Strain-based non-intrusive approaches for measuring the pressure of pipes have attracted widespread attention due to their great convenience and ability to avoid destroying the integrity of structures. However, the mentioned method usually measures the dynamic pressure based only on the static strain–pressure sensitivity coefficients (SSSCs) instead of the dynamic strain–pressure sensitivity coefficients (DSSCs) due to its complicated calibration, which will inevitably affect the accuracy significantly. To address this issue, a model-driven scheme with dual stages is proposed in the present study to compensate the dynamic pressure measurement. The DSSCs are analytically derived for the first time from the axial governing equations of the pipe, considering the general boundary conditions for the thin-walled and thick-walled pipes simultaneously. In the first stage, the physical parameters involved in the DSSCs are calibrated by minimizing the residual of the experimental results and the theoretical counterparts. In the second stage, the DSSCs calculated from the calibrated analytical model are utilized to compensate the dynamic pressure based on the relationship between the DSSCs and the SSSCs. The proposed method is applied to an industrial hydraulic pipe system, and the experimental results show that the relative error is reduced greatly after the compensation is implemented, demonstrating the validity of the proposed compensation method.

A Model-Driven Scheme to Compensate the Strain-Based Non-Intrusive Dynamic Pressure Measurement for Hydraulic Pipe

Vibration analysis and control technologies of hydraulic pipeline system in aircraft: A review

This paper investigates the reliability design optimization of an aeronautical hydraulic pipeline system, in which the constraint locations are treated as design parameters. To reduce the size of the optimization problem, two non-probabilistic global sensitivity indices are introduced and modified to screen out those constraint locations which have no or little effect on the optimization target. Considering the rest of constraint locations as design variables, the complexity of the optimization problem is dramatically reduced. The optimization of the pipeline systems demonstrates that the proposed method is superior to the traditional direct optimization method in both the optimization efficiency and results. This work indicates that introduction of sensitivity analysis can greatly improve the efficiency and performance of optimization, especially in those complex engineering problems involving a large number of design variables.

Application of non-probabilistic sensitivity analysis in the optimization of aeronautical hydraulic pipelines

A dynamic modeling approach for nonlinear vibration analysis of the L-type pipeline system with clamps

Multi-parameter dynamical measuring system using fibre Bragg grating sensors for industrial hydraulic piping

DOI: 10.2514/1.C031660 Inthispaperamodalanalysisofanaircrafthydraulic-pipelinevibrationiscarriedoutconsideringtheinfluenceof the fluid–structure interaction. Aircraft hydraulic pipes are always configured in curved rather than straight lines for the limited aircraft space. Therefore, the influence of curvature and friction of the pipeline should be accounted for in the vibration analysis. A mathematical model is developed to predict the vibration of the selected aircraft hydraulic-pipeline section. Vibration testing of the aircraft hydraulic pipeline is carried out, and comparison between the developed model and test is given, which illustrates that the developed model is correct. The research resultsalsoshowthatthemostimportantfactorfortheresonantfrequencyofthepipelinesectionisitslengthbetween the two supports, which can obviously increase the resonant frequency. The friction influence on the resonant frequency of the pipeline is less than the influence of the curvature. And the friction influence can reduce the stress response range, especially at the pressure peaks.

Modal Analysis of the Aircraft Hydraulic-System Pipeline

BIM-based automated design for HVAC system of office buildings—An experimental study

This paper aims to further the understanding of the mixing process of in-line twin synthetic jets (SJs) and their impact in the near-wall region in a flat-plate laminar boundary layer. A numerical study has been carried out, in which colored fluid particles and the Q criterion are used to track the SJ-induced vortex structures at the early stage of the evolution. Interacting vortex structures at four selected phase differences are presented and analyzed. It is found that the fluid injected at the early stage of the blowing stroke mainly contributes to the formation of the hairpin legs, the fluid injected near the maximum blowing mainly contributes to the formation of the hairpin head, and the fluid injected at the late stage of the blowing stroke contributes very little to the formation of the hairpin vortex. It is also confirmed that, irrespective of the phase difference, the hairpin vortex issued from the upstream actuator is more capable of maintaining its coherence than its counterpart issued from the downstream actuator. The influence of the interacting vortex structures on the boundary layer is also studied through investigating excess wall shear stress. In all cases, a pair of streaks of high wall shear stress can be observed with similar size. Among them, the streaks have the strongest wall shear stress, with the largest gap at phase difference 0 when partially interacting vortex structures are produced. The findings can provide valuable guiding information for the applications of synthetic jets in heat transfer, mixing control, and flow control in a crossflow.

/pdf/evolution-and-near-wall-effect-of-the-vortex-structures-3mxtyhgp.pdf

Evolution and Near-Wall Effect of the Vortex Structures Induced by In-Line Twin Synthetic Jets in a Crossflow

Fault diagnosis and classification (FDC) is an important part of prognostics and health management for ensuring safety and performance in the flight. However, it is challenging to achieve accurate FDC only based on single senor readings. In this paper, a fused FDC model among multiple different sensors is stabled by a hybrid deep learning architecture combining a sparse autoencoder (SAE) and a convolutional neural network (CNN). The hybrid model uses the SAE to enhance the hidden fault signal features in the multiple sensor signals, and then classifies the obtained feature map using the CNN. This method, which combines the advantages of the SAE in feature extraction and of the CNN in local feature recognition, fully utilizes the spatiotemporal coupling characteristics of multi-sensor signals. The FDC accuracy obtained by the proposed method when applied to a flight test data set is 93.78%, compared with 66.67% obtained using the combined SAE and feedforward neural network method and 83.11% obtained using the CNN only.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09928333.pdf

Hongxin Wang

Papers

Modal Analysis of the Aircraft Hydraulic-System Pipeline

BIM-based automated design for HVAC system of office buildings—An experimental study

Evolution and Near-Wall Effect of the Vortex Structures Induced by In-Line Twin Synthetic Jets in a Crossflow

Flight Test Sensor Fault Diagnosis Based on Data-Fusion and Machine Learning Method