Abstract
The various aspects of experimental rocket engine development, utilizing different fuels, including model studies, component-level tests, and simulated flight conditions, play an essential role in the Rocket propulsion system. The experimental development of a rocket engine or propulsion system depends on the design of the experiment. The challenges faced by classical hybrid rockets, their real-world importance, and potential mitigation methods, ensuring simplicity, cost-effectiveness, and safety without compromising their advantages. It aims to establish state-of-the-art sustainable technologies based on global findings, as most current technologies are still in early stages of commercialization. The development of a rocket engine and propulsion system depends on model studies, component-level tests, and system-level tests. When rocket engines and power plants are tested on the ground, specific techniques and equipment are used to ensure safety. Advanced hybrid rockets, which combine fast-burning fuels, composite motor construction, and innovative design, offer high performance while maintaining cost-effectiveness, environmental sustainability, and simplicity. Future space missions could be revolutionized by these technologies, requiring short-term investments that are green, safe, affordable, and high-performance. The effectiveness of Machine Learning (ML) models is contingent on the availability of high-quality training data, which can be a limiting factor in their application. Incorporating ML into existing engineering workflows poses challenges, requiring interdisciplinary collaboration and expertise.
Acknowledgments
The author expresses his sincere gratitude to the Visvesvaraya National Institute of Technology, Nagpur.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The Author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI, and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
List of abbreviations
Abbreviation full form
- AM
-
Additive Manufacturing
- AMBER
-
Advanced Model Based Experimental Rocket
- CFD
-
Computational Fluid Dynamics
- CNN
-
Convolutional Neural Networks
- DAQ
-
Data Acquisition
- DB-1
-
Double-base Propellants without Aluminum
- DLR
-
Deutsches Zentrum für Luft-und Raumfahrt (German Aerospace Center)
- DRL
-
Deep Reinforcement Learning
- EOS
-
Equation of State
- FEM
-
Finite Element Method
- FKP
-
Federal Space Program (Federal’naya Kosmicheskaya Programma)
- GEO
-
Geostationary Orbit
- GH2
-
Gaseous Hydrogen
- GOx
-
Gaseous Oxygen
- HEO
-
High Earth Orbit
- HP
-
Hydrogen Peroxide
- HRE
-
Hybrid Rocket Engine
- HTP
-
High-Test Peroxide
- HTPB
-
Hydroxyl-Terminated Polybutadiene
- IR
-
Infrared
- ISP
-
Specific Impulse
- LEO
-
Low Earth Orbit
- LOX
-
Liquid Oxygen
- LPREs
-
Liquid-Propellant Rocket Engines
- LRE
-
Liquid Rocket Engines
- LH2
-
Liquid Hydrogen
- LSTM
-
Long Short-Term Memory Network
- MEO
-
Medium Earth Orbit
- ML
-
Machine Learning
- Mpc
-
Model Predictive Control
- NASDA
-
National Space Development Agency (Japan)
- NASA
-
National Aeronautics and Space Administration
- N2O
-
Nitrous Oxide
- O/F
-
Oxidizer-to-Fuel Ratio
- PDE
-
Partial Differential Equation
- PE
-
Polyethylene
- RB0146D
-
Rocket Engine Model RB-146D
- RB0150
-
Rocket Engine Model RB-150
- RKP
-
Russian Space Program (Rossiyskaya Kosmicheskaya Programma)
- R&D
-
Research and Development
- RDRE
-
Rotating Detonation Rocket Engine
- SLS
-
System-Level Simulation
- SSO
-
Sun-Synchronous Orbit
- SRM
-
Solid Rocket Motor
- SST
-
Shear Stress Transport
- TFM
-
Total Flow Mass
- UV-VIS
-
Ultraviolet-Visible
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