As hardware platforms mature and evolve to contain higher compute capacity, Small Unmanned Aerial Systems (sUAS) are increasingly capable of operating as fully-integrated, cooperative inspection systems. A variety of lightweight sensing payloads are emerging for efficient multi-modal data collection. Deep learning algorithms applied to this sensor data significantly reduce the burden on system operators and enable the fusion of data from multiple sources for enhanced decision making.
The Air Force Civil Engineer Center (AFCEC) and TORC Robotics are developing a Rapid Airfield Damage Assessment System (RADAS) that uses simultaneous data streams from multiple sUAS and ground sensors for computer-aided condition assessment and planning of airfield repair. Operators, aided by intelligent algorithms, remotely monitor incoming data and software tools to identify a Minimum Airfield Operating Surface (MAOS).
Recent developments by AFCEC and TORC use deep learning algorithms to eliminate the bottleneck of human-in-the-loop interpretation of multiple simultaneous data sources. These advances provide a supervised autonomous workflow in: (a) identification of damages from multiple incoming sUAS video streams, (b) automated tasking of decisions based on that data, and (c) adjustment of decisions based on additional incoming information.
Preliminary results demonstrate significant reduction in airfield assessment time, increased assessment accuracy, and remove humans from danger during the inspection process. This work is part of the RADAS program funded by the Air Force Civil Engineering Center (AFCEC).
Recent advancements in composite materials technologies have broken further from traditional designs and require advanced instrumentation and analysis capabilities. Success or failure is highly dependent on design analysis and manufacturing processes. By monitoring smart structures throughout manufacturing and service life, residual and operational stresses can be assessed and structural integrity maintained. Composite smart structures can be manufactured by integrating fiber optic sensors into existing composite materials processes such as ply layup, filament winding and three-dimensional weaving. In this work optical fiber was integrated into 3D woven composite parts at a commercial woven products manufacturing facility. The fiber was then used to monitor the structures during a VARTM manufacturing process, and subsequent static and dynamic testing. Low cost telecommunications-grade optical fiber acts as the sensor using a high resolution commercial Optical Frequency Domain Reflectometer (OFDR) system providing distributed strain measurement at spatial resolutions as low as 2mm. Strain measurements using the optical fiber sensors are correlated to resistive strain gage measurements during static structural loading.
Recent advances in materials science have resulted in a proliferation of flexible structures for high-performance civil,
mechanical, and aerospace applications. Large aspect-ratio aircraft wings, composite wind turbine blades, and
suspension bridges are all designed to meet critical performance targets while adapting to dynamic loading conditions.
By monitoring the distributed shape of a flexible component, fiber optic shape sensing technology has the potential to
provide valuable data during design, testing, and operation of these smart structures. This work presents a demonstration
of such an extended-range fiber optic shape sensing technology. Three-dimensional distributed shape and position
sensing is demonstrated over a 30m length using a monolithic silica fiber with multiple optical cores. A novel, helicallywound
geometry endows the fiber with the capability to convert distributed strain measurements, made using Optical
Frequency-Domain Reflectometry (OFDR), to a measurement of curvature, twist, and 3D shape along its entire length.
Laboratory testing of the extended-range shape sensing technology shows
Raman spectroscopy has become an established method for determining the composition of gaseous samples at low
temperatures (<1000°C). However, the design of a Raman sensor which operates at high temperatures (>1000°C)
remains elusive. This work investigates the feasibility of high-temperature Raman spectroscopy utilizing a monolithic
sapphire tube as a sample cell and signal collection optic. The insertion loss of small-diameter, single-crystal sapphire
tubing is measured to be 0.26-0.40dB/cm, proving its potential for use as a short-distance waveguide. Relevant system
losses are characterized for a fiber-based, reflection mode Raman sensor, and expected Raman signal powers are
predicted by simulation for the gaseous combustion products of ethylene: N2, CO, CO2, H2, and H2O. The successful implementation of a Raman sensor as described by this research could enable real-time analysis of exhaust gases from a hydrocarbon combustor. Furthermore, the extension of Raman spectroscopy to high temperatures would be a critical step
towards more precisely controlled, fuel-efficient technologies.
An optical fiber Single/Multi-/Single-mode Intrinsic Fabry-Pérot Interferometer (SMS-IFPI) pressure sensor has been
demonstrated using a silica tube-based pressure transducer hermetically sealed by thermal fusion bonding. The sensor,
made entirely of fused silica, contains an IFPI strain sensor enclosed by a CO2 laser-bonded outer tube. A sensor
prototype is constructed and demonstrated for single point pressure sensing at high temperature (600°C), with temperature
compensation achieved through co-location of an SMS-IFPI temperature sensor. The inline geometry and low
transmission loss of the SMS-IFPI sensor makes it suitable for frequency division multiplexing (FDM) in a single fiber
branch. In future work, we envision multiplexing of up to eight such IFPI pressure sensors along a single fiber branch for
quasi-distributed pressure measurement.
Direct bonding between two epitaxy-ready (EPI polished) sapphire wafers is demonstrated as the basis for an all-sapphire
pressure sensor. Through chemical processing, hydrogen pre-bonding, and a final high-temperature bakeout, the
two single-crystal wafers are directly bonded without the use of any adhesive or intermediate layer. Dicing across the
edge of the structure and inspection of the diced pieces with a scanning electron microscope (SEM) indicates a
successful direct bond. Control of the bonding wave generates an air bubble sealed between the two bonded sapphire
wafers. Optical interference-based measurements of the bubble height and shape at pressures from 0 to 60psig prove that
the bubble is sealed by the bonded wafers and demonstrate the potential for sapphire direct bonding as a means of
constructing an all-sapphire pressure sensor. Since the structure contains no adhesives, such an all-sapphire sensor is
ideal for pressure sensing in extremely harsh, high-temperature environments, potentially operating at temperatures over