Multiphysics Model Research Articles

Integrated modeling to control vaporization-induced composition change during additive manufacturing of nickel-based superalloys

A critical issue in laser powder bed fusion (LPBF) additive manufacturing is the selective vaporization of alloying elements resulting in poor mechanical properties and corrosion resistance of parts. The process also alters the part’s chemical composition compared to the feedstock. Here we present a novel multi-physics modeling framework, integrating heat and fluid flow simulations, thermodynamic calculations, and evaporation modeling to estimate and control the composition change during LPBF of nickel-based superalloys. Experimental validation confirms the accuracy of our model. Moreover, we quantify the relative vulnerabilities of different nickel-based superalloys to composition change quantitatively and we examine the effect of remelting due to the layer-by-layer deposition during the LPBF process. Spatial variations in evaporative flux and compositions for each element were determined, providing valuable insights into the LPBF process and product attributes. The results of this study can be used to optimize the LPBF process parameters such as laser power, scanning speed, and powder layer thickness to ensure the production of high-quality components with desired chemical compositions.

A Comprehensive Flow–Mass–Thermal–Electrochemical Coupling Model for a VRFB Stack and Its Application in a Stack Temperature Control Strategy

In this work, a comprehensive multi-physics electrochemical hybrid stack model is developed for a vanadium redox flow battery (VRFB) stack considering electrolyte flow, mass transport, electrochemical reactions, shunt currents, and as heat generation and transfer simultaneously. Compared with other VRFB stack models, this model is more comprehensive in considering the influence of multiple factors. Based on the established model, the electrolyte flow rate distribution across cells in the stack is investigated. The distribution and variation in shunt currents, single-cell current and single-cell voltage are analyzed. The distribution and variation in temperature and heat generation and heat transfer are also researched. It can be found that the VRFB stack temperature will exceed 40 °C when operating at 60 A and 100 mA cm−2 at an ambient temperature of 30 °C, which will lead to electrolyte ion precipitation, affecting the performance and safety of the battery. To control the stack temperature below 40 °C, a new tank cooling control strategy is proposed, and the suitable starting cooling point and the controlled temperature are specified. Compared with the common room cooling strategy, the new tank cooling strategy reduces energy consumption by 27.18% during 20 charge–discharge cycles.

A multi-step calibration strategy for reliable parameter determination of salt rock mechanics constitutive models

The storage of renewable hydrogen in salt caverns requires fast injection and production rates to cope with the imbalance between energy production and consumption. This raises concerns about the mechanical stability of salt caverns under such operational conditions. The use of appropriate constitutive models for salt mechanics is an important step in investigating this issue, therefore many constitutive models with several parameters have been presented in the literature. However, a robust calibration strategy to reliably determine which model and parameter set represents the given rock, based on stress–strain data sets, remains an unsolved challenge. In this paper, for the first time in the community, we present a multi-step strategy to determine a single parameter set based on many deformation data sets for salt rocks. Towards this end, we first develop a comprehensive constitutive model able to capture all relevant nonlinear deformation physics of transient, reverse, and steady-state creep. The determination of the single set of representative material parameters is then achieved by framing the calibration process as an optimization problem, for which the global Particle Swarm Optimization algorithm is employed. To allow for dynamic data integration, a multi-step calibration strategy is developed for a situation where experiments are included one at a time, as they become available. Additionally, due to the existing mild heterogeneity in the experimental rock samples, our optimization strategy is made flexible to allow for this slight heterogeneity. The devised optimization strategy, based on the multi-physics comprehensive constitutive modeling framework, results in a single set of representative material properties of all the deformation data sets. As a rigorous mathematical analysis for the presented method and the lack of relevant experimental data sets, we consider a wide range of synthetic experimental data sets, inspired by the existing sparse relevant data in the literature. The results of our performance analyses show that the proposed calibration strategy is robust. Moreover, the results become increasingly more accurate as more data sets become available.

Numerical investigation of arc characteristics and metal properties in ELAMF-assisted WAAM with wire retraction

In this study, a multi-physics integrated model of wire arc additive manufacturing (WAAM) with wire retraction and external longitudinal alternating magnetic field (ELAMF) assistance is proposed and validated, and the effect of ELAMF on the arc plasma characteristics and metal properties are systematically and quantitatively investigated. The model comprehensively takes the arc plasma heating, wire dynamic retraction, droplet transfer, the formation and diffusion of metal vapor, the melting and solidification of molten metal, and the stirring effect of ELAMF into account. The simulated results show that ELAMF has compressed the arc morphology, and decreased the temperature, potential, current density and static pressure, which are most pronounced when the wire is at an elevated position and the transient current is at its peak phase. Meanwhile, the formation of low-temperature and low-velocity cavities in the center of arc column are promoted by ELAMF, consequently diminishing the energy transferred from the arc plasma to molten pool through thermal conduction and mitigating the squeezing of the arc plasma on the surface of molten pool. The results also demonstrate that the ELAMF has reduced the temperature gradient of liquid metal inside the molten pool, and intensified the forced convection for the occurrence of multiple circulations.

Research on performance optimization of electrolytic cell: Non-parameter topology and parameter optimization

The technology of water electrolysis for hydrogen production converts renewable energy into hydrogen, enabling efficient storage and utilization of clean energy. The key to enabling the large-scale development of water electrolysis technology lies in enhancing the performance of electrolytic cells and minimizing energy consumption. The mastoid process structure obviously influences the electrolytic cell's flow and electrochemical performance. Therefore, this paper studied the sensitivity of the mastoid process structure to the optimization target, designed the shape topology and the parameter optimization structure, and further analyzed the influence of the two optimization methods on the flow and electrochemical performance of the electrolytic cell based on the multi-physics coupling model. The results show that under the same deformation area, the pressure drop of the topology and the parameter optimization structure is reduced by 17.17 % and 11.89 % compared with that of the original structure. The electrochemical properties were almost unaffected, and the change value was less than 0.1 %. Topology optimization design can achieve optimal performance within limited deformation constraints, and parameter optimization design is more conducive to industrial processing.

Research on the high-performance computing method for the neutron diffusion spatiotemporal kinetics equation based on the convolutional neural network

Due to the uncertainty of computational results and the lack of interpretability of models in solving physical field equations in current deep learning, this paper designs a convolutional neural network that can be used to solve the neutron diffusion spatiotemporal kinetics equation in polar and cylindrical coordinate systems. This algorithm directly utilizes the macroscopic cross-section of the material without using the lattice homogenization method, replaces the finite volume method with the extended matrices, and solves the extended matrices using the convolutional kernels instead of the iterative algorithms. Taking the simplified Tsinghua High Flux Reactor (THFR) as an example, the feasibility of the algorithm is verified on the PyTorch platform and compared with the calculation results of the source iteration method running on the GPU. The calculation results show that when the number of grids in the radial and axial sections of the simplified THFR model is 804,600 and 3,576,000, respectively, and the algorithm is iterated 3000 times, the normalized power of the convolutional neural network and the source iteration method converges to 10−10, and the maximum point by point error of the neutron flux density of the above two algorithms converges to 10−5. The computational time consumed by the convolutional neural network is approximately 880.64 s and 3729.62 s, which reduces the computational time by 4.66% and 5.05% compared to the GPU parallel accelerated source iteration method, and the former consumes 43.75% less memory compared to the latter. The convolutional neural network is mainly used as the virtual physics engine for the THFR digital twin system, in addition to solving the neutron diffusion spatiotemporal kinetics equation and further improving computational speed. The algorithm directly utilizes the neutron macroscopic cross-section of the material to calculate the neutron flux density distribution without using the lattice homogenization, providing theoretical guidance and algorithm support for developing the high-precision multi-physical field coupling model.

Diagnosis of Abnormal Overcharge Internal Pressure in Lithium Battery Based on Physics‐Informed Neural Network's Residual Distribution

Changes in the internal pressure of lithium batteries often reflect the state of the batteries. Rapid diagnosis of abnormal internal pressure is importance for battery safety. This article proposes a battery overcharge internal pressure abnormality diagnosis method based on the detection of safety vent strain. First, this method establishes a multiphysical model between the strain of the battery's safety vent, surface temperature rise, and state of charge (SOC) under normal internal pressure. Second, a physics‐informed neural network is used to enhance the multiphysical model's accuracy. Third, the residual sequence generated from actual measurements and estimated values, along with an improved quartile range, is used to diagnose internal pressure abnormalities. Battery overcharge experiment results show that this method can diagnose internal pressure abnormalities within 260 s after overcharging and alarm. At this time, the battery has slight bulging, and the SOC is 102.1%. By conducting charging experiments under different conditions, the low false alarm rate of this method is only 0.575%. The proposed method can identify overcharging abnormalities before the lithium battery safety vent is opened, which greatly ensures the safe operation of the battery system.

Performance investigation of a thermoelectric generator for vehicle exhaust recovery using graded pore density foam metal

Improving the efficiency of thermoelectric generators (TEGs) used to harness residual heat from automobile exhausts is crucial for their widespread adoption. To enhance fluid heat transfer, the Kelvin tetrahedron model is employed for metal foam, and a multiphysical field model of the thermoelectric generator based on metal foam is established. The effects of inserting metal foam with uniform and gradient pore densities into the heat exchanger on the performance of the TEG are investigated. Experimental verification is conducted by constructing a test bench with dimensions identical to those of the model The findings suggest that inserting foam metal significantly enhances the output performance of the TEG, resulting in increases in both output power and efficiency as pore density rises. At Ta = 573 K and ma = 30 g/s, the output power of the TEG with inserted 20 PPI foam metal is enhanced by 140.46 %, while the efficiency experiences a remarkable increase of 197.50 % compared to a smooth pipe. Compared to the performance metrics of uniform foam metal, the positive gradient foam metal exhibits a maximum power increase of 7.89 % and a maximum efficiency increase of 34.46 %, along with an average pressure drop reduction of 27.29 %.

Study on heat and mass transfer behavior of coupled electrochemical reaction in porous structure of proton exchange membrane fuel cell

Proton exchange membrane fuel cell (PEMFC) is widely used in transportation, aviation and energy storage due to its unique advantages. Improving the performance of fuel cells depends largely on effective hydrothermal management. In this work, a multi-physical field coupling model based on the lattice Boltzmann method (LBM) is established to analyze the oxygen transport, liquid water transport, heat transfer and electrochemical reaction, mainly considering the difference between the overpotential of the cell and the surface wettability of the structure. The results show that the overpotential is closely related to the reaction process. The rate of water production is significantly accelerated by increasing the overpotential, and the breakage and merging of water clusters are observed. The increase of overpotential will lead to more energy loss in the form of heat, thus increasing the heat production. Enhancing the hydrophobicity of the structure has a positive effect on the performance of the fuel cell, and the possibility of local hot spots in the low-hydrophobic structure is greater. It is feasible to improve water management and fuel cell performance through wettability design.

Influence of Immersion Orientation on Microstructural Evolution and Deformation Behavior of 40Cr Steel Automobile Front Axle during Oil Quenching.

This study employs the finite element method to investigate the microstructural evolution and deformation behavior of a 40Cr steel automobile front axle during the quenching process. By establishing a multi-physics field coupling model, the study elucidates the variation patterns of the microstructure field in the quenching process of the front axle under different immersion orientations. It is found that along the length direction, the bainite and martensite structures decrease from the center to the edge region, while the ferrite structure shows an increasing trend. Additionally, the influence of immersion orientation on the hardness of the front axle's microstructure and deformation behavior is thoroughly discussed. The results indicate that, firstly, when quenched horizontally, the hardness difference among different regions of the front axle is approximately 8.2 HRC, whereas it reaches 10.3 HRC when quenched vertically. Considering the uniformity of the microstructure, the horizontal immersion method is preferable. Secondly, due to the different immersion sequences in different regions of the front axle leading to varying heat transfer rates, as well as the different amounts of martensite structures obtained in different regions, the deformation decreases along the length direction from the center to the edge region. Horizontal immersion quenching, compared to vertical immersion, results in a reduction of approximately 56.2% and 48.9% in deformation on the representative central cross-section (A-A) and the total length of the front axle, respectively. Therefore, considering aspects such as microstructure uniformity and deformation, the horizontal immersion quenching orientation is more favorable.

Theoretical investigation of stacked supercapacitors under extreme mechanical impact

Power sources for microsystems in extreme mechanical environments are crucial. Supercapacitors, as a common power source for microsystems, may experience failures, fires, and explosions in extreme mechanical impact scenarios such as smart helmets, vehicle collisions, and high-speed aircraft. It is rare to have supercapacitors specifically designed for extreme mechanical impacts, as well as research on their physical processes when subjected to impacts. This research introduces a multi-physics model to describe the energy storage and voltage fluctuations of the proposed stacked supercapacitors under extreme mechanical impacts. With the help of finite element simulation software and interpretable machine learning, we analyzed the influence of various parameters on stacked supercapacitors under extreme mechanical impacts. Furthermore, a prototype of the non-separator stacked supercapacitor, resisting extreme mechanical impacts has been developed. The impact resistance of such supercapacitor was tested using a machete hammer, verifying the consistency between theoretical analysis and experiments. Finally, an iterative framework has been proposed to optimize the energy storage and impact resistance of proposed supercapacitors. This study provides a design prototype, theoretical model, and optimization framework for supercapacitors intended for impact resistance, enhancing the potential application of micro-supercapacitors in extreme mechanical environments.

Life Degradation of Lithium-Ion Batteries Under Vehicle-to-Grid Operations Based on a Multi-physics Model

Life Degradation of Lithium-Ion Batteries Under Vehicle-to-Grid Operations Based on a Multi-physics Model

Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development

Fast growing demands for electric vehicles require better longevity, safety and reliability for next-generation high-energy battery technologies. A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics. Nowadays, Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance, health and safety, but is hurdled by a scarcity of data. To mitigate this issue, this study presents one of the first studies for data development through both experimental studies and three-dimensional (3-D) multi-physics modeling to underpin a deep learning framework with in-depth examination for battery performance and thermal risk prediction. Specifically, Part I focused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model accuracy by two steps: firstly, we validated the multi-physics model against two commercial Lithium-ion batteries, i.e., Panasonic NCR18650B and 18650BD; Then, the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model, such as voltage evolution and maximum local temperature (hot spot). The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory (CNN-LSTM) in part II.

Framework for electrochemical-mechanical behavior of all-solid-state batteries: From the reconstruction method to multi-physics and multi-scale modeling

All-solid-state batteries (ASSBs) are high-energy, high-power batteries. To enhance the understanding of the electrochemical-mechanical behavior in ASSBs across different scales, we developed a multi-physics and multi-scale modeling framework. This framework incorporates elastoplastic finite deformation and electrode microstructures of ASSBs, and the role of gradient plasticity in the governing equation for multiple physical fields was discussed. Utilizing X-ray computed tomography, we reconstructed the microstructure through a machine learning (ML)-informed image segmentation process. Our study clarifies the impact of electrode microstructures on concentration, stress, voltage, delamination and buckling from AM to electrode scale. Comparative analysis of the Feret diameter distribution of active materials (AMs) shows that ML-informed image segmentation outperforms two traditional segmentation methods. We observed that the asynchronous diffusion saturation of AMs, varying in shape and size, significantly influences the electrochemical-mechanical behavior of ASSBs, resulting in complicated debonding indices and J-integral distribution at the interface. The proposed upscaling homogenization procedure is demonstrated to be efficient for buckling analysis, with the shape mode closely matching existing experimental observations. These results shed light on the critical multi-physics and multi-scale coupling mechanisms in ASSBs.

A quasi-3D SinZZ model-driven multi-field Chebyshev FEM for nonlinear vibration control in multilayer multiferroic composite plates

This paper presents an advanced numerical method for modeling and mitigating nonlinear vibrations in thin multilayered fiber-reinforced multiferroic composite plates, addressing complex multi-physical interactions. The proposed methodology leverages a multi-physical coupling Chebyshev finite element formulation, utilizing high-order shape functions derived from Chebyshev polynomials. By integrating the strengths of spectral element methods and Legendre spectral finite element methods, this approach effectively overcomes challenges such as shear locking and spurious zero energy modes while ensuring high convergence rates in multi-physical problems. A quasi-3D refined model, incorporating the Murakami zig-zag model and sinusoidal shear deformation theory, is employed to accurately capture the nonlinear Von Kármán strain–displacement relationship and the magneto-electro-elastic coupling in multilayer structures with thickness-dependent material properties. To suppress nonlinear vibrations, the study utilizes a closed-loop multiphysical Chebyshev finite element model for time-domain analysis of viscoelastically damped systems, employing the Golla–Hughes–McTavish model. The results underscore the significant influence of multiferroic properties and the strategic distribution of ferroelectric fibers within the substrate on the dynamic behavior of the plate. The numerical validation, supported by rigorous verification, demonstrates the robustness of the proposed method in effectively simulating and controlling multilayer fiber-reinforced multiferroic composite plates. Additionally, this research highlights the potential for significant vibration reduction through semi-active damping mechanisms, offering valuable insights for practical applications in industries where precision and stability are critical.

Numerical investigation of the effects of variable fluid properties on cilia-driven flow of tangent hyperbolic fluid in a channel with heat and mass transfer: A new approach to microfluidic pumps

Heat, mass transfer, and non-Newtonian fluid flow processes have gained significant interest in various industrial applications due to their substantial significance in the fields of technology, engineering, and science. The aforementioned processes hold significance in the context of polymer solutions, porous industrial materials, ceramic processing, oil recovery, and fluid beds. This study aims to investigate the impact of temperature-dependent viscosity and thermal conductivity on the cilia-driven flow of tangent hyperbolic fluid with mass and heat transfer. The objectives include analyzing how these variable properties affect the velocity, temperature, and concentration profiles within the fluid flow, thereby providing insights into potential applications in bioenergy systems and enhancing the understanding of non-Newtonian fluid dynamics. Viscous dissipation has been taken into consideration. Firstly, governing nonlinear coupled equations are solved in a fixed frame, and then the results are tracked in the wave frame. MATHEMATICA's NDSolve command is utilized to graphically discuss the findings for several different flow parameters. Analytically stated, solving a problem with such a coupled and nonlinear system is tough. The effect of new parameters is discussed on velocity and temperature profile and graphically shown. It has been observed that the thermal conductivity is improved at moderately low temperatures, whereas the opposite pattern is seen at comparatively high temperatures. On the other hand, the temperature distribution demonstrates a behaviour consistent with lowering as the number of heat Bolus increases. The findings that were provided offer beneficial insight into bioenergy systems and serve as a helpful benchmark for both experimental and extra-progressive computational Multiphysics models.

Multiphysics modeling and simulation of monolayer flow and die swelling in industrial tube extrusion process

AbstractIn a regular extrusion process for thermoplastic specimen, the material behavior in dies and its rheological properties have a major influence on the mechanical properties and the quality of the extruded product. This study outlines the multiphysical model, which has been adapted to address the specific issues associated with polyamide 12 polymer extrusion. A series of numerical simulations, based on the modeling of material behavior, have been conducted using the computational code Comsol Multiphysics®. These simulations have been applied to the study of polymer melt flows through an industrial extrusion die. The objective of this study is to analyze the influence of the imposed extrusion parameters on the physical field distribution and the material displacement within the die. The die swell at the exit of the extrusion die has been investigated as a function of the imposed pressure. The progression of the polymer flow front inside the extrusion tool and the swelling rates have been analyzed. In particular, the phenomenon of delayed swelling at high pressure has been highlighted at high pressure.Highlights Polymer swelling at industrial extrusion die exit is investigated. Polymer melt extrusion within industrial extrusion tool is numerically simulated. Flow front progression and extrudate swell are analyzed. Effect of extrusion pressure on instabilities and die swell is highlighted.

Optimization method for geometric shape of DC GIL insulators based on electric thermal multi-physics field coupling model

Optimization method for geometric shape of DC GIL insulators based on electric thermal multi-physics field coupling model

Thermodynamic and electrochemical performance coupling analysis of single ammonia-fueled tubular solid oxide fuel cell toward low operating temperatures

The primary development strategy for ammonia-fueled solid oxide fuel cell (SOFC) focuses on enhancing the cell lifetime by reducing the operating temperature, albeit inevitably affecting the cell output performance. This study has established a multi-physics model of the single ammonia-fueled tubular SOFC and conducted a thorough analysis and weighing of the improvements in cell stress and the losses in output performance at low temperature operating conditions. As the operating temperature is reduced from 800 °C to 700 °C, the inhomogeneity in its internal temperature distribution and stress distribution is significantly reduced, albeit with a concomitant increase in ohmic impedance by 1.75 Ω cm2 and a reduction in maximum power density by 42.16 %. The study employs the reduction of electrolyte thickness to compensate for the ohmic losses incurred by low temperature, while simultaneously incorporating a material thermal deformation constraint mechanism. As the electrolyte thickness is reduced from 200 μm to 20 μm, the overall thermal deformation variance ranges from 0.0977 to 0.1214. When the electrolyte thickness falls below 80 μm, there is a significant increase in thermal deformation variance. However, adjusting the electrolyte thickness within the range of 80–200 μm results in a relatively small change in overall thermal deformation variance, while also achieving a power density improvement of 0–30.57 %.

A High-Performance Microwave Heating Device Based on a Coaxial Structure

Continuous-flow microwave heating stands out for its ability to rapidly and uniformly heat substances, making it widely applicable in chemical production. However, in practical applications, the permittivity of the heated liquid changes dramatically as the reaction progresses, affecting the efficiency and uniformity of continuous-flow heating. Herein, this work presents a novel microwave heating device based on a coaxial structure for high-performance heating. Our approach commenced with the development of a multiphysical field model, incorporating spiraled polytetrafluoroethylene (PTFE) as a water channel and the coaxial waveguide as a container. The analysis shows that the uniform distribution of the sectional electric field of electromagnetic waves in the TEM mode within the coaxial structure can enhance heating uniformity. Then, a continuous-flow microwave heating system for different liquid loads was established, and experimental measurements were conducted. The heating efficiency for all loads exceeded 90%, which basely matched the simulation results, validating the accuracy of the model. Finally, the heating efficiency and uniformity under different permittivity loads were analyzed, as well as the impact of channel radius on heating efficiency. The device exhibits high heating efficiency under different loads, with uniform radial electric field distribution and stable heating uniformity. This continuous-flow microwave device is suitable for chemical research and production because of its high adaptability to the large dynamic range of permittivity, contributing to the promotion of microwave energy applications in the chemical industry.