Aging in Structural Changes of Amorphous Solids: A Study of First Passage Time and Persistence Time Distribution

The time distribution of relaxation events in an aging system is investigated via molecular dynamics simulations. The focus is on the distribution functions of the first passage time, $p_1(\Delta t)$, and the persistence time, $p(\tau)$. In contrast to previous reports, both $p_1$ and $p$ are found to evolve with time upon aging. The age dependence of the persistence time distribution is shown to be sensitive to the details of the algorithm used to extract it from particle trajectories. By updating the reference point in event detection algorithm and accounting for the event specific aging time, we uncover age dependence of $p(\tau)$, hidden to previous studies. Moreover, the apparent age-dependence of $p_1$ in continuous time random walk with an age independent $p(\tau)$ is shown to result from an implicit synchronization of all the random walkers at the starting time

Maintaining the equipartition theorem in small heterogeneous molecular dynamics ensembles

It has been reported recently that the equipartition theorem is violated in molecular dynamics simulations with periodic boundary condition [Shirts et al, J. Chem. Phys. 125, 164102 (2006)]. This effect is associated with the conservation of the center of mass momentum. Here, we propose a fluctuating center of mass molecular dynamics approach (FCMMD) to solve this problem. Using the analogy to a system exchanging momentum with its surroundings, we work out --and validate via simulations-- an expression for the rate at which fluctuations shall be added to the system. The restoration of equipartition within the FCMMD is then shown both at equilibrium as well as beyond equilibrium in the linear response regime

Microtexture investigation of orientation gradients and grain subdivision in rolled coarse-grained niobium

Orientation effects concerning grain subdivision and further annealing behavior of three neighboring grains were observed in 80% cold-rolled coarse-grained niobium. The present study which was conducted as a cooperation on the basis of DAAD and CAPES funding attempts to clarify the microstructural evolution of deformed niobium and the differences in terms of stored energy (boundary distribution) using high-resolution electron backscattering diffraction (FE-EBSD)

Orientation dependence of recrystallization in aluminum – Simulation and experiment

In this project the orientation dependence of recrystallization in aluminum (99.99%) was studied by means of experimental methods and computer simulation. Samples with columnar grain morphology were deformed in plane strain compression in a channel die setup up to a technical thickness reduction of 50% in several defined steps. After the last deformation step the samples were recrystallized at different temperatures. After each of the deformation steps and each of the recrystallization steps the samples were analyzed by means of electron backscattering diffraction technique. With the orientation data gained from the last deformation step an input file was generated for a recrystallization simulation based on a probabilistic cellular automaton

Instrument landing systems for the space shuttle

Comparison of instrument landing systems for space shuttle and aircraf

Wissensnetzwerk Controlling – Evaluationsergebnis der didaktisch-pädagogischen Begleitergruppe

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Methylome Alterations “Mark” New Therapeutic Opportunities in Glioblastoma

In this issue of Cancer Cell, Sturm et al. report that global DNA methylation patterns in glioblastoma multiforme divide adult and pediatric tumors into subgroups that have characteristic DNA mutations, mRNA profiles, and most importantly, different clinical behaviors. These findings suggest novel opportunities for therapeutics for this dreaded disease

Computational Discovery of Energy-Efficient Heat Treatment for Microstructure Design using Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is employed to develop autonomously optimized and custom-designed heat-treatment processes that are both, microstructure-sensitive and energy efficient. Different from conventional supervised machine learning, DRL does not rely on static neural network training from data alone, but a learning agent autonomously develops optimal solutions, based on reward and penalty elements, with reduced or no supervision. In our approach, a temperature-dependent Allen-Cahn model for phase transformation is used as the environment for the DRL agent, serving as the model world in which it gains experience and takes autonomous decisions. The agent of the DRL algorithm is controlling the temperature of the system, as a model furnace for heat-treatment of alloys. Microstructure goals are defined for the agent based on the desired microstructure of the phases. After training, the agent can generate temperature-time profiles for a variety of initial microstructure states to reach the final desired microstructure state. The agent's performance and the physical meaning of the heat-treatment profiles generated are investigated in detail. In particular, the agent is capable of controlling the temperature to reach the desired microstructure starting from a variety of initial conditions. This capability of the agent in handling a variety of conditions paves the way for using such an approach also for recycling-oriented heat treatment process design where the initial composition can vary from batch to batch, due to impurity intrusion, and also for the design of energy-efficient heat treatments. For testing this hypothesis, an agent without penalty on the total consumed energy is compared with one that considers energy costs. The energy cost penalty is imposed as an additional criterion on the agent for finding the optimal temperature-time profile