Machined ferrous metal components may carry a magnetic field, which in rotation disturb the output of electrical sensors. To minimize the effect on the electrical instrumentation, the rotating components are usually demagnetized. However, even after the demagnetization process, a residual magnetism unavoidably remains. This paper presents a methodology to predict the effects of a rotating magnetic field induced on thin film measurements. In addition to the prediction of the magnetic effects, a procedure to correct the spurious variation in the readings of thin film gauges has been developed to enhance the fidelity of the measurements. An analytical model was developed to reproduce the bias on the electrical signal from sensors exposed to rotor airfoils with magnets. The model is based on the Biot–Savart law to generate the magnetic field, and the Faraday's law to calculate the electromotive force induced along the measurement circuit. The model was assessed by means of controlled experiments varying the rotor tip clearance and rotational speed. The presented methodologies allowed the correction of the magnetic field effects. The raw signal of the thin film sensors, in the absence of any correction, is prone to deliver errors in the heat flux amounting to about 8% of the mean overall value. Thanks to the developed corrective approach, the residual magnetic effect contribution to the heat flux error would be 2% at most.

References

References
1.
Knauss
,
H.
,
Roediger
,
T.
,
Gaisbauer
,
U.
, and
Kraemer
,
E.
,
2006
, “
A Novel Sensor for Fast Heat Flux Measurements
,”
AIAA
Paper No. 2006-3637.
2.
Tipler
,
P. A.
, and
Mosca
,
G.
,
2005
, “
Physics for Science and Technology
,”
Electricity and Magnetism
, Vol.
2A
, Editorial Reverté, Barcelona.
3.
Sohre
,
J. S.
, and
Nippes
,
P. I.
,
1988
, “
Electromagnetic Shaft Currents and Demagnetization on Rotors of Turbines and Compressors
,”
17th Turbomachinery Symposium
,
Texas A&M University
,
College Station, TX
, Nov. 8–10, pp. 13–33.
4.
Schultz
,
D. L.
, and
Jones
,
T. V.
,
1973
, “
Heat Transfer Measurements in Short Duration Facilities
,” Advisory Group for Aerospace Research and Development, Neuilly-sur-Seine, France, AGARDograph Report No. 165.
5.
Mikolanda
,
T.
,
Kosec
,
M.
, and
Richter
,
A.
,
2009
, “
Magnetic Field of Permanent Magnets: Measurement, Modelling, Visualization
,” Technical University of Liberec, Liberec, Czech Republic.
6.
Ljung
,
L.
,
2014
,
Matlab & Simulink: System Identification Toolbox™ 7 User's Guide
, MathWorks, Inc., Natick, MA.
7.
Garnier
,
H.
,
Mensler
,
M.
, and
Richard
,
A.
,
2003
, “
Continuous-Time Model Identification From Sampled Data: Implementation Issues and Performance Evaluation
,”
Int. J. Control
,
76
(
13
), pp.
1337
1357
.
8.
Ljung
,
L.
,
2009
, “
Experiments With Identification of Continuous-Time Models
,”
15th IFAC Symposium on System Identification
, Saint-Malo, France, July 6–8, pp. 1175–1180.
9.
Young
,
P. C.
, and
Jakeman
,
A. J.
,
1980
, “
Refined Instrumental Variable Methods of Time-Series Analysis: Part III, Extensions
,”
Int. J. Control
,
31
(
4
), pp.
741
764
.
10.
Seber
,
G. A. F.
, and
Wild
,
C. J.
,
2003
,
Nonlinear Regression
,
Wiley-Interscience
,
Hoboken, NJ
.
11.
DuMouchel
,
W. H.
, and
O'Brien
,
F. L.
,
1989
, “
Integrating a Robust Option Into a Multiple Regression Computing Environment
,”
21st Symposium on the Interface
,
Alexandria, VA
, Apr. 9–12, pp. 297–301.
12.
Holland
,
P. W.
, and
Welsch
,
R. E.
,
1977
, “
Robust Regression Using Iteratively Reweighted Least-Squares
,”
Commun. Stat.
,
6
(
9
), pp.
813
827
.
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