Real World Engineering Projects Research Articles

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

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

Collaborative scheduling of energy-saving spare parts manufacturing and equipment operation strategy using a self-adaptive two-stage memetic algorithm

Previous studies on production scheduling predominantly focus on developing optimal production plans in the context of deterministic customer requirements, achieving certain optimization goals related to the manufacturer. However, little research has been conducted on scheduling customized spare parts manufacturing on the supply side and equipment operation strategy on the demand side simultaneously. In real-world engineering projects, an efficient collaborative scheduling solution not only helps the manufacturer develop optimal production plans for critical spare parts, but also guides equipment users to achieve maximum output. Therefore, this paper studies a collaborative scheduling problem that both considers energy-saving spare parts manufacturing in a flexible job shop and equipment operation strategy of distributed users. To solve this problem effectively, a self-adaptive two-stage memetic algorithm (STMA) is proposed to minizine total energy consumption of the manufacturer and maximize total operation utility of equipment simultaneously. In detail, four heuristic rules are designed to generate a high-quality initial population. In the first stage, the crossover and mutation are utilized for global exploration, and a self-adaptive local search operator is presented to enhance the local development ability of the STMA. In the second stage, several energy-saving and utility-improving strategies are proposed to further optimize the Pareto front solutions generated in the previous period. Extensive experiments are conducted, and the numerical results indicate that STMA is the most promising compared to other well-known algorithms.

Monocular out-of-plane vibration measurement using parametric homography

Vision-based measurement is an emerging method for vibration tests due to its non-contact nature and high spatial resolution, which make the full-field measurement possible. Conventional vision-based measurement requires two or more cameras to produce an out-of-plane response measurement. However, the additional cost of another camera and synchronization system makes applying this kind of measurement method to real-world engineering projects more challenging. This paper presents a single-camera method for measuring the out-of-plane vibration of plate structures. To accomplish that, the image variation induced by the motion of a plane is represented using a motion-parametric homography. Under the Lucas–Kanade framework, the motion-parametric homographies are estimated separately for each region of interest (ROI) between images, and the out-of-plane displacement response is obtained concurrently. The proposed method’s results are confirmed in good agreement with those of the laser Doppler vibrometer through an impact test on a steel plate structure.

Simulation of Test Arch Based on Concrete Damage Plasticity Model and Damage Evolution Analysis

In light of the limited research on latent damages during the construction of large-span suspension arches, this study introduced a method to simulate structural damage utilizing random porosity. Initially, based on data from real-world engineering projects, the most susceptible areas within the arch structure were pinpointed. Subsequently, multiple test arch simulation models were constructed. Employing Python, commands for random porosity were implemented within ABAQUS and distinct mesh modules were devised to depict structures under varying degrees of damage. The current investigation delved into the structural responses of these susceptible areas under different damage rates, shedding light on damage progression patterns. Notably, our findings demonstrated that concealed damages on the top plate of the arch foot profoundly influenced structural integrity, whereas damages at the arch hance were comparatively minimal and predominantly manifest at the arch base. The pronounced localized damage at both the arch base and hance initiated and intensified at sectional corners, necessitating enhanced anti-crack measures in these regions. Moreover, depending on the stresses of the arch structure, diverse reinforcement strategies could be employed, optimizing the balance between load-bearing efficiency and cost considerations.

Predicting near-term, out-of-sample fish passage, guidance, and movement across diverse river environments by cognitively relating momentary behavioral decisions to multiscale memories of past hydrodynamic experiences

Predicting the behavior of individuals acting under their own motivation is a challenge shared across multiple scientific fields, from economic to ecological systems. In rivers, fish frequently change their orientation even when stimuli are unchanged, which makes understanding and predicting their movement in time-varying environments near built infrastructure particularly challenging. Cognition is central to fish movement, and our lack of understanding is costly in terms of time and resources needed to design and manage water operations infrastructure that is able to meet the multiple needs of human society while preserving valuable living resources. An open question is how best to cognitively account for the multi-modal, -attribute, -alternative, and context-dependent decision-making of fish near infrastructure. Here, we leverage agent- and individual-based modeling techniques to encode a cognitive approach to mechanistic fish movement behavior that operates at the scale in which water operations river infrastructure is engineered and managed. Our cognitive approach to mechanistic behavior modeling uses a Eulerian-Lagrangian-agent method (ELAM) to interpret and quantitatively predict fish movement and passage/entrainment near infrastructure across different and time-varying river conditions. A goal of our methodology is to leverage theory and equations that can provide an interpretable version of animal movement behavior in complex environments that requires a minimal number of parameters in order to facilitate the application to new data in real-world engineering and management design projects. We first describe concepts, theory, and mathematics applicable to animals across aquatic, terrestrial, avian, and subterranean domains. Then, we detail our application to juvenile Pacific salmonids in the Bay-Delta of California. We reproduce observations of salmon movement and passage/entrainment with one field season of measurements, year 2009, using five simulated behavior responses to 3-D hydrodynamics. Then, using the ELAM model calibrated from year 2009 data, we predict the movement and passage/entrainment of salmon for a later field season, year 2014, which included a novel engineered fish guidance boom not present in 2009. Central to the fish behavior model’s performance is the notion that individuals are attuned to more than one hydrodynamic signal and more than one timescale. We find that multi-timescale perception can disentangle multiplex hydrodynamic signals and inform the context-based behavioral choice of a fish. Simulated fish make movement decisions within a rapidly changing environment without global information, knowledge of which direction is downriver/upriver, or path integration. The key hydrodynamic stimuli are water speed, the spatial gradient in water speed, water acceleration, and fish swim bladder pressure. We find that selective tidal stream transport in the Bay-Delta is a superset of the fish-hydrodynamic behavior repertoire that reproduces salmon movement and passage in dam reservoir environments. From a cognitive movement ecology perspective, we describe how a behavior can emerge from a repertoire of multiple fish-hydrodynamic responses that are each tailored to suit the animal’s recent past experience (localized environmental context). From a movement behavior perspective, we describe how different fish swim paths can emerge from the same local hydrodynamic stimuli. Our findings demonstrate that a cognitive approach to mechanistic fish movement behavior modeling does not always require the maximum possible spatiotemporal resolution for representing the river environmental stimuli although there are concomitant tradeoffs in resolving features at different scales. From a water operations perspective, we show that a decision-support tool can successfully operate outside the calibration conditions, which is a necessary attribute for tools informing future engineering design and management actions in a world that will invariably look different than the past.

Interdisciplinary Project-Based Learning: Experiences and Reflections From Teaching Electronic Engineering in China

Continuous developments in the electronics industry have led to constantly changing career roles and graduate skills requirements. Students in the University of Electronic Science and Technology of China (UESTC) have complained that numerous courses in electronic engineering are heavily focused on theoretical knowledge that is disconnected from the needs of the industry. Thus, in an effort toward delivering student-centered educational programmes that meet the needs of the industry, this article introduces an innovative course that was developed using the project-based learning (PBL) method and situated in the electronic engineering undergraduate programme at UESTC. Since real-world engineering projects require teams to collaborate on ill-defined problems, we focused this innovative course on developing professional and technical skills, drawing from a range of more typical electronic engineering courses. We provide full details of two projects that were created for this PBL approach and evaluate them as practice examples that demonstrate the impact of this practical pedagogic innovation in UESTC. According to our evaluation, completed by all 40 of our enrolled students, our innovative course based on interdisciplinary PBL exercises demonstrated a significant improvement in student satisfaction and 65% of students preferred the interdisciplinary PBL course in comparison to traditional lecture-based courses.

Heat transfer performance of deep borehole heat exchanger with different operation modes

The deep borehole heat exchanger (DBHE) is an essential component of medium-depth geothermal heat pump systems (MD-GHPs). Compared with the conventional borehole heat exchanger, the DBHE has the advantages of minimal land area need, good seasonal heat storage performance and high efficiency in winter. However, most current DBHE research is focused on a single operation mode, and few studies on multiple operation modes. In this study, a DBHE heat transfer model is developed and its governing equations are discretized using the Finite Difference Method (FDM). The model is then validated using measured data from a real-world engineering project. This is followed by an analysis of the outlet water temperature, heat loss rate, maximum heat affected radius, and rock and soil heat recovery characteristics of DBHE with a variety of run-stop ratios (i.e., the ratio of the running time to the intermission time in a day). The following are the key findings: thermal recovery of rock and soil, as well as DBHE operation stability, are negatively correlated with the run-stop ratio, but DBHE heat loss rate is positively correlated with the run-stop ratio; when the run-stop ratio is 24:0 after 15 years of operation, the heat affected radius of DBHE on the surrounding rock and soil is 91.36 m; the other three run-stop ratios (i.e., 16:8,12:12 and 8:16) maybe reduced by 9.07%, 17.34% and 24.86%, respectively. The results of this study have implications for the design of DBHE with different operation modes.

Preparing first-year engineering students for cooperation in real-world projects

ABSTRACT Problem-based learning is an active student-centred approach, which requires engineering students to work together in solving real-world problems. This study aims to review the incorporation of cooperative learning principles in an introductory first-year engineering course that aimed to develop students’ group skills while working on problem-based projects. A total of 367 students participated in 65 groups. No formal examination was written, as this course only consists of practical work. Data collection included an assessment matrix and reflective sheets where students’ individual and group contribution to the project were recorded. The data were structured and visually integrated into tables. Based on the findings, this research suggests that the integration of cooperative learning principles enhanced the development of essential group skills and team performance in some of the groups while working on real-world engineering projects.

Drawing with SAT: four methods and A tool for producing railway infrastructure schematics

Schematic drawings showing railway tracks and equipment are commonly usedto visualize railway operations and to communicate system specifications andconstruction blueprints. Recent advances in on-line collaboration and modelingtools have raised the expectations for quickly making changes to models,resulting in frequent changes to layouts, text, and/or symbols in schematicdrawings. Automating the creation of high-quality schematic views fromgeographical and topological models can help engineers produce and updatedrawings efficiently. This paper introduces four methods for automatically producingschematic railway drawings with increasing level of quality and controlover the result. The final method, implemented in the open-source tool that we have developed,can use any combination of the following optimization criteria, which can havedifferent priorities in different use cases: width and height of the drawing,the diagonal line lengths, and the number of bends. We show how to encode schematic railway drawings as an optimizationproblem over Boolean and numerical domains, using combinations of unary numberencoding, lazy difference constraints, and numerical optimization into anincremental SAT formulation. We compare drawings resulting from each of the four methods, applied tomodels of real-world engineering projects and existing railway infrastructure.We also show how to add symbols and labels to the track plan, which isimportant for the usefulness of the final outputs. Since the proposed tool iscustomizable and efficiently produces high-quality drawings from railML 2.xmodels, it can be used (as it is or extended) both as an integrated module inan industrial design tool like RailCOMPLETE, or by researchers forvisualization purposes.

A Systematic Interdisciplinary Engineering and Technology Model Using Cutting-Edge Technologies for STEM Education

Contribution: A systematic interdisciplinary engineering and technology instructional model using cutting-edge technologies is proposed in this research. This model consists of four key components: 1) interdisciplinary collaboration; 2) hands-on projects; 3) real-world simulations; and 4) cutting-edge technologies. The model was designed to engage students in the real-world engineering projects and experience through the following activities: 1) performing the design, fabrication, and lab testing; 2) modeling and analyzing testing samples; 3) conducting on-site experiments; and 4) performing data processing and analysis. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Engineering programs are challenged by the rapid development of innovative technologies. To address this challenge, it is required to provide engineering graduates with “soft skills” for the capability of project management, especially the communication and collaboration among disciplines through university education. Therefore, it is desired to develop a systematic interdisciplinary engineering and technology educational strategy. Research questions: 1) is the proposed systematic interdisciplinary instructional model appropriate and feasible to be integrated into the engineering curriculum? 2) what were the students’ perceptions and experiences about the interdisciplinary model? 3) does the interdisciplinary model facilitate engineering students’ soft skills, such as interdisciplinary communication and collaboration? and 4) does the interdisciplinary model help to enhance engineering students’ real-world professional experience, develop design thinking and problem-solving skills, which will help them to transfer their learning experience from the classroom setting to the real-world setting? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</i> A systematic interdisciplinary engineering and technology model was developed, implemented, and evaluated. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> The feasibility and effectiveness of the proposed systematic instructional model were validated by various data sources. The tested model is recommended for broader adaptation in various STEM disciplines.

Experiment of 3D Seismic Reflection Technique for Forward Probing on TBM Tunnel Face

Geophysical technologies are used to mitigate geological hazard caused by adverse geological conditions in front of a tunnel face. The prevailing method for forward probing for tunnels constructed by a tunnel boring machine (TBM) for advance prediction is based on seismic detection. Conventional tunnel seismic prediction technology uses P- and S-waves with sources fired on the tunnel wall or face and layout receivers on the tunnel wall to acquire the reflected waves. However, the results show that most of these methods have different deficiencies that are in either low detection accuracy, short detection depth, and/or multiplicity in imaging. This paper proposes a new high resolution tunnel advance prediction technology on the face based on 3D seismic wave detection. It arranges the 3D high-density source and recording geometry on the tunnel face to receive reflected P-waves for 3D imaging. By using the 3D numerical simulation, we first analyze the energy distribution and propagation characteristics of the wave field, which proves that our method is feasible. Compared with the conventional technologies, the seismic energy propagating towards the tunnel face is stronger and produces rich reflected information. The reflected wave has the advantages of bandwidth, strong energy and little interferences from surface wave, so that the seismic phases are easy to be identified. On this basis, we present the high resolution true 3D prediction technology to obtain more comprehensive and abundant azimuth information. Our approach is further validated by an application experiment in a real-world engineering project of water conveyance tunnel. The results show that the new technique has a greater detection length, higher detection accuracy and more reliable imaging results.

Modelling indirect interactions during failure spreading in a project activity network

Spreading broadly refers to the notion of an entity propagating throughout a networked system via its interacting components. Evidence of its ubiquity and severity can be seen in a range of phenomena, from disease epidemics to financial systemic risk. In order to understand the dynamics of these critical phenomena, computational models map the probability of propagation as a function of direct exposure, typically in the form of pairwise interactions between components. By doing so, the important role of indirect interactions remains unexplored. In response, we develop a simple model that accounts for the effect of both direct and subsequent exposure, which we deploy in the novel context of failure propagation within a real-world engineering project. We show that subsequent exposure has a significant effect in key aspects, including the: (a) final spreading event size, (b) propagation rate, and (c) spreading event structure. In addition, we demonstrate the existence of ‘hidden influentials’ in large-scale spreading events, and evaluate the role of direct and subsequent exposure in their emergence. Given the evidence of the importance of subsequent exposure, our findings offer new insight on particular aspects that need to be included when modelling network dynamics in general, and spreading processes specifically.

The canadian satellite design challenge: building future engineering capability in canadian universities

The CSDC is a Canadian university competition to design and build a small satellite, called a “cubesat,” which will conduct a science mission. The CSDC simulates a real-world engineering project, requiring the multi-disciplinary teams to prepare mandatory documentation, and give a detailed design review in front of industry experts. The CSDC also includes mentored “hands-on” workshops, providing a valuable career-development educational experience.

Application of localized meshless methods to 2D shallow water equation problems

This study aims to apply the meshless local radial-basis-function differential quadrature (LRBFDQ) method to solve the shallow water equations (SWE). This localized approach is developed from the differential quadrature (DQ) method by employing the radial-basis functions (RBFs) as the trial functions. Comparing with global-type meshless methods, the present method is more appropriate to large-scale problems with complex shapes. Moreover the drawbacks rising from the poor selection of shape parameter and also the full resultant matrix with high condition number are reduced. For real hydraulic-engineering applications located in irregular domains, the LRBFDQ method is very suitable to solve these kinds of shallow-water problems. In this work, the numerical models are applied to simulate three typical 2D SWE problems: (1) a tidal-wave propagation, (2) a dam-break problem and (3) an inverse engineering problem: the numerical analysis of the inflow discharge of the Yuanshantze Flood Diversion (YFD) project in Taiwan. As a result, the adopted meshless method not only shows its algorithm superiority over other mesh-dependent numerical schemes, but also brings more efficiency than several conventional mesh or meshless methods. The application of YFD project also delivers its applicability of this meshless scheme to solve real-world engineering projects.

Understanding the Impacts of Indirect Stakeholder Relationships Stakeholder Value Network Analysis and Its Application to Large Engineering Projects

In order to understand the strategic implications of indirect stakeholder relationships on large engineering projects, this research develops a qualitative/quantitative network approach, namely the Stakeholder Value Network analysis, to model the multiple relationships between stakeholders as value exchanges. With theoretical support from the Social Exchange Theory, this approach unifies both social and economic relationships into a common framework and also defines network statistics to effectively measure indirect stakeholder influence in a “Network” model as against a more traditional “Hub-and-Spoke” model. The usefulness of this approach and the strategic implications of these network statistics are then demonstrated by a retrospective case study for a large real-world engineering project, with improved understanding of relationships management for various stakeholders. The retrospective case is conducted on a multi-billion dollar, 5-year upgrade and turnaround of a major chemical processing plant for a Global 500 firm. In addition, we discuss the synergy between the Stakeholder Value Network approach and Strategic Issue Management, and outline a feasible plan to integrate these two approaches as an important extension of the original framework of the Stakeholder Value Network analysis.

MetaMOOSE—an object-oriented framework for the construction of CASE tools

This paper describes certain problems which can occur when attempting to build complex CASEtools with facilities not envisaged by the Metatool builders. A solution, based upon an object oriented approach combined with an interpreted OO language has been used to build the MetaMOOSE MetaCASE tool. MetaMOOSE uses an object model to describe the entities and behaviour of the SE development process. Use of the Itcl language gives platform independence and speeds the tool development cycle. A persistent object database ensures integration of the resulting CASE tools. In addition, the successful use of MetaMOOSE to construct a full lifecycle CASE toolset (MOOSE) and its subsequent use in real world engineering projects is described.