Evidence map›Paper›PMID 39875557›Full record

ArticleScientific reports2025

Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning.

Ali Basem, M Yasiri, Pooya Ghodratallah, Kamal Sharma, Nimesh Raj, S Abdul Ameer, Mohammed Yaseen Abdullah, Abbas Hameed Abdul Hussein, Mohammed Y Al-Khuzaie, M Ahmedi

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Ali BasemFaculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, Iraq.
M YasiriDepartmet of Chemical Engineering, Al-Amarah University, Maysan, Iraq.
Pooya GhodratallahDepartment of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil, Iraq. pooya.ghodratallah@cihanuniversity.edu.iq.
Kamal SharmaDepartment of Mechanical Engineering, Institute of Engineering and Technology, GLA University, Mathura (U.P), India.
Nimesh RajCentre of Research Impact and Outcome, Chitkara University, Rajpura-140417, Punjab, India.
S Abdul AmeerDepartment of Automobile Engineering, College of Engineering, Al-Musayab, University of Babylon, Babylon, Iraq.
Mohammed Yaseen AbdullahAl-Noor University College, Nineveh, Iraq.
Abbas Hameed Abdul HusseinAhl Al Bayt University, Kerbala, Iraq.
Mohammed Y Al-KhuzaieCollege of Technical Engineering, National University of Science and Technology, Dhi Qar, 64001, Iraq.
M AhmediDepartmet of Chemical Engineering, Al-Amarah University, Maysan, Iraq.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, the usage of a predictive surrogate model for the estimate of flow variables in the transient phase of coolant injection from the nose cone by combining the Long Short-Term Memory (LSTM) and Proper Orthogonal Decomposition (POD) technique. The velocity, pressure, and mass fraction of the counterflow jet is evaluated via this hybrid technique and the source of discrepancy of a predictive surrogate model with Full order model is explained in this study. The POD modes for the efficient prediction of the different flow variables are defined. The performance of the POD + LSTM for different ranges of training and test is evaluated and it is found that the performance of this hybrid technique is acceptable when 80% of the available data is training test. The predictive errors of coolant mass fraction and axial velocity is higher due to the complexity of the vortex in the recirculation region.

Indexed as

Coolant jetLSTMMachine learningNose conePODSupersonic flow

Identifiers

PMID39875557
PMCPMC11775303

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