Multiscale Process Research Articles

Multiscale method for identifying and marking the multiform fractures from visible-light rock-mass images

Multiform fractures have a direct impact on the mechanical performance of rock masses. To accurately identify multiform fractures, the distribution patterns of grayscale and the differential features of fractures in their neighborhoods are summarized. Based on this, a multiscale processing algorithm is proposed. The multiscale process is as follows. On the neighborhood of pixels, a grayscale continuous function is constructed using bilinear interpolation, the smoothing of the grayscale function is realized by Gaussian local filtering, and the grayscale gradient and Hessian matrix are calculated with high accuracy. On small-scale blocks, the pixels are classified by adaptively setting the grayscale threshold to identify potential line segments and mini-fillings. On the global image, potential line segments and mini-fillings are spliced together by progressing the block frontier layer-by-layer to identify and mark multiform fractures. The accuracy of identifying multiform fractures is improved by constructing a grayscale continuous function and adaptively setting the grayscale thresholds on small-scale blocks. And the layer-by-layer splicing algorithm is performed only on the domain of the 2-layer small-scale blocks, reducing the complexity. By using rock mass images with different fracture types as examples, the identification results show that the proposed algorithm can accurately identify the multiform fractures, which lays the foundation for calculating the mechanical parameters of rock masses.

Intracellular Dynamics of Human Cardiac Ventricular Myocyte: ORd Model

Human heart is elegantly articulated to mechanically contract in response to electrical excitation. Cardiac electrical activity may be described as a multiscale process from sub-cellular to cellular to tissue level. Ion movement at the cellular level through ion channels results in an action potential that propagates as an electrical wave in tissue. A first-principles-based mathematical description of the cellular-level dynamics of cardiac electrophysiological behavior provides a better understanding of the functioning of the heart. The mathematical models describing cellular dynamics often involve a coupled system of ordinary differential equations (ODEs) with variables including transmembrane voltage, ion concentrations and ion channel gating variables, whose evolution describes activation/inactivation of ion channels. In this study we discuss a mathematical model of the human ventricular myocyte (O’Hara–Rudy model), defined as a system of 41 ODEs, with variables involving membrane voltage, 29 gating variables describing activation of Na[Formula: see text], K[Formula: see text], Ca[Formula: see text] channels, 11 variables describing ion concentrations and Ca[Formula: see text] related flux. Runge–Kutta method with variable order and variable time step was adopted to solve the system numerically. We discuss the action potential (AP) profile of a healthy human ventricular myocyte and corresponding dominant ionic currents. We present a phase plot that describes the change in voltage and its rate as the system evolves over time. The phase plot seems to provide more details of the underlying events than the AP curve.

Motile bacteria leverage bioconvection for eco-physiological benefits in a natural aquatic environment.

Bioconvection, a phenomenon characterized by the collective upward swimming of motile microorganisms, has mainly been investigated within controlled laboratory settings, leaving a knowledge gap regarding its ecological implications in natural aquatic environments. This study aims to address this question by investigating the influence of bioconvection on the eco-physiology of the anoxygenic phototrophic sulfur bacteria community of meromictic Lake Cadagno. Here we comprehensively explore its effects by comparing the physicochemical profiles of the water column and the physiological traits of the main populations of the bacterial layer (BL). The search for eco-physiological effects of bioconvection involved a comparative analysis between two time points during the warm season, one featuring bioconvection (July) and the other without it (September). A prominent distinction in the physicochemical profiles of the water column centers on light availability, which is significantly higher in July. This minimum threshold of light intensity is essential for sustaining the physiological CO2 fixation activity of Chromatium okenii, the microorganism responsible for bioconvection. Furthermore, the turbulence generated by bioconvection redistributes sulfides to the upper region of the BL and displaces other microorganisms from their optimal ecological niches. The findings underscore the influence of bioconvection on the physiology of C. okenii and demonstrate its functional role in improving its metabolic advantage over coexisting phototrophic sulfur bacteria. However, additional research is necessary to confirm these results and to unravel the multiscale processes activated by C. okenii's motility mechanisms.

Unified gas-kinetic particle method for frequency-dependent radiation transport

This paper proposes a unified gas-kinetic particle (UGKP) method for the frequency-dependent photon transport process. The photon transport is a typical multiscale process governed by the nonlinear radiative transfer equations (RTE). The flow regime of photon transport varies from the ballistic regime to the diffusive regime with respect to optical depth and photon frequency. The UGKP method is an asymptotic preserving (AP) scheme and a regime adaptive scheme. The teleportation error is significantly reduced, and the computational efficiency is remarkably improved in the diffusive regime. Distinguished from the standard multigroup treatment, the proposed UGKP method solves the frequency space in a non-discretized way. Therefore the Rosseland diffusion system can be precisely preserved in the optically thick regime. Taking advantage of the local integral solution of RTE, the distribution of the emitted photon can be constructed from its macroscopic moments. The Monte Carlo particles in the UGKP method need only be tracked before their first collision events, and a re-sampling process is performed to close the photon distribution for each time step. The high computational cost of calculating excessive scattering events can be saved, especially in the optically thick regime. The proposed UGKP method is implicit and removes the light speed constraint on the time step. The particle tracking approach combining the implicit formulation makes the proposed UGKP method an efficient solution algorithm for frequency-dependent radiative transfer problems. We demonstrate with numerical examples the capability of the proposed multi-frequency UGKP method.

A multiscale electrochemo-mechanical model of porous electrode in lithium-ion batteries: The coupling of reaction and finite deformation

In this study, we present a novel multiscale electrochemo-mechanical model for porous electrodes in lithium-ion batteries (LIBs). Building upon the theory of finite deformation and the pseudo-two-dimensional framework introduced by Doyle et al. (Journal of the Electrochemical Society, 140, 6(1993)), our model incorporates the interaction between particle deformation and multiscale electrochemical processes, which has been lacking in previous research. The model is validated by the rate performance tests on SiO-based electrodes. By utilizing the model, we analyze the coupling effects among reaction distribution, porosity variation, and local geometrical distortion of the electrode at high current rates. We also investigate the relative importance of different electrochemo-mechanical coupling paths for materials with large volume expansion ratios. Comparing the comprehensive model with simplified versions, we find that diffusion stress is a priority due to its significant impact on ionic diffusion within particles. Porosity variation becomes equally significant at high current rates, as ionic transport in the electrolyte becomes the rate-limiting step. This work contributes to enhance understanding of the interplay between electrochemical and mechanical phenomena in LIB electrodes, especially for silicon-based materials.

Multiscale modeling of shape memory polymers foams nanocomposites

Shape memory polymer foams (SMPFs) are defined as lightweight cellular materials with the ability to recover their undeformed shape by applying an external stimulus like light, temperature, magnetic field, etc. Reinforcement of the material with nano-clay filler leads to a significant improvement in its physical properties. As a result, a wide range of applications is occupied by SMPFs, especially in industry and biomedical applications. Because of the heterogeneous nature of the SMPFs at different scale levels, modeling the material with one scale numerical model which able to capture all the effects of the lower scale material structure is really a complex task. Within this work, a full multiscale modeling approach is employed to investigate the thermomechanical response of the SMPFs. Furthermore, a combination of the 3D Representative Volume Element (RVE) and finite element method is used to explore the effects of the nano-filler weight fractions and the material's cellular structure at different length scales on the material dynamics. The multiscale modeling process gives great and promising results with good agreement with the experimental results. Moreover, the model shows relatively high sensitivity parameters that are initiated from the lower scale level.

Multiscale computational fluid dynamics modelling of spatial ALD on porous li-ion battery electrodes

The self-limiting surface reaction characteristic of atomic layer deposition (ALD) makes it ideal for the surface modification of electrode materials for lithium-ion batteries (LIBs). Spatial ALD shows promise as a scalable method for the coating on pre-fabricated electrode sheets. As a strong-coupled multiscale process, various process conditions and microstructure parameters have great influences on the macroscale fluid dynamics and the pore-scale diffusion–reaction process, thus affecting the coating efficiency. This study presents a multiscale numerical model that combines computational fluid dynamics (CFD) with multilevel pore-scale diffusion–reaction kinetics to explore the spatial ALD process on porous LIB electrodes. The dynamic mesh method is utilized to simulate electrode movement. The considerable active surface-to-volume ratio of the porous structure limits the precursor infiltration depth due to the low diffusion rate and inadequate precursor supply. As the electrode velocity increases, an asymmetric distribution of precursor concentration under the injector is observed with a rapid decrease. Elevating both the precursor concentration and inlet gas velocity augments the coating depth by enhancing the supply of the precursor. The experimental data aligns well with our numerical results, verifying the accuracy of the multiscale CFD model. Our observations reveal that a relatively lower operating pressure, around 0.1 atm, compared to 0.01 atm and 1 atm, optimizes the deposition rate along the electrode depth during the half-ALD cycle, especially when the pore size is larger. Electrode porosity of about 0.4 notably improves coating uniformity by elevating the precursor diffusion rate. Predictions show that with a substrate velocity of 0.2 m/s, the coating depth on an electrode having higher porosity at the top compared to the bottom via atmospheric spatial ALD could reach a depth of 38 μm with a precursor utilization of 78 %.

Diagenetic Evolution of Upper Cretaceous Kawagarh Carbonates from Attock Hazara Fold and Thrust Belt, Pakistan

A recent hydrocarbons discovery in 2021 in the Kawagarh Formation has brought attention to the significance of sedimentology and specifically diagenesis for understanding and characterizing the reservoir properties. The diagenetic history and multiscale processes that contributed to diagenesis were vaguely known. This study aimed to reconstruct various diagenetic phases, paragenetic sequences, and the interrelationship of these phases in the Kawagarh Formation. The diagenetic processes were identified and characterized through an integrated methodology utilizing the outcrop, petrographic, and geochemical analyses. Early calcite cementation was found to occur in the early stages of marine burial diagenesis involving pore fluid originating from the dissolution of aragonite in interlayer marl/mudstone beds and reprecipitating as microspar in adjacent limestone beds. The absence of mechanical compaction in wackstone and mudstone facies and the presence of late compaction in lithified packstones clearly imply that early calcite cementation occurred prior to compaction. Dolomitization with stylolites coupled with significant negative oxygen (δ18O) isotope values implies a fault-related hydrothermal dolomitization model. Uplift introduced the fractures and low Mg fresh fluids to the system which caused calcitisation in shallow burial settings. The depleted δ13C and negative δ18O values indicate the mixing of surface-derived waters with hot burial fluids during the calcitization. This study offers valuable insights into several aspects related to the formation and the basin itself, including burial depths, fluid influx, and geochemical gradients. It also sheds light on the evolution of reservoir properties such as porosity and permeability in dolomitization fronts. Such insights can be used to gain a deeper understanding about the burial history, basin evaluation, and reservoir characterization for hydrocarbon exploration.

Steric heights of submesoscale processes and internal gravity waves in the subtropical northwestern Pacific and northern South China Sea as revealed by moored observations

The Surface Water and Ocean Topography (SWOT) altimeter mission provides a good opportunity to globally detect submesoscale processes (submesoscales) from high-resolution sea surface height (SSH) measurements. However, in addition to the balanced mesoscale eddies and submesoscales, the measured SSH also contains signals of unbalanced internal gravity waves (IGWs) including internal tides (ITs) and supertidal IGWs (IGWs with frequencies of 3–8 cycles per day). Therefore, a quantitative understanding of the contributions of the above multiscale dynamic processes to the SSH is a prerequisite for exploring submesoscales from the SWOT-derived SSH data. Here, this issue was investigated through analyzing one-year moored observation-derived near-surface steric heights (SHs) in the subtropical northwestern Pacific (NWP) and northern South China Sea (SCS) which have different dynamic backgrounds. In the sense of annual mean, the root-mean-squared (RMS) SHs of submesoscales (defined as processes with periods of 2–15 days here) and IGWs in the SCS are 1.30 and 5.54 cm, respectively, while they are only 0.70 and 3.19 cm in the NWP. Seasonally, the RMS SH of submesoscales is larger in winter than summer in both regions; with respect to the IGWs, their RMS SH is larger in summer than winter in the SCS but it is opposite in the NWP. Generally, the SHs of diurnal and semidiurnal ITs are dominated by the stationary component in both regions but the proportion of the nonstationary component is much larger in the NWP than the SCS. The sum of the SHs by nonstationary diurnal and semidiurnal ITs exceeds that of submesoscales except in winter in the SCS. Furthermore, we also found that the SHs of supertidal IGWs are always larger than those of submesoscales in both regions and both seasons. By decomposing the IGWs with frequencies of 1–8 cpd (i.e., cycles per day) into different baroclinic modes, we found that for all of the IGWs bands, the SHs are overwhelmingly attributed to the first mode. The results in this study will provide useful information for the application of SWOT data in regions with active IGWs.

How fluctuation intensity flux drives SOL expansion

Predictions of heat load widths λq based on particle orbits alone are very pessimistic. This paper shows that pedestal peeling-ballooning (P-B) magnetohydrodynamic (MHD) turbulence broadens the stable scrape-off layer (SOL) by the transport, or spreading, of fluctuation energy from the pedestal. λq is seen to increase with Γε , the fluctuation energy density flux. We elucidate the fundamental physics of the spreading process. Γε increases with pressure fluctuation correlation length. P-B turbulence is seen to be especially effective at spreading, on account of its large effective mixing length. Spreading is shown to be a multiscale process, which is enhanced by the synergy of large and small-scale modes. Pressure fluctuation skewness correlates well with the spreading flux–with the zero crossing of skewness and Γε spatially coincident–suggesting the role of coherent fluctuation structures and the presence of intermittency in λq broadening. λq∼Bp−1 scaling persists for the broadened SOL. We show that the spreading flux increases for increasing pedestal pressure gradient ∇P0 and for decreasing pedestal collisionality υped∗ . This trend is due to the dominance of peeling modes for large ∇P0 and low υped∗ . Ultimately, we see that a state of weak MHD turbulence, as for small ELMs, is very attractive for heat load management. Our findings have transformative implications for future fusion reactor designs and call for experimental investigations to validate the observed trends.

Multiscale Process Modeling of Semicrystalline PEEK for Tailored Thermomechanical Properties.

Polyether ether ketone (PEEK) is a semicrystalline thermoplastic that is used in high-performance composites for a wide range of applications. Because the crystalline phase has a higher mass density than that of the amorphous phase, the evolution of the crystalline phase during high-temperature annealing processing steps results in the formation of residual stresses and laminate deformations, which can adversely affect the composite laminate performance. Multiscale process modeling, utilizing molecular dynamics, micromechanics, and phenomenological PEEK crystal kinetic laws, is used to predict the evolution of volumetric shrinkage, elastic properties, and thermal properties, as a function of crystalline phase evolution, and thus annealing time, in the 306-328 °C temperature range. The results indicate that lower annealing temperatures in this range result in a faster evolution of thermomechanical properties and shrinkage toward the pure crystalline values. Therefore, from the perspective of composite processing, it may be more advantageous to choose the higher annealing rates in this range to slow the volumetric shrinkage and allow PEEK stress relaxation mechanisms more time to relax internal residual stresses in PEEK composite laminates and structures.

An implicit unified gas-kinetic wave–particle method for radiative transport process

The unified gas-kinetic wave–particle method (UGKWP) has been developed for the multiscale gas, plasma, and multiphase flow transport processes for the past years. In this work, we propose an implicit UGKWP (IUGKWP) method to remove the Courant–Friedrichs–Lewy time step constraint. Based on the local integral solution of the radiative transfer equation (RTE), the particle transport processes are categorized into the long-λ streaming process and the short-λ streaming process compared to a local physical characteristic time tp. In the construction of the IUGKWP method, the long-λ streaming process is tracked by the implicit Monte Carlo method; the short-λ streaming process is evolved by solving the implicit moment equations; and the photon distribution is closed by a local integral solution of RTE. In the IUGKWP method, the multiscale flux of radiation energy and the multiscale closure of photon distribution are constructed based on the local integral solution. The IUGKWP method preserves the second-order asymptotic expansion of RTE in the optically thick regime and adapts its computational complexity to the flow regime. The numerical dissipation is well controlled, and the teleportation error is significantly reduced in the optically thick regime. The computational complexity of the IUGKWP method decreases exponentially as the Knudsen number approaches zero, and the computational efficiency is remarkably improved in the optically thick regime. The IUGKWP is formulated on a generalized unstructured mesh, and multidimensional 2D and 3D algorithms are developed. Numerical tests are presented to validate the capability of IUGKWP in capturing the multiscale photon transport process. The algorithm and code will apply in the engineering applications of inertial confinement fusion.

FitMultiCell: simulating and parameterizing computational models of multi-scale and multi-cellular processes.

Biological tissues are dynamic and highly organized. Multi-scale models are helpful tools to analyse and understand the processes determining tissue dynamics. These models usually depend on parameters that need to be inferred from experimental data to achieve a quantitative understanding, to predict the response to perturbations, and to evaluate competing hypotheses. However, even advanced inference approaches such as approximate Bayesian computation (ABC) are difficult to apply due to the computational complexity of the simulation of multi-scale models. Thus, there is a need for a scalable pipeline for modeling, simulating, and parameterizing multi-scale models of multi-cellular processes. Here, we present FitMultiCell, a computationally efficient and user-friendly open-source pipeline that can handle the full workflow of modeling, simulating, and parameterizing for multi-scale models of multi-cellular processes. The pipeline is modular and integrates the modeling and simulation tool Morpheus and the statistical inference tool pyABC. The easy integration of high-performance infrastructure allows to scale to computationally expensive problems. The introduction of a novel standard for the formulation of parameter inference problems for multi-scale models additionally ensures reproducibility and reusability. By applying the pipeline to multiple biological problems, we demonstrate its broad applicability, which will benefit in particular image-based systems biology. FitMultiCell is available open-source at https://gitlab.com/fitmulticell/fit.

A novel multiscale process simulation to predict the impact of intrinsic heat treatment on local microstructure gradients and bulk hardness of AISI 4140 manufactured by laser powder bed fusion

ABSTRACT Although finite element model based process simulations for the laser powder bed fusion additive manufacturing process have become more common in the recent years, the proposed approaches are often only viable for materials without complex phase transformations. Process simulations for materials such as the quench and tempering steel AISI 4140 typically lead to higher computational cost due to the finer mesh and time steps needed for more complex material models. This study proposes a novel multiscale approach to combine the advantages of the macroscale and mesoscale models into one framework, in order to reduce computational cost while retaining the high accuracy. The implementation of these multiscale methods was validated by experimentally analyzing multiple parameter combinations regarding bulk hardness and local microstructure differences. The results show an accurate prediction of bulk hardness and localised tempering effects while reducing the computational cost in order to simulate the component scale.

Weighted IMF-based denoising and multi-scale kurtosis weighted K singular value decomposition dictionary learning model for bearing fault diagnosis

Generally, bearing early failure-induced impulses is weak and easily submerged by strong background noise. How to extract weak impulses is a big challenge. In this paper, based on the K Singular Value Decomposition (KSVD) algorithm, an improved dictionary learning model (WID-MSKW-KSVD) is proposed, which integrates a special denoising method and a multi-scale transformation into a typical dictionary learning model and it successfully overcomes two major shortcomings of traditional KSVD model including the lack of multi-scale learning process and the ability of dictionary atoms to resist strong noise interference. Furthermore, the kurtosis of the envelope spectrum is weighted into the proposed model as a fault matrix to enhance the fault feature. Finally, the feasibility of the proposed model is demonstrated by numerical simulation and practical experiments, and the superiority and reliability of this model in suppressing noise interference and extracting fault features are verified by comparison with other state-of-the-art methods.

Multi-scale study of turbulent mass transfer process in different shaped-adsorbent packed bed

Insufficient consideration of the coupled effect of multi-scale and turbulent mass transfer impedes thorough understanding of the dynamic adsorption process in the fixed bed. To solve this problem, a novel PR-CMT model, integrated particle resolution method and computational mass transfer theory, is proposed and verified to simulate the gas adsorption process on particles by combining the computational mass transfer method and the particle dissolution method. The proposed model not only rigorously accounts for the turbulent diffusion by incorporating the equation of concentration variance c2‾ and concentration variance dissipation εc, but also establishes connections between different scales of fixed-bed particle-fluid systems. Through this work, it is found that there is a preferred fluid path in the particle filling structure, which can lead to significant changes in local mass transfer performance. Additionally, the turbulent diffusion enhances the mass and heat transfer between the gas and particles, resulting in a more uniform distribution in the radial direction. Using the proposed model, the effects of the adsorbent characteristics on the multi-scale adsorption process, such as the adsorbent porosity and shape, are further examined. The results indicate that increasing the adsorbent porosity leads to enhanced overall adsorption rate in the fixed bed, and the turbulent diffusion is strongest in the fixed bed with cylinder adsorbents.

CPA-YOLOv7: Contextual and pyramid attention-based improvement of YOLOv7 for drones scene target detection

Target detection in unmanned aerial vehicle application scenarios has other problems, such as dense targets. The existing unmanned aerial vehicle target detection model with high computational complexity makes it difficult to meet real-time unmanned aerial vehicle target detection, and the detection accuracy of small targets is low. To address these problems, we propose an improved YOLOv7 small target detection model based on context and pyramidal attention that can cope with dense unmanned aerial vehicle scenarios - CPA-YOLOv7. This model embeds our proposed lightweight multi-scale attentional feature spatial pyramid pooling module, which can better distinguish between small and large target features, reducing the computational effort while improving the detection accuracy of the model. Secondly, we design a contextual dynamic fusion attention module in the network to fuse global and local contextual information and dynamically assign features to multiple groups of channels; in the multi-scale fusion process, it effectively increases the characterization ability of small target features and enables the network to better focus on small target information. Finally, we improve Wise-Intersection-over-Union loss as the regression loss function, add a moderation factor to retain some of the high and low-quality sample weights to improve the regression accuracy of high-quality anchor frames, and use the dynamic non-monotonic focusing mechanism to increase the model's focus on ordinary quality anchor frames to improve the model's localization performance and robustness to low-quality samples. Numerous experimental results show that on the unmanned aerial vehicle datasets VisDrone2021-DET and AI-TOD, the mAP values of our model are 2.3% and 1.1% higher than those of the YOLOv7 model with fewer parameters introduced, and the computational speed reaches 146 frames per second (FPS), which can meet the real-time requirements of unmanned aerial vehicle detection.

Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles

A multi-scale physical process management system is presented in this paper, taking the plug-in hybrid electric vehicle system as the physical interface connecting the macro traffic system to the micro energy conversion process, with the ultimate goal of global energy management in the full temporal–spatial domain for autonomous plug-in hybrid electric vehicles. This novel method adopts a macro traffic flow model at a large time scale, in which only the initial conditions and the traffic information of key road sections are required, and a car following model at the micro scale. Furthermore, local replanning of energy management is carried out by adjusting the power threshold and the efficiency weight through the type of reinforcement learning that is closest to human learning, once a short term speed disturbance is induced by unknown disturbances in the macro traffic flow. Due to the nonlinear relationship between speed fluctuation and power fluctuation, it is necessary to map the vehicle speed and acceleration characteristics to the power characteristics, instead of directly utilizing the traffic model characterized by the speed and acceleration characteristics. The results show that novel multi-scale physical management can achieve a smaller deviation from the global optimal solution and enhanced robustness of global energy management. Additionally, close coupling between the dynamic characteristics of vehicle components and speed fluctuation ensures correct tracking of the optimized target value.

Harnessing mechanical cues in the cellular microenvironment for bone regeneration.

At the macroscale, bones experience a variety of compressive and tensile loads, and these loads cause deformations of the cortical and trabecular microstructure. These deformations produce a variety of stimuli in the cellular microenvironment that can influence the differentiation of marrow stromal cells (MSCs) and the activity of cells of the MSC lineage, including osteoblasts, osteocytes, and chondrocytes. Mechanotransduction, or conversion of mechanical stimuli to biochemical and biological signals, is thus part of a multiscale mechanobiological process that drives bone modeling, remodeling, fracture healing, and implant osseointegration. Despite strong evidence of the influence of a variety of mechanical cues, and multiple paradigms proposed to explain the influence of these cues on tissue growth and differentiation, even a working understanding of how skeletal cells respond to the complex combinations of stimuli in their microenvironments remains elusive. This review covers the current understanding of what types of microenvironmental mechanical cues MSCs respond to and what is known about how they respond in the presence of multiple such cues. We argue that in order to realize the vast potential for harnessing the cellular microenvironment for the enhancement of bone regeneration, additional investigations of how combinations of mechanical cues influence bone regeneration are needed.

Probing Localised Corrosion Inhibition of AA2024-T3 by Integrating Electrode Array, SVET, SECM, and SEM-EDS Techniques

This work demonstrates an approach towards the understanding of multi-scale and open-circuit localised electrochemical processes of AA2024-T3 in the presence and absence of an environmentally friendly rare-earth inhibitor; cerium diphenyl phosphate (Ce(dpp)3). At high temporal resolution, a wire bean electrode (WBE) made from 100 identical AA2024-T3 wires revealed sudden increases in galvanic anodic and cathodic activities immediately after dosing of 50 and 100 ppm of the inhibitor and an overall suppression of macro-scale activities by increasing the inhibitor concentration to 200 ppm, suggesting it as a fast-screening tool for inhibitors and measuring inhibition efficiency. At high spatial resolutions, scanning probe electrochemical techniques confirmed local activation of corroding microstructures on individual AA2024-T3 wires similarly by dosing the inhibitor up to 100 ppm. In agreement with WBE findings, the effective shutdown of both anodic and cathodic activities occurred after increasing the inhibitor concentration to 200 ppm confirming the optimal concentration of the Ce(dpp)3 and the mixed mode inhibition mechanism of this selected inhibitor on AA2024-T3.