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.

The research aims to produce, model, and optimise the mechanical properties of novel composite material through a structured multidisciplinary approach. The primary objective is to combine materials science, mechanical engineering, and statistical concepts to ensure Design for Manufacturability (DFM) from the industrial perspective. More specifically, the article is intended to determine the optimal mixture components and predictive model of Al-Si alloy with Al₂O₃ by accommodating multi-responses that enable DFM. The study adopted ASTM standards to prepare and test the novel composite material. Additionally, the Mixture Design of Experiment (DOE) approach was used to design the experimentation and subsequent analysis. In addition, microstructural images, Cox Response Trace plot, and Response Optimiser plot are effectively utilised to draw robust inferences. For multi-response modelling and optimisation, the composite material’s mechanical properties, like impact strength, hardness, density, and tensile strength, are considered. The study determines that innovative composite material will yield better results when Al-Alloy is 94.65 wt% and Al₂O₃ is 5.35 wt% from a multi-responses perspective. Further, it provides predictive models with a high level of predictability. Besides, the research shows that novel composite material has better mechanical properties from a practical perspective. The article not only provides the mechanical properties of a new class of material but also shows the effective utilisation of material science and statistical concepts to develop the novel material in a structured manner. This composite material can be used as a replacement for various parts of automobiles and aircraft. Additionally, researchers can use the article’s modelling and optimisation approach as a paradigm to create durable composite materials.

The research presents a novel approach to develop high-strength functionally graded composite materials (FGCMs) by using recycled coconut shell ash (CSA) particles as reinforcement for a hypereutectic Al-Si alloy matrix. Using a centrifugal casting technique, test specimens are prepared for the study under ASTM standards. The optimal combination of materials to maximise the materials’ overall tensile strength is obtained through the mixture methodology approach. The results show that CSA particles in the matrix material increase the tensile strength of the produced material. Process parameters, melting temperature and rotating speed were found to play a pivotal role in determining the tensile strength. A better tensile strength of the material is obtained when Al-Si = 90.5 wt%, CSA = 9.5 wt%, rotating speed = 800 RPM, and melting temperature = 800 °C; the proposed regression model developed has substantial predictability for tensile strength. This work presents a methodology for enhancing the tensile strength of FGCMs by optimising both the material composition and processing parameters. The achieved tensile strength of 197.4 MPa, at 800 RPM and 800 °C, for a concentration of 7.5 wt% CSA particles, makes these FGCMs suitable for use in multiple engineering sectors.

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.

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.