Piezoelectric actuators find extensive application in delivering precision motion in the micrometer to nanometer range. The advantages of a broader range of motion, rapid response, higher stiffness, and large actuation force from piezoelectric actuators make them suitable for precision positioning applications. However, the inherent nonlinearity in the piezoelectric actuators under dynamic working conditions severely affects the accuracy of the generated motion. The nonlinearity in the piezoelectric actuators arises from hysteresis, creep, and vibration, which affect the performance of the piezoelectric actuator. Thus, there is a need for appropriate modeling and control approaches for piezoelectric actuators, which can model the nonlinearity phenomenon and provide adequate compensation to achieve higher motion accuracy. The present review covers different methods adopted for overcoming the nonlinearity issues in piezoelectric actuators. This review highlights the charge-based and voltage-based control methods that drive the piezoelectric actuators. The survey also includes different modeling approaches for the creep and hysteresis phenomenon of the piezoelectric actuators. In addition, the present review also highlights different control strategies and their applications in various types of piezoelectric actuators. An attempt is also made to compare the piezoelectric actuator’s different modeling and control approaches and highlight prospects.

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.

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.

In this paper, a vertically held thin elastic plate subjected to viscous fluid flow and undergoing flow-induced deformation is examined. Two-dimensional numerical simulations are carried out at low Reynolds number, Re = 100. The simulations are performed using an in-house fluid structure interaction code that combines the sharp-interface immersed boundary (IB) fluid solver with the finite-element method structural solver. The fluid solver and the structural solver are being coupled using the partitioned approach. The system's dynamic behaviour is investigated by considering the effect of parameters such as mass ratio (M) and bending rigidity (K_b). When the plate undergoes deformation, the amplitude and frequency response for different reduced velocity (Ur) are obtained. We observe six distinct regions. The first is Low Amplitude region (LA) ($U_r=0to1.0)$, second is Initial branch (IB) $\left(U_r=1.0to2.5\right)$, the third is Intermediate region (I) $\left(U_r=2.5to3.0\right)$, the fourth is Upper Branch (UB) $\left(U_r=3.0to4.6\right)\text{,}$ the fifth is Bi-stable region (BS) $\left(U_r=4.6to6.0\right)$ and finally we have Lower Branch (LB) $\left(U_r=6.0to8.0\right)$. Further we observe synchronization of vortex shedding frequency ($f_v)$ with structural frequency $\left(f_s\right)$ at $U_r=3.0$ with first mode of natural frequency $\left(f_{n1}\right)$ being the dominant frequency. In order to understand the dynamics of flow-induced deformation of vertical mounted elastic plates in viscous fluid flow we have analysed the amplitude, frequency and displacement response of structure and corresponding behavior of the fluid owing to the complexity of computationally modelling such interactions. An Artificial Neural Network machine learning model is also used to predict the plate oscillation frequency. The study further emphasizes the importance of using machine learning based prediction models to reduce computational expense.