arXiv subjects

Mohammad Nasim Hasan

Publications and source records attributed to Mohammad Nasim Hasan.

8 recordsLinked to original sources

arXiv2026

Effect of Grain Size and Local Chemical Order on Creep Resistance in MoNbTaW Refractory High-Entropy Alloy: A Molecular Dynamics Study

Refractory high-entropy alloy (RHEA) is a promising class of materials with potential applications in extreme environments, where the dominant failure mode is thermal creep. The design of these alloys, therefore, requires an understanding of how their microstructure and local chemical distribution affect creep behavior. In this study, we performed high-fidelity atomistic simulations using machine-learning interatomic potentials to explore the creep deformation of MoNbTaW RHEA under a wide range of stress and temperature conditions. We parametrized grain size and local chemical order (LCO) to investigate the effects of these two important design variables, which can be controlled during the alloy fabrication process, on creep deformation process. Our investigation revealed that resistance to creep deformation is enhanced with larger grain size due to the reduced grain boundary area, which limits grain-boundary dominated deformation mechanisms such as Coble creep and grain boundary sliding. Introducing LCO in the microstructure has the same effect of increasing resistance to creep deformation by strengthening grain boundary. This study highlights the importance of utilizing LCO in conjunction with other microstructural properties when designing RHEAs for extreme environmental applications.

cond-mat.mtrl-sci↗

arXiv2025

Atomistic insights into Cu segregation effects on irradiation-induced defect dynamics in medium-entropy alloys

Copper (Cu) segregation in medium and high-entropy alloys (M/HEAs) has shown significant influence on alloy properties. In this study, we investigate the effect of Cu segregation on evolution of irradiation-induced defects in FeNiCu, a model MEA, using hybrid molecular dynamics (MD) and Monte Carlo (MC) simulations. Thermodynamically driven hybrid MC/MD annealing at low temperature resulted in a partially decomposed Cu-segregated structure (CSS) and was compared with a random solid solution (RSS) and pure Ni. Results through cumulative displacement cascades reveal that Cu-rich domains in CSS act as defect traps, accelerating interstitial-vacancy recombination and suppressing defect cluster growth. The complex potential energy landscape (PEL) in CSS disrupts dislocation propagation, leading to spatially dispersed networks. Notably, CSS exhibits reduced stair-rod dislocation density compared to RSS, highlighting its superior resistance to irradiation swelling. Localized shear strain causes dislocations to preferentially nucleate in/near Cu-rich regions though their growth is hindered by chemical heterogeneity of the alloy. Notably, prolonged irradiation induces slow Cu segregation in the RSS structure, while slowly annihilating pre-existing Cu clusters in CSS simultaneously. The findings provide atomic-scale insights into the interplay between Cu segregation and irradiation-induced defect evolution in MEAs.

cond-mat.mtrl-sci↗

arXiv2024

Graph neural network framework for energy mapping of hybrid monte-carlo molecular dynamics simulations of Medium Entropy Alloys

Machine learning (ML) methods have drawn significant interest in material design and discovery. Graph neural networks (GNNs), in particular, have demonstrated strong potential for predicting material properties. The present study proposes a graph-based representation for modeling medium-entropy alloys (MEAs). Hybrid Monte-Carlo molecular dynamics (MC/MD) simulations are employed to achieve thermally stable structures across various annealing temperatures in an MEA. These simulations generate dump files and potential energy labels, which are used to construct graph representations of the atomic configurations. Edges are created between each atom and its 12 nearest neighbors without incorporating explicit edge features. These graphs then serve as input for a Graph Convolutional Neural Network (GCNN) based ML model to predict the system's potential energy. The GCNN architecture effectively captures the local environment and chemical ordering within the MEA structure. The GCNN-based ML model demonstrates strong performance in predicting potential energy at different steps, showing satisfactory results on both the training data and unseen configurations. Our approach presents a graph-based modeling framework for MEAs and high-entropy alloys (HEAs), which effectively captures the local chemical order (LCO) within the alloy structure. This allows us to predict key material properties influenced by LCO in both MEAs and HEAs, providing deeper insights into how atomic-scale arrangements affect the properties of these alloys.

cond-mat.mtrl-sci↗

arXiv2022

Understanding Mechanical Characteristics of FeNiCrCoCu HEA in Nanoscale Laser Powder Bed Fusion via Molecular Dynamics

The concept of alloying multiple principal elements at high concentrations has led to the development of High Entropy Alloys (HEAs) with exceptional mechanical properties, making them the focus of major recent scientific endeavors. Geometrically complex HEAs with tailored microstructural characteristics can be produced using additive manufacturing technologies such as powder bed fusion (PBF). However, an in-depth study on the effect of process thermal conditions during PBF is required to effectively fabricate HEAs with desirable mechanical characteristics. Thus, in our present molecular dynamic (MD) study we have explored the implication of PBF process thermal conditions on the mechanical characteristics of FeNiCrCoCu HEA by systematically varying laser scan speed from 0.4 Å/ps to 0.1 Å/ps, unidirectional and reversing laser passes from 1 to 4, and laser power from 100 microwatts to 220 microwatts. Our investigation suggests that reducing the laser scanning speed up to a critical velocity of 0.2 Å/ps considerably improves mechanical strengths, with further reduction creating severe surface defects. Decreased ultimate tensile strength (UTS) is associated with the annihilation of the bulk sessile dislocations during tensile straining marking an early yield failure. Alternately, the material's strength could be improved by annealing with several unidirectional laser passes over the same target region, resulting in enhanced UTS due to subtler yield points. Increasing laser power aids in ameliorating material density ultimately leading to higher UTS even in non-dislocation-free structures. These findings will assist researchers to understand the underlying effects and optimize process thermal parameters to fabricate superior HEAs utilizing additive manufacturing.

cond-mat.mtrl-sci↗

arXiv2022

Effect of powder bed fusion process parameters on microstructural and mechanical properties of FeCrNi MEA: An atomistic study

In our study, molecular dynamics (MD) simulations of laser powder bed fusion (LPBF) have been conducted on equimolar FeNiCr medium entropy alloy (MEA) powders. With the development of newer LPBF technologies capable of printing at the microscale, an even deeper understanding of the underlying atomistic effects of the process parameters on the microstructural and mechanical properties of the manufactured FeNiCr MEA products is required. In accordance with previous literature, the parameters of the LPBF process have been systematically varied, including layer resolution from 1 to 6, laser power from 100 μW to 220 μW, bed temperature from 300 K to 1200 K, and laser scan speed from 0.5 Å/ps to 0.0625 Å/ps. Consistent with prior macroscopic experimental findings, the atomistic results suggest that additive manufacturing using thinner layers imparts higher ultimate tensile strength (UTS) than fabricating with thicker layers. The latter, however, requires a shorter process time but induces keyhole defect formation if the laser-induced temperature is not sufficiently high enough. Increasing the temperature proves useful in mitigating this problem. Enhancement of UTS for the multi-rowed powders has been observed by raising the substrate temperature to 600 K or laser power to 160 μW during production. Beyond these critical limits, however, the UTS of the product diminishes due to the emergence of multiple vacancies. The results of our present study will help researchers to find a good balance between the production speed and strength of additive manufactured products at the nanoscale.

cond-mat.mtrl-sci↗

arXiv2020

Extraction of Material Properties through Multi-fidelity Deep Learning from Molecular Dynamics Simulation

Simulation of reasonable timescales for any long physical process using molecular dynamics (MD) is a major challenge in computational physics. In this study, we have implemented an approach based on multi-fidelity physics informed neural network (MPINN) to achieve long-range MD simulation results over a large sample space with significantly less computational cost. The fidelity of our present multi-fidelity study is based on the integration timestep size of MD simulations. While MD simulations with larger timestep produce results with lower level of accuracy, it can provide enough computationally cheap training data for MPINN to learn an accurate relationship between these low-fidelity results and high-fidelity MD results obtained using smaller simulation timestep. We have performed two benchmark studies, involving one and two component LJ systems, to determine the optimum percentage of high-fidelity training data required to achieve accurate results with high computational saving. The results show that important system properties such as system energy per atom, system pressure and diffusion coefficients can be determined with high accuracy while saving 68% computational costs. Finally, as a demonstration of the applicability of our present methodology in practical MD studies, we have studied the viscosity of argon-copper nanofluid and its variation with temperature and volume fraction by MD simulation using MPINN. Then we have compared them with numerous previous studies and theoretical models. Our results indicate that MPINN can predict accurate nanofluid viscosity at a wide range of sample space with significantly small number of MD simulations. Our present methodology is the first implementation of MPINN in conjunction with MD simulation for predicting nanoscale properties. This can pave pathways to investigate more complex engineering problems that demand long-range MD simulations.

physics.comp-ph↗

arXiv2020

Mixed Convection in a Differentially Heated Cavity with Local Flow Modulation via Rotating Flat Plates

Mixed Convection inside a cavity resulting from thermal buoyancy force under local modulation via rotating flat plate has been investigated. The present model consists of a square cavity with the left and right vertical walls fixed at constant high and low temperatures respectively while the top and bottom walls are supposed to be adiabatic. Two clockwise rotating flat plates, having negligible thickness in comparison to their lengths, acting as flow modulators have been placed vertically along the centerline of the cavity. The moving boundary problem due to plate motion in this study has been solved by implementing \textit{Arbitrary Lagrangian Eulerian (ALE)} finite element formulation with triangular discretization scheme.Simulations are conducted for air ($Pr=0.71$) at different Rayleigh numbers ($10^2 \leq Ra \leq 10^6$). Rotational Reynolds number based on plate dynamic condition has been considered to be constant at 430. Numerical results identify critical Rayleigh number $Ra_{cr}=0.41\times10^6$ beyond which two smaller flow modulators are more effective than a single larger modulator. Thermal oscillating frequency was observed to be insensitive to Rayleigh number for the case of double modulators.

physics.flu-dyn↗

arXiv2020

Mixed Convective Heat Transfer Enhancement in a Ventilated Cavity by Flow Modulation via Rotating Plate

The present study numerically explores the mixed convection phenomena in a differentially heated ventilated square cavity with active flow modulation via a rotating plate. Forced convection flow in the cavity is attained by maintaining an external fluid flow through an opening at the bottom of the left cavity wall while leaving it through another opening at the right cavity wall. A counter-clockwise rotating plate at the center of the cavity acts as active flow modulator. Moving mesh approach is used for the rotation of the plate and the numerical solution is achieved using Arbitrary Lagrangian-Eulerian (ALE) finite element formulation with a quadrilateral discretization scheme. Transient parametric simulations have been performed for various frequency of the rotating plate for a fixed Reynolds number (Re) of 100 based on maximum inlet flow velocity while the Richardson number (Ri) is maintained at unity. Heat transfer performance has been evaluated in terms of spatially averaged Nusselt number and time-averaged Nusselt number along the heated wall. Power spectrum analysis in the frequency domain obtained from the fast Fourier transform (FFT) analysis indicates that thermal frequency and plate frequency start to deviate from each other at higher values of velocity ratio (> 4).

physics.flu-dyn↗