Photonic doppler velocimeter

光子多普勒测速仪

• 批准号: 406667-2011
• 负责人: Higgins, Andrew
• 金额: $ 10.2万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments - Category 1 (<$150,000)
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=472532
• 关键词: Photonic; doppler; velocimeter

The proposed instrument to be acquired is a Photonic Doppler Velocimeter (PDV), used to obtain extremely accurate material velocity histories along two axes in high speed ballistic impact and shock wave experiments. The ability to generate this type of high quality velocity history data is essential to McGill University's continued participation in state-of-the-art research in the fields of ballistic protection, shock wave physics, and detonics. With the development of numerical modeling over the past decade, continuous data sources are necessary for the validation of new models. Experimental work involving discrete "time of arrival" data collection is now considered to be obsolete by researchers in the field. The ability to continuously record the material velocity history in an experiment will permit insights into a number of important problems. In particular, this program will use the system in evaluating novel ceramic-metal composite and shear thickening fluid armours, in simulating hypervelocity impacts caused by orbital debris, and in studying nonideal detonation propagation in commercial blasting explosives. The ability to study these processes in a high-resolution, continuous history measurement will permit models and computer simulations to be validated, which in turn can be used to predict and improve how these processes occur in applications.

拟获取的仪器是光子多普勒测速仪(PDV)，用于在高速弹道碰撞和冲击波实验中获得沿两个轴的极精确的材料速度历史。生成这种类型的高质量速度历史数据的能力对于麦吉尔大学继续参与弹道保护、冲击波物理和爆炸领域的最先进研究至关重要。在过去的十年中，随着数值模拟的发展，对新模型的验证需要连续的数据源。该领域的研究人员现在认为，涉及离散“到达时间”数据收集的实验工作已经过时。在实验中连续记录材料速度历史的能力将使人们能够深入了解一些重要问题。特别是，该系统将用于评估新型陶瓷-金属复合材料和剪切增稠流体装甲，模拟轨道碎片引起的超高速撞击，以及研究商业爆炸炸药中的非理想爆轰传播。在高分辨率、连续的历史测量中研究这些过程的能力将使模型和计算机模拟得到验证，这反过来又可以用于预测和改进这些过程在应用中如何发生。