Optimization of a Suspension of a Micromechanical Accelerometer in an Angular Velocity Measurement Unit

Optimization of the suspension of the micromechanical accelerometer, which is a part of an angular velocity measurement unit, was considered. This problem was faced because of the need to improve the design of the existing device to meet new technical requirements. Shock loads up to 1 000 g exceed ultimate loads for micromechanical accelerometers by several orders of magnitude. During the study, it turned out that the measurement unit shell works as a resonator, so the shock loads on the micromechanical accelerometer can even exceed 1 000g. In this case, suspensions of the micromechanical accelerometer are deformed in the plane of the accelerometer, which causes their destruction. The solution to this problem was hampered by the strict limits on the overall dimensions of the device. The existing shock dampers that could absorb such shock loads simply did not fit into the attachment points. Therefore, an original spring was designed and manufactured for this purpose. At the final stage, an additional system of passive heat outlets from the accelerometer to the device shell was designed and installed.

Angle of attack prediction using recurrent neural networks in flight conditions with faulty sensors in the case of F-16 fighter jet

The angle of attack (AOA) is one of the critical parameters in a fixed-wing aircraft because all aerodynamic forces are functions of the AOA. Most methods for estimation of the AOA do not provide information on the method’s performance in the presence of noise, faulty total velocity measurement, and faulty pitch rate measurement. This paper investigates data-driven modeling of the F-16 fighter jet and AOA prediction in flight conditions with faulty sensor measurements using recurrent neural networks (RNNs). The F-16 fighter jet is modeled in several architectures: simpleRNN (sRNN), long-short-term memory (LSTM), gated recurrent unit (GRU), and the combinations LSTM-GRU, sRNN-GRU, and sRNN-LSTM. The developed models are tested by their performance to predict the AOA of the F-16 fighter jet in flight conditions with faulty sensor measurements: faulty total velocity measurement, faulty pitch rate and total velocity measurement, and faulty AOA measurement. We show the model obtained using sRNN trained with the adaptive momentum estimation algorithm (Adam) produces more exact predictions during faulty total velocity measurement and faulty total velocity and pitch rate measurement but fails to perform well during faulty AOA measurement. The sRNN-GRU combinations with the GRU layer closer to the output layer performed better than all the other networks. When using this architecture, the correlation and mean squared error (MSE) between the true (real) value and the predicted value during faulty AOA measurement increased by 0.12 correlation value and the MSE decreased by 4.3 degrees if one uses only sRNN. In the sRNN-GRU combined architecture, moving the GRU closer to the output layer produced a model with better predicted values.

Research on spatial filtering velocity measurement method of interdigital electrostatic sensor

Particle velocity is an important parameter to describe the flow characteristics of gas-solid two-phase flow, and it is also a tricky problem for the parameter measurement of gas-solid flow. Aiming at the problem that the spatial filtering effect of electrostatic sensor suffering from the effect of spatial distribution of solid particles, a new type of interdigital electrostatic sensor is proposed to resolve it. Firstly, the spatial filtering characteristics of the interdigital electrostatic sensor are analyzed through finite element simulation, obtained that the spatial position and size of the solid particles have no effect on the peak frequency of the sensor output signal power spectrum, and the quantitative relationship between the velocity and peak frequency is derived. Then the experimental verification is carried out on an experimental platform of gravity conveying particle flow. Simulation and experimental results show that the interdigital electrostatic sensor eliminates the influence of particle spatial position and particle size on the velocity measurement results. In the velocity range of 2.97m/~4.95m/s, the relative error of the measurement system is better than 5%, and the relative standard deviation of repeated measurements is within 3%.

An effective, low-cost method to improve the movement velocity measurement of a smartphone app during the bench press exercise

Some studies have reported considerable errors in the movement velocity measurement when using the My Lift app. This study aimed to investigate whether these errors may be related to the use of a range of movement (ROM) statically measured prior to the movement (ROMMYLIFT) instead of ROM dynamically monitored. Ten young adults performed two repetitions of the bench press exercise on a Smith machine with loads that allowed two velocity conditions (above and below 0.6 m s−1). The exercises were monitored by the My Lift app, a magnet and a rotary encoder. After, 15 older adults performed the same exercise at different percentages of 1RM, monitored by the My Lift app and a magnet. The results revealed that ROM dynamically obtained by encoder (reference method) with the mean velocity above (0.497 ± 0.069 m) and below (0.450 ± 0.056 m) 0.6 m s−1 were quite different ( p < 0.05; large effect) from the ROMMYLIFT (0.385 ± 0.040 m). These errors provided highly biased and heteroscedastic mean velocity measurements (mean errors approximately 22%). The errors observed in adults were also observed in the older participants, except for loads equal to 85% of 1RM. The magnet method proved to be valid, presenting measurements very close to the encoder (mean errors approximately 1.7%; r > 0.99). In conclusion, the use of ROMMYLIFT is inadequate, as the higher the movement velocity, the higher the errors, both for young and older adults. Thus, to improve the measurement of the My Lift app, it is recommended that the magnet method be used in conjunction with the app to more accurately determine the ROM.