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.

Point-of-care testing is essential for individuals with various diseases, as it allows for timely and effective management, preventing complications and promoting overall health. Colorimetric detection is a valuable tool for disease diagnosis due to its simplicity, affordability, and ability to provide rapid, qualitative results at the point of care. In this endeavour, we have designed a novel, affordable point-of-care diagnostic device utilizing microfluidic principles, a smartphone camera, and established laboratory colorimetric methods for accurate colorimetry. Our proposed microfluidic device comprises of layers of adhesive poly-vinyl films with micro-wells precision-cut using a cutting printer and stacked on a glass slide. As part of this pilot study, we employed the glucose-oxidase/peroxidase reaction on the microfluidic platform as a demonstration, successfully achieving enzymatic glucose determination. The resulting coloured complex, formed by phenol and 4-aminoantipyrine in the presence of hydrogen peroxide generated during glucose oxidation, is captured at various glucose concentrations using a smartphone camera. Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 97% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms. Furthermore, the classifier exhibits outstanding precision, recall, and F1 score of 96.6%, 96.2%, and 96%, respectively, as validated through our study, showcasing its exceptional predictive capability. The developed CNN model can be successfully used as pre-trained model for future glucose concentration predictions.

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.

Growing interest in Lighter-Than-Air (LTA) platforms such as Airships and Super-Pressure Balloons has exposed the need for efficient, integrated design tools, since their development otherwise demands convoluted 3D CAD alongside computationally expensive aerostat analysis. This paper introduces STRATOS, an open-source computational framework automating the conceptual design, geometric realization, and physical evaluation of LTA platforms. Built on a modular Python architecture, STRATOS uses Constructive Solid Geometry (CSG) through SALOME to generate watertight hulls (single and multi-lobe), aerodynamic appendages, and super-pressure balloons. Native physical modules compute full $6\times6$ added-mass matrices via a Boundary Element Method (BEM), reducing reliance on early-stage CFD, while aerostat, thermal, and structural analyses predict operational limits such as burst altitude and steady-level flight profiles. A 2D arc-length petalling module bridges digital design with fabrication. By exporting analysis-ready meshes and automating the design loop, STRATOS provides a scalable, end-to-end foundation for LTA engineering.