TY - JOUR
T1 - Analytical modeling and optimization of electrostatic particle sensors for particle number detection
T2 - incorporating particle size influence
AU - Wallner, Tanja
AU - Bainschab, Markus
AU - Kubicka, Manuel
AU - Klambauer, Reinhard
AU - Bergmann, Alexander
N1 - Publisher Copyright:
© 2024 The Author(s). Published by IOP Publishing Ltd.
PY - 2024/5
Y1 - 2024/5
N2 - Developing measurement devices and methods to track increasingly stringent emission standards, including particle number, is crucial. This paper presents a novel analytical model to describe the signal response of an electrostatic particle sensor not only to particle mass concentration, but also to the particle number concentration of in-flowing particles. The uniqueness of this model lies in its ability to calculate the signal as a function of particle diameter, enabling the determination of particle number concentration from the signal. The model considers the effects of aerosol flow, electrode voltage and temperature, and can be used for the optimization of the sensor geometry parameters, length, width, and electrode gap. The model was designed to optimize the sensor’s geometry and signal retrieval as well as the optimization of the electric field between the electrodes. Comparative analysis was conducted between the proposed model and a model from the literature as well as experimental data from literature and experimental data collected in this paper.
AB - Developing measurement devices and methods to track increasingly stringent emission standards, including particle number, is crucial. This paper presents a novel analytical model to describe the signal response of an electrostatic particle sensor not only to particle mass concentration, but also to the particle number concentration of in-flowing particles. The uniqueness of this model lies in its ability to calculate the signal as a function of particle diameter, enabling the determination of particle number concentration from the signal. The model considers the effects of aerosol flow, electrode voltage and temperature, and can be used for the optimization of the sensor geometry parameters, length, width, and electrode gap. The model was designed to optimize the sensor’s geometry and signal retrieval as well as the optimization of the electric field between the electrodes. Comparative analysis was conducted between the proposed model and a model from the literature as well as experimental data from literature and experimental data collected in this paper.
KW - analytic model for sensor optimization and signal retrieval
KW - dendrite formation
KW - electrostatic particle sensor
KW - exhaust particles
UR - http://www.scopus.com/inward/record.url?scp=85185391059&partnerID=8YFLogxK
U2 - 10.1088/1361-6501/ad27c9
DO - 10.1088/1361-6501/ad27c9
M3 - Article
AN - SCOPUS:85185391059
SN - 0957-0233
VL - 35
JO - Measurement Science and Technology
JF - Measurement Science and Technology
IS - 5
M1 - 055112
ER -