivation concept based on the development process of FSAE racing products, a step-type engineering ability cultivation system has been constructed based on the development process of FSAE racing products; with the construction of characteristic practice platform as the starting point, the innovation ability training system has been established corresponding to grade and progressive; Aiming at cultivating the ability to solve complex engineering problems, the independent learning and autonomous management mechanism has been established, a good automobile innovative talent cultivation ecosystem has been constructed, the regression of practical education project and the experimental demonstration and promotion of the ecosystem model have been realized.

Dynamic reliability analysis using the extended support vector regression (X-SVR)

For engineering applications, the dynamic system responses can be significantly affected by uncertainties in the system parameters including material and geometric properties as well as by uncertainties in the excitations. The reliability of dynamic systems is widely evaluated based on the first-passage theory. To improve the computational efficiency, surrogate models are widely used to approximate the relationship between the system inputs and outputs. In this paper, a new machine learning based metamodel, namely the extended support vector regression (X-SVR), is proposed for the reliability analysis of dynamic systems via utilizing the first-passage theory. Furthermore, the capability of X-SVR is enhanced by a new kernel function developed from the vectorized Gegenbauer polynomial, especially for solving complex engineering problems. Through the proposed approach, the relationship between the extremum of the dynamic responses and the input uncertain parameters is approximated by training the X-SVR model such that the probability of failure can be efficiently predicted without using other computational tools for numerical analysis, such as the finite element analysis (FEM). The feasibility and performance of the proposed surrogate model in dynamic reliability analysis is investigated by comparing it with the conventional ε-insensitive support vector regression (ε-SVR) with Gaussian kernel and Monte Carlo simulation (MSC). Four numerical examples are adopted to evidently demonstrate the practicability and efficiency of the proposed X-SVR method.

非Newton流体的物质点法模拟研究

Simulation of the non-Newtonian fluid flow is an interesting problem for engineers. As a relatively new particle-based method, the material point method (MPM), combining the advantages of both the Lagrangian algorithm and the Eulerian algorithm, has been widely and effectively used to solve complex engineering problems. The plane Poiseuille flow and Couette flow of the shear thickening and shear thinning cross fluid and power-law fluid were studied with the artificial state equations for the MPM. The results show that, the simulation with the MPM for the Newtonian fluid is in good agreement with the theoretical solution and the MPM simulates the shear thinning and shearing thickening of the non-Newtonian fluid exactly. The results confirm the applicability of the MPM for simulation of the non-Newtonian fluid flow and expand the application field of the MPM.

Development of Crosspoint Modification for Isogeometric Analysis in AI

Isogeometric Analysis (IGA) has emerged as a powerful computational method that seamlessly integrates computer-aided design (CAD) and finite element analysis (FEA). It provides an innovative approach for solving complex engineering problems by employing the same basis functions used in CAD representations, such as Non-Uniform Rational B-Splines (NURBS), to discretize the computational domain. However, one of the challenges in IGA is the occurrence of crosspoints, which are regions where multiple NURBS patches meet. This presents a novel development of a crosspoint modification technique for Isogeometric Analysis, leveraging the capabilities of Artificial Intelligence (AI) algorithms. The aim is to overcome the limitations posed by crosspoints and enhance the accuracy and efficiency of IGA simulations. It utilizes AI-based methods, such as deep learning and pattern recognition, to identify and modify the crosspoint regions in the NURBS-based representation. The AI model is trained on a large dataset of known crosspoint configurations and their corresponding modifications, enabling it to learn the underlying patterns and relationships between the geometry and the required modifications. This trained model is then employed to automatically identify and modify crosspoints in new IGA problems, streamlining the pre-processing stage and reducing human intervention. The crosspoint modification technique incorporates various strategies to ensure geometric continuity and smooth transition across the modified crosspoint regions. These strategies include local refinement of the NURBS control points, adaptively adjusting the degrees of the adjacent patches, and optimizing the blending functions in the crosspoint vicinity. Several numerical experiments and comparative studies are conducted. The results demonstrate that the crosspoint modification using AI significantly improves the accuracy of the IGA simulations, particularly in regions affected by crosspoints. Moreover, the computational efficiency is enhanced as the automated modification reduces the manual effort required for crosspoint handling.
 This research contributes to the advancement of Isogeometric Analysis by introducing a novel AI-based crosspoint modification technique. By effectively addressing the challenges associated with crosspoints, the proposed method enhances the accuracy and efficiency of IGA simulations. The integration of AI algorithms with IGA holds great potential for future applications in various engineering disciplines, including structural analysis, fluid dynamics, and electromagnetics.

Recent Advances on Swarm Intelligence for Solving Complex Engineering Problems

Recent Advances on Swarm Intelligence for Solving Complex Engineering Problems

Сравнение генетических алгоритмов MOGA и NSGA-II на задаче оптимизации формы рабочего колеса гидротурбины

Проведено сравнение генетических алгоритмов MOGA и NSGA-II на тестовых задачах и задаче оптимизации формы рабочего колеса гидротурбины. Рассмотрен модифицированный алгоритм NSGA-IIm, в котором операторы рекомбинации и мутации заимствованы из MOGA. Для сравнения скорости сходимости алгоритмов использована метрика, характеризующая расстояние от приближенного фронта Парето до точного. Представлены результаты решения тестовой задачи Purpose. The main goal of this article is to compare two popular multi-objective genetic algorithms MOGA and NSGA-II by solving test problems and a practical problem of hydraulic turbine runner optimization. Modification of NSGA-II called NSGA-IIm is also considered. The major problem is to compare convergence rates of approximate solution to the exact Pareto front. Methodology. The genetic algorithms MOGA and NSGA-II are described in detail. The modified algorithm NSGA-IIm is NSGA-II with the recombination and mutation operators taken from the MOGA algorithm. The known problems ZDT3 (2 objectives, 30 and 12 parameters, no constraints) and OSY (2 objectives, 6 parameters, 6 constraints) are taken as test problems. These algorithms are compared by solving runner optimization problem with 24 free parameters and 2 or 3 objectives. To compare the algorithms, a metric characterizing the distance from the approximate Pareto front to the exact one is introduced. Since the algorithms are stochastic in nature, the value of the metric was averaged over 100 runs of the algorithm. In the two-objective runner optimization problem the metric value was averaged over 3 runs of the algorithm. Findings. Solving the test problems, it was found that in the first 50 generations the MOGA algorithm converges faster than other algorithms, but after 50 generations the NSGA-II algorithm has shown the best result. The MOGA algorithm gives an approximate front containing more solutions than NSGA-II. When solving the two-objective and the three-objective runner optimization problem the similar results were obtained. The approximate Pareto front, obtained by the MOGA algorithm, is distributed more uniformly than other algorithms and contains a larger number of solutions. The advantage of the algorithms NSGA-II and NSGA-IIm is a slightly better definition of extreme values of target functionals. However, the obtained differences are not very significant. Originality/value. Obtained results show that both MOGA and NSGA-II are very similar in terms of convergence rate and can be applied for solving complex engineering problems.

Research on Practical Teaching of Information Specialty from the Perspective of Complex Engineering Problems

In this paper ,the teaching difficulties in the course of Data Structure Design is discussed in the view of Complex Engineering problems. On the basis of the analysis of the characteristics of Complex Engineering problems, the teaching guidance, the teaching implementation and the assessment forms are reconsidered and redesigned. The concept of solving Complex Engineering problems is integrated into the details of the content and the form of Data Structure Design. This paper attempts to provide reference in direction and content for teaching reform of practical courses of information specialty under the background of engineering education accreditation.

Flow Regime Algorithm (FRA): a physics-based meta-heuristics algorithm

In this research study, a physics-based optimization algorithm, namely Flow Regime Algorithm (FRA) is proposed. The main sources of inspiration are classical fluid mechanics and flow regimes. The flow regime usually is being divided into two categories which are laminar and turbulent flows. Reynolds number is the parameter which defines that the flow regime is laminar or turbulent. In this research study, a similar number to Reynolds has been defined which indicates the search type (global or local) of the algorithm and is called search type factor. For the purpose of developing the local and global searches equations, the concept of boundary layer in fluid mechanics has been used. The performance of the proposed algorithm has been evaluated using 26 benchmark functions and has been compared with seven popular and well-known algorithms which are simulated annealing, particle swarm optimization, firefly algorithm, cuckoo search, flower pollination algorithm, krill herd and monarch butterfly. Finally, the heat wheel optimization problem and horizontal axis marine current turbine (tidal turbine) problem, which are real-case engineering problems, have been solved using FRA. The results indicated that FRA can be a great candidate in solving complex engineering problems.

Flooding of the Saguenay region in 1996. Part 2: Aux Sables River flood mitigation and environmental impact assessment

After the flooding of the Saguenay region in July 1996, several rivers, including the Aux Sables River, experienced unusual water discharge, causing flooding and morphological damage. This paper addresses flood mitigation and environmental impact assessment of the Aux Sables River following the July 1996 flooding. The consequences of the flood are summarized followed by a review of various proposed solutions for a similar flood. The option of dredging the Aux Sables River to increase discharge without causing flooding raises the issue of suspended sediment concentration, since the intake water at Jonquiere would be at risk. Utilizing newly developed software, UMHYSER-1D, the suspended sediment impact assessment for the Aux Sables River enables the maximum permissible sediment discharge to be released into the river while avoiding any risk of pollution for the population of Jonquiere city. Using UMHYSER-1D to mitigate this risk confirms the important role of numerical modeling in solving complex engineering problems.

Bat algorithm for dam–reservoir operation

Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, Bat Algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation.

Numerical Modeling of Gas Flow in Coal Using a Modified Dual-Porosity Model: A Multi-Mechanistic Approach and Finite Difference Method

Numerical simulation, a cost-effective technique used to describe and predict gas flow behavior in coal, can provide a reliable means of evaluating gas well performance, solving complex engineering problems, and determining how variations in reservoir properties and gas drainage practice affect wellbore performance at the field scale. This paper first presents a multi-mechanistic gas flow model using a modified dual-porosity model by incorporating the effects of multiscale transport mechanisms in coal, effective stress evaluation, and matrix shrinkage. Then, a numerical model and a simulator are derived using the finite difference method to solve the proposed model and are successfully tested against two sets of in situ gas extraction field data. Subsequently, the effect of parametric variations on gas transport behavior in dry coal seams is quantified through a series of simulations. The simulated results imply that (1) a greater initial pore pressure or a lower gas drainage pressure will lead to a higher gas flow rate; (2) the fracture permeability as well as matrix permeability plays significant roles in gas production profiles, and the former mainly influences the early initial depletion stage, while the latter controls gas flow rate and production decay rate at the late stage; (3) the mass transfer rate between matrix blocks and fractures varies with the distance from the matrix block to the free coal surface, and a shorter distance will result in a greater mass transfer rate; and (4) the decrease of the matrix radius increases the gas released from matrix and leads to a greater gas flow rate and a slower decay rate. The results also indicate that size and matrix permeability are two key parameters affecting the overall gas deliverability, and play a critical role in gas production at the late stage.

In‐memory integration of existing software components for parallel adaptive unstructured mesh workflows

SummaryReliable mesh‐based simulations are needed to solve complex engineering problems. Mesh adaptivity can increase reliability by reducing discretization errors but requires multiple software components to exchange information. Often, components exchange information by reading and writing a common file format. This file‐based approach becomes a problem on massively parallel computers where filesystem bandwidth is a critical performance bottleneck. Our approach using data streams and component interfaces avoids the filesystem bottleneck. In this paper, we present these techniques and their use for coupling mesh adaptivity to the PHASTA computational fluid dynamics solver, the Albany multi‐physics framework, and the Omega3P linear accelerator frequency analysis applications. Performance results are reported on up to 16,384 cores of an Intel Knights Landing‐based system.

An Improved Crow Search Algorithm Applied to Energy Problems

The efficient use of energy in electrical systems has become a relevant topic due to its environmental impact. Parameter identification in induction motors and capacitor allocation in distribution networks are two representative problems that have strong implications in the massive use of energy. From an optimization perspective, both problems are considered extremely complex due to their non-linearity, discontinuity, and high multi-modality. These characteristics make difficult to solve them by using standard optimization techniques. On the other hand, metaheuristic methods have been widely used as alternative optimization algorithms to solve complex engineering problems. The Crow Search Algorithm (CSA) is a recent metaheuristic method based on the intelligent group behavior of crows. Although CSA presents interesting characteristics, its search strategy presents great difficulties when it faces high multi-modal formulations. In this paper, an improved version of the CSA method is presented to solve complex optimization problems of energy. In the new algorithm, two features of the original CSA are modified: (I) the awareness probability (AP) and (II) the random perturbation. With such adaptations, the new approach preserves solution diversity and improves the convergence to difficult high multi-modal optima. In order to evaluate its performance, the proposed algorithm has been tested in a set of four optimization problems which involve induction motors and distribution networks. The results demonstrate the high performance of the proposed method when it is compared with other popular approaches.

Patch load resistance of longitudinally stiffened webs: Modeling via support vector machines

Steel girders are the structural members often used for passing long spans. Mostly being subjected to patch loading, or concentrated loading, steel girders are likely to face sudden deformation or damage e.g., web breathing. Horizontal or vertical stiffeners are employed to overcome this phenomenon. This study aims at assessing the feasibility of a machine learning method, namely the support vector machines (SVM) in predicting the patch loading resistance of longitudinally stiffened webs. A database consisting of 162 test data is utilized to develop SVM models and the model with best performance is selected for further inspection. Existing formulations proposed by other researchers are also investigated for comparison. BS5400 and other existing models (model I, model II and model III) appear to yield underestimated predictions with a large scatter; i.e., mean experimental-to-predicted ratios of 1.517, 1.092, 1.155 and 1.256, respectively; whereas the selected SVM model has high prediction accuracy with significantly less scatter. Robust nature and accurate predictions of SVM confirms its feasibility of potential use in solving complex engineering problems.

First Conference of Computational Methods in Offshore Technology (COTech2017)

November 30 – December 1, 2017, University of Stavanger, NorwayPrefaceThis First Conference of Computational Methods in Offshore Technology (COTech2017) is a result of a Program Area for Research (COTech) that was established in 2016 at Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, Norway. The motivation to focus on Computational Methods is because of the fact that numerical computation approach is nowadays not only serving as a means to cultivate and realize innovative ideas, but also it is becoming the primary choice to solve complex engineering problems such as offshore. Based on the defined goals of the COTech Program Area for Research, the aim of this COTech2017 conference is to provide a platform for academics and professionals in application of computational methods with focus on, but not limited to, Offshore Technology to come together, present their recent works in the area, exchange ideas, and establish professional networks. It is intended and will serve as a forum for a multidisciplinary research and brings together Norwegian and invited foreign researchers to enable them exchange their research experience and disseminate their results within application of computational methods in design, analysis and simulation of offshore systems and structures.List of Conference Organizing Committee, Topic Area coordinators, Invited Keynote Speakers, International Scientific Committee Members and Reviewers are available in this pdf.

Research on the Integrated Implementation of Mechanical Professional Curriculum Designs Based on Virtual Prototype Technology

For the problems such as lacking close relationship among these mechanical professional curriculum designs, curriculum knowledge division alone, without integrating the relevant knowledge and failing to highlight the characteristics of mechanical major, an integrated implementation system is established for the engineering practice teaching in view of the mechanical professional engineering certification requirements. This system breaks the relatively independent practice of these curriculum designs and fuses to form a whole, as well as brings innovation design idea into the curriculum design. For the target to solve complex engineering problems, the curriculum design and its content will be set in stages based on virtual prototype technology. The teaching reform can strengthen students' abilities such as operation, practical skills, problem-solving, exploration and innovation. So the reform project for mechanical professional curriculum designs is of important academic significance and engineering application value.

A new descriptor for image matching based on bionic principles

After millions of years of evolution, nature has developed a wide variety of interesting structures, each with their own singularities and properties. Such structures provide several unique and innovative models which may be extended to solve complex engineering problems. One example of such structures is the so-called orb webs, built by many species of a spider as a part of their survival tactics. Orb webs are highly optimized structure, specifically devised to capture prey by efficiently covering a whole area with sticky threads. In this paper, a new feature descriptor called spider local image features (SLIF) is proposed. In the proposed approach, feature vectors are built by selectively extracting pictorial information from a set of previously detected interest point. This is achieved by considering a set of efficiently distributed sampling points, which emulate the intersection nodes formed by the threads of an orb web structure. The SLIF method produces simple low-dimensional feature descriptors, which are robust to several image transformation and distortions, such as scaling, rotation, bright shifts and viewpoint changes. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known feature description methods, such as those presented on the scale-invariant feature transform, speeded-up robust features, binary robust scalable keypoints and fast retina keypoints. The comparison examines several different images, commonly considered as a benchmark within the image matching literature. Our experimental results evidence SLIF’s high performance and robustness against common image transformations and distortions and further show its viability for many of computer vision applications.

ON THE QUESTION OF MASS-ENERGY CHARACTERISTICS OF VARIOUS AVIATION SOURCES OF ELECTRICITY

The article raises the issue of the synthesis of power supply systems for new generation aircraft, which is understood as: the power supply system (complex) of the aircraft, which includes the power and information structures closely integrated. Using this method, the power generation, transformation and distribution functions are assigned to the power structure, and the information structure provides work algorithms. The problematic of the synthesis of the described systems is formed and its relevance is justified. The main work done abroad and on the territory of the Russian Federation aimed at solving this problem are analyzed. As a solution to the problem, it is proposed to use the actual, from the point of view of the authors, method – the structural-functional method. It is shown that the structural-functional method is applicable to solving complex engineering problems, as shown in the examples [16, 18]. The chosen method of solving this problem, like any other one, requires a sufficient number of bench-mark data. When applying the structural-functional method, which is data of the "constraint" type, i.e. GOST (All-Union State Standard) and OST (All-Union Standard) requirements, technical specifications, supplemented by data on possible elements of the synthesized scheme. This article is mainly devoted to the choice of parameters of possible elements of the synthesized circuit, namely primary electric power sources. The article defines a technique for converting discrete values of primary energy sources into functional dependencies, as well as restrictions imposed on their approximating functions. The example shows the obtaining of functional dependencies for mass-energy indicators of nickel-cadmium storage batteries produced by VARTA and SAFT. The analysis of the obtained results is shown, which showed their sufficient reliability and, as follows, their applicability in the development of aircraft power supply systems.

A multidisciplinary design and analysis environment and its application to aircraft flight dynamics analysis

Abstract Complex engineering problems have to be jointly solved by many disciplinary teams with multitudinous computational software packages and physical experiment systems. Thus, the efficiency and effectiveness of solving complex engineering problems largely depend on effective collaboration among specialist engineers, seamless integration of disparate cyber-physical systems, and transparent interoperations of heterogeneous data sources. This paper presents a multidisciplinary design and analysis (MDA) environment in conjunction with its application to aircraft flight dynamics analyses. The MDA infrastructure builds a cybernetic platform that integrates structure analysis and flow computation systems with wind tunnel experiment systems; reconciles and interoperates diverse data sources generated by the cyber-physical systems. It is demonstrated that application of the MDA environment cost-effectively enhances work efficiency, team collaboration, and solution optimality in aircraft flight dynamics engineering processes.