Solving Engineering Problems Research Articles

A dynamic solvent chamber propagation estimation framework using RNN for warm solvent injection in heterogeneous reservoirs

Warm solvent injection (WSI), injecting low-temperature solvent into formations to reduce the viscosity of heavy oil, is a clean technology for heavy oil production through reducing greenhouse gas emissions and water usage. The success of WSI operation depends on the uniform development and propagation of solvent chambers in reservoirs. However, reservoir heterogeneity stemming from shale barriers plays a detrimental role in the conformance of solvent chamber development and oil production rate. In this work, we developed a novel recurrent neural network (RNN)-based framework with the capability of efficiently tracking and estimating the solvent chamber positions in heterogeneous reservoirs based on only production time-series data. The developed estimation model utilizes the “sequence-to-sequence" mapping methodology to correlate observed production time-series sequence and solvent chamber edge sequence via a long short-term memory (LSTM) algorithm. The trained RNN models exhibit high accuracy, evidenced by the predicted dynamic solvent chamber locations match the corresponding true locations from numerical simulation, with a high coefficient of determination (R2) and a low mean squared error. Specifically, the achieved R2 values exceed 0.98 on both the training and testing data. The developed RNN-based workflow was tested via several cases from both regularly- and irregularly-shaped shale barriers, and the results were promising. The predicted solvent chambers showed strong agreement with those obtained from numerical simulations. The major benefits of this workflow include reducing computational time and saving overall monitoring and tracking costs for conventional techniques. The present work would provide a good demonstration of the capability of practical integration of machine learning methods in solving engineering problems.

Fluid–structure interaction in engineering based on its applications: A plenary and up-to-date review

The present study reviews the relevant research done on the fluid–structure interaction (FSI) in channels and vessels. The cases of FSI in channels with elastic plates, channels with variable geometry, flow airfoils, blood vessels, and heart valves are presented, using data from credible research articles. The review is carried out with a focus on the performance of the FSI method in solving engineering problems in channels, reporting its findings such as the effect of power-law exponent, the Nusselt number in different parts of a fluid, lift, and drag for a rigid cylinder and a plate in Newtonian fluids, the formation of eddy current, and the distribution of velocity and pressure in a fluid and on walls. The review is instrumental for researchers, engineers, and academics in providing comprehensive insight into methodologies and applications of fluid–structure interaction.

A nonlinear randomly reuse-based mutated whale optimization algorithm and its application for solving engineering problems

The whale optimization algorithm (WOA) is a meta-heuristic optimization algorithm inspired by the social behavior of whale hunting. However, the WOA has the defects of easily falling into local optimum and slow convergence speed. Therefore, to overcome the shortcomings of the original WOA, a novel variant of WOA called nonlinear randomly reuse-based mutated whale optimization algorithm (NRRMWOA) is proposed in this study. In detail, the proposed NRRMWOA includes three novel strategies as compared with original WOA. Firstly, a nonlinear adaptive parameter strategy is introduced to achieve nonlinear adjustment of search pattern along with the iteration time. The second improvement is the random reuse strategy, which can fully utilize the current optimal whale to improve the solution accuracy. The thirdly proposed late disturbance mutation strategy has the function of increasing population diversity in the later stage of iteration. The three proposed strategies complement each other and significantly improve the convergence efficiency and global exploration capability of WOA. To verify the superiority of the proposed NRRMWOA, a series of typical benchmark functions are utilized to conduct comprehensive numerical experiments. Compared with three sets of comparison algorithms (five variants of WOA, five advanced algorithms, and five well-known algorithms), the ARVs (average ranking values) obtained by NRRMWOA are 1.4667, 1.9167, and 2.1333, respectively. The rankings are all the best in the corresponding group. The detailed experimental and statistics results indicate that the presented NRRMWOA has higher convergence speed and better solution quality compared with the three sets of existing algorithms. In addition, 15 real-world optimization problems are employed to further test the performance of NRRMWOA for solving engineering optimization problems. For the results of 15 real-world optimization problems, the proposed NRRMWOA achieves the best results including 8 best “Mean”, 10 best “Min.”, and 7 best “Max.”. As a conclusion, the proposed NRRMWOA is a promising and excellent algorithm.

SDO: A novel sled dog-inspired optimizer for solving engineering problems

A new bio-inspired meta-heuristic algorithm, named Sled Dog Optimizer (SDO), is proposed in this paper. The inspiration for SDO is primarily drawn from the various behavioral patterns of sled dogs. It focuses on constructing mathematical models by simulating the process of dog sledding, training and retiring behaviors. The mutual constraints of multiple steps and the variation of several parameters make the exploitation and exploration capabilities of SDO well balanced. To test the ability of SDO, this paper sets up several different sets of experiments to be analyzed from a number of different aspects. First, SDO is qualitatively analyzed through several performance metrics, which include search history, search trajectory and population diversity. Second, SDO is compared with several novel and excellent metaheuristics at CEC 2017 as well as CEC 2020. The results show that SDO has extremely good performance in solving unconstrained optimization problems. SDO is then further applied to the CEC 2020 real-world problem test set to test its ability to solve real-world optimization problems with constraints. Finally, this paper extends and proposes a new path planning model to which SDO is applied. In these problems with constraints, SDO performs well and has a promising application. SDO demonstrates outstanding control of exploration and exploitation capabilities in all experiments. It can be said that SDO promotes the progress of meta-heuristic algorithms and provides another new powerful technology for complex optimization problems in the real world.

Comparing effects of different goal type orders on collaborative simulation‐based inquiry in engineering problem solving: A process modelling approach

Comparing effects of different goal type orders on collaborative simulation‐based inquiry in engineering problem solving: A process modelling approach

Improving Reinforcement Learning Exploration by Autoencoders

Reinforcement learning is a field with massive potential related to solving engineering problems without field knowledge. However, the problem of exploration and exploitation emerges when one tries to balance a system between the learning phase and proper execution. In this paper, a new method is proposed that utilizes autoencoders to manage the exploration rate in an epsilon-greedy exploration algorithm. The error between the real state and the reconstructed state by the autoencoder becomes the base of the exploration-exploitation rate. The proposed method is then examined in two experiments: one benchmark is the cartpole experiment while the other is a gridworld example created for this paper to examine long-term exploration. Both experiments show results such that the proposed method performs better in these scenarios.

Development of Technological Problem Solving Class Design Competency Diagnostic Tool for Technology Teachers

Development of Technological Problem Solving Class Design Competency Diagnostic Tool for Technology Teachers

Adaptive crossover-based marine predators algorithm for global optimization problems

Abstract The Marine Predators Algorithm (MPA) is a swarm intelligence algorithm developed based on the foraging behavior of the ocean’s predators. This algorithm has drawbacks including, insufficient population diversity, leading to trapping in local optima and poor convergence. To mitigate these drawbacks, this paper introduces an enhanced MPA based on Adaptive Sampling with Maximin Distance Criterion (AM) and the horizontal and vertical crossover operators – i.e., Adaptive Crossover-based MPA (AC-MPA). The AM approach is used to generate diverse and well-distributed candidate solutions. Whereas the horizontal and vertical crossover operators maintain the population diversity during the search process. The performance of AC-MPA was tested using 51 benchmark functions from CEC2017, CEC2020, and CEC2022, with varying degrees of dimensionality, and the findings are compared with those of its basic version, variants, and numerous well-established metaheuristics. Additionally, 11 engineering optimization problems were utilized to verify the capabilities of the AC-MPA in handling real-world optimization problems. The findings clearly show that AC-MPA performs well in terms of its solution accuracy, convergence, and robustness. Furthermore, the proposed algorithm demonstrates considerable advantages in solving engineering problems, proving its effectiveness and adaptability.

Considering personalized individual semantics with ordinal and cardinal consensus reaching processes via three-way decision and regret theory

The seamless integration of computerized methodologies into industrial engineering problem-solving is pivotal for optimizing efficiency. In the specific domain of multi-attribute group decision-making (MAGDM) with probabilistic linguistic term sets (PLTSs), these methodologies offer systematic approaches to consensus building, ensuring effective decision processes in intricate scenarios. Within the realm of PLTSs, the consensus-reaching process (CRP) for MAGDM is gaining prominence. This paper addresses this evolving area by proposing ordinal and cardinal CRPs within the framework of PLTSs, specifically incorporating the regret theory (RT) of three-way decisions (TWD). The paper introduces an initial distance formula under PLTSs, providing a complementary approach to assess similarity relations among decision-makers (DMs). To account for diverse semantics across DMs, personalized individual semantics (PIS) is integrated into the CRP, recognizing variations in DMs’ alternatives and attributes. To enhance realism, the paper introduces the concepts of individual alternative sets and individual attribute sets. Additionally, the paper integrates ordinal and cardinal consensus, establishing a dynamic feedback adjustment mechanism grounded in the principles of RT and TWD. The method’s reasonableness is validated through a real case study, and a comparative analysis with the existing methods underscores the superiority of the approach presented in this paper.

Reach, Adoption, and Implementation Strategies of a Telehealth Fall Prevention Program: Perspectives From Francophone Communities Across Canada.

Introduction. A fall may impact a person's physical, emotional, and psychological well-being. Fall prevention programs are being implemented to reduce these negative outcomes. However, linguistic barriers in health services may reduce access to such prevention programs. A telehealth fall prevention program was designed to increase access to such programs in French for Francophone minority communities in Canada. This capacity-building project aimed to support community partners to deliver this telehealth program and document strategies used to reach, adopt, and implement the program within various Francophone and Acadian Minority Communities. Methods. A sequential explanatory mixed methodology was used to document reach, adoption, and implementation strategies and describe the lived experiences of program facilitators and organization representatives. Reach, adoption, and implementation were documented and analyzed descriptively, while lived experiences were analyzed using content analysis following the Consortium Framework for Implementation Research. Results. Twelve organization representatives or program facilitators from eight organizations operating in four different provinces participated in the study. Three themes emerged from the qualitative data on reach and adoption: external context, internal context, and capacity building. Four themes were identified as barriers and facilitators to implementation: level of preparation and time management, interpersonal relations and telepresence, exercise facilitation and safety, and technological problem-solving. Conclusion. Using tailored reach and adoption strategies such as prioritizing provinces with higher proportions of needs and training local community program facilitators may lead to the successful implementation of a new telehealth fall prevention program. Results from this study could potentially inform other primary prevention programs or telehealth program implementation.

Developing a Framework for the Integration of Artificial Intelligence in Technology Education

This study aimed to create a foundation for the integration of AI in technology education. The framework of this study was based on the important problem-solving learning process in technology education. The developed framework structures the problem-solving steps of such types of problems into problem solving, design, development, and evaluation. Each technological problem and problem-solving step area can be implemented using Intelligent Tutoring System (ITS), Dialogue-Based Tutoring System (DBTS), and Exploratory Learning Environment (ELE) of AI convergence education. Technology education supports learners develop Technological knowledge and develop critical and creative thinking when attempting to resolve real-life technological glitches. Since the process of solving technological problems is complex in itself, it was necessary to categorize technological problems in real life and organize the procedure for resolving technical issues into a series for formal education in schools. In this process of technological problem solving learning, explicit knowledge may be necessary, and interpretive or procedural knowledge may be necessary. Methods of utilizing AI convergence education vary depending on the target knowledge. Therefore, structuring and presenting a plan to apply various AI convergence educations to the process of solving highly complex technological problems is meaningful in that it suggests the basis for educational direction. Based on the framework developed in this study, expected that AI convergence education methods appropriate for each problem-solving process of various technological problems will be systematically researched and implemented in the future.

Development and Implementation of Visualization of CPU Components using AR

Augmented Reality (AR) technology presents innovative solutions for visualizing complex concepts and systems. This paper investigates the utilization of AR in the visualization of CPU (Central Processing Unit) components, with the objective of augmenting comprehension and learning within computer architecture. By developing an AR application, CPU components are depicted in three dimensions, enabling users to interactively explore and comprehend their functionalities and interconnections. The paper delineates the design and execution of the AR system and discusses its potential advantages for education, training, and technical problem-solving in computer science and engineering. Keywords: Augmented Reality, CPU Components, Visualization, Computer Architecture.

Hydro-mechanical model for small-strain stiffness of unsaturated soil

This study aims to investigate a novel method for predicting the small-strain stiffness of unsaturated soil. A hydro-mechanical model is proposed to estimate the small-strain stiffness of unsaturated soil with various matric suction and stress states. A series of saturated and unsaturated bender element tests are conducted, and the test results indicate that the small-strain stiffnesses of saturated and unsaturated soil have similar changing trends with the variation of the stress state. The small-strain stiffness of unsaturated soil is calculated by the proposed model based on the test results of saturated soil. The model has two parameters that have a physical meaning and can be measured by tests. Thus, combined with the mature prediction model and measurement method of saturated soil and the proposed hydro-mechanical model, the complex unsaturated bender element tests are unnecessary. The proposed model is anticipated to be of practical value for solving engineering problems during the determination of small-strain parameters in the design of pits and foundations.

Interdisciplinary Transition Innovation, Management, and Engineering (InTIME) Design: an intersection analysis of design approaches for whole-system sustainability

AbstractInterdisciplinary transition innovation, management, and engineering (InTIME) Design has been developed to overcome sustainability transition challenges in complex systems. The intersections of InTIME Design with a range of reported design for sustainability (DfS) approaches were analysed. Results demonstrate similar core principles across DfS approaches. InTIME Design accomplishes convergence of the studied approaches, and organises the DfS approaches into workflow phases, adds a complimentary wicked problem definition, and deploys systems engineering problem solving.

2022 개정 기술과 교육과정 개발과 주요 특징

The purpose of this study was to develop the 2022 revised technology and curriculum. The study was divided into primary and secondary research according to the government's curriculum development process. The research methods used in this study included literature research, expert consultation, Delphi survey, and policy consultation. One of the most important features of the 2022 revised technology subject curriculum development is that the participation of professors at technology teacher training institutions and current technology teachers increased compared to the previous curriculum. As a result of this participation and discussion, the results of the 2022 revised technology subject curriculum development study were derived. The results of this study are summarized as follows. First, the middle school technology curriculum was developed into two major areas: technological problem solving and innovation, and sustainable technology and convergence. Second, the high school technology curriculum was composed and developed with technology and home economics as general electives, robots and the world of engineering as career electives, and creative engineering design and general intellectual property as convergence electives. Third, all curriculum were developed by dividing them into changed content elements: knowledge/understanding, process/skill, value/attitude, and achievement standards were developed through combination of content elements. Fourth, the high school technology curriculum strengthened the nature of the engineering field and attempted to preserve the purpose of the high school credit system. The development process and main features of the 2022 revised technology subject curriculum can be used as explanatory material for future curriculum implementation, monitoring of future curriculum, and basic data for the next curriculum.

IHHO: an improved Harris Hawks optimization algorithm for solving engineering problems

Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Hawks optimization (IHHO) algorithm for solving different engineering tasks. The proposed algorithm focused on random location-based habitats during the exploration phase and on strategies 1, 3, and 4 during the exploitation phase. The proposed modified Harris hawks in the wild would change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. To avoid being stuck in a local solution, random values were generated using logarithms and exponentials to explore new regions more quickly and locations. To evaluate the performance of the proposed algorithm, IHHO was compared to other five recent algorithms [grey wolf optimization, BAT algorithm, teaching–learning-based optimization, moth-flame optimization, and whale optimization algorithm] as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). These optimizers had been applied to different benchmarks, namely standard benchmarks, CEC2017, CEC2019, CEC2020, and other 52 standard benchmark functions. Moreover, six classical real-world engineering problems were tested against the IHHO to prove the efficiency of the proposed algorithm. The numerical results showed the superiority of the proposed algorithm IHHO against other algorithms, which was proved visually using different convergence curves. Friedman's mean rank statistical test was also inducted to calculate the rank of IHHO against other algorithms. The results of the Friedman test indicated that the proposed algorithm was ranked first as compared to the other algorithms as well as three other modifications of HHO.

MCHIAO: a modified coronavirus herd immunity-Aquila optimization algorithm based on chaotic behavior for solving engineering problems

This paper proposes a hybrid Modified Coronavirus Herd Immunity Aquila Optimization Algorithm (MCHIAO) that compiles the Enhanced Coronavirus Herd Immunity Optimizer (ECHIO) algorithm and Aquila Optimizer (AO). As one of the competitive human-based optimization algorithms, the Coronavirus Herd Immunity Optimizer (CHIO) exceeds some other biological-inspired algorithms. Compared to other optimization algorithms, CHIO showed good results. However, CHIO gets confined to local optima, and the accuracy of large-scale global optimization problems is decreased. On the other hand, although AO has significant local exploitation capabilities, its global exploration capabilities are insufficient. Subsequently, a novel metaheuristic optimizer, Modified Coronavirus Herd Immunity Aquila Optimizer (MCHIAO), is presented to overcome these restrictions and adapt it to solve feature selection challenges. In this paper, MCHIAO is proposed with three main enhancements to overcome these issues and reach higher optimal results which are cases categorizing, enhancing the new genes’ value equation using the chaotic system as inspired by the chaotic behavior of the coronavirus and generating a new formula to switch between expanded and narrowed exploitation. MCHIAO demonstrates it’s worth contra ten well-known state-of-the-art optimization algorithms (GOA, MFO, MPA, GWO, HHO, SSA, WOA, IAO, NOA, NGO) in addition to AO and CHIO. Friedman average rank and Wilcoxon statistical analysis (p-value) are conducted on all state-of-the-art algorithms testing 23 benchmark functions. Wilcoxon test and Friedman are conducted as well on the 29 CEC2017 functions. Moreover, some statistical tests are conducted on the 10 CEC2019 benchmark functions. Six real-world problems are used to validate the proposed MCHIAO against the same twelve state-of-the-art algorithms. On classical functions, including 24 unimodal and 44 multimodal functions, respectively, the exploitative and explorative behavior of the hybrid algorithm MCHIAO is evaluated. The statistical significance of the proposed technique for all functions is demonstrated by the p-values calculated using the Wilcoxon rank-sum test, as these p-values are found to be less than 0.05.

A hybrid particle swarm optimization algorithm for solving engineering problem

To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function (f1-f13\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${f}_{1}-{f}_{13}$$\\end{document}) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems.

An Application of TRIZ Inventive Approach in Development Strategy into Fourth-party Logistics

Purpose: The study aims to affirm the application of TRIZ in logistics service and then develop innovative strategies for Vietnamese local logistics service providers (LSPs) to transform into fourth-party logistics (4PL) through TRIZ inventive methodology. Methodology/Approach: The integration of Savransky’s methodology into TRIZ proposals enables to identify the core correlated dependencies of selected parameters. The analyses of the theoretical background and the application of the TRIZ matrix are comprehensively conducted to create innovation for three important attributes of 4PL. Findings: The research results propose seven strategies through Savransky’s suggested steps from the analytical application of TRIZ. The established recommendations are categorised into short-term and long-term strategies and can be flexibly applied depending on the corporate growth process. Research Limitation/implication: TRIZ approach was initially constructed for solving engineering problems. As a result, several inventive principles in the contradiction matrix were not entirely appropriate to the service area. In such cases, the authors can’t apply all suggested solutions from the contradiction matrix. Originality/Value of paper: The study provides local LSPs in Vietnam with comprehensive strategies for improving their capabilities to meet the requirements of 4PLs’ power.

Hybrid Strategies Based Seagull Optimization Algorithm for Solving Engineering Design Problems

The seagull optimization algorithm (SOA) is a meta-heuristic algorithm proposed in 2019. It has the advantages of structural simplicity, few parameters and easy implementation. However, it also has some defects including the three main drawbacks of slow convergence speed, simple search method and poor ability of balancing global exploration and local exploitation. Besides, most of the improved SOA algorithms in the literature have not considered the drawbacks of the SOA comprehensively enough. This paper proposes a hybrid strategies based algorithm (ISOA) to overcome the three main drawbacks of the SOA. Firstly, a hyperbolic tangent function is used to adjust the spiral radius. The spiral radius can change dynamically with the iteration of the algorithm, so that the algorithm can converge quickly. Secondly, an adaptive weight factor improves the position updating method by adjusting the proportion of the best individual to balance the global and local search abilities. Finally, to overcome the single search mode, an improved chaotic local search strategy is introduced for secondary search. A comprehensive comparison between the ISOA and other related algorithms is presented, considering twelve test functions and four engineering design problems. The comparison results indicate that the ISOA has an outstanding performance and a significant advantage in solving engineering problems, especially with an average improvement of 14.67% in solving welded beam design problem.