AI Spacecraft Shielding Profiles for Radiation-Mass Tradeoffs
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Solution Overview
Problem
Existing spacecraft shielding designs struggle to optimize radiation protection for both crew and electronics due to the complexity of cosmic radiation interactions and the limitations of traditional materials, particularly for long-duration missions, where current shielding is inadequate for both mass and volume constraints.
Innovation Solution
A system utilizing a genetic algorithm and generative model to optimize spacecraft shielding profiles, incorporating a fitness test and evolutionary processes to generate diverse shielding profiles that minimize radiation exposure while considering weight, cost, and material properties, using a combination of topology optimization and Monte Carlo simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional shielding materials and designs are used, then engineering feasibility is maintained, but radiation protection effectiveness deteriorates due to inadequate shielding for long-duration missions
Solution Approach 1:
The patent applies parameter changes by systematically varying shielding material composition, density, and geometric configuration as design parameters. The AI optimization algorithm iterates through different parameter combinations to identify configurations that maximize radiation protection effectiveness while satisfying mission constraints, thereby resolving the contradiction between improved protection and design complexity.
Solution Approach 2:
The patent employs composite materials by combining multiple shielding materials with different properties (e.g., hydrogen-rich polymers, water, aluminum) in optimized ratios and arrangements. This composite approach enables tailored shielding characteristics that achieve superior radiation protection compared to single materials, while the AI system manages the complexity of multi-material integration.
2Reliability
If shielding design is optimized to minimize radiation exposure, then radiation protection improves, but the ability to provide meaningful feedback and iterate the design process deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by using AI algorithms that automatically evaluate shielding designs against radiation transport models and mission requirements. The system provides quantitative feedback on protection effectiveness and generates iterative design recommendations, enabling efficient optimization without manual iteration complexity.
Solution Approach 2:
The patent substitutes manual design iteration processes with automated AI-based optimization systems. Instead of requiring engineers to manually adjust and re-evaluate shielding designs, the AI algorithm automatically performs iterations, generating optimized configurations that maximize radiation protection while maintaining ease of operation through automated decision-making.
3Reliability
If more shielding material is added to protect against cosmic radiation, then radiation protection improves, but spacecraft mass and volume constraints are violated
Solution Approach 1:
The patent applies parameter changes by optimizing the density, composition, and distribution of shielding materials to achieve maximum protection with minimum mass. The AI algorithm varies material parameters and geometric configurations to identify optimal solutions that satisfy both radiation protection requirements and spacecraft mass constraints, resolving the contradiction between protection effectiveness and mass limits.
Solution Approach 2:
The patent employs local quality by implementing non-uniform shielding distributions where material density and composition are optimized for specific radiation exposure zones rather than applying uniform shielding throughout. This localized approach provides enhanced protection where needed while minimizing overall spacecraft mass, addressing the contradiction between protection effectiveness and mass constraints.
Data Source
AI summary
Methods, systems, and non-transitory computer-readable storage media for using Artificial Intelligence (AI) to determine optimal design framework and topology for space craft shielding. A system can receive measured extravehicular or intravehicular activity radiation fields and generate a plurality of shielding profiles. The system can then repeatedly execute an optimization algorithm until a minimum number of iterations is performed. The optimization algorithm can include: scoring each shielding profile with respect to the measured intravehicular activity radiation fields; pairing the shielding profiles within the plurality of shielding profiles, resulting in paired shielding profiles; for each pair of shielding profiles within the paired shielding profiles, selecting the shielding profile with the higher score as a parent profile, resulting in parent shielding profiles; generating new shielding profiles using pairs of the parent shielding profiles; and adding the new shielding profiles to the plurality of shielding profiles.