Method And System for Enhancing Vehicle Performance and Design Using Parametric Modeling and Gradient-Based Control Integration
Aerospace
Method And System for Enhancing Vehicle Performance and Design Using Parametric Modeling and Gradient-Based Control Integration (TOP2-326)
A Method for Flight Vehicle Design that Considers Physical and Guidance, Navigation, and Control Systems
Overview
Aerospace vehicles, including aircraft, spacecraft, and autonomous systems, require a balance of physical and control systems to achieve optimal performance. Traditional design approaches treat physical and control parameters separately, leading to suboptimal designs that may not meet stringent operational requirements under real-world conditions. NASA Ames has developed a novel parametric modeling approach that integrates physical design (e.g., geometry, structural load) with guidance, navigation, and control (GNC) systems, allowing for the co-optimization of these subsystems. This innovative technique applies multi-variable calculus and gradient-based methods to iterate on design parameters, enabling aerospace vehicles to achieve better performance, stability, and fuel efficiency. The technology reduces the development time and ensures a more robust design by accounting for real-world variables such as aerodynamic uncertainties, environmental disturbances, and sensor noise.
The Technology
The parametric modeling system allows for the integrated design and optimization of aerospace vehicles by unifying physical and control subsystems within a single computational model. The system includes representations of the vehicle’s geometry, structural load, propulsion, energy storage, and GNC systems. The system performs sensitivity analysis on key performance metrics (e.g., fuel consumption, heat load, and mechanical forces) to determine how changes in design parameters affect overall performance. By incorporating real-world conditions, such as wind variations and sensor noise, the system allows for the use of real-time feedback to refine vehicle designs. The optimization process uses a gradient-based algorithm to iteratively adjust parameters so that constraints such as structural integrity, thermal protection, and fuel capacity are met. The system generates a Pareto front representing trade-offs between performance metrics that allow engineers to visualize optimal designs for different mission profiles, which enhances design accuracy while reducing the need for expensive physical testing.

Benefits
- Improves both physical and guidance, navigation, and control (GNC) systems, resulting in better fuel efficiency, structural integrity, and operational reliability
- Accounts for real-world variables such as aerodynamic disturbances, sensor noise, and environmental uncertainties
- Streamlines the iterative design process, reducing the need for costly physical testing and wind tunnel experiments
- Applicable to a variety of vehicles, including aircraft, space entry systems, spacecraft, electric vertical takeoff and landing (eVTOL) vehicles, and others that require a combination of physical and control system design
- Provides a more accurate, efficient, and robust solution for designing flight vehicles by integrating physical and GNC systems into a unified design framework that optimizes or enhances multiple subsystems simultaneously
- The use of a sweeping gradient method (SGM) for trajectory analysis may allow for a comprehensive optimization process that significantly reduces the engineering effort while providing more precise data about real-world performance
Applications
- Aerospace industry
- Aircraft and spacecraft manufacturers
- Autonomous vehicle development
- electric Vertical Take-Off and Landing (eVTOL) industry
- eVTOL manufacturers
Technology Details
Aerospace
TOP2-326
ARC-18856-1
https://link.springer.com/article/10.1007/s10957-023-02303-3
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Multi-Objective Flight Control Optimization Framework
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Aerodynamic Framework for Parachute Deployment from Aerial Vehicle
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Residual Mode Filters
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Multistage Free-Flight Testing System
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