Example of Computational Particle Mechanics format
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Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format
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Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format Example of Computational Particle Mechanics format
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Computational Particle Mechanics — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Computational Mathematics #43 of 152 down down by 26 ranks
Numerical Analysis #19 of 66 down down by 14 ranks
Computational Mechanics #27 of 79 down down by 20 ranks
Modeling and Simulation #105 of 290 down down by 63 ranks
Civil and Structural Engineering #116 of 318 down down by 75 ranks
Fluid Flow and Transfer Processes #32 of 83 down down by 24 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 197 Published Papers | 651 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 09/06/2020
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

3.3

CiteRatio for Computational Particle Mechanics from 2016 - 2020
Year Value
2020 3.3
2019 3.3
2018 3.5
2017 4.2
graph view Graph view
table view Table view

0.483

17% from 2019

SJR for Computational Particle Mechanics from 2017 - 2020
Year Value
2020 0.483
2019 0.579
2018 0.86
2017 0.888
graph view Graph view
table view Table view

0.902

12% from 2019

SNIP for Computational Particle Mechanics from 2017 - 2020
Year Value
2020 0.902
2019 1.021
2018 1.397
2017 1.464
graph view Graph view
table view Table view

insights Insights

  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 17% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 12% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Computational Particle Mechanics

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Springer

Computational Particle Mechanics

Approved by publishing and review experts on SciSpace, this template is built as per for Computational Particle Mechanics formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 551 authors to write and format their manuscripts to this journal.

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Last updated on
09 Jun 2020
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ISSN
2196-4386
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Open Access
Hybrid
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Author Year
(Blonder et al, 1982)
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Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1007/S40571-015-0082-3
Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results
P. Van Liedekerke1, Margriet Palm1, Nick Jagiella1, Dirk Drasdo1

Abstract:

In this paper we present an overview of agent-based models that are used to simulate mechanical and physiological phenomena in cells and tissues, and we discuss underlying concepts, limitations, and future perspectives of these models. As the interest in cell and tissue mechanics increase, agent-based models are becoming more... In this paper we present an overview of agent-based models that are used to simulate mechanical and physiological phenomena in cells and tissues, and we discuss underlying concepts, limitations, and future perspectives of these models. As the interest in cell and tissue mechanics increase, agent-based models are becoming more common the modeling community. We overview the physical aspects, complexity, shortcomings, and capabilities of the major agent-based model categories: lattice-based models (cellular automata, lattice gas cellular automata, cellular Potts models), off-lattice models (center-based models, deformable cell models, vertex models), and hybrid discrete-continuum models. In this way, we hope to assist future researchers in choosing a model for the phenomenon they want to model and understand. The article also contains some novel results. read more read less

Topics:

Cellular automaton (52%)52% related to the paper
View PDF
233 Citations
open accessOpen access Journal Article DOI: 10.1007/S40571-020-00354-1
Grand challenges for Smoothed Particle Hydrodynamics numerical schemes

Abstract:

This paper presents a brief review of grand challenges of Smoothed Particle Hydrodynamics (SPH) method. As a meshless method, SPH can simulate a large range of applications from astrophysics to free-surface flows, to complex mixing problems in industry and has had notable successes. As a young computational method, the SPH me... This paper presents a brief review of grand challenges of Smoothed Particle Hydrodynamics (SPH) method. As a meshless method, SPH can simulate a large range of applications from astrophysics to free-surface flows, to complex mixing problems in industry and has had notable successes. As a young computational method, the SPH method still requires development to address important elements which prevent more widespread use. This effort has been led by members of the SPH rEsearch and engineeRing International Community (SPHERIC) who have identified SPH Grand Challenges. The SPHERIC SPH Grand Challenges (GCs) have been grouped into 5 categories: (GC1) convergence, consistency and stability, (GC2) boundary conditions, (GC3) adaptivity, (GC4) coupling to other models, and (GC5) applicability to industry. The SPH Grand Challenges have been formulated to focus the attention and activities of researchers, developers, and users around the world. The status of each SPH Grand Challenge is presented in this paper with a discussion on the areas for future development. read more read less

Topics:

Grand Challenges (56%)56% related to the paper
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103 Citations
Journal Article DOI: 10.1007/S40571-015-0085-0
Application of particle and lattice codes to simulation of hydraulic fracturing

Abstract:

With the development of unconventional oil and gas reservoirs over the last 15 years, the understanding and capability to model the propagation of hydraulic fractures in inhomogeneous and naturally fractured reservoirs has become very important for the petroleum industry (but also for some other industries like mining and geo... With the development of unconventional oil and gas reservoirs over the last 15 years, the understanding and capability to model the propagation of hydraulic fractures in inhomogeneous and naturally fractured reservoirs has become very important for the petroleum industry (but also for some other industries like mining and geothermal). Particle-based models provide advantages over other models and solutions for the simulation of fracturing of rock masses that cannot be assumed to be continuous and homogeneous. It has been demonstrated (Potyondy and Cundall Int J Rock Mech Min Sci Geomech Abstr 41:1329–1364, 2004) that particle models based on a simple force criterion for fracture propagation match theoretical solutions and scale effects derived using the principles of linear elastic fracture mechanics (LEFM). The challenge is how to apply these models effectively (i.e., with acceptable models sizes and computer run times) to the coupled hydro-mechanical problems of relevant time and length scales for practical field applications (i.e., reservoir scale and hours of injection time). A formulation of a fully coupled hydro-mechanical particle-based model and its application to the simulation of hydraulic treatment of unconventional reservoirs are presented. Model validation by comparing with available analytical asymptotic solutions (penny-shape crack) and some examples of field application (e.g., interaction with DFN) are also included. read more read less

Topics:

Hydraulic fracturing (55%)55% related to the paper
89 Citations
open accessOpen access Journal Article DOI: 10.1007/S40571-014-0010-Y
Discrete element modelling of large scale particle systems—I: exact scaling laws
Yuntian Feng1, David R. Owen1

Abstract:

The discrete element method has emerged as a powerful predictive tool for the numerical modelling of many scientific and engineering problems involving discrete and discontinuous phenomena. There are nevertheless computational challenges to resolve before industrial scale applications can be effectively simulated. This multi-... The discrete element method has emerged as a powerful predictive tool for the numerical modelling of many scientific and engineering problems involving discrete and discontinuous phenomena. There are nevertheless computational challenges to resolve before industrial scale applications can be effectively simulated. This multi-part paper aims to address some of the theoretical and computational issues central to achieving this goal. In the first part of this paper, a simple but generic theoretical framework is established for the development of a comprehensive set of scaling conditions, under which a scaled discrete element model can exactly reproduce the mechanical behaviour of a physical model. In particular, three basic physical quantities and their scale factors can be freely chosen. A special selection leads to a unique set of scale factors governing an exact scaling, which also gives rise to the requirement that all the interaction laws employed in a scaled model be scale-invariant. The subsequent examination reveals that most commonly used interaction laws, if all material (mechanical and physical) properties are treated as constant, do not possess such a feature and therefore cannot be directly employed in a scaled model. The problem can be solved by treating the scaled particles as pseudo-particles and by properly scaling the interaction laws. The resulting scaled interaction laws become scale-invariant and thus can be used in a scaled model. read more read less

Topics:

Scale (ratio) (55%)55% related to the paper, Discrete element method (55%)55% related to the paper, Scaling (53%)53% related to the paper, Scale factor (51%)51% related to the paper, Scale invariance (50%)50% related to the paper
View PDF
88 Citations
open accessOpen access Journal Article DOI: 10.1007/S40571-015-0044-9
A local constitutive model for the discrete element method. Application to geomaterials and concrete

Abstract:

This paper presents a local constitutive model for modelling the linear and non linear behavior of soft and hard cohesive materials with the discrete element method (DEM) We present the results obtained in the analysis with the DEM of cylindrical samples of cement, concrete and shale rock materials under a uniaxial compressiv... This paper presents a local constitutive model for modelling the linear and non linear behavior of soft and hard cohesive materials with the discrete element method (DEM) We present the results obtained in the analysis with the DEM of cylindrical samples of cement, concrete and shale rock materials under a uniaxial compressive strength test, different triaxial tests, a uniaxial strain compaction test and a Brazilian tensile strength test DEM results compare well with the experimental values in all cases read more read less

Topics:

Discrete element method (57%)57% related to the paper, Ultimate tensile strength (56%)56% related to the paper, Constitutive equation (53%)53% related to the paper, Proctor compaction test (52%)52% related to the paper
View PDF
75 Citations
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Frequently asked questions

1. Can I write Computational Particle Mechanics in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Computational Particle Mechanics guidelines and auto format it.

2. Do you follow the Computational Particle Mechanics guidelines?

Yes, the template is compliant with the Computational Particle Mechanics guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Computational Particle Mechanics?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Computational Particle Mechanics citation style.

4. Can I use the Computational Particle Mechanics templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Computational Particle Mechanics.

5. Can I use a manuscript in Computational Particle Mechanics that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Computational Particle Mechanics that you can download at the end.

6. How long does it usually take you to format my papers in Computational Particle Mechanics?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Computational Particle Mechanics.

7. Where can I find the template for the Computational Particle Mechanics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Computational Particle Mechanics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Computational Particle Mechanics's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

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SciSpace's Computational Particle Mechanics is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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After writing your paper autoformatting in Computational Particle Mechanics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Computational Particle Mechanics's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Computational Particle Mechanics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Computational Particle Mechanics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Computational Particle Mechanics?

The 5 most common citation types in order of usage for Computational Particle Mechanics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Computational Particle Mechanics?

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16. Can I download Computational Particle Mechanics in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Computational Particle Mechanics Endnote style according to Elsevier guidelines.

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