Method And System for Enhancing Vehicle Performance and Design Using Parametric Modeling and Gradient-Based Control Integration

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. 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.