ABSTRACT
Ultralight architected materials enabled by advanced manufacturing processes have achieved density-normalized strength and stiffness properties that are inaccessible to bulk materials. However, the majority of this work has focused on static loading and elastic-wave propagation. Fundamental understanding of the mechanical behavior of architected materials under large-deformation dynamic conditions remains limited, due to the complexity of mechanical responses and shortcomings of characterization methods. Here, we present a microscale suspended-plate impact testing framework for three-dimensional micro-architected materials, where supersonic microparticles to velocities of up to 850 m/s are accelerated against a substrate-decoupled architected material to quantify its energy dissipation characteristics. Using ultra-high-speed imaging, we perform in situ quantification of the impact energetics on two types of architected materials as well as their constituent nonarchitected monolithic polymer, indicating a 47% or greater increase in mass-normalized energy dissipation under a given impact condition through use of architecture. Post-mortem characterization, supported by a series of quasi-static experiments and high-fidelity simulations, shed light on two coupled mechanisms of energy dissipation: material compaction and particle-induced fracture. Together, experiments and simulations indicate that architecture-specific resistance to compaction and fracture can explain a difference in dynamic impact response across architectures. We complement our experimental and numerical efforts with dimensional analysis which provides a predictive framework for kinetic-energy absorption as a function of material parameters and impact conditions. We envision that enhanced understanding of energy dissipation mechanisms in architected materials will serve to define design considerations toward the creation of lightweight impact-mitigating materials for protective applications.
ABSTRACT
Stress responses vary drastically for a given set of stimuli, individuals, or points in time. A potential source of this variance that is not well characterized arises from the theory of stress as a dynamical system, which implies a complex, nonlinear relationship between environmental/situational inputs and the development/experience of stress. In this framework, stress vs. non-stress states exist as attractor basins in a physiologic phase space. Here, we develop a model of stress as a dynamical system by coupling closed loop physiologic control to a dynamic oscillator in an attractor landscape. By characterizing the evolution of this model through phase space, we demonstrate strong sensitivity to the parameters controlling the dynamics and demonstrate multiple features of stress responses found in current research, implying that these parameters may contribute to a significant source of variability observed in empiric stress research.
ABSTRACT
In this paper, we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50-74 compartments per neuron. After making some modifications to the source code for GENESIS and its parallel implementation, PGENESIS, particularly to improve memory usage, we find that PGENESIS is able to efficiently scale on supercomputing resources to network sizes as large as 9 × 106 neurons with 18 × 109 synapses and 2.2 × 106 neurons with 45 × 109 synapses. The modifications to GENESIS that enabled these large scale simulations have been incorporated into the May 2019 Official Release of PGENESIS 2.4 available for download from the GENESIS web site (genesis-sim.org).
