BEAST Building Sustainable Data Infrastructure for NASA Thermal Protection Research

NASA’s current data infrastructure for thermal protection system (TPS) research remains fragmented and project-specific. Arcjet test results are often stored as spreadsheets summarizing individual campaigns, with raw data scattered across local drives or cloud folders. Once projects conclude, these datasets are difficult to locate or reuse, and the absence of standardized metadata, version control, and provenance tracking limits cross-campaign analysis and long-term reproducibility.

To address these limitations, we developed the BEAST (Backend for Experiment Analysis, Storage, and Traceability) platform, a centralized database that ingests, structures, and visualizes arcjet data. BEAST provides queryable access to time-series and metadata records, enabling statistical analysis, material property tracking, and reproducible cross-test comparison. The system embeds version control, facility-configuration tracking, and material-sample lineage within a unified, searchable framework, establishing a sustainable foundation for data-driven TPS development. Beyond archival functions, BEAST streamlines facility operations through automated report generation, component-usage tracking, and interactive dashboards for operators and analysts. A shared web interface further supports coordinated test planning between principal investigators and test engineers.

Under the same initiative, companion tools extend automation to data generation and analysis. arcjetCV applies computer vision and machine learning to high-speed video to extract real-time measurements of material recession and shock standoff, while STARscan provides rapid 3D surface reconstructions of test samples before and after exposure. All tools share BEAST’s data schema and provenance model, enabling seamless integration of imagery, mesh, and metadata.

The BEAST ecosystem represents a critical step toward reproducible, data-rich, and efficient operations across NASA’s arcjet facilities linking data management, automation, and analysis to enhance scientific fidelity and mission readiness.