PLASMA THERMAL DYNAMICS IN THE MAGNETIC FIELD OSCILLATING AMPLIFIED (MOA) THRUSTER: AN EXAMINATION OF ADIABATIC HEATING PROCESSES
Abstract
The Magnetic Field Oscillating Amplified (MOA) thruster presents a novel approach to plasma propulsion, leveraging oscillating magnetic fields to enhance ion acceleration and plasma confinement. This study investigates the thermal dynamics of plasma within the MOA thruster, with a particular focus on the role of adiabatic heating. We examine the temporal and spatial evolution of plasma temperature and pressure under varying magnetic field strengths, using computational simulations and laboratory diagnostics. Results indicate that adiabatic compression driven by magnetic field oscillations significantly contributes to plasma heating, increasing thermal energy and ion velocities. The findings offer insights into optimizing MOA thruster designs for efficient energy transfer and enhanced propulsion performance.
Keywords
References
Most read articles by the same author(s)
- Dr.Daniel Williams, Dr. Alexei M. Ivanov, OPTIMIZING VEHICLE DESIGN FOR EFFICIENCY: PRESSURE GRADIENT AND AERODYNAMICS EVALUATION USING CFD , International Journal of Next-Generation Engineering and Technology: Vol. 2 No. 03 (2025): Volume 02 Issue 03
- Dr. Alejandro Cortés-Mendoza, Cloud Computing As A Socio-Technical And Environmental Infrastructure: Integrating Security, Sustainability, And Strategic Governance In The Post-Traditional Hosting Era , International Journal of Next-Generation Engineering and Technology: Vol. 2 No. 12 (2025): Volume 02 Issue 12
- Joshua Hoffman, The Algorithmic Frontier of Financial Intermediation: A Comprehensive Analysis of Agentic AI, Large Language Models, And Blockchain Integration in Modern Fintech Ecosystems , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Sneha Reddy, Optimizing Complex Processing Ecosystems using Event-Centric Approaches for Enhanced Durability , International Journal of Next-Generation Engineering and Technology: Vol. 2 No. 04 (2025): Volume 02 Issue 04
- Mateo Laurent Dubois, Adaptive Chaos Engineering and AI-Driven Dependability Modeling for Resilient Cloud-Native and Safety-Critical Systems , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. Youssef El-Masry, Statistical Learning Driven Virtual Counterpart Systems Evaluating Healthcare Coverage Administration Analysis , International Journal of Next-Generation Engineering and Technology: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Ismoyilov Diyorbek Bektemir og’li, Fayzillayeva Oykhon Qodir qizi, Esanova Dilsinoy Dilmurod qizi, Artificial Intelligence Today And In The Future , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Dr. Eleanor Whitfield, Architecting Trustworthy and Equitable Artificial Intelligence in Clinical Research and Care: Ethical, Regulatory, and Workforce Imperatives for Responsible Translation , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Raka Pratama, Siti Maharani, Policy-Based Automation for Secure Governance of Machine and Workload Identities in Cloud IAM , International Journal of Next-Generation Engineering and Technology: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Dr. Alistair J. Sterling, Architectural Frameworks for Multimodal Learning Analytics and Autonomic System Feedback: Integrating Physiological, Inertial, And Temporal Data for Enhanced Skill Acquisition , International Journal of Next-Generation Engineering and Technology: Vol. 2 No. 12 (2025): Volume 02 Issue 12