Example of Advanced Modeling and Simulation in Engineering Sciences format
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Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format Example of Advanced Modeling and Simulation in Engineering Sciences format
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Advanced Modeling and Simulation in Engineering Sciences — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Engineering (miscellaneous) #27 of 77 down down by 12 ranks
Applied Mathematics #197 of 548 down down by 90 ranks
Modeling and Simulation #140 of 290 down down by 63 ranks
Computer Science Applications #339 of 693 down down by 120 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 95 Published Papers | 243 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 01/06/2020
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Related Journals

open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 8.6
SJR: 0.53
SNIP: 2.363
open access Open Access

Springer

Quality:  
High
CiteRatio: 3.4
SJR: 0.291
SNIP: 0.951
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 6.1
SJR: 1.882
SNIP: 1.743
open access Open Access

Elsevier

Quality:  
High
CiteRatio: 4.9
SJR: 0.774
SNIP: 1.158

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.

2.6

26% from 2019

CiteRatio for Advanced Modeling and Simulation in Engineering Sciences from 2016 - 2020
Year Value
2020 2.6
2019 3.5
2018 2.8
2017 2.9
graph view Graph view
table view Table view

1.01

7% from 2019

SJR for Advanced Modeling and Simulation in Engineering Sciences from 2018 - 2020
Year Value
2020 1.01
2019 1.081
2018 1.026
graph view Graph view
table view Table view

0.941

13% from 2019

SNIP for Advanced Modeling and Simulation in Engineering Sciences from 2017 - 2020
Year Value
2020 0.941
2019 1.081
2018 1.235
2017 0.913
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 26% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 7% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 13% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Advanced Modeling and Simulation in Engineering Sciences

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Springer

Advanced Modeling and Simulation in Engineering Sciences

Approved by publishing and review experts on SciSpace, this template is built as per for Advanced Modeling and Simulation in Engineering Sciences formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 314 authors to write and format their manuscripts to this journal.

CSMA

i
Last updated on
01 Jun 2020
i
ISSN
1606-8610
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
White faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Citation Type
Author Year
(Blonder et al, 1982)
i
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: 10.1186/S40323-020-00147-4
How to tell the difference between a model and a digital twin
Louise Wright1, Stuart Davidson1

Abstract:

“When I use a word, it means whatever I want it to mean”: Humpty Dumpty in Alice’s Adventures Through The Looking Glass, Lewis Carroll. “Digital twin” is currently a term applied in a wide variety of ways. Some differences are variations from sector to sector, but definitions within a sector can also vary significantly. Withi... “When I use a word, it means whatever I want it to mean”: Humpty Dumpty in Alice’s Adventures Through The Looking Glass, Lewis Carroll. “Digital twin” is currently a term applied in a wide variety of ways. Some differences are variations from sector to sector, but definitions within a sector can also vary significantly. Within engineering, claims are made regarding the benefits of using digital twinning for design, optimisation, process control, virtual testing, predictive maintenance, and lifetime estimation. In many of its usages, the distinction between a model and a digital twin is not made clear. The danger of this variety and vagueness is that a poor or inconsistent definition and explanation of a digital twin may lead people to reject it as just hype, so that once the hype and the inevitable backlash are over the final level of interest and use (the “plateau of productivity”) may fall well below the maximum potential of the technology. The basic components of a digital twin (essentially a model and some data) are generally comparatively mature and well-understood. Many of the aspects of using data in models are similarly well-understood, from long experience in model validation and verification and from development of boundary, initial and loading conditions from measured values. However, many interesting open questions exist, some connected with the volume and speed of data, some connected with reliability and uncertainty, and some to do with dynamic model updating. In this paper we highlight the essential differences between a model and a digital twin, outline some of the key benefits of using digital twins, and suggest directions for further research to fully exploit the potential of the approach. read more read less
View PDF
236 Citations
open accessOpen access Journal Article DOI: 10.1186/S40323-015-0031-Y
Efficient and accurate numerical quadrature for immersed boundary methods
László Kudela1, N. Zander1, Tino Bog1, Stefan Kollmannsberger1, Ernst Rank1

Abstract:

One question in the context of immersed boundary or fictitious domain methods is how to compute discontinuous integrands in cut elements accurately. A frequently used method is to apply a composed Gaussian quadrature based on a spacetree subdivision. Although this approach works robustly on any geometry, the resulting integra... One question in the context of immersed boundary or fictitious domain methods is how to compute discontinuous integrands in cut elements accurately. A frequently used method is to apply a composed Gaussian quadrature based on a spacetree subdivision. Although this approach works robustly on any geometry, the resulting integration mesh yields a low order representation of the boundary. If high order shape functions are employed to approximate the solution, this lack of geometric approximation power prevents exponential convergence in the asymptotic range. In this paper we present an algorithmic subdivision approach that aims to be as robust as the spacetree decomposition even for close-to-degenerate cases—but remains geometrically accurate at the same time. Based on 2D numerical examples, we will show that optimal convergence rates can be obtained with a nearly optimal number of integration points. read more read less

Topics:

Gauss–Kronrod quadrature formula (62%)62% related to the paper, Tanh-sinh quadrature (59%)59% related to the paper, Numerical integration (59%)59% related to the paper, Gaussian quadrature (57%)57% related to the paper, Boundary (topology) (55%)55% related to the paper
View PDF
82 Citations
open accessOpen access Journal Article DOI: 10.1186/S40323-015-0055-3
Robust model reduction by $$L^{1}$$ L 1 -norm minimization and approximation via dictionaries: application to nonlinear hyperbolic problems
Rémi Abgrall1, David Amsallem2, R Crisovan1

Abstract:

We propose a novel model reduction approach for the approximation of non linear hyperbolic equations in the scalar and the system cases. The approach relies on an offline computation of a dictionary of solutions together with an online $$L^1$$ L 1 - norm minimization of the residual. It is shown why this is a natural frame... We propose a novel model reduction approach for the approximation of non linear hyperbolic equations in the scalar and the system cases. The approach relies on an offline computation of a dictionary of solutions together with an online $$L^1$$ L 1 - norm minimization of the residual. It is shown why this is a natural framework for hyperbolic problems and tested on nonlinear problems such as Burgers’ equation and the one-dimensional Euler equations involving shocks and discontinuities. Efficient algorithms are presented for the computation of the $$L^1$$ L 1 -norm minimizer, both in the cases of linear and nonlinear residuals. Results indicate that the method has the potential of being accurate when involving only very few modes, generating physically acceptable, oscillation-free, solutions. read more read less

Topics:

Hyperbolic partial differential equation (59%)59% related to the paper, Nonlinear system (57%)57% related to the paper, Euler equations (56%)56% related to the paper, Norm (mathematics) (54%)54% related to the paper
View PDF
71 Citations
open accessOpen access Journal Article DOI: 10.1186/S40323-016-0070-Z
Toward 4D mechanical correlation

Abstract:

The goal of the present study is to illustrate the full integration of sensor and imaging data into numerical procedures for the purpose of identification of constitutive laws and their validation. The feasibility of such approaches is proven in the context of in situ tests monitored by tomography. The bridging tool consists ... The goal of the present study is to illustrate the full integration of sensor and imaging data into numerical procedures for the purpose of identification of constitutive laws and their validation. The feasibility of such approaches is proven in the context of in situ tests monitored by tomography. The bridging tool consists of spatiotemporal (i.e., 4D) analyses with dedicated (integrated) correlation algorithms. A tensile test on nodular graphite cast iron sample is performed within a lab tomograph. The reconstructed volumes are registered via integrated digital volume correlation (DVC) that incorporates a finite element modeling of the test, thereby performing a mechanical integration in 4D registration of a series of 3D images. In the present case a non-intrusive procedure is developed in which the 4D sensitivity fields are obtained with a commercial finite element code, allowing for a large versatility in meshing and incorporation of complex constitutive laws. Convergence studies can thus be performed in which the quality of the discretization is controlled both for the simulation and the registration. Incremental DVC analyses are carried out with the scans acquired during the in situ mechanical test. For DVC, the mesh size results from a compromise between measurement uncertainties and its spatial resolution. Conversely, a numerically good mesh may reveal too fine for the considered material microstructure. With the integrated framework proposed herein, 4D registrations can be performed and missing boundary conditions of the reference state as well as mechanical parameters of an elastoplastic constitutive law are determined in fair condition both for DVC and simulation. read more read less

Topics:

Finite element method (51%)51% related to the paper
View PDF
64 Citations
open accessOpen access Journal Article DOI: 10.1186/2213-7467-1-8
A frontal approach to hex-dominant mesh generation

Abstract:

Indirect quad mesh generation methods rely on an initial triangular mesh. So called triangle-merge techniques are then used to recombine the triangles of the initial mesh into quadrilaterals. This way, high-quality full-quad meshes suitable for finite element calculations can be generated for arbitrary two-dimensional geometr... Indirect quad mesh generation methods rely on an initial triangular mesh. So called triangle-merge techniques are then used to recombine the triangles of the initial mesh into quadrilaterals. This way, high-quality full-quad meshes suitable for finite element calculations can be generated for arbitrary two-dimensional geometries. In this paper, a similar indirect approach is applied to the three-dimensional case, i.e., a method to recombine tetrahedra into hexahedra. Contrary to the 2D case, a 100% recombination rate is seldom attained in 3D. Instead, part of the remaining tetrahedra are combined into prisms and pyramids, eventually yielding a mixed mesh. We show that the percentage of recombined hexahedra strongly depends on the location of the vertices in the initial 3D mesh. If the vertices are placed at random, less than 50% of the tetrahedra will be combined into hexahedra. In order to reach larger ratios, the vertices of the initial mesh need to be anticipatively organized into a lattice-like structure. This can be achieved with a frontal algorithm, which is applicable to both the two- and three-dimensional cases. The quality of the vertex alignment inside the volumes relies on the quality of the alignment on the surfaces. Once the vertex placement process is completed, the region is tetrahedralized with a Delaunay kernel. A maximum number of tetrahedra are then merged into hexahedra using the algorithm of Yamakawa-Shimada. Non-uniform mixed meshes obtained following our approach show a volumic percentage of hexahedra that usually exceeds 80%. The execution times are reasonable. However, non-conformal quadrilateral faces adjacent to triangular faces are present in the final meshes. read more read less

Topics:

Mesh generation (65%)65% related to the paper, Triangle mesh (58%)58% related to the paper, Volume mesh (57%)57% related to the paper, Polygon mesh (55%)55% related to the paper, Tetrahedron (52%)52% related to the paper
View PDF
55 Citations
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Frequently asked questions

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13. What is Sherpa RoMEO Archiving Policy for Advanced Modeling and Simulation in Engineering Sciences?

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 Advanced Modeling and Simulation in Engineering Sciences. 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 Advanced Modeling and Simulation in Engineering Sciences?

The 5 most common citation types in order of usage for Advanced Modeling and Simulation in Engineering Sciences are:.

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

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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 Advanced Modeling and Simulation in Engineering Sciences Endnote style according to Elsevier guidelines.

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