Search Results (349)

This review critically analyzes the evolution of passive heat transfer enhancement in internal flows, charting a paradigm shift from momentum-based flow perturbation to the precise engineering of vortex structures. The central thesis is that the highest-performance, next-generation thermal systems will be realized through ‘flow field programming’—a unified design paradigm that intelligently architects vortex-topology and surface architecture across scales using smart materials, additive manufacturing, and artificial intelligence. This progression is traced from classical devices such as twisted tapes, which generate global swirl, to bio-inspired aerofoil inserts that efficiently produce discrete longitudinal vortices. The synergy achieved in compound systems—through the integration of geometries or the combination of inserts with advanced fluids—is identified as a key mechanism for surpassing traditional performance limits. Furthermore, applications in microscale and phase-change heat transfer, where surface engineering dominates, are explored. The novelty of this work lies in its synthesis of the underlying vortex-generation physics across diverse techniques and scales, introducing ‘flow field programming’ as a forward-looking framework for adaptive thermal management. This evolution—from static geometries to intelligent, responsive designs—is positioned to dramatically improve energy sustainability by enabling more compact, efficient, and adaptive thermal management across power generation, advanced electronics, and renewable energy systems. Full article

Bio-inspired metaheuristic optimization offers flexible search mechanisms for high-dimensional predictive problems under operational constraints. In administrative risk prediction settings, class imbalance and feature redundancy challenge conventional learning pipelines. This study evaluates a wrapper-based metaheuristic feature selection framework for post-compliance income declaration prediction using real longitudinal administrative records. The proposed approach integrates swarm-inspired optimization with supervised classifiers under a weighted objective function jointly prioritizing minority-class recall and subset compactness. Robustness is assessed through 31 independent stochastic runs per configuration. The empirical results indicate that performance effects are learner-dependent. For variance-prone classifiers, substantial minority-class recall gains are observed, with recall increasing from 0.284 to 0.849 for k-nearest neighbors and from 0.471 to 0.932 for Random Forest under optimized configurations. For LightGBM, optimized models maintain high recall levels (0.935–0.943 on average) with low dispersion, suggesting representational stabilization and dimensional compression rather than large absolute recall improvements. Optimized subsets retain approximately 16–33 features on average from the original 76-variable space. Within the evaluated experimental protocol, the findings show that metaheuristic-driven wrapper feature selection can reshape predictive representations under class imbalance, enabling simultaneous control of minority-class performance and feature dimensionality. Formal institutional deployment and cross-domain generalization remain subjects for future investigation. Full article

Porous tubular structures are of significant interest in engineering due to their exceptional potential for lightweight design, energy absorption, and multifunctional integration. Inspired by the unique net architecture of natural luffa sponges, this study introduces a novel design approach for such structure, namely bio-inspired Voronoi Tube (BVT). This design employs Voronoi tessellation patterns, parametrically controlled through the spatial distribution of seed points and integrates iterative optimization algorithms, to achieve precise coordinated regulation over the randomness and continuity of the resulting spatial network, closely mimicking the biological paradigm. Then, specimens are fabricated via additive manufacturing and then quasi-statically compressed axially, followed by systematic mechanical testing of the base material. The experimental results are analyzed to reveal the BVT structure’s mechanical responses and simultaneously validate finite-element simulation model. Subsequently, a systematic numerical analysis is performed to further understand the deformation mechanisms of the BVT structure and the influence of key geometric parameters. The results indicate that the iteratively optimized BVT structure successfully replicates the characteristic energy absorption behavior of the natural luffa sponge, confirming the effectiveness of the bio-inspired design. A rise in diameter from 0.6 mm to 1.0 mm results in a 78.32% increase in the specific energy absorption (SEA). Under identical mass conditions, tailored adjustments to the geometry can enhance the SEA by up to 34.57%. Full article

The absolute positioning accuracy of industrial manipulators is frequently bottlenecked by the interplay of geometric tolerances and complex, unmodeled non-geometric parameter drifts. Traditional static kinematic models, predicated on rigid-body assumptions, often struggle to characterize these state-dependent dynamic behaviors. To bridge this gap, this study introduces a PSO-Driven Neuro-Symbolic State-Space Framework incorporating Adaptive Wavelet Networks, drawing inspiration from two biological principles: the collective swarm intelligence observed in bird flocking and fish schooling, and the localized receptive field structure of mammalian visual cortex neurons. By reformulating calibration as a latent state estimation problem, we model kinematic parameters as stochastic states. Crucially, the observation model fuses symbolic Denavit–Hartenberg (D–H) predictions with an Adaptive Wavelet Network (AWNN). The AWNN utilizes Mexican Hat kernels, whose morphology mirrors the center-surround antagonism of cortical receptive fields, and leverages their precise time–frequency localization to effectively learn complex, configuration-dependent residuals. The framework employs a robust decoupled strategy. First, Particle Swarm Optimization (PSO) executes meta-optimization to autonomously determine hyperparameters, thereby mitigating initialization sensitivity. Second, a recursive inference engine estimates the hybrid states. Third, a global batch optimization refines the symbolic parameters against a frozen non-geometric error field. Experimental validation on an ABB IRB 120 robot (400 datasets) yielded a test RMSE of 0.73 mm. Compared to the standard Levenberg–Marquardt method, our approach reduced the RMSE by 40.16% and the maximum error by 35.71% (down to 0.99 mm). Moreover, it outperforms the state-of-the-art RPSO-DCFNN baseline by 12.05% while maintaining high computational efficiency (convergence within 20.15 s). These findings underscore the superiority of the proposed bio-inspired state-space fusion strategy for high-precision industrial applications. Full article

Polydopamine (PDA) is a bioinspired polymer known for its strong adhesiveness, biocompatibility, and functional properties, making it highly useful in biomedical applications. This review highlights recent progress in PDA-based biomaterials, with a focus on their morphology, synthesis techniques, and various biomedical uses. It examines how PDA composites, which are formed at the nanoscale and macroscale levels, contribute to drug delivery, tissue engineering, wound healing, and cancer treatment. The ability of PDA to create stable, functional coatings and composites that bond well with different biomaterials enhances its therapeutic potential. This review also discusses challenges such as structural stability, toxicity, and production scale. Additionally, it covers different polymerization mechanisms and their implications for future clinical use. With ongoing advancements, PDA-based materials hold great promise for personalized medicine, including targeted drug delivery, photothermal therapy, and tissue regeneration. Overall, this overview emphasizes the vital role of PDA in the progression of biomedical technology and its potential for future applications. Full article

The commercialization of proton-exchange-membrane fuel cells is constrained by the limitations of perfluorosulfonic acid membranes like Nafion, which suffer from high methanol crossover, humidity-dependent conductivity, high cost, and poor environmental sustainability. This review presents a comprehensive analysis of aquaporin-inspired chitosan/cellulose (AQP-CS) composite membranes as a transformative, bio-inspired alternative. The central design paradigm integrates a sustainable chitosan/cellulose matrix—which offers inherent mechanical stability, tunable proton conduction, and excellent fuel barrier properties—with biomimetic water channels engineered for selective hydration transport. This synergistic architecture aims to fundamentally decouple water management from proton conduction, directly addressing the core performance flaw of conventional membranes. The review is structured to explicitly trace the logical pathway from the foundational material properties of chitosan and cellulose to the functional requirements for integrating synthetic aquaporin-mimetic components. Experimental evidence from advanced chitosan composites, demonstrating proton conductivities up to 0.131 S cm⁻¹ alongside drastically reduced methanol permeability, validates the potential of this approach. Consequently, AQP-CS composites establish a novel framework for developing next-generation fuel cell membranes that combine high performance with ecological design. However, key challenges in the stable integration of biomimetic channels, long-term operational durability, and scalable manufacturing must be resolved to enable practical deployment and mark a significant leap toward sustainable energy conversion technologies. Full article

Bio-inspired engineering draws on principles refined by natural evolution to tackle persistent challenges in fluid mechanics, structural dynamics, and thermal transport. This article presents a critical, mechanism-driven narrative review that integrates recent advances across three complementary domains that are often treated independently, namely: flow-control strategies such as leading-edge tubercles, alula-like devices, riblets, superhydrophobic skins, and hybrid low-Reynolds-number fliers; fluid-structure interactions inspired by aquatic and aerial organisms that leverage compliant foils, flexible filaments, ciliary arrays, and piezoelectric fluttering plates for propulsion, wake regulation, mixing, and energy harvesting; and phase-change heat-transfer surfaces modeled after stomata, porous biological networks, and textured cuticles that enhance nucleation control, liquid replenishment, and droplet or bubble removal. Rather than providing an exhaustive catalog of biological analogues, this review emphasizes the underlying physical mechanisms that link these domains and enable multifunctional performance. These developments reveal shared physical principles, including multiscale geometry, capillary- and vortex-mediated transport, and compliance-enabled flow tuning, which motivate the integrated treatment of aerodynamic, hydrodynamic, and thermal systems in applications spanning aerospace, energy conversion, and microscale thermal management. The review assesses persistent challenges associated with scaling biological architectures, ensuring long-term durability, and modeling tightly coupled fluid-thermal-structural interactions. By synthesizing insights across flow control, fluid-structure interaction, and phase-change heat transfer, this review provides a unifying conceptual framework that distinguishes it from prior domain-specific reviews. Emerging opportunities in hybrid multi-mechanism designs, data-driven optimization, multiscale modeling, and advanced fabrication are identified as promising pathways to accelerate the translation of biological strategies into robust, multifunctional thermal–fluid systems. Full article

Bio-inspired and bio-based materials are increasingly recognized as powerful enablers of climate-responsive and low-carbon architecture. By learning from natural systems, such as adaptability, self-regulation, and resource efficiency, these materials offer promising solutions to the escalating environmental pressures faced by the built environment. However, their systematic integration into building design remains limited, particularly in tropical monsoon climates. To address this gap, this study applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to identify, prioritize, and map the interdependencies among ten bio-based and bio-inspired strategies for sustainable building design. The results highlight five dominant solutions: living building systems, bio-composite exterior cladding for weather resistance, mycelium-based insulation for humidity control, bio-based natural ventilation and passive cooling, and bio-inspired self-shading systems. The causal analysis reveals three key characteristics: (1) living building systems function as a central integrative nexus, (2) bio-composite cladding acts as a primary driver of durability and climate resilience, and (3) bio-based water filtration and local timber exhibit lower systemic leverage despite their environmental benefits. Theoretically, this study advances biomimetic design research by introducing a causal, system-level framework for understanding interactions among nature-inspired strategies. Practically, it provides architects, engineers, and policymakers with an evidence-based decision-support tool to prioritize climate-adapted, bio-inspired solutions, contributing to the development of resilient and regenerative architecture in rapidly changing climates. Full article

Particle swarm optimization (PSO) is a widely used bio-inspired optimization algorithm, yet maintaining an effective balance between exploration and exploitation remains challenging. Most existing PSO variants rely on static or predefined regulation strategies, which restrict their adaptability to evolving search states and may lead to premature convergence or search stagnation. Inspired by division of labor and competitive selection mechanisms in biological populations, this paper proposes a dual-subpopulation competitive particle swarm optimization (DCPSO). In DCPSO, the population is explicitly partitioned into exploration and exploitation subpopulations with distinct search roles. A dynamic competition mechanism is designed to evaluate recent search progress, based on which stagnated particles are adaptively migrated between subpopulations, enabling flexible reallocation of computational resources during the optimization process. Experimental results on the CEC2017 benchmark suite demonstrate that DCPSO consistently outperforms standard PSO and several representative state-of-the-art algorithms, achieving statistically significant improvements on the majority of benchmark functions, particularly on hybrid and composition problems. Additional experiments on engineering design problems further verify the robustness, convergence stability, and practical effectiveness of DCPSO. Full article

Many complex engineering problems can be formulated as mathematical optimization tasks, for which bio-inspired metaheuristic algorithms have demonstrated outstanding effectiveness. Drawing inspiration from snake behavior, the Snake Optimizer (SO) algorithm provides a promising framework but suffers from random population initialization, insufficient global search capability, and slow convergence. To address these drawbacks, the study proposes a Modified Snake Optimizer (MSO) that integrates three key strategies: a dual mapping strategy based on Latin hypercube sampling and logistic mapping for population initialization; an opposition-based learning mechanism with scaling factors for exploration; and integration of the soft-rime search strategy from RIME optimization during exploitation. The performance of MSO was benchmarked against nine representative algorithms using the CEC2017 and further validated on three engineering application problems—pressure vessel, tension/compression spring, and hydrostatic thrust bearing design, and two UAV path planning scenarios. Experimental results show that MSO achieves faster convergence speed, stronger robustness and greater stability, effectively extending the biomimetic principles of the original SO and confirming its superiority for solving optimization problems. Full article

The efficacy of swarm intelligence algorithms in navigating high-dimensional, non-convex landscapes depends on the dynamic balance between global exploration and local exploitation. Drawing inspiration from the intricate social dynamics of Pyrrhura molinae, this study proposes a novel bio-inspired metaheuristic, the Comprehensive Learning Parrot Optimizer (CL-PO). While the original Parrot Optimizer (PO) simulates collective foraging and communication, it often suffers from population homogenization and entrapment in local optima due to its reliance on single-source social learning. To address these limitations, CL-PO incorporates a dimension-wise multi-exemplar social learning mechanism analogous to the cross-individual knowledge transfer observed in avian colonies. This strategy enables stagnant individuals to reconstruct their search trajectories by learning from multiple superior peers, thereby sustaining population diversity and facilitating adaptive exploration. Rigorous benchmarking on 29 test functions from the CEC 2017 suite reveals that CL-PO achieves statistically superior performance compared to nine state-of-the-art algorithms, securing a top-tier average Friedman rank of 1.28. Furthermore, the practical utility of CL-PO is substantiated through a complex reservoir production optimization task using the Egg benchmark model, where it consistently maximizes the net present value (NPV), reaching $9.625\times {10}^8$ USD. These findings demonstrate that CL-PO is a powerful and reliable solver for addressing large-scale engineering optimization problems under complex constraints. Full article

This experimental review discusses evolutionarily approved, naturally pre-designed skeletal architectures of marine keratosan sponges in the form of 3D scaffolds, which have garnered increasing interest in the fields of structural and functional biomimetics as well as in tissue engineering. It has been demonstrated that these renewable, ready-to-use natural scaffolds can undergo further modifications through specialized treatments such as metallization and carbonization, enabling the creation of functional biomaterials while maintaining the species-specific hierarchical 3D structure. The study presented remarkable findings, including the demonstration of the unique shape-memory behavior of these scaffolds even after two months of exposure to high mechanical pressure at temperatures exceeding 100 °C. Additionally, the cytocompatibility and biological performance of natural and carbonized (1200 °C) spongin scaffolds, derived from selected bath sponges, were comparatively investigated with respect to growth and proliferation of human MG-63 osteoblastic cells. Understanding whether carbonization universally enhances osteogenic capabilities or selectively amplifies the inherent architectural advantages remains to be critical for the rational design of sponge-derived scaffolds in bone and structural tissue engineering applications. Full article

Myriapods achieve remarkable obstacle-crossing capability through inter-segment pitch adjustment and coordinated anterior–posterior propulsion, providing valuable biomimetic inspiration for engineering design. Articulated tracked vehicles, connecting front and rear units via pitch mechanisms, exhibit functional similarity to myriapod body segments. This study develops a comprehensive dynamic model for articulated tracked vehicle pitch motion to reveal its biomimetic connection with myriapod locomotion. A quadratic-function-based non-uniform track–ground contact pressure distribution method with zero-gradient boundary conditions is proposed, effectively eliminating the non-physical negative pressure issue inherent in traditional assumptions. Systematic analyses reveal that the front unit provides primary traction under pitch-up conditions, forming a front-pulling-rear driving mode, while the rear unit dominates under pitch-down and acceleration conditions, forming a rear-pushing-front driving mode. Through pitch attitude adjustment, the maximum surmountable vertical-wall height increased from 263 to 593 mm, representing a 125.45% improvement. This traction distribution pattern closely matches the anterior-guidance and posterior-propulsion mechanism observed in myriapod locomotion. This study quantitatively validates the functional analogy between articulated tracked vehicle pitch dynamics and myriapod inter-segment coordination, providing theoretical foundations for bio-inspired tracked vehicle design. Full article

With the electrification of automotive powertrains, aerodynamic noise has emerged as the primary factor affecting vehicle comfort. This systematic review, adhering to PRISMA 2020 guidelines, bridges the gap between biological fluid mechanics and automotive engineering by synthesizing recent advances in aerodynamic mechanisms and bionic control strategies. Based on a comprehensive search of Web of Science, ScienceDirect, SAE Mobilus, and Google Scholar for the literature published between 2016 and 2025, 90 eligible studies were analyzed to provide a rigorous evidence-based synthesis. The review details complex flow phenomena, such as turbulent separation and vortex shedding across key regions like A-pillars and mirrors, drawing parallels to bio-inspired fluid–structure interactions. Numerical prediction methods, including large eddy simulation (LES), detached eddy simulation (DES), and lattice boltzmann method (LBM), are critically examined for their efficacy in resolving both conventional and bionic flow structures. A significant focus is placed on bio-inspired mitigation technologies, where quantitative findings demonstrate substantial noise suppression: specifically, the reviewed data shows that bionic riblet surfaces on tires can reduce noise levels by up to 5.18 dB, while beetle-head-inspired protuberances on exterior mirrors can achieve reductions of up to 10 dB. Finally, this work suggests future research directions in integrated fluid–acoustic–structural simulation frameworks and self-adaptive bionic systems, providing a robust reference for developing high-performance, low-noise vehicles inspired by natural organisms. Full article

4D printing, as an advanced evolution of 3D bioprinting, introduces time as an active design dimension, enabling printed constructs to undergo programmed morphological or functional transformations in response to external or endogenous stimuli. By integrating stimuli-responsive smart materials with precise additive manufacturing, 4D printing provides a bio-inspired strategy to overcome the inherent limitations of static scaffolds and to achieve spatiotemporal dynamic matching with the evolving biological microenvironment during tissue regeneration. Over the past decade, significant progress has been made in applying 4D printing to structurally and functionally complex tissues, including bone, muscle, vasculature, nerve repair, wound closure, and other emerging biomedical scenarios. Rather than emphasizing shape change alone, recent advances demonstrate that 4D-printed constructs can emulate key biological processes such as morphogenesis, contraction, directional guidance, electrophysiological signaling, and microenvironment-responsive regulation, thereby enhancing tissue integration and functional recovery. This review systematically summarizes materials, stimulus–response mechanisms, and representative applications of 4D printing from a bio-inspired perspective, while critically discussing current challenges related to material performance, mechanistic understanding, manufacturing precision, and clinical translation. Finally, future perspectives are outlined, highlighting the importance of interdisciplinary integration, intelligent manufacturing, and clinically oriented evaluation frameworks to advance 4D printing toward personalized and precision regenerative medicine. Full article