Multiple Engineering Disciplines Research Articles

Constructing multivariate distribution of rainfall characteristics: A Bayesian vine algorithm

Constructing a multivariate distribution model for rainfall characteristics is an important task for risk-based hydrologic design. However, most stochastic rainfall models are limited to bivariate frequentist distribution and cannot be easily generalized to multivariate (≥3) Bayesian settings. Towards this problem, this study presents a Bayesian vine algorithm. The presented algorithm translates the high-dimensional Bayesian update task into multiple lower-dimensional update tasks using the vine approach. The algorithm also computes the evidence, allowing Bayesian model selection of marginals and copulas in the joint distribution. Two numerical examples, a synthetic rainfall data from Canada, and a real rainfall data from China are utilized to demonstrate the algorithm’s capability. The results suggest that the algorithm can satisfactorily identify and characterize the underlying marginal and dependence for the rainfall characteristics. Since the high-dimensional Bayesian update task is translated into lower-dimensional Bayesian updates, the algorithm is scalable, stable, and computationally efficient in higher dimensions. A 20-year continuous hourly rainfall record can be characterized for its depth, duration, and intensity in around 60 s without any convergence or tuning issues. Overall, this study provides a powerful Bayesian tool for constructing stochastic rainfall models, which in turn can be used in multiple civil engineering disciplines concerned with rainfall interaction. This study does not consider temporal/serial dependence but only the cross-dependence among the rainfall characteristics.

Impact of 3D-printed laboratory equipment in vibrations and controls courses on student engineering identity, motivation, and mindset

This paper describes the implementation of innovative 3D-printed laboratory equipment linked to inquiry-based learning activities designed to improve learning, increase engineering identity and motivation, and foster a growth mindset in students taking undergraduate level mechanical vibrations courses, control theory courses, and associated laboratories. These innovative designs create new opportunities for hands-on learning, are low-cost, portable, and can be adapted for use in multiple science and engineering disciplines. The learning activities are based on the POGIL model, which has been used across a variety of disciplines including engineering. We describe the features of three separate devices (spring-connected sliding carts, compliant parallel arms with fixed-free ends and a slider mass, and a pendulum with variable tip load) implemented using a quasi-experimental approach with 510 duplicated students across three semesters during the COVID-19 pandemic in multiple lecture courses and laboratory sections. We also present an assessment of impact based on descriptive statistical analyses of survey data for student-reported learning gains and pre-post paired comparison tests on validated instruments measuring perceptions of engineering identity, engineering motivation, and growth mindset. Further, we conducted a student focus group and include salient instructor observations. Results show most students participating in the learning activities using these devices report that it supported their learning “a lot” or “a great deal.” In addition, on six of seven surveyed learning outcomes, most students reported feeling confident enough to complete them on their own or even teach them to someone else. Our data did not show a measurable impact on engineering identity, engineering motivation, or growth mindset, though it does suggest further investigation is merited.

Exploration of phase change material melt front during latent heat thermal energy storage charging

Latent Heat Thermal Energy Storage (LHTES) is a technology that utilises the phase change energy of a material to store energy and is used in multiple engineering disciplines. The use of this technology at high temperatures presents new material challenges that need to be considered. The exploration of Phase Change Material (PCMs) melting phenomena was completed with finite volume methods to model advection-diffusion interactions of different PCM classes undergoing pure substance melting. This study modelled a two-dimensional system with heat flux elicited by an isothermal wall – producing a melt front in the PCM. The investigation focussed on retention of a Solid Salt Barrier (SSB) between the liquid PCM and the storage tank wall. The importance of such a barrier is to mitigate the corrosion between the liquid PCM and the containment vessel. Results reveal the possibility of SSB retention through the adaptation of a LHTES system aspect ratio. A thermal storage inefficiency via unmelted PCM has been demonstrated and quantified when an SSB is maintained. Through varying geometry and PCM choice (carbonate, fluoride, and chloride eutectics), it was also shown that SSB retention is possible; however, when optimising the rate of internal enthalpy change, the corresponding aspect causes the SSB protected wall to be insignificant compared to exposed surfaces at the top and base. This paper provides insight into the implications of aspect ratio choice for thermal energy storage tank designs; as well, suggestions are made regarding exploration of divergent geometries to greater utilise the PCM energy storage capacity available in SSB systems.

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain.

Modal-Based Analysis for Aiding 3D Elastic Metastructure Design

The engineered topological structures of the unit cell endow elastic metamaterials (EMMs) with the extraordinary capability to attenuate elastic waves. In real-life scenarios, a practical EMM (i.e., metastructure), consisting of a limited number of unit cells, is the truncation of the infinite EMM, which may detriment seriously the attenuation capability. To understand the mechanism behind the detriment, an efficient way to evaluate and analyze the stopbands for metastructures is essential. Instead of relying on the commonly adopted frequency response analysis to characterize stopbands for metastructures, which are sensitive to frequency sweeping steps and short of information of truncated resonance affecting wave attenuation performance, a novel modal-based method (MM) is proposed to assess the wave attenuation of 3D metastructures. Specifically, the modal-based analysis scheme incorporating the modal superposition principle and a freshly developed layer-based strain energy ratio (SER) measurement method is proposed. Through employing the MM, the opening and ceasing of the stopbands are evaluated by characterizing dominant eigenmodes developed in metastructures. Accordingly, the influences of eigenmodes induced by different mechanisms on wave attenuation performance are investigated, and the stopband formation mechanisms are elaborated. Furthermore, under the guidance of a freshly proposed modal-based design framework, metastructures with modified geometrical parameter settings (GPSs) are demonstrated with enlarged normalized bandwidths (NBs) at lower frequencies, which show improved applicability in multiple engineering disciplines.

Enhancing Product Development Skills of Engineering Students through Diversified Group Activities

Abstract : The paper aims to examine the effect of diversity on student performance and the skill sets in an Engineering exploration and design project ( E E D P ) c o u r s e . T h e c o u r s e c o n s i s t s o f multidisciplinary modules, and each module is designed and delivered with diversified group activities considering the skill requirement of the product development process. The course aims to develop multidisciplinary product development culture in the first year of engineering studies with a diversified approach. The diversified student project groups are formed with a maximum group size of four. Considering the need statements, week-wise activity sessions within a group are planned so that students can acquire technical and soft skills. The course is designed and taught by a group of four faculties from multiple engineering disciplines using Project-Based Learning (PBL) pedagogy. As a result of this activity, every year, more than 600 students have been trained, and more than 120 prototypes/products have been developed to fulfill the stakeholders' needs. During the student project exhibition, it is observed that students have acquired outstanding product development skills such as project management, teamwork, communication, interpersonal skills, and technical as well as research skills. Keywords : Diversity; Engineering Education; Project-based learning; Engineering exploration

Towards a method to quantitatively measure toolchain interoperability in the engineering lifecycle: A case study of digital hardware design

The engineering lifecycle of cyber-physical systems is becoming more challenging than ever. Multiple engineering disciplines must be orchestrated to produce both a virtual and physical version of the system. Each engineering discipline makes use of their own methods and tools generating different types of work products that must be consistently linked together and reused throughout the lifecycle. Requirements, logical/descriptive and physical/analytical models, 3D designs, test case descriptions, product lines, ontologies, evidence argumentations, and many other work products are continuously being produced and integrated to implement the technical engineering and technical management processes established in standards such as the ISO/IEC/IEEE 15288:2015 “Systems and software engineering-System life cycle processes”. Toolchains are then created as a set of collaborative tools to provide an executable version of the required technical processes. In this engineering environment, there is a need for technical interoperability enabling tools to easily exchange data and invoke operations among them under different protocols, formats, and schemas. However, this automation of tasks and lifecycle processes does not come free of charge. Although enterprise integration patterns, shared and standardized data schemas and business process management tools are being used to implement toolchains, the reality shows that in many cases, the integration of tools within a toolchain is implemented through point-to-point connectors or applying some architectural style such as a communication bus to ease data exchange and to invoke operations. In this context, the ability to measure the current and expected degree of interoperability becomes relevant: 1) to understand the implications of defining a toolchain (need of different protocols, formats, schemas and tool interconnections) and 2) to measure the effort to implement the desired toolchain. To improve the management of the engineering lifecycle, a method is defined: 1) to measure the degree of interoperability within a technical engineering process implemented with a toolchain and 2) to estimate the effort to transition from an existing toolchain to another. A case study in the field of digital hardware design comprising 6 different technical engineering processes and 7 domain engineering tools is conducted to demonstrate and validate the proposed method.

Enhancing Flood Risk Assessment and Mitigation through Numerical Modeling: A Case Study

Evaluation of dike-break-induced flood risk has been a worldwide concern due to its enormous economic, environmental, and societal importance. The mitigation of flood risk in detention basins is a complex decision making process that could span multiple engineering and scientific disciplines. In this paper, an integrated dike-break-induced flood modeling, analysis, and management framework is proposed. The MIKE21-based numerical approach is first adopted to model the flood routing process in detention basins. With the physical behavior of the flood well understood, physics-informed approaches are proposed to better quantify the dike-break-induced flood risks, e.g., human life, economic and environmental losses, offering valuable information for engineers and policymakers to formulate targeted contingency plans. The Zhuhu detention basin in the Poyang Lake district in China is investigated using the proposed framework. The flood movement in the detention basin is first numerically simulated using the MIKE21-based model. Based on the results, i.e., inundation area, water depth, flow velocity, and arrival time of flood peak, the detention basin is divided into several flood disaster zones. The induced flood risks are then estimated for the different zones. Customized emergency evacuation plans are also formulated for the different flood disaster zones. The study of the Zhuhu detention basin confirms that the proposed framework effectively fuses numerical modeling, physics-informed analysis, and management of flood events, providing an integrated and enhanced decision making process for flood warning and risk mitigation in flood detention basins or at other places.

Control and Optimization of Multi-Agent Systems and Complex Networks for Systems Engineering

Systems engineering crosses multiple engineering disciplines for the design, control, and overall management of engineered systems [...]

Improved Well-Control Progressive Optimization with Generalized Barycentric Coordinates and Manifold Mapping

Summary Well-control management is nowadays frequently approached by means of mathematical optimization. However, in many practical situations the optimization algorithms used are still computationally expensive. In this paper, we present progressive optimization (PO), a simulator-nonintrusive four-stage methodology to accelerate optimal search substantially in well-control applications. The first stage of PO comprises a global exploration of the search space using design of experiments (DOEs). Thereafter, in the second stage, a fast-to-evaluate proxy model is constructed with the points considered in the experimental design. This proxy is based on generalized barycentric coordinates (GBCs), a generalization of the concept of barycentric coordinates used within a triangle. GBCs can be especially suited to problems in which nonlinearities are not strong, as is the case often for well-control optimization. This fact is supported by the good performance in these types of optimization problems of techniques that rely strongly on linearity assumptions, such as trajectory piecewise linearization, a procedure that is not always applicable due to its simulator-intrusive nature. In the third stage, the precision of the proxy model is iteratively improved and the enhanced surrogate model is reoptimized by means of manifold mapping (MM), a method that combines models with different levels of accuracy. MM has solid theoretical foundations and leads to efficient optimization schemes in multiple engineering disciplines. The final and fourth stage aims at additional improvement, resorting to direct optimization of the best solution from the previous stages. Nonlinear (operational) constraints are handled in PO with the filter method. The optimal search may be finalized earlier than at the fourth stage whenever the solution obtained is of satisfactory quality. PO is tested on two waterflooding problems built upon a synthetic model previously studied in well-control optimization literature. In these problems, which have 120 and 40 well controls and include nonlinear constraints, we observe for PO reductions in computational cost, for solutions of comparable quality, of approximately 30% and 50% with respect to Hooke-Jeeves direct search (HJDS), which, in turn, outperforms particle swarm optimization (PSO). HJDS and PSO are simulator-nonintrusive algorithms that usually perform well in optimization for oilfield operations. The novel concepts of GBC and MM within the framework of the PO paradigm can be extremely helpful for practitioners to efficiently deal with optimized well-control management. Savings of 50% in computing cost may be translated in practice into days of computations for just a single field and optimization run.

Design a Mechatronic System (Robot) to Transfer Table Tennis Balls

Advancement of technology and human needs has contributed to the convergence of multiple engineering disciplines. Mechatronics engineering is an applied discipline that focuses on the design and study of complete engineering systems including mechanical, electrical, electronic, and control systems. This paper is concentrated on design a mechatronic system (robot arms) to transfer Ping-Pong balls. The aim of this paper is to explain the design process of producing a robot that will compete in a competition. The robot will be operated either wired or wireless within its designated area in performing the ball collection during a two-round match. Duration of 10 minutes is going to be allocated for every round. Top two teams that collect most balls between the 2 rounds will proceed to subsequent round (if quite 4 teams are competing). The performance evaluations will be in terms of number of Ping-Pong balls successfully placed inside the collection buckets.

Optimal control and learning for cyber‐physical systems

Optimal control and learning for <scp>cyber‐physical</scp> systems

Software Architectures for Smart Grid System—A Bibliographical Survey

Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems, in which system architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope with current Information and Communication Technologies (ICT). The distributed generation in a smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible, and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review of architectures for smart grids. An in depth analysis of layered and agent-based architectures based on the National Institute of Standards and Technology (NIST) conceptual model is presented. Also presented is a set of smart grid Reference Architectures dealing with cross domain technology.

Integrating Computational Science Tools into a Thermodynamics Course

Computational tools and methods have permeated multiple science and engineering disciplines, because they enable scientists and engineers to process large amounts of data, represent abstract phenomena, and to model and simulate complex concepts. In order to prepare future engineers with the ability to use computational tools in the context of their disciplines, some universities have started to integrate these tools within core courses. This paper evaluates the effect of introducing three computational modules within a thermodynamics course on student disciplinary learning and self-beliefs about computation. The results suggest that using worked examples paired to computer simulations to implement these modules have a positive effect on (1) student disciplinary learning, (2) student perceived ability to do scientific computing, and (3) student perceived ability to do computer programming. These effects were identified regardless of the students’ prior experiences with computer programming.

System Lifecycle Management - An Approach for Developing Cybertronic Systems in Consideration of Sustainability Aspects

Contemporary technological products are systems based on multiple engineering disciplines. Requirements, risen due to global competition and legislation, lead to complexity explosion especially with regard to the information flow within the product development process. Embedded intelligence and interconnectivity used for an autonomous decision-finding by the products increases complexity furthermore. This paper presents an approach and framework for model-based development of Cybertronic Systems supported by System Lifecycle Management for provision of data availability and traceability. An example based on eco-design of a Cybertronic Element – wheeled excavator – within a Cybertronic System – autonomous construction area – is presented.

A knowledge-based master model approach exemplified with jet engine structural design

Successful product development requires the consideration of multiple engineering disciplines and the quantification of tradeoffs among conflicting objectives from the very early design phases. The single-largest challenge to do so is the lack of detailed design information. A possible remedy of this issue is knowledge-based engineering. This paper presents a knowledge-based master model approach that enables the management of concurrent design and analysis models within different engineering disciplines in relation to the same governing product definition. The approach is exemplified on an early phase structural design of a turbo-fan jet engine. The model allows geometric-, structural mechanics- and rotor-dynamic- models to be concurrently integrated into a multi-disciplinary design and optimization loop.

An Experience of CACSD for Networked Control Systems: From Mechatronic Platform Identification to Control Implementation

Continual advances in information and communication technologies (ICT) are revolutionizing virtual education and bringing new tools on the market that provide virtual solutions to a range of problems. Nevertheless, nonvirtual experimentation using computer-aided control system design tools is still fundamental for future engineers. This paper describes a control course laboratory element, which is designed to teach electronic control engineering, in which students use a networked control system to steer and drive an electric car prototype. Multidisciplinary in nature, this laboratory allows students to take advantage of their previous academic training, as they model the plant from an open-loop test and contrast the simulated behavior with experimental results obtained on the prototype. A goal in this work was that students, across multiple engineering disciplines, should be able to integrate the knowledge acquired throughout their degree studies. An evaluation of students' grades, and their perception of the course, was conducted; this confirmed the educational benefits of the approach.

Early-Stage Design for Electric Ship

One of the truisms of ship design is that the decisions of greatest impact are made in the early stages of design when the least information and the greatest uncertainty are present. In response to this, ongoing efforts in developing ship design tools are directed toward making more information available sooner and pushing decision points later. The integrated nature of electric ship in which performance of support systems directly affects performance of primary mission systems compounds this concern; design and simulation of support systems are needed earlier, and collaborative design among multiple engineering disciplines is required in the process. This paper reviews the Navy ship design process and recent developments in ship design tools to illuminate the areas in which further development is necessary.

Tracking Student Participants From A REU Site With NAE Grand Challenges As The Common Theme

The National Academy of Engineering (NAE) Grand Challenges provides the theme for this NSF-funded Research Experience for Undergraduates (REU) site. Research topics, with their broad societal impact, allow undergraduate students from multiple engineering disciplines and computer science to work together on exciting and critical problems. The approach to addressing the need for research in critical technical areas includes: providing research training in multi-disciplinary research fields; developing technical and professional skills; networking with fellow REU students and participating faculty advisors; and raising student interest and awareness in both graduate studies and our nation’s most critical problems. The Grand Challenges investigated by student participants include: making solar energy economical; providing access to clean water; advancing health informatics; securing cyberspace; restoring and improving urban infrastructure; engineering the tools of scientific discovery; engineering better medicines; and advancing personalized learning. Over a three-year period, 34 students participated in the REU Site and 58.8% were students underrepresented in engineering. The student participants published their work and gave presentations in regional and national conferences. Several graduate students gained leadership experience by assisting in program coordination. Based on surveys, overall, student participants value most the opportunity to contribute to a research group on exciting and relevant problems. Tracking efforts resulted in locating 100% of the student participants. We found 38.2% completed their undergraduate degrees and now have positions in industry; 38.2% are currently in graduate school and 8.8% have completed their graduate degrees; 14.7% are still pursuing engineering or computer science undergraduate degrees.

Changes in Teachers’ Adaptive Expertise in an Engineering Professional Development Course

Although the consensus seems to be that high-school-level introductory engineering courses should focus on design, this creates a problem for teacher training. Traditionally, math and science teachers are trained to teach and assess factual knowledge and closed-ended problemsolving techniques specific to a particular discipline, which is unsuited for teaching design skills for open-ended problems that may involve multiple engineering disciplines. Instead, engineering teacher training should use the more fluid framework of adaptive expertise which values the ability to apply knowledge in innovative ways as well as recall facts and solve problems using conventional techniques. In this study, we examined a 6-week program to train math/science teachers to teach high school design engineering. For each curriculum unit, we had a pre-posttest to assess the teachers’ factual knowledge and ability to solve typical problems (termed ‘‘efficiency’’) and their ability to apply their knowledge to reason through open-ended problems (termed ‘‘innovation’’). In addition, we conducted a pre-posttest to see whether teachers’ attitudes and beliefs related to adaptive expertise changed over the course of the program.