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MIRRAS is a low-cost miniature sensor technology to measure the instantaneous surface regression in hybrid fuels, solid propellants and ablative materials at a fraction of the cost, size and complexity of ultrasonic, X-ray or optical methods. Each sensor provides highly accurate surface regression and material thickness data, along with information on transients that often go undetected. This allows developers to improve the overall quality and reliability of their propulsion systems. MIRRAS sensors (MIniature Resistive Regression and Ablation Sensors) are designed to be embedded directly in propellant material and regress along with it during firing. The resistance of the sensor is related to its instantaneous length, allowing the exact thickness of the host material to be recorded. The time derivative of the data reveals the instantaneous surface regression rate as a function of time. Testing using MIRRAS sensors begins by casting them directly into a host material, or by inserting them into the material after it has been formed or cured. The sensor signals are transmitted from inside the high-pressure chamber to the ambient environment through sealed lead wires in the motor case. Each set of up to six sensors is connected to a multi-channel signal conditioner that provides localized instantaneous material thickness and surface regression rate data as a function of time to a data acquisition system. No calibration is required. BENEFITS Advancements are continuously being sought in hybrid fuels and oxidizers, solid propellants and ablative materials. The surface characteristics of these materials need to be determined as a function of key operational parameters to establish their overall performance, and to support propulsion system design and development. A similar need exists in propellant and material health monitoring, but the implementation and end-user training costs of ultrasonic, X-ray and optical regression rate testing methods can be too costly for many early-stage applications. * Low-cost solution that requires no specialized end user training * Does not effect regression rate or combustion * Reduces the total number of required developmental tests * Provides more data to improve quality and reliability * Small size and low mass are ideal for flight applications |
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