Solve Complex Engineering Problems Research Articles

New bandgap analysis method for metamaterial structures using variational principle

This paper presents an efficient bandgap analysis method for metamaterial structures based on variational principle and linear expression technique. This is in view of the challenges in constructing a displacement function that satisfies the Bloch boundary when applying the traditional energy method to analyze the dispersion relations of metamaterial structures. Also, the inclusion of the wave numbers in the displacement function leads to inefficient computation. While there have been methods, such as virtual springs, proposed to deal with such problems, issues remain to be resolved. The main idea of the current paper is the solution of linear uncorrelated coefficients by processing the constraints. In this way, the unknown coefficients in the equations of motion can be linearly expressed, thereby allowing the equations to become variational. Both Gaussian elimination and null-space techniques are used to implement the method. The solution procedures of the two techniques are detailed and demonstrated by using one-dimensional and two-dimensional metamaterial structures. The accuracy of the method is verified by comparing the results with those from using finite element method. The calculation efficiency is compared with that of the traditional energy method in terms of both the matrix dimension and the number of the points of wave numbers. The results show that the method has a much higher calculation efficiency than that of the traditional energy method. The method proposed in this paper is free of convergence problems in dealing with boundary problems. Furthermore, in the discussion of combined periodic structures, it is shown that the method still has good geometrical applicability to structures containing multiple boundary conditions, showing great potential in solving complex engineering problems.

Physics-informed neural networks in groundwater flow modeling: Advantages and future directions

In recent years, there has been enormous development in soft computing, especially artificial intelligence (AI), which has developed robust methods for solving complex engineering problems. Researchers in the field of water resources engineering have applied these AI methods to solve a variety of hydrological problems. Despite their widespread use in the surface and atmospheric hydrology fields, groundwater hydrologists have not widely used AI methods in their routine field-scale modeling efforts. This is because AI models have been primarily considered black box models that lack physical meaning. Furthermore, using AI models to generate the space-time distribution of transient groundwater level variations is challenging and requires further flux balance and mass transport analyses. More recently, a new type of physics-informed neural network (PINN) model has been developed to address several limitations by integrating governing physics (groundwater flow equations) into the AI tools. This study presents the systematic advantages of the PINN algorithm for solving groundwater problems using a set of classic test problems. As discussed in detail in the article, these advantages and potentials are associated with the meshless nature of PINN, its continuous time and space dimensions, its independence from time-stepping and incremental marching in space, and its efficiency in running time. However, despite PINN's promising attributes, it is important to acknowledge its nascent stage of development and the inherent limitations of all neural network models, such as training challenges and hyperparameter selection. Thus, collaborative efforts between groundwater modelers and computer scientists are imperative to explore and exploit the full potential of PINN in tackling increasingly complex groundwater problems and nurturing PINN into a dependable modeling tool in industry and academia.

Platform Design of Passive Target Perception and Localization Based on Sensor Networks

With the rapid development, extensive knowledge, and diverse application scenarios of target perception and positioning technology in sensor networks, a passive target perception and localization platform based on Wireless Sensor Networks (WSN) has been designed. The platform is mainly applied for the teaching of electronic information, communication, and Internet of Things (IoT) engineering. The platform follows the teaching concept of “developing students’ ability to solve complex engineering problems” in the emerging engineering discipline and combines experimental simulation with real-world testing, as well as integrating scientific research and teaching. It encompasses technical elements such as perception and localization theory, sparse representation classification modeling, solving underdetermined equations, data analysis, and sparse coding, as well as nontechnical aspects such as team collaboration and cost budgeting. The platform boasts high fidelity and scalability, providing students with the opportunity to develop comprehensive practical and innovative skills in solving complex engineering problems.

An inspired chaos-based estimation-theory optimization for low-density parity-check (LDPC) code decoding

Optimization techniques have emerged as powerful tools for solving complex engineering problems in real-time applications, surpassing traditional heuristic methods. This paper introduces a novel chaos optimization algorithm that integrates the bisection search method to enhance the convergence ability, solution accuracy, and search speed of conventional optimization problems. The proposed algorithm is applied to the design and analysis of a reduced complexity decoding scheme using Low-Density Parity-Check (LDPC) codes. By leveraging the Linear Minimum Mean Square Error (LMMSE) estimation criterion, the proposed method approximates the estimated parameters acquired during the message computation process. This combined approach effectively mitigates error propagation and premature convergence issues. The presented research offers a low-complexity decoding algorithm for LDPC codes, leveraging an optimized chaos bisection search method. The adoption of the LMMSE criterion overcomes data dependency and error propagation concerns during the estimation process. The modified chaos optimization technique, integrated with the bisection search, efficiently determines optimal values from perturbed observations, leading to improved convergence rates and data processing precision by reducing the observation dimension (search space). Simulation results validate the proposed approach, showcasing its ability to mitigate error propagation and premature convergence while maintaining coding gain accuracy. Experimental validation on an FPGA RF-SoC device (model XCZU27DR) demonstrates that the proposed method achieves a maximum throughput of 8.7 Gbps at a clock frequency of 386.72 MHz, with reduced hardware complexity. The experimental verification on an FPGA RF-SoC device highlights the algorithm's minimal hardware complexity, superior decoding throughput, and Bit Error Rate (BER) performance.

The Assessment Method Reform of “Machine Design” Course Based on OBE Education Concept

In order to strengthen students' grasp of the knowledge of "Machine Design" and to improve students' ability to solve complex engineering problems and innovate, based on the OBE education concept, this paper analyzes the problems existing in the course of "Machine Design" and reforms the assessment method of "Machine Design" course. The reform highlights the character of the course and the main position of the students, and gives play to the incentive role of the assessment method. Starting from the concrete practice, under the premise of ensuring students' good adaptability, a number of measures focusing on cultivating students' engineering ability and innovation ability are introduced in the assessment. Students' attention to practical projects, academic performance and competition results have been significantly improved, which reflects the effectiveness of this reform to a certain extent.

Reform of the Comprehensive Practical Course System for Mechanical and Electronic Engineering Majors Under the Background of New Engineering

Under the background of new engineering, the reform of the comprehensive practical course system formechanical and electronic engineering majors actively responds to the challenges posed by the new round of technological revolution and industrial transformation to higher education, cultivating top-notch innovative intellectuals with comprehensive engineering qualities, meeting the requirements of being able to solve complex engineering problems rather than just cognitive capabilities, forming two core courses through reconstructing and reshaping the core courses of the major. The core courses include Drive, Measurement, and Control I and Drive, Measurement, and Control II, which highlight the comprehensive framework of mechanical and electronic engineering professional knowledge, continuing the comprehensive practical course system based on the unity of knowledge and practice, following the trend of new engineering, highlighting the practicality of professional innovation, assisting engineering education reform, and promoting high-quality development of new engineering professionals cultivation.

An LSTM-stacked autoencoder multisource response prediction and constraint optimization for scaled expansion tubes

Deep learning is attracting increasing attention due to its excellent predictive power and its ability to be applied in traditional research areas. In this paper, we propose a multisource response prediction network architecture based on a long short-term memory (LSTM)-stacked autoencoder to predict key crashworthiness indicators and complete curve reconstruction. Taking an expansion tube as an example using an equivalent scaling research method, a scaled expandable tubular (SET) finite element model was established and verified using a quasistatic compression test and a full-size coupler and buffer system experiment. A design of experiments (DOE) approach was used to obtain a dataset for training the prediction network. Neural network hyperparameters are critical to network prediction accuracy, and after comparison, the multisource response prediction network architecture showed good computational efficiency and satisfactory prediction accuracy when appropriate hyperparameters were selected. Subsequently, multiobjective constraint optimization was performed using the nondominated sorting genetic algorithm-II (NSGA-II) based on a prediction network architecture, which greatly improved the energy-absorption structure optimization accuracy. The results are expected to provide a research methodology for solving complex engineering problems by establishing a new framework for deep learning algorithms combined with optimization methods.

Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

Discrete Mathematics Teaching Reform and Practice for Engineering Certification in Computer Science

Engineering education professional certification is the certification of engineering majors in higher education institutions in accordance with the standards and processes of the Washington Agreement. It is an effective means to improve the quality of engineering education, promote continuous improvement of the profession, and enhance the international competitiveness of professional talents. Discrete mathematics is the core backbone course of computer science and technology and related majors. Under the guidance of the certification concept of engineering education, the curriculum outline of discrete mathematics, the core basic course of computer science and technology, has been revised to focus on cultivating students’ graduation abilities, refine teaching objectives, closely adhere to graduation requirements, support graduation indicators, and continuously reform teaching methods, teaching modes, and assessment forms. Teaching practice has shown that students’ mastery of computer mathematics theoretical knowledge is more solid, their ability to solve complex engineering problems is improved, and good teaching results are achieved.

Computational Engineering Advancements: General Review of Mathematical Modeling in Computer Engineering Applications

The comprehensive review explored the advancements in computational engineering with a focus on the role of mathematical modeling in computer engineering applications. The research investigated various mathematical models that used in different computer engineering fields, including software engineering, hardware design, and networking. It also examined the importance of mathematical modeling and its impact on solving complex engineering problems. The study highlighted emerging trends in mathematical modeling, such as reduced-order modeling, multiscale modeling, and uncertainty quantification. It also explored cutting-edge computational tools and techniques, such as high-performance computing, GPU acceleration, and meshless methods, which enabled engineers to conduct more efficient and accurate simulations. Furthermore, the integration of artificial intelligence and machine learning in mathematical modeling was discussed, emphasized the growing significance of data-driven models and surrogate modeling in computer engineering applications. The research identified industries that was benefit from enhanced computational engineering approaches, including aerospace, energy, healthcare, finance, and manufacturing. Through case studies, successful implementations of mathematical models in real-world engineering projects were presented, showcasing the practical implications of these advancements. The review also addressed challenges in adopting advanced mathematical models, such as computational complexity, data limitations, and model interpretability, and proposes potential solutions to address these issues.

Understanding Design Thinking as an Innovative Approach in Engineers in Society Course from Civil Engineering Student’s Perceptions

Design Thinking (DT) is a robust framework to identify and solve various human problems creatively. It offers a systematic solution to train engineering students in critical and creative thinking to solve complex engineering problems. The DT method is integrated within the Engineers in Society (EIS) course to develop problem-solving skillset among the final year engineering students. This paper presents the understanding of DT towards achieving the program outcome related to Engineers and Society attribute for the final year civil engineering students taking the EIS as one of the culminating courses. Community problem-based learning (Com-PBL) was used as a teaching and learning method in the course where the students participated in a community project. The findings from the survey questionnaires responded by 165 indicate that most students demonstrated good understanding on the Design Thinking approach implemented in the course. It can be concluded that the DT approach is a good method to improve the cognitive learning domains in developing student attributes toward the understanding of complex engineering problem-solving in societal contexts. Overall, from a Continual Quality Improvement (CQI) perspective, this study is important to improve, develop and evaluate the teaching, learning and assessment methods adopted for engineering education.

Preface

Fourth Conference of Computational Methods & Ocean Technology (COTech 2023) November 30 – December 1, 2023, University of Stavanger, Stavanger, Norway The COTech (Computational Methods & Ocean Technology) conference started as part of the research and dissemination activities of the Program Area for research “COTech - Computational methods in Offshore Technology” at Faculty of Science and Technology, University of Stavanger (UiS) with its first event in 2017. This Program Area for Research was founded in 2015 with seven professors, four associate professors, two adjunct professors and five research (PhD) students from the Department of Mechanical and Structural Engineering and Materials Science (IMBM), whose expertise and competence lies primarily within use of computational methods such as finite element methods, boundary and volume element methods, computational fluid dynamics and the like in marine and subsea technology, marine operations, design and analysis of mechanical systems, integrity and reliability of offshore structures and mechanical systems, renewable energy and wind engineering. In the ocean-related engineering area in particular, 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 for the harsh and unfriendly environment in the Arctic.Like the previous conference, this year’s COTech conference is conducted as part of the dissemination activities of the Institute Strategic Program (ISP) entitled “Computational Methods and Ocean Technology” established under the research activities of the Department of Mechanical and Structural Engineering and Materials Science. This three-year program (2021 - 2023) intends to bring researchers of the department under four selected research areas:1. Ocean Energy Conversion - installations & dynamic analysis of offshore wind turbines, structural health monitoring, corrosion assisted fatigue, ocean wave energy.2. Aquaculture Technology - efficient farming techniques, advanced numerical modeling and computation, fluid-structure interaction, design effective maintenance programs for aquaculture.3. Marine and Subsea Technology - sediment erosion (scour), scour prediction CFD models, structural integrity and fatigue life of offshore structures, adaptive control of ROVs in subsea application, predictive maintenance of subsea structures4. Innovative Solutions - 3D printing based innovative solutions, inspection drones, augmented reality for maintenance training,…In addition to the thematic areas covered in the last three conferences, the 4th COTech conference accommodated some of the emerging technologies and applications such as machine learning techniques, additive manufacturing technologies, 3D printing in healthcare technology, smart energy storage and bio-inspired design.In general, the conference is intended to provide a platform for academics and professionals working within diverse forms of the computational methods, Ocean/Offshore technology and Oil and Gas technologies to come together, present their recent works in the area, exchange ideas, and establish professional networks. It will serve as a forum for multidisciplinary research and bring together enable them to exchange their research experience and disseminate their results within the involved fields.List of Conference Organizing Committee, Volume Editors, Invited Keynote Speakers, Additional plenary session speakers, Technical Committee Members and Reviewers are available in this Pdf.

Practical Research on Promoting The Construction of New Engineering Education through Discipline Competition by PDCA Cycle

<p>At the beginning of the emergence of the concept of new engineering, its connotation interpretation and paradigm change theme have become the focus of attention. The new engineering should not only pay attention to the construction of new industries and new specialties under the new technology, but also pay attention to the continuous training effect of talents under the new engineering education. Discipline competition is an effective way to cultivate the innovation and entrepreneurship ability of college students and improve their comprehensive quality. In order to promote the sustainable and good construction of new engineering education in Colleges and universities, this paper uses PDCA cycle as a means to integrate multi-disciplinary technology through discipline competition, promote the ability of college students to solve complex engineering problems, and thus improve the teaching quality of new engineering. Practice has proved that PDCA is applied to discipline competition in a circular way, providing a feasible path for the implementation of new engineering. After three years of tracking assessment on three different classes of computer science, the graduation design score has been raised from about 82 points to about 85 points, and the students’ theoretical level, practical ability, innovation ability and discipline competition design level have been significantly improved.</p> <p> </p>

Promoting higher engineering education: Three‐level progressive cultivation of mechanical thinking based on multi‐classroom integration

AbstractTo improve students' ability to solve complex engineering problems, cultivate their advanced mechanical thinking, and promote higher engineering education, in this paper, a reform of the teaching mode with the goal of advanced mechanical thinking cultivation and output orientation is carried out, and a three‐level theoretical mechanics teaching method based on multi‐classroom integration is proposed by adopting the concept of progressive cultivation of mechanical thinking. The meaning of three different levels of mechanics thinking, primary, intermediate, and advanced, and the implementation strategy of the three‐level progressive cultivation of mechanical thinking are clarified. An online and offline blended teaching method based on multi‐classroom integration is designed. The effectiveness of the proposed teaching method is verified by the combination of the evaluation of the examination score, the achievement of course objectives, and students' learning satisfaction. The results show that the final evaluation score in 2022 is 17.4% higher than that in 2021, and the score distribution shows a skewed distribution, with most students' scores skewing toward the excellent range, indicating that after adopting this teaching method, different students can eventually have similar excellent learning outcomes even though they start learning with different foundations and abilities. In addition, the three‐course objectives in 2022 are achieved at high degrees of 78.3, 81.5, and 81.3, respectively, which are significantly higher than those in 2021. Meanwhile, the questionnaire results show that students are satisfied with the teaching method, which fully proves that the teaching mode can achieve good teaching effectiveness.

Control and performance analyses of a DC motor using optimized PIDs and fuzzy logic controller

Based on the no-free-lunch theorem, researchers have been proposing optimization algorithms for solving complex engineering problems. This paper analyzes the performance of five metaheuristics: Equilibrium Optimizer (EO), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Differential Evolution (DE), and Genetic Algorithm (GA) in fine-tuning the gains of a Proportional-Integral-Derivative (PID) to control the speed of a DC motor. The selected metaheuristics, in addition to being from distinct classes, are well established in their respective groups. The methods and findings of this study can be summarized in three phases. First, the mathematical model of the DC motor is deduced. Second, detailed descriptions of the aforementioned algorithms are presented. Furthermore, the structures of the applied controllers are discussed. Third, comparisons based on statistical indicators and analyses in the time and frequency domains, in addition to robustness and load disturbance tests, are performed. The results revealed that if a sufficient number of runs is given for each metaheuristic, despite being in different runs, all algorithms are able to propose the same optimal gain values. TLBO presented the highest speed, while GA and DE were the slowest in finding optimal values. Additionally, the results were compared with the Opposition-Based Learning Henry Gas Solubility Optimization (OBL/HBO)-based PID, reported to have better results than some previously published works on this topic, and a Fuzzy Logic Controller (FLC). The five optimized controllers obtained approximately the same results and outperformed the OBL/HGO-based PID, but the FLC was superior compared to the metaheuristic-based PIDs.

Exploration and Practice of Project-Based Teaching Reform Oriented to Engineering Education

With the shortening of knowledge iteration cycle, enterprises urgently need innovative practitioners to solve complex engineering problems, and engineering education reform is imminent. Taking the wireless sensor network course of Guangdong University of Science and Technology as an example, this paper explores the related elements of project-based teaching based on real social engineering projects, and constructs a project-based teaching mode with project as the carrier, learning as the center, and engineering quality training as the core. It is hoped to provide reference for quality goals, knowledge deconstruction, implementation process and evaluation system for the transformation of new engineering and the promotion of engineering education.

Developing and assessing a collaborative open-ended design project with a renewable energy focus in an introductory thermal sciences course

This paper describes the development and assessment of a collaborative open-ended design project for a second-year Introduction to Thermal Sciences course, in which groups had to design a renewable energy portfolio that would meet the University of San Diego's energy needs. The project was broken into three individual tasks, and the deliverables were a technical report and an oral presentation. Final reports submitted by groups in spring 2022 were directly assessed using rubrics to determine the efficacy of the project at meeting four of the student outcomes addressed by the course (1—ability to solve complex engineering problems, 2—ability to apply the engineering design process, 4—consider the impact of engineering solutions, and 7—ability to acquire and apply new knowledge). Four out of the six groups achieved student outcome 1, and five of the groups achieved student outcomes 2, 4, and 7. A self- and peer-evaluation form was used to indirectly assess student outcome 5—ability to function effectively on a team, and results showed that 27 of the 29 students achieved the outcome. Indirect assessment was performed using an anonymous survey instrument, and students’ responses about how the project improved their abilities in all skills that were assessed were statistically different from neutral in the positive direction. Students’ responses about their perception of the project organization, tasks expectations, timing, and difficulty were also statistically different from neutral in the positive direction. Open-ended feedback indicated that students appreciated learning about renewable energy, having real data from a realistic customer, and the amount of autonomy they were given.

A machine learning-based comparative analysis of surrogate models for design optimisation in computational fluid dynamics

Complex computer codes are frequently used in engineering to generate outputs based on inputs, which can make it difficult for designers to understand the relationship between inputs and outputs and to determine the best input values. One solution to this issue is to use design of experiments (DOE) in combination with surrogate models. However, there is a lack of guidance on how to select the appropriate model for a given data set. This study compares two surrogate modelling techniques, polynomial regression (PR) and kriging-based models, and analyses critical issues in design optimisation, such as DOE selection, design sensitivity, and model adequacy. The study concludes that PR is more efficient for model generation, while kriging-based models are better for assessing max-min search results due to their ability to predict a broader range of objective values. The number and location of design points can affect the performance of the model, and the error of kriging-based models is lower than that of PR. Furthermore, design sensitivity information is important for improving surrogate model efficiency, and PR is better suited to determining the design variable with the greatest impact on response. The findings of this study will be valuable to engineering simulation practitioners and researchers by providing insight into the selection of appropriate surrogate models. All in all, the study demonstrates surrogate modelling techniques can be used to solve complex engineering problems effectively.

DESIGN AND IMPLEMENTATION OF THE INTEGRATED PRACTICUM OF GEOMATICS ACCORDING TO ENGINEERING EDUCATION ACCREDITATION

Abstract. Engineering education accreditation is a new engineering education model promoted by the Ministry of Education in China, which aims to cultivate the ability of students to solve complex engineering problems. A course named “integrated practicum of Geomatics” (IPG) was innovatively designed for the undergraduate specialty of Geomatics. This course involves in the current advanced technologies to lay a solid foundation for students to engage in Geomatics through aerial photography technology design, unmanned aerial vehicle photography, control point selection, GNSS-RTK control point survey, photo interpretation and sketch, 3D (DEM, DOM, and DLG) product production, inputting 3D products into the Geodatabase, and GIS application software development. This paper expounds on the objectives, contents, evaluation methods, and continuous improvement of IPG.

The decay of Six Sigma and the rise of Quality 4.0 in manufacturing innovation

Smart manufacturing (SM) processes exhibit rapidly increasing complexity, nonlinear patterns in hyperdimensional spaces, high volumes of data, transient sources of variations, reduced lifetime, ultrahigh conformance, and non-Gaussian pseudo-chaotic behaviors. Standard quality control techniques and paradigms are not up to handling all these dynamics. Therefore, quality engineers went stagnant, with little innovation to offer to the manufacturing industry. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) have been applied to solve complex engineering problems and drive innovation. This new era where computer science principles are applied to quality control is called Quality 4.0 (Q4.0). However, the Six Sigma five-step problem-solving strategy (define, measure, analyze, improve, and control) does not fit the full ML cycle. The limitations of the Six Sigma techniques and paradigms in driving manufacturing innovation are discussed. A case study where a 3D quality pattern that can be easily detected by an MLA is not detected by traditional process monitoring methods. Early results motivate the development of the new era of Q4.0 without the limitations of Six Sigma and the potential of AI.