Engineering Design Challenges Research Articles

Mesoscopic ultrafast nonlinear optics—the emergence of multimode quantum non-Gaussian physics

Over the last few decades, nonlinear optics has become significantly more nonlinear, traversing nearly a billionfold improvement in energy efficiency, with ultrafast nonlinear nanophotonics in particular emerging as a frontier for combining both spatial and temporal engineering. At present, cutting-edge experiments in nonlinear nanophotonics place us just above the mesoscopic regime, where a few hundred photons suffice to trigger highly nonlinear dynamics. In contrast to classical or deep-quantum optics, the mesoscale is characterized by dynamical interactions between mean-field, Gaussian, and non-Gaussian quantum features, all within a close hierarchy of scales. When combined with the inherent multimode complexity of optical fields, such hybrid quantum-classical dynamics present theoretical, experimental, and engineering challenges to the contemporary framework of quantum optics. In this review, we highlight the unique physics that emerges in multimode nonlinear optics at the mesoscale and outline key principles for exploiting both classical and quantum features to engineer novel functionalities. We briefly survey the experimental landscape and draw attention to outstanding technical challenges in materials, dispersion engineering, and device design for accessing mesoscopic operation. Finally, we speculate on how these capabilities might usher in some new paradigms in quantum photonics, from quantum-augmented information processing to nonclassical-light-driven dynamics and phenomena to all-optical non-Gaussian measurement and sensing. The physics unlocked at the mesoscale present significant challenges and opportunities in theory and experiment alike, and this review is intended to serve as a guide to navigating this new frontier in ultrafast quantum nonlinear optics.

Constructive Student Roles for Authentic Collaboration in Engineering Design Challenges

Constructive Student Roles for Authentic Collaboration in Engineering Design Challenges

Convex Combination Search Algorithm: A Novel Metaheuristic Optimization Algorithm for Solving Global Optimization and Engineering Design Problems

In this paper, a novel metaheuristic optimization algorithm (MHOA) called convex combination search (CCS) is proposed as a solution to global optimization problems and engineering design problems. CCS is based on a combination of rules that depend upon the concept of the linear convex combination. These rules are mathematically modeled to guarantee the variety of solutions at the initialization stage and achieve equilibrium between exploitation, exploration capabilities at the generation stage, the algorithm’s convergence, and robustness. A detailed mathematical model of the algorithm is offered. As an advantage for the CCS algorithm, it requires just two parameters which are the population size and the number of generations for determining the global optimal solution of any optimization problem. The effectiveness of the suggested algorithm is investigated on 17 unconstrained multimodal test functions, and 7 constrained benchmark problems having different characteristics. In addition, five engineering design challenges are resolved to confirm the robustness and dependability of CCS in resolving engineering applications. The efficiency and competitiveness of the proposed algorithm were illustrated in comparison with other methods. A statistical analysis of the results has been carried out to illustrate the competitiveness and power effectiveness of the proposed algorithm. Finally, the sensitivity of the CCS parameters is presented to show the sensitivities of these parameters to the performance of the proposed algorithm.

Designing healthcare systems for earlier diagnosis and prevention of dementia

AbstractRecent clinical trials have successfully slowed Alzheimer's disease dementia progression, but only in early-stage patients. Society must therefore shift to early diagnosis. By framing this is as an engineering design challenge, we argue that a systems approach will identify solutions by providing the means to validate dementia medical technologies from multiple levels and perspectives: society, government, public health, healthcare, and patient ecosystems. We show that new data-enabled design methods can facilitate these different granularities of thinking and outline the need for designers.

Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm

This research introduces a novel multi-objective adaptation of the Geometric Mean Optimizer (GMO), termed the Multi-Objective Geometric Mean Optimizer (MOGMO). MOGMO melds the traditional GMO with an elite non-dominated sorting approach, allowing it to pinpoint Pareto optimal solutions through offspring creation and selection. A Crowding Distance (CD) coupled with an Information Feedback Mechanism (IFM) selection strategy is employed to maintain and amplify the convergence and diversity of potential solutions. MOGMO efficacy and capabilities are assessed using thirty notable case studies. This encompasses nineteen multi-objective benchmark problems without constraints, six with constraints and five multi-objective engineering design challenges. Based on the optimization results, the proposed MOGMO is better 54.83% in terms of GD, 64.51% in terms of IGD, 67.74% in terms of SP, 70.96% in terms of SD, 64.51% in terms of HV and 77.41% in terms of RT. Therefore, MOGMO has a better convergence and diversity for solving un-constraint, constraint and real-world application. Statistical outcomes from MOGMO are compared with those from Multi-Objective Equilibrium Optimizer (MOEO), Decomposition-Based Multi-Objective Symbiotic Organism Search (MOSOS/D), Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Multi-Verse Optimization (MOMVO) and Multi-Objective Plasma Generation Optimizer (MOPGO) algorithms, utilizing identical performance measures. This comparison reveals that MOGMO consistently exhibits robustness and excels in addressing an array of multi-objective challenges. The MOGMO source code is available at https://github.com/kanak02/MOGMO.

MSAO: A multi-strategy boosted snow ablation optimizer for global optimization and real-world engineering applications

Snow Ablation Optimizer (SAO) is a cutting-edge nature-inspired meta-heuristic technique that mimics the sublimation and melting processes of snow in its quest for optimal solution to complex problems. While SAO has demonstrated competitive performance in comparison to classical algorithms in early research, it still exhibits certain limitations including low convergence accuracy, a lack of population diversity, and premature convergence, particularly when addressing high-dimensional intricate challenges. To mitigate the above-mentioned adverse factors, this paper introduces a novel variant of SAO with featuring four enhancement strategies collectively referred as MSAO. Firstly, the good point set initialization strategy is employed to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain rapidly. Secondly, the greedy selection method is adopted to reserve better candidate solutions for the next iteration, thus striking a robust exploration–exploitation balance. Then, the Differential Evolution (DE) scheme is introduced to expand the search range and enhance the exploitation capability of the algorithm for higher convergence accuracy. Finally, to reduce the risk of falling into local optima, a Dynamic Lens Opposition-Based Learning (DLOBL) strategy is developed to operate on the current optimal solution dimension by dimension. With the blessing of these strategies, the optimization performance of MSAO is comprehensively improved. To comprehensively evaluate the optimization performance of MSAO, a series of numerical optimization experiments are conducted using the IEEE CEC2017 & CEC2022 test sets. In the IEEE CEC2017 experiments, the optimal crossover probability CR=0.8 is determined and the effectiveness of each improvement strategy is ablatively verified. MSAO is compared with the basic SAO, various state-of-the-art optimizers, and CEC2017 champion algorithms in terms of solution accuracy, convergence speed, robustness, and scalability. In the IEEE CEC2022 experiments, MSAO is compared with some recently developed improved algorithms to further validate its superiority. The results demonstrate that MSAO has excellent overall optimization performance, with the smallest Friedman mean rankings of 1.66 and 1.25 on both test suites, respectively. In the majority of test cases, MSAO can provide more accurate and reliable solutions than other competitors. Furthermore, six realistic constrained engineering design challenges and one photovoltaic model parameter estimation issue are employed to demonstrate the practicality of MSAO. Our findings suggest that MSAO has excellent optimization capacity and broad application potential.

Leveraging an Open-Access Digital Design Notebook for Graduate Biomedical Engineering Education in Nigeria

Amidst the dual challenges of an eight-month university closure from nationwide public university strikes in Nigeria and the lingering impacts of the Covid-19 pandemic, we needed to innovate the delivery of BME graduate curriculum to ensure graduate students continued to progress in their studies. To ensure BME graduate students were engaging in team-based, clinician-identified engineering design challenges, we developed a digital design notebook (DDN) using Google Sites as an open-access, collaborative tool for scaffolding and documenting the engineering design process. Student design teams remotely uploaded digital content documenting their project work onto scaffolded DDNs created by program instructors. DDNs were purposefully designed to shepherd students through the design process such that each phase of the design process corresponded to an editable “page” of the DDN. Video lectures, learning resources, assignments, and other program information were embedded into the DDN for students to access throughout their design challenge. Project mentors and program instructors remotely monitored and assessed students’ work using the DDN. At the end of the design challenge, students effectively created an e-portfolio which showcased the work they conducted to build a biomedical prototype. Designing and implementing the DDN builds on previous research which demonstrates that “structured” design notebooks can be used as effective tools in engineering design and design thinking education. Our work also leverages educational frameworks for infusing engineering design into existing graduate biomedical engineering curriculum in Nigeria.

Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho.

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its multi-objective version, the multi-objective EDO (MOEDO), enhanced with elite non-dominated sorting and crowding distance mechanisms. An information feedback mechanism (IFM) is integrated into MOEDO, aiming to balance exploration and exploitation, thus improving convergence and mitigating the stagnation in local optima, a notable limitation in traditional approaches. Our research demonstrates MOEDO's superiority over renowned algorithms such as MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO. This is evident in 72.58% of test scenarios, utilizing performance metrics like GD, IGD, HV, SP, SD and RT across benchmark test collections (DTLZ, ZDT and various constraint problems) and five real-world engineering design challenges. The Wilcoxon Rank Sum Test (WRST) further confirms MOEDO as a competitive multi-objective optimization algorithm, particularly in scenarios where existing methods struggle with balancing diversity and convergence efficiency. MOEDO's robust performance, even in complex real-world applications, underscores its potential as an innovative solution in the optimization domain. The MOEDO source code is available at: https://github.com/kanak02/MOEDO.

Growing the Entrepreneurial Mindset in First Year Engineering Students Using Sociotechnical Design Challenges

Engineering graduates must acquire both technical knowledge and a diverse range of professionalskills to effectively address the current global challenges. Equally important is aprofound understanding of how technological solutions are influenced by the human andnatural environments in which they are implemented. An open-ended, team-based designchallenge integrates entrepreneurial-minded (EM) skill development into an interdisciplinaryfirst-year engineering course that approaches engineering from a sociotechnical perspective.A mixed-methods study using a post-course reflective questionnaire explored students’self-perceived development of EM skills. Quantitative results from a series of 5-point Likerttypequestions indicate that students felt they developed EM skills in all three areas of the 3-Cframework, with average mean scores above 4.0 in all three categories. Scores were significantlyhigher in the Connections and Create Value subscales (mean 4.31 ± 0.62 and 4.23 ± 0.76,respectively) compared to the Curiosity subscale (mean 4.04 ± 0.84). Student comments onopen response questions support the overall value of the project and the broader sociotechnicallearning outcomes that were achieved. Overall, this study suggests the effectiveness ofincorporating open-ended, sociotechnical engineering design challenges to develop skills thatwill better prepare students for collaborative work on complex, interdisciplinary problemsthey may encounter in their professional careers.

Using Computer Graphics to Make Science Visible in Engineering Education.

Science plays a crucial role in engineering. But science tends to be obscure to students, especially when they are overwhelmed by complex engineering design challenges that involve many variables. This article shows how computer graphics can be used to visualize science concepts and operationalize inquiry practices in engineering design to support integrated learning and teaching of science and engineering. Based on these graphical capabilities, generative design driven by evolutionary computation can also be visually illustrated to give students a glimpse into how artificial intelligence is transforming engineering design. The article provides real-world examples in the field of sustainable energy engineering based on Aladdin, an open-source design and analysis Web app. It also presents evidence of learning from pilot tests at culturally diverse high schools. Science educators interested in incorporating engineering design into their lesson plans may find this article helpful.

Dujiangyan irrigation system optimization (DISO): A novel metaheuristic algorithm for dam safety monitoring

This work utilizes a novel metaheuristic algorithm named Dujiangyan Irrigation System Optimization (DISO) to resolve the dam deformation prediction problem. The Dujiangyan irrigation system (DIS) served as the model for the DISO algorithm, which has good metaheuristic algorithmic properties to handle various issues. This study utilizes the Fish Mouth project, the Baopingkou project, and the DIS's water separation and sand discharge principle. With the help of a mathematical model and implementation, DISO can perform the optimization process in various search spaces. Two comparative experiments with various characteristics are performed to evaluate the effectiveness of the DISO algorithm. 29 CEC 2017 benchmark test functions, 4 high-constrained test functions, and 2 typical engineering design challenges are chosen in the first step. 14 advanced heuristic techniques are chosen from the literature, and the statistical outputs of these techniques are examined and compared. Three nonparametric statistical tests are performed to confirm the proposed algorithm's effectiveness. The results demonstrate that the DISO algorithm outperforms all the evaluated algorithms. In the following stage, the proposed algorithm is applied to the dam deformation prediction problem, and a KICA-DISO-SVM spatial–temporal monitoring model is established for dams. The findings of the studies indicate that the proposed method can effectively monitor the influence of dam displacement on dam operation. The proposed model overcomes the inability of the standard single measurement point mode to capture the intrinsic relationship of the spatial deformation field accurately. The suggested DISO technique offers encouraging outcomes in resolving complex optimization problems.

Using an open-ended socio-technical design challenge for entrepreneurship education in a first-year engineering course

Engineering graduates must be prepared with sound technical knowledge and a range of 21st century competencies and professional skills such as creativity, interdisciplinary collaboration, communication, and innovation to successfully solve today’s complex, global problems. Equally important is a deep appreciation of the degree to which technological solutions are situated within the context of human and natural environments. Despite calls from the National Academy of Engineering and several professional organizations to broaden engineering education to embrace these skills, most engineering programs persistently focus on the importance of technical skills. This paper describes an open-ended team-based design challenge that integrates entrepreneurial-minded (EM) skill development into an interdisciplinary first-year engineering course that approaches engineering from a socio-technical perspective. The challenge was implemented in two simultaneous first-year classes (n = 49), with the goal of fostering students’ broad professional skills and their appreciation of the links between engineering technologies and societal context. The action research study used a quasi-experimental design with convenience sampling and no control group to explore students’ self-perceived entrepreneurial-minded (EM) skills development. Data were collected with a retrospective questionnaire comprised of a series of 5-point Likert-type questions that asked students to assess the development of their EM skills in all three areas of the EM framework: Exhibit Curiosity, Establish Connections, and Create Value (the “3C” framework). Results indicate that students felt they developed EM skills in all three areas of the 3C Framework, with more fully developed skills in the Establish Connections and Create Value categories. Overall, this study suggests the effectiveness of using open-ended, socio-technical engineering design challenges for developing skills that will better prepare students to work collaboratively on complex and interdisciplinary problems they will face in their professional careers.

CO2 electrolysis: Advances and challenges in electrocatalyst engineering and reactor design

CO2 electrolysis: Advances and challenges in electrocatalyst engineering and reactor design

Multi-objective chaos game optimization

The Chaos Game Optimization (CGO) has only recently gained popularity, but its effective searching capabilities have a lot of potential for addressing single-objective optimization issues. Despite its advantages, this method can only tackle problems formulated with one objective. The multi-objective CGO proposed in this study is utilized to handle the problems with several objectives (MOCGO). In MOCGO, Pareto-optimal solutions are stored in a fixed-sized external archive. In addition, the leader selection functionality needed to carry out multi-objective optimization has been included in CGO. The technique is also applied to eight real-world engineering design challenges with multiple objectives. The MOCGO algorithm uses several mathematical models in chaos theory and fractals inherited from CGO. This algorithm's performance is evaluated using seventeen case studies, such as CEC-09, ZDT, and DTLZ. Six well-known multi-objective algorithms are compared with MOCGO using four different performance metrics. The results demonstrate that the suggested method is better than existing ones. These Pareto-optimal solutions show excellent convergence and coverage.

Recognition of Design Failure by Fourth-Grade Students During an Engineering Design Challenge

The practice of persisting and learning from design failures is essential to engineering design and offers unique ways of knowing and learning for K-12 students. To understand how students engage in the practice of persisting and learning from design failures, we must first understand how, if at all, they recognize that a design failure has occurred. We studied a classroom of fourth-grade students engaged in an engineering design challenge and examined the ways in which design failure occurred and how students recognized, neglected to recognize, or misinterpreted design failure. We found that, in addition to anticipating failure, conducting fair tests, and making focused observations, students must have an understanding and awareness of the evolving criteria and constraints of the design problem in order to recognize design failure. If lacking an understanding and awareness of criteria and constraints represents a barrier to recognizing an initial design failure, it also represents a barrier to recognizing any subsequent design failures in the design process and thus a barrier to persisting and learning from design failures.

Serval Optimization Algorithm: A New Bio-Inspired Approach for Solving Optimization Problems.

This article introduces a new metaheuristic algorithm called the Serval Optimization Algorithm (SOA), which imitates the natural behavior of serval in nature. The fundamental inspiration of SOA is the serval's hunting strategy, which attacks the selected prey and then hunts the prey in a chasing process. The steps of SOA implementation in two phases of exploration and exploitation are mathematically modeled. The capability of SOA in solving optimization problems is challenged in the optimization of thirty-nine standard benchmark functions from the CEC 2017 test suite and CEC 2019 test suite. The proposed SOA approach is compared with the performance of twelve well-known metaheuristic algorithms to evaluate further. The optimization results show that the proposed SOA approach, due to the appropriate balancing exploration and exploitation, is provided better solutions for most of the mentioned benchmark functions and has superior performance compared to competing algorithms. SOA implementation on the CEC 2011 test suite and four engineering design challenges shows the high efficiency of the proposed approach in handling real-world optimization applications.

Coal-derived graphene foam and micron-sized silicon composite anodes for lithium-ion batteries

Silicon-based materials demonstrate significant potential as lithium-ion batteries (LIBs) anode, but their expansion and degradation present engineering design challenges for commercial application. In this study, a porous three-dimensional (3D) graphene and micron-sized silicon composite anode (Si@G foam) was synthesized using humic acid (HA) derived from coal as a graphene precursor. In-situ formation of graphene structure through reducing HA was confirmed by Raman spectra. The reduced HA (rHA) shows similar electrical conductivity compared to the commercial conductive carbon. SEM images depict a 3D skeleton of coal-derived graphene, with silicon particles distributed on the 3D graphene foam's internal surface. The Si@G composite-anode displays a good reversible capacity of ∼656 mAh/g at a current density of 50 mA/g, as well as a high-rate capability of ∼433 mAh/g at a current density of 800 mA/g, and outstanding cycling stability –89.8% capacity retention after 300 cycles, which is significantly higher than that of other foam structures. During lithiation and de-lithiation, the graphene foam serves as a matrix of electrical conductors and a volume expansion support for silicon. This 3D graphene network will be beneficial for developing advanced silicon-based anodes for high-performance LIBs.

“Teacher, I know how to do it”: An engineering design-based STEM activity on the concepts of forces and floating/sinking for young problem solvers

This article aims to implement an engineering design-based STEM activity for young children and evaluate the implementation process of the activity, along with providing some revision ideas. The activity was held as a 90-minute weekend workshop with the participation of six children aged four to seven. Data were collected through digital observations and field notes. In the activity, the children followed a four-step engineering design process. They solved the cartoon characters’ problem by designing a floatable boat that could take the characters across the lake. In addition, the children tried to move their toy boats on the water without touching them. Throughout the activity, children gained hands-on experience with the concept of force. They made predictions about and tested what material would suit their designs. Thus they had the opportunity to think about what might affect the sinking or floating of objects in the water. They heard each other’s ideas and observed the testing process of both their own and other children’s designs. In all these respects, this article suggests that playful and developmentally appropriate engineering design challenges can be effective in creating STEM mindsets. This article also includes some revision ideas for integrating activity into the curriculum in early childhood education settings in line with the experiences obtained from the implementation process.

Middle School Engineering Teachers’ Enactments of Pedagogies Rooted in Funds of Knowledge and Translanguaging

Multilingual students should have opportunities to learn and do engineering in learning environments that foreground and sustain their cultural and linguistic practices. However, little is known about how middle school engineering teachers enact these environments. To address this gap in research and practice, this comparative case study describes the different ways in which two middle school technology and engineering teachers enacted pedagogies rooted in funds of knowledge and translanguaging. The teachers and research team engaged in a professional learning community, which focused on funds of knowledge and language-based pedagogies, for over one year. The teachers enacted pedagogies that incorporated principles of funds of knowledge and translanguaging, reflected on their teaching, and sought to improve their pedagogy in subsequent iterations of teaching. In this context, the research team analyzed the following data sources: (a) transcripts from classroom observations during a minimum of four engineering design challenges per teacher; (b) teacher artifacts (e.g., lesson plans, curriculum materials); and (c) monthly reflection sessions and interviews with each teacher. Thematic analyses of these data were used to answer the following research question: How did the teachers enact funds of knowledge and translanguaging pedagogies? Analyses indicated similarities and differences in how the teachers enacted funds of knowledge and translanguaging pedagogies. Similarities included (a) providing students with bilingual materials; (b) grounding engineering design challenges in the context of students’ communities; and (c) positioning parents as core intellectual resources. While the two teachers enacted pedagogies in similar ways, they also demonstrated differences. Alex, a bilingual teacher who had lived in Peru, enacted funds of knowledge pedagogies through sharing narratives and explaining how they mapped onto the engineering design challenges. He also frequently spoke to students in Spanish. Andrew, a white monolingual teacher who had lived and taught in his community for decades, enacted funds of knowledge pedagogies through experiential and place-based learning, and through incorporating popular culture. Despite his efforts to sustain bilingualism through providing bilingual curriculum materials, English remained the primary medium of instruction within his class, consistent with his school’s de facto English-only policy. This study indicates that funds of knowledge and translanguaging pedagogies may be fostered through community-oriented engineering design challenges and through school-level policies and practices that explicitly encourage multilingualism.