Complex Engineering Problems Research Articles

Управление человеческим капиталом промышленного предприятия

Effective management of a modern industrial enterprise involves solving complex technological problems, competent management of financial flows, work with innovations, their implementation and movement, which is possible only if the intellectual resource component of the enterprise's activities and the availability of high-level human capital are included. The high level of human capital of an industrial enterprise contributes to a rapid response to changes, the spread of innovations, the growth of added value, increased labor productivity, a high culture of communication and active communications. There is a lack of sufficient research in Russia on the application of the concept of human capital in the practice of managing industrial enterprises, the subsystem of human capital management has not been allocated and structured in enterprise management systems, employers do not always recognize the expediency of investing in human capital. The solution of these problems is facilitated by the awareness of the expediency of investments in human capital and the allocation of their main directions – investments in additional professional education, the creation of comfortable working conditions, maintaining the physical health of employees, the formation and development of corporate spirit. Human capital management at an industrial enterprise at the individual and corporate levels can be carried out using the proposed management tools. The construction of the human capital management system of an industrial enterprise is facilitated by the allocation of human capital characteristics and human capital management subsystems. An example of the adaptation of the proposed human capital management system to the activities of a specific industrial enterprise (Astrakhan Gas Processing Plant LLC). As s a result the structuring of human resource management functions aimed at formation and increment of human capital is presented.

Polynomial response surface-based transformation function for the performance improvement of low-fidelity models for concrete gravity dams

The behavior of concrete gravity dams under seismic loading is a complex engineering problem dependent on a wide range of variables. Probabilistic methods can be used to evaluate the capacity of an individual or a portfolio of dams to withstand seismic events. However, due to the high number of re-evaluations required by such methods, simplified models that may not fully capture the complexity of the problem are frequently adopted in the evaluations. For a portfolio of dams that requires geometric uncertainty to be included in the list of controlled variables, the number of re-evaluations increases even further. This is a common engineering problem, where the cost-performance trade-off must be evaluated for every project. To address this issue, this study proposes a machine learning-based transformation function that improves the results obtained with a simplified method by converting low-fidelity data into high-fidelity data. The proposed procedure is applied to analyze the seismic response of a dam-reservoir-foundation system considering three approaches for geometric uncertainty, with increasing complexity. The final function takes as input low-fidelity observations, as well as geometric, material and seismic parameters, and outputs improved observations, with accuracy levels comparable to those obtained with a high-fidelity model but at a much lower cost. The sliding factor of safety resulting from a pseudostatic analysis is taken as the low-fidelity observation, and the sliding displacement from a nonlinear finite element analysis is selected as the high-fidelity observation. The resulting transformation function is then used to generate fragility curves for a well-documented case study dam, and the results using the proposed methodology and from a traditional fragility analysis are compared. It is observed that the proposed methodology to generate transformation functions is capable of correlating methods with radically different hypotheses, precision levels and even different outputs.

Direct point-cloud-based numerical analysis using octree meshes

Point-cloud-based geometry acquisition techniques have seen an increased deployment in recent years and can be used in many branches of science and engineering. Different from conventional computer aided design (CAD) models, point-cloud data can be directly obtained by applying surveying technology, such as laser scanning devices. This opens a pathway to an efficient and highly automatic acquisition of the geometric model. However, in order to perform numerical analysis on point-cloud models using conventional finite element approaches, a surface reconstruction is required, which is both time consuming and error-prone. Therefore, a direct numerical analysis framework based on point-cloud data in conjunction with polyhedral element techniques is developed in this contribution. To this end, a robust and efficient meshing paradigm based on the direct use of point-cloud data (without surface reconstruction) is proposed. From the geometric model, a trimmed octree mesh containing only a limited number of master elements (element patterns) is directly generated, which is highly suitable for pre-computation techniques and parallelization (high-performance computing–HPC). By computing the element stiffness and mass matrices from pre-computed master elements via scaling, a fully automatic HPC framework is developed. By means of six numerical examples, we demonstrate the robustness, versatility, and efficiency of the developed methodology in handling (geometrically) complex engineering problems.

Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology

Physics-Informed Neural Networks (PINNs) and extended PINNs (XPINNs) have emerged as a promising approach in computational science and engineering for solving partial differential equations (PDEs) by combining the power of artificial intelligence (AI) with the underlying physics to accurately model and predict the solutions to complex problems in science and engineering. In this work, we propose the augmented physics-informed neural network (APINN), which adopts soft and trainable domain decomposition and flexible parameter sharing to further improve the extended PINN (XPINN) as well as the vanilla PINN methods. Concretely, a trainable gate network is employed to mimic the hard decomposition of XPINN, which can be flexibly fine-tuned for discovering a potentially better partition. The gate network satisfying the partition-of-unity property, weighted averages several sub-networks as the final output. APINN does not require complex interface conditions, whose sub-nets can utilize all training samples rather than just part of the training data in their subdomains. Lastly, each sub-net shares part of the common parameters to capture the similar components in each decomposed function. Furthermore, following the PINN generalization theory (Hu et al., 2022), APINN is shown to improve generalization by proper gate network initialization and general domain & function decomposition. Extensive experiments on different partial differential equations (PDEs) demonstrate how APINN improves PINN and XPINN. Specifically, we present examples where XPINN performs similarly to or worse than PINN, so that APINN can significantly improve both. We also show cases where XPINN is already better than PINN, so APINN can still slightly improve XPINN. Furthermore, we visualize the optimized gating networks and their optimization trajectories, and connect them with their performance, which helps discover the possibly optimal decomposition. Interestingly, if initialized by different decomposition, the performances of corresponding APINNs can differ drastically. This, in turn, shows the potential to design an optimal domain decomposition for the PDE under consideration.

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.

Uniaxial constitutive model for fiber reinforced concrete: A physics-based data-driven framework

Fiber reinforced concrete (FRC) has improved strength and ductility, making it suitable for a wider range of engineering structures. The development of a constitutive model is crucial for analyzing mechanical behavior of these structures, while this still remains challenging as the complex composition of FRC makes it difficult to formulate explicit relationships among a range of critical material parameters. With the latest development of data and digital technologies, data-driven approaches have emerged as a powerful alternative that are capable of solving advanced and complex engineering problems. Developing data-driven methods based on objective data analysis and decision-making to predict mechanical behavior of engineering structures is an important direction of such development. In this paper, a uniaxial constitutive model of fiber-reinforced concrete is developed by a physics-based data-driven framework. The framework consists of three important parts, including construction of experimental database, parameters calibration of physical models, and implementation of neural network. From the experimental data collected from published literature, an experimental database is built first. A physical model is proposed by modifying the uniaxial constitute model for normal concrete, so that it is more convenient and straightforward to consider the effect of fibers on fiber reinforced concrete subjected to compression, tension and repeat loading conditions. The parameters of the model are calibrated against experimental data by the swarm intelligence optimization algorithms. With the calibrated parameters of the physical model, a Fully Connected Neural Network (FCNN) is trained to be used to predict the physical parameters of fiber reinforced concrete. By comparing with independent experimental data, the proposed fiber-reinforced concrete uniaxial constitutive model constructed using the physics-based data-driven framework can accurately predict the stress–strain relationships of a range of FRC, which suggests that the FRC material model can be used in the numerical simulation and design of FRC components and structures. In addition, the proposed model is applicable to multiple loading conditions.

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.

Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation

Archimedes' optimization algorithm (AOA) is a recently proposed physics-based optimization algorithm inspired by Archimedes' Principle. However, for optimization of complex engineering problems, it shows slow convergence and can easily fall into local minima. Chaotic maps have non-repeatability properties they can effectively scan the search space with fast convergence in optimization algorithms. In this study, knacks of physics-based optimization along with chaotic maps are exploited for the parameter estimation of autoregressive exogenous (ARX), nonlinear input ARX (NI-ARX), LD-Didactic temperature process plant models and electrically stimulated muscle model. Ten chaotic maps have been applied to AOA. The fitness function of both models is constructed based on the square error between true and estimated response. The chaotic variants indicate that AOA with a chaotic Circle map (CAOA2) gives the best performance in terms of accuracy and convergence against reptile search heuristics, sine cosine optimization approach, harris hawks optimizer (HHO) and whale optimization algorithm (WOA) for different variations of generation and noise levels. Statistics based on mean square error, convergence plots, statistical plots, Taguchi test, average fitness, t-test, and Friedman test for repeated measures approve the reliability of CAOA2 for parameter estimation of ARX, NI-ARX, LD-Didactic temperature process plant models and electrically stimulated muscle model.

Quantum algorithm for smoothed particle hydrodynamics

We present a quantum computing algorithm for the smoothed particle hydrodynamics (SPH) method. We use a normalization procedure to encode the SPH operators and domain discretization in a quantum register. We then perform the SPH summation via an inner product of quantum registers. Using a one-dimensional function, we test the approach in a classical sense for the kernel sum and first and second derivatives of a one-dimensional function, using both the Gaussian and Wendland kernel functions, and compare various register sizes against analytical results. Error convergence is exponentially fast in the number of qubits. We extend the method to solve the one-dimensional advection and diffusion partial differential equations, which are commonly encountered in fluids simulations. This work provides a foundation for a more general SPH algorithm, eventually leading to highly efficient simulations of complex engineering problems on gate-based quantum computers.

Application of machine learning approaches for modelling crack growth rates

AbstractThe scientific community has widely accepted the use of machine learning techniques to tackle complex engineering problems. Among the most intriguing problems is finding the correlation between alloy steel properties and cyclic fatigue and crack growth rate. Employing machine‐learning models can provide more robust and accurate predictive models to address such challenges. This paper presents the application of four machine learning models, namely decision trees (DT), random forest (RF), adoptive boosting (AdaBoost), and gradient boosting regression tree (GBRT) to predict the crack growth rate of steel/alloys. The study utilizes a large database gathered from literature to construct the predictive models and compares the results using various statistical metrics and graphical representation. The study's findings demonstrate the effectiveness and suitability of machine learning techniques to handle complex databases related to fatigue problems.

A knowledge-driven co-evolutionary algorithm assisted by cross-regional interactive learning

Differential evolution (DE) and Estimation of distribution algorithm (EDA) exhibit complementary superiority in solving complex continuous optimization and engineering problems. The design of appropriate strategies coordinated with the two algorithms to balance exploration and exploitation is conducive to obtaining high-precision solutions. A knowledge-driven co-evolutionary algorithm assisted by a cross-regional interactive learning mechanism (KCACIL) is proposed to achieve a comprehensive collaboration between the algorithms, diverse strategies, and cross-regional individuals. Various elite-guided mutation strategies and a self-feedback strategy based on successful experience in light of implicit knowledge are devoted to fulfilling self-learning and cross-regional interactive learning to accomplish individual collaboration and knowledge transfer in the three regions. Reinforcement learning based on ε−greedy and simulated annealing is employed as feedback on the cross-regional individual information to promote the collaboration between opposition-based learning, interaction learning mechanism, and the revised strategy of inferior solutions with small Q values and high distance density. The dynamic self-adaptive adjustment strategies of multiple parameters are adopted to balance diversity and convergence. KCACIL is verified on the CEC 2014, 2017, 2020 benchmark test suites, and engineering applications. Experimental results indicate KCACIL is superior to the state-of-the-art comparison algorithms.

Improved crow search algorithm for optimal flexible manufacturing process planning

The enhancement of manufacturing processes due to the growing demand for products and services is a problem that must be constantly updated. Several schemas have been conducted to improve the manufacturing processes. These schemas are based on the flexible use of machines, tools, and tool access directions to determine the best process planning (PP) for a specific piece or part. The combination of different tools and machines leads to the formulation of complicated combinatory problems that can be treated as optimization problems. On the other hand, bioinspired optimization techniques have been widely employed for solving several complex engineering and real-life problems showing competitive results. The crow search algorithm (CSA) is an optimization technique based on the intelligent foraging behavior of crows. Our work uses an improved CSA (ICSA) to determine the optimal flexible process planning (FPP) with a low computational effort. Several AND/OR networks FPP problems are employed in the experimentation stage, where the production cost and production time are considered as fitness functions. To determine the performance and accuracy of the proposed technique, different results previously reported in the literature are used for a comparison showing that ICSA-FPP is able to outperform similar approaches.

Utilization of Additive Manufacturing for the Rapid Prototyping of C-Band Radiofrequency Loads

Additive manufacturing is a versatile technique that shows promise in providing quick and dynamic manufacturing for complex engineering problems. Research has been ongoing into the use of additive manufacturing for potential applications in radiofrequency (RF) component technologies. Here, we present a method for developing an effective prototype load produced from 316L stainless steel on a direct metal laser sintering machine. The model was tested using simulation software to verify the validity of the design. The load structure was manufactured by an online digital manufacturing company, showing the viability of using easily accessible tools to manufacture RF structures. The produced load was able to produce an S11 value of −22.8 dB at a C-band frequency of 5.712 GHz while under a vacuum. In a high-power test, the load was able to terminate a peak power of 8.1 MW. The discussion includes future applications of the present method and how it will help to improve the implementation of future accelerator concepts.

Physics-supervised deep learning–based optimization (PSDLO) with accuracy and efficiency

Identifying efficient and accurate optimization algorithms is a long-desired goal for the scientific community. At present, a combination of evolutionary and deep-learning methods is widely used for optimization. In this paper, we demonstrate three cases involving different physics and conclude that no matter how accurate a deep-learning model is for a single, specific problem, a simple combination of evolutionary and deep-learning methods cannot achieve the desired optimization because of the intrinsic nature of the evolutionary method. We begin by using a physics-supervised deep-learning optimization algorithm (PSDLO) to supervise the results from the deep-learning model. We then intervene in the evolutionary process to eventually achieve simultaneous accuracy and efficiency. PSDLO is successfully demonstrated using both sufficient and insufficient datasets. PSDLO offers a perspective for solving optimization problems and can tackle complex science and engineering problems having many features. This approach to optimization algorithms holds tremendous potential for application in real-world engineering domains.

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.

Sequential Design-Space Reduction and Its Application to Hull-Form Optimization

Hull-form optimization is a complex engineering problem. Owing to the several numerical simulations and complex design-performance spaces, hull-form optimization is considered an inefficient process, which makes determining the global optimum difficult. This study used rough set theory (RST) to acquire knowledge and reduce the design space for hull-form optimization. Furthermore, we studied one of the hull-form optimization problems by practically applying RST to the appropriate number of sampling points. To solve this problem, we proposed the RST-based sequential design-space reduction (SDSR) method that uses interval theory to calculate subspace intersections and unions, as well as test calculations to choose an appropriate stopping criterion. Finally, SDSR was used to optimize a KRISO container ship to minimize the wave-making resistance. The results were compared to those of direct optimization and one-time design-space reduction, thus proving the feasibility of this method.

Teaching Reform in The Demonstration Professional Construction Site of “Curriculum Ideology and Politics”

This major is guided by the principle of "application first, strengthening engineering practice ability, integrating industry and education, highlighting the background of optoelectronic industry, combining industry and management, and showing quality management characteristics". It is dedicated to cultivating application-oriented engineering talents who can meet the needs of regional optoelectronic industry and have the ability to design, implement and apply quality control systems for measurement and control systems and instrumentation production processes, and can engage in technology development, engineering application, production operation, quality management and other work in instrumentation and related fields. The orientation of the major is clear, and the service orientation is clear, which is in line with the development orientation and school direction of the university. Curriculum ideological and political teaching reform for this professional course has the unparalleled advantages of traditional science and technology courses. The education in humanistic cultivation, good moral development and solving complex engineering problems is to explore the resources of curriculum ideology and politics in the curriculum teaching.

Review on Chemical Engineering Capstone Design Teaching Model to Drive Continuous Improvement and Achieving Program Outcomes

Project-based Learning (PjBL) is a well-known paradigm for engineering design education, with numerous case studies published in the literature. Capstone is intended to provide students with an opportunity to demonstrate their readiness for professional practice. Consequently, this paper suggests a teaching and learning model as a new paradigm for capstone design projects in an effort to perpetually enhance engineering education, particularly in chemical engineering design courses. Rigorously assessing students’ knowledge of the design process is essential for understanding how to best create learning environments to facilitate the development of such knowledge. Such assessment is also quite difficult and hence there is a lack of assessment tools capable of measuring the design process knowledge of every student. The model also intends to assess whether students can explain their tasks through PBL. Besides, it provides such a structure for aligning course learning outcomes, methods of teaching including teaching strategy and learning activity, and methods for assessing students’ performance. Instead of prioritizing student outcomes and mapping them to direct metrics related to curriculum, the model is also used to highlight areas of engineering education where significant opportunities exist for improving the preparedness of our students for capstone and ultimately for professional practice. This paper also addresses an early stage of a study to seek the challenges in incorporating complex engineering problems during designing a capstone design model.

The M3 pressure-differential wave energy converter in Persian shores; numerical modeling and design

Following a double objective, this paper develops a numerical model for the M3 fixed-bottom pressure-differential wave energy converter (WEC) and then employs the model in the design of the WEC with available wave data for three Persian shores. To reach the first objective, using the potential theory, a simple numerical method, based on the conventional finite element method (FEM), has been developed for calculating the added mass and damping coefficients of the WEC. This paper shows that a complex engineering problem can be solved easily by making adjustments and making simple assumptions. These assumptions, though some of which may not be applicable to other types of WECs, are shown to be very effective in the modeling of the WEC considered in this paper. By comparing the results of the proposed method with those previously reported in the literature, the obtained results are validated. With a simple and practical numerical tool in hand, on the sequel and as the second objective, the energy absorption of the WEC is investigated based on the wave data in the Persian Gulf and the Gulf of Oman. For this purpose, several cases are considered for the dimensions of the WEC. Other effective parameters on the energy absorption of the WEC, i.e. the stiffness and damping coefficient of the power take-off (PTO) system, are optimized for each dimensional case to achieve the maximum absorbed power. A comparison of the absorbed power in different dimensional cases suggests appropriate WEC dimensions and the corresponding stiffness and damping coefficient of the PTO system for different application locations.