Aero-Engine Fuel Tank Static Load Testing
Project name: Aero-Engine Fuel Tank Static Load Testing
A leading aerospace propulsion manufacturer utilizes the Dynatronic 240-channel SE-86H High-Speed Static Stress-Strain Testing and Analysis System to conduct static load monitoring during fuel tank pressurization tests.
1. Test Objectives
Structural Integrity Verification – Validates fuel tank deformation limits under extreme pressure conditions.
Stress Distribution Mapping – Identifies localized stress concentrations (welds/curvatures) through multi-point strain measurement.
Leakage Risk Assessment – Correlates strain data with pressure thresholds to predict potential failure points.
2. SE-86H System Capabilities
Ultra-High Channel Capacity – 240 synchronized channels for full-field strain gauge coverage (foil/FBG sensors).
Precision Static Measurement – Resolves micro-strain (με) levels at 0.1% accuracy under quasi-static loading.
Real-Time Visualization – Displays strain cloud maps and pressure-strain curves during ramp loading.
3. Testing Protocol
Instrumentation Phase
Strain rosettes installed on tank welds, domes, and mounting interfaces.
Pressure transducers integrated with SE-86H for load-strain synchronization.
Pressurization Test
Stepwise pressure increase (0→150% design limit) with strain recorded at 10Hz sampling.
Hold cycles at critical pressures to monitor creep effects.
Post-Test Analysis
Finite Element Model (FEM) correlation using strain contour plots.
Burst pressure prediction via strain-rate extrapolation.
4. Technical Innovations
Temperature-Compensated Strain Analysis – Automatic thermal drift correction for cryogenic/ambient tests.
Overload Protection – Hardware interlocks pause testing upon detecting abnormal strain gradients.
AS9100-Compliant Reporting – Generates certification-ready test documentation.
5. Industry Applications
Composite Tank Validation – Delamination detection in CFRP tanks through asymmetric strain patterns.
Reusable Vehicle Certification – Accumulated strain analysis for lifespan estimation.
6. Next-Gen Upgrades
AI-Based Anomaly Detection – Machine learning algorithms flag micro-yielding events in real time.
Digital Twin Integration – Live test data feeds into computational models for predictive maintenance.
