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Abstract Immersed boundary methods (IBMs) have evolved over the past 50 years from a specialized technique in biofluid dynamics and applied mathematics to a cornerstone of computational fluid dynamics. Many recent advancements in immersed boundary methods have centered on sharp-interface immersed boundary methods, which offer enhanced accuracy and fidelity for flow simulations. This paper outlines the key principles that have driven our own efforts in the development of sharp-interface immersed boundary methods over the past 25 years. We also highlight the power and versatility of these methods by showcasing a range of applications, spanning biolocomotion (i.e., swimming and flying), physiological flows, compressible aerodynamics, fluid–structure interaction (FSI), and flow-induced noise. 

Abstract Trade-offs between producing costly movements for gathering information (‘explore’) and using previously acquired information to achieve a goal (‘exploit’) arise in a wide variety of problems, including foraging, reinforcement learning and sensorimotor control. Determining the optimal balance between exploration and exploitation is computationally intractable, necessitating heuristic solutions. Here we show that the electric fishEigenmannia virescensuses a salience-dependent mode-switching strategy to solve the explore–exploit conflict during a refuge-tracking task in which the same category of movement (fore-aft swimming) is used for both gathering information and achieving task goals. The fish produced distinctive non-Gaussian distributions of movement velocities characterized by sharp peaks for slower, task-oriented ‘exploit’ movements and broad shoulders for faster ‘explore’ movements. The measures of non-normality increased with increased sensory salience, corresponding to a decrease in the prevalence of fast explore movements. We found the same sensory salience-dependent mode-switching behaviour across ten phylogenetically diverse organisms, from amoebae to humans, performing tasks such as postural balance and target tracking. We propose a state-uncertainty-based mode-switching heuristic that reproduces the distinctive velocity distribution, rationalizes modulation by sensory salience and outperforms the classic persistent excitation approach while using less energy. This mode-switching heuristic provides insights into purposeful exploratory behaviours in organisms, as well as a framework for more efficient state estimation and control of robots. 

We employ a novel computational modelling framework to perform high-fidelity direct numerical simulations of aero-structural interactions in bat-inspired membrane wings. The wing of a bat consists of an elastic membrane supported by a highly articulated skeleton, enabling localised control over wing movement and deformation during flight. By modelling these complex deformations, along with realistic wing movements and interactions with the surrounding airflow, we expect to gain new insights into the performance of these unique wings. Our model achieves a high degree of realism by incorporating experimental measurements of the skeleton’s joint movements to guide the fluid–structure interaction simulations. The simulations reveal that different segments of the wing undergo distinct aeroelastic deformations, impacting the flow dynamics and aerodynamic loads. Specifically, the simulations show significant variations in the effectiveness of the wing in generating lift, drag and thrust forces across different segments and regions of the wing. We employ a force partitioning method to analyse the causality of pressure loads over the wing, demonstrating that vortex-induced pressure forces are dominant while added-mass contributions to aerodynamic loads are minimal. This approach also elucidates the role of various flow structures in shaping pressure distributions. Finally, we compare the fully articulated, flexible bat wing with equivalent stiff wings derived from the same kinematics, demonstrating the critical impact of wing articulation and deformation on aerodynamic efficiency. 

Echolocating bats often encounter clutter as they pursue insect prey. To probe the adaptive behaviors bats employ to mitigate the effects of clutter, this study quantified echolocation call features and head movements of big brown bats (Eptesicus fuscus) as they tracked a moving prey target in the dark. Bats were trained to rest on a perch and track an approaching target for a food reward. Clutter was positioned at different distances and angular offsets from the bat and the path of a moving target. This study hypothesized that bats dynamically adjust call features and head direction to facilitate target localization in the presence of clutter. The results show that bats shortened call duration and interval and increased head movements when the target was close to clutter. The study also revealed that bats increase the production of sonar strobe groups in cluttered environments, which may sharpen sonar spatial resolution. Spectral analysis showed that maximum call power shifted to lower frequencies when clutter was close to the target. These data demonstrate the big brown bat's range of adaptive behaviors that support target tracking in cluttered environments. 

This article presents the evolutionary history of immersed boundary methods (IBMs), tracing their origins to the very beginning of computational fluid dynamics in the late 1950s all the way to the present day. The article highlights the advancements in this simulation methodology over the last 50 years and explores the interplay between IBMs and body-conformal grid methods during this time. Drawing upon the authors’ combined experience of more than 40 years in this arena, the perspective offered is personal and subjective. By employing a critical and comparative approach through the chronological lens, we hope that this article empowers the reader to understand both the capabilities and limitations of these methods, and to pursue advancements that fill the key gaps and break new ground. 

Biological mechanosensation has been a source of inspiration for advancements in artificial sensory systems. Animals rely on sensory feedback to guide and adapt their behaviors and are equipped with a wide variety of sensors that carry stimulus information from the environment. Hair and hair-like sensors have evolved to support survival behaviors in different ecological niches. Here, we review the diversity of biological hair and hair-like sensors across the animal kingdom and their roles in behaviors, such as locomotion, exploration, navigation, and feeding, which point to shared functional properties of hair and hair-like structures among invertebrates and vertebrates. By reviewing research on the role of biological hair and hair-like sensors in diverse species, we aim to highlight biological sensors that could inspire the engineering community and contribute to the advancement of mechanosensing in artificial systems, such as robotics.