Developing a Scalable SQL-Based Framework for Ingesting and Analyzing Aerospace Simulation Output Data

Abstract

Typical aircraft external loads processes were originally developed for smaller models under legacy computational constraints. While these processes have been adapted to handle modern model sizes, they were not designed to scale efficiently with the growing volume and complexity of simulation data. As a result, downstream plotting and critical case selection workflows remain inefficient and lack consistent traceability across aircraft and analysis loops. To address these limitations, a relational SQL-based framework was developed to structure and scale external loads data across millions of records and multiple analysis types into a unified and structured dataset. This framework enables automated post-processing tools for envelope visualization and comparison, along with a reduction methodology that identifies the smallest subset of critical cases required to preserve structural design conditions within a specified tolerance. The database supports efficient extraction of one-dimensional envelope extrema and optional two-dimensional envelope boundaries using convex hull methods. These envelope requirements are used to define a constraint set, which is solved using a greedy set-cover algorithm with a user-defined tolerance (e.g., 3%) to determine the minimal set of representative conditions. SQL enables efficient filtering, aggregation, and envelope extraction over large datasets, while higher-order geometric and combinatorial operations are performed in Python after data reduction. Application of the framework to a representative dataset containing 4,914 flight conditions produced approximately 696 one-dimensional envelope requirements, which were further reduced to a final subset of 82–94 critical cases depending on the inclusion of multidimensional constraints. The reduction process maintained envelope fidelity within the specified tolerance and completed within practical computational time. This approach consolidates large-scale external loads data into a single, structured, and traceable system and enables repeatable, automated workflows for plotting and case reduction. The resulting process significantly reduces manual engineering effort while maintaining alignment with established analysis practices, providing a scalable and reproducible solution for managing large external loads datasets.