| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mechanical and Aerospace Engineering | 204 | 42 | 64 | 13 |
| Engineering and Technology | 506 | 57 | 72 | 16 |
The objective of Computational particle mechanics is to combine knowledge in the areas of Mechanics, Discrete element method, Finite element method, Smoothed-particle hydrodynamics and Computational Science and Engineering. Mechanics research is concerned with Flow (psychology) in particular. The research on Discrete element method featured in the journal combines topics in other fields like Mathematical analysis, Granular material, Composite material, Fracture (geology) and Structural engineering.
The concepts on Finite element method presented in it can also apply to other research fields, including Coupling, Numerical analysis, Free surface and Deformation (meteorology). It links adjacent topics like Smoothed-particle hydrodynamics with Computer simulation. The journal focused on works that combine different research areas such as Computational Science and Engineering and Applied mathematics.
Most of the works presented in Computational particle mechanics deals with Applied mathematics but it intersects with the subject of Meshfree methods. The journal features Material point method research that overlaps with concepts in Discretization.
The most cited articles mainly tackle studies in Mechanics, Computational Science and Engineering, Smoothed-particle hydrodynamics, Finite element method and Discrete element method. While work presented in the published articles provide substantial information on Mechanics, it also covers topics in Basis (linear algebra) and Penetration (firestop). The study of Discrete element method in the most cited publications encompasses disciplines such as Rock mechanics, as well as fields such as Fracture (geology) and Packing problems, all of which overlap with one another.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Computational particle mechanics (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Computational particle mechanics (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 3.74% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 13.59% were posted by at least one author from the top 10 institutions publishing in the journal. Another 18.45% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 22.33% of all publications and 45.63% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
José M. Domínguez;Georgios Fourtakas;Corrado Altomare;Ricardo B. Canelas
(2021)Renato Vacondio;Corrado Altomare;Corrado Altomare;Matthieu De Leffe;Xiangyu Hu
(2021)A. English;J. M. Domínguez;R. Vacondio;A. J. C. Crespo
(2021)Jan-Philipp Fürstenau;Henning Wessels;Christian Weißenfels;Peter Wriggers
(2020)Tsung Hui Huang;Haoyan Wei;Jiun Shyan Chen;Michael C. Hillman
(2020)Renwei Liu;Renwei Liu;Jiale Yan;Jiale Yan;Shaofan Li;Shaofan Li
(2020)Yuya Yamaguchi;Shinsuke Takase;Shuji Moriguchi;Kenjiro Terada
(2020)Alejandro Cornejo;Vicente Mataix;Francisco Zárate;Eugenio Oñate
(2020)Georgios Moutsanidis;Jacob J. Koester;Michael R. Tupek;Jiun-Shyan Chen
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