Several practical issues related to real-time parameter estimation for a linear longitudinal dynamics model in atmospheric turbulence using indirect turbulence measurements were examined and discussed. The frequency response of the airflow vanes, wing response to atmospheric turbulence, and the structural response of the air data boom were identified as sources of colored noise. The frequency response of the airflow vanes for a wide variety of combinations of natural frequency and damping ratio did not adversely impact parameter estimation results in turbulence, except for CZq and CZGe. This was attributed to noisy
explanatory variables in the atmospheric turbulence implementation and relatively low aerodynamic contributions as compared to CZD. Airflow vane dynamics do not substantially contribute to the scatter in
the estimates, because all of the added dynamics are outside the range of frequencies analyzed. The structural modes of the boom lead to similar conclusions, because the natural frequencies of the boom structural modes are outside the range of frequencies used for dynamic modeling.
A major source of colored noise was identified as the frequency-dependent upwash and time delay induced by the wing-bound vortex system and the longitudinal separation between the AOA measurement and the wing. It was also surmised that the frequency-dependent sidewash was a major source of colored
noise. The frequency-dependent upwash and time delay appear to be significant contributors to biasing and increased scatter and uncertainty for parameter estimates in atmospheric turbulence.
Practical issues were examined using data from a Twin Otter DHC-6 longitudinal linear simulation, with realistic noise sequences added to the computed aircraft responses. This allowed a clear view of the effect of each source of colored noise to the modeling problem, because the true values of the model parameters were known. This approach was used initially to show that real-time parameter estimation can be done accurately in all atmospheric turbulence conditions if the AOA and angle of sideslip
measurements are accurate. Flight test data from the GRC DHC-6 Twin Otter aircraft was used to validate the effect of the identified colored noise sources.
Based on these findings, several practical recommendations for flight testing in atmospheric turbulence are suggested, as well as areas for future study. First, mount the air data boom at the nose of the aircraft to minimize the effect of the wing-bound and wing-tip vortices. Second, if the air data boom is mounted on the nose of the aircraft, an upwash and time delay calibration as a function of frequency is required. Third, airflow angle measurements must be corrected for frequency dependent upwash, sidewash, and time delay corrections.
The following Task 2 and Task 3 results were carried out assuming that these precautions had been taken care of. This assumed that the frequency response was adequate (Good assumption based on the results of this section for a well calibrated, stiff, nose mounted sensor). This left the sensor’s static performance to be evaluated in Task 2 and Task 3. In other words, if these precautions are taken, the RT- PID estimates will behave similarly inside and outside of atmospheric turbulence.
12.2
Task 2: Effect of Sensor Noise and Turbulence
RT-PID
x In general, the original MOS for message accuracy were not achieved since the errors in the estimates and in the error bounds increase as a function of turbulence intensity. A new MOS criterion was formulated where the error in the estimates and in the error bounds with atmospheric turbulence is tolerated as long as the ICEPro messages and AOA brackets are equivalently displayed inside and outside of turbulence.
x In practice, for flight in zero, light, and moderate atmospheric turbulence the average airflow angles are similar. In severe turbulence, the average airflow angles are significantly different due to wing stall. The differences do not affect message accuracy, but do affect the AOA bracket accuracy. The data from stalled runs was not considered useful because stalled flight does not produce reliable data. As a result, all severe turbulence test conditions have been omitted from Task 2 analysis.
x With the exception of the roll degrade message in calm atmosphere, there were no false positives or false negatives. The roll degrade message was caused by low pilot activity resulting in poor RT-PID estimates. In each of the turbulence cases, the additional aileron inputs provided by the virtual pilot improved the RT-PID estimates so that no false positives (roll degrade message) were annunciated.
x In general the new MOS message criterion was considered to be successful.
D-ICES
x It was found that the application of turbulence and sensor noise on D-ICES had minimal impact on performance without filtering.
ż Only one condition was found that yielded a false positive that would have caused the system to enter ID Mode when no icing was present.
ż There was one condition where the elevator deflection Theil inequality Coefficient for an iced airplane failed to exceed the threshold and two where the value originally exceeded the threshold, but with time decreased until it was below the threshold.
x However, when the state vectors were low pass filtered to remove higher frequency disturbances due to turbulence and noise, D-ICES accurately detected all icing conditions in the presence of turbulence and sensor noise. Filtering did not have any adverse ramifications on logic, thresholds, or other system parameters or functions.
x Three potential false positive cases were found that showed the elevator Theil Coefficient built up over time to exceed the threshold when no ice was present. An examination of the data in this region led to supposition that the D-ICES inversion process had trouble determining the correct elevator deflection with pitch due to elevator characteristics at those flight conditions. This condition is specific to the Twin Otter flight model.
Using the results of Task 2, it was determined that the objective to identify an acceptable error in the wind measurement was not required and that the performance of an air data probe such as the AIMMS-20 probe was satisfactory.