Kevin G. Wang

Predictive Simulation of Underwater Implosion: Coupling Multi-Material Compressible Fluids With Cracking Structures

The implosive collapse of a gas-filled underwater structure can lead to strong pressure pulses and high-speed fragments that form a potential threat to adjacent structures. In this work, a high-fidelity, fluid-structure coupled computational approach is developed to simulate such an event. It allows quantitative prediction of the dynamics of acoustic and shock waves in water and the initiation and propagation of cracks in the structure. This computational approach features an extended finite element method (XFEM) for the highly-nonlinear structural dynamics characterized by large plastic deformation and fracture. It also features a finite volume method with exact two-phase Riemann solvers (FIVER) for the solution of the multi-material flow problem arising from the contact of gas and water after the structure fractures. The Eulerian computational fluid dynamics (CFD) solver and the Lagrangian computational structural dynamics (CSD) solver are coupled by means of an embedded boundary method of second-order accuracy in space. The capabilities and performance of this computational approach are explored and discussed in the full-scale simulations of a laboratory implosion experiment with hydrostatic loading and a three-dimensional manufactured implosion problem with explosion loading.Copyright

Acceleration of diffusive molecular dynamics simulations through mean field approximation and subcycling time integration

Diffusive Molecular Dynamics (DMD) is a class of recently developed computational models for the simulation of long-term diffusive mass transport at atomistic length scales. Compared to previous atomistic models, e.g., transition state theory based accelerated molecular dynamics, DMD allows the use of larger time-step sizes, but has a higher computational complexity at each time-step due to the need to solve a nonlinear optimization problem at every time-step. This paper presents two numerical methods to accelerate DMD based simulations. First, we show that when a many-body potential function, e.g., embedded atom method (EAM), is employed, the cost of DMD is dominated by the computation of the mean of the potential function and its derivatives, which are high-dimensional random variables. To reduce the cost, we explore both first- and second-order mean field approximations. Specifically, we show that the first-order approximation, which uses a point estimate to calculate the mean, can reduce the cost by two to three orders of magnitude, but may introduce relatively large error in the solution. We show that adding an approximate second-order correction term can significantly reduce the error without much increase in computational cost. Second, we show that DMD can be significantly accelerated through subcycling time integration, as the cost of integrating the empirical diffusion equation is much lower than that of the optimization solver. To assess the DMD model and the numerical approximation methods, we present two groups of numerical experiments that simulate the diffusion of hydrogen in palladium nanoparticles. In particular, we show that the computational framework is capable of capturing the propagation of an atomically sharp phase boundary over a time window of more than 30 seconds. The effects of the proposed numerical methods on solution accuracy and computation time are also assessed quantitatively.

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.

A Fluid-Structure Coupled Computational Framework for Fluid-Induced Failure and Fracture

This work extends and generalizes a recently developed fluid-structure coupled computational framework to model and simulate fluid-induced failure and fracture. In particular, a novel surface representation approach is proposed to represent a fractured fluid-structure interface in the context of embedded boundary method. This approach is generic in the sense that it is applicable to many different computational fracture models and methods, including the element deletion (ED) technique and the extended finite element method (XFEM). Two three-dimensional model problems are presented to demonstrate the salient features of the computational framework, and to compare the performance of ED and XFEM in the context of fluid-induced failure and fracture.Copyright

Harvesting environmental thermal energy using solid/liquid phase change materials

This article investigates the feasibility of using solid/liquid phase change materials to harvest the renewable thermal energy in various natural environments, which is often associated with a low temperature differential. The basic idea is to move the phase change material cyclically through the temperature differential and convert a fraction of the energy absorbed by the phase change material in its melting process into mechanical or electrical energy. In this work, we first develop a thermodynamic model for an idealized setting, thereby deriving a theoretical upper limit of the thermal efficiency. Next, we couple the thermodynamic model with a structural mechanics model based on Kirchhoff–Love plate theory, in order to predict the performance of specific devices. To validate the thermomechanical model and demonstrate the feasibility of the underlying approach, we develop a prototype that uses pentadecane (C15H32) as the phase change material. The measured specific energy agrees favorably with the model prediction. Finally, we employ the validated model to conduct a parameter study. The result implies that stiffer structures and phase change materials with high solid/liquid density ratio are preferred. The study also suggests that compared to bismuth telluride (Bi2Te3)-based thermoelectric generators, the phase change material–based approach may yield significantly higher efficiency when the temperature differential is less than 100°C.

High-Fidelity Fluid Structure Coupled Simulations for Underwater Propulsion Using Flexible Biomimetic Fins

The ability of fish to maneuver in tight places, perform stable high acceleration maneuvers, and hover efficiently has inspired the development of underwater robots propelled by flexible fins mimicking those of fish. In general, fin propulsion is a challenging fluid-structure interaction (FSI) problem characterized by large structural deformation and strong added-mass effect. It was recently reported that a simplified computational model using the vortex panel method for the fluid flow is not able to accurately predict thrust generation. In this work, a high-fidelity, fluid-structure coupled computational framework is applied to predict the propulsive performance of a series of biomimetic fins of various dimensions, shapes, and stiffness. This computational framework couples a three-dimensional finite-volume Navier-Stokes computational fluid dynamics (CFD) solver and a nonlinear, finite-element computational structural dynamics (CSD) solver in a partitioned procedure. The large motion and deformation of the fluid-structure interface is handled using a validated, state-of-the-art embedded boundary method. The notorious numerical added-mass effect, that is, a numerical instability issue commonly encountered in FSI simulations involving incompressible fluid flows and light (compared to fluid) structures, is suppressed by accounting for water compressibility in the CFD model and applying a low-Mach preconditioner in the CFD solver. Both one-way and two-way coupled simulations are performed for a series of flexible fins with different thickness. Satisfactory agreement between the simulation prediction and the corresponding experimental data is achieved.Copyright

Atomistic Models of Long-Term Hydrogen Diffusion in Metals

The effective and efficient storage of hydrogen is one of the key challenges in developing a hydrogen economy. Recently, intensive research has been focused on developing and optimizing metal-based nanomaterials for high-speed, high-capacity, reversible hydrogen storage applications. Notably, the absorption and desorption of hydrogen in nanomaterials is characterized by an atomic, deformation-diffusion coupled process with a time scale of the order of seconds to hours--far beyond the time windows of existing simulation technologies such as Molecular Dynamics (MD) and Monte Carlo (MC) methods. In this work, we present a novel deformation-diffusion coupled computational framework, which allows the long-term simulation of such slow processes and at the same time maintains a strictly atomistic description of the material. Specifically, we first propose a theory of non-equilibrium statistical thermodynamics for multi-species particulate solids based on Jaynes maximum entropy principle and the meanfield approximation approach. This non-equilibrium statistical thermodynamics model is then coupled with novel discrete kinetics laws, which governs the diffusion of mass--and possibly also conduction of heat--at atomic scale. Finally, this thermo-chemo-mechanical coupled system is solved numerically using a staggered procedure. The salient features of this computational framework are demonstrated in the simulation of a specific hydrogen diffusion problem using palladium nanofilms, which comes with a simulation time of one second. More generally, the proposed computational framework can be considered as an ideal tool for the study of many deformation-diffusion coupled phenomena in hydrogen-storage-related applications including, but not limited to, hydrogen embrittlement, grain boundary diffusion, and various cyclic behaviors.

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.

Computational Algorithms for Tracking Dynamic Fluid-Structure Interfaces in Embedded/Immersed Boundary Methods

A robust, accurate, and computationally efficient interface tracking algorithm is a key component of an embedded/immersed computational framework for the solution of fluid-structure interaction problems with complex and deformable geometries. To a large extent, the design of such an algorithm has focused on the case of a closed embedded interface and a Cartesian Computational Fluid Dynamics (CFD) grid. Here, two robust and efficient interface tracking computational algorithms capable of operating on structured as well as unstructured three-dimensional CFD grids are presented. The first one is based on a projection approach, whereas the second one is based on a collision approach. The first algorithm is faster. However, it is restricted to closed interfaces and resolved enclosed volumes. The second algorithm is therefore slower. However, it can handle open shell surfaces and underresolved enclosed volumes. Both computational algorithms exploit the bounding box hierarchy technique and its parallel distributed implementation to efficiently store and retrieve the elements of the discretized embedded interface. They are illustrated, and their respective performances are assessed and contrasted, with the solution of three-dimensional, nonlinear, dynamic fluid-structure interaction problems pertaining to aeroelastic and underwater implosion applications.