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Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

In this paper, a strategy for analyzing a problem of the transient thermal coupling with the elastoplastic finite deformation is presented. A general constitutive equation is deduced by assuming the material properties to be temperature‐dependent. The thermal and mechanical coupling problem is solved with a staggered algorithm, which partitions the coupled problem into an elasto‐plastic problem at the known temperature field and a pure heat transfer problem at the fixed configuration. In this procedure, the elasto‐plastic mechanical analysis is based on the static‐explicit solution algorithm, which applies the finite deformation theory to describe the nonlinear behavior of the deformation body and its contact interaction with the tools during the forming process induced by the ordinary external loading and the “thermal loading”. In addition, both the ordinary heat transfer boundary conditions and the mechanical terms are taken into account in the implicit finite element analysis of the heat transfer. A special method based on the R‐minimum strategy is presented to solve the interaction problem between the static‐explicit mechanical analysis and the implicit thermal analysis. Furthermore, as examples, the analyses of sheet warm forming processes are demonstrated.

It is suggested that, to be successful, innovation teams should be small and consist of people with key expertise who want to participate and develop new solutions within their organisations. When it comes to conducting innovation work, I suggest shared leadership may be a factor influencing success. In this chapter, a theoretical framework is presented on the shared leadership of innovation teams. The key to establishing shared leadership in innovation teams is to plan for it as the team is created, not after the team has already been formed, as this may result in various problems in the intended innovation project. The proposed framework details key aspects to consider; some of which are related to external factors such as management and resources, and some to internal factors such as the team’s size, competencies, and their ability to develop norms and ways of working together. The proposed framework is applicable for managers, innovation leaders, and team members, and contributes to previous research on shared leadership and innovation leadership. Further research on the proposed framework is suggested.

This paper aims to explore the impact of product market competition (PMC) on companies’ investment in external auditing.

This paper applies a conceptual framework derived from demand–supply analysis and a panel data set of 2,263 listed manufacturing companies in China covering the period 2012–2019. In the assessment of PMC, this study measures industry-level competition intensity and company-specific market power separately.

Industries appear to engage in a lower average level of external auditing if industrial competition intensity is either too high or too low. Similarly, companies spend less on external auditing if their market power is either too strong or too weak, and the company-level inverted U-shaped relationship is much more evident in industries with weak PMC.

This paper shows that a company’s external audit strategy is affected by the level of competition it faces in its market. The findings of this paper can improve the current linear PMC–auditing theoretical framework and provide insights into the strategic auditing of listed companies in China. The findings also have significant implications for policy recommendations regarding corporate governance and market scrutiny regulations.

This paper shows that volatility persistence in GARCH models and spurious long memory in autoregressive models may arise if the possibility of structural changes is not incorporated in the time series model. It also describes a tractable hidden Markov model (HMM) in which the regression parameters and error variances may undergo abrupt changes at unknown time points, while staying constant between adjacent change-points. Applications to real and simulated financial time series are given to illustrate the issues and methods.

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.

During the industrial design process, a product is usually modified and analyzed repeatedly until reaching the final design. Modifying the model and regenerating a mesh for every update during this process is very time consuming. To improve efficiency, it is necessary to circumvent the computer-aided design modeling stage when possible and directly modify the meshes to save valuable time. The purpose of this paper is to develop a method for mesh modifications.

In contrast to existing studies, which focus on one or a class of modifications, this paper comprehensively studies mesh union, mesh gluing, mesh cutting and mesh partitioning. To improve the efficiency of the method, the paper presents a fast and effective surface mesh remeshing algorithm based on a ball-packing method and controls the remeshing regions with a size field.

Examples and results show that the proposed mesh modification method is efficient and effective. The proposed method can be also applied to meshes with different material properties, which is very different with previous work that is only suitable for the meshes with same material property.

This paper proposes an efficient and comprehensive tetrahedral mesh modification method, through which engineers can directly modify meshes instead of models and save time.

Sheet metal forming is a process of shaping thin sheets of metal by applying pressure through male or female dies or both. In most of used sheet‐formating processes the metal is subjected to primarily tensile or compressive stresses or both. During the last three decades considerable advances have been made in the applications of numerical techniques, especially the finite element methods, to analyze physical phenomena in the field of structural, solid and fluid mechanics as well as to simulate various processes in engineering. These methods are useful because one can use them to find out facts or study the processes in a way that no other tool can accomplish. Finite element methods applied to sheet metal forming are the subjects of this paper. The reason for writing this bibliography is to save time for readers looking for information dealing with sheet metal forming, not having an access to large databases or willingness to spend own time with uncertain information retrieval. This paper is organized into two parts. In the first one, each topic is handled and current trends in the application of finite element techniques are briefly mentioned. The second part, an Appendix, lists papers published in the open literature. More than 900 references to papers, conference proceedings and theses/dissertations dealing with subjects that were published in 1995‐2003 are listed.

Deadlock is a rather undesirable phenomenon and must be well solved in flexible manufacturing systems (FMS). This paper aims to propose a general iterative deadlock control method for a class of generalized Petri nets (GPN), namely, G-systems, which can model an FMS with assembly and disassembly operations of multiple resource acquisition. When given an uncontrolled G-system prone to deadlocks, the work focuses on the synthesis of a near-optimal, non-blocking supervisor based on reachability graph (RG) analysis.

The concept of a global idle place (GIP) for an original uncontrolled G-system is presented. To simplify the RG computation of an uncontrolled G-system, a GIP is added temporarily to the net model during monitor computation steps. Starting with one token and then by gradually increasing the number of tokens in the GIP at each iteration step, the related net system is obtained. The minimal-covered-set of all bad markings of the related net system suffering from deadlock can be identified and then removed by additional monitors through an established place-invariant control method. Consequently, all related systems are live, and the GIP is finally removed when the non-blockingness of the controlled system is achieved. Meanwhile, the redundancy of monitors is also checked.

A typical G-system example is provided to demonstrate the applicability and effectiveness of the proposed method. Experiments show that the proposed method is easy to use and provides very high behavioral permissiveness for G-system. Generally, it can achieve an optimal or a near-optimal solution of the non-blocking supervisor.

In this work, the concept of GIP for G-systems is presented for synthesis non-blocking supervisors based on RG analysis. By using GIP, an effective deadlock control method is proposed. Generally, the proposed method can achieve an optimal or a near-optimal, non-blocking supervisor for an uncontrolled G-system prone to deadlocks.