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A Coupled Approach to Developing Damage Prognosis Solutions

机译：开发损伤预测解决方案的耦合方法

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摘要

An approach to developing damage prognosis (DP) solution that is being developed at Los Alamos National Laboratory (LANL) is summarized in this paper. This approach integrates aadvanced sensing technology, data interrogation procedures for state awareness, novel model validation and uncertainty quantification techniques, and reliability-based decision-making algorithms in an effort to transition the concept of damage prognosis to actual practice. In parallel with this development, experimental efforts are underway to deliver a proof-of-principle technology demonstration. This demonstration will assess impact damage and predict the subsequent fatigue damage accumulation in a composite plate. Although the project focus will be DP for composite materials, most of this technology can generalize to many other applications. The unique aspects of this approach discussed herein include: 1) multi-length scale damage models analyzed on tera-scale computer platforms that discretize composites on an individual lamina level, 2) integration of advanced sensors with Los Alamos's flight-hardened data acquisition system, 3) damage detection based on a statistical pattern recognition approach, and 4) reliability-based metamodels with quantified uncertainty that can be deployed on microprocessors integrated with the sensing system for autonomous damage prognosis.
机译：本文总结了一种由洛斯阿拉莫斯国家实验室（LANL）开发的开发损害预后（DP）解决方案的方法。这种方法集成了先进的传感技术，用于状态感知的数据询问程序，新颖的模型验证和不确定性量化技术以及基于可靠性的决策算法，旨在将破坏预后的概念转变为实际操作。与这一发展同时，正在进行实验性工作以提供原理证明技术演示。该演示将评估冲击损伤并预测复合板中随后的疲劳损伤累积。尽管该项目的重点将是用于复合材料的DP，但该技术的大多数可以推广到许多其他应用。本文讨论的这种方法的独特方面包括：1）在万亿级计算机平台上分析的多尺度损伤模型，这些模型在单个薄片层面离散化了复合材料； 2）先进传感器与Los Alamos的飞行硬化数据采集系统集成在一起， 3）基于统计模式识别方法的损伤检测，以及4）具有量化不确定性的基于可靠性的元模型，可以将其部署在与传感系统集成的微处理器上，以进行自主损伤预后。

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