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Technology Focus: Sensors 

# Cryogenic Flow Sensor 

Marshall Space Flight Center, Alabama 

An acousto-optic cryogenic flow sen- 
sor (CFS) determines mass flow of 
cryogens for spacecraft propellant 
management. The CFS operates unob- 
trusively in a high-pressure, high-flow- 
rate cryogenic environment to provide 
measurements for fluid quality as well 
as mass flow rate. Experimental hard- 
ware uses an optical “plane-of-light” 
(POL) to detect the onset of two-phase 
flow, and the presence of particles in 
the flow of water. 

Acousto-optic devices are used in 
laser equipment for electronic control 
of the intensity and position of the laser 
beam. Acousto-optic interaction occurs 
in all optical media when an acoustic 

wave and a laser beam are present. 
When an acoustic wave is launched into 
the optical medium, it generates a re- 
fractive index wave that behaves like a si- 
nusoidal grating. An incident laser 
beam passing through this grating will 
diffract the laser beam into several or- 
ders. Its angular position is linearly pro- 
portional to the acoustic frequency, so 
that the higher the frequency, the larger 
the diffracted angle. 

If the acoustic wave is traveling in a 
moving fluid, the fluid velocity will af- 
fect the frequency of the traveling wave, 
relative to a stationary sensor. This fre- 
quency shift changes the angle of dif- 
fraction, hence, fluid velocity can be de- 

termined from the diffraction angle. 
The CFS acoustic Bragg grating data 
test indicates that it is capable of accu- 
rately determining flow from 0 to 10 
meters per second. The same sensor 
can be used in flow velocities exceeding 
100 m/s. The POL module has success- 
fully determined the onset of two-phase 
flow, and can distinguish vapor bubbles 
from debris. 

This work was done by John Justak of Ad- 
vanced Technologies Group, Inc. for Marshall 
Space Flight Center. For more 
information, contact Sammy Nabors, MSFC 
Commercialization Assistance Lead, at Refer to MFS- 
32730 - 1 . 

# Multi-Sensor Mud Detection 

This technology is also applicable to terrain hazard assessment in terrestrial or planetary 

NASA’s Jet Propulsion Laboratory, Pasadena, California 

Robust mud detection is a critical per- 
ception requirement for Unmanned 
Ground Vehicle (UGV) autonomous off- 
road navigation. A military UGV stuck in a 

A General Dynamics Robotic Systems (GDRS) ex- 
perimental unmanned vehicle (XUV) navigates 
through a muddy grass field during a data col- 
lection for the Daytime Mud Detection System. 

mud body during a mission may have to be 
sacrificed or rescued, both of which are un- 
attractive options. There are several char- 
acteristics of mud that may be detectable 
with appropriate UGV-mounted sensors. 
For example, mud only occurs on the 
ground surface, is cooler than surround- 
ing dry soil during the daytime under 
nominal weather conditions, is generally 
darker than surrounding dry soil in visible 
imagery, and is highly polarized. However, 
none of these cues are definitive on their 
own. Dry soil also occurs on the ground 
surface, shadows, snow, ice, and water can 
also be cooler than surrounding dry soil, 
shadows are also darker than surrounding 
dry soil in visible imagery, and cars, water, 
and some vegetation are also highly polar- 
ized. Shadows, snow, ice, water, cars, and 
vegetation can all be disambiguated from 
mud by using a suite of sensors that span 
multiple bands in the electromagnetic 
spectrum. Because there are military oper- 
ations when it is imperative for UGV’s to 
operate without emitting strong, de- 

tectable electromagnetic signals, passive 
sensors are desirable. 

JPL has developed a daytime mud de- 
tection capability using multiple passive 
imaging sensors. Cues for mud from 
multiple passive imaging sensors are 
fused into a single mud detection image 
using a rule base, and the resultant mud 
detection is localized in a terrain map 
using range data generated from a stereo 
pair of color cameras. Thus far at the 
time of this reporting, JPL has: 

1. Performed daytime data collections, 
on wet and dry soil, with several candi- 
date passive imaging sensors, including 
multi-spectral (blue, green, red, and 
near-infrared bands), short-wave in- 
frared, mid-wave infrared, long-wave 
infrared, polarization, and a stereo 
pair of color cameras. 

2. Characterized the advantages and dis- 
advantages of each passive imaging 
sensor to provide cues for mud. 

3. Implemented a first-generation mud 
detector that uses a stereo pair of color 

NASA Tech Briefs, January 2010