Mechanics' Institute

Not all particulate matter carried by fluid flows has constant buoyancy. In some cases, the buoyancy of a particle can change dynamically based on the local flow. We refer to this phenomenon as ‘active buoyancy.’ Although actively buoyant particles are found throughout nature, their dynamics is not well understood, particularly when they are also highly inertial. Motivated by the problem of the transport of firebrands in wildfires, whose effective buoyancy is modulated by conductive and convective heat transfer to the surrounding fluid, we conducted a series of experiments to investigate the effects of active buoyancy on particle settling in quiescent fluid. We find that, depending on the control parameters, active buoyancy can either hinder or enhance settling, in some cases to a large extent. The details of this settling modulation, however, cannot be simply captured by any single control parameter. Analysis of the trajectories of the falling particles showed that they fall along nearly sinusoidal paths even though the particle Reynolds number is higher than expected for this regime, suggesting that active buoyancy may act to stabilise their wakes. Our results suggest both that models of actively buoyant particles such as firebrands must account for the effects of active buoyancy and that there is still much to be understood about the behaviour of these complex particles.

van der Waals (vdW) heterostructures serve as a highly tunable platform for manipulating electronic and spin properties in two-dimensional (2D) materials. Their capability to achieve electric field control of topological magnetic phases is particularly promising, offering an energy-efficient pathway for advancing spintronic technologies. Here, using first-principles calculations and atomic spin dynamics simulations, we report a tunable two-dimensional multiferroic platform based on the CrSeTe/In2Se3 heterostructures for spintronic applications. Utilizing the intrinsic polar nature of CrSeTe, the total polarization of the heterostructures can be precisely modulated through reversible polarization switching in In2Se3. Furthermore, the application of an electric field and vertical strain induces a diverse array of spin textures in CrSeTe/In2Se3 heterostructures, notably skyrmions and bimerons. Our findings reveal that polarization reversal in ferroelectric materials can effectively modulate multiferroicity in 2D heterostructures, guiding the direction of realizing the spintronic nanomaterials and nanodevices.

Dielectric elastomer actuators (DEAs) have been widely studied as artificial muscles, owing to their large actuation strain and high energy density. To generate out-of-plane actuation, which can be conveniently coupled in motions such as grasping, walking, and swinging, asymmetrically structured DEAs (AS-DEAs) have been developed by attaching active dielectric elastomer (DE) films to passive substrates. However, traditional AS-DEAs usually suffer from a short actuation life caused by stress concentration at the soft–stiff interface. Here, inspired by the human bone–cartilage tissue interface, we developed gradient structured DEAs (GS-DEAs) by preparing thin DE films in which the modulus was increased stepwise and inserting them as buffer layers between active DE films and substrates. This gradient structure effectively reduces the interface shear stresses to below the interfacial bonding strength, thus greatly improving the durability of the actuator. GS-DEAs maintain large out-of-plane actuation and force output after 100,000 cycles and successfully drive a robotic fish for long-term underwater swimming.

Laser-induced microparticle impact testing (LIPIT) provides an effective experimental method for revealing the ultrahigh strain-rate dynamic response of materials by launching a single microparticle through laser-induced film expansion to impact a target material. Understanding the coupling effects of laser ablation and the dynamic responses of both the films and microparticles during the launch process in LIPIT is essential for achieving higher launch velocity of the microparticle. In this study, the influences of elastomer film thickness and microparticle diameter on the launch process are investigated by numerical simulations. The results show that the laser-induced plasma pressure experiences a sudden increase in the launch system with a thin or without elastomer film due to the inertial effect of microparticle and the reflection of laser-induced shock waves at the microparticle–film interface. As the elastomer film thickness increases, the onset time of the sudden increase in pressure delays and the amplitude of the peak pressure decreases. When the elastomer film thickness exceeds about 10 μm, the influence of elastomer film thickness is negligible. Additionally, the microparticle launch velocity decreases with increasing elastomer film thickness. In a launch system without an elastomer film, increasing the microparticle diameter leads to a greater amplitude of the sudden increase in plasma pressure, resulting in a higher launch velocity. However, when the microparticle diameter exceeds 9.2 μm, the launch velocity begins to decrease with increasing diameter. This study discloses the coupling effects during the microparticle launch process in LIPIT and provides insights into experimental design and optimization of the LIPIT system.

As global warming intensifies, geological carbon storage (GCS) in saline aquifers could play a vital role in mitigating CO2 emissions. CO2 trapping occurs mainly through solubility and residual trapping, requiring an accurate prediction using solubility trapping (STI) and residual trapping (RTI) indices. Machine learning shows promise for estimating CO2 trapping in saline aquifers, but current models often lack effective feature selection, parameter optimization, and advanced deep learning techniques, limiting their performance. This study develops predictive models for RTI and STI using CNN, LSTM, and hybrid algorithms by combining them with growth optimization (GO) and cuckoo optimization (COA). An extensive data set of 6,811 global data points was analyzed, with feature selection using the nondominated sorting genetic algorithm and random forest analysis. Model performance was based on independent testing data, and Shapley additive explanation (SHAP) analysis identified key features. For RTI, residual gas saturation (RGS), injection rate (IR), permeability (Perm), elapsed time (Te), porosity (Por), and salinity (Sal) were the most influential. Conversely, RGS, thickness (Th), Te, Perm, Sal, and Por were most critical for STI. The results confirm that hybrid DL models outperformed standard DL models, with metaheuristic optimization enhancing accuracy and generalization. The CNN-COA model achieved the lowest root-mean-square error (RMSE) for RTI (0.0011 for training; 0.0035 for testing) and STI (0.0005 for training; 0.0028 for testing) predictions. SHAP analysis revealed RGS and Perm as the most and least influential features for RTI predictions and Th and Perm as the most and least influential features, respectively, for STI predictions. This study is innovative in its integration of advanced feature selection methods and hybrid deep learning algorithms with effective optimization and feature selection. This integration leads to improved GCS model prediction performance, robustness, and adaptability to diverse geological conditions.

This paper introduces a mathematical framework for explicit structural dynamics, employing approximate dual functionals and row-sum mass lumping. We demonstrate that the Petrov–Galerkin method developed in our previous work—utilizing row-sum mass lumping—can be interpreted as a Galerkin method with a customized higher-order accurate mass matrix. Unlike prior work, our method correctly incorporates Dirichlet boundary conditions, while preserving optimal spatial accuracy. The mathematical analysis is substantiated by spectral analysis, two-dimensional linear benchmarks illustrating robustness under mesh distortion and a three-dimensional geometrically non-linear forced vibration analysis. The obtained results reveal that our approach achieves accuracy and robustness comparable to a traditional Galerkin method employing the consistent mass formulation, while retaining the explicit nature of the lumped mass formulation.

Oscillators exhibiting an Andronov-Hopf bifurcation are candidates to mimic the functionality of the cochlea, since the transfer response of these oscillators is compressive and frequency selective. The former implies that small stimuli are amplified and strong stimuli are attenuated, while the latter means that the oscillator only reacts in a (small) frequency band. However, this implies that many oscillators are needed to cover a relevant frequency band. By introducing the notion of tunable characteristic frequencies, i.e., the characteristic frequency can be adjusted by a controllable input, the number of oscillators can be eventually reduced. Subsequently, the tunability enhancement of coupled oscillators is investigated by analyzing the local dynamics of a network of oscillators. For this, necessary conditions for the emergence of Andronov-Hopf bifurcations are determined for networks consisting of two groups, i.e., a group is a network of identical oscillators. By choosing the eigenvalues of the product of the cross-coupling matrix as bifurcation parameters and exploiting the structure of the transfer matrix of this network, the critical points and, thus, the characteristic frequency at this point can be derived. Tunability of the characteristic frequency is then enabled by controlling the asymmetry between the groups of oscillators.