We describe a general strategy we have found effective for parallelizing solid mechanics simulations. Such simulations often have several computationally intensive parts, including finite element integration, detection of material contacts, and particle interaction if smoothed particle hydrodynamics is used to model highly deforming materials. The need to balance all of these computations simultaneously is a difficult challenge that has kept many commercial and government codes from being used effectively on parallel supercomputers with hundreds or thousands of processors. Our strategy is to load-balance each of the significant computations independently with whatever balancing technique is most appropriate. The chief benefit is that each computation can be scalably parallelized. The drawback is the data exchange between processors and extra coding that must be written to maintain multiple decompositions in a single code. We discuss these trade-offs and give performance results showing this strategy has led to a parallel implementation of a widely-used solid mechanics code that can now be run efficiently on thousands of processors of the Pentium-based Sandia/Intel TFLOPS machine. We illustrate with several examples the kinds of high-resolution, million-element models that can now be simulated routinely. We also look to the future and discuss what possibilities this new capability promises, as well as the new set of challenges it poses in material models, computational techniques, and computing infrastructure.
ALEGRA is an arbitrary Lagrangian-Eulerian finite element code that emphasizes large distortion and shock propagation. This document describes the user input language for the code.
This report provides a review of the open literature relating to numerical methods for simulating deep penetration events. The objective of this review is to provide recommendations for future development of the ALEGRA shock physics code to support earth penetrating weapon applications. While this report focuses on coupled EulerianLagrangian methods, a number of complementary methods are also discussed which warrant further investigation. Several recommendations are made for development activities within ALEGRA to support earth penetrating weapon applications in the short, intermediate, and long term.
Virtual reality simulators are increasingly used to gain robotic surgical skills. This study compared use of the da Vinci Surgical Skills Simulator (dVSSS) to the standard da Vinci (SdV) robot for skills acquisition in a prospective randomized study. Residents from urology, gynecology, and general surgery programs performed three virtual reality tasks (thread the ring, ring rail, and tubes) on the dvSSS. Participants were then randomized to one of the two study groups (dVSSS and SdV). Each participant then practiced on either the dVSSS or the SdV (depending on randomization) for 30 min per week over a 4-week time period. The dVSSS arm was not permitted to practice ring rail (due to no similar practice scenario available for the SdV group). Following 4 weeks of practice, participants performed the same three virtual reality tasks and the results were recorded and compared to baseline. Overall and percent improvement were recorded for all participants from pre-test to post-test. Two-way ANOVA analyses were used to compare the dVSSS and SdV groups and three tasks. Initially, 30 participants were identified and enrolled in the study. Randomization resulted in 15 participants in each arm. During the course of the study, four participants were unable to complete all tasks and practice sessions and were, therefore, excluded. This resulted in a total of 26 participants (15 in the dVSSS group and 11 in the SdV group) who completed the study. Overall total improvement score was found to be 23.23 and 23.48 for the SdV and dVSSS groups, respectively (p = 0.9245). The percent improvement was 60 and 47 % for the SdV and dVSSS groups respectively, which was a statistically significant difference between the two groups and three tasks. Practicing on the standard da Vinci is comparable to practicing on the da Vinci simulator for acquiring robotic surgical skills. In spite of several potential advantages, the dVSSS arm performed no better than the SdV arm in the final assessment of participant scores. Our findings indicate that both the SdV and dVSSS can be beneficial to residents in improving their robotic surgery skills.
This document provides general guidance for the design and analysis of bolted joint connections. An overview of the current methods used to analyze bolted joint connections is given. Several methods for the design and analysis of bolted joint connections are presented. Guidance is provided for general bolted joint design, computation of preload uncertainty and preload loss, and the calculation of the bolted joint factor of safety. Axial loads, shear loads, thermal loads, and thread tear out are used in factor of safety calculations. Additionally, limited guidance is provided for fatigue considerations. An overview of an associated Mathcad © Worksheet containing all bolted joint design formulae presented is also provided.
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