Environmental Engineering Reference
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1.980
1.960
1.940
1.920
1.900
s a nd #1
sand #2
sand #3
1.880
1.860
0.00
0.04
0.08
0.12
0.16
0.20
(cm 3 /c m 3 )
θ
Fig. 5.18 Calibration curves relating the resonant frequency of the antenna ( f res )tothecor-
responding moisture content of the MUT (
θ
) for the three types of sand [12]
As a further validation of these results and to investigate how the imaginary per-
mittivity influences the S 11 (
, full-wave simulations were performed through a
commercial simulator, CST Microwave Studio. More specifically, the time-domain
solver, based on the well-known Finite Integration Technique, was used. Simula-
tions were performed by varying the value of the electrical conductivity of the MUT
(
f
)
σ MUT ), for several fixed values of the real part of the dielectric permittivity (
ε MUT ).
Fig. 5.19 shows how the minimum of the S 11 (
f
)
magnitude changes as a result
of variations of
σ MUT . It can be seen that when the permittivity is higher than 3,
an increase of the electric conductivity causes
to decrease. Simulation re-
sults in Fig. 5.19 are also compared to measurements performed on sand samples
moistened with approximately the same amount of bi-distilled water (
|
S 11 (
f
) |
θ =
8%), with
a conductivity ranging from 0 to 107 mS m 1 . The experimental trend is in good
agreement with the simulations.
5.6.2.4
Sensing Volume
Another aspect that is worth taking into account is the sensing volume, which rep-
resents an issue also for traditional moisture content measurements. For a given an-
tenna, the sensing volume mainly depends on the relative permittivity of the MUT.
To assess how the relative permittivity of the MUT affects the sensing volume, full-
wave simulations were performed through CST Microwave Studio. For different
fixed values of dielectric permittivity, the thickness of the MUT was progressively
increased, and the corresponding changes in the resonant frequency of the antenna
were observed. Results of these simulations are shown in Fig. 5.20.
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