A Data-Based Architecture for Flight Test without Test Points

2025-06-02

AI Summary

Traditional flight testing relies on predefined test points, requiring pilots to precisely replicate modeled flight conditions; even minor deviations invalidate the model, revealing a fundamental limitation of the “point-wise validation” paradigm. This paper proposes a novel “test-point-free” flight test architecture: leveraging high-fidelity aerodynamic modeling, we construct a machine learning–based reduced-order model (ROM) that integrates Gaussian process regression (GPR) with second-order longitudinal dynamic equivalent modeling. The resulting framework enables dynamic prediction, online model updating, and closed-loop feedback under arbitrary real-flight conditions. Validated on T-38C flight test data, the method successfully builds and continuously updates a pitch-motion hypersurface ROM, automatically generating dynamic evaluation metrics compliant with MIL-STD-1797B. By shifting the paradigm from discrete point validation to continuous condition-space modeling, the approach significantly enhances model fidelity, robustness, and engineering applicability.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The justification for the"test point"derives from the test pilot's obligation to reproduce faithfully the pre-specified conditions of some model prediction. Pilot deviation from those conditions invalidates the model assumptions. Flight test aids have been proposed to increase accuracy on more challenging test points. However, the very existence of databands and tolerances is the problem more fundamental than inadequate pilot skill. We propose a novel approach, which eliminates test points. We start with a high-fidelity digital model of an air vehicle. Instead of using this model to generate a point prediction, we use a machine learning method to produce a reduced-order model (ROM). The ROM has two important properties. First, it can generate a prediction based on any set of conditions the pilot flies. Second, if the test result at those conditions differ from the prediction, the ROM can be updated using the new data. The outcome of flight test is thus a refined ROM at whatever conditions were flown. This ROM in turn updates and validates the high-fidelity model. We present a single example of this"point-less"architecture, using T-38C flight test data. We first use a generic aircraft model to build a ROM of longitudinal pitching motion as a hypersurface. We then ingest unconstrained flight test data and use Gaussian Process Regression to update and condition the hypersurface. By proposing a second-order equivalent system for the T-38C, this hypersurface then generates parameters necessary to assess MIL-STD-1797B compliance for longitudinal dynamics.

Problem

• Eliminates need for predefined test points in flight testing
• Uses machine learning to create adaptive reduced-order models
• Validates aircraft models with real-time flight data updates

Innovation

• Uses high-fidelity digital model for air vehicle
• Employs machine learning for reduced-order model
• Updates model with Gaussian Process Regression