High-Speed 2-D Raman and Rayleigh Imaging of a Hydrogen Jet Issued from a Hollow-Cone Piezo Injector

Bin Wu, Priybrat Sharma, Tao Yu, Lucia Palombi, Hao Wu, Moez Ben Houidi, Niraj Panthi, William Roberts, Gaetano Magnotti

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

This paper reports high-speed (10 kHz and 100 kHz) 2-D Raman/Rayleigh measurements of a hydrogen (H2) jet issued from a Bosch HDEV4 hollow-cone piezo injector in a high-volume constant pressure vessel. During the experiments, a Pa = 10 bar ambient environment with pure nitrogen (N2) is created in the chamber at T = 298 K, and pure H2 is injected vertically with an injection pressure of Pi = 51 bar. To accommodate the transient nature of the injections, a kHz-rate burst-mode laser system with second harmonic output at λ = 532 nm and high-speed CMOS cameras are employed. By sequentially separating the scattered light using dichroic mirrors and bandpass filters, both elastic Rayleigh (λ = 532 nm) and inelastic N2 (λ = 607 nm) and H2 (λ = 683 nm) Raman signals are recorded on individual cameras. With the help of the wavelet denoising algorithm, the detection limit of 2-D Raman imaging is greatly expanded. The H2 mole fraction distribution is then derived directly from scattering signals at 10 kHz for Raman and 100 kHz for Rayleigh, with a spatial resolution of approximately 200 μm (5.0 lp/mm). The current work successfully demonstrates the feasibility of high-speed 2-D Raman and Rayleigh imaging in gaseous fuel injection and the experimental technique could potentially contribute to the design of next-generation high-pressure, high-flowrate H2 injectors.

Original languageEnglish
JournalSAE Technical Papers
DOIs
StatePublished - 2023
Externally publishedYes
EventSAE 16th International Conference on Engines and Vehicles, ICE 2023 - Capri, Italy
Duration: Sep 10 2023Sep 14 2023

Funding

The authors thank Dr. Matthew Dunn from the University of Sidney for sharing the MATLAB codes of the WATR algorithm. The research reported in this work was funded by King Abdullah University of Science and Technology (KAUST).

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