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<p>Nuclear propulsion’s capability to rely on high levels of power for sustained timeframes would enable the exploration in areas where no other technology can wander, increasing payload fraction and minimizing radiation exposure. The objective of this paper is to propose and analyze the performance of an electric thruster to be coupled with a nuclear space reactor, in alignment with the specific requirements of innovative missions relying on nuclear electric propulsion. The applied-field magnetoplasmadynamic technology is selected given its performances in terms of thrust, specific impulse, and relatively high readiness level. To assess the extent of this system, a low-power correction to the voltage model is derived according to a multivariable linear regression, allowing the use of genetic algorithms to optimize both geometric and operational system’s variables in terms of thrust and specific impulse. The proposed low-power voltage correction reduced the mean relative error from 46% to 2% against experimental datasets, ensuring higher reliability in the optimization process. Results identify two distinct optimal configurations at 25 kWe: a high-thrust design providing 1.1 N with a specific impulse of 2250&#xa0;s and 49% efficiency, and a high-specific-impulse design reaching over 6600&#xa0;s with 84% efficiency.</p>

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Modeling and Optimization of Applied-Field Magnetoplasmadynamic Technology for NEP

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月20日

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  • Lorenzo Tutolo,
  • Stefano Lorenzi,
  • Riccardo Boccelli

摘要

Nuclear propulsion’s capability to rely on high levels of power for sustained timeframes would enable the exploration in areas where no other technology can wander, increasing payload fraction and minimizing radiation exposure. The objective of this paper is to propose and analyze the performance of an electric thruster to be coupled with a nuclear space reactor, in alignment with the specific requirements of innovative missions relying on nuclear electric propulsion. The applied-field magnetoplasmadynamic technology is selected given its performances in terms of thrust, specific impulse, and relatively high readiness level. To assess the extent of this system, a low-power correction to the voltage model is derived according to a multivariable linear regression, allowing the use of genetic algorithms to optimize both geometric and operational system’s variables in terms of thrust and specific impulse. The proposed low-power voltage correction reduced the mean relative error from 46% to 2% against experimental datasets, ensuring higher reliability in the optimization process. Results identify two distinct optimal configurations at 25 kWe: a high-thrust design providing 1.1 N with a specific impulse of 2250 s and 49% efficiency, and a high-specific-impulse design reaching over 6600 s with 84% efficiency.