FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer): Efficient Predictions of Particle Trajectories and Erosion-Reference-Cited by-同舟云学术

FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer): Efficient Predictions of Particle Trajectories and Erosion

Published:2023-09-08 Issue:37 Volume:62 Page:15278-15289
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Yang Steve D.¹^ORCID,Ali Zulfikhar A.²,Wong Bryan M.¹²^ORCID

Affiliation:

1. Materials Science & Engineering Program, University of California-Riverside, Riverside, California 92521, United States

2. Department of Chemistry and Department of Physics & Astronomy, University of California-Riverside, Riverside, California 92521, United States

Funder

National Energy Technology Laboratory

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.3c01639

Reference41 articles.

1. Raghu, D.; McKee, B.; Wu, J. B. C.; Sheriff, C. High Temperature Erosion Resistant Materials for Petroleum Refinery Equipment. In CORROSION 2001; OnePetro, 2001.

2. Solid Particle Erosion Testing of Helicopter Rotor Blade Materials

3. Erosion- and corrosion-resistant coatings for aircraft compressor blades

4. CFD application for coal/air balancing in power plants

5. CFD based prediction of erosion rate in large scale wall-fired boiler

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