Multiphysics Model Research Articles

Extending a Multiphysics Li-Ion Battery Model from Normal Operation to Short Circuit and Venting

Mitigation of Li-ion battery system fires consists of reliable fault detection and proactive, fast discharge control. Both require modeling of failure modes due to high temperatures and currents between normal operation and thermal runaway. In this work, we present a control-oriented, reduced-order, multiphysics model that captures the electrochemical, thermal, gas generation, mechanical expansion, and venting behavior of NMC pouch cells undergoing an external short circuit (ESC) from different initial state-of-charge (SOC). The model is parameterized through experiments by fitting the solid-electrolyte interphase (SEI) decomposition rate, the cell’s thermal parameters, and the particle solid-phase diffusion parameters to capture the first venting timing, peak temperature, and diffusion-limited electrical behavior at high currents. Using a single parameter set, the multiphysics model can capture behavior during an ESC to predict whether a cell will generate gas and vent, predict the vent timing within 10 seconds of it occurring in the experiment, and maximum cell expansion pressure within 10 kPa for cells that did not vent. The model can also predict the SOC trajectory for cells with a high initial SOC within 6% SOC for the 15-minute discharge or until the cell vents.

Sideband Vibro-Acoustics Suppression and Numerical Prediction of Permanent Magnet Synchronous Motor Based on Markov Chain Random Carrier Frequency Modulation

This paper presents a Markov chain random carrier frequency modulation (MRCFM) technique for suppressing sideband vibro-acoustic responses caused by discontinuous pulse-width modulation (DPWM) in permanent magnet synchronous motors (PMSMs) for new energy vehicles. Firstly, the spectral and order distributions of the sideband current harmonics and radial electromagnetic forces introduced by DPWM are characterized and identified. Then, the principle and implementation method of three-state Markov chain random number generation are proposed, and particle swarm optimization (PSO) algorithm is chosen to quickly find the key parameters of transition probability and random gain. A Simulink and JMAG multi-physics field co-simulation model is built to simulate and predict the suppression effect of the MRCFM method on the sideband vibro-acoustic response. Finally, a 12-slot-10-pole PMSM test platform is built for experimental testing. The results show that the sideband current harmonics and vibro-acoustic response are effectively suppressed after the optimization of Markov chain algorithm. The constructed multi-physics field co-simulation model can accurately predict the amplitude characteristics of the sideband current harmonics and vibro-acoustic response.

Multiphysics modeling and optimization of an innovative shape memory alloy-polymer active composite

Shape memory alloys (SMAs) are a unique class of smart materials capable of recovering significant deformations through temperature variations, making them attractive for adaptive structures and morphing applications. However, integrating SMAs into polymer composites poses significant challenges, such as interfacial delamination and matrix overheating during thermal activation, in addition predicting the stress acting on the SMA during the actuation is pivotal. Addressing these issues is crucial for realizing the full potential of SMA-based active composites in aerospace, automotive, and renewable energy sectors. This study presents a novel multi-material design strategy for SMA-polymer active composites, featuring a rigid PC/ABS layer for mechanical integrity, a soft high-temperature silicone coating for encapsulating SMA wires, and aluminum terminals for wire crimping. A comprehensive multiphysics FEM model was developed to accurately capture the coupled thermo-mechanical response. Extensive experimental characterization and validation were conducted, followed by systematic parametric studies to investigate the effects of critical design parameters on key performance metrics. The developed prototype exhibited remarkable shape morphing capabilities, with a maximum tip deflection of 68 mm (deflection-to-length ratio of 0.4). Excellent agreement between experiments and simulations was achieved, with a maximum error of 1.7 % in tip deflection, validating the accuracy of the multiphysics model. Parametric analyses quantified the trade-offs between geometric parameters, revealing their influence on activation temperature, deflection, SMA wire stress, and fatigue life. Optimal configurations enabling low activation temperatures (<150 °C), low stress levels (<400 MPa), and high predicted fatigue life (>50,000 cycles) were identified. The multi-material design approach effectively addressed common issues in SMA-polymer composites, enabling reliable actuation cycles without damage accumulation. The validated multiphysics model and parametric insights provide a comprehensive framework for optimizing the design and performance of SMA-polymer active composites, paving the way for their widespread adoption in shape morphing applications. Potential implications include the development of adaptive aerodynamic surfaces, deployable structures, and energy-efficient systems across various industries.

SEAHOWL: Partitioned Multiphysics and Multifidelity Modelling of Wind Turbines with Monolithically Coupled Elastodynamics

We introduce SEAHOWL (Servo-Elasto-Aero-Hydro Offshore Wind Lab), a novel multiphysics multifidelity simulation framework for large-scale onshore, offshore, and floating wind turbines. For structural dynamics, multibody and finite element problems are coupled monolithically and solved within a single system of equations through Project Chrono, providing high numerical stability and optimal coupling accuracy. Combined with partitioned multiphysics, modularity is at the heart of the framework with various numerical methods and solvers (internal or external) available for each physics. This flexibility allows for the selection a target trade-off between numerical accuracy and computational efficiency on a use-case basis. Simulations showcased here have been selected through the prism of industrial R&D challenges, covering: controller response and loads over the operational wind range, 3P effect for different levels of fidelity for the blades, floating wind turbine response under irregular waves and turbulent wind, and innovative multibody application for 3P fatigue mitigation. SEAHOWL is cross-validated against the reference OpenFAST whole-turbine simulator from NREL, showing overall good agreement while differences between the two frameworks are highlighted.

Laser-induced stress by multi-beam femtosecond pulses in fused silica

Ultrafast laser technology presents the unique capacity to process glass materials with an outstanding processing quality; however, combining high quality and high throughput is still a crucial issue because glass is brittle and highly heat sensitive. One strategy to overcome this limitation is to split in space the main laser beam into multiple beams for process parallelization. In the present paper, the simultaneous interaction of several femtosecond laser beams at the surface of fused silica targets is addressed experimentally and theoretically. This work is devoted to highlight the beams cooperation for inducing stress in the material. The experiment consists in irradiating the target with multiple laser pulses with a wavelength of 1030 nm and a duration of 500 fs. The induced stress is observed through post-mortem cross-polarized microscopy. A multiscale and multiphysics model describing laser energy deposition into the material and its mechanical response is developed. The influence of various laser parameters is studied: number and position of laser beams, repetition rate, and fluence. Both experimental and modeling results, which are in a good agreement, show significant cooperative effects for stress formation with large enough laser energy deposition, possibly leading to detrimental cracks.

Carbon Binder Domain Inhomogeneity in Silicon-Monoxide/Graphite Composite Anode by 2D Multiphysics Modeling.

The Carbon-binder domain (CBD) plays a pivotal role in the performance of lithium-ion battery electrodes. The heterogeneous distribution of CBD across the electrode has garnered significant attention. However, a thorough understanding of how this CBD inhomogeneity affects anode performance remains a crucial pursuit, especially when considering the inherent material variations present in the SiO/Graphite (SiO/Gr) composite anode. In this study, an electro-chemo-mechanical model is established that provides a detailed geometric description of the particles. This model allows to quantitatively uncover the effects of CBD inhomogeneity on the fundamental behaviors of the SiO/Gr composite anode. The findings indicate that reducing the proportion of CBD in the upper domain (near the anode surface) compared to the lower domain (near the current collector) positively influences electrochemical performance, particularly in terms of capacity and Li plating. However, such an arrangement introduces potential risks of mechanical failures, and it is recommended to incorporate a higher proportion of CBD alongside the SiO particles. Finally, an anode design with a lower CBD proportion in the upper domain exhibits superior rate performance. This study represents a pioneering modeling exploration of CBD inhomogeneity, offering a promising multiphysics model with significant potential for informing advanced battery design considerations.

Voxel Design of Grayscale DLP 3D-Printed Soft Robots.

Grayscale digital light processing (DLP) printing is a simple yet effective way to realize the variation of material properties by tuning the grayscale value. However, there is a lack of available design methods for grayscale DLP 3D-printed structures due to the complexities arising from the voxel-level grayscale distribution, nonlinear material properties, and intricate structures. Inspired by the dexterous motions of natural organisms, a design and fabrication framework for grayscale DLP-printed soft robots is developed by combining a grayscale-dependent hyperelastic constitutive model and a voxel-based finite-element model. The constitutive model establishes the relationship between the projected grayscale value and the nonlinear mechanical properties, while the voxel-based finite-element model enables fast and efficient calculation of the mechanical performances with arbitrarily distributed material properties. A multiphysics modeling and experimental method is developed to validate the homogenization assumption of the degree of conversion(DoC) variation in a single voxel. The design framework is used to design structures with reduced stress concentration and programmable multimodal motions. This work paves the way for integrated design and fabrication of functional structures using grayscale DLP 3Dprinting.

Prediction of plasma path and analysis of axial fracturing properties in rock fragmentation by high-voltage pulsed discharge (RHPD)

Rock fragmentation by high-voltage pulsed discharge (RHPD) is widely utilized in resource recovery and energy extraction due to its low energy consumption and high efficiency. However, observing the plasma development process during RHPD presents challenges, and the frequency dependence of dielectric properties further complicates plasma path prediction and the analysis of fracturing properties in the axial direction of the plasma. To address these issues, we analyzed the development properties of plasma within rocks and established a segmented breakdown criterion that considered the propagation velocity of plasma. Additionally, utilizing a transient electromagnetic field model and a particle flow model (PFM), we established a multi-physics field model and proposed a predictive method for the plasma path in a rock–liquid combination environment. This allows for the quantification of the velocity, potential, and length of the plasma. Furthermore, we computed the time response of shock waves and analyzed the loading mechanism of shock waves. Based on the spatial distribution of plasma, the PFM was applied to simulate the fracturing properties of rocks under shock wave loading. Finally, we established a comprehensive experimental platform for RHPD and conducted three-dimensional reconstructions of the fractured area to validate the accuracy of plasma path prediction methods and fracturing properties analysis. This study significantly advances plasma development theory and provides insights for optimizing rock fragmentation efficiency.

Characterization of cracking-healing cycle of asphalt mixture based on air-coupled second harmonic measurement using Duffing-van der pol oscillator

Second harmonic generation (SHG) technique using Rayleigh surface wave has been successfully attempted for the non-destruction evaluation of asphalt mixture during the self-healing process. The air-coupled transducer placement is desirable for the convenient implementation of SHG for the large-scale detection. Nevertheless, the pronounced attenuation of ultrasonic wave by air-coupled propagation will cause the second harmonic induced by microcracks to be barely detectable, particularly under the influence of inevitable noise. In this study, a novel approach by combining Duffing oscillator and van der pol equation is developed to identify and quantify the second harmonic in noise-contained signals based on the phase trajectory and periodic trajectory area exponent. The multi-physics finite element model is first established to simulate the air-asphalt propagation of Rayleigh waves, and proof-of-concept numerical simulations are carried out to estimate the second harmonic amplitude of noise-contaminated signals. The air-coupled SHG experiments are conducted, and the approach of the Duffing-van der pol oscillator is applied to extract the second harmonic in experimental data, and the self-healing behavior of asphalt mixture is analyzed through the variation of the nonlinear parameter of SHG measurements. The results indicate that the contactless SHG method combined with the Duffing-van der pol oscillator can effectively realize the convenient and reliable estimation of the nonlinear ultrasonic signal during the cycle of asphalt deterioration and healing, which is important for the property characterization of asphalt mixture considering its high attenuation to the ultrasonic waves.

Multiphysics integrated model of NMC111 battery module for micro-mobility applications using PCM as intercell material

The widespread use of e-bikes and e-scooters powered by lithium-ion batteries in urban settings has heightened safety concerns, particularly the risk of thermal runaway, which can result in dangerous incidents. The surge in battery-related fire incidents highlights the critical need for a deep understanding of thermal runaway phenomena. This study addresses this imperative by introducing a Multiphysics model combining pseudo-bidimensional electrochemical for simulating electrical performance, lumped thermal model for simulating the heat transfer behavior inside the module, and a thermal runaway kinetic model for propagation evaluation. This approach facilitates precise predictions of battery performance and proactively addresses safety concerns by incorporating thermal behavior simulations and predicting potential thermal runaway events. The model proposed allows the evaluation of different strategies for improving battery safety and performance, such as phase change materials and thermal insulators. Simulations reveal that implementing sodium hydrogen phosphate dodecahydrate (Na2HPO4··12H2O) as an intercell material substantially enhances thermal management. This material reduced the maximum temperature of the battery module by up to 30% during critical thermal runaway events. Additionally, it slowed the rate of temperature increase by 50%, significantly decreasing the possibility of adjacent cells reaching thermal runaway conditions. The research findings demonstrate the effectiveness of advanced thermal management strategies using PCMs to prevent potentially catastrophic thermal events in battery modules typical of micro-mobility devices.

A digital twin model of the axial temperature field of a DC cable for millisecond calculations

The most important features of digital twin technology for transmission and transformation equipment are symbiotic evolution and state prediction. In constructing the digital twin model for DC cables, the transient and steady-state temperature fields need to be considered comprehensively in the modeling process to consider the multi-physical field coupling, mathematical model, operational data, and environmental conditions in specific cases, which makes it difficult to satisfy the real-time requirements of the digital twin. To solve this problem, in this paper, a digital twin technology architecture for DC cables is proposed, and a neural network learning model is constructed to minimize the latency due to the complexity of the model simulation. Moreover, an efficient digital twin model that is based on the model-driven approach is developed for the fast computation of the axial temperature field of DC cables. The use of the Sequence-to-Sequence residual neural network algorithm for the reduced-order multi-physics field coupling modeling reduces the computational time and ensures as much accuracy as possible up to the finite element model simulation. The simulation results show that the use of the digital twin model reduces the simulation time from 275 s to 0.56 s, and the relative error between the accuracy of the digital twin model and the finite element model for the axial temperature field of the DC cable is 0.46 %. Finally, verification of the reasonableness and feasibility of the proposed method is carried out via an experimental platform according to the standard of CIGRE TB496. In particular, the maximum value of the DC cable temperature is verified to have a small error compared with the result of the Sequence-to-Sequence calculation under specific working conditions.

A Study of the Thermal Management and Discharge Strategies of Lithium-Ion Batteries in a Wide Temperature Range

The performance of lithium-ion batteries is greatly influenced by various factors within their operating environment, which can significantly impact their overall efficiency and effectiveness. In this paper, a multi-physics field electrochemical thermal model is established to measure the physical parameters of a battery module during the charge/discharge process. The effects of working temperature, current rate, and convective heat transfer coefficient are investigated by establishing an electrochemical and thermal model. The results are obtained by conducting numerous parameterized scans to analyze the system’s state across various operating conditions, enabling the determination of its temperature and the selection of appropriate cooling measures accordingly. Based on the internal and external conditions of battery operation, parameter selection corresponding to the operating range is divided into several stages, with thermal management strategies provided for each stage. The existing framework facilitates the design of battery packs equipped with efficient thermal management strategies, thereby enhancing the battery systems’ reliability and performance. Furthermore, it aids in establishing optimal operational and safety boundaries for batteries.

A fully automated tool for constructing multiphysics model and performing simulations of kW-scale solid oxide cell stacks

Multiphysics simulations of solid oxide cell (SOC) require extensive experience and can be labor intensive. This paper reports the first successful implementation of automated multiphysics simulation process for industrial-scale planar SOC stacks. The automation is realized through Python based Ansys SpaceClaim scripting for building geometric model, Python based Ansys Workbench Meshing scripting for setting numerical grids, and Scheme based Ansys Fluent TUI commands for performing multiphysics simulations. The model building process allows for customized dimensions of all stack components and the corresponding edge grid counts. The automation of simulations streamlines all the computational steps in multiphysics modeling. The multiphysics model is shown to agree well with the experimental data. The automated tool is illustrated in a parametric analysis of SOC operations, illuminating the influences of size, shape and thermal boundary on the stack performance. The automation makes the simulations simple and efficient, invaluable for the SOC design and operation analysis.

Optimizing the chemical vapor deposition process of 4H–SiC epitaxial layer growth with machine-learning-assisted multiphysics simulations

This work addresses a novel technique for selecting the best process parameters for the 4H–SiC epitaxial layer in a horizontal hot-wall chemical vapor reactor using a transient multi-physical (thermal-fluid-chemical) simulation model and combined with a machine-learning model. An experiment was performed to validate the feasibility of the numerical model. Secondly, a single-factor analysis was conducted to investigate the effects of process parameters, including the deposition temperature, inlet-flow volume, rotational speed of the susceptor, and cavity pressure, on the quality of the 4H–SiC epitaxial layer. Finally, a machine learning algorithm, the ant colony optimization-back propagation neural network (ACO–BPNN), was employed to develop the input/output model and optimize process parameters for obtaining a high-quality epitaxial layer and reducing the optimization cycle and costs. Notably, the optimized process was validated by real experiments, where the error between calculation and experiment is 4.03 % for deposition rate and 0.49 % for coefficient of variation, respectively. The results highlight the model as reliable and lay the foundation for the CVD growth of the 4H–SiC epitaxial layer.

Development of an ultrasound-mediated nano-sized drug-delivery system for cancer treatment: from theory to experiment.

Aim: To establish a methodology for understanding how ultrasound (US) induces drug release from nano-sized drug-delivery systems (NSDDSs) and enhances drug penetration and uptake in tumors. This aims to advance cancer treatment strategies.Materials & methods: We developed a multi-physics mathematical model to elucidate and understand the intricate mechanisms governing drug release, transportand delivery. Unique in vitro models (monolayer, multilayer, spheroid) and a tailored US exposure setup were introduced to evaluate drug penetration and uptake.Results: The results highlight the potential advantages of US-mediated NSDDSs over conventional NSDDSs and chemotherapy, notably in enhancing drug release and inducing cell death.Conclusion: Our sophisticated numerical and experimental methods aid in determining and quantifying drug penetration and uptake into solid tumors.

Hypersonic inlet flow field reconstruction dominated by shock wave and boundary layer based on small sample physics-informed neural networks

High Mach number inlets face complex challenges such as shock wave/boundary layer interference, adversely impacting the aerodynamic characteristics and stable operating boundaries. Traditional computational fluid dynamics (CFD) simulations for performance and flow field analysis are slow, and data-driven deep learning technologies struggle with predicting flow fields in complex aerodynamic phenomena like shock wave/boundary layer interactions, boundary layer separation, and Mach disks. This study introduces a rapid, robust method for reconstructing hypersonic viscous inlet flow fields using inlet geometry design parameters. This method is called shock wave and boundary layer dominated two-dimensional multiphysical field reconstruction model based on multi-scale sensory field fusion residual physical information neural network (PIMSRF_ResNet), ensuring high-precision training data. Using the optimal Pareto solution set from multi-objective optimization with a small sample intelligence method, 150 sets of multi-physics fields under Ma10 conditions with varying geometric design parameters are calculated. Compared to traditional pure data-driven models, PIMSRF_ResNet's structural similarity in the test set reaches 0.9773, with a peak signal-to-noise ratio close to 30 dB and a correlation coefficient exceeding 98%. These experimental results demonstrated that the proposed method is a promising tool for real-time prediction of complex internal flow fields dominated by high Mach number shock waves and boundary layers.

A bottom-up multi-physics model correlating burnishing factors to fatigue life

Burnishing is type of contact based non-metal removal finishing processes that is used to enhance the service performance of hard material like Ti-6Al-4V through modification of roughness, exerting work hardened surface layer and inducing compressive residual stress. However, the design of process aiming at anti-fatigue machining is a big challenge, since on one hand the experimental trial and errors of fatigue tests are time consuming and costly, on the other hand there is lack of predictive models that directly correlate the process factors to lifetime. Therefore, to study the fatigue behavior of burnished samples, a holistic predictive framework is required. The present study takes lead to solve the problem by developing a model which correlates the surface integrity and fatigue life of the materials processed. Here firstly the roughness, microhardness and residual stress of the burnished samples are modeled and correlated to process factors through multiphysics of burnishing. Then they were correlated to fatigue life by using stress-based approach that includes surface geometrical, mechanical and metallurgical aspects. The accuracy of developed models were compared with the series of experiments which were carried out on as-turned Ti-6Al-4V. The results revealed that the developed analytical model can predict the lifetime of the burnished samples with acceptable accuracy where the average prediction error for 16 data sets is 13.6 % within the range of 6.3 % to 22 %. The verified model was then utilized to identify the mechanism of lifetime variation and to understand how the process factors determines the fatigue life. It was obtained that the static force as a parameter contributed to loading has greatest impact on variation of fatigue life by adjusting all the surface integrity aspects i.e. roughness, microhardness and residual stress. It was also found that the main impact of feed rate on fatigue life is attributed to its influence surface roughness at high static force.

Partial differential equation modelling of the solid–liquid phase process in iron sintering and interface control with backstepping method

Due to the highly continuous temporal and spatial distribution characteristics of the sintering process, the solid–liquid phase interface during sintering is uneven and discontinuous, which seriously affects the quality of sintered ore. In order to achieve real-time and effective control of the solid–liquid interface in the sintering system, this study proposes a spatiotemporally distributed partial differential equation (PDE) thermal system with Stefan interface boundaries. Slice the 3D sintering mixing space and establish a discrete 1D space multi-distributed system to achieve distributed control of the sintering thermal system. Based on this system, a distributed boundary controller is designed using the backstepping method to drive the solid–liquid phase interface to the desired position. This method fully adapts to the nonlinear problems of the sintering thermal system and can quickly respond to system changes. Based on real data from the sintering site, a multi-physics simulation model was designed to verify the correctness of the theoretical research. Comparing the simulation results with the actual sintering state shows that this control method can effectively control the position of the sintering solid–liquid interface, which plays a vital role in improving the quality of sinter and sintering efficiency.

Overall performance evaluation of a novel optical truncation method for compound parabolic concentrated photovoltaic-thermal system

A variable geometric concentration ratio (VGCR) truncation method was proposed to enhance the performance of compound parabolic concentrated photovoltaic-thermal (CPC-PVT) system in this study. Based on a multi-physics field model of optical-thermal-electrical coupling, numerical investigations using Monte Carlo ray tracing (MCRT) and finite volume method (FVM) were conducted to analyze the effects of the variation rate of truncation ratio (k) and light incident angle (θin) on the overall performance (optical efficiency (ηop), instantaneous thermal efficiency (ηth), instantaneous electrical efficiency (ηele,total), instantaneous electrical power (P), instantaneous exergy efficiency (ηu,total) and economic performance) of CPC-PVT system. Also, the overall performance between the VGCR-CPC-PVT system and the full CPC-PVT (FCPC-PVT) system was compared. The results indicate that, when θin is smaller, there is little disparity in the overall performance between the two systems. However, as k increases, ηop, ηele,total, ηth and ηu,total of VGCR-CPC-PVT system monotonically increase, and the area of the high-temperature region on the PV panel surface decreases. When θin is larger, the VGCR-CPC-PVT system has a smaller shaded area, ηop, ηele,total, P and ηu,total are significantly improved compared with the FCPC-PVT system. Additionally, k has a significant impact on the overall performance of the system, and P, ηu,total and saved reflective surface area (S) are the criteria for selecting k. When using P and ηu,total as the selection criteria, the optimal k value is 0.04. For 0°≤θin≤38°, the average ηop, average ηele,total, average ηth, average P and average ηu,total of the VGCR-CPC-PVT system with k = 0.04 are relatively improved by 1.91 %, 4.59 %, 2.42 %, 3.12 % and 4.04 %, respectively, while the reflector area decreases by 17.5 %, compared with the FCPC-PVT system. The cost-effectiveness of concentrator increases significantly with k.

A comprehensive study of parameters distribution in a short PEM water electrolyzer stack utilizing a full-scale multi-physics model

Proton exchange membrane (PEM) electrolyzer is regarded as one of the most promising technologies for hydrogen production. To meet the requirements of large-scale hydrogen production, PEM electrolyzer stack that composed by series units are necessary. However, the composition brings extra problems such as maldistribution from the manifold to each unit, which results in parameters differences among the units and further gives rise to stack degradation. In this study, a full-scale three-dimensional multi-physics model is developed to simulate a short PEM electrolyzer stack with four single electrolyzer units. All the electrolyzer components and connections (e.g. manifold) are included in the model, and the temperature distribution, liquid saturation, membrane water content and electrolyzer performance are investigated in detail. The results indicate that performance of each single electrolyzer decreases from the first to the last unit along the water flow direction. It is recommended that the inlet temperature of electrolyzer stack should not exceed 60°C. To ease the thermal management, the water supply mode for cathode side can obviously lower the stack temperature to a preferred range, and improve the temperature uniformity. This study provides a comprehensive understanding of the distribution behaviors inside the electrolyzer stack and guides the operating parameters optimization.