Computationally-efficient stochastic cluster dynamics method for modeling damage accumulation in irradiated materials

An improved version of a recently developed stochastic cluster dynamics (SCD) method {[}Marian, J. and Bulatov, V. V., {\it J. Nucl. Mater.} \textbf{415} (2014) 84-95{]} is introduced as an alternative to rate theory (RT) methods for solving coupled ordinary differential equation (ODE) systems for irradiation damage simulations. SCD circumvents by design the curse of dimensionality of the variable space that renders traditional ODE-based RT approaches inefficient when handling complex defect population comprised of multiple (more than two) defect species. Several improvements introduced here enable efficient and accurate simulations of irradiated materials up to realistic (high) damage doses characteristic of next-generation nuclear systems. The first improvement is a procedure for efficiently updating the defect reaction-network and event selection in the context of a dynamically expanding reaction-network. Next is a novel implementation of the $\tau$-leaping method that speeds up SCD simulations by advancing the state of the reaction network in large time increments when appropriate. Lastly, a volume rescaling procedure is introduced to control the computational complexity of the expanding reaction-network through occasional reductions of the defect population while maintaining accurate statistics. The enhanced SCD method is then applied to model defect cluster accumulation in iron thin films subjected to triple ion-beam ($\text{Fe}^{3+}$, $\text{He}^{+}$ and $$\text{H\ensuremath{{}^{+}}}$$) irradiations, for which standard RT or spatially-resolved kinetic Monte Carlo simulations are prohibitively expensive

The 1999 Center for Simulation of Dynamic Response in Materials Annual Technical Report

Introduction: This annual report describes research accomplishments for FY 99 of the Center for Simulation of Dynamic Response of Materials. The Center is constructing a virtual shock physics facility in which the full three dimensional response of a variety of target materials can be computed for a wide range of compressive, ten- sional, and shear loadings, including those produced by detonation of energetic materials. The goals are to facilitate computation of a variety of experiments in which strong shock and detonation waves are made to impinge on targets consisting of various combinations of materials, compute the subsequent dy- namic response of the target materials, and validate these computations against experimental data

The 1998 Center for Simulation of Dynamic Response in Materials Annual Technical Report

Introduction: This annual report describes research accomplishments for FY 98 of the Center for Simulation of Dynamic Response of Materials. The Center is constructing a virtual shock physics facility in which the full three dimensional response of a variety of target materials can be computed for a wide range of compressive, tensional, and shear loadings, including those produced by detonation of energetic materials. The goals are to facilitate computation of a variety of experiments in which strong shock and detonation waves are made to impinge on targets consisting of various combinations of materials, compute the subsequent dynamic response of the target materials, and validate these computations against experimental data

Microstructure modeling and crystal plasticity parameter identification for predicting the cyclic mechanical behavior of polycrystalline metals

Computational homogenization permits to capture the influence of the microstructure on the cyclic mechanical behavior of polycrystalline metals. In this work we investigate methods to compute Laguerre tessellations as computational cells of polycrystalline microstructures, propose a new method to assign crystallographic orientations to the Laguerre cells and use Bayesian optimization to find suitable parameters for the underlying micromechanical model from macroscopic experiments

Influence of chemistry and structure on interfacial segregation in NbMoTaW with high-throughput atomistic simulations

Refractory multi-principal element alloys exhibiting promising mechanical properties such as excellent strength retention at elevated temperatures have been attracting increasing attention. Although their inherent chemical complexity is considered a defining feature, a challenge arises in predicting local chemical ordering, particularly in grain boundary regions with enhanced structural disorder. In this study, we use atomistic simulations of a large group of bicrystal models to sample a wide variety of interfacial sites (grain boundary) in NbMoTaW and explore emergent trends in interfacial segregation and the underlying structural and chemical driving factors. Sampling hundreds of bicrystals along the [001] symmetric tilt axis and analyzing more than one hundred and thirty thousand grain boundary sites with a variety of local atomic environments, we uncover segregation trends in NbMoTaW. While Nb is the dominant segregant, more notable are the segregation patterns that deviate from expected behavior and mark situations where local structural and chemical driving forces lead to interesting segregation events. For example, incomplete depletion of Ta in low-angle boundaries results from chemical pinning due to favorable local compositional environments associated with chemical short-range ordering. Finally, machine learning models capturing and comparing the structural and chemical features of interfacial sites are developed to weigh their relative importance and contributions to segregation tendency, revealing a significant increase in predictive capability when including local chemical information. Overall, this work, highlighting the complex interplay between local grain boundary structure and chemical short-range ordering, suggest tunable segregation and chemical ordering by tailoring grain boundary structure in multi-principal element alloys

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Ferroelectric domains in barium titanate by Bragg coherent X-ray diffraction imaging

My PhD work focused on studying the domain structures and the strain fields inside barium titanate (BaTiO3) nanocrystals. The results on the domain structure study have already been published. The results on the stripe-like strain fields inside nanocrystals are finalized and there is a plan for publication. The first question my PhD work wants to address is what the domain structures inside BTO nanoparticles exist and how they evolve with temperature and when crossing the phase transition. Bragg coherent X-ray diffraction imaging (BCDI) experiments on nominal 200 nm size BTO nanoparticles were carried out at the Diamond I13-1 beamline and the Advanced Photon Source 34-ID-C beamline. The 90° domain walls were tracked in detail when crossing the tetragonal-cubic phase transition. This is presented in Chapter 3. Upon studying the domain structure inside BTO nanocrystals, some unexpected stripe-like strain fields were found. Crystals with clear facets were chosen to restore resolve the crystallographic direction, after which the strain field direction and periodicity were studied in detail. This is shown in Chapter 4. To understand the temperature dependence of the strain stripes, in-situ BCDI experiments were done at ESRF ID-01 beamline. Faceted BTO nanocrystals were chosen for temperature study. The strain stripes were found to be stable and preserved at both tetragonal and cubic phase with at elevated temperatures. This is illustrated in Chapter 5. The Finite element analysis (FEA) approach was utilized to understand the origins of the strain stripes. Different piezoelectric blocks were defined to simulate the domain structures inside a BTO crystal. 180° domain walls were found to give more strain stripes features than 90° domain walls in the simulation. This is covered in Chapter 6. The same patch of BTO nanocrystals were also studied using an X-ray Free-electron Laser as a function of time delay after laser excitation. Rather than seeing any significant thermal expansion effects, the diffraction peaks were found to move perpendicular to the momentum transfer direction. This suggests a laser driven rotation of the crystal lattice, which is delayed by the aggregated state of the crystals. Internal deformations associated with crystal contacts were also observed. These are shown in Chapter 7

Bayesian network analysis of multi-compartmentalized immune responses in a murine model of sepsis and direct lung injury

Abstract Background Inflammatory disease processes involve complex and interrelated systems of mediators. Determining the causal relationships among these mediators becomes more complicated when two, concurrent inflammatory conditions occur. In those cases, the outcome may also be dependent upon the timing, severity and compartmentalization of the insults. Unfortunately, standard methods of experimentation and analysis of data sets may investigate a single scenario without uncovering many potential associations among mediators. However, Bayesian network analysis is able to model linear, nonlinear, combinatorial, and stochastic relationships among variables to explore complex inflammatory disease systems. In these studies, we modeled the development of acute lung injury from an indirect insult (sepsis induced by cecal ligation and puncture) complicated by a direct lung insult (aspiration). To replicate multiple clinical situations, the aspiration injury was delivered at different severities and at different time intervals relative to the septic insult. For each scenario, we measured numerous inflammatory cell types and cytokines in samples from the local compartments (peritoneal and bronchoalveolar lavage fluids) and the systemic compartment (plasma). We then analyzed these data by Bayesian networks and standard methods. Results Standard data analysis demonstrated that the lung injury was actually reduced when two insults were involved as compared to one lung injury alone. Bayesian network analysis determined that both the severity of lung insult and presence of sepsis influenced neutrophil recruitment and the amount of injury to the lung. However, the levels of chemoattractant cytokines responsible for neutrophil recruitment were more strongly linked to the timing and severity of the lung insult compared to the presence of sepsis. This suggests that something other than sepsis-driven exacerbation of chemokine levels was influencing the lung injury, contrary to previous theories. Conclusions To our knowledge, these studies are the first to use Bayesian networks together with experimental studies to examine the pathogenesis of sepsis-associated lung injury. Compared to standard statistical analysis and inference, these analyses elucidated more intricate relationships among the mediators, immune cells and insult-related variables (timing, compartmentalization and severity) that cause lung injury. Bayesian networks are an effective tool for evaluating complex models of inflammation.http://deepblue.lib.umich.edu/bitstream/2027.42/113666/1/13104_2015_Article_1488.pd

Immunomodulatory biomimetic nanoparticles target articular cartilage trauma after systemic administration

Post-traumatic osteoarthritis (PTOA) is one of the leading causes of disability in developed countries and accounts for 12% of all osteoarthritis cases in the United States. After trauma, inflammatory cells (macrophages amongst others) are quickly recruited within the inflamed synovium and infiltrate the joint space, initiating dysregulation of cartilage tissue homeostasis. Current therapeutic strategies are ineffective, and PTOA remains an open clinical challenge. Here, the targeting potential of liposome-based nanoparticles (NPs) is evaluated in a PTOA mouse model, during the acute phase of inflammation, in both sexes. NPs are composed of biomimetic phospholipids or functionalized with macrophage membrane proteins. Intravenous administra-tion of NPs in the acute phase of PTOA and advanced in vivo imaging techniques reveal prefer-ential accumulation of NPs within the injured joint for up to 7 days post injury, in comparison to controls. Finally, imaging mass cytometry uncovers an extraordinary immunomodulatory effect of NPs that are capable of decreasing the amount of immune cells infiltrating the joint and conditioning their phenotype. Thus, biomimetic NPs could be a powerful theranostic tool for PTOA as their accumulation in injury sites allows their identification and they have an intrinsic immunomodulatory effect

Efficient fast Fourier transform-based solvers for computing the thermomechanical behavior of applied materials

The mechanical behavior of many applied materials arises from their microstructure. Thus, to aid the design, development and industrialization of new materials, robust computational homogenization methods are indispensable. The present thesis is devoted to investigating and developing FFT-based micromechanics solvers for efficiently computing the (thermo)mechanical response of nonlinear composite materials with complex microstructures