Evaluation of Blow-Off Dynamics in Aero-Engine Combustors Using Recurrence Quantification Analysis

2021 ◽  
Author(s):  
Ho Yin Leung ◽  
Efstathios Karlis ◽  
Yannis Hardalupas ◽  
Andrea Giusti
Keyword(s):  
Recurrence Rate ◽  
Future Research ◽  
Pressure Measurements ◽  
Flame Extinction ◽  
Linear Dynamical Systems ◽  
Intermittent Behavior ◽  
Aero Engine ◽  
Quantification Analysis ◽  
Stable Flame ◽  
Flight Measurements

Abstract The lean blow-out performance of an engine and the ability to re-ignite the flame, especially at high-altitude conditions, are important aspects for the safe operability of airplanes. The operability margins of the engine could be extended if it was possible to predict the occurrence of flame blowout from in-flight measurements and take actions to dynamically control the flame behaviour before complete extinction. In this work, the use of Re-currence Quantification Analysis (RQA), an established tool for the analysis of non-linear dynamical systems, is explored to reconstruct and study the blow-off dynamics starting from pressure measurements taken from blow-off experiments of an engine rig. It is shown that the dynamics of the combustor exhibit chaotic characteristics far away from blow-off and that the dynamics become more coherent as the blow-off condition is approached. The degree of determinism and recurrence rate are studied during the entire combustor’s dynamics, from stable flame to flame extinction. It is shown that the flame extinction is anticipated by an increase of the degree of determinism and recurrence rate at all investigated conditions, which indicates intermittent behavior of the combustor before the blow-off condition is reached. Therefore, in the configuration investigated here, the determinism and the recurrence rate of the system could be good predictors of blow-off occurrence and could potentially enable control actions to avoid flame extinction. This study opens up new possibilities for engine control and operability. The development of real-time RQA should be addressed in future research.

scholarly journals Poisson balanced spiking networks

2019 ◽  
Author(s):  
Camille E. Rullán Buxó ◽  
Jonathan W. Pillow
Keyword(s):  
Linear Models ◽  
Nonlinear Function ◽  
Threshold Function ◽  
Future Research ◽  
Linear Dynamical Systems ◽  
Integrate And Fire ◽  
Network Output ◽  
Spiking Networks ◽  
Different Populations ◽  
Positive Correlations

AbstractAn important problem in computational neuroscience is to understand how networks of spiking neurons can carry out various computations underlying behavior. Balanced spiking networks (BSNs) provide a powerful framework for implementing arbitrary linear dynamical systems in networks of integrate-and-fire neurons (Boerlin et al. [1]). However, the classic BSN model requires near-instantaneous transmission of spikes between neurons, which is biologically implausible. Introducing realistic synaptic delays leads to an pathological regime known as “ping-ponging”, in which different populations spike maximally in alternating time bins, causing network output to overshoot the target solution. Here we document this phenomenon and provide a novel solution: we show that a network can have realistic synaptic delays while maintaining accuracy and stability if neurons are endowed with conditionally Poisson firing. Formally, we propose two alternate formulations of Poisson balanced spiking networks: (1) a “local” framework, which replaces the hard integrate-and-fire spiking rule within each neuron by a “soft” threshold function, such that firing probability grows as a smooth nonlinear function of membrane potential; and (2) a “population” framework, which reformulates the BSN objective function in terms of expected spike counts over the entire population. We show that both approaches offer improved robustness, allowing for accurate implementation of network dynamics with realistic synaptic delays between neurons. Moreover, both models produce positive correlations between similarly tuned neurons, a feature of real neural populations that is not found in the original BSN. This work unifies balanced spiking networks with Poisson generalized linear models and suggests several promising avenues for future research.

scholarly journals IoMT-based biomedical measurement systems for healthcare monitoring: a review

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 174
Author(s):  
Imran Ahmed ◽  
Eulalia Balestrieri ◽  
Francesco Lamonaca
Keyword(s):  
Healthcare Systems ◽  
Future Research ◽  
Pressure Measurements ◽  
Healthcare Applications ◽  
Measurement Systems ◽  
Biomedical Equipment ◽  
Research Activities ◽  
Biomedical Measurement ◽  
Healthcare Monitoring ◽  
Internal Body

<p class="Abstract"><span lang="EN-US">Biomedical measurement systems (BMS) have provided new solutions for healthcare monitoring and the diagnosis of various chronic diseases. With a growing demand for BMS in the field of medical applications, researchers are focusing on advancing these systems, including Internet of Medical Things (IoMT)-based BMS, with the aim of improving bioprocesses, healthcare systems and technologies for biomedical equipment. This paper presents an overview of recent activities towards the development of IoMT-based BMS for various healthcare applications. Different methods and approaches used in the development of these systems are presented and discussed, taking into account some metrological aspects related to the requirement for accuracy, reliability and calibration. The presented IoMT-based BMS are applied to healthcare applications concerning, in particular, heart, brain and blood sugar diseases as well as internal body sound and blood pressure measurements. Finally, the paper provides a discussion about the shortcomings and challenges that need to be addressed along with some possible directions for future research activities.</span></p>

scholarly journals STRONG LAWS FOR RECURRENCE QUANTIFICATION ANALYSIS

2013 ◽  
Vol 23 (08) ◽  
pp. 1350147 ◽  
Author(s):  
M. GRENDÁR ◽  
J. MAJEROVÁ ◽  
V. ŠPITALSKÝ
Keyword(s):  
Recurrence Rate ◽  
Recurrence Quantification Analysis ◽  
Complexity Measures ◽  
Laws Of Large Numbers ◽  
Strong Laws ◽  
Large Numbers ◽  
Recurrence Quantification ◽  
Quantification Analysis

The recurrence rate and determinism are two of the basic complexity measures studied in the recurrence quantification analysis. In this paper, the recurrence rate and determinism are expressed in terms of the correlation sum, and strong laws of large numbers are given for them.

Quantitative analysis tool for clinical functional MRI in mild traumatic brain injury

2019 ◽  
pp. 245-249
Author(s):  
Peter E. Cartwright ◽  
◽  
Thomas G. Perkins ◽  
Priya Santhanam ◽  
Lindell K. Weaver ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Quantitative Analysis ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Sequence Data ◽  
Future Research ◽  
Analysis Tool ◽  
Clinical Interpretation ◽  
Quantification Analysis ◽  
Study Participants

Functional magnetic resonance imaging (fMRI) has been available commercially for clinical diagnostic use for many years. However, both clinical interpretation of fMRI by a neuroradiologist and quantitative analysis of fMRI data can require significant personnel resources that exceed reimbursement. In this report, a fully automated computerbased quantification methodology (Enumerated Auditory Response, EAR) has been developed to provide an auditory fMRI assessment of patients who have suffered a mild traumatic brain injury. Fifty-five study participants with interpretable auditory fMRI sequence data were assessed by EAR analysis, as well as both clinical radiologist fMRI interpretation and voxelwise general linear model (GLM) analysis. Comparison between the clinical interpretation and the two computer analysis methods resulted in 67% concordance (identical), 32% near-concordance (one level difference), and 1% discordant. Comparison between the clinical computer-based quantification (EAR) and GLM analysis yielded significant correlations in right and left ear responses (p<0.05) for the full subject group. Automated fMRI quantification analysis equivalent to EAR might be appropriate for both future research projects with constrained resources, as well as possible routine clinical use.

scholarly journals Poisson balanced spiking networks

2020 ◽  
Vol 16 (11) ◽  
pp. e1008261
Author(s):  
Camille E. Rullán Buxó ◽  
Jonathan W. Pillow
Keyword(s):  
Linear Models ◽  
Nonlinear Function ◽  
Threshold Function ◽  
Future Research ◽  
Linear Dynamical Systems ◽  
Integrate And Fire ◽  
Network Output ◽  
Spiking Networks ◽  
Different Populations ◽  
Positive Correlations

An important problem in computational neuroscience is to understand how networks of spiking neurons can carry out various computations underlying behavior. Balanced spiking networks (BSNs) provide a powerful framework for implementing arbitrary linear dynamical systems in networks of integrate-and-fire neurons. However, the classic BSN model requires near-instantaneous transmission of spikes between neurons, which is biologically implausible. Introducing realistic synaptic delays leads to an pathological regime known as “ping-ponging”, in which different populations spike maximally in alternating time bins, causing network output to overshoot the target solution. Here we document this phenomenon and provide a novel solution: we show that a network can have realistic synaptic delays while maintaining accuracy and stability if neurons are endowed with conditionally Poisson firing. Formally, we propose two alternate formulations of Poisson balanced spiking networks: (1) a “local” framework, which replaces the hard integrate-and-fire spiking rule within each neuron by a “soft” threshold function, such that firing probability grows as a smooth nonlinear function of membrane potential; and (2) a “population” framework, which reformulates the BSN objective function in terms of expected spike counts over the entire population. We show that both approaches offer improved robustness, allowing for accurate implementation of network dynamics with realistic synaptic delays between neurons. Both Poisson frameworks preserve the coding accuracy and robustness to neuron loss of the original model and, moreover, produce positive correlations between similarly tuned neurons, a feature of real neural populations that is not found in the deterministic BSN. This work unifies balanced spiking networks with Poisson generalized linear models and suggests several promising avenues for future research.

scholarly journals Aero-Engine Real-Time Models and Their Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jiajie Chen ◽  
Zhongzhi Hu ◽  
Jiqiang Wang
Keyword(s):  
Real Time ◽  
Future Research ◽  
Component Level ◽  
Time Model ◽  
Modeling Methods ◽  
Time Performance ◽  
Model Based ◽  
Time Modeling ◽  
Aero Engine ◽  
Flight Envelope

Aero-engine real-time models are widely used in control system design, integration, and testing. They can be used as the basis for model-based engine intelligent controls and health management, which is critical to improve engine safety, reliability, economy, and other performance indicators. This article provides an up-to-date review on aero-engine real-time modeling methods, model adaptation techniques, and applications for the last several decades. Besides, future research directions are also discussed, mainly focusing on the following four areas:(1) verification of the aero-engine real-time model over the full flight envelope; (2) better balance between real-time performance and accuracy in simplified methods for the aero-thermodynamic component level models; (3) further improvement in the real-time performance for the identified nonlinear models over the full flight envelope; (4) improvement of hybrid on-board adaptive real-time models combining the advantages of both model-based and data-based on-board adaptive real-time modeling methods.

scholarly journals Parcellation of the human amygdala using recurrence quantification analysis

2020 ◽  
Author(s):  
Krzysztof Bielski ◽  
Sylwia Adamus ◽  
Emilia Kolada ◽  
Joanna Rączaszek-Leonardi ◽  
Iwona Szatkowska
Keyword(s):  
Functional Connectivity ◽  
Structural Connectivity ◽  
Recurrence Quantification Analysis ◽  
Future Research ◽  
Valuable Insight ◽  
List Type ◽  
Brain Organization ◽  
Recurrence Quantification ◽  
Connectivity Patterns ◽  
Quantification Analysis

ABSTRACTSeveral previous attempts have been made to divide the human amygdala into smaller subregions based on the unique functional properties of the subregions. Although these attempts have provided valuable insight into the functional heterogeneity in this structure, the possibility that spatial patterns of functional characteristics can quickly change over time has been neglected in previous studies. In the present study, we explicitly account for the dynamic nature of amygdala activity. Our goal was not only to develop another parcellation method but also to augment existing methods with novel information about amygdala subdivisions. We performed state-specific amygdala parcellation using resting-state fMRI (rsfMRI) data and recurrence quantification analysis (RQA). RsfMRI data from 102 subjects were acquired with a 3T Trio Siemens scanner. We analyzed values of several RQA measures across all voxels in the amygdala and found two amygdala subdivisions, the ventrolateral (VL) and dorsomedial (DM) subdivisions, that differ with respect to one of the RQA measures, Shannon’s entropy of diagonal lines. Compared to the DM subdivision, the VL subdivision can be characterized by a higher value of entropy. The results suggest that VL activity is determined and influenced by more brain structures than is DM activity. To assess the biological validity of the obtained subdivisions, we compared them with histological atlases and currently available parcellations based on structural connectivity patterns (Anatomy Probability Maps) and cytoarchitectonic features (SPM Anatomy toolbox). Moreover, we examined their cortical and subcortical functional connectivity. The obtained results are similar to those previously reported on parcellation performed on the basis of structural connectivity patterns. Functional connectivity analysis revealed that the VL subdivision has strong connections to several cortical areas, whereas the DM subdivision is mainly connected to subcortical regions. This finding suggests that the VL subdivision corresponds to the basolateral subdivision of the amygdala (BLA), while the DM subdivision has some characteristics typical of the centromedial amygdala (CMA). The similarity in functional connectivity patterns between the VL subdivision and BLA, as well as between the DM subdivision and CMA, confirm the utility of our parcellation method. Overall, the study shows that parcellation based on BOLD signal dynamics is a powerful tool for identifying distinct functional systems within the amygdala. This tool might be useful for future research on functional brain organization.HighlightsA new method for parcellation of the human amygdala was developedThe ventrolateral and dorsomedial subdivisions of the amygdala were revealedThe two subdivisions correspond to the anatomically defined regions of the amygdalaThe two subdivisions differ with respect to values of entropyA new parcellation method provides novel information about amygdala subdivisions

scholarly journals Key Problems Affecting the Anti-Erosion Coating Performance of Aero-Engine Compressor: A Review

Coatings ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 821 ◽  
Author(s):  
Guangyu He ◽  
Danyang Sun ◽  
Jiao Chen ◽  
Xiao Han ◽  
Zhaolu Zhang ◽  
...  
Keyword(s):  
Erosion Resistance ◽  
Preparation Method ◽  
Hard Coating ◽  
Future Research ◽  
Surface Microstructure ◽  
Coating Performance ◽  
Sand Erosion ◽  
Aero Engine ◽  
Engine Compressor ◽  
Aero Engines

Sand erosion has always been a key threat to the performance and service life of aero-engines. The compressor, the key component installed at the front of the aero-engine, suffers the most from sand erosion, especially compressors serving in deserts. Ceramic hard coating is a traditional way to improve the hardness and wear resistance of cutting and grinding tools. It may also be used to improve the erosion resistance of aero- engine compressor. However, the mechanism of erosion damage is complicated, which may include wear, secondary erosion, anisotropic erosion, impact, and fatigue. Recent research discovered the major problems with ceramic hard coating on aero-engine compressors. In this paper, these following problems are discussed: the design of coating material and structure, the preparation method and technology, the effects of droplets and clusters of coating surface, microstructure and characteristics of interface. The review of the major problems and possible solutions discussed in this paper may contribute to the future research on erosion coating theoretically and practically.

Vibration Fault Diagnosis of Aero-Engine Rotor System Based on Recurrence Quantification Analysis

2011 ◽  
Vol 105-107 ◽  
pp. 680-684
Author(s):  
Le Xi Li ◽  
Sheng Li Hou ◽  
Ren Heng Bo ◽  
Li Qiao ◽  
Tao Wang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Recurrence Plot ◽  
Recurrence Quantification Analysis ◽  
Rotor System ◽  
Classification Rate ◽  
Recurrence Quantification ◽  
Aero Engine ◽  
Quantification Analysis ◽  
Engine Rotor

Aero-engine rotor system is the core component of engine. Aim at difficulties of fault diagnosis of engine rotor system, a method to detect the fault feature is proposed, which is based on recurrence plot (RP) and recurrence quantification analysis(RQA) by research of the characteristics and the mechanism of faults. An experiment is used to detect the fault of rotor system by using this new method. The results showed that the RQA is an effective way to extract features from vibration signal and by the use of quantitative features it is possible to identify and classify different types of rotor. Comparing with classical statistical features, the proposed algorithm has better classification rate. The research will be helpful in the further study of fault diagnosis of rotor system.

Assessment of Bearing Heat Generation Prediction by the Program ADORE With Respect to Experimental Results and SHABERTH Predictions

2017 ◽  
Author(s):  
Brian D. Nicholson ◽  
Garry D. Givan ◽  
Kevin L. Thompson ◽  
Justin Mason ◽  
Pradeep K. Gupta ◽  
...  
Keyword(s):  
Heat Generation ◽  
Empirical Models ◽  
Experimental Results ◽  
Future Research ◽  
Main Shaft ◽  
Analysis Program ◽  
High Speeds ◽  
Aero Engine ◽  
Rolling Elements ◽  
Good Agreement

This work details the heat generation analysis of a turbine aero-engine main-shaft bearing using the computer program Advanced Dynamics Of Rolling Elements (ADORE). The empirical models used for traction and churning heat generation are detailed. The predictions of ADORE are shown to demonstrate the differing contributions of traction and churning to total heat generation at different load/speed regimes. These results are then compared with experimental results. In addition, the results of ADORE are also compared with results from the well-known bearing analysis program SHABERTH (Shaft Bearing Thermal Analysis). The comparisons showed good agreement between ADORE and the experimental results for loads between 13.35 and 53.40 kN and speeds between 1.8 and 2.2 MDN. The results also showed under prediction of heat generation by SHABERTH in this regime. Limitations of both programs were identified and speculated to include limitations in the empirical models due to the lack of available experimental traction data at high speeds/loads. Finally, recommendations for future research are provided which will likely provide significant improvements in the ability to predict bearing heat generation in turbine aero-engine applications.

Sign in / Sign up

Export Citation Format

Share Document