Accurate Measurement of Blast Shock Wave Pressure using Neural Network Sensor | #Sciencefather #Researcherawards


Introduction

Accurate measurement of blast shock wave pressure is a crucial aspect of defense, aerospace, and industrial safety research. However, the process often suffers from mechanical vibrations and strong shocks that distort sensor readings, especially during the critical compression phase. To address these challenges, an enhanced sensor system was designed, integrating a buffer device to stabilize the sensor’s dynamic response to rapid pressure changes. By combining advanced experimental setups like a double-diaphragm shock tube and computational modeling through neural networks, this study aims to refine the accuracy of transient pressure detection and establish a framework for precise dynamic pressure measurement in harsh environments.

Impact of Mechanical Vibrations on Sensor Dynamics

Mechanical vibrations and sudden impacts can significantly degrade the accuracy of pressure sensors during blast events. These disturbances alter the sensor’s dynamic response, leading to delayed signal detection and underestimation of true pressure peaks. Understanding the influence of such factors is vital for designing a system capable of withstanding extreme conditions. By analyzing these effects, researchers can develop improved mechanical structures and filtering algorithms to mitigate vibration-induced errors and ensure reliability in high-intensity shock environments.

Design and Functionality of the Buffer Device

The specialized buffer device developed in this study serves to improve the stability and dynamic performance of the pressure sensor. By regulating the transmission of shock energy, the buffer effectively reduces abrupt mechanical loads that compromise signal integrity. This component ensures smoother pressure transitions and enhances the sensor’s ability to detect transient events accurately. However, while the buffer improves protection and stability, it also introduces bandwidth limitations, necessitating further optimization through computational compensation methods.

Dynamic Calibration Using Double-Diaphragm Shock Tube

To validate the performance of the enhanced sensor system, a double-diaphragm shock tube was employed for dynamic calibration. This setup allows precise control over shock wave generation and propagation, enabling accurate assessment of the sensor’s temporal response. Through a series of controlled experiments, the influence of the buffer device on signal behavior was quantified, providing essential data for modeling and compensation. This calibration process bridges the gap between theoretical design and practical performance in extreme pressure conditions.

Neural Network-Based Dynamic Compensation

A backpropagation (BP) neural network was implemented to develop a dynamic compensation model for the sensor system. This AI-based approach learns the nonlinear characteristics of the pressure response, enabling real-time correction of distortions caused by the buffer device. The BP neural network effectively extended the operational bandwidth of the sensor, improving both sensitivity and accuracy during the compression phase. This fusion of artificial intelligence with sensor technology marks a significant advancement in dynamic pressure measurement.

Research Significance and Practical Applications

The integration of mechanical enhancement and neural network compensation represents a major step forward in high-precision pressure sensing. This research not only improves accuracy in dynamic measurements but also offers valuable insights for designing robust sensor systems in defense testing, aerospace propulsion studies, and explosion research. The combination of experimental calibration and AI-driven modeling provides a scalable framework for future developments in smart sensing and adaptive measurement systems capable of operating in complex and high-impact environments.

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#Sciencefather, #Reseacherawards, #ShockWavePressure, #BlastMeasurement, #DynamicCalibration, #PressureSensor, #NeuralNetworkCompensation, #BPModel, #BufferDevice, #SensorTechnology, #ExperimentalPhysics, #HighPressureMeasurement, #BlastResearch, #CompressionPhase, #SmartSensing, #AIinSensors, #FluidDynamics, #AerospaceResearch, #MechanicalVibrationControl, #DynamicResponse, #DataModeling, #ResearchInnovation,

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