0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessAchieving real-time measurements of ultra-fine dry chemical agents (UDCA) is extremely challenging, with their complex composition, high injection concentrations, and susceptibility to contamination. In this study, a high-concentration particulate matter measurement method was proposed based on the extinction principle to reduce the powder contamination effect during calibrations. Through theoretical analysis and experimental verification, a functional relationship was established. Calibration experiments were conducted using sodium bicarbonate (SBA) and potassium bicarbonate (PBA) UDCAs, which are the most representative. The results showed that the measurement ranges of SBA and PBA reached 227.15 g/m3 and 122.55 g/m3, respectively. Compared with the uncorrected calibration method, the maximum indicated errors of SBA and PBA with the powder contamination effect corrected were reduced from 11.25% and 22.68% to 5.98% and 9.21%. This method provides a strategy for measuring high concentration powder, especially for achieving the measurement of UDCA in aircraft engine compartments.
Weitong Ma, Shaonan Liu, Song Lu, Long Shi, Heping Zhang (2023). Anti-contamination calibration method for ultra-fine dry chemical agent: Achieving wide measurement range and high measurement accuracy. Measurement, 223, pp. 113743-113743, DOI: 10.1016/j.measurement.2023.113743.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2023
Authors
5
Datasets
0
Total Files
0
Language
English
Journal
Measurement
DOI
10.1016/j.measurement.2023.113743
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access