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Impact identification for damage mitigation using smart materials.

机译：使用智能材料的减震识别。

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In the design of automobiles, occupant and pedestrian safety are important design issues. If a vehicle were to be involved in a collision or near-collision, it would be desirable, if the on-board systems could detect impact or potential impact, handle safety through deployment of various safety systems, and steer away from impact. For this, an impact study to find the location and magnitude of impact and damage identification is relevant. Two different scenarios are discussed: (1) Non-damaging impact—the severity of impact is considered to be limited as not to cause structural damage; (2) Damaging impact—the impact is severe enough to cause structural damage.; For non-damaging impacts, a generalized dynamic methodology is developed based on the Mindlin Plate Theory, the Rayleigh-Ritz energy approach, and the Hamilton principle. This procedure is demonstrated for a simply-supported rectangular plate for static and impact loads, with point and area contacts. The forward model results (predicting strains for known forces) are compared with FEA and known analytical solutions and are found to be in good agreement. An algorithm using deconvolution for identifying the impact location and magnitude for a set of known strains (inverse model) is developed and compared to a forward model study. The known impact force history in the forward model and the output from the inverse algorithm compared favorably.; In order to implement sensor integration, the sensor gain factor and parameters that affect smart sensor performance are also investigated using a Taguchi method. Gain factors obtained using FEA and experimental work are used in both forward and inverse models.; In the case of a damaging impact, forward dynamic analyses for undamaged and damaged plates are performed for a known set of forces. Analyses are carried out for nine different damage locations on a simply supported plate and six different damage sizes (0.01% to 10%). Changes in frequency and mode shape assist in assessing damage severity.; This work, using distributed smart systems is useful for impact identification and damage assessment in a structure. Future application of this approach is envisioned in the area of automobile and passenger safety.
机译：在汽车设计中，乘员和行人安全是重要的设计问题。如果车辆要发生碰撞或接近碰撞，则车载系统可以检测到碰撞或潜在碰撞，通过部署各种安全系统来处理安全问题，并避免碰撞，这将是合乎需要的。为此，进行影响研究以找到影响和损害识别的位置和大小是很重要的。讨论了两种不同的情况：（1）非破坏性影响-认为影响的严重程度受到限制，不会造成结构性破坏； （2）破坏性冲击-冲击严重到足以造成结构性损坏。对于非破坏性影响，基于Mindlin板理论，Rayleigh-Ritz能量方法和Hamilton原理开发了一种广义的动力学方法。对于简单支撑的矩形板，通过点和面接触，可以承受静载荷和冲击载荷，演示了此过程。将正向模型结果（预测已知力的应变）与FEA和已知分析解决方案进行比较，发现它们具有很好的一致性。开发了一种使用反卷积算法来识别一组已知应变（逆模型）的冲击位置和大小的算法，并将其与正向模型研究进行了比较。 ;正向模型中的已知冲击力历史记录和逆向算法的输出可比。为了实现传感器集成，还使用Taguchi方法研究了影响智能传感器性能的传感器增益因子和参数。使用有限元分析和实验工作获得的增益因子可用于正向和反向模型。在有破坏性冲击的情况下，针对一组已知的力对未破坏和损坏的板进行前向动力学分析。在简单支撑的板上对9个不同的损坏位置和6个不同的损坏大小（0.01％至10％）进行了分析。频率和振型的变化有助于评估损伤的严重程度。使用分布式智能系统的这项工作对于结构中的影响识别和损害评估非常有用。在汽车和乘客安全领域中，可以预想该方法的未来应用。

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