Seite - 19 - in Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Bild der Seite - 19 -
Text der Seite - 19 -
2.1. ElectromagneticHeating
0.1 1 10
ε'
ε' ≈ εs
ε' ≈ ε∞
log(ωτ)
log(ωτ)
ε''
Figure2.3. Amplitudesofε′ andε′′ atdifferent frequencies [Met96].
According to above equations, the varying curves of ε′ and ε′′ are
showninfigure 2.3.
Intuitively, the real amplitudeε′ can be considered as the in-phase re-
sponse with the external electric field −→
E, which defines the amount
of energy that can be stored within the dielectric and does not cause
energy loss. The imaginary amplitude ε′′ can be considered as the
response that has 90◦ phase shift with −→
E, which determines the
amount of energy dissipation from the external electric field and the
heat generation within the dielectric. Correspondingly, the density
of power dissipated into the dielectric due to the dipole relaxation is
[Mer98]
pd= 1
2 ωε0ε
′′(ω) ∥∥∥−→E∥∥∥2 , (2.4)
whereε0= 8.85×10−12F/m isthepermittivityofvacuum. Itisevident
from above equations as well as figure 2.3 thatf0= 1/τ is the critical
pointwherethedipolepolarizationfailstofollowthedirectionchange
of −→
E. Thereforeit ismoreefficienttousemicrowaveswithfrequencies
19
Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
- Titel
- Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
- Autor
- Yiming Sun
- Verlag
- KIT Scientific Publishing
- Ort
- Karlsruhe
- Datum
- 2016
- Sprache
- englisch
- Lizenz
- CC BY-SA 3.0
- ISBN
- 978-3-7315-0467-2
- Abmessungen
- 14.8 x 21.0 cm
- Seiten
- 260
- Schlagwörter
- Mikrowellenerwärmung, Mehrgrößenregelung, Modellprädiktive Regelung, Künstliches neuronales Netz, Bestärkendes Lernenmicrowave heating, multiple-input multiple-output (MIMO), model predictive control (MPC), neural network, reinforcement learning
- Kategorie
- Technik