首页> 外文会议>International Conference on Environmental Systems >Mapping of Spacecraft Atmosphere Monitor Signal to Major Constituent Abundances
【24h】

Mapping of Spacecraft Atmosphere Monitor Signal to Major Constituent Abundances

机译:航天器气氛监测信号的映射到主要的组成丰富

获取原文

摘要

The Spacecraft Atmosphere Monitor (S.A.M.) follows the JPL's commitment to introduce and develop next-generation instrumentation concepts for sensing the air quality on manned space flights via continuous sampling, measuring, and reporting in 2s intervals on all gaseous pollutants. The S.A.M. will have two modes of operation: the Major Constituent Analysis (MCA) mode and the Trace Gas Analysis (TGA) mode. The MCA mode will report on molecular analytes such as CH4, H2O, N2, O2, Ar, and CO2 while the TGA mode will acquire minute amounts of volatile organic compounds. Both modes assess the composition of the ambient air with twenty full mass spectra per second giving rise to a substantial amount of data to be processed by a set of small footprint software stacks hosted by an on-board computer. Mass spectra will be accumulated as the number of counts recorded in a given mass-to-charge channel and converted into the absolute abundances of detected species using an efficient algorithm. The decomposition algorithm contains four units: peak identification, mass calibration, background and dead time correction, and an abundance analysis unit. The abundance analysis module identifies target species through their characteristic fragmentation patterns in the presence of molecular isobars, such as CO and N2. For example, in order to identify N2 analyte, the code will simultaneously monitor abundance ratios of the 14, 28 and 29 Th signals and will adapt to any instability caused by a decrease in ambient pressure or changes in humidity. This requirement becomes critical for instruments designed to monitor the near real-time quality of cabin air and promptly provide accurate feedbacks.
机译:航天器气氛监测(S.A.M.)遵循JPL的承诺,介绍和开发下一代仪器,通过连续采样,测量和在所有气态污染物上以2S间隔以2S间隔报告载有载人的空间航班上的空气质量。 S.A.M.将有两种操作模式:主要的组成分析(MCA)模式和痕量气体分析(TGA)模式。 MCA模式将报告分子分析物,例如CH 4,H 2 O,N 2,O 2,Ar和CO 2,而TGA模式将获得微量挥发性有机化合物。两种模式评估了每秒20个全质谱的环境空气的组成,从车载计算机托管的一组小型足迹软件堆栈产生大量数据。将质谱将累积为记录在给定的质量到充电通道中的计数的数量,并使用高效算法转换成检测物种的绝对丰度。分解算法包含四个单位:峰值识别,质量校准,背景和死区时间校正,以及丰度分析单元。丰度分析模块通过其特征碎片模式识别在分子烯烃的存在下,例如CO和N2。例如,为了识别N2分析物,代码将同时监测14,28和第29个信号的丰度比,并且将适应由环境压力降低或湿度变化引起的任何不稳定性。这一要求对于旨在监控驾驶室空气的近实时质量并及时提供准确反馈的仪器至关重要。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号