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The purpose of this paper is to develop a coupled approach to solve the pressure–velocity-coupled problem efficiently in the three-dimensional injection molding simulation.

A fully coupled pressure–velocity algorithm is developed to solve the coupled problem, by treating the pressure gradient term implicitly. And, the Schur complement preconditioned FGMRES is applied to decompose the resulting coupled pressure–velocity equation into pressure and velocity subsystems. Then, BoomerAMG is adopted to solve the pressure subsystem, and block Jacobi preconditioned FGMRES is applied to the velocity subsystem.

According to the several experiments, the fully coupled pressure–velocity algorithm was demonstrated to have faster convergence than the traditional SIMPLE algorithm, and the calculating time was reduced by up to 70 per cent. And, the Schur complement preconditioned FGMRES worked more efficiently than block Gauss–Seidel preconditioned FGMRES, block-selective AMG and AMG with block ILU(0) smoother and could take at least 47.4 per cent less time. The proposed solver had good scalability for different-size problems, including various cases with different numbers of elements. It also kept good speedup and efficiency in parallel performance.

A coupled solver has been proposed to effectively solve the coupled problem in the three-dimensional injection molding simulation, which is more robust and efficient than existing methods.

This paper aims to develop an integrated cooling simulation for the temperature history of the panel during the forming process.

A local one‐dimensional transient analysis in the thickness direction is adopted for the panel part, which employs finite‐difference method. And a three‐dimensional, boundary element method is used for the numerical implementation of the heat transfer analysis in the mold region, which is considered as three‐dimensional conduction. The Renormalization‐Group turbulence model is applied for the jet impinging cooling. The part and mold analyses are coupled so as to match the temperature and heat flux on the glass‐mold interface.

The paper provides mathematical model and numerical strategy adapted to the problem, with experimental verification that shows a good agreement.

Cooling design in the forming operation of picture tube panel is of great importance because it significantly affects the part quality associated with residual stresses and productivity. The developed simulation package is a tool in the optimization of processing parameters and in‐mold cooling system structure design.

This paper presents a realistic, integrated, and coupled numerical model for analyzing the panel cooling process that retains important aspects of the problem. The paper could be very valuable to the researchers in this field as a benchmark for their analyses.

The purpose of this paper is to propose an efficient iterative method for large-scale finite element equations of bad numerical stability arising from deformation analysis with multi-point constraint using Lagrange multiplier method.

In this paper, taking warpage analysis of polymer injection molding based on surface model as an example, the performance of several popular Krylov subspace methods, including conjugate gradient, BiCGSTAB and generalized minimal residual (GMRES), with diffident Incomplete LU (ILU)-type preconditions is investigated and compared. For controlling memory usage, GMRES(m) is also considered. And the ordering technique, commonly used in the direct method, is introduced into the presented iterative method to improve the preconditioner.

It is found that the proposed preconditioned GMRES method is robust and effective for solving problems considered in this paper, and approximate minimum degree (AMD) ordering is most beneficial for the reduction of fill-ins in the ILU preconditioner and acceleration of the convergence, especially for relatively accurate ILU-type preconditioning. And because of concerns about memory usage, GMRES(m) is a good choice if necessary.

In this paper, for overcoming difficulties of bad numerical stability resulting from Lagrange multiplier method, together with increasing scale of problems in engineering applications and limited hardware conditions of computer, a stable and efficient preconditioned iterative method is proposed for practical purpose. Before the preconditioning, AMD reordering, commonly used in the direct method, is introduced to improve the preconditioner. The numerical experiments show the good performance of the proposed iterative method for practical cases, which is implemented in in-house and commercial codes on PC.

The purpose of this paper is to overcome the application limitations of other multi-variable regression based on polynomials due to the huge computation room and time cost.

In this paper, based on the idea of feature selection and cascaded regression, two strategies including Laguerre polynomials and manifolds optimization are proposed to enhance the accuracy of multi-variable regression. Laguerre polynomials were combined with the genetic algorithm to enhance the capacity of polynomials approximation and the manifolds optimization method was introduced to solve the co-related optimization problem.

Two multi-variable Laguerre polynomials regression methods are designed. Firstly, Laguerre polynomials are combined with feature selection method. Secondly, manifolds component analysis is adopted in cascaded Laguerre polynomials regression method. Two methods are brought to enhance the accuracy of multi-variable regression method.

With the increasing number of variables in regression problem, the stable accuracy performance might not be kept by using manifold-based optimization method. Moreover, the methods mentioned in this paper are not suitable for the classification problem.

Experiments are conducted on three types of datasets to evaluate the performance of the proposed regression methods. The best accuracy was achieved by the combination of cascade, manifold optimization and Chebyshev polynomials, which implies that the manifolds optimization has stronger contribution than the genetic algorithm and Laguerre polynomials.

As part of a cross-cultural research, the purpose of this paper is to provide further insights into the existing product-country image (PCI) of the Chinese consumers’ perceptions of Spain, its products, and its people, thereby providing an overall contemporary Spanish PCI within the Chinese market.

In total, 215 valid structured questionnaires were gathered throughout China. Following a pre-investigation of 259 usable questionnaires, a 52-item construct was drawn from the existing widely used PCI item scales, designing a structured PCI construct, covering: “country image,” “personal image,” “product image,” “general knowledge about Spain,” and “personal data.”

The study provides: a PCI pre-investigation research results as well as a literature review on PCI topic; research results for the five hypotheses concerning Chinese citizens’ view of Spanish product price, quality, technicality, inventiveness, and known brands view; descriptive statistics as well as result graphs for each of the covered PCI sections; our PCI construct variable correlations with the tested five product image variables; and general implications.

Standardizing PCI longitudinal studies, as well as focusing on how the Spanish Government and private sector should determine the long-term criteria to facilitate a clearer representation of the Spanish PCI evolution within the Chinese market, is advised.

The current study intends to capture insights for Spain and other country’s public and private sector decision makers to better integrate market and non-market strategies. The results would further assist delineating strategies to avoid the liability of foreignness of Spanish organizations within China.

The purpose of this paper is to facilitate and clarify the perceptions of Spanish consumers towards China, its people, and its products, while outlining the overall contemporary Chinese product-country image (PCI) within the Spanish market.

A 52-item construct was adapted from former PCI scales from cross-cultural equivalence, including “country image”, “personal image”, “product image”, “general knowledge about China”, and “personal data”. Overall, 215 valid structured questionnaires were gathered.

The current study provides: a PCI literature review; hypotheses results concerning Spanish citizens’ views of Chinese products’ price, quality, technicality, inventiveness, and known brands concepts; descriptive statistics and results graphs for each of the PCI sections; and correlations of other variables with the five product image variables within the construct.

Implementing standardized, longitudinal consumer studies that facilitate better comprehension of PCI evolution within a specific market emerged as a future research agenda.

The data informed both the Chinese public and private organizations’ managers of the importance of adapting to the market and non-market environments within Spain to avoid the liability of country of origin effect.