Poornesh Mangalore

In many undergraduate mechanical engineering programs, students only participate in Real World Research at the end of their degree requirements through a capstone or thesis project (Nkhoma et al., ...

Feedback is a crucial element of the learning process, offering students valuable insights into their progress, strengths, and areas for improvement (Hill and West 2020). Traditionally, feedback is...

Engineering education must evolve to prepare graduates as ethical, socially responsible, and sustainable designers responding to climate, resource, urbanization, and equity challenges, not just technical specifications. This paper examines sustainability in engineering education through the integrated lenses of transformative learning theory (TLT), triple bottom line (TBL), and education for sustainable development (ESD) to link pedagogy with measurable learning and impact. The research question guiding this study is stated once and consistently: In what ways do distinctive pedagogical approaches and curricular changes within engineering education facilitate transformative learning processes that develop students’ sustainability competencies?. Using a mixed‑methods design, the study analyzes fifteen qualitative case studies (curricular, co‑curricular, outreach, industry/research) for evidence of disorienting dilemmas, critical reflection, and perspective transformation, and synthesizes quantitative indicators (participation, tangible outputs, composite Sustainability Impact Score) across five representative cases. Findings show that experiential, interdisciplinary, and community‑engaged formats catalyze holistic TBL reasoning, ethics and stewardship, systems and critical thinking, and theory‑to‑practice application; technically intensive projects yield clearer measurable outcomes, while community initiatives benefit from stronger pre/post assessment. The integrated TLT–TBL–ESD model and the Sustainability Impact Score offer practical guidance for course design, faculty development, and institutional change aligned to WFEO 2030 competencies, supporting scalable, evidence‑based transformation in engineering education.

This article discusses contemporary strategies to deal with immersed boundary (IB) frameworks useful for analyzing flow–structure interaction in complex settings. It focuses on immense advancements in various fields: biology, oscillation of structures due to fluid flow, deformable materials, thermal processes, settling particles, multiphase systems, and sound propagation. The discussion also involves a review of techniques addressing moving boundary conditions at complex interfaces. Evaluating practical examples and theoretical challenges that have been addressed by these frameworks are another focus of the article. Important results highlight the integration of IB methods with adaptive mesh refinement and high-order accuracy techniques, which enormously improve computational efficiency and precision in modeling complex solid–fluid interactions. The article also describes the evolution of IB methodologies in tackling problems of energy harvesting, bio-inspiration propulsion, and thermal-fluid coupling, which extends IB methodologies broadly in many scientific and industrial areas. More importantly, by bringing together different insights and paradigms from across disciplines, the study highlights the emerging trends in IB methodologies towards solving some of the most intricate challenges within the technical and scientific domains.

The “immersed boundary method (IBM)” is considered to be the most efficacious and versatile technique to solve flow problems associated with intricate geometries. The first part of this review examines recent advancements in IBM, essential for the simulation of “fluid–structure interactions (FSIs)” in sophisticated systems. This review highlights significant developments in turbulence modeling, adaptive mesh refinement, and complex geometric simulations, demonstrating IB methods’ capacity to seamlessly integrate arbitrary geometries into structured computational grids while preserving computational efficiency. Various IB techniques are analyzed for enforcing boundary conditions on dynamic immersed boundaries, with notable breakthroughs in managing velocity discontinuities, spurious oscillations, and large-scale deformations. Recent findings illustrate the versatility of IB methods, with applications encompassing biological fluid dynamics, turbulent multiphase flows, and cavitating flows. These innovations not only enhance computational performance but also address evolving challenges across engineering and scientific fields, establishing IB methods as a robust tool for resolving complex, multidisciplinary problems with high accuracy and efficiency.

Purpose. The purpose of this study was to utilize Machine Learning (ML) to optimize the hardness of aluminum-silicon (Al-Si) alloy composites reinforced with Coconut Shell Ash (CSA). Specifically, the study focused on leveraging ML techniques to accurately predict material properties and streamline optimization. Also, the study intended to optimize the composition and process parameters simultaneously through the ML approach. Methodology. The study used Minitab’s Automated Machine Learning (AutoML), specifically the TreeNet model, to develop a predictive model for hardness. The input parameters included Al-Si alloy content, CSA content, melting temperature, and stirring speed. The model was trained and validated using experimental data, and hyperparameter tuning was performed to improve accuracy. The optimal settings were then experimentally verified to assess the model’s reliability. Findings. The optimal composition for maximizing hardness was 90 wt% Al-Si alloy and 10 wt% CSA, with a melting temperature of 800 °C and a stirring speed of 800 rpm. The experimental results validated the model’s predictions, with a hardness value of 70.9 BHN. The study demonstrated that CSA can be an effective, eco-friendly reinforcement for Al-Si composites, enhancing mechanical properties and promoting sustainability. Practical Implications. The findings have significant implications for industries such as automotive, aerospace, and defense, where lightweight, high-strength materials are critical. The ML-based approach used in this study can reduce the need for extensive experimental testing, offering a practical and efficient method for optimizing composite materials. Using CSA as a reinforcement also contributes to sustainable manufacturing by utilizing agricultural waste. Originality. This study presents an innovative approach by integrating ML into optimizing metal matrix composites, specifically using an eco-friendly reinforcement material. The article offers a novel contribution to materials science and sustainable engineering through a novel and structured step-by-step AutoML approach.

POSITIVE POINTS: * PRACTICAL AND INDUSTRY-FOCUSED APPROACH: The article provides a well-structured, practical framework for curriculum development that aligns with industrial needs, ensuring graduates are well-prepared for employment. * COMPREHENS...

* While the abstract outlines a significant issue in engineering education and suggests a comprehensive solution involving a flexible, outcome-based curriculum, it could benefit from greater clarity in terms of the specific methodology used in the r...