Multi-physics Phenomena Research Articles

Modeling kinetic effects of charged vacancies on electromechanical responses of ferroelectrics: Rayleighian approach

Understanding the time-dependent effects of charged vacancies on the electromechanical responses of materials is at the forefront of research for designing materials exhibiting metal-insulator transitions and memristive behavior. A Rayleighian approach is used to develop a model for studying the nonlinear kinetics of the reaction leading to generation of vacancies and electrons via the dissociation of vacancy-electron pairs. Also, diffusion and elastic effects of charged vacancies are considered to model polarization-electric potential and strain-electric potential hysteresis loops. The model captures multiphysics phenomena by introducing couplings among polarization, the electric potential, stress, strain, and concentrations of charged (multivalent) vacancies and electrons (treated as classical negatively charged particles), where the concentrations can vary due to association-dissociation reactions. A derivation of coupled time-dependent equations based on the Rayleighian approach is presented. Three limiting cases of the governing equations are considered, highlighting the effects of (1) nonlinear reaction kinetics on the generation of charged vacancies and electrons, (2) Vegard's law (i.e., the concentration-dependent local strain) on asymmetric strain-electric potential relations, and (3) coupling between a fast component and the slow component of the net polarization on the polarization-electric-field relations. The Rayleighian approach discussed in this work should pave the way for developing a multiscale modeling framework in a thermodynamically consistent manner while capturing multiphysics phenomena in ferroelectric materials. Published by the American Physical Society 2025

Machine Learning-Driven Analysis of Heat Transfer in Chemically Reactive Fluid Flow Considering Soret-Dufour Effects

ObjectiveThe study investigates tangent hyperbolic nanofluid flow across an extended media, emphasizing magnetohydrodynamic influences, mixed convection, and nanoparticle transport with the help of an innovative and advanced technique. MethodologyIt employs a sophisticated Gaussian Neural Network and Hybrid Cuckoo Search to mimic complex fluid dynamics accurately, ensuring efficient computation and solution precision. By transforming the governing system of nonlinear partial differential equations into ordinary differential equations via similarity transformation, the methodology attains computational efficiency and solution precision. Core FindingsStatistical measures confirm the model's resilience, indicating absolute error variations from E−05 to E−12 and mean squared errors continuously between E−02 − E−07, highlighting the reliability of the model, while the Error in Nash-Sutcliffe Efficiency values fall within the interval E−05 − E−15. The Theil's Inequality Coefficient values lie in the range of E−01 − 10−07. Future Direction and ApplicationsThe findings underscore the model's capacity to improve thermal management, energy efficiency, and process dependability in sectors like chemical processing, environmental engineering, and sustainable energy production. This study positions the Gaussian Neural Networks framework as a robust computational instrument, providing a foundation for subsequent investigations into intricate geometries, multi-physics phenomena, and magnetohydrodynamic systems for novel technology applications.

A Differentiable Physics-Informed Machine Learning Approach to Model Laser-based Micro-Manufacturing Process

Abstract Advanced manufacturing processes are often based on complex multiphysics phenomena that are either poorly understood or are computationally too expensive to simulate in the context of process design, control or planning. Traditionally, simplified physics models with prescribed heuristics or purely data-driven surrogate models are used as alternatives in such applications. The concept of physics-informed machine learning (PIML) has been shown to have unique advantages over both of these alternatives in various fields of complex system analysis. In this paper, a new physics-informed machine learning (PIML) approach is presented to model the geometry of the cut produced by a Magnetically-assisted Laser Induced Plasma Micro Machining (M-LIPMM) process. This PIML architecture uses a neural network to auto-adapt the parametric boundary condition and physical properties used in a simplified finite difference based physics model (of 2D heat conduction), as a function of the inputs namely the laser settings. This network also estimates the scaling and shifting parameters used by a convolutional neural network that takes the temperature profile predicted by the simplified heat conduction model to predict the width and depth of the machined cut. Trained on physical experiment data, the PIML approach compares favorably to a pure data-driven neural network model in extrapolation tests, while also providing physical insights (that the latter cannot). The PIML approach also provides an 85% better accuracy overall compared to the simplified physics model with heuristic settings.

Mathematical basis and toolchain for hierarchical optimization of biochemical networks.

Biological signalling systems are complex, and efforts to build mechanistic models must confront a huge parameter space, indirect and sparse data, and frequently encounter multiscale and multiphysics phenomena. We present HOSS, a framework for Hierarchical Optimization of Systems Simulations, to address such problems. HOSS operates by breaking down extensive systems models into individual pathway blocks organized in a nested hierarchy. At the first level, dependencies are solely on signalling inputs, and subsequent levels rely only on the preceding ones. We demonstrate that each independent pathway in every level can be efficiently optimized. Once optimized, its parameters are held constant while the pathway serves as input for succeeding levels. We develop an algorithmic approach to identify the necessary nested hierarchies for the application of HOSS in any given biochemical network. Furthermore, we devise two parallelizable variants that generate numerous model instances using stochastic scrambling of parameters during initial and intermediate stages of optimization. Our results indicate that these variants produce superior models and offer an estimate of solution degeneracy. Additionally, we showcase the effectiveness of the optimization methods for both abstracted, event-based simulations and ODE-based models.

Three-Dimensional Model of Ionic Species Concentration and Flux During Localised Corrosion of Steel in Marine Environment

Localized corrosion, exemplified by pitting and crevice corrosion, presents a significant challenge in industrial and marine settings due to the presence of chloride ions in seawater, brines and industrial fluids. It initiates and spreads at specific points on metal surfaces and its hidden rapid metal degradation beneath intact surface layers complicates detection and monitoring, increasing the risk of unforeseen structural failures and jeopardizing critical infrastructure integrity. Factors influencing its severity include chloride concentration, pH levels, temperature variations and aggressive ion presence, which degrade protective oxide layers and expose metal to accelerated corrosion. This study aims to enhance predictive capabilities to effectively manage corrosion in chloride-rich environments. Using COMSOL Multiphysics software, this study models a microscale geometry representing a micropit and simulates chloride-induced localized corrosion in steel, by integrating Nernst-Planck equations to incorporate electrochemical reactions, ion transport dynamics and three-dimensional geometrical features. Key parameters such as chloride concentration, pH and material properties are crucial in the modelling approach to simulate the formation and growth of corrosion sites. For 0 to 800 seconds simulation, the model displays the flux of ionic species into and out of the pit. At 800 second, the results demonstrate a peak of Fe2+ concentration of approximately 2.53×103 mol/m3 at the deepest section, the Cl- of 7×103 mol/m3 and an increase in H+ inside the pit, reducing the pH from 8 to about 4.9. These concentration changes indicate substantial metal dissolution is actively occurring inside the pit, making COMSOL Multiphysics software a versatile platform for coupling multiphysical phenomena, enabling comprehensive analysis and 3D visualization of corrosion processes.

Performance analysis of heat pipe micro-reactor with Stirling engine based on full-scope multi-physics coupled simulation

Heat pipe micro-reactors (HPMRs) with Stirling engines for power conversion are emerging as a promising surface power technology. We developed a comprehensive multi-physics solver using the open-source finite-volume code OpenFOAM to perform high-fidelity analysis of HPMR systems’ steady-state and dynamic performance. The solver couples a 3D coarse-mesh multi-group neutron diffusion model with a 3D fine-mesh thermal conduction model using a mapping approach to accurately simulate multi-physics phenomena in the reactor core. Additionally, it integrates a 2D high-temperature heat pipe model as boundary conditions for heat transfer in the core, a 1D Stirling engine model for energy conversion, and a lumped parameter radiator model for heat dissipation. Applied to the HOMER-15 reactor system, the solver aided to identify the optimal working point of the system, achieving 3 kW of electric power with a conversion efficiency of 25.2 % under 20-Hz Stirling engine frequency and 3.34-MPa pressure. The transient full-scope simulation with the solver shows that the load-follow strategy based on gas charging and venting in Stirling engines is capable to accommodate 35 % step increase of external load. When the unexpected reactivity insertion is within 0.1$, the peak temperature in the reactor core is below the allowable limit temperature. Analysis of single failure of Stirling engines indicates that the system safety and reliability can be enhanced by either incorporating an appropriate number of engines or enhancing heat transfer between Stirling engine channels.

Development of a multi-physics numerical model for a multi-component thermoelectric generator with discontinuous porosity in the exhaust gas channel

A critical technical challenge in the energy harvesting performance of a thermoelectric generator (TEG) is the optimal utilization of the thermal energy from exhaust gas. In most TEG applications, the exhaust gas flowing through the exhaust channel exhibits high inertia forces, causing significant temperature maldistribution on the hot surfaces of thermoelectric modules (TEMs). To address this challenge, we developed a numerical model that incorporates the primary multi-physics phenomena associated with TEGs, designed with a porous flow conditioner and extended surfaces for practical use. A reliable three-zone method is employed to simulate thermoelectric energy conversion phenomena, including the resulting heat pumping and Joule heating effects in individual TEMs. The model accounts for the communication effect between electrically connected TEMs by integrating Kirchhoff’s voltage and current laws into the solving process. A selective porous media method is proposed for accurately analyzing the pressure jump induced by the flow conditioner and the porosity jump occurring at the interfaces between the flow conditioner and plate fins while ensuring model convergence reliability. The developed numerical model aligns with experimental results, showing a maximum error of 5 %. Comprehensive analyses of TEM-wise heat absorption rates and channel-wise flow distribution characteristics were conducted to identify the optimal position and porosity of the flow conditioner. The findings demonstrate that optimal use of the flow conditioner improves the net output power of the TEG by 31.5 % compared to the case without any flow conditioner.

Analysis of Entropy Generation Due to Heat and Mass Transfer in Porous Electrode of PEFC

During operation of polymer electrolyte fuel cells (PEFCs), the complicated multi-physics phenomena including heat and mass transfer, ionic and electronic transport and electrochemical reaction simultaneously occur in porous electrodes. It has been well known that the kinetic-theory based oxygen reduction reaction (ORR) and two-phase transport within cathode electrodes cause the severe overpotentials and performance degradation. In the previous studies, the energy loss in fuel cells and electrochemical devices has been experimentally investigated by using electrochemical impedance spectroscopy (EIS) and classified into the activation, ohmic and concentration polarization. EIS technique is a powerful method to analyze the sources of their performance loss, however, it cannot locally identify the key factors and transport processes causing the loss. To further enhance the power density and efficiency of PEFCs, it’s necessary to discriminate the critical phenomena influencing on the performance degradation using an alternative approach and design the better electrode/channel structure for reducing the energy loss. Several researchers attempted to apply the analysis of entropy generation to the elucidation of performance loss mechanisms in fuel cells and electrochemical devices [1-3]. Entropy is a physical property representing the thermodynamic irreversibility. The entropy generation analysis enables to identify the dominant phenomena inducing the overpotential in such devices and contributes to design the optimum structure minimizing the energy dissipation. Kjelstrup and Røsjorde [1] numerically estimated the local entropy production in various layers of a one-dimensional PEFC and found that the dissipation of energy due to charge transport in the electrolyte membrane is more important than the that of diffusion in the electrode. The author introduced the entropy generation analysis into the two-phase flow simulation for the cathode diffusion media of a PEFC and succeeded to locally estimate the entropy production rate due to oxygen diffusion within the porous electrode in the past work [3]. This study further analyzed the distributions of entropy production rate due to the heat transfer, diffusion and electrochemical reaction in the cathode porous electrode in more detail using the conventional two-phase flow PEFC model and extracted the significant process affecting the cathode overpotential. The author also investigated the effect of land/channel geometry on the local entropy generations of these phenomena in the electrode and discussed how to design its structure reducing the entropy generation.Fig. 1 shows the numerical results of the two-phase flow simulation and the entropy generation analysis in the cathode electrode. The two-phase flow model used in this study was developed by Natarajan and Nguyen [4]. (a), (b) and (c) denote the through-plane distribution of temperature across the cell and the profiles of oxygen concentration and water saturation in the cathode gas diffusion layer (GDL), respectively. (d), (e) and (f) are the distributions of entropy production rate due to heat flux, oxygen diffusion and ORR in the electrode. The fuel cell was operated for 50 s at the temperature of 80 oC and the current density of 1.0 A/cm2. It was noted that the entropy generation due to the electrochemical reaction gives a larger effect on the cathode overpotential than that of diffusion and heat transfer. That is very well known. The entropy production rate of ORR at the catalyst layer under the channel is larger than that under the land because the sufficient oxygen supply progresses the reaction more actively. Furthermore, the land/channel geometry has an influence on the entropy generation of diffusion and heat transfer in the cathode electrode. The entropy production rates due to oxygen diffusion and heat flux become remarkably high under the boundary between the channel and land owing to the large gradients of its concentration and temperature. To reduce the entropy generation in the porous electrode with the land/channel configuration, it’s necessary to design the cell structure uniformizing the reaction distribution and alleviating the gradients of current, concentration and temperature.

Electromagnetic–thermal–mechanical coupling analysis of bent rotor straightening via electromagnetic induction heating

Abstract Rotors of steam turbines in power plants can be locally deformed by undesired situations, such as rubbing between the rotors and stationary parts. A straightening process is required to correct bending without causing additional damage because a rotor bending displacement of approximately 0.15 mm can stop turbine unit operation. In this study, a numerical framework was established to simulate the straightening process using electromagnetic induction heating, which is straightforward and economical among the methods for straightening bent rotors. The straightening process involves complex coupling of electromagnetic, thermal, and mechanical phenomena. For efficiency, sequential coupling was used in the simulations, dividing the multiphysics phenomena into electromagnetic–thermal and thermal–mechanical fields. The temperature distributions resulting from electromagnetic induction heating were calculated through two-way coupling of the electromagnetic–thermal analysis. The thermal deformations of the rotors were obtained by solving the coupled equations for the thermal field obtained from the electromagnetic–thermal analysis and the mechanical field. Using the established numerical framework, the thermal–mechanical behaviors and straightening mechanisms of bent rotors were investigated. Furthermore, the effects of process parameters, including the direction of gravity and heating and cooling conditions, on the straightening performance were determined. Appropriate parameters were identified to achieve the desired straightening performance with final bending displacements of less than 0.1 mm for bent rotors with initial bending displacements of 0.15–0.3 mm. For a rotor made of A182 F11 Class 2, the best straightening performance was obtained by heating the rotor to a maximum temperature of 650 °C for 20 h under insulation, followed by natural cooling. The simulation results revealed that the straightening performance can be improved when the rotor is rapidly heated to a high maximum temperature and cooled immediately, as long as the temperature conditions do not cause phase transformation or unintended plastic deformation of the bent rotors.

Defect dynamics modeling of mesoscale plasticity

The collective motion of defects and their interaction are the basic building blocks for plastic deformation and corresponding mechanical behaviors of crystalline metals. Especially, dislocations among various defects are the “carrier” of plastic deformation in many crystalline materials, particularly ductile materials. To get a fundamental understanding of plastic deformation mechanisms, it calls for an integrated computational platform to simultaneously capture detailed defects characteristics across several length scales together with corresponding macroscopic mechanical response. In this paper, we present a three-dimensional mesoscale defect dynamics model to directly couple the three dimensional discrete dislocation dynamics model with continuum finite element method, aiming at capturing both size dependent plasticity at micron-, and submicron scale and constitutive behaviors at larger scales where such size-dependence disappear. Using non-singular dislocation theories, our model could accurately consider both short- and long-range elastic interactions between multiple dislocation segments with even higher computational efficiency than traditional dislocation dynamics simulations, together with the careful consideration of crystal/material rotation in the coupled framework. In addition, our model could directly model dislocation nucleation from stress concentrators such as a void, crack and indentor tip, which could allow us to investigate various defects’ motion and their mutual interactions, predicting macroscopic mechanical response of complex structures. The developed concurrently coupled model could also consider multiphysical phenomena by solving coupled governing equations in finite element framework, which could shed light on complex defect behaviors under various physical environments.

Numerical investigation of spreading phenomena of molten corium using EISPH method

Understanding the behavior of molten corium is crucial for effective heat management and ensuring the integrity of reactor containment during severe nuclear incidents. Corium spreading is a complex multi-physics phenomenon influenced by hydrodynamics, heat transfer, and phase change. The Smoothed Particle Hydrodynamics (SPH) method is utilized to study corium flow, leveraging its ability to handle complex fluid dynamics and its compatibility with parallel computing architectures. This research examines two specific corium spreading cases: the VULCANO VE-U7 experiment, which is characterized by a wide mushy zone, and the FARO L26S experiment, noted for its narrow mushy zone. The study compares spreading lengths and temperature profiles over time with experimental data, offering a detailed analysis of the observed spreading behaviors. Variations in physicochemical properties result in distinct spreading patterns between the cases, highlighting the complexity of corium behavior under different scenarios. The findings demonstrate the applicability and capability of the SPH method in capturing various characteristics of corium spreading effectively.

Data-driven reduced order surrogate modeling for coronary in-stent restenosis

Background:The intricate process of coronary in-stent restenosis (ISR) involves the interplay between different mediators, including platelet-derived growth factor, transforming growth factor-β, extracellular matrix, smooth muscle cells, endothelial cells, and drug elution from the stent. Modeling such complex multiphysics phenomena demands extensive computational resources and time. Methods:This paper proposes a novel non-intrusive data-driven reduced order modeling approach for the underlying multiphysics time-dependent parametrized problem. In the offline phase, a 3D convolutional autoencoder, comprising an encoder and decoder, is trained to achieve dimensionality reduction. The encoder condenses the full-order solution into a lower-dimensional latent space, while the decoder facilitates the reconstruction of the full solution from the latent space. To deal with the 5D input datasets (3D geometry + time series + multiple output channels), two ingredients are explored. The first approach incorporates time as an additional parameter and applies 3D convolution on individual time steps, encoding a distinct latent variable for each parameter instance within each time step. The second approach reshapes the 3D geometry into a 2D plane along a less interactive axis and stacks all time steps in the third direction for each parameter instance. This rearrangement generates a larger and complete dataset for one parameter instance, resulting in a singular latent variable across the entire discrete time-series. In both approaches, the multiple outputs are considered automatically in the convolutions. Moreover, Gaussian process regression is applied to establish correlations between the latent variable and the input parameter. Results:The constitutive model reveals a significant acceleration in neointimal growth between 30−60 days post percutaneous coronary intervention (PCI). The surrogate models applying both approaches exhibit high accuracy in pointwise error, with the first approach showcasing smaller errors across the entire evaluation period for all outputs. The parameter study on drug dosage against ISR rates provides noteworthy insights of neointimal growth, where the nonlinear dependence of ISR rates on the peak drug flux exhibits intriguing periodic patterns. Applying the trained model, the rate of ISR is effectively evaluated, and the optimal parameter range for drug dosage is identified. Conclusion:The demonstrated non-intrusive reduced order surrogate model proves to be a powerful tool for predicting ISR outcomes. Moreover, the proposed method lays the foundation for real-time simulations and optimization of PCI parameters.

A comprehensive numerical procedure for high-intensity focused ultrasound ablation of breast tumour on an anatomically realistic breast phantom.

High-Intensity Focused Ultrasound (HIFU) as a promising and impactful modality for breast tumor ablation, entails the precise focalization of high-intensity ultrasonic waves onto the tumor site, culminating in the generation of extreme heat, thus ablation of malignant tissues. In this paper, a comprehensive three-dimensional (3D) Finite Element Method (FEM)-based numerical procedure is introduced, which provides exceptional capacity for simulating the intricate multiphysics phenomena associated with HIFU. Furthermore, the application of numerical procedures to an anatomically realistic breast phantom (ARBP) has not been explored before. The integrity of the present numerical procedure has been established through rigorous validation, incorporating comparative assessments with previous two-dimensional (2D) simulations and empirical data. For ARBP ablation, the administration of a 0.1 MPa pressure input pulse at a frequency of 1.5 MHz, sustained at the focal point for 10 seconds, manifests an ensuing temperature elevation to 80°C. It is noteworthy that, in contrast, the prior 2D simulation using a 2D phantom geometry reached just 72°C temperature under the identical treatment regimen, underscoring the insufficiency of 2D models, ascribed to their inherent limitations in spatially representing acoustic energy, which compromises their overall effectiveness. To underscore the versatility of this numerical platform, a simulation of a more clinically relevant HIFU therapy procedure has been conducted. This scenario involves the repositioning of the ultrasound focal point to three separate lesions, each spaced at 3 mm intervals, with ultrasound exposure durations of 6 seconds each and a 5-second interval for movement between focal points. This approach resulted in a more uniform high-temperature distribution at different areas of the tumour, leading to the ablation of almost all parts of the tumour, including its verges. In the end, the effects of different abnormal tissue shapes are investigated briefly as well. For solid mass tumors, 67.67% was successfully ablated with one lesion, while rim-enhancing tumors showed only 34.48% ablation and non-mass enhancement tumors exhibited 20.32% ablation, underscoring the need for multiple lesions and tailored treatment plans for more complex cases.

Assessment of Machine Learning Techniques for Simulating Reacting Flow: From Plasma-Assisted Ignition to Turbulent Flame Propagation

Combustion involves the study of multiphysics phenomena that includes fluid and chemical kinetics, chemical reactions and complex nonlinear processes across various time and space scales. Accurate simulation of combustion is essential for designing energy conversion systems. Nonetheless, due to its multiscale, multiphysics nature, simulating these systems at full resolution is typically difficult. The massive and complex data generated from experiments and simulations, particularly in turbulent combustion, presents both a challenge and a research opportunity for advancing combustion studies. Machine learning facilitates data-driven techniques to manage the substantial amount of combustion data that is either obtained through experiments or simulations, and thereby can find the hidden patterns underlying these data. Alternatively, machine learning models can be useful to make predictions with comparable accuracy to existing models, while reducing computational costs significantly. In this era of big data, machine learning is rapidly evolving, offering promising opportunities to explore its integration with combustion research. This work provides an in-depth overview of machine learning applications in turbulent combustion modeling and presents the application of machine learning models: Decision Trees (DT) and Random Forests (RF), for the spatio-temporal prediction of plasma-assisted ignition kernels, based on the initial degree of ionization, with model validations against DNS data. The results demonstrate that properly trained machine learning models can accurately predict the spatio-temporal ignition kernel profile based on the initial energy deposition and distribution.

Multi-physics numerical simulation study on thermo-sensitive gel delivery for a local post-tumor surgery treatment

Numerous studies in the literature have proposed the use of thermo-responsive hydrogels for filling cavities after tumor resection. However, optimizing the injection process is challenging due to the complex interplay of various multi-physics phenomena, such as the coupling of flow and heat transfer, multi-phase interactions, and phase-change dynamics. Therefore, gaining a fundamental understanding of these processes is crucial. In this study, we introduce a thermo-sensitive hydrogel formulated with poloxamer 407 and Gellan gum as a promising filling agent, offering an ideal phase-transition temperature along with suitable elastic and viscous modulus properties.We performed multi-physics simulations to predict the flow and temperature distributions during hydrogel injection. The results suggested that the hydrogel should be kept at 4 °C and injected within 90 s to avoid reaching the transition temperature. Cavity filling simulations indicated a symmetric distribution of the hydrogel, with minimal influence from the syringe's position.The temperature gradient at the cavity edge delays gelation during injection, which is essential to guarantee its administration as a liquid. The hydrogel's viscosity follows a sigmoidal function relative to temperature, taking five minutes to reach its maximum value. In summary, the multi-physics simulations carried out in this study confirm the potential of thermo-responsive hydrogels for use in post-tumor surgery treatment and define the conditions for a proper administration. Furthermore, the proposed model can be widely applied to other thermo-responsive hydrogels or under different conditions.

Thermal energy and electro-osmotic for biomimetic artificial olfactory cilia in tri-hybrid nanofluids: entropy-defying approaches

Biomimetic artificial olfactory cilia have demonstrated potential in identifying specific volatile organic compounds linked to various diseases, including certain cancers, metabolic disorders, and respiratory conditions. These sensors may facilitate non-invasive disease diagnosis and monitoring. Cilia Motility is the coordinated movement of cilia, which are hair-like projections present on the surface of particular cells in different species. Cilia serve an important part in several biological functions, including motility, fluid movement, and sensory reception. Cilia motility is a complicated process that requires the coordinated interaction of structural components and molecular pathways. Cilia are made up of a highly structured structure known as the axoneme, which is made up of microtubules grouped in a unique pattern. The axoneme is made up of nine outer doublet microtubules and a core pair of singlet microtubules. This arrangement offers structural support and serves as a scaffold for the proteins involved in ciliary movement. Our latest endeavors investigate these Multiphysics phenomena in ciliary beating flows that are inspired by biology, utilizing copper, gold, and titania nanoparticles. We examine their functions in biological systems such as peristaltic transport computationally. Our models give precise two- and three-dimensional velocity, temperature, and concentration solutions by integrating transverse magnetohydrodynamics with laser heating. Furthermore, at the channel wall expressions, the skin friction coefficient, Sherwood number, Nusselt number and optimization of entropy generation are acquired and analyzed. Important properties of the velocity and scalar profiles are revealed by a thorough analysis of dimensionless parameters. The simplified examination provides more insight into the trapping patterns that result from the complex interaction between nanofluid rheology and optics. These findings greatly contribute to our knowledge and improvement of nanofluidic transport technologies in a variety of fields supporting industry, sustainability, and medicine. Our combined computational and experimental methodology clarifies the complex dynamics in these systems and provides design guidance for the engineering of improved fluidic devices that make use of multifunctional nanomaterial interfaces and peristaltic motion.

Thermal–Electrical–Mechanical Coupled Finite Element Models for Battery Electric Vehicle

The safety of lithium-ion batteries is critical to the safety of battery electric vehicles (BEVs). The purpose of this work is to develop a method to predict battery thermal runaway in full electric vehicle crash simulation. The thermal–electrical–mechanical-coupled finite element analysis is used to model an individual lithium-ion battery cell, a battery module, a battery pack, and a battery electric vehicle with 24 battery modules in a live circuit connection. The lithium-ion battery is modeled using a representative approach, with each internal battery component individually modeled to represent its geometric shape and realistic thermal, mechanical, and electrical properties. A resistance heating solver and Randles circuit model built with a generalized voltage source are used to simulate the electrical behavior of the battery. The thermal simulation of the battery considers the heat capacity and thermal conductivity of different cell components, as well as heat conduction, radiation, and convection at their interfaces. The mechanical property of battery cell and battery module models is validated using spherical punch tests. The electrical property of the battery cell and battery module models is verified against CircuitLab simulation in an external short-circuit test. The simulation results for the battery module’s internal resistance are consistent with both experimental data and literature values. The multi-physics coupling phenomenon is demonstrated with a cylindrical compression simulation on the battery module. The multi-physics BEV model with 24 live battery modules is used to simulate the external short-circuit test and the side pole impact test. The simulation run time is less than 24 h. The results demonstrated the feasibility of using a representative battery model and multi-physics analysis to predict battery thermal runaway in full electric vehicle crash analysis.

Accuracy and scalability of incompressible inductionless MHD codes applied to fusion technologies

Abstract It is well-known that magnetohydrodynamics (MHD) dominates the dynamic of the liquid metal flows inside the breeding blankets (BB) of future nuclear fusion plants by magnetic confinement. MHD is a multiphysics phenomenon involving both electromagnetism and incompressible fluid mechanics. From the computational point of view, the simulation of MHD flows in fusion relevant conditions entails a significant challenge. Indeed, due to the shape of the induced electrical currents inside the bulk of the fluid, high spatial resolutions are needed to capture the large gradients found in boundary layers and 3D effects. Besides, solving the equations accurately typically requires very small time steps for the transient algorithms. Over the past few decades, some parallel MHD codes have been developed with success to simulate complex flows in increasingly realistic geometries. Among them, the MHD tools of commercial CFD platforms have attracted attention due to their relatively soft learning curve. Most of these codes are based on the so called ϕ-formulation which, by applying the divergence free condition of the current density to the Ohms law, reduces the electromagnetic part of the problem to a single Poisson equation. As a downside, the approach segregates the fluid and electromagnetic problem. In practice, this establishes important limits to the mesh element size, to the mesh quality and to the time-step needed to obtain accurate and stable solutions that maintains charge conservation at a discrete level. In this work, these limits are explored for the commercial platform ANSYS-Fluent using a test geometry under different conditions. As an alternative, a new code based on Finite Element Methods (FEM) is introduced as well. This open-source code, called GridapMHD (https://github.com/gridapapps/GridapMHD.jl), aims at solving the full set of MHD equations using a monolithic approach. GridapMHD is still in early stages of development but it has already shown promising results.

Fomite disinfection using spray systems: A computational multi-physics framework

Disinfecting inanimate objects or materials carrying infectious agents, i.e., fomites, using spray systems reduces healthcare-associated infections in medical settings and community-acquired infections in non-medical environments. However, an accurate prediction of such systems is challenging as these systems embrace multi-physics phenomena depending on several parameters. Therefore, this paper presents a computational modeling-based multi-physics framework to evaluate the performance and effectiveness of spray systems employed in disinfecting fomites with non-porous hydrophilic surfaces. The framework includes four key phases: (i) atomizing the liquid disinfectant jet into the disinfectant droplets; (ii) interactions between disinfectant droplets and the surrounding air; (iii) impingements created by the disinfectant droplets on the fomite surface; (iv) interactions between the disinfectant depositions and pathogens causing fomite disinfection. The accuracy of the framework is evaluated using two sets of experimental data on the reduction of viable Bacillus atrophaeus spores over an 1800-second period. The results show that the framework can predict fomite disinfection via spray systems, with the deviations from the measured data being 2.73% and 2.38%. By presenting a detailed perception of the dynamics involved in fomite disinfection, this framework has the potential to improve public health practices and lead to the development of more effective and targeted disinfection strategies in diverse settings.

Hybrid laser-ECM processing of advanced materials in neutral electrolyte without aggressive reagents: Machining performance, removal characteristics and sustainability aspects

The growing need for more energy efficient systems has led to advent of hard and corrosion resistant materials which are difficult to machine with both conventional and non-conventional machining techniques. Although ECM offers athermal and force-free dissolution, the workpiece surface integrity in multicomponent materials is limited with ECM due to several multi-physical phenomena occurring on the surface like selective phase dissolution, passivation, pitting, particle breakout, etc. To tackle these challenges, aggressive acidic and basic reagents are added to the electrolytes. Although these reagents improve the machining performance, they are not sustainable for the environment and human life as they generate harmful waste which is difficult to dispose and recycle. Furthermore, the handling of these aggressive electrolytes requires specialized coatings, resins, filters, catalysts, etc. to protect the machine tools and equipment from damage.Hybrid laser-ECM (LECM) is a relatively novel drill-mill processing technique to process these advanced materials while using neutral electrolytes, thereby potentially reducing the negative impact of using acidic/basic reagents in electrolytes. Under controlled conditions and proper laser alignment, LECM can simultaneously apply laser and EC process energies in the same machining zone to provide synergistic benefits manifesting in terms of dominance of kinetics and thermodynamics over pure electrodynamics in ECM. This provides multi-fold benefits in improving passivation weakening and homogenize multiphase dissolution. Therefore, this study investigates for the first time the machining performance, removal behaviour, reaction products and power/resource consumption of LECM in neutral electrolyte in comparison to ECM in various neutral and basic electrolytes. The NbC–Ni cermet was used as the workpiece material, which is a cobalt-free alternative to conventional WC-Co. The results revealed that LECM in neutral NaNO3 compared to ECM with NaOH addition not only provided better Sa (2.02 μm, 24% ↓), M.R.R. (153 μg/s, 31% ↑), aspect ratio (0.048, 14% ↑) and similar passivation weakening capability while maintaining a nearly neutral pH (8) of the reaction products, but also reduced electrolyte usage by 23.6% with a 16.4% increase in power consumption. These aspects make LECM a sustainable and cost-effective candidate for processing modern passivating multiphase materials like newer cermets, super alloys, high entropy alloys, etc. with nearly same performance as achieved by using ECM with acidic/basic electrolytes. Further fundamental and applied research is needed in LECM to meet the industrial specifications.