Engineering Experience Research Articles

Transforming Learning Orientations Through STEM Interdisciplinary Project-Based Learning

Science, technology, engineering, and math (STEM) education is challenged by industries to incorporate business, engineering, and communication experiences to prepare students for workplace success. In this study, we outline an approach—the STEM Oriented Alliance for Research (SOAR)—to enhance student experience by offering interdisciplinary project-based learning (IPBL) for undergraduate students majoring in electrical engineering, communications, and marketing. We examined how students’ disciplinary and cooperative orientations toward learning shifted in response to their experiences in a semester-long interdisciplinary project-based learning experience with authentic industry outputs. Using a multi-method approach, we explored how interdisciplinary projects influenced student experiences in terms of five collaboration abilities: positive interdependence, accountability, promotive interaction, group processing, and social skills. Further, we observed a shift from fixed- to more growth-oriented mindsets, and from a primarily disciplinary to interdisciplinary focus for their future professional work. The outcomes of the SOAR project make clear that providing structure for professional cooperation on interdisciplinary projects can have profound effects on how students learn to cooperate and position themselves as learners. For most SOAR participants, the experience was deeply formative and contributed to their readiness to cooperate and learn within the interdisciplinary and STEM-oriented workforce.

Life Prediction and Reliability Evaluation of Reinforced Concrete Frame Structures Considering the Collision Response Under Earthquake Conditions

Firstly, in this study, we utilize the high-order element model (truss link model) simulation method in OpenSees (3.7.0) software and verify the feasibility of this method by comparing it with the shaking table test. Secondly, the structural dynamic response of adjacent structures with different performance levels, spacing, and layout forms under large earthquakes is analyzed, and the corresponding structural failure probability is studied. Furthermore, the life distribution within the design service life of the structure is predicted according to the nonparametric Kaplan–Meier estimation model. Finally, the reliability of adjacent structures is evaluated by using the joint engineering demand parameters. The analysis method of replacing the theoretical analysis based on engineering experience and certainty in the current specification with probability analysis is proposed, which provides a more reliable theoretical basis for decision-making regarding the reinforcement, maintenance, or demolition of structures in the later stage.

Investigation of STEM Supported Engineering and Design Experiences of Students Participating in Design and Innovation Workshop in the Context of Situational Interest Theory

This study aims to examine the engineering and design-supported STEM (Science, Technology, Engineering, and Mathematics) experiences of students who participated in the "Design and Innovation Workshop" organized at the Science and Art Center (SAC) during the semester break. The experiences and perceptions of 17 students (10 males and 7 females) were analyzed in depth in this study, which was conducted using a phenomenology design from qualitative research methods. Semi-structured interview form, student diaries, field notes and STEM product drawing form were used as data collection tools. The research findings showed that students' participation in design and engineering-based STEM activities enhanced their problem-solving skills, creativity and teamwork. The theory accompanying the research suggests that these activities increase students' interest and motivation in STEM disciplines. The research results suggest that design and innovation workshops can positively influence students' attitudes and skills towards STEM fields.

A representation learning-based approach to enhancing manufacturing quality for low-voltage electrical products

In low-voltage electrical product manufacturing, resolving quality issues is heavily reliant on engineering experience, and can be time-consuming and error-prone. Through quality management systems, a large number of historical defect cases can be consolidated for analysis along with relevant causes. However, these defect descriptions are often casually described with a mix of Chinese and English language, containing domain-specific terms. Additionally, defect product features have varieties and complex relationships. Therefore, historical defect cases have not been effectively utilized to support manufacturing quality issues. To address this challenge, this study proposes a representation learning-based approach to enhance manufacturing quality. Key research contributions include: (1) A two-stage word embedding technique based on the pre-trained language model. First, TSDAE is utilized for unsupervised pre-training on a large amount of unlabeled data. Then, Sentence-BERT is utilized for fine-tuning on a small set of labeled similar sentence pairs. This process yields a pre-trained language model specific to low-voltage electrical product defects. (2) NSHPSAGE graph embedding model based on the constructed product feature knowledge graph. We select more valuable neighboring nodes during sampling and explore different aggregation functions to enhance graph embedding performance. This model effectively aggregates product feature information into “Defect_Case” nodes, yielding graph embedding vectors. The model exhibits good Weighted-Precision and Weighted-Recall with a short training duration, and it can handle new nodes, addressing the issue of heterogeneous graph embedding. (3) A defect case recommendation technique that fuses word embedding and graph embedding. We use Multi-Head Attention Fusion in the late-fusion to obtain defect case vectors. This approach comprehensively considers defect description semantic knowledge and complex product feature relationships, enabling accurate defect case recommendation with the prototype system.

Applications and Prospects of Smooth Particle Hydrodynamics in Tunnel and Underground Engineering

Smoothed particle hydrodynamics (SPH) is a state-of-the-art numerical simulation method in fluid mechanics. It is a novel approach for modeling and comprehending complex fluid behaviors. In contrast to traditional grid-dependent techniques like finite element and finite difference methods, SPH utilizes a meshless, purely Lagrangian approach, offering significant advantages in fluid simulations. By leveraging a set of arbitrarily distributed particles to represent the continuous fluid medium, SPH enables the precise estimation of partial differential equations. This grid-free methodology effectively addresses many challenges associated with conventional methods, providing a more adaptable and efficient solution framework. SPH’s versatility is evident across a broad spectrum of applications, ranging from advanced computational fluid dynamics (CFD) to complex computational solid mechanics (CSM), and proves effective across various scales—from micro to macro and even astronomical phenomena. Although SPH excels in tackling problems involving multiple degrees of freedom, complex boundaries, and large discontinuous deformations, it is still in its developmental phase and has not yet been widely adopted. As such, a thorough understanding and systematic analysis of SPH’s foundational theories are critical. This paper offers a comprehensive review of the defining characteristics and theoretical foundations of the SPH method, supported by practical examples derived from the Navier–Stokes (N-S) equations. It also provides a critical examination of successful SPH applications across various fields. Additionally, the paper presents case studies of SPH’s application in tunnel and underground engineering based on practical engineering experiences and long-term on-site monitoring, highlighting SPH’s alignment with real-world conditions. The theory and application of SPH have thus emerged as highly dynamic and rapidly evolving research areas. The detailed theoretical analysis and case studies presented in this paper offer valuable insights and practical guidance for scholars and practitioners alike.

Rapid identification and early warning of axial compressor stall based on multiscale CNN-SVM-FC model

Early prewarning of compressor stall and surge is crucial to avoid aircraft engine instability, yet it is challenging due to the complex and unstable flow field characterized by multiple modes and multiscale features. To enhance the multi-scale feature representation capability of Convolutional Neural Network-Support Vector Machine (CNN-SVM) algorithm, a novel classifier modelling method combined multiscale windows with CNN-SVM is introduced for stall prewarning in this paper, named Multiscale CNN-SVM-FC. Multiscale detection windows are utilized to adaptively identify various pressure features during the stall process. Additionally, to reduce the false alarm rate, a fuzzy control algorithm is integrated with the temporal accumulation of prediction results from the multi-branch network for joint analysis. A series of test data from a five-stage axial compressor at different operating speeds is used to verify this method. The results indicate that the proposed Multiscale CNN-SVM-FC method enhances the accuracy of classification and reduces the false alarm rate compared to the standard CNN-SVM model, achieving over 99% accuracy in identifying unstable states under various speeds. Compared to three traditional stall prewarning methods, the Multiscale CNN-SVM-FC model provides an average warning signal 164 milliseconds ahead of stall, and reduces the uncertainty associated with threshold selection, which typically relies on engineering experience.

Multi-Objective Parameter Configuration Optimization of Hydrogen Fuel Cell Hybrid Power System for Locomotives

Conventional methods of parameterizing fuel cell hybrid power systems (FCHPS) often rely on engineering experience, which leads to problems such as increased economic costs and excessive weight of the system. These shortcomings limit the performance of FCHPS in real-world applications. To address these issues, this paper proposes a novel method for optimizing the parameter configuration of FCHPS. First, the power and energy requirements of the vehicle are determined through traction calculations, and a real-time energy management strategy is used to ensure efficient power distribution. On this basis, a multi-objective parameter configuration optimization model is developed, which comprehensively considers economic cost and system weight, and uses a particle swarm optimization (PSO) algorithm to determine the optimal configuration of each power source. The optimization results show that the system economic cost is reduced by 8.76% and 18.05% and the weight is reduced by 11.47% and 9.13%, respectively, compared with the initial configuration. These results verify the effectiveness of the proposed optimization strategy and demonstrate its potential to improve the overall performance of the FCHPS.

GenAI in the classroom: Customized GPT roleplay for process safety education

This paper outlines innovative pedagogical approaches in chemical engineering safety education, utilising three key strategies: (1) Integration of the direct experiences of experienced professional engineers to teach expertise-aligned process safety case studies; (2) Shifting from traditional short oral presentations to more comprehensive and dynamic ‘engineering meeting’ formats to drive constructive student learning; and (3) The use of Generative AI to enhance (1) and (2), with cost/time scalability, improved student access and by accommodating greater learning-style diversity. Evaluation of these educational innovations is performed through a mixed-methods approach and reveals positive impacts on student learning and engagement. The paper provides a detailed outline of classroom implementation, with supporting resources, for straightforward integration by other academics.

An open-source tool for automated human-level circling behavior detection

Quantitatively relating behavior to underlying biology is crucial in life science. Although progress in keypoint tracking tools has reduced barriers to recording postural data, identifying specific behaviors from this data remains challenging. Manual behavior coding is labor-intensive and inconsistent, while automatic methods struggle to explicitly define complex behaviors, even when they seem obvious to the human eye. Here, we demonstrate an effective technique for detecting circling in mice, a form of locomotion characterized by stereotyped spinning. Despite circling's extensive history as a behavioral marker, there currently exists no standard automated detection method. We developed a circling detection technique using simple postprocessing of keypoint data obtained from videos of freely-exploring (Cib2−/−;Cib3−/−) mutant mice, a strain previously found to exhibit circling behavior. Our technique achieves statistical parity with independent human observers in matching occurrence times based on human consensus, and it accurately distinguishes between videos of wild type mice and mutants. Our pipeline provides a convenient, noninvasive, quantitative tool for analyzing circling mouse models without the need for software engineering experience. Additionally, as the concepts underlying our approach are agnostic to the behavior being analyzed, and indeed to the modality of the recorded data, our results support the feasibility of algorithmically detecting specific research-relevant behaviors using readily-interpretable parameters tuned on the basis of human consensus.

Integration of extended reality and CAE in the context of industry 4.0

ABSTRACT Simulation plays a major role in the development of new products and is considered one of the nine technological pillars of Industry 4.0. By encapsulating a plethora of digital tools, simulation has transitioned towards what is known as Computer Aided Engineering (CAE), which further enables engineers to validate the design and ensure product performance. The development of cutting-edge digital technologies, such as Extended Reality (XR), has also gained attention aiming at the facilitation of human-machine interface (HMI) and the creation of communication channels for improved remote collaboration. The contribution of this study lies in the design and development of a cloud-based framework for the integration of CAE tools with Mixed Reality (MR), specifically, Computational Fluid Dynamics (CFD), focusing on industrial refrigeration systems design and editing applications. Furthermore, the developed solution provides augmented 3D visualizations overlaid on the existing installations. The proposed framework supports the virtual co-existence of engineers during the design phase, thus enabling collaboration and mentoring between experienced and young engineers. Additionally, it can be realized as a Cloud platform, which supports the distribution of several services such as storage of CAD files, simulation results, and the XR application for the setup and visualization of simulation results.

토성의 등장과 축성기술의 형성

This paper examines the emergence of ancient earthen fortifications and the construction techniques that laid their foundations, under the premise that the appearance of ancient fortresses coincides with the formation of fortress construction techniques. Ancient fortresses first appeared as defensive structures characterized by walls and later evolved into significant political and administrative symbols for states. Among these, earthen fortifications, as suggested by the character ‘城’, represent an early form of such defensive walls, indicating a long-standing history. Literature records show that these fortifications existed prior to the Three Kingdoms period. Archaeological investigations of Bronze Age ditch enclosures indicate that the soil excavated from creating these enclosures was used to build earthen walls, which gradually developed into the defensive settlements known as earthen fortifications. In terms of the formation of construction techniques, the ability to build earthen fortifications likely did not emerge suddenly during the Three Kingdoms period. Constructing walls with soil requires appropriate techniques, specifically the technique of compaction, which means ‘to firmly pack soil.’ This compaction technique stemmed from accumulated civil engineering experiences since the Neolithic period and laid the groundwork for building earthen fortifications and other earthen structures. Recent findings of compaction marks in domestic earthen fortifications reveal that this technology had matured by the Three Kingdoms period. Therefore, it can be concluded that the practice of compacting soil began as early as the Neolithic period, evolving through the earthen walls of Bronze Age ditch enclosures into the form of fortifications. However, there remains a gap between the earthen walls of the Bronze Age and the construction of Three Kingdoms period fortifications. Future archaeological research is expected to further elucidate the emergence of these fortifications and the early techniques used in their construction.

Electrical breakdown mechanism and life prediction of thermal-aged epoxy/glass fibre composites

Epoxy/glass fibre composites possess excellent mechanical and electrical properties and are widely utilised in electrical and electronic power equipment. However, the composites exhibit relatively poor thermal conductivity, causing the temperature of the composites to increase during the operation of power equipment, resulting in a significant reduction in the electrical breakdown strength. Although the effects of thermal aging on polymeric materials have been widely studied, its influence on electrical strength mechanisms has not been investigated at the molecular level. In this study, epoxy/glass fibre composite specimens were subjected to accelerated thermal aging treatment for 360 h at 180 °C. Functional groups, molecular chain dynamics, and electrical breakdown are characterised using infrared spectroscopy, dielectric spectroscopy, and breakdown measurement. Subsequently, electrical breakdown mechanism and life prediction of the thermally aged composites are discussed. During thermal aging, the epoxy resin molecular chains undergo continuous oxidation and chain scission, which generate numerous polar functional groups and short chains and an increase in the free volume. This triggers an enhancement in the chain segmental dynamics, thereby significantly reducing the activation energy of the epoxy resin. After 360 h, activation energy decreased from 0.78 eV to 0.67 eV. The DC breakdown voltages of the specimens decreased from 168.28 kV/mm to 134.91 kV/mm. An insulation life prediction model for thermally aged epoxy/glass fibre composites is established based on the time-temperature equivalence theory. The prediction results indicate that the service life of the operational composites is approximately 11.2 years at 353 K, which is consistent with engineering experience.

A multi-objective co-optimization method of controller parameters for the overall system of small pressurized water reactor

Small pressurized water reactors (SPWRs) normally have complicated and varying working conditions, and become an important direction for nuclear energy development. The overall system of SPWR, including the reactor coolant average temperature control system, the feedwater control system of the once-through steam generator (OTSG), the steam turbine speed control system and the steam dump control system, is used to guarantee the safe and stable operation. The overall system of SPWR is designed with multiple PI controllers. To accomplish intelligent self-tuning of PI controller parameters, reduce the dependence on human engineering experience, and improve the control performance of the overall system, a multi-objective co-optimization method(MMOCO) with PI controller parameters for overall system of SPWR is proposed. Firstly, based on the coordinated control theory and PI control algorithm, the overall system of SPWR is established. Then, a MMOCO for the overall system of SPWR is designed with the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, to compare the performance of the controller parameters derived from the MMOCO and the controller parameters obtained via the engineering tuning method, step load decrease transients are selected. The results show that the ability to control the overall system of SPWR is effectively improved after applying the MMOCO.

Effects of storm events on nutrient characteristics in a stratified drinking water reservoir: Behavior, transmission pathways and management strategy

Storm events result in nutrient fluctuations and deterioration of reservoir water supply quality. Understanding of nutrient dynamics (e.g., concentration, composition, loads and transport pathways) and adoption of effective management strategies are critical for safeguarding water quality. A comprehensive monitoring was conducted for three storm events during the rainy season in 2023. Results showed nitrogen (N) and phosphorus (P) dynamics demonstrate a significant response to hydrological process. Rainfall resulted in the highest event mean concentrations (EMCs) of total nitrogen (TN), nitrate nitrogen (NO3−-N), ammonia nitrogen (NH4+-N), total phosphorus (TP), and particulate phosphorus (PP) in the runoff being 1.97, 2.15, 2.30, 44.17, and 62.38 times higher than those observed in baseflow. On average, NO3−-N/PP accounted for 82 %/96 % of N/P exports. Hysteresis analyses reveal that NH4+-N and PP were mainly transported by surface runoff from over-land sources, whereas TN and NO3−-N were primarily delivered by subsurface runoff. Additionally, nutrient concentrations were significantly higher in the intrusive layer in reservoir compared to the pre-storm period, which gradually decreased from the tail to the head as particulate sedimentation and water column mixing occurred. Water-lifting-aerators (WLAs) were employed to alter the reservoir thermal stratification regime via artificial mixing to affect the intrusive layer of storm runoff. Comparison of the intrusive layer for three storms reveals that WLAs triggers the storm runoff to form an underflow via increasing the reservoir bottom water temperature above that the runoff, ensuring that water quality at the intake position remains unaffected by inflows. These findings serve as a reference for the response of reservoir eutrophication levels to storm events and present practical engineering experience for enhancing water quality safety during the rainy season.

Technology-rich Engineering Experiences in Indigenous and Rural Schools

Indigenous populations, constituting 6.2% of the global population, face challenges in STEM education due to systemic barriers and limited exposure to science and engineering. Our research, part of a federally funded project, aimed to address these challenges by implementing Community-Based Engineering (CBE) education in an elementary school located on a Native American Reservation in the United States. In this paper, we used CBE as our theoretical framework situating engineering within the context of students' communities and cultures. Our participants included 15 students and two Native American teachers with varying teaching experience. We employed mixed methods and combined quantitative tools such as the Engineering Identity Development Scale and the Engineering Technology subscale of the S-STEM survey, with qualitative data from teacher and student interviews. Our analysis revealed significant changes in students' perceptions of engineering for their communities and their personal engineering identities after they engaged with CBE lessons. We also found that the cultural connections to community were evident in student interviews. Furthermore, teachers appreciated CBE and emphasized that these engineering lessons enrich their rich traditions and practices. This study highlights the effectiveness of CBE and demonstrates how engineering education can be more inclusive and resonant with Indigenous students.

Capacity optimization of a large-scale photovoltaic power generation system coupled with hydrogen production and storage based on economic analysis

Abstract The renewable energy system coupled with hydrogen storage has proven to be a suitable method to reduce the variability of output power and meet stable hydrogen supply demand. However, the excessive cost of hydrogen through water electrolysis using renewable energy restricts its application, and the capacity configuration of electrolyzers and gaseous hydrogen storage tanks is affected by the immoderate reliance on engineering experience, leading to the unbalance of generation side and hydrogen demand side. In this study, a 300 MW photovoltaic power generation system has been proposed to fit the raw material demand of a synthetic ammonia plant, i.e., around 1000 kg/h hydrogen. A simplified mathematical model including an electrolyzer and a hydrogen tank is proposed to get the best capacity configuration. The levelized cost of hydrogen (LCOH) is chosen as an optimization function, and a particle swarm optimization algorithm is adopted to get the optimal results. The simulation results indicate that the optimal capacity configuration is 176.36 MW for the electrolyzer and 14644.2 Nm3 for the hydrogen tank, and the LCOH is 30.31 Yuan/kg. Compared with the empirical model, the LCOH based on the optimization model is 8.87% lower than that of the empirical model, indicating better economic benefits of the optimization model.

Research on methods for controlling strand sag in main cables

The accuracy of main cable construction in suspension bridges is directly influenced by the sag of the strands during the erection process. Thus, effective methods for controlling strand sag are crucial. However, the current control standards and methods are primarily based on practical engineering experience, lacking quantitative analysis and a sufficient basis. This paper aims to address this gap by summarizing four commonly used strand sag control methods and proposing a quantitative analysis model that considers the influence of random factors. The model quantifies the impact of these methods on the main cable shape and strand tension after cable tightening. To illustrate the practical application, a suspension bridge is utilized as a case study. The results of the study illustrate a linear relationship between the main cable sag and inter-strand distance, with each control method exhibiting a varying linear change rate. Moreover, the discrepancy in strand distance contributes to uneven strand tension after cable tightening. Random errors contribute to the dispersion of the main cable sag and strand tension, which are further exacerbated by cumulative errors between layers of general strands. Based on the study’s results, this paper provides valuable references for the erection and control of the main cable in suspension bridges.

Edge detection in x-ray images of drill mast welds based on an improved Scharr operator

Rotary drilling rigs are extensively used in foundational construction, with main components including the mast, drill rods, drill bits, power head, and cockpit. The mast, a critical component, must be of high quality to ensure operational reliability. To ensure the reliability of the mast during operation, it is necessary to detect defects in the most stress-prone areas. Based on finite element analysis and practical engineering experience, the welds on the large circular disc of the drill mast are identified as the most vulnerable to damage. Accordingly, this paper prepared test specimens specifically from these weld locations and conducted X-ray inspections, obtaining images for analysis. Given the inevitable presence of noise due to objective conditions, the images first underwent median filtering. For effective subsequent defect detection, edge detection was performed on the images. Among various edge detection operators, the Scharr operator was found to be highly effective. To further enhance the edge detection performance, this paper introduces an improved Scharr edge detection algorithm that incorporates dynamic thresholds and Markov Random Field (MRF) techniques. The enhanced algorithm was evaluated using signal-to-noise ratio and edge localization accuracy as metrics, and it was found to significantly improve performance.

Automated design and optimization of distributed filter circuits using reinforcement learning

Abstract Designing distributed filter circuits (DFCs) is complex and time-consuming, involving setting and optimizing multiple hyperparameters. Traditional optimization methods, such as using the commercial finite element solver HFSS (High-Frequency Structure Simulator) to enumerate all parameter combinations with fixed steps and then simulate each combination, are not only time-consuming and labor-intensive but also rely heavily on the expertise and experience of electronics engineers, making it difficult to adapt to rapidly changing design requirements. Additionally, these commercial tools struggle with precise adjustments when parameters are sensitive to numerical changes, resulting in limited optimization effectiveness. This study proposes a novel end-to-end automated method for DFC design. The proposed method harnesses reinforcement learning (RL) algorithms, eliminating the dependence on the design experience of engineers. Thus, it significantly reduces the subjectivity and constraints associated with circuit design. The experimental findings demonstrate clear improvements in design efficiency and quality when comparing the proposed method with traditional engineer-driven methods. Furthermore, the proposed method achieves superior performance when designing complex or rapidly evolving DFCs, highlighting the substantial potential of RL in circuit design automation. In particular, compared to the existing DFC automation design method CircuitGNN, our method achieves an average performance improvement of 8.72%. Additionally, the execution efficiency of our method is 2000 times higher than CircuitGNN on the CPU and 241 times higher on the GPU.

A multiobjective optimization framework for site investigation program based on Bayesian approach and NSGA‐II

AbstractSite investigation provides essential geotechnical parameter information for analysis and design. However, three conflicting objectives, namely exploration effort, robustness and parameter uncertainty, pose a challenge to the development of an optimal site investigation program. In this study, a three objective optimization framework for the site investigation program is proposed based on the Bayesian approach and the non‐dominated sorting genetic algorithm (NSGA‐II). The only inputs required by the proposed framework are prior distribution of geotechnical parameters and error information. The prior distribution of geotechnical parameters is derived from integrating engineering experience and measurements from basic exploration boreholes. The error information is obtained based on literature and expert judgment related to the specific project. Firstly, a design pool of candidate investigation programs is generated using Bayesian approach to determine the locations and number of exploration boreholes. The NSGA‐II is then applied to identify the optimal program that balances lower cost, higher robustness, and lower uncertainty. The proposed multiobjective optimization framework is illustrated and validated through a real site investigation case in Chongqing, China, aimed at determining the ultimate bearing capacity of the rock foundation. The spatial correlation of parameters within the study area is also considered. The optimal program is represented by the location and number of exploration boreholes. By comparing measurements with predictions from different site investigation programs, the efficiency of the proposed multiobjective framework is demonstrated. Additionally, the influence of engineering experience and random field modeling on the investigation program is discussed.