scholarly journals Calculation of passive intermodulation product on nonlinear area source using reciprocity principle

2020 ◽  
Vol 62 (11) ◽  
pp. 3401-3406
Author(s):  
Qianwen Chen ◽  
Xiong Chen
Keyword(s):  
Reciprocity Principle ◽  
Area Source ◽  
Passive Intermodulation

Compact Passive Intermodulation Mitigation Method using Planar Nonlinearity Injection

2020 ◽  
Author(s):  
Xiong Chen ◽  
Peng Zhang ◽  
Ming Yu ◽  
David Pommerenke
Keyword(s):  
Passive Intermodulation

scholarly journals A Method for Measuring the Maximum Measurable Gain of a Passive Intermodulation Chamber

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 770
Author(s):  
Zhanghua Cai ◽  
Yantao Zhou ◽  
Lie Liu ◽  
Francesco de Paulis ◽  
Yihong Qi ◽  
...  
Keyword(s):  
Approximate Method ◽  
Path Loss ◽  
High Gain ◽  
Directional Antenna ◽  
Anechoic Chamber ◽  
Measurement Interval ◽  
High Gain Antenna ◽  
Specific Relationship ◽  
Method Of Measurement ◽  
Passive Intermodulation

This paper presents an approximate method that allows the calculation of the maximum measurable gain (MMG) in an anechoic chamber. This method is realized by using a low passive intermodulation (PIM) medium-gain directional antenna. By reducing the distance between the antenna and the wall of the chamber to reduce path loss, the purpose of replacing a high-gain antenna with a medium-gain antenna is achieved. The specific relationship between distance and equivalent gain is given in this paper. The measurement interval is determined by the 3 dB beamwidth of the measurement antenna to scan the whole chamber. A set of corresponding data for the residual PIM level and the MMG of the chamber can be obtained by the method of measurement outlined herein. The feasibility of this method was verified by measurements in two PIM measurement chambers.

Reciprocity principle for the detection of planar cracks in anisotropic elastic material

2012 ◽  
Vol 28 (8) ◽  
pp. 085010 ◽  
Author(s):  
P Steinhorst ◽  
A-M Sändig
Keyword(s):  
Elastic Material ◽  
Reciprocity Principle ◽  
Anisotropic Elastic ◽  
Anisotropic Elastic Material

Aerial short-range dispersion of volatilized pesticides from an area source

2002 ◽  
Vol 46 (3) ◽  
pp. 126-135 ◽  
Author(s):  
Wittich K.-P. ◽  
Siebers J.
Keyword(s):  
Short Range ◽  
Area Source

Second-order Passive Intermodulation Distortion Modeling Comparision in Wireless Relay Systems

2021 ◽  
Vol 58 (1) ◽  
pp. 3-12
Author(s):  
Jinzi Song ◽  
Jungyu Choi ◽  
Bobae Kim ◽  
Zhihui Jin ◽  
Sungbin Im
Keyword(s):  
Second Order ◽  
Intermodulation Distortion ◽  
Relay Systems ◽  
Wireless Relay ◽  
Passive Intermodulation

Analysis on the contribution rates of point and area source emissions to wuhan SO2, NO2, PM2.5 concentrations and atmospheric environmental capacity

2021 ◽  
pp. 101209
Author(s):  
Ting Zhou ◽  
Hui Hu ◽  
Jiaxin Chen ◽  
Ruoqiao Bai ◽  
Feifei Wang ◽  
...  
Keyword(s):  
Environmental Capacity ◽  
Area Source

Inverse AERMOD and SCIPUFF Dispersion Modeling for Farm-Level PM10 Emission Rate Assessment

2021 ◽  
Vol 64 (3) ◽  
pp. 801-817
Author(s):  
Bin Cheng ◽  
Aditya Padavagod Shiv Kumar ◽  
Lingjuan Wang-Li
Keyword(s):  
Wind Speed ◽  
Egg Production ◽  
Single Point ◽  
Dispersion Modeling ◽  
Emission Rates ◽  
Farm Level ◽  
Area Source ◽  
Inverse Dispersion ◽  
Pm10 Emission ◽  
Inverse Dispersion Modeling

HighlightsAERMOD and SCIPUFF were employed to back-calculate farm-level PM10 emission rates based on inverse modeling.Both AERMOD and SCIPUFF did not capture the diurnal and seasonal variations of farm-level PM10 emission rates.AERMOD modeling results were affected by wind speed, with higher wind speed leading to higher emission rates.Higher numbers of receptors and PM10 measurements with greater time resolution may be recommended in the future.Abstract. Air pollutant emissions from animal feeding operations (AFOs) have become a serious concern for public health and ambient air quality. Particulate matter with aerodynamic equivalent diameter less than or equal to 10 µm (PM10) is one of the major air pollutants emitted from AFOs. To assess the impacts of PM10 emissions from AFOs, knowledge about farm-level PM10 emission rates is needed but is challenging to obtain through field measurements. The inverse dispersion modeling approach provides an alternative way to estimate farm-level PM10 emission rates. In this study, two dispersion models, AERMOD and SCIPUFF, were employed to back-calculate farm-level PM10 emission rates based on hourly PM10 concentration measurements at four downwind locations in the vicinity of a commercial egg production farm in the southeast U.S. Onsite meteorological data were simultaneously recorded using a 10 m weather tower to facilitate the dispersion modeling. The modeling results were compared with PM10 emission measurements from two layer houses on the farm. Single-area source, double-area source, and double-volume source were used in AERMOD, while only single-point source was used in SCIPUFF. The inverse modeling results indicated that both SCIPUFF and AERMOD did not capture the diurnal and seasonal variations of the farm-level PM10 emission rates. In addition, the AERMOD modeling results were affected by wind speed, and higher emission rates may be predicted at higher wind speeds. The single-point source for SCIPUFF, the plume rise simplification for AERMOD, and insufficient concentration measurement resolution in response to temporal changes in wind direction may have added uncertainties to the modeling results. The results of this study suggest that more receptors covering more representative downwind locations should be considered in future modeling for farm-level emissions assessment. Moreover, ambient data collection with greater time resolution (e.g., less than one hour) is recommended to capture diurnal and seasonal patterns more rigorously. Only in this way can researchers achieve a better understanding of the effectiveness of inverse dispersion modeling for estimation of pollutant emission rates. Keywords: AERMOD, Animal feeding operations, Egg production, Farm-level emission rate, Inverse dispersion modeling, PM10, SCIPUFF.

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