Dynamic Pressure Signal Research Articles

Diagnosis of Combustion Instability Using Waveform Pattern of Dynamic Pressure Signal

Diagnosis of Combustion Instability Using Waveform Pattern of Dynamic Pressure Signal

Dual-parameter stretchable, transferable mesh piezoresistive sensor for electronic skin detection of strain and temperature changes

Abstract Wearable sensors integrating multiple functions have great potential in artificial intelligence and flexible electronics at this stage and can perceive various external stimuli with high sensitivity and accuracy, such as strain, stress, and temperature. However, because multiple parameters do affect each other and reduce the sensing performance, making a single device that can detect multiple functions simultaneously is a huge challenge. In this paper, a strain-temperature dual-parameter sensor is developed with a planar structure design and used poly(3,4-ethylenedioxythiophene): poly(styrenesulfonic acid) and multi-walled carbon nanotubes polymerization materials to prepare a micron-sized film. The influence of two-dimensional structures on sensing performance is explored through simulation, and a structure with large deformation is selected to improve the strain detection range. The sensor can detect static and dynamic strain signals, and can maintain good linearity and response speed below 100 ms within a large strain range of 20%. In addition, the sensor also exhibits good temperature detection capability, with a temperature sensitivity of 18.2 μV K−1 and the ability to detect static and dynamic temperature changes with long-term stability. Finally, the sensor is tested in some actual scenarios, reflecting that the sensor manufactured has the dual-detection ability, showing sensitive strain monitoring and temperature perception decoupled between the dual signals. The sensor is realized with circuit board acquisition and wireless communication, combining multi-channel applications. Our research provides a feasible method for constructing multi-parameter human-computer interaction sensors.

Highly robust mechanical sensing platform inspired by a scorpion structure for 3D-mechanical signal perception

Three-dimensional force sensors play a crucial role in the aerospace industry and precision mechanical manufacturing. However, the current sensors still lack sufficient stability in the sensing of spatial mechanical forces and struggle to distinguish dynamic and static pressure signals. Coincidentally, scorpions in nature show a remarkable capacity to perceive three-dimensional stress with exceptional stability and sensitivity. This is primarily attributed to the highly sensitive sensing structure and stable multilayer structure in the slit sensilla of a scorpion. Inspired by this design, we develop a biomimetic sensing platform capable of accurately detecting the magnitude, position, and loading rate of a spatial force. This bionic system consists of an alternating combination of carbon fiber board (CFB) and hydroxylated multiwalled carbon nanotubes-styrene–isoprene (HMCNSI). Specifically, the flexible HMCNSI polymer enhances fatigue resistance, while the rigid CFB offers support and protection, resulting in exceptional robustness and stability of the sensing platform. The platform exhibits a rapid response time (14 ms), a fast recovery time (28 ms), and excellent sensitivity (GF = 46 for strain 0–3 %; GF = 118.3 for strain 3–6 %). Furthermore, the mechanical sensing platform can distinguish dynamic and static loads, identify exertion force rates in the range of 0.1–1 m/s and force magnitudes within 30 N, and provide a high positioning accuracy of 1 mm. The proposed biomimetic sensing platform exhibits promising applications in the field of precision detection of spatial forces such as human-machine interactions and intelligent robotics.

Flow regime classification using various dimensionality reduction methods and AutoML

Accurate identification of flow regimes is paramount in several industries, especially in chemical and hydrocarbon sectors. This paper describes a comprehensive data-driven workflow for flow regime identification. The workflow encompasses: i) the collection of dynamic pressure signals using an experimentally verified numerical two-phase flow model for three different flow regimes: stratified, slug and annular flow, ii) feature extraction from pressure signals using Discrete Wavelet Transformation (DWT), iii) Evaluation and testing of 12 different Dimensionality Reduction (DR) techniques, iv) the application of an AutoML framework for automated Machine Learning classifier selection among K-Nearest Neighbors, Artificial Neural Networks, Support Vector Machines, Gradient Boosting, Random Forest, and Logistic Regression, with hyper-parameter tuning. Kernel Fisher Discriminant Analysis (KFDA) is the best DR technique, exhibiting superior goodness of clustering, while KNN proved to be the top classifier with an accuracy of 92.5 % and excellent repeatability. The combination of DWT, KFDA and KNN was used to produce a virtual flow regime map. The proposed workflow represents a significant step forward in automating flow regime identification and enhancing the interpretability of ML classifiers, allowing its application to opaque pipes fitted with pressure sensors for achieving flow assurance and automatic monitoring of two-phase flow in various process industries.

A study on dynamic pressure sensor based on Pitot tube structure.

To meet the demand for the accurate measurements of the dynamic pressure of a shock wave, a composite dynamic pressure sensor design method is proposed based on the formation mechanism, propagation characteristics, special testing environment of the dynamic pressure, and Pitot tube structure. The dynamic pressure of the shock wave is evaluated by the total pressure and static pressure units installed in the composite sensor. FLUENT simulation software was used to analyze the aerodynamic characteristics of the dynamic pressure sensor, and parameters such as the structural size and inlet position of the sensor were determined. In response to the special experimental environment of the shock wave, the requirements for the dynamic pressure measurements under damage conditions were analyzed, and a dynamic pressure testing system was established. Dynamic pressure tests with four 2,4,6-trinitrotoluene [C7H5(NO2)3] equivalents of 1, 2, 15, and 20kg were carried out. The experimental results show that the proposed sensor design method can accurately and effectively measure the dynamic pressure signal, and the dynamic pressure gain multiple decreases with an increase in the proportional distance. This provides an effective testing method for evaluating the dynamic pressure damage effect of ammunition systems.

Two-phase flow regime identification using multi-method feature extraction and explainable kernel Fisher discriminant analysis

PurposeIdentifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.Design/methodology/approachA numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.FindingsThe results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.Practical implicationsThis workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.Originality/valueThis paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

Aeroengine Stall Warning by Multicorrelation Analysis

The present work aims to develop a stall warning method that is reliable even when the compressor encounters inlet distortion. For this purpose, multicorrelation analysis (MCA) is proposed to provide an effective indicator for the stall warning method. The relationship between MCA and correlation measures was discussed. It was demonstrated that MCA can provide an overall evaluation of both temporal and spatial correlations of multiple signals collected by different sensors. An experimental study of MCA was carried out on a low-speed axial compressor. Sensors were mounted on the casing to sample the dynamic pressure signals used for stall warning. The correlation of the signals was found to be influenced by the steady blade loading and the disturbance energy. Autocorrelation and cross-correlation were adequate when the inlet flow was uniform, but the accuracy of these methods could be affected by distortion. In contrast, MCA was robust under uniform and distorted inlet conditions.

Propagation behaviors of kerosene-fueled rotating detonation wave with varied atomizer locations

In this study, we tested a rotating detonation engine (RDE) fueled by liquid kerosene. A scheme of circumferential array atomization with an injection throat upstream of the annular combustor was used to realize the kerosene-fueled rotating detonation. The propagation characteristics of rotating detonation waves (RDWs) were analyzed by varying the distance between the pressure-swirl atomizers and the injection throat. The dynamic pressure signals and the visualized high-speed images were captured in the combustor. When the fuel atomizers were close to the injection throat, an unstable detonation mode appeared during the operation of the combustor. There was a periodical extinction and reinitiation of detonation waves in the combustor because the wave formed in the combustor could not propagate self-sustainably. In the fuel-lean tests, the rotating detonation degenerated to the combustion mode intermittently. Subsequently, unstable detonation was detected during the recovery stage from combustion to detonation mode. As the equivalence ratio increased, the rotating detonation process exhibited better continuity, but the duration of unstable detonation also increased. The continuity and stability of the rotating detonation process could be significantly improved by lengthening a certain distance between the atomizers and the injection throat. The experimental results indicated an optimal location of the fuel atomizers for stable operation, which could serve as a reference for the design of the injection structure of liquid-fueled RDEs.

Signal-processing-based characterization of thermoacoustic coupling in a lean premix swirling gas turbine combustor

This study investigates the strength of the thermoacoustic feedback coupling between the acoustic field and the thermal field in a combustor with the coherence function between dynamic pressure (DP) and heat release rate (HR) signals. The time domain signals of DP and HR - which are measured synchronously from a lean premix combustor experiencing combustion instability (CI) - were used. The coherence function between DP and HR signals in the time-frequency domain reflects the degree of linear interdependence and the mutual causality of signals from the two fields. The results of the coherence analysis revealed the following: (i) the coherences are far from unity due to random properties in both signals during stable combustion, thus implying decoupling of the two fields; (ii) they are close to unity around the acoustic eigenfrequencies of the combustor during unstable combustion, thus demonstrating the linear interdependence and mutual causality between the two signals frequency-wise; and (iii) the formation of nearly linear interdependence between the two signals at the first eigenfrequency precedes the onset of limit cycle oscillation. Based on these findings, we propose an equivalent harmonic oscillator model for a combustion process under severe instability and formulate the damping ratio expression accordingly.

Experimental analysis of performance and tip dynamic pressure in a compressor cascade with high-speed moving endwall

Abstract This study measured the aerodynamic performance and dynamic pressure signals of a compressor cascade platform with high-speed rotating endwall. Instead of translational movement, the endwall features an innovative large rotating disk. Measurements were conducted on a controlled diffusion airfoil (CDA) under different conditions: tip clearances (3 mm and 2.5 mm), inlet incidences (+6° and −6°), and stationary or high-speed rotating states at 0.5 Ma inflow. The results reveal that endwall movement amplifies circumferential leakage losses, increases kinetic energy, deviates the leakage flow path, and reduces total pressure loss in the leakage core region. Dynamic pressure results reveal greater unsteadiness in the tip region under positive incidence conditions and with larger clearances. Characteristic frequency ranges (8000 Hz for system vibration and 150∼200 Hz for leakage flow development) are identified. Further experimental measurements and high-precision simulations are needed the determine the matching relationship between complex flow behaviour in the blade tip region and characteristic frequency.

A rotating stall warning method for aero-engine compressor based on DeepESVDD-CNN

The compressor is one of the key components of the aircraft power system, and its rotating stall will greatly affect the performance of the whole system. Therefore, to ensure the stable and safe operation of the aircraft power system, it is necessary to develop a warning method for the compressor rotational stall. Based on the dynamic pressure signal collected from the compressor outlet pressure sensor of the aero-engine, this paper combines anomaly detection (AD) with deep learning to realize the compressor rotational stall warning system, which is named DeepESVDD-CNN. Specifically, a new anomaly detection method, deep ellipsoid support vector data description (DeepESVDD), is proposed to extract stall precursor based on dynamic pressure signals, and then to use the obtained warning signal as the input of convolution neural network (CNN) for training a classification model. This method only needs normal samples as training sets for unsupervised learning, and stores the characteristics of all normal samples in a deep network. The network parameters fully represent the stall warning model, which greatly simplifies the model training process and improves the performance of the stall warning model. The effectiveness and performance of this method are verified at four different compressor operating modes. In addition, two traditional warning methods for compressor stall are introduced and their performances are compared and tested. The experimental results show that DeepESVDD-CNN can identify the stall precursor and give the warning signal more accurately. It has good performance in the warning time, practicability, stability and real-time, and has good prospect in compressor stall warning tasks.

Measurement of Inertial and Acoustic Waves in a Turbine Chute Rim Seal Cavity

AbstractA chute rim seal cavity has been instrumented with time-resolved and time-averaged pressure transducers as well as a gas concentration measurement system at the Oxford Rotor Facility. The unsteady pressure transducers were unevenly distributed along the circumference generating 15 combinations of angular spacing to conduct a phase analysis. The test operating conditions were modified through different configurations of mainstream flow, a range of rotational speeds and several rates of purge flow to obtain test data in the regimes of rotationally-driven, pressure-driven, and combined ingestion. The rotating unsteady flow structures within the rim seal cavity were studied through a combination of phase analysis of dynamic pressure signals and inspection of the frequency domain. The introduction of mainstream flow (axial and swirled) increased the overall unsteadiness exciting an additional band of lower frequencies. The phase analysis revealed these were associated with acoustic waves, providing the first experimental evidence of the presence and coexistence of inertial and acoustic waves in the rim seal cavity. The interaction of the inertial and acoustic waves strongly depends on the operating conditions, and may be linked to changes in sealing effectiveness.

Numerical Gas–Liquid Two-Phase Flow Regime Identification in a Horizontal Pipe Using Dynamic Pressure Data

Gas–liquid two-phase flow is very common in industrial pipelines. Flow regime identification is the first step to design, analyze, and operate the gas–liquid system successfully. The purpose of this study is to develop a methodology for identification of a two-phase flow regime using post signal processing techniques, namely Fast Fourier Transform (FFT) and Probabilistic Density Function (PDF). Three different flow regimes were simulated in a 6 m horizontal pipe with a 0.050 m inner diameter. A Level-Set (LS) method coupled with Volume of Fluid (VOF) method is used to model the air–water interface. After validation of the numerical method, dynamic pressure readings were collected with the intent to identify the associated flow regimes by post-processing of these signals. It was concluded that dynamic pressure signals of different flow regimes show different characteristics (like dominant frequency, FFT amplitude, PDF location and PDF magnitude) in the time and frequency domains. These characteristics can be potentially used as differentiating factors to distinguish different flow regimes. This research is limited to stratified, slug, and annular flow in the horizontal pipe. This paper uses a new approach to identify the flow regime in a horizontal pipe by Fast Fourier Transform and Probability Density Function of dynamic pressure readings obtained by using numerical simulation.

Identification of Horizontal Gas-Liquid Two-Phase Flow Regime using Deep Learning

Two-phase flow is of great importance in various industrial processes. A characteristic feature of two-phase flow is that it can acquire various spatial distribution of phases to form different flow patterns/regimes. The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems and it holds great significance in petrochemical and thermonuclear industries today. The objective of this study is to develop a methodology for identification of flow regime using dynamic pressure signals and deep learning techniques. Stratified, slug and annular flow regimes were simulated using a Level-Set (LS) method coupled with Volume of Fluid (VOF) method in a 6 m horizontal pipe with 0.050 m inner diameter. Dynamic pressure signals were collected at a strategic location. These signals were converted to scalograms and used as inputs in deep learning architectures like ResNet-50 and ShuffleNet. Both architectures were effective in classifying different flow regime and recorded testing accuracies of 85.7% and 82.9% respectively. According to our knowledge no similar research has been reported in literature, where various Convolutional Neural Networks are used along with dynamic pressure signals to identify flow regime in horizontal pipe. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This study can be extended by collecting data over broader range of flow parameters and different geometries.

Forecasting thermoacoustic instabilities in liquid propellant rocket engines using multimodal Bayesian deep learning

We present a method that combines multiple sensory modalities in a rocket thrust chamber to predict impending thermoacoustic instabilities with uncertainties. This is accomplished by training an autoregressive Bayesian neural network model that forecasts the future amplitude of the dynamic pressure time series, using multiple sensor measurements (injector pressure/ temperature measurements, static chamber pressure, high-frequency dynamic pressure measurements, high-frequency OH* chemiluminescence measurements) and future flow rate control signals as input. The method is validated using experimental data from a representative cryogenic research thrust chamber. The Bayesian nature of our algorithms allows us to work with a dataset whose size is restricted by the expense of each experimental run, without making overconfident extrapolations. We find that the networks are able to accurately forecast the evolution of the pressure amplitude and anticipate instability events on unseen experimental runs 500 milliseconds in advance. We compare the predictive accuracy of multiple models using different combinations of sensor inputs. We find that the high-frequency dynamic pressure signal is particularly informative. We also use the technique of integrated gradients to interpret the influence of different sensor inputs on the model prediction. The negative log-likelihood of data points in the test dataset indicates that prediction uncertainties are well-characterized by our model and simulating a sensor failure event results in a dramatic increase in the epistemic component of the uncertainty, as would be expected when a Bayesian method encounters unfamiliar, out-of-distribution inputs.

Suppression of pressure feedback of the rotating detonation combustor by a Tesla inlet configuration

With the increase in the inlet throat area, the rotating detonation combustor (RDC) can obtain higher pressure gain. However, the pressure feedback caused by the rotating detonation wave (RDW) can affect the upstream components of combustion chamber and interfere with the stable operation of RDC. Therefore, to obtain outstanding performance, it is imperative to investigate the suppression of pressure feedback. In this study, kerosene and 28.5% oxygen-enriched air (at room temperature) were taken as the propellant, and the tesla valve inlet configuration was applied to RDC, and the effect was compared with that of the traditional inlet configuration to suppress pressure feedback. A series of experiments were conducted to examine the operation characteristics of Tesla RDC and the effect and mechanism of suppressing pressure feedback. Under similar conditions, Tesla RDC can effectively transform the longitudinal pulsed detonation (LPD) mode into the rotating detonation (RD) mode, thus expanding the operating range of RDC. RDC under the exit nozzle with a small area ratio (0.25) was successfully realized. The operating range of Tesla RDC can be expanded by 300% and 94.7% at an exit area ratio of 0.5 and 0.65, respectively. The bypass channel in Tesla valve inlet configuration is the key to expanding the operating range. Directing pressure feedback to the bypass channel can effectively weaken the influence on the main inlet channel and shorten the inlet recovery time. Two quantitative evaluation indexes of pressure feedback were established; one is the percentage of the pressure fluctuating amplitude in the air plenum to the pressure amplitude of the detonation wave (PFdw), and the other is the static pressure of the air plenum, PFap. By analyzing the dynamic pressure signal upstream of the combustor, it was found that pressure disturbance is in the form of a pressure wave during the upstream return of the combustor, and the return speed is close to the local sound speed. Pressure feedback is mainly suppressed at the inlet throat, and PFdw is reduced by approximately 14% due to drainage of the bypass channel. Tesla RDC exhibits a good suppression effect on LPD mode, PFdw is significantly lower in the large exit nozzle area ratio, and the minimum value is 23%. PFap has an approximately linear relationship with the equivalence ratio, and the minimum value is 7.7%. Overall, this study demonstrates that Tesla RDC has excellent potential in widening the operation range and suppressing the pressure feedback, providing an innovative method for improving the performance of RDC.

Analysis of Pressure Fluctuation Characteristics of Central Swirl Combustors Based on Empirical Mode Decomposition.

In order to study the characteristics of pressure fluctuation during unstable combustion, experimental studies had been conducted on the mechanism model of the swirl combustor and the industrial swirl combustor. The signal of dynamic pressure, heat release rate, and the high-speed flame image in the two combustors were synchronously collected by using dynamic pressure sensors, a photoelectric sensor, and a high-speed camera under normal temperature and pressure. After empirical mode decomposition of the dynamic pressure signal, several intrinsic mode functions were obtained. It was found that the pressure pulsation energy is concentrated in the first three order intrinsic mode function. Through fast Fourier transform spectrum calculation, it was found that the first three order intrinsic mode function pulsation can characterize the changes of heat release rate and air flow pulsation under cold state and flame instability. It showed that the decomposition of the dynamic pressure in the combustor by this method can obtain the main physical processes in its connotation, and provide data processing methods for the induction mechanism of oscillating combustion and combustion diagnosis in an industrial combustor test.

Self-powered and flexible piezo-sensors based on conductivity-controlled GaN nanowire-arrays for mimicking rapid- and slow-adapting mechanoreceptors

Human skin contains slowly adaptive (SA) and rapidly adaptive (RA) mechanoreceptors, which respond differently to external stimuli. Based on human tactile perception principles, the fabrication of a self-powered electronic skin (e-skin) that simultaneously mimics SA- and RA-mechanoreceptors is a prime need for robots and artificial prosthetics to interact with the surrounding environment. However, the complex process of merging multimode sensors to mimic SA- and RA-mechanoreceptors hinders their utilization in e-skins. We proposed SA- and RA-mechanoreceptors based on n-type and semi-insulating GaN nanowire arrays. The SA- and RA-mechanoreceptors demonstrated distinguished features such as grasping of objects and detection of their surface textures. Based on piezoelectric sensing principles, the proposed e-skin can simultaneously mimic static and dynamic pressure signals. Mechanoreceptors further detected several stimuli of various pressures with low and high frequencies. The response and reset times showed by SA-mechanoreceptors were 11 and 18 ms under 1-Hz frequency, which are rapid enough for practical e-skin applications.

Flow Regime Identification in Gas-Liquid Two-Phase Flow in Horizontal Pipe by Deep Learning

Two phase flow commonly occurs in industrial pipelines, heat exchangers and nuclear power plants. A characteristic feature of two-phase flow is that it can acquire various spatial distribution of phases to form different flow patterns/regimes. The first step to successfully design, analyze, and operate gas-liquid system is flow regime identification. Flow regime identification is of huge importance to the effective operation of facilities for the handling and transportation of multiphase fluids, and it represents one of the most significant challenges in petrochemical and thermonuclear industries today. The objective of this study is to develop a methodology for identification of flow regime using dynamic pressure signals and deep learning techniques. Three different flow regimes were simulated using a Level-Set (LS) method coupled with Volume of Fluid (VOF) method in a 6 m horizontal pipe with 0.050 m inner diameter. Dynamic pressure readings were collected at a strategic location and were converted to scalograms to be used as inputs in deep learning architectures like ResNet-50 and ShuffleNet. Both architectures performed effectively in classifying different flow regime and recorded testing accuracies of 85.7% and 82.9% respectively. According to our knowledge no similar research has been reported in literature, where various Convolutional Neural Networks are used along with dynamic pressure signals to identify flow regime in horizontal pipe.

Shaking table test for seismic optimization of soil slope reinforced by new EPS pile under earthquake

To explore the optimal seismic performance of new EPS pile, we carried out a series of shaking table test by comparing different pile types. We measured and obtained the seismic data with various attributes, such as the characteristic image of different probability horizontal seismic action, acceleration and dynamic soil pressure. By analyzing the time-domain characteristics of multi-attribute data such as deformation characteristics and acceleration, we revealed the instability mode of the slope model and the regional response characteristics of particle spatial position. Based on the Fast Fourier Transform (FFT) and Statistical Probability Sperm Matrix (SPSM), we proposed the damage level correlation between the spectral characteristics of acceleration and dynamic soil pressure. We obtained the relationship between the variation of the frequency and specific period between acceleration and dynamic soil pressure at different seismic stages through in-depth study, and then calculated the regional differences of spectrum displacement (Sd) by using Seism Signal. In the frequency domain of acceleration and dynamic soil pressure signals, the predominant frequencies were mostly concentrated in 0.5–8 Hz. Under high seismic acceleration, the amplitude frequency of acceleration spectrum of conventional pile and EPS pile was both 1.51 Hz, and the dominant frequency of dynamic soil pressure was significantly different. There was a positive correlation between seismic acceleration and dynamic soil pressure. The shift of frequency profile and main frequency band of EPS pile was mainly caused by the propagation stage after the acceleration main shock. Under high seismic acceleration, the deformation of EPS pile and conventional pile occurred to some extent. EPS plate was used as the damping layer to buffer and dissipated the deformation of the slope under earthquake, and its effect was linked to the seismic data attributes. The spectral displacement of EPS pile was reduced as a whole was higher than that of conventional pile. The slip surface of pile body was sensitive to earthquake, and it was easy to become the weak seismic link of the EPS pile. These results are helpful for us to provide a theoretical basis for improving the reliability optimization design of dynamic parameters of the pile structure.