Search Results (92)

Electrochemical impedance spectroscopy (EIS) resolves the internal physicochemical processes of lithium-ion batteries across timescales—from ohmic conduction through charge-transfer kinetics to solid-state diffusion. Despite this analytical power, EIS deployment remains largely confined to laboratory electrochemical workstations that are bulky, expensive, and incapable of online multi-cell operation under dynamic DC bias conditions. This study presents a multi-channel EIS measurement device that simultaneously addresses three requirements for practical battery diagnostics: workstation-grade measurement accuracy, multi-cell synchronous acquisition, and tolerance to the DC bias voltage present across battery terminals during operation. The device employs a master–slave distributed architecture: each slave unit is built around the DNB1101 battery-dedicated impedance measurement chip with a Kelvin four-wire sensing configuration, while the STM32F407-based master controller coordinates measurement scheduling and data communication under FreeRTOS. A four-channel slave board with a differential daisy-chain communication topology and hardware broadcast trigger mechanism supports multi-cell synchronous acquisition. The device operates over a frequency range of 0.01 Hz to 5620 Hz with logarithmic spacing, and a C#-based host application provides real-time Nyquist and Bode visualization along with MATLAB R2024a-based post-processing for outlier rejection and data smoothing. Validation was conducted using Panasonic NCR18650 ternary (NCA) and LiFePO₄ (LFP) 18650 cells, benchmarked against a CorrTest CS350 electrochemical workstation at SOC = 40% and 25 °C. The device achieves a maximum impedance magnitude error of 1.55% and a maximum phase error of 1.22%. Equivalent circuit model fitting via ZSimpWin yields parameter differences below 1% between the device and the reference workstation. Under online conditions with a 3.6 V DC bias, the impedance measurement deviation of a 20 mΩ precision resistor remains below 0.69% across the full frequency range. Multi-channel synchronous measurements across four cells demonstrate inter-channel amplitude variance below 2.13%. Cross-chemistry validation with LiFePO₄ cells yields magnitude and phase errors below 0.92%. These results demonstrate that the proposed device provides laboratory-grade EIS accuracy with multi-channel, online, and cross-chemistry capabilities, offering a practical platform for integrating EIS-based diagnostics into next-generation battery management systems. Full article

Aiming at the limitation that the traditional integer-order dynamic model of a liquid crystal optical phased array (LCOPA) cannot simultaneously characterize the liquid crystal deformation memory effect, cross-coupling of two-dimensional deflection channels and time-delay characteristics, this paper proposes a two-dimensional coupled fractional-order dynamic modeling and parameter identification method based on fractional calculus. First, the two-dimensional beam deflection mechanism of LCOPA is analyzed to establish a static beam propagation model. Then, a fractional-order generalized Kelvin constitutive equation is adopted to construct a coupled dynamic model integrating multi-element coupling, channel cross-coupling and time-delay characteristics. A Legendre wavelet integral operational matrix is constructed to simplify fractional operations, combined with the least squares algorithm to achieve synchronous high-precision identification of multiple parameters. Finally, an experimental platform is built for verification. The results show that the proposed model achieves a fitting degree of 98% for beam deflection dynamics, with steady-state prediction error less than 0.15% and dynamic RMSE no more than 0.00020 rad, significantly outperforming integer-order models. This research provides theoretical support and engineering reference for modeling and controller design of high-precision non-mechanical beam steering systems. Full article

Air-assist atomization exploits high-velocity gas streams to shear liquid phases, achieving fine droplets at supply pressures significantly lower than those required by conventional pressure atomization. This technology is extensively deployed across diverse sectors, including pesticide spraying, food spray drying, sanitization/disinfection, and combustion atomization. Although specific operational fields dictate vastly contrasting droplet size distributions, velocities, and deposition uniformities—with target diameters spanning from <50 μm to >200 μm—their foundational atomization mechanisms remain inherently unified, governed primarily by toroidal vortex-induced primary breakup and Kelvin–Helmholtz/Rayleigh–Taylor (KH–RT) or Taylor Analogy Breakup (TAB) secondary breakup. This review systematically parses the atomization mechanisms, critical performance metrics, numerical simulation frameworks, and experimental characterization methodologies of air-assist nozzles. Crucially, from a novel “cross-disciplinary custom design” perspective, we contrast the optimal droplet parameter windows across the agricultural, food, sanitization, and combustion sectors (e.g., electrostatic plant protection yields a 203–1350% increase in abaxial leaf deposition; mine wind-assisted misting achieves a >90% collection efficiency for PM₁₀ dust; and SCR air-assisted injectors reduce the Sauter Mean Diameter (SMD) to 25 μm). Synthesized insights reveal that segmented VOF-to-DPM transition frameworks, corner-vortex-induced breakup theories, and closed-loop adaptive control architectures possess substantial cross-domain migration value. Current bottleneck challenges are highlighted, including the mesh dependency of droplet collision–coalescence models in dense spray regimes, the absence of robust atomization constitutive formulations for non-Newtonian fluids, and the ongoing paradigm shift from open-loop presetting to intelligent closed-loop regulation. This work establishes a comprehensive theoretical foundation and technical roadmap for cross-disciplinary integration and next-generation smart nozzle design in air-assist atomization. Full article

The magnetic susceptibilities of various electrically insulating ordinary fluids depend on their local states, such as their density and temperature. When such fluids, which can be characterized as either paramagnetic or diamagnetic and occur commonly in nature, are subjected to magnetic field gradients, it induces an effective body force—the Kelvin force. This force, which depends on the susceptibility and the gradient of the square of the magnetic field strength, can become one of the effective mechanisms for modulating the flow and transport, particularly where terrestrial gravity becomes negligible, such as in free space or under microgravity conditions. For the first time, we developed a theoretical model demonstrating that a viscous current can be generated due to the contrasts between the magnetic susceptibilities of the intruding and ambient fluids in the presence of gradients in magnetic fields, analogous to the viscous gravity current in terrestrial situations. We derived similarity solutions for the two-dimensional and axisymmetric currents arising from a balance between the Kelvin buoyancy and viscous forces with a prescribed power law for the magnetic field strength. These determine the shape and various spreading relationships of the viscous current. For a prescribed time variation in the source flux, it is shown that a family of scaling laws exists for the spreading rate and the thickness of the current, which depend on the steepness of the magnetic field gradient. Unlike gravity, since the driving horizontal buoyancy arising from the Kelvin force is externally specified, it potentially offers a mechanism to control the characteristic shape and the rate of motion of the viscous current. Full article

Triboelectric nanogenerators (TENGs) offer a promising route for self-powered microscale energy harvesting, yet their design optimisation remains empirically challenging due to the complex interplay of material surface physics, device geometry and operating mode. In this work, we present an integrated framework that combines atomic force microscopy (AFM) characterisation, COMSOL Multiphysics 6.0 finite element simulation and physics-informed conditional variational autoencoder (cVAE) to predict and optimise TENG output performance. Four polymer dielectric materials, HDPE, LDPE, TPU, and PMMA, were characterised via Kelvin Probe Force microscopy (KPFM) for work function, surface potential and surface roughness. Surface charge density was calculated from measured KPFM potential using the parallel plate capacitor model and used as a boundary condition in COMSOL Multiphysics simulations for contact-separation and lateral sliding TENG mode for dielectric film thicknesses of 50 µm and 100 µm. The simulated open circuit voltage (Voc) and short circuit charge (Qsc) across gap distances up to 150 mm formed the training dataset for a cVAE model with eight physicochemical condition features. The trained model demonstrated strong reconstruction accuracy (R² ≥ 0.94) and enables generative prediction across unseen design spaces. Results reveal that the LDPE/TPU pair at 50 µm thickness consistently achieves the highest electric outputs in both modes, and the sliding mode yields 25–30% higher voltages than the contact separation mode across all material pairs. This study provides a transferable data-efficient methodology for accelerating TENG material and geometry optimisation. Full article

Atmospheric equatorial Kelvin waves—convective disturbances that manipulate tropical wind and rainfall patterns—can propagate eastward at speeds ranging from nearly stationary to 30 m/s, with variability determined by moist processes and advection by the background wind. Current studies on Kelvin waves lack a comprehensive climatology that explains how their structure and propagation speeds change in different background states. Thus, this work builds a variable regression model that uses ERA5 reanalysis data to reconstruct Kelvin waves during different background wind shear conditions and phases of the Madden–Julian Oscillation (MJO) and the El Niño–Southern Oscillation (ENSO) over the Pacific. Overall, Kelvin waves tend to speed up during background conditions that generate upper-tropospheric westerlies and slow down during upper-tropospheric easterlies. East Pacific Kelvin waves are faster than West Pacific Kelvin waves because of climatological westerly shear in the former and easterly shear in the latter. However, strong westerly shear over the East Pacific allows extratropical Rossby waves to impede on the Kelvin wave, while strong easterly shear over the West Pacific distorts classical Kelvin wave structure. The results provide references for weather prediction models to accurately resolve the interaction between Kelvin waves and background circulation. Full article

Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment. Full article

The term “pristine emulsion” is used for differentiating emulsions that consist of only water and oil with no surfactant from the Pickering emulsions, which are also surfactant-free but stabilized with colloidal particles. We review 22 papers dedicated to such emulsions prepared from a wide variety of liquids. We studied here the evolution of one such emulsion, hexadecane-in-water at 4% vl, over a long period of time, from days to weeks. We discovered that the droplet size grows with time, with a rate that depends on mixing conditions, which supports a coalescence hypothesis. However, this coalescence is unusual because the size reaches a certain constant value, which contradicts typical coalescence behavior. To explain this peculiarity and such emulsification in general, we employ a theoretical model that was developed for explaining pristine nano-bubble stability. We hypothesize the existence of a layer of structured water molecules at the interface, following Eastoe and Ellis (Adv in Colloid and Interface Sci., 134–135, 89–95, 2007) and others. We point out that the Electric Double Layer exerts a force on the water dipole moments in this layer (dielectrostatic force) that compensates Kelvin’s pressure. The droplet size calculated using this model is close to the measured size. The second factor associated with this layer is the repulsion of the water dipole moments, which we show can compensate for the surface tension tangential to the interface. After ruling out alternative hypotheses with our data, we conclude that the model suggested for explaining the stability of nano-bubbles is also consistent with our results for these “pristine emulsions”. Full article

Structural adhesives in aeronautical applications are routinely exposed to complex loading histories that generate time-dependent deformation, making accurate prediction of their viscoelastic response essential for reliable assessment of joint integrity. This work presents an integrated experimental and modeling study of the aerospace-grade epoxy adhesive 3M Scotch-Weld EC-2216 using multi-step creep and stress-relaxation tests performed at room temperature and controlled loading rates, combined with fractional viscoelastic modeling. Unlike traditional single-step characterizations, the multi-step protocol employed here captures the cumulative loading effects and fading-memory dynamics that govern the adhesive’s mechanical response. The experimental data were analyzed using fractional Maxwell, Voigt–Kelvin, and Zener formulations. Statistical evaluation based on the Bayesian Information Criterion (BIC) consistently identified the Fractional Zener Model (FZM) as the most robust representation of the stress-relaxation behavior, effectively capturing both the unrelaxed and relaxed modulus. The results demonstrate that EC-2216 exhibits hierarchical relaxation mechanisms and history-dependent viscoelasticity that cannot be accurately described by classical integer-order models. Overall, the study validates the use of fractional operators to represent the broad and hierarchical relaxation spectra typical of toughened aerospace epoxies and provides a rigorous framework for durability assessment and predictive modeling of adhesively bonded structures. Full article

This paper discusses the phenomenon of concrete creep and its impact on bridge structures, with particular emphasis on the mechanical models used to describe it. Classical rheological models, such as the Maxwell and Kelvin–Voigt, along with their generalized and fractional extensions incorporating fractional-order derivatives, are presented. These models differ in their complexity and in the accuracy of fit to laboratory test results. The use of non-classical, fractional-order rheological models (the fractional Kelvin–Voigt model and the fractional Zener model) enables better model fitting. The paper further describes methods for estimating creep effects in bridge design. The most popular is the effective modulus method, which is easy to implement but does not account for the load application history. More accurate approaches (e.g., Trost, Bažant, incremental method according to linear elasticity theory) are based on iterative procedures and require advanced computer implementation. The consequences of creep in bridge structures are highlighted: geometric (changes in elevation) and static (redistribution of internal forces and support reactions, changes in sectional stresses). These effects are particularly important in structures erected in stages, such as bridges built using the balanced cantilever method. The analytical section presents the influence of various creep models on changes in static quantities for a three-span prestressed bridge constructed by the cantilever method. The importance of proper selection of the creep model for the accuracy of engineering calculations and for the correct assessment of the long-term behavior of the structure is emphasized. Full article

For concrete structures dominated by fracture failure, e.g., containment and gravity dams, sustained load deformations primarily arise from crack extension and concrete viscoelasticity. As cracks progressively grow under sustained loads, accurate prediction of the time-dependent fracture process in concrete accounting for crack-viscoelasticity interactions are crucial for the stability and safe design of concrete structures. This paper presents an initial fracture toughness ($K_{\mathrm{IC}}^{ini}$)-based numerical model to predict the time-dependent crack extension in concrete under sustained loads. The model integrates a time-dependent tension-softening constitutive relation, the generalized Kelvin chain model for viscoelastic behavior and $K_{\mathrm{IC}}^{ini}$-based criterion for crack extension. The accuracy of the model was verified with two sets of experimental data available in the literature. The results indicated that the tension-softening constitutive law that quantifies the relation cohesive stress (s_w), loading time (t), and COD can be successfully implemented in the numerical model. The predicted CMOD versus time and crack length versus time curves show good agreements with the test results regardless of loading level, specimen configuration and material property, demonstrating the predictive capability of the model in describing the crack extension in concrete exposed to sustained loads. Full article

Accurate thermal modeling of Terminal Test Masses (TTMs) is crucial for optimizing the sensitivity of gravitational wave interferometers like Virgo. In fact, in such gravitational wave detectors even minimal laser power absorption can induce performance-limiting thermal effects. This paper presents a detailed investigation into the steady-state thermal behavior of TTMs. In particular, future scenarios of increased intracavity laser beam power and optical coating absorption are considered. We develop and compare two numerical models: a comprehensive model incorporating volumetric heat absorption in both the multilayer coating and the bulk substrate, and a simplified reduced model where the coating’s thermal impact is represented as an effective surface boundary condition on the substrate. Our simulations were focused on a ternary coating design, which is a candidate for use in next-generation detectors. Results reveal that higher coating absorption localizes peak temperatures near the coating–vacuum interface. Importantly, the comparative analysis demonstrates that temperature predictions from the reduced model differ from the detailed model by only milli-Kelvins, a discrepancy often within the experimental uncertainties of the system’s thermo-physical parameters. This finding suggests that computationally efficient reduced models can provide sufficiently accurate results for thermal management and first-order distortion analyses. Moreover, the critical role of accurately characterizing the total power absorbed by the coating is emphasized. Full article

Carbon black (CB)-filled rubber has been widely used in engineering. However, its time-dependent behavior, such as creep, is undesirable during the service process. In addition, the long-term creep test is time- and cost-consuming. To this end, the objective of this paper aims to predict the creep behavior from the short-term storage modulus by use of the collocation method. First, the master curve of storage modulus was constructed based on the time–temperature superposition principle (TTSP), and the validation of shift factors was verified by use of the Williams–Landel–Ferry (WLF) equation. Second, the generalized Kelvin model was used to describe the master curve of storage modulus by use of the collocation method, and the corresponding parameters were obtained. Compared with the existing works, the collocation method had the advantages of avoiding the occurrence of waviness of the fitting curve. Lastly, the creep compliance of CB-filled rubber was calculated by substituting the fitting parameters into the creep compliance expression. In order to verify the reliability of the calculation result, the creep tests were carried out. It was obvious that the calculation result is in good agreement with the experimental one with a RMSE value of 0.0055, which means that the calculation result is reliable. Full article

The control of ventilation systems is often performed by automatic algorithms which often do not consider the future evolution of the system in its control politics. Digital twins allow system forecasting for a more sophisticated control. This paper explores a novel methodology to create a Machine Learning (ML) model for the predictive control of a ventilation system combining Computational Fluid Dynamics (CFD) with Artificial Intelligence (AI). This predictive model was created to forecast the temperature and humidity evolution of a ventilated room to be implemented in a digital twin for better unsupervised control strategies. To replicate the full range of annual conditions, a series of CFD simulations were configured and executed based on seasonal data collected by sensors positioned inside and outside the room. These simulations generated a dataset used to develop the predictive model, which was based on a Deep Neural Network (DNN) with fully connected layers. The model’s performance was evaluated, yielding final average absolute errors of 0.34 degrees Kelvin for temperature and 2.2 percentage points for relative humidity. The presented results highlight the potential of this methodology to create AI-driven digital twins for the control of room ventilation. Full article

Marine soft clay is characterized by a high water content and low strength, exhibiting pronounced creep deformation under long-term loading that threatens the serviceability and durability of coastal infrastructure. Accordingly, this study develops a creep constitutive model that combines elastic, plastic, and viscous effects and quantitatively evaluates time-dependent deformation under varying water contents and stress levels to provide reliable prediction tools for tunnel, excavation, and pile-foundation design. Cyclic creep tests were carried out on reconstituted marine soft clay with water contents of 40–60% and stress ratios of 0.4–1.2 using a pneumatic, fully digital, closed-loop triaxial apparatus. A “nonlinear spring–Bingham slider–dual viscous dashpot in parallel with a standard Kelvin dashpot” element assembly was proposed, and the complete stress–strain relationship was derived. Experimental data were fitted with Python to generate a creep-strain polynomial and verify the model accuracy. The predicted–measured creep difference remained within 10%, and the surface-fit coefficient of determination reached R² = 0.97, enabling rapid estimation of deformation for the given stress and time conditions. The findings offer an effective method for the precise long-term settlement prediction of marine soft clay and significantly enhance the reliability of the deformation assessments in coastal civil-engineering projects. Full article