Journal article
Prism signal processing of coriolis meter data for gasoline fuel injection monitoring
- Abstract:
- Prism Signal Processing is a new recursive FIR technique offering rapid filter design and calculation. It has previously been applied to Coriolis mass flow metering to generate fast (48 kHz) flow measurement updates, facilitating for the first time the direct mass flow measurement of individual fuel pulses injected into a laboratory diesel fuel injection test bench. In this paper we describe an augmented sensor signal filtering scheme which enables rapid tracking of the desired mode of flow tube vibration while notching out undesired modes. The new scheme is applied to a gasoline injection test bench where the vibrational interference is greater than for the previously described diesel system due to increased hydraulic shock. The paper presents experimental findings which illustrate the further challenges to be overcome in order to achieve the goal of traceable direct mass flow measurement of individual fuel injection pulses. For example, when a fuel pulse is shorter than the resonant period of the flow tube, the observed phase difference appears to show dependence on the instantaneous phase of the flow tube vibration.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 2.3MB, Terms of use)
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- Publisher copy:
- 10.1016/j.flowmeasinst.2019.101645
Authors
- Publisher:
- Elsevier
- Journal:
- Flow Measurement and Instrumentation More from this journal
- Volume:
- 70
- Article number:
- 101645
- Publication date:
- 2019-10-03
- Acceptance date:
- 2019-09-30
- DOI:
- EISSN:
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1873-6998
- ISSN:
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0955-5986
- Language:
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English
- Keywords:
- Pubs id:
-
pubs:1059260
- UUID:
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uuid:aa578c65-dce4-4cf8-83f3-c78e5c8a4baf
- Local pid:
-
pubs:1059260
- Source identifiers:
-
1059260
- Deposit date:
-
2019-10-01
Terms of use
- Copyright holder:
- Elsevier
- Copyright date:
- 2019
- Rights statement:
- © 2019 Elsevier Ltd. All rights reserved.
- Notes:
- This is the accepted manuscript version of the article, available under the terms of a Creative Commons, Attribution, Non-Commercial, No Derivatives licence. The final version is available online from Elsevier at: https://doi.org/10.1016/j.flowmeasinst.2019.101645
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