Multiphysics Modeling Approach Research Articles

Fractal Circuit Architectures for Spatially Controlled Heating and Multi-Modal Shape Programming of Electro-Active Shape Memory Polymers

Shape memory polymers (SMPs) are commonly activated through external heating or rigid embedded heaters, which lack precise temperature control and restrict programmable shape transformations. This study demonstrates integrating thin films of hard electronic materials patterned in fractal designs into SMPs yields novel thermomechanical responses, enabling flexible implementation of stretchable electronic functionality. Space-filling Hilbert, Moore and Peano fractal curves generate distributed electronic circuitry patterns within the SMP matrices. To investigate the coupled electro-thermal-mechanical behavior, a multi-physics modeling approach is adopted. A nonlinear thermo-visco-hyperelastic constitutive model captures the SMP’s shape memory characteristics. Governing equations for the multi-physics problem are formulated. Experimental data validates the model’s predictions of the SMP’s nonlinear material response under uniaxial loading. This validated framework analyzes SMP composites with integrated fractal circuits. Findings reveal composites with higher-order fractal circuits’ exhibit faster, more uniform resistive heating and smaller electrical resistance changes during stretching, enabling consistent and predictable shape recovery under various deformations. Notably, these advantageous heating and resistance characteristics enhance the force recovery rate during the shape memory cycle. Furthermore, the composites demonstrate excellent shape recovery under bending. Remarkably, uniaxial stretching can induce controlled out-of-plane shape transformations, enabling novel actuation modalities. This work provides insights into leveraging fractal circuit integration to enhance SMP performance and expand capabilities, enabling multi-functional actuators, sensors and programmable soft grippers.

Multiphysics Modeling of Electrochemical Impedance Spectroscopy Responses of SAM-Modified Screen-Printed Electrodes

In this work, we present a multiphysics modeling approach capable of simulating electrochemical impedance spectroscopy (EIS) responses of screen-printed electrodes (SPEs) modified with self-assembled monolayers of 11-Mercaptoundecanoic acid (MUA). Commercially available gold SPEs are electrochemically characterized through experimental cyclic voltammetry and EIS measurements with 10 mM [Fe(CN)6]3−/4− redox couple in phosphate buffered saline before and after the surface immobilization of MUA at different concentrations. We design the multiphysics model through COMSOL Multiphysics® based on the 3D geometry of the devices under test. The model includes four different physics considering the metal/solution interface electrochemical phenomena, the ion and electron potentials and currents, and the measurement set-up. The model is calibrated through a set of experimental measurements, allowing the tuning of the parameters used by the model. We use the calibrated model to simulate the EIS response of MUA-modified SPEs, comparing the results with experimental data. The simulations fit the experimental curves well, following the variation of MUA concentration on the surface from 1 µM to 100 µM. The EIS parameters, retrieved through a CPE-modified Randles’ circuit, confirm the consistency with the experimental data. Notably, the simulated surface coverage estimates and the variation of charge transfer resistance due to MUA-immobilization are well matched with their experimental counterparts, reporting only a 2% difference and being consistent with the experimental electrochemical behavior of the SPEs.

A combined multiphysics modeling and deep learning framework to predict thermal runaway in cylindrical Li-ion batteries

In this paper, machine learning (ML)/deep learning (DL) has been employed to predict the thermal runaway (TR) occurrence in Li-ion batteries (LIBs). State-of-the-art convolutional neural networks (CNNs) are adopted to forecast the evolution of the TR phenomenon with a classification approach of three distinct stages, namely, safe operation, critical condition of triggered the TR, and the actual TR occurrence. In addition, the “you look only once” (YOLO) object detection ML model is used to identify the location of the TR heat sources within the battery. The neural networks are trained on simulated thermal images, computed with the multiphysics modeling of LIBs. The multiphysics modeling approach comprises a coupled thermal, electrochemical (P2D model), and degradation sub-models. The degradation phenomenon leading to the TR considered in this paper is the solid electrolyte interface (SEI) formation/decomposition on the negative electrode. The proposed ML model exhibits high accuracy in predicting the TR evolution stages and heat source locations. The combined multiphysics and ML modeling approach developed in this work provides qualitative insights for ‘on-the-fly’ prediction of the TR, as well as a framework for extending data-driven methodologies to broad applications in electrochemistry.

Size design strategy for scaling up alkaline water electrolysis stack integrated with renewable energy source: A multiphysics modeling approach

Large-capacity hydrogen production systems are crucial for promoting green hydrogen development. However, the scaling-up of electrolysis stacks through larger electrode areas or additional electrodes may increase the bubble coverage effect and shunt current effect, leading to efficiency degradation. Therefore, a quantitative model that considers interrelated factors is needed to optimize the size design during the scaling-up process. This paper presents a stack-level multiphysics model that describes the electrochemical and two-phase flow processes within the stack. The model is validated experimentally, with a relative error of the current–voltage (IV) curve within 4%. Based on the model, the energy efficiency-reaction current density (ER) curves of different stacks are analyzed and used to provide performance optimization strategies for stacks coupled with renewable energy sources (RES). The findings demonstrate that in on-grid mode, the small cell design leads to a hydrogen production rate increase of over 6%, driving a trend towards miniaturization of cell areas. In addition, during off-grid operation, the hydrogen production variations among different designs can exceed 4% due to performance differences under heavy and light loads. Therefore, optimizing electrolyzer performance requires considering power source fluctuations and conducting specialized computational optimizations based on specific scenarios. In summary, this paper proposes optimization strategies of size design for scaling up electrolysis stacks to improve stack performance, with the goal of driving the advancement of green hydrogen.

Fully Coupled Analysis of Aerothermoelastic Deformation of a Scramjet Inlet

Although supersonic combustion ramjets—scramjets—provide a fuel-efficient method for propulsion at hypersonic speeds, current challenges with the engine prohibit the robustness necessary for space accessibility and trans-atmospheric flight. One such challenge the engine faces is the vehicle and inlet’s compliance under harsh thermal and mechanical loads at hypersonic speeds. The deformation of the inlet has ramifications on the downstream components and the engine as a whole, creating conditions outside of the original design envelope. Additionally, the deformations impact the vehicle’s aerodynamic performance due to the integrated airframe/inlet design. One mitigation technique that works in tandem with thermal management is active cooling. It is important to understand the impacts of active cooling on the inlet and engine performance; in order to do so, a multiphysics modeling approach is used to capture the coupled aerothermostructural response of the inlet, and a multifidelity approach is used to model the remaining components of the scramjet. The system is found to be extremely sensitive to the changes in deformation, leading to increased flow separation and heating and to deviations of the engine performance and efficiency from the original design point.

Modeling High-Power Density Ceria-Based Direct Ammonia Fueled SOFC Stacks for Mobile Applications

The transport sector currently accounts for 23% of the global energy-related CO2 emissions, with an average annual growth of +1.8% since 2010, which is faster than for any other end-use sector [1]. Decarbonization of the mobility sector is particularly challenging due to the highly strenuous space, weight and efficiency demands for a power generation system applied for vehicle propulsion. Batteries have established themselves as a solution for the electric propulsion of passenger cars. However, their low energy densities and high recharging durations hinders their deployment in vehicles with large gross weight including trucks, ships, trains and airplanes [2, 3]. High-temperature solid oxide fuel cells (SOFCs) possess many advantageous characteristics compared to polymer electrolyte membrane (PEM) fuel cells that can be effectively exploited in a vehicle, such as high efficiency, fuel flexibility and impurity tolerance. SOFCs can be directly run with a great variety of fuels, such as NH3, which is currently regarded to be a very promising energy and hydrogen carrier, mostly due to its carbon neutrality, favorable volumetric energy density (12.7 MJ/L) and transportability [4].In this contribution, we assess the performance of intermediate temperature direct NH3-fueled SOFC stacks as a highly efficient power generation source for mobile applications by means of a detailed multi-physics modeling approach, comprising investigations on electrode, button cell, and stack scales. The modeled SOFC stack is intended to be integrated in a hybrid system concept by coupling with a gas turbine.Firstly, a 1D model of cell containing a gadolinium-doped ceria (GDC) electrolyte is developed based on an established multi-scale computational framework [2]. The electrochemical sub-model is subsequently parametrized and validated through reproducing measurements collected on a high-power density Ni-GDC/GDC/SSC-GDC cell. In order to physically account for the electronic leakage current due to mixed ionic-electronic conduction (MIEC) properties of the ceria-based electrolyte, the model contains the implementation of a distributed charge-transfer model that solves individual electronic and ionic conduction pathways across all constituents of the membrane-electrode assembly (MEA) [5]. The decomposition of NH3 on the surface of the Ni particles in the fuel electrode is modeled based on a thermodynamically consistent 12-step elementary kinetic mechanism [6]. The thermo-catalytic and electrochemical sub-frameworks are then integrated into a 3D stack model, which is built upon a lightweight design.The numerical simulations indicate that the performance of the SOFC stack based on the anode-supported GDC-electrolyte cell design is not only highly sensitive to the temperature, but also to the selection of the absolute pressure level. Results suggest the stack to reach a very promising performance in the intermediate temperature range with a predicted ASR of ~0.7 Ω cm2 at 550 °C, pure NH3 feed and 50% anode off-gas recirculation. By predicting species, temperature and current density distributions across the stack, the developed model proves itself to be highly instrumental for the identification of design points that provide a feasible trade-off between performance and safety metrics.

Modeling the radiative, thermal and chemical microenvironment of 3D scanned corals

Reef building corals are efficient biological collectors of solar radiation and consist of a thin stratified tissue layer spread over a light scattering calcium carbonate skeleton surface that together construct complex three dimensional (3D) colony structures forming the foundation of coral reefs. They exhibit a vast diversity of structural forms to maximize photosynthesis of their dinoflagellate endosymbionts (Symbiodiniaceae), while simultaneously minimizing photodamage, offer resistance to hydrodynamic stress, reduce attack by predators and increase prey capture and heterotrophic feeding. The symbiosis takes place in the presence of dynamic gradients of light, temperature and chemical species that are affected by the interaction of incident irradiance and water flow with the coral colony. We developed a multiphysics modelling approach to simulate the microscale spatial distribution of light, temperature and O2 in a coral fragment with its morphology determined by 3D scanning techniques. Model results compared well with spatial measurements of light, O2 and temperature under similar flow and light conditions. The model enabled us to infer the effect of coral morphology and light scattering in tissue and skeleton on the internal light environment experienced by the endosymbionts, as well as the combined contribution of light, water flow and ciliary movement on O2 and temperature distributions in the coral.

Continuous flow hydrothermal synthesis of zeolite LTA in intensified reactor. Experimental and multiphysics CFD modeling approach

Zeolites are conventionally synthesized in batch-wise hydrothermal processes that usually take several hours or days to complete. In current research work, continuous flow process is developed on pilot scale for the hydrothermal synthesis of zeolite LTA. The pilot reactor was able to produce highly crystalline zeolite nanoparticles without any clogging, which often encounters for particle flow in tubular reactors. Steady-state zeolite production rates of 55–80 g⋅h −1 (dry basis) were achieved with a residence time below 10 min, with corresponding space time yields (STY) between ca. 700 and 1100 g⋅L −1⋅h −1. A couple CFD model with a population balance model was developed to investigate the continuous flow hydrothermal process. The average particle size obtained from the model was in good agreement with the experimental data. The model was further applied to study the effects of critical parameters, such as temperature and flow rate, to highlight potential process optimization directions.

A novel high-dimensional and multi-physics modeling approach of proton exchange membrane fuel cell for real-time simulation

Fuel cell model provides state observation and quantitative analysis for Hardware-In-the-Loop (HIL) test platform. For a high-dimensional multi-physics model, the observed distribution of physical properties is meaningful for fuel cell stack optimization, manufacturing process and model-based control development. However, traditional modeling approach is difficult to consider both the calculation accuracy and efficiency. To solve this problem, this paper proposes a high-dimensional multi-physics real-time modeling approach for fuel cells based on partial differential equations (PDEs)-tridiagonal matrix equation transformation (TMET) and symmetric successive overrelaxation (SSOR) algorithm. In the proposed method, a generalized framework of TMET and a novel staggered grid technique is developed. In this case, both the convection and diffusion phenomena can be accurately described. For different fuel cells or parameters change, supplementary model modification or development is not required, only the global variables need to be re-assigned. Moreover, SSOR with bi-directional iteration mechanism is used to increase the probability of computational convergence and thus improve the calculation efficiency. Compared with commercial COMSOL model, by using the proposed modeling approach, the mean absolute percentage error (MAPE) of the simulated polarization output characteristics is 1.77%, the MAPE of different distribution characteristics can remain within 10%, and the calculation time can be shortened from hours even days to seconds.

Mitigating distortions during debinding of a monolithic solid oxide fuel cell stack using a multiscale, multiphysics model

Improving the power density of solid oxide fuel cell stacks would significantly enhance this technology for transportation. Using a monolithic structure to downsize the stack dimension offers a key to elevate the power density of solid oxide fuel cell stacks. This innovative design is promising but manufacturing is a challenge. The monolith is co-sintered in one firing step, and the gas channels are formed by burning off sacrificial organic materials. Structure distortion or fracture was observed in post-mortem investigations. In this work a multiscale, multiphysics modelling approach is proposed to describe and resolve this challenge in the debinding process occurring in a monolithic stack, i.e. the burning of organics and transportation of gases through the gradually opening microstructure, as well as the pressure build-up in the microstructure due to gas development. Simulation results show that a prominent pressure peak is experienced in the stack when a plasticiser (polyethylene glycol) and a pore-former (polymethyl methacrylate) are decomposed simultaneously. To reduce the high pressures, we investigate two possible strategies: (i) changing the mixture of organic additives; (ii) modifying the debinding temperature profile. Three tapes with different pore-formers are prepared, and the generated pressures during debinding of the three stacks are compared. The corresponding stack shapes after debinding are recorded. Numerical investigations show a good agreement with the post-mortem observations. By changing the composition of organics the distortion or fracturing of the stack can be avoided. Furthermore, to facilitate stack manufacturing, the high pressures can also be reduced by adjusting the heating rates and dwell temperatures of debinding. By using the new temperature profile suggested by the simulation study, the duration of debinding can also be reduced.

Percolation-assisted coating of metal-organic frameworks on porous substrates

In the past two decades, significant advancements have been made toward thin film fabrication of metal-organic frameworks (MOFs) on porous solid substrates for gas separation and catalysis applications. To enhance the physical and chemical stability of the films, various top-down and bottom-up approaches have been implemented. While both approaches have some advantages, they are mostly limited due to a lack of adhesion with the substrate, cracking of the films, and non-uniform coverages. Therefore, there is a need for improvement in the coating processes for the fabrication of robust, uniform, and scalable films. Here, we develop a novel percolation-assisted coating (PAC) process that combines top-down and bottom-up approaches in a continuous-flow microfluidic device to deposit HKUST-1 on a porous substrate, yielding controlled film thicknesses and mass loading. The PAC process is optimized using a Multiphysics modeling approach to design the microfluidic reactor, which can be readily scaled up and deployed to fabricate MOF films on various porous substrates. The desired thickness in the range of 10–200 μm of the MOF film can be achieved by controlling the residence time and temperature of the reaction mixture. The synthesized film is characterized for physical adhesion using sonication, film coverage using scanning electron microscopy, and porosity using a porosimeter. The performance of synthesized films is benchmarked for the effective separation of 50 vol% CH4–H2 gas mixture with the separation factor of 4. The microfluidic device is also applicable to synthesize films of various MOFs like MOF-5, MOF-505 (also reported here), UIO-66, etc., over a wide range of porous substrates. Lastly, we propose a multi-chamber design of the microfluidic device for high-throughput screening of thin-film growth using the PAC process.

Plasma formation in ambient fluid from hypervelocity impacts

The generation of plasma from hypervelocity impacts is an active research topic due to its important science and engineering ramifications in various applications. Previous studies have mainly focused on the ionization of the solid materials that constitute the projectile and the target. In this letter, we consider impact events that occur in a fluid (e.g., gas) medium, and present a multiphysics computational modeling approach and associated analysis to predict the behavior of the dynamic fluid–solid interaction that causes the surrounding fluid to ionize. The proposed computational framework is applied to a specific case involving a system of three interacting domains: a copper rod projectile impacting onto a soda lime glass target in a neon gas environment. The impact velocity is varied between 3km/s and 6km/s in different simulations. The computational model couples the compressible inviscid Navier–Stokes equations with the Saha ionization equations. The three material interfaces formed among the projectile, the target, and the ambient gas are tracked implicitly by solving two level set equations that share the same velocity field. The mass, momentum, and energy fluxes across the interfaces are computed using the FInite Volume method with Exact two-material Riemann problems (FIVER). The simulation result reveals a region of neon gas with high velocity, temperature, pressure, and mass density, formed in the early stage of the impact mainly due to the hypersonic compression of the fluid between the projectile and the target. For impact velocities higher than 4km/s, ionization is predicted in this region.

Efficient reactive electrochemical carbon membranes for organic pollutants removal from aqueous solution: A study of multi-physics modeling and simulation approach

A novel application of three metal oxide catalysts CuOx, Fe2O3, and CoO onto coal-based carbon membrane (CM) in the enhancement of electrochemical removal of high concentration organic pollutants is presented. Their morphology, structure, and elemental composition were characterized. Analysis of their performance in the treatment of highly concentrated Rhodamine B (RhB), Rose Bengal, and phenol pollutants with respective concentrations of 600 mg/l, 637 mg/l, and 50 mg/l presented excellent removal efficiency (all >97 %). A study of the degradation mechanism of pollutants at the membrane surface was approached through multi-physics modeling and simulation of the reaction kinetics of complex indirect RhB oxidation chemistry occurring at the copper oxide membrane surface which involved oxidation of resistant intermediates. The results gave a degradation efficiency of 99.82 % after 60 s of reaction at the CM-CuOx surface which is very close to the experimental results. An understanding of the pollutants concentration distribution across the electrocatalytic filtration system was achieved through simulation of the transport, diffusion, and convection of degraded products through the membrane walls. No fouling was observed for RhB treatment and a new insight into the interactions between pollutants molecules and reactive membrane surfaces was brought by the mean of Monte-Carlo simulations of their adsorption mechanisms. Finally, a comparison with other Electrochemical Advanced Oxidation Processes (EAOPs), revealed that the membranes fabricated in this work possessed significant advantages both in terms of permeability and removal efficiency.

Predicting Thermal Failures Using an Advanced Data-Driven Modeling Framework in a Cylindrical Li-Ion Battery Pack

Li-ion cells have been widely used in battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and stationary energy storage units due to their high specific energy and excellent cycle life. However, the safety of Li-ion battery (LIB) packs has become a vital issue, especially under extreme or abusive conditions. Fire and explosion due to the onset of thermal runaway often lead to catastrophic damages. Thermal failures are caused by drastic and successive processes and reactions that feed into one another, eventually leading to fire and explosion. Thus, one of the most crucial current research goals is to steer LIBs’ technology toward long-lasting and safe operation.In this work, we employ data-driven methods to predict and potentially prevent the thermal runaway (TRA) in a LIB battery pack with various cooling systems. Specifically, we use several deep learning (DL) techniques to learn and predict the likelihood of the TRA, using thermal images acquired from the multi-physics modeling of LIBs. The multi-physics modeling approach comprises a coupled thermal, electrochemical (P2D) model1 and degradation due to the solid electrolyte interface (SEI) formation/decomposition sub-models.2 The geometrical and material properties of the battery pack are taken from the 2170 cell as presently used in some BEV. The presented multi-physics model has been formulated as a three-dimensional model to facilitate efficient and accurate numerical simulations in different conditions. Under general working conditions, the cells in a pack are exposed to a continuous charge/discharge load, where heat is generated inside the cells due to several electrochemical and degradation processes. The multi-physics modeling results in the form of the temperature profiles of the battery surface as well as the SEI formation/decomposition rate at various charge/discharge cycles are validated against the prior literature’s experimental data. Thermal images of the battery pack from different views are collected as sequences of video frames during the battery operation from multiple simulations to be used in DL.Based upon the multi-physics modeling results, Convolutional Neural Networks (CNNs) are proposed to predict the TRA-like events.3–5 Hyperparameters optimization has been performed to identify variables for the best performing ML method. The proposed combined multi-physics and machine learning modeling methodology provide interesting insight and predictive capabilities of the TRA. Thus, the developed combined multi-physics and ML modeling approach establishes a basis for 'on-the-fly' prediction of the TRA as well as a framework for extending machine learning methodologies to broad applications in battery failure prediction.References J. Newman and W. Tiedemann, J. Electrochem. Soc., 140, 1–5 (1993).X. Fen, X. He, M. Ouyang, L. Wang, L. Lu, D. Ren and S. Santhanagopalan, J. Electrochem. Soc., 165, A3748–A3765 (2018).J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016-Decem, 779–788 (2016).K. He, X. Zhang, S. Ren, and J. Sun, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016-Decem, 770–778 (2016).K. Simonyan and A. Zisserman, 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., 1–14 (2015).

A multiphysics modeling approach for in-stent restenosis: Theoretical aspects and finite element implementation

Development of in silico models that capture progression of diseases in soft biological tissues are intrinsic in the validation of the hypothesized cellular and molecular mechanisms involved in the respective pathologies. In addition, they also aid in patient-specific adaptation of interventional procedures. In this regard, a fully-coupled high-fidelity Lagrangian finite element framework is proposed within this work which replicates the pathology of in-stent restenosis observed post stent implantation in a coronary artery. Advection–reaction–diffusion equations are set up to track the concentrations of the platelet-derived growth factor, the transforming growth factor-β, the extracellular matrix, and the density of the smooth muscle cells. A continuum mechanical description of volumetric growth involved in the restenotic process, coupled to the evolution of the previously defined vessel wall constituents, is presented. Further, the finite element implementation of the model is discussed, and the behavior of the computational model is investigated via suitable numerical examples. Qualitative validation of the computational model is presented by emulating a stented artery. Patient-specific data are intended to be integrated into the model to predict the risk of in-stent restenosis, and thereby assist in the tuning of stent implantation parameters to mitigate the risk.

Multi-physics modelling and simulation approach with experimental validation for electrocatalytic filtration process: Part A. Optimization of electrodeposition of metal oxides on carbon membranes

• The model helped in a fast and accurate determination of optimum parameters for uniform electrodeposition results. • The model allowed the control and manipulation of deposition parameters. • RMSE values described good agreement between the model and the experimental studies. • The model gave a generalization of the application to various metal oxides. The integration of metal oxidized catalysts on conductive membrane substrates for electrochemical filtration process is an ingenious way to improve their water treatment performance. However, the determination of the optimum parameters for a uniform distribution of the metal particles over the membrane surface conventionally necessitates multiple experiments and is even more difficult to attain uniformity when coating onto non-flat surface geometries. In this work part A of the study, based on finite element method (FEM) in combination with error control and adaptive meshing using the UMFPACK solver of COMSOL Multi-physics software, we developed a model which through simulations helped in a fast and accurate determination of optimum parameters for uniform electrodeposition results of various metals onto tubular geometry coal-based carbon membrane. Experimental studies were also conducted in order to demonstrate the accuracy of the model and the Pearson's correlation coefficient and the root-mean-square error (RMSE) obtained in this work gave values of [0.88, 0.99] and [0.01, 0.1] respectively, confirming the agreement of the model data with experimental data. The difference in optimum parameters observed for each system studied is another evidence that the model allows the control and manipulation of parameters regardless of the depositing species which marks a significant step forward accurate prediction of electrodeposition of catalysts on carbon membranes for electrocatalytic wastewater treatment.

A Combined Multi-Physics Modelling and Machine Learning to Predict Electro-Thermal Failures of Cylindrical Li-Ion Batteries

Safety aspects of Li-ion batteries (LIBs) operation become increasingly important due to their integration into large-scale systems such as electric vehicles (EVs). Recent events involving many EVs explosions endangering consumers’ lives have shown that electro-thermal aspects of LIBs are far from being adequately managed. Thus, one of the most crucial goals of current research is to steer LIBs’ technology toward long-lasting and safe operation.In this work, we employ data-driven methods to predict and potentially prevent the thermal runaway (TRA) in LIBs. Specifically, we use various machine learning (ML) techniques to learn and predict the likelihood of the TRA, using thermal images acquired from the multi-physics modeling of LIBs. The multi-physics modeling approach is comprised of a coupled thermal, electrochemical (P2D) model1, and degradation due to the solid electrolyte interface (SEI) formation/decomposition sub-models.2 The presented multi-physics model has been formulated as a three-dimensional model to facilitate efficient and accurate numerical simulations in different conditions. Under general working conditions, the cell is exposed to a continuous charge/discharge load, where heat is generated inside the cell due to several electrochemical and degradation processes. Thermal images of the battery surface are collected as sequences of video frames during the battery operation from multiple simulations to be used in ML. LIB’s geometrical and material properties are taken from the 2170 cell as used in the current Tesla vehicles (model S 2021, model 3 2021). Based upon the multi-physics modeling results, two deep learning techniques, i.e., Convolutional Neural Networks (CNN) and object detection using YOLO, are proposed to address the problem of predicting the TRA like-events.3–5 The multi-physics modeling results in the form of the temperature profiles of the battery surface as well as the SEI formation/decomposition rate at various charge/discharge cycles are validated against the prior literature experimental data. Two regimes with single and double hot spots in the cylindrical LIB are investigated for a potential TRA occurrence. The CNN predictions of the TRA-like event on unknown data set show an excellent accuracy reaching up to 90% for all the different CNN architectures used. The object detection technique is found to be very efficient in identifying the location of TRA initiation at various conditions. Thus, the developed combined multi-physics and ML modeling approach establishes a basis for 'on-the-fly' prediction of the TRA as well as a framework for extending machine learning methodologies to broad applications in electrochemistry.References J. Newman and W. Tiedemann, J. Electrochem. Soc., 140, 1–5 (1993).X. Fen, X. He, M. Ouyang, L. Wang, L. Lu, D. Ren and S. Santhanagopalan, J. Electrochem. Soc., 165, A3748–A3765 (2018).J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016-Decem, 779–788 (2016).K. He, X. Zhang, S. Ren, and J. Sun, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016-Decem, 770–778 (2016).K. Simonyan and A. Zisserman, 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., 1–14 (2015).

Fast Multi-Physics Simulation of Microwave Filters via Deep Hybrid Neural Network

One fundamental difficulty in multiphysics numerical simulation is the complex interactions between different physics domains leading to plenty of computational costs. Although neural networks have recently been introduced in multiphysics simulations, the modeling complexity and the enormous amount of training data required may still pose significant challenges to researchers. In this work, we introduce a low-cost, electromagnetic-centric, multiphysics modeling approach to simulate microwave filters. With ground-truth datasets being generated from the finite element method, a novel deep hybrid neural network (DHNN) model structure is introduced, which uses the sigmoid and the ReLU functions as activators to mimic the diversity of biological neurons. A new, more feasible training algorithm is proposed for the efficient development of the DHNN model. The algorithm adopts the design-of-experiment (DOE) sampling technique and is specifically designed for the simulation of multiphysics responses. The strong approximation ability of the DHNN can lead to high-accuracy modeling with fewer training data and less resource consumption. Another advantage of this approach is that the modeling process is more concise and easier to apply compared with other modeling technologies. Numerical examples show that the DHNN can achieve higher accurate results with much less training data compared to traditional ANNs. The advantages of the proposed method in computational efficiency are more pronounced, especially when the amount of input data increases.

Assessing flooding impact to riverine bridges: an integrated analysis

Abstract. Flood events are the most frequent cause of damage to infrastructure compared to any other natural hazard, and global changes (climate, socioeconomic, technological) are likely to increase this damage. Transportation infrastructure systems are responsible for moving people, goods and services, and ensuring connection within and among urban areas. A failed link in these systems can impact the community by threatening evacuation capability, recovery operations and the overall economy. Bridges are critical links in the wider urban system since they are associated with little redundancy and a high (re)construction cost. Riverine bridges are particularly prone to failure during flood events; in fact, the risks to bridges from high river flows and erosion have been recognized as crucial at global level. The interaction of flow, structure and network is complex, and not fully understood. This study aims to establish a rigorous, multiphysics modeling approach for the assessment of the hydrodynamic forces impacting inundated bridges, and the subsequent structural response, while understanding the consequences of such impact on the surrounding network. The objectives of this study are to model hydrodynamic forces as demand on the bridge structure, to advance a performance evaluation of the structure under the modeled loading, and to assess the overall impact at systemic level. The flood-prone city of Carlisle (UK) is used as a case study and a proof of concept. Implications of the hydrodynamic impact on the performance and functionality of the surrounding transport network are discussed. This research will help to fill the gap between current guidance for design and assessment of bridges within the overall transport system.

An inventive multi-scale, multiphysics modeling approach and comparative analysis of distinctive features of planar ionization waves in air: I. Negative streamers

A multiscale-multiphysics model of two different patterns of negative discharge (droplets and branches) based on the well-known electro-hydrodynamic equations in a two-dimensional non-axisymmetric configuration is developed to investigate the main features of the negative discharge (e.g. bifurcation, stochastic branching, and evolution of all charged particles including positive and negative ions and electrons). We discuss the challenges behind the finite-element-based streamer models and illustrate the robustness and efficiency of introduced three different meshing techniques via coupled fixed and adaptively refined meshes in terms of the accuracy of results and computational burden. The capability of the model in representing an outstanding spatial resolution of the streamer propagation and its stages is demonstrated under both uniform and non-uniform electric fields. A further required extension of the presented model for the representation of the positive streamer is presented in part II of this series paper. Also, in this part, the distinctive characteristics of both positive and negative streamers are presented, and the different features achieved from the model are thoroughly discussed and compared with the prior experimental results.