Abstract: The present disclosure provides a drop impact prediction method and system for heavy equipment airdrop based on a neural network. The drop impact prediction method includes the following steps: S1: acquiring a plurality of sets of sample data by using a finite element model for drop simulation of heavy equipment airdrop; S2: determining structural parameters of a BP neural network, and pre-processing the structural parameters; S3: constructing a BP neural network model; and S4: predicting a drop impact situation of heavy equipment airdrop in an actual application process by using the trained BP neural network model.

Abstract: Disclosed is an efficient recycling system for exhaust energy of an internal combustion engine. Firstly, a thermoelectric generation device recycles high-temperature waste heat energy in the exhaust of the internal combustion engine and recycles high-temperature heat that originally radiates into the ambient atmosphere on the surface of the volute (24). Secondly, pressure energy in the exhaust of the internal combustion engine is efficiently recycled by using a turbocharging device, and the efficiency of the turbocharging device is improved through the graphite sealing device. Finally, low-temperature waste heat energy in the exhaust of the internal combustion engine is efficiently recycled by using an organic Rankine cycle device. The risk of decomposition caused by the fact that the working medium used by organic Rankine cycle works in a high-temperature environment is avoided, thereby ensuring the cycle efficiency and the working reliability of the organic Rankine cycle.

Abstract: Disclosed is an efficient recycling system for exhaust energy of an internal combustion engine. Firstly, a thermoelectric generation device recycles high-temperature waste heat energy in the exhaust of the internal combustion engine and recycles high-temperature heat that originally radiates into the ambient atmosphere on the surface of the volute (24). Secondly, pressure energy in the exhaust of the internal combustion engine is efficiently recycled by using a turbocharging device, and the efficiency of the turbocharging device is improved through the graphite sealing device. Finally, low-temperature waste heat energy in the exhaust of the internal combustion engine is efficiently recycled by using an organic Rankine cycle device. The risk of decomposition caused by the fact that the working medium used by organic Rankine cycle works in a high-temperature environment is avoided, thereby ensuring the cycle efficiency and the working reliability of the organic Rankine cycle.

Abstract: A method for processing ultrasonic response signals collected from a plurality of measurement locations along a weld to determine the presence of a defect in the weld may include filtering an ultrasonic response signal from each of the measurement locations to produce a filtered response signal for each of the measurement locations. Thereafter, an ultrasonic energy for each of the measurement locations is calculated with the corresponding filtered response signal. The ultrasonic energy for each measurement location is then compared to the ultrasonic energy of adjacent measurement locations to identify potential defect locations. When the ultrasonic energy of a measurement location is less than the ultrasonic energy of the adjacent measurement locations, the measurement location is a potential defect location. The presence of a defect in the weld is then determined by analyzing fluctuations in the ultrasonic energy at measurement locations neighboring the potential defect locations.

Abstract: A method for determining the type of a defect in a weld may include determining a defect location and a corresponding defect signal by analyzing ultrasonic response signals collected from a plurality of measurement locations along the weld. The defect signal and the plurality of defect proximity signals corresponding to ultrasonic response signals from measurement locations on each side of the defect location may then be input into a trained artificial neural network. The trained artificial neural network may be operable to identify the type of the defect located at the defect location based on the defect signal and the plurality of defect proximity signals and output the type of the defect located at the defect location. The trained artificial neural network may also be operable to determine a defect severity classification based on the defect signal and the plurality of defect proximity signals and output the severity classification.

Abstract: A method for determining the type of a defect in a weld may include determining a defect location and a corresponding defect signal by analyzing ultrasonic response signals collected from a plurality of measurement locations along the weld. The defect signal and the plurality of defect proximity signals corresponding to ultrasonic response signals from measurement locations on each side of the defect location may then be input into a trained artificial neural network. The trained artificial neural network may be operable to identify the type of the defect located at the defect location based on the defect signal and the plurality of defect proximity signals and output the type of the defect located at the defect location. The trained artificial neural network may also be operable to determine a defect severity classification based on the defect signal and the plurality of defect proximity signals and output the severity classification.

Abstract: A method for processing ultrasonic response signals collected from a plurality of measurement locations along a weld to determine the presence of a defect in the weld may include filtering an ultrasonic response signal from each of the measurement locations to produce a filtered response signal for each of the measurement locations. Thereafter, an ultrasonic energy for each of the measurement locations is calculated with the corresponding filtered response signal. The ultrasonic energy for each measurement location is then compared to the ultrasonic energy of adjacent measurement locations to identify potential defect locations. When the ultrasonic energy of a measurement location is less than the ultrasonic energy of the adjacent measurement locations, the measurement location is a potential defect location. The presence of a defect in the weld is then determined by analyzing fluctuations in the ultrasonic energy at measurement locations neighboring the potential defect locations.