Experimental and machine learning-driven assessment of IS 2062 steel under double-base propellant combustion conditions
Abstract The degradation behavior of IS 2062 structural steel under extreme combustion conditions induced by double-base propellant exposure is crucial for aerospace and defense applications.This study addresses in understanding steel performance under high-temperature, short-duration rocket motor c...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10033-x |
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| Summary: | Abstract The degradation behavior of IS 2062 structural steel under extreme combustion conditions induced by double-base propellant exposure is crucial for aerospace and defense applications.This study addresses in understanding steel performance under high-temperature, short-duration rocket motor combustion, providing insights into material resilience, oxidation, and erosion trends. A series of seven static firing tests were conducted with varying nozzle throat diameters, systematically analyzing chamber pressure, burn rates, and structural integrity. The optimal test, Test-06achieved stable combustion with an average chamber pressure of 313 ksc and a burn rate of 36–37 mm/s, demonstrating minimal material degradation. Conversely, Test-07 exceeded 355–372 ksc with burn rates surpassing 41 mm/s, exhibiting erosive burning and accelerated steel erosion.Material characterization techniques, including X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Raman Spectroscopy, Field Emission Scanning Electron Microscopy (FESEM), and Energy Dispersive Spectroscopy (EDS), revealed significant oxidation, scale formation, and localized surface degradation, in high-temperature regions around 2500 Kelvinat the nozzle throat inducing structural changes. To enhance predictive accuracy, machine learning models Linear Regression, Random Forest Regression, Support Vector Machines (SVM), K-Means Clustering, and Artificial Neural Networks (ANN) were employed to analyze combustion-induced degradation trends, confirming Test-06 as the optimal balance of stability and high performance. Findings emphasize that while IS 2062 steel maintains integrity under transient high-temperature exposure, prolonged operation may lead to thermal fatigue and microstructural weakening, necessitating protective coatings or alternative alloy compositions for long-term durability. This study contributes to improving material selection, structural design, and predictive modeling for defence propulsion systems. |
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| ISSN: | 2045-2322 |