Abstract:

In many micro and nanoscale applications of thin film phase transition, identifying the circumstances that allows stable liquid contact with a heated surface is critical. Using molecular dynamics (MD) simulations, our current data driven machine learning-based study attempts to examine the properties of liquid interaction with a solid surface at nanoscale. A few nanometer-thick liquid argon layer over a platinum surface has been used to simulate a liquid-solid contact system. The wall temperature is raised linearly after necessary initial equilibration of the entire system, with different boundary heating rates for various surface wetting conditions, namely hydrophobic, hydrophilic, and superhydrophilic. Our current investigation shows that the heating condition, liquid film thickness, and surface wetting condition all have a significant impact on the type of liquid wall contact that persists during the phase transition phenomena of thin liquid argon film (i.e., normal evaporation or explosive boiling). In the event of normal evaporation, a stable liquid contact with the solid surface continues, however in the case of explosive boiling, the liquid film is splashed away from the solid surface resembling the macroscopic Leidenfrost effect. For various system configurations in regard to liquid initial film thickness, liquid heating rate as well as solid-liquid interaction, a wide variation of the onset time as well as the wall temperature of boiling explosion have been found in the present study. An accurate mapping of the Leidenfrost conditions in context to nanoscale thin film liquid-vapor phase process has been generated using a predictive model based on deep neural networks that has been designed, trained and cross validated against molecular dynamics data of the present study.

Abstract:

Additive manufacturing (AM) has gained significant attention due to its ability to drive technological development as a sustainable, flexible, and customizable manufacturing scheme. Among the various AM techniques, direct ink writing (DIW) has emerged as the most versatile 3D printing technique for the broadest range of materials. DIW allows printing of practically any material, as long as the precursor ink can be engineered to demonstrate appropriate rheological behavior. This technique acts as a unique pathway to introduce design freedom, multifunctionality, and stability simultaneously into its printed structures. Here, a comprehensive review of DIW of complex 3D structures from various materials, including polymers, ceramics, glass, cement, graphene, metals, and their combinations through multimaterial printing is presented. The review begins with an overview of the fundamentals of ink rheology, followed by an in-depth discussion of the various methods to tailor the ink for DIW of different classes of materials. Then, the diverse applications of DIW ranging from electronics to food to biomedical industries are discussed. Finally, the current challenges and limitations of this technique are highlighted, followed by its prospects as a guideline toward possible futuristic innovations.

Abstract:

The refining process of petroleum crude oil generates asphaltenes, which poses complicated problems during the production of cleaner fuels. Following refining, asphaltenes are typically combusted for reuse as fuel or discarded into tailing ponds and landfills, leading to economic and environmental disruption. Here, we show that low-value asphaltenes can be converted into a high-value carbon allotrope, asphaltene-derived flash graphene (AFG), via the flash joule heating (FJH) process. After successful conversion, we develop nanocomposites by dispersing AFG into a polymer effectively, which have superior mechanical, thermal, and corrosion-resistant properties compared to the bare polymer. In addition, the life cycle and technoeconomic analysis show that the FJH process leads to reduced environmental impact compared to the traditional processing of asphaltene and lower production cost compared to other FJH precursors. Thus, our work suggests an alternative pathway to the existing asphaltene processing that directs toward a higher value stream while sequestering downstream emissions from the processing.

Abstract:

Understanding of the material properties of layered transition-metal dichalcogenides (TMDs) is critical for their applications in flexible electronics. Data-driven machine learning (ML)-based approaches are being developed in contrast to the traditional experimental or computational methods to predict and understand material properties under varied operating conditions. In this study, we used two ML algorithms, namely, long short-term memory (LSTM) and feed forward neural network (FFNN), combined with molecular dynamics (MD) simulations to predict the mechanical properties of MX2 (M = Mo, W and X = S, Se) TMDs. The LSTM model is found to be capable of predicting the entire stress–strain response, whereas the FFNN is used to predict material properties such as fracture stress, fracture strain, and Young’s modulus. The effects of operating temperature, chiral orientation, and pre-existing crack size on the mechanical properties are thoroughly investigated. We carried out 1440 MD simulations to produce the input dataset for the neural network models. Our results indicate that both LSTM and FFNN are capable of predicting the mechanical response of monolayer TMDs under different conditions with more than 95% accuracy. The FFNN model exhibits lower computational cost than LSTM; however, the capability of the LSTM model to predict the entire stress–strain curve is advantageous for assessing material properties. The study paves the pathway toward extending this approach to predict other important properties, such as optical, electrical, and magnetic properties of TMDs.

Abstract:

Functionally grading and coreshell are two interesting engineering modifications to nanomaterials structure for tailored applications in electronics, energy conversion devices and so on. Understanding the mechanical response of these nanostructures are of particular importance to ensure the reliability of these devices under service conditions. In this paper, functionally graded and coreshell Silver–Gold (Ag–Au) nanospheres are studied under compression load using molecular dynamics simulation. The fracture and deformation mechanism along with the incipient plasticity through dislocation nucleation and propagation has been studied for both functionally graded and coreshell structure for different percentages of Ag and Au of the nanosphere. Our results indicate that plastic deformation is dictated by the partial dislocation nucleation and propagation from the contact surface which varies as the alloying percentages of the coreshell and functionally graded nanosphere. An inverse size effect is observed for the mechanical properties which also affects the deformation mechanism of the nanosphere by forming stacking fault tetrahedra for both the nanostructures. For a range of Ag percentages in Au, the coreshell nanospheres showed higher compressive strength compared to functionally graded nanospheres. As coreshell and functionally grading are two promising nanoscale materials design, current work will inspire developing new metal nanospheres to harness the materials potential for different engineering applications.

Abstract:

Functionally graded material (FGM) is a class of advanced materials, consisting of two (or more) different constituents, that possesses a continuously varying composition profile. With the advancement of nanotechnology, applications of FGMs have shifted from their conventional usage towards sophisticated micro and nanoscale electronics and energy conversion devices. Therefore, the study of mechanical and vibrational properties of different FGM nanostructures is crucial in exploring their feasibility for different applications. In this study, for the first time, we employed molecular dynamics (MD) simulations to investigate the mechanical and vibrational properties of radially graded Cu–Ni FGM nanowires (NW). Distribution of Cu and Ni along the radial direction follows power-law, exponential and sigmoid functions for FGM NWs under consideration. Our results demonstrate that distribution function parameters play an important role in modulating the mechanical (elastic modulus and ultimate tensile strength) and vibrational (natural frequency and quality factor) properties of FGM NWs. The study also suggests that elastic moduli of FGM NWs can be predicted with relatively good accuracy using Tamura and Reuss micromechanical models, regardless of NW diameter. We found that Euler-Bernoulli beam theory under-predicts the natural frequencies of FGM NWs, whereas He-Lilley model closely approximates the MD results. Interestingly, FGM NWs are always found to exhibit beat vibration because of their asymmetrical cross sections. Finally, this is the first atomistic scale study of FGMs that directly compares MD simulations with continuum theories and micromechanical models to understand the underlying mechanisms that govern the mechanical and vibrational properties of FGM NWs in nanoscale.

Abstract:

Phase change characteristics of thin film liquid argon subjected to ultrafast boundary heating for different liquid film thicknesses (3 nm  6 nm), boundary heating rates (8 × 109 K/s  320 × 109 K/s) for different surface wetting conditions are main objectives of the present study. Molecular dynamics (MD) simulation has been conducted involving a three-phase domain where liquid and vapor argon (Ar) atoms are placed over the solid platinum-like (Pt) surface. Depending on the combination of boundary heating rate and liquid film thickness, two types of phase change phenomena have been observed namely-diffusive evaporation and explosive boiling. The variations in the system temperature, net evaporation number and wall heat flux normal to the surface over time are closely investigated to explicate the evolution of thin film phase change characteristics. Besides, to get a better understanding of phase change phenomena of thin film liquid, the time-averaged wall heat flux (qavg) obtained from the MD simulation has been compared with classical thermodynamics prediction. The thermodynamic heat flux (qtherm) values are in excellent agreement with the time-averaged wall heat flux (qavg) for diffusive evaporation cases while they differ significantly for explosive boiling cases. A comparative study has been performed on the estimation of cumulative energy density in the liquid film prior to the explosive boiling both from macroscopic as well as MD viewpoints based on simplified control volume approach. The cumulative energy density within the liquid film as obtained from macroscopic viewpoint reasonably matches with that obtained in MD approach for hydrophilic and super-hydrophilic surfaces. Interestingly, for all explosive boiling cases, accumulated energy density at the boiling explosion assumes a mean value with 95% confidence level within 5.6% of the mean, which refers to a critical condition in context to energy content of the liquid film in atomistic approach which is in agreement with other macroscopic model prediction.

Abstract:

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.

Abstract:

Nanoindentation is a powerful tool capable of providing fundamental insights of material elastic and plastic response at the nanoscale. Alloys at nanoscale are particularly interesting as the local heterogeneity and deformation mechanism revealed by atomistic study offers a better way to understand hardening mechanism to build a stronger material. In this work, nanoindentation in Al-Cu alloys are studied using atomistic simulations to investigate the effects of loading direction, alloying percentages of Cu via dislocation-driven mechanisms. Also, a low-fidelity finite element (FE) model has been developed for nanoindentation simulations where nanoscale materials properties are used from atomistic simulations. Material properties, such as hardness and reduced modulus, are computed from both the FE and MD simulations and then compared. Considering the fundamental difference between these two numerical approaches, the FE results obtained from the present study conform fairly with those from MD simulations. This paves a way into finding material properties of alloys with reduced simulation time and cost by using FE where high-fidelity results are not required. The results have been presented as load-displacement analysis, dislocation density, dislocation loops nucleation and propagation, von-Mises stress distribution and surface imprints. The techniques adopted in this paper to incorporate atomistic data into FE simulations can be further extended for finding other mechanical and fracture properties for complex alloy materials.

Abstract:

An atomistic model of functionally gradient wettability (FGW) surface for molecular dynamics (MD) simulation has been proposed and developed. Using the present model, a non-equilibrium MD study has been conducted to investigate the effects of FGW on liquid thin film phase change characteristics over the FGW surface. A power function has been considered as the wettability governing function of the FGW surface and by varying its function parameter, various FGW surfaces have been studied. The simulation results show that the function parameter can be a significant modulation parameter for heat transfer characteristics associated with the phase transition. To gain insight into any additional heat transfer mode associated with the FGW surface, the wall heat fluxes have been compared with linear mixture rule predictions. It is found that, along with conduction heat transfer through the interface between solid FGW surface and liquid thin film, there exists convective heat transfer along the wettability gradient direction. This additional heat transfer mode, which is not present for uniformly wetted surfaces, causes significant enhancement of phase change characteristics. The results of the present MD simulation have been found consistent with macroscopic prediction based on classical thermodynamics theory.

Abstract:

A numerical study has been conducted on mixed convection heat transfer enhancement in a long horizontal channel provided with periodically distributed rotating blades. The upper wall of the channel is maintained at a constant low temperature (Tc) while the lower wall is kept hot at a constant high temperature (Th). A series of rotating blades having negligible thickness in comparison to its length is placed periodically along the centerline of the channel with the spacing between two successive blades’ rotational axes being equal to the height of the channel under consideration. The mathematical model of the present problem is governed by two-dimensional laminar transient continuity, momentum and energy equations. The governing equations are transformed to non-dimensional forms and then the moving mesh problem due to blade motion is solved by implementing Arbitrary Lagrangian-Eulerian (ALE) finite element formulation with triangular discretization scheme. Three different working fluids have been considered such as water, air and liquid Gallium that essentially cover a wide range of Prandtl Number (Pr) from 0.026 to 7.1. The dynamic condition of the rotating blades has been represented by Reynolds Number (Re) that is varied in the range of 1 to 500 and its effect on fluid flow and heat transfer has been investigated for the case of pure mixed convection heat transfer, characterized by Richardson number (Ri) of unity. Numerical results have been presented and analyzed in terms of the distribution of streamline and isotherm patterns, spatially averaged Nusselt number and normalized average Nusselt number variation along the hot wall for different parametric system configurations. The results of the present study show that, presence of rotating blades increases the heat transfer significantly in the channel. Heat transfer increases with increasing Prandtl Number (Pr) and the enhancement becomes more significant at higher Reynolds Numbers (Re).Power Spectrum analysis in frequency domain obtained from the FFT analysis indicates that, the rotating blade oscillation frequency and the oscillation frequency of Nusselt number differ at higher range of Reynolds Number (Re) and Prandtl Number (Pr). Therefore, dynamic condition of the rotating blades together with the thermophysical properties of working fluid play vital role in modulating the heat transfer characteristics and fluid flow behavior within the long horizontal channel.

Abstract. Functionally graded materials (FGM) eliminate the stress singularity in the interface between two different materials and therefore have a wide range of applications in high temperature environments such as engines, nuclear reactors, spacecrafts etc. Therefore, it is essential to study the mechanical properties of different FGM materials. This paper aims at establishing a method for modelling FGMs in molecular dynamics (MD) to get a better insight of their mechanical properties. In this study, the mechanical characteristics of Cu-Ni FGM nanowires (NW) under uniaxial loading have been investigated using the proposed method through MD simulations. In order to describe the inter-atomic forces and hence predict the properties properly, EAM (Embedded atom model) potential has been used. The nanowire is composed of an alloying constituent in the core and the other constituent graded functionally along the outward radial direction. Simple Linear and Exponential functions have been considered as the functions which defines the grading pattern. The alloying percentage on the surface has been varied from 0% to 50% for both Cu-cored and Ni-cored nanowires. All the simulations have been carried out at 300 K. The L/D ratios are 10.56 and 10.67 for Cu-cored and Ni-cored NWs, respectively. This study suggests that Ultimate Tensile Stress (UTS) and Young’s modulus (E) increase with increasing surface Ni percentage in Cu-cored NWs. However, in Ni-cored NWs these values decrease with the increase of surface Cu percentage. Also, for the same surface percentage of Ni in Cu-cored NW, the values are higher in linearly graded FGMs than that in exponentially graded FGMs. While in Ni-cored NWs, exponentially graded FGM shows higher values of UTS and E than those in linearly graded FGM. Thus, grading functions and surface percentages can be used as parameters for modulating the mechanical properties of FGM nanowires. 

Abstract:

A numerical study of two-dimensional, laminar, steady mixed convection heat transfer in a Cu-water nanofluid filled lid-driven square cavity with an isothermally heated cylinder has been conducted. The wall of the cylinder is maintained at a constant high temperature, whereas the walls of the cavity (including the moving lid) are maintained at a constant low temperature. The isothermally heated cylinder is placed at the center of the cavity. The fluid flow in the cavity is driven by the combined effect of the buoyancy force due to temperature gradient and forced flow due to the top moving wall in the +x direction. The developed mathematical model is governed by the two-dimensional continuity, momentum and energy equations, which are solved by using Galerkin finite element method. The working fluid inside the cavity is Cu-water nanofluid, where water has been considered as the base fluid. The influence of the Reynolds number (1 ≤ Re ≤ 500) and the solid volume fraction of the Cu nanoparticle (0≤ ϕ ≤0.05) on fluid flow and heat transfer has been numerically investigated for the case of pure mixed convection heat transfer. Numerical results are presented in terms of the distribution of streamlines and isothermal contours, local as well as average Nusselt number variation on the cylinder surface for different parametric conditions. It is observed that enhancement of heat transfer occurs significantly as Reynolds number and solid volume fraction of nanoparticle change continuously. Thus, the dynamic condition of the moving lid and solid volume fraction of the nanoparticle can be used as parameters for enhancing the heat transfer characteristics and flow behavior in that cavity.