Sensor systems for the Altair Lunar Lander:
The Altair Lunar Lander will enable astronauts to learn to live and work on the moon for extended periods of time, providing the experience needed to expand human exploration farther into the solar system. My overriding recommendation: Use independent and complementary [sometimes referred to as 'orthogonal'] techniques to disambiguate confounding/interfering signals. E.g.: a mass spectrometer ['MS'], which currently serves as a Majority Constituent Analyzer ['MCA'] can be very valuable in detecting the presence of a gaseous specie, so long as it falls on a mass-to-charge ratio ['m/z'] that is not already occupied by a majority constituent of cabin air. Consider the toxic gas, CO. Both N{sub 2} and CO have parent peaks of m/z = 28, and CO{sub 2} has a fragment peak at m/z = 28 [and at 16 and 12], so the N{sub 2} and CO{sub 2} m/z=28 signals could mask low, but potentially-dangerous levels of CO. However there are numerous surface-sensitive CO detectors, as well as tunable-diode-laser-based CO sensors that could provide independent monitoring of CO. Also, by appending a gas chromatograph ['GC'] as the front-end sample processer, prior to the inlet of the MS, one can rely upon the GC to separate CO from N{sub 2} and CO{sub 2}, providing the crew with another CO monitor. If the Altair Lunar Lander is able to include a Raman-based MCA for N{sub 2}, O{sub 2}, H{sub 2}O, and CO{sub 2}, then each type of MCA would have cross-references, providing more confidence in the ongoing performance of each technique, and decreasing the risk that one instrument might fail to perform properly, without being noticed. See, also Dr. Pete Snyder's work, which states 'An orthogonal technologies sensor system appears to be attractive for a high confidence detection of presence and temporal characterization of bioaerosols.' Another recommendation: Use data fusion for event detection to decrease uncertainty: tie together the outputs from multiple sensing modalities - eNose, solid-state sensors, GC-IMS, GC-MS - via nonlinear algorithms, such as an 'artificial neural net.' MA Ryan at the JPL and Henry Abarbanel at UCSD are possible candidates to implement such an approach.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 1020340
- Report Number(s):
- LLNL-TR-421882; TRN: US201116%%767
- Country of Publication:
- United States
- Language:
- English
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