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A multi-physics process model is developed to analyze reactive melt infiltration (RMI) fabrication of ceramic-matrix composite (CMC) materials and components. The paper aims to discuss this issue.

Within this model, the following key physical phenomena governing this process are accounted for: capillary and gravity-driven unsaturated flow of the molten silicon into the SiC/SiC CMC preform; chemical reactions between the silicon melt and carbon (either the one produced by the polymer-binder pyrolysis or the one residing within the dried matrix slurry); thermal-energy transfer and source/sink phenomena accompanying reactive-flow infiltration; volumetric changes accompanying chemical reactions of the molten silicon with the SiC preform and cooling of the as-fabricated CMC component to room temperature; development of residual stresses within, and thermal distortions of, the as-fabricated CMC component; and grain-microstructure development within the SiC matrix during RMI.

The model is validated, at the material level, by comparing its predictions with the experimental and modeling results available in the open literature. The model is subsequently applied to simulate RMI fabrication of a prototypical gas-turbine engine hot-section component, i.e. a shroud. The latter portion of the work revealed the utility of the present computational approach to model fabrication of complex-geometry CMC components via the RMI process.

To the authors’ knowledge, the present work constitutes the first reported attempt to apply a multi-physics RMI process model to a gas-turbine CMC component.

The purpose of this paper is to develop multi-physics computational model for the conventional gas metal arc welding (GMAW) joining process has been improved with respect to its predictive capabilities regarding the spatial distribution of the mechanical properties (strength, in particular) within the weld.

The improved GMAW process model is next applied to the case of butt-welding of MIL A46100 (a prototypical high-hardness armor-grade martensitic steel) workpieces using filler-metal electrodes made of the same material. A critical assessment is conducted of the basic foundation of the model, including its five modules, each dedicated to handling a specific aspect of the GMAW process, i.e.: first, electro-dynamics of the welding-gun; second, radiation/convection controlled heat transfer from the electric arc to the workpiece and mass transfer from the filler-metal consumable electrode to the weld; third, prediction of the temporal evolution and the spatial distribution of thermal and mechanical fields within the weld region during the GMAW joining process; fourth, the resulting temporal evolution and spatial distribution of the material microstructure throughout the weld region; and fifth, spatial distribution of the as-welded material mechanical properties.

The predictions of the improved GMAW process model pertaining to the spatial distribution of the material microstructure and properties within the MIL A46100 butt-weld are found to be consistent with general expectations and prior observations.

To explain microstructure/property relationships within different portions of the weld, advanced physical-metallurgy concepts and principles are identified, and their governing equations parameterized and applied within a post-processing data-reduction procedure.

The purpose of this paper is to discuss the recently developed multi-physics computational model for the conventional Gas Metal Arc Welding (GMAW) joining process that has been upgraded with respect to its predictive capabilities regarding the spatial distribution of the mechanical properties controlling the ballistic limit (i.e. penetration resistance) of the weld.

The original model consists of five modules, each dedicated to handling a specific aspect of the GMAW process, i.e.: electro-dynamics of the welding-gun; radiation-/convection-controlled heat transfer from the electric arc to the workpiece and mass transfer from the filler-metal consumable electrode to the weld; prediction of the temporal evolution and the spatial distribution of thermal and mechanical fields within the weld region during the GMAW joining process; the resulting temporal evolution and spatial distribution of the material microstructure throughout the weld region; and spatial distribution of the as-welded material mechanical properties. The model is upgraded through the introduction of the sixth module in the present work in recognition of the fact that in thick steel GMAW weldments, the overall ballistic performance of the armor may become controlled by the (often inferior) ballistic limits of its weld (fusion and heat-affected) zones.

The upgraded GMAW process model is next applied to the case of butt-welding of MIL A46100 (a prototypical high-hardness armor-grade martensitic steel) workpieces using filler-metal electrodes made of the same material. The predictions of the upgraded GMAW process model pertaining to the spatial distribution of the material microstructure and ballistic-limit-controlling mechanical properties within the MIL A46100 butt-weld are found to be consistent with general expectations and prior observations.

To the authors’ knowledge, the present work is the first reported attempt to establish, using computational modeling, functional relationships between the GMAW process parameters and the mechanical properties controlling the ballistic limit of the resulting weld.

The purpose of this paper is to clarify the effect of material hardening model and lump-pass method on the thermal-elastic-plastic (TEP) finite element (FE) simulation of residual stress induced by multi-pass welding of materials with cyclic plasticity.

Nickel-base alloy and stainless steel, which are used in J-type weld for manufacturing the nuclear reactor pressure head, can easily harden during multi-pass welding. The J-weld welding experiment is carried out and the temperature cycle and residual stress are measured to validate the TEP simulation. Thermal-mechanical sequence coupling method is employed to get the welding residual stress. The lumped-pass model and pass-by-pass FE model are built and two materials hardening models, kinematic hardening model and mixed hardening model, are adopted during the simulations. The effects of material hardening models and lumped-pass method on the residual stress in J-weld are distinguished.

Based on the kinematic hardening model, the stresses simulated with the lumped-pass FE model are almost consistent with those obtained by the pass-by-pass FE model; while with the mixed hardening material model, the lumped-pass method has great effect on the simulated stress.

A computation with mixed isotropic-kinematic material seems not to be the appropriate solution when using the lumped-pass method to save the computation time.

In the simulation of multi-pass welding residual stress involved in materials with cyclic plasticity, the material hardening model should be carefully considered. The kinematic hardening model with lump-pass FE model can be used to get better simulation results with less computation time. The results give a direction for welding residual stress simulation for the large structure such as the reactor pressure vessel.

The uniformity of electrodeposition is the key to successful application of pattern plating because the quality of electrodeposited copper layer has a huge impact on the performance of printed circuit boards (PCBs). The multi-physics coupling technology was used to accurately analyze and forecast the characteristics of electrochemical system. Further, an optimized plating bath was used to achieve a uniform electrodeposition.

A multi-physics coupling numerical simulation based on the finite element method was used to optimize electrodeposition conditions in pattern plating process. The influences of geometric and electrochemical factors on uniformity of current distribution and electrodeposited layer thickness were discussed by multi-physics coupling.

The model results showed that the distance between cathode and anode and the insulating shield had a great impact on uniformity of electrodeposition. By numerical simulation, it had been proved that using an auxiliary cathode was an effective and simple way to improve uniformity of electrodeposition due to redistributing of the current. This helped to achieve more uniform surface of the copper patterns by preventing the edge effect and the roughness of the copper layer was reduced to 1 per cent in the secondary current distribution model.

The research is still in progress with the development of high-performance computers.

A multi-physics coupling platform is an excellent tool for quickly and cheaply studying the process behaviors under a variety of operating conditions.

The numerical simulation method has laid the foundation for the design and improvement of the plating bath.

By multi-physics coupling technology, we built a bridge between theoretical and experimental study for control of uniformity of pattern plating in PCB manufacturing. This method can help optimize the design of plating bath and uniformity of pattern plating in PCB manufacturing.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

The design of electromagnetic systems for a variety of applications such as induction heating, electrical machines, actuators and transformers requires the solution of a multi-physics problem often involving thermal, structural and mechanical coupling to the electromagnetic system. This results in a complex analysis system embedded within an optimization process. The appearance of high-performance computing systems over the past few years has made coupled simulations feasible for the design engineer. When coupled with surrogate modelling techniques, it is possible to significantly reduce the wall clock time for generating a complete design while including the impact of the multi-physics performance on the device.

An architecture is proposed for linking multiple singe physics analysis tools through the material models and a controller which schedules the execution of the various software tools. The combination of tools is implemented on a series of computational nodes operating in parallel and creating a “super node” cluster within a collection of interconnected processors.

The proposed architecture and job scheduling system can allow a parallel exploration of the design space for a device.

The originality of the work derives from the organization of the parallel computing system into a series of “super nodes” and the creation of a materials database suitable for multi-physics interactions.

Natural gas hydrate (NGH) has been regarded as one of the most important resources due to NGH's large amounts of reserve. However, NGH development still faces many technical challenges, such as low production rate and reservoir instability resulting from NGH decomposition. Therefore, developing a fully coupled THMC model for simulating the hydrate decomposition and studying its mechanical behavior is very important and necessary. The purpose of this article is to develop and solve a multi-phase, strong nonlinearity and large-scale fully coupled thermal-hydro-mechanical–chemical (THMC) model for simulating the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and rock deformation during NGH decomposition.

In this paper, a multi-phase, strong nonlinearity and large-scale fully coupled THMC model is developed for simulating the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and rock deformation during NGH dissociation. The fully coupled THMC model is solved by using a fully implicit finite element method, in which the gas pressure, water pressure, temperature and displacement are taken as basic unknown variables. The proposed model is validated against with the experimental data, showing high accuracy and reliability.

A multi-phase, strong nonlinearity and large-scale fully coupled THMC model is developed for simulating the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and rock deformation during NGH decomposition. The proposed model is validated against with the experimental data, showing high accuracy and reliability.

Some assumptions are made to make the model tractable, including (1) the composition gas of hydrate is pure methane; (2) the gas-liquid multi-phase flow in the pore obeys Darcy's law; (3) hydrate occurs on the surface of soil particles, both of them form the composite consolidation material; (4) the small-strain assumption is applied to composite solid materials, which are treated as skeletons and cannot be moved; (5) momentum change caused by phase change is not considered.

NGH has been regarded as one of the most important resources due to its large amounts of reserve. However, NGH development still faces many technical challenges, such as low production rate and reservoir instability resulting from NGH decomposition. Most of the existing studies decouple the process with solid deformation and seepage behavior, but the accuracy of the numerical results will be sacrificed to certain extent. Therefore, it is very important and necessary to develop a fully coupled THMC model for simulating the hydrate decomposition and studying its mechanical behavior.

NGH, widely distributed in shallow seabed or permanent frozen region, has the characteristics of high energy density and high combustion efficiency (Yan et al., 2020). A total of around 7.5 × 1,018 m³ has been proved to exist around the world and 1 m³ of NGH can release about 160–180 m³ of natural gas (Kvenvolden and Lorenson) under normal conditions. Safely and sustainably extracting NGH commercially can effectively relieve global energy pressure and contribute to achieving carbon reduction goals.

The novelty of the present work lies in mainly two aspects. First, a fully coupled THMC model is developed for studying the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and solid deformation during NGH dissociation. Second, the numerical solution is obtained by using a fully implicit finite element method (FEM) and is validated against experimental data.

The purpose of this paper is to present a reduced order computational strategy for a multi-physics simulation involving a fluid flow, electromagnetism and heat transfer in a hot-wall chemical vapour deposition reactor. The main goal is to produce a multi-parametric solution for fast exploration of the design space to perform numerical prototyping and process optimisation.

Different reduced order techniques are applied. In particular, proper generalized decomposition is used to solve the parameterised heat transfer equation in a five-dimensional space.

The solution of the state problem is provided in a compact separated-variable format allowing a fast evaluation of the process-specific quantities of interest that are involved in the optimisation algorithm. This is completely decoupled from the solution of the underlying state problem. Therefore, once the whole parameterised solution is known, the evaluation of the objective function is done on-the-fly.

Reduced order modelling is applied to solve a multi-parametric multi-physics problem and generate a fast estimator needed for preliminary process optimisation. Different order reduction techniques are combined to treat the flow, heat transfer and electromagnetism problems in the framework of separated-variable representations.

Friction stir welding (FSW) butt-joining involving the use of a dissimilar filler metal insert between the retreating and advancing portions of the workpiece is investigated computationally using a combined Eulerian-Lagrangian (CEL) finite element analysis (FEA). The emphasis of the computational analysis was placed on the understanding of the inter-material mixing and weld-flaw formation during a dissimilar-material FSW process. The paper aims to discuss these issues.

The FEA employed is of a two-way thermo-mechanical character (i.e. frictional-sliding/plastic-work dissipation was taken to act as a heat source in the energy conservation equation), while temperature is allowed to affect mechanical aspects of the model through temperature-dependent material properties. Within the analysis, the workpiece and the filler-metal insert are treated as different materials within the Eulerian subdomain, while the tool was treated as a conventional Lagrangian subdomain. The use of the CEL formulation within the workpiece insert helped avoid numerical difficulties associated with excessive Lagrangian element distortion.

The results obtained revealed that, in order to obtain flaw-free FSW joints with properly mixed filler and base materials, process parameters including the location of the tool relative to the centerline of the weld must be selected judiciously.

To the authors’ knowledge, the present work is the first reported attempt to simulate FSW of dissimilar materials.