Multiscale Characterization, Mechanical Behavior and Computational Simulation of Bulk Materials, Metallic Powders and/or Nanoparticles

Topic Information

Dear Colleagues,

Metallic powders and nanoparticles (NPs) play a pivotal role in advanced manufacturing, energy storage, catalysis and biomedical applications, driven by their unique size-dependent properties and potential for tailoring microstructures. This Topic aims to explore the interdisciplinary landscape of multiscale characterization, mechanical behavior, and computational simulation of these materials, addressing fundamental science and engineering challenges across scales—from atomic/molecular interactions to macroscale performance. Key Focus Areas: Multiscale Characterization: Research will investigate structural, morphological, and chemical properties across scales using experimental techniques (e.g., SEM/TEM, XRD, AFM, spectroscopy) and theoretical frameworks. Studies may include surface/interface phenomena in nanoparticles, powder particle size distribution, grain boundary effects, phase transformations during processing (e.g., sintering and additive manufacturing) and the evolution of hierarchical architectures. Special emphasis is placed on bridging nanoscale features (e.g., defect structures, alloying effects) with meso/macroscale characteristics to establish structure–property relationships. Mechanical Behavior: Research will explore the deformation, failure and functional mechanical responses of metallic powders and NPs, including compaction behavior in powder metallurgy, strength–ductility trade-offs in nanoparticle-reinforced composites, size-dependent plasticity (e.g., Hall–Petch relationships at nanoscales), fatigue and creep. Studies may also address dynamic loading scenarios (e.g., shock, high strain rates) and the role of processing-induced defects (e.g., porosity, agglomeration) on mechanical performance. Cross-scale couplings—such as how nanoscale grain boundaries influence macroscale ductility—are of particular interest. Computational Simulation: Research will advance modeling approaches spanning quantum mechanics, molecular dynamics (MD), discrete dislocation dynamics (DDD), finite element analysis (FEA) and phase-field methods to simulate synthesis, processing and mechanical behavior. The focus includes bridging scales via multiscale modeling frameworks (e.g., MD-to-FEA coupling), predicting powder flow and compaction, simulating sintering kinetics and forecasting mechanical responses under complex loading. Novel algorithms for data-driven modeling (e.g., machine learning-aided material design) and uncertainty quantification in simulations are also welcome. Scope and Applications: Contributions may address pure metallic systems, alloys and nanocomposites (e.g., metal–organic frameworks, core–shell NPs). Applications range from additive manufacturing (e.g., binder jetting, laser powder bed fusion) and thermal management to catalysis and energy storage. Studies integrating experimental characterization with computational tools to validate models or guide material design are highly encouraged, as are investigations into emerging challenges like the environmental stability of nanoparticles and scalable production techniques. Submission Types: Original research articles, reviews, perspectives and methodology papers are welcome, provided they align with the topic’s focus on multiscale analysis, mechanical phenomena and computational innovation. Interdisciplinary works linking materials science, physics, chemistry and engineering are particularly valued.

Dr. Xiangnan Pan
Prof. Dr. Qing Peng
Prof. Dr. Hui Qi
Prof. Dr. Raj Das
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Alloys alloys	-	3.2	2022	24.7 Days	CHF 1000	Submit
Applied Mechanics applmech	1.5	3.5	2020	20.4 Days	CHF 1400	Submit
Crystals crystals	2.4	5.0	2011	12.7 Days	CHF 2100	Submit
Journal of Composites Science jcs	3.7	5.8	2017	16.2 Days	CHF 1800	Submit
Powders powders	-	-	2022	27.6 Days	CHF 1000	Submit
Nanomaterials nanomaterials	4.3	9.2	2010	15.4 Days	CHF 2400	Submit

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Published Papers (1 paper)

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Journals

All Alloys Applied Mechanics Crystals J. Compos. Sci. Powders Nanomaterials

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35 pages, 33285 KB  

Open AccessArticle

Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam Subject to Random Parametric Error

by Lin Sun, Xudong Li and Xiaopei Liu

J. Compos. Sci. 2025, 9(8), 442; https://doi.org/10.3390/jcs9080442 - 17 Aug 2025

Viewed by 241

Abstract

Random parametric errors (RPEs) are introduced into the model establishment of a laminated composite cantilever beam (LCCB) to demonstrate the accuracy and robustness of a recurrent neural network (RNN) in predicting the chaotic vibration of a LCCB, and a comparative analysis of training [...] Read more.

Random parametric errors (RPEs) are introduced into the model establishment of a laminated composite cantilever beam (LCCB) to demonstrate the accuracy and robustness of a recurrent neural network (RNN) in predicting the chaotic vibration of a LCCB, and a comparative analysis of training performance and generalization capability is conducted with a convolutional neural network (CNN). In the process of dynamic modeling, the nonlinear dynamic system of a LCCB is established by considering RPEs. The displacement and velocity time series obtained from numerical simulation are used to train and test the RNN model. The RNN model converts the original data into a multi-step supervised learning format and normalizes it using the MinMaxScaler method. The prediction performance is comprehensively evaluated through three performance indicators: coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results show that, under the condition of introducing RPEs, the RNN model still exhibits high prediction accuracy, with the maximum R² reaching 0.999984548634328, the maximum MAE being 0.075, and the maximum RMSE being 0.121. Furthermore, performing predictions at the free end of the LCCB verifies the applicability and robustness of the RNN model with respect to spatial position variations. These results fully demonstrate the accuracy and robustness of the RNN model in predicting the chaotic vibration of a LCCB. Full article

(This article belongs to the Topic Multiscale Characterization, Mechanical Behavior and Computational Simulation of Bulk Materials, Metallic Powders and/or Nanoparticles)

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