Analysis and numerical solution of dynamic Jiles–Atherton model applied to hysteresis modeling for giant magnetostrictive materials

PurposeOwing to the excellent performance, giant magnetostrictive materials (GMMs) are widely used in many engineering fields. The dynamic Jiles–Atherton (J-A) model, derived from physical mechanism, is often used to describe the hysteresis characteristics of GMM. However, this model, despite cited by many different literature studies, seems not to possess unique expressions, which may cause great trouble to the subsequent application. This paper aims to provide the rational expressions of the dynamic J-A model and propose a numerical computation scheme to obtain the model results with high accuracy and fast speed.Design/methodology/approachThis paper analyzes different published papers and provides a reasonable form of the dynamic J-A model based on functional properties and physical explanations. Then, a numerical computation scheme, combining the Newton method and the explicit Adams method, is designed to solve the modified model. In addition, the error source and transmission path of the numerical solution are investigated, and the influence of model parameters on the calculation error is explored. Finally, some attempts are made to study the influence of numerical scheme parameters on the accuracy and time of the computation process. Subsequently, an optimization procedure is proposed.FindingsA rational form of the dynamic J-A model is concluded in this paper. Using the proposed numerical calculation scheme, the maximum calculation error, while computing the modified model, can remain below 2 A/m under different model parameter combinations, and the computation time is always less than 0.5 s. After optimization, the calculation speed can be enhanced with the computation accuracy guaranteed.Originality/valueTo the best of the authors’ knowledge, this paper is the first one trying to provide a rational form of the dynamic J-A model among different citations. No other research studies focus on designing a detailed computation scheme targeting the fast and accurate calculation of this model as well. And the performance of the proposed calculation method is validated in different conditions.

Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption

The discovery and description of patterns in electric energy consumption time series is fundamental for timely management of the system. A bicluster describes a subset of observation points in a time period in which a consumption pattern occurs as abrupt changes or instabilities homogeneously. Nevertheless, the pattern detection complexity increases with the number of observation points and samples of the study period. In this context, current bi-clustering techniques may not detect significant patterns given the increased search space. This study develops a parallel evolutionary computation scheme to find biclusters in electric energy. Numerical simulations show the benefits of the proposed approach, discovering significantly more electricity consumption patterns compared to a state-of-the-art non-parallel competitive algorithm.

Scaling Scientific Cellular Automata Microstructure Evolution Model of Static Recrystallization toward Practical Industrial Calculations

An attempt to bridge the gap between capabilities offered by advanced full-field microstructure evolution models based on the cellular automata method and their practical applications to daily industrial technology design was the goal of the work. High-performance parallelization techniques applied to the cellular automata static recrystallization (CA-SRX) model were selected as a case study. Basic assumptions of the CA-SRX model and developed modifications allowing high-performance computing are presented within the paper. Particular attention is placed on the development of the parallel computation scheme allowing numerical simulations even for a large volume of material. The development of new approaches to handle communication within the distributed environment is also addressed in the paper as a means to obtain higher computational efficiency. Evaluation of model limits was based on the scalability analysis. The investigation was carried out for the 3D and 2D case studies. Therefore, the complex static recrystallization cellular automata simulation taking into account the influence of recovery, nucleation based on accumulated energy, and the progress of recrystallization as a function of stored energy and grain boundary mobility with high-performance computing capabilities is now possible. The research highlighted that parallelization is more effective with an increasing number of cellular automata cells processed during the entire simulation. It was also proven that the developed parallelization scheme and communication mechanism provides a possibility of obtaining scaled speedup over 700 times for 2D and over 800 times for 3D computational domains, which is crucial for future applications in industrial practice. Therefore, the presented approach’s main advantage is based on the possibility of running the calculation based on input data obtained directly from high-resolution 3D imaging of the microstructure. With that, the full immersion of the experimental results into the numerical model is possible. The second novelty aspect of this work is related to the identification of the quality of model predictions as a function of model size reductions.

On the performance of equiangular mascon solution in GRACE-like missions

Mass concentration (mascon) solutions for GRACE (Gravity Recovery and Climate Experiment) data are widely used in various regional-to-global mass change studies. The current advances in the mascon solution have mainly concentrated on improving the spatial resolution of the solution, enhancing the applied least-squares regularization, and the characterization of the solution errors. Most of the mascon solutions are obtained on the equal-area grid, inducing complexities in creating the grid and its presentation. In this regard, estimation of the mascon solutions on equiangular grids can be appealing. Furthermore, in the equal-area methods, there is no global criterion to determine the size of the mascon areas. The mascon size is usually chosen in a subjective manner which hampers the objective application of different mascon solutions. In view of these challenges, two main questions are addressed in this study: i) what kind of modifications should be made in computation scheme of the mascon solution if equiangular grids are used to account for different areas of the grid patches, and ii) in case of non-equiangular solutions, how to define an objective criterion for the patch sizes based on the resolution of both the observation and the signal of interest. We investigate the performance of the high-resolution mascon-based approach, proposed by Abedini et al. [2021], which uses GRACE-like observations similar to level-1 data for a period of one month over the Greenland region. Two main practical issues are studied on the estimation of the surface density changes as follows. First, we show that for equiangular grids, the area of the patches should be accounted for in the regularization by introducing area-affected weights for the unknown parameters. We investigate the effect of three different area-affected weighting strategies on the derived solution. Secondly in order to obtain proper size for the patches, a novel approach is presented to investigate the performance of the mascon solution using the analysis of the resolution matrix entries. The proposed resolution analysis is used to obtain the optimal patch size for the discretization of the area of interest. Based on the results, it is demonstrated that the minimum legible patch size in the Greenland area for the current settings of the GRACE observations is 0.5 degree in the NS direction and a latitude-adaptive grid-size rather than equiangular grids at high latitude regions in the EW direction.

Publicly Verifiable Outsourcing Computation for QR Decomposition Based on Blockchain

In the Big Data Era, outsourcing computation has become increasingly significant as it supplies computation resources for clients with limited resources. However, there are still many security challenges such as payment fairness, privacy protection, and verification. In this paper, we propose a secure publicly verifiable outsourcing computation scheme for the large-scale matrix QR decomposition. In the proposed scheme, client can pay for outsourcing services through blockchain-based payment system which achieves the payment fairness. Moreover, to protect privacy, both permutation matrix and block diagonal matrix are applied in encryption process. Meanwhile, to achieve the public verification, the computational complexity is reduced by using the matrix digest technology. It is worth mentioning that our scheme is provable and secure under the co-CDH assumption.