Submit to Materials Review for Materials Propose a Special Issue

Journal Menu

Journal Browser

Computational Modelling and Design of Novel Engineering Materials (Second Edition)

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".

Deadline for manuscript submissions: closed (10 February 2024) | Viewed by 8112

Share This Special Issue

Special Issue Editors

Prof. Dr. Tadeusz Burczyński

E-Mail Website
Guest Editor

Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
Interests: information and computational science and engineering; computational intelligence; soft computing; sensitivity analysis and optimization; inverse problems; stochastic modelling and fuzzy systems; multiscale modelling and design new 2D materials
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wacław Kuś

E-Mail Website
Guest Editor

Department of Computational Mechanics and Engineering, Silesian University of Technology, Gliwice, Poland
Interests: multiscale modeling; nanostructures optimization; bioinspired optimization; parallel computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Łukasz Madej

E-Mail Website
Guest Editor

Department of Applied Computer Science and Modelling, AGH University of Science and Technology, Krakow, Poland
Interests: metal forming; materials forming; multiscale modeling; discrete modeling techniques; finite element method; microstructure evolution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Discovering new materials is an important direction for the development of science worldwide. The use of advanced numerical models makes it possible to reduce the time required for developing and obtaining novel materials with predefined mechanical, thermal, optical, or electronic properties.

Computer methods not only allow the determination of material properties at nano, micro, and macro scales, but also allow for multi-scale analyses of the phenomena that occur in those materials at various time and length scales. Ab initio methods, such as DFT, MD, MC, and CA, but also FEM, BEM, and FDM, are some of the most commonly used methods in the analysis of direct problems.

Designing new materials often requires the selection of the appropriate chemical composition, thermomechanical treatment, or shape of microstructural features, as well as their topology. These tasks can be solved using inverse techniques based on both global and local optimization algorithms.

This Special Issue welcomes the submission of all papers in which aspects of the computer modelling of new materials are discussed.

Therefore, we kindly invite you to submit a manuscript to this Special Issue. Full papers, communications, and reviews are all welcome.

Prof. Dr. Tadeusz Burczyński
Prof. Dr. Wacław Kuś
Prof. Dr. Łukasz Madej
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

computational materials science
digital material representation models
image-based modelling
multiscale modelling
optimisation of structures and materials
nanostructures and 2D material modelling
gradient and hybrid material modelling
metallic and nonmetallic material modelling
experimental verification of computational models of materials
computational efficiency in material modelling and design

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

35 pages, 4349 KiB

Open AccessArticle

Dynamics in Explicit Gradient Elasticity: Material Frame-Indifference, Boundary Conditions and Consistent Euler–Bernoulli Beam Theory

by Charalampos Tsakmakis, Carsten Broese and Stergios Alexandros Sideris

Materials 2024, 17(8), 1760; https://doi.org/10.3390/ma17081760 - 11 Apr 2024

Cited by 1 | Viewed by 817

Abstract

The paper is concerned with the boundary conditions of explicit gradient elasticity of Mindlin’s type in dynamics. It has been argued in an earlier paper that acceleration terms should not be present in the boundary tractions because of objectivity arguments. This is discussed in the present paper in more detail, and it is supplemented by assuming the validity of the principle of material frame indifference. Furthermore, new examples are discussed in order to illustrate that significant differences exist in the responses predicted by boundary tractions with and without acceleration terms. Full article

(This article belongs to the Special Issue Computational Modelling and Design of Novel Engineering Materials (Second Edition))

► Show Figures

Figure 1

14 pages, 9021 KiB

Open AccessArticle

Harnessing a Dielectric/Plasma Photonic Crystal as an Optical Microwave Filter: Role of Defect Layers and External Magnetic Fields

by Hassen Dakhlaoui, Walid Belhadj, Haykel Elabidi, Najla S. Al-Shameri, Fatih Ungan and Bryan M. Wong

Materials 2024, 17(3), 559; https://doi.org/10.3390/ma17030559 - 24 Jan 2024

Cited by 1 | Viewed by 965

Abstract

We investigate the transmittance spectrum of a multichannel filter composed of dielectric

(A)

and plasma

(P)

materials in the microwave region within the transfer matrix formalism. Two configurations of the proposed filter are studied under the influence of an [...] Read more.

We investigate the transmittance spectrum of a multichannel filter composed of dielectric

(A)

and plasma

(P)

materials in the microwave region within the transfer matrix formalism. Two configurations of the proposed filter are studied under the influence of an applied magnetic field: (1) a periodic structure containing

{(A / P)}^{N}

unit cells surrounded by air and (2) the introduction of a second dielectric material

(D)

acting as a defect layer to produce an

(A P)^{N / 2} / D / (A P)^{N / 2}

structure. Our findings reveal that in the periodic case, the number of resonant states of the transmittance increases with number

N

; however, the observed blue and red shifts depend on the intensity and orientation of the applied magnetic field. We present contour plots of the transmission coefficients that show the effect of the incident angle on the shifts of the photonic band gaps. Furthermore, we find that the introduction of a defect layer generates additional resonant states and merges the central resonant peak into a miniband of resonances. Moreover, we show that the number of resonant peaks and their locations can be modulated by increasing the unit cell number,

N

, as well as increasing the width of the inserted defect layer. Our proposed structures enable the design of novel photonic filters using magnetized plasma materials operating in the microwave region. Full article

(This article belongs to the Special Issue Computational Modelling and Design of Novel Engineering Materials (Second Edition))

► Show Figures

Figure 1

14 pages, 6019 KiB

Open AccessEditor’s ChoiceArticle

Multiscale Analysis of Composite Structures with Artificial Neural Network Support for Micromodel Stress Determination

by Wacław Kuś, Waldemar Mucha and Iyasu Tafese Jiregna

Materials 2024, 17(1), 154; https://doi.org/10.3390/ma17010154 - 27 Dec 2023

Cited by 4 | Viewed by 1353

Abstract

Structures made of heterogeneous materials, such as composites, often require a multiscale approach when their behavior is simulated using the finite element method. By solving the boundary value problem of the macroscale model, for previously homogenized material properties, the resulting stress maps can be obtained. However, such stress results do not describe the actual behavior of the material and are often significantly different from the actual stresses in the heterogeneous microstructure. Finding high-accuracy stress results for such materials leads to time-consuming analyses in both scales. This paper focuses on the application of machine learning to multiscale analysis of structures made of composite materials, to substantially decrease the time of computations of such localization problems. The presented methodology was validated by a numerical example where a structure made of resin epoxy with randomly distributed short glass fibers was analyzed using a computational multiscale approach. Carefully prepared training data allowed artificial neural networks to learn relationships between two scales and significantly increased the efficiency of the multiscale approach. Full article

(This article belongs to the Special Issue Computational Modelling and Design of Novel Engineering Materials (Second Edition))

► Show Figures

Figure 1

20 pages, 5005 KiB

Open AccessArticle

Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning

by Gordana Marković, Vaso Manojlović, Jovana Ružić and Miroslav Sokić

Materials 2023, 16(19), 6355; https://doi.org/10.3390/ma16196355 - 22 Sep 2023

Cited by 3 | Viewed by 1715

Abstract

Titanium alloys have been present for decades as the main components for the production of various orthopedic and dental elements. However, modern times require titanium alloys with a low Young’s modulus, and without the presence of cytotoxic alloying elements. Machine learning was used with aim to analyze biocompatible titanium alloys and predict the composition of Ti alloys with a low Young’s modulus. A database was created using experimental data for alloy composition, Young’s modulus, and mechanical and thermal properties of biocompatible titanium alloys. The Extra Tree Regression model was built to predict the Young’s modulus of titanium alloys. By processing data of 246 alloys, the specific heat was discovered to be the most influential parameter that contributes to the lowering of the Young’s modulus of titanium alloys. Further, the Monte Carlo method was used to predict the composition of future alloys with the desired properties. Simulation results of ten million samples, with predefined conditions for obtaining titanium alloys with a Young’s modulus lower than 70 GPa, show that it is possible to obtain several multicomponent alloys, consisting of five main elements: titanium, zirconium, tin, manganese and niobium. Full article

(This article belongs to the Special Issue Computational Modelling and Design of Novel Engineering Materials (Second Edition))

► Show Figures

Figure 1

20 pages, 25470 KiB

Open AccessArticle

Computational Fracture Evolution Analysis of Steel-Fiber-Reinforced Concrete Using Concrete Continuous Damage and Fiber Progressive Models

by Iwona Pokorska, Mariusz Poński, Wojciech Kubissa, Tomasz Libura, Adam Brodecki and Zbigniew Kowalewski

Materials 2023, 16(16), 5635; https://doi.org/10.3390/ma16165635 - 15 Aug 2023

Cited by 2 | Viewed by 1314

Abstract

The process of concrete cracking is a common problem because the first micro-cracks due to the loss of moisture may appear even before the concrete is loaded. The application of fracture mechanics allows for a better understanding of this problem. Steel-fiber-reinforced concrete (SFRC) samples with a notch were subjected to a three-point bending test, and the results for crack energy were used to analyze the concrete’s material properties. In this paper, an experimental and numerical analysis of SFRC with rapid changes in the force (F) crack mouth opening displacement (CMOD) curve (F-CMOD) is presented. In order to obtain the relevant F-CMOD diagrams, three-point bending tests were carried out with non-standard samples with a thickness equal to one-third of the width of standard samples. For analysis purposes, crimped steel fibers were adopted. A probabilistic analysis of the most important parameters describing the material in question, such as peak strength, post-cracking strength, crack mouth opening displacement (CMOD), fracture energy, and the post-cracking deformation modulus, was conducted. The tests and the analysis of their results show that the quasi-static numerical method can be applied to obtain suitable results. However, significant dynamic effects during experiments that influence the F-CMOD curves are hard to reflect well in numerical calculations. Full article

(This article belongs to the Special Issue Computational Modelling and Design of Novel Engineering Materials (Second Edition))

► Show Figures

Figure 1

15 pages, 3932 KiB

Open AccessArticle

Crystallisation Degree Analysis during Cryopreservation of Biological Tissue Applying Interval Arithmetic

by Alicja Piasecka-Belkhayat and Anna Skorupa

Materials 2023, 16(6), 2186; https://doi.org/10.3390/ma16062186 - 9 Mar 2023

Cited by 1 | Viewed by 1086

Abstract

This paper presents the numerical modelling of heat transfer and changes proceeding in the homogeneous sample, caused by the crystallisation phenomenon during cryopreservation by vitrification. Heat transfer was simulated in a microfluidic system in which the working fluid flowed in micro-channels. The analysed process included single-phase flow during warming, and two-phase flow during cooling. In the model under consideration, interval parameters were assumed. The base of the mathematical model is given by the Fourier equation, with a heat source including the degree of ice crystallisation. The formulated problem has been solved using the interval version of the finite difference method, with the rules of the directed interval arithmetic. The fourth order Runge–Kutta algorithm has been applied to determine the degree of crystallisation. In the final part of this paper, examples of numerical computations are presented. Full article

(This article belongs to the Special Issue Computational Modelling and Design of Novel Engineering Materials (Second Edition))

► Show Figures

Journal Menu

Journal Browser

Computational Modelling and Design of Novel Engineering Materials (Second Edition)

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (6 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI