Deformation-diffusion coupled analysis of long-term hydrogen diffusion in nanofilms

The absorption and desorption of hydrogen in nanomaterials can be characterized by an atomic, deformation-diffusion coupled process with a time scale of the order of seconds to hours. This time scale is beyond the time windows of conventional atomistic computational models such as molecular dynamics (MD) and transition state theory based accelerated MD. In this paper, we present a novel, deformation-diffusion coupled computational model basing on non-equilibrium statistical mechanics, which allows long-term simulation of hydrogen absorption and desorption at atomic scale. Specifically, we propose a carefully designed trial Hamiltonian in order to construct our meanfield based approximation, then apply it to investigate the palladium-hydrogen (Pd-H) system. Specifically, here we combine the meanfield model with a discrete kinetic law for hydrogen diffusion in palladium nanofilms. This combination in practice defines the evolution of hydrogen atomic fractions and lattice constants, which facilitates the characterization of the deformation-diffusion process of hydrogen over both space and time. Using the embedded atom model (EAM) potential, we investigate the deformation-diffusion problem of hydrogen desorption and absorption in palladium nanofilms and compare our results with experiments both in equilibrium and non-equilibrium cases

Rapid Quantification of Dynamic and Spall Strength of Metals Across Strain Rates

The response of metals and their microstructures under extreme dynamic conditions can be markedly different from that under quasistatic conditions. Traditionally, high strain rates and shock stresses are measured using cumbersome and expensive methods such as the Kolsky bar or large spall experiments. These methods are low throughput and do not facilitate high-fidelity microstructure-property linkages. In this work, we combine two powerful small-scale testing methods, custom nanoindentation, and laser-driven micro-flyer shock, to measure the dynamic and spall strength of metals. The nanoindentation system is configured to test samples from quasistatic to dynamic strain rate regimes (10$^{-3}$ s$^{-1}$ to 10$^{+4}$ s$^{-1}$). The laser-driven micro-flyer shock system can test samples through impact loading between 10$^{+5}$ s$^{-1}$ to 10$^{+7}$ s$^{-1}$ strain rates, triggering spall failure. The model material used for testing is Magnesium alloys, which are lightweight, possess high-specific strengths and have historically been challenging to design and strengthen due to their mechanical anisotropy. Here, we modulate their microstructure by adding or removing precipitates to demonstrate interesting upticks in strain rate sensitivity and evolution of dynamic strength. At high shock loading rates, we unravel an interesting paradigm where the spall strength of these materials converges, but the failure mechanisms are markedly different. Peak aging, considered to be a standard method to strengthen metallic alloys, causes catastrophic failure, faring much worse than solutionized alloys. Our high throughput testing framework not only quantifies strength but also teases out unexplored failure mechanisms at extreme strain rates, providing valuable insights for the rapid design and improvement of metals for extreme environments

Uncertainty Quantification of Material Properties in Ballistic Impact of Magnesium Alloys

The design and development of cutting-edge light materials for extreme conditions including high-speed impact remains a continuing and significant challenge in spite of steady advances. Magnesium (Mg) and its alloys have gained much attention, due to their high strength-to-weight ratio and potential of further improvements in material properties such as strength and ductility. In this paper, a recently developed computational framework is adopted to quantify the effects of material uncertainties on the ballistic performance of Mg alloys. The framework is able to determine the largest deviation in the performance measure resulting from a finite variation in the corresponding material properties. It can also provide rigorous upper bounds on the probability of failure using known information about uncertainties and the system, and then conservative safety design and certification can be achieved. This work specifically focuses on AZ31B Mg alloys, and it is assumed that the material is well-characterized by the Johnson–Cook constitutive and failure models, but the model parameters are uncertain. The ordering of uncertainty contributions for model parameters and the corresponding behavior regimes where those parameters play a crucial role are determined. Finally, it is shown that how this ordering provides insight on the improvement of ballistic performance and the development of new material models for Mg alloys

Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

he accurate forecasting of carbon dioxide (CO2) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β) arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn’t consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE) objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO2 emission of the World top‒5 CO2 emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE) and mean absolute error (MAE) are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree

Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

he accurate forecasting of carbon dioxide (CO2) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β) arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn’t consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE) objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO2 emission of the World top‒5 CO2 emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE) and mean absolute error (MAE) are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree

Long-term atomistic simulation of hydrogen absorption in palladium nanocubes using a diffusive molecular dynamics method

Understanding the transport of hydrogen within metallic nanomaterials is crucial for the advancement of energy storage and the mitigation of hydrogen embrittlement. Using nanosized palladium particles as a model, recent experimental studies have revealed several interesting phenomena that occur over long time periods. The time scale of these phenomena is beyond the capability of established atomistic models such as molecular dynamics. In this work, we present the application of a new approach, referred to as diffusive molecular dynamics (DMD), to the simulation of long-term diffusive mass transport at the atomic scale. Specifically, we simulate the absorption of hydrogen by palladium nanocubes with edge lengths in the range of 4 nm and 16 nm. We find that the absorption process is dominated by the initiation and propagation of an atomistically sharp α/β Pd-H phase boundary, with thickness in the range of 0.2 to 1.0 nm, which separates an α phase core from a β phase shell. The evolution of phase boundary and the resulting local lattice deformation are described in this paper in detail. The effects of size on both equilibrium and kinetic properties are also assessed

Atomistic Simulation of Hydrogen Diffusion in Palladium Nanoparticles Using a Diffusive Molecular Dynamics Method

Understanding the transport of hydrogen within metals is crucial for the advancement of energy storage and the mitigation of hydrogen embrittlement. Using nanosized palladium particles as a model, recent experimental studies have revealed several highly nonlinear phenomena that occur over a long period of time. The time scale of these phenomena is beyond the capability of established atomistic models. In this work, we present the application of a new model, referred to as diffusive molecular dynamics (DMD), to simulating long-term diffusive mass transport at atomistic length scale. Specifically, we validate the model for the long-term dynamics of a single hydrogen atom on palladium lattice. We show that the DMD result is in satisfactory agreement with the result of the classical random walk model. Then, we apply the DMD model to simulate the absorption of hydrogen by a palladium nanocube with an edge length of 16 nm. We show that the absorption process is dominated by the propagation of a sharp, coherent α/β hydride phase boundary. We also characterize the local lattice deformation near the dynamic phase boundary using the mean positions of the palladium and hydrogen atoms

Long-term atomistic simulation of hydrogen absorption in palladium nanocubes using a diffusive molecular dynamics method

Understanding the transport of hydrogen within metallic nanomaterials is crucial for the advancement of energy storage and the mitigation of hydrogen embrittlement. Using nanosized palladium particles as a model, recent experimental studies have revealed several interesting phenomena that occur over long time periods. The time scale of these phenomena is beyond the capability of established atomistic models such as molecular dynamics. In this work, we present the application of a new approach, referred to as diffusive molecular dynamics (DMD), to the simulation of long-term diffusive mass transport at the atomic scale. Specifically, we simulate the absorption of hydrogen by palladium nanocubes with edge lengths in the range of 4 nm and 16 nm. We find that the absorption process is dominated by the initiation and propagation of an atomistically sharp α/β Pd-H phase boundary, with thickness in the range of 0.2 to 1.0 nm, which separates an α phase core from a β phase shell. The evolution of phase boundary and the resulting local lattice deformation are described in this paper in detail. The effects of size on both equilibrium and kinetic properties are also assessed