Fuel savings model after aero-engine washing based on convolutional neural network prediction

Cui, Zhiquan; Zhong, Shisheng; Yan, Zhiqi

doi:10.1016/j.measurement.2019.107180

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Results show that the method is verified to do the diagnosis. Cui et al [41] studied the predictive method of fuel saving on the washing engine; the method they used was singular value decomposition, convolutional neural network, and empirical mode decomposition. To improve the prediction accuracy, they replaced the continuous flight data by discrete data to train the model which would be used to predict the quantity of fuel savings.…”

Section: Introductionmentioning

confidence: 99%

The Optimization Research of Diesel Cylinder Gasket Parameters Based on Hybrid Neutral Network and Improved Grey Wolf Algorithm

Dong¹,

Liu²,

Liu³

et al. 2020

Mathematical Problems in Engineering

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In order to improve reliability and fatigue life of cylinder gaskets in heavy duty diesel engine, several methods and algorithms are applied to optimize operating factors of gaskets. Finite element method is utilized to figure out and analyze the temperature fields, thermal-mechanical coupling stress fields, and deformations of gasket. After determining the maximum values of three state parameters, the orthogonal experimental design method is adopted to analyze the influence rules of five operating factors on three state parameters of the gaskets and four factors which most significantly affect these state parameters are determined. Then, the method which uses operating factors to predict state parameters is established on the application of hybrid neuron network based on partial least squares regression and deep neural network. The comparison results between the predicted values and real values verified the accuracy of the hybrid neuron network method. Based on artificial bee colony algorithm, improvement is attached to the way three kinds of grey wolves locate preys in grey wolf algorithm and the way how using different hierarchy wolfs in grey wolf algorithm to determine three weight coefficients and the location of prey is put forward with. The method using artificial bee colony algorithm to optimize the grey wolf algorithm is called ABC and GWO. The proposed HNN and the ABC and GWO method are applied to work out operating factors values which correspond to optimal state parameters of gasket, and the gaskets are optimized according to the optimal values. It has been demonstrated by finite element analysis results that maximum temperature, maximum coupling stress, and the maximum deformation decrease to 6 K, 12.57 MPa, and 0.0925 mm compared to the original values, respectively, which proves the accuracy of the algorithm and the validity of the improvement.

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Section: Introductionmentioning

confidence: 99%

The Optimization Research of Diesel Cylinder Gasket Parameters Based on Hybrid Neutral Network and Improved Grey Wolf Algorithm

Dong¹,

Liu²,

Liu³

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Oliveira et al [17] also used a Monte Carlo method, but for calculating measurement uncertainty in solid-oxide fuel cells. Additionally, Cui et al [19] emphasize the advantage of adopting AI tools, such as Artificial Neural Networks, for quantifying fuel savings, showing the competitiveness of a data-driven model; this reasoning, moreover, can be adopted in the fuel quantity estimation process, thus minimizing the fuel carried in excess. While efforts like those by Uzun et al [18] explore merging physics-guided principles with machine learning in fuel consumption modeling, this project emphasizes the crucial role of comprehensive data availability.…”

mentioning

confidence: 99%

Computational Tool for Aircraft Fuel System Analysis

Di Marzo,

Calil,

Najafabadi

et al. 2024

Aerospace

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Fuel level gauging in aircraft presents a significant flight mechanics challenge due to the influence of aircraft movements on measurements. Moreover, it constitutes a multidimensional problem where various sensors distributed within the tank must converge to yield a precise and single measurement, independent of the aircraft’s attitude. Furthermore, fuel distribution across multiple tanks of irregular geometries complicates the readings even further. These issues critically impact safety and economy, as gauging errors may compromise flight security and lead to carrying excess weight. In response to these challenges, this research introduces a multi-stage project in aircraft fuel gauging systems, as a continuum of studies, where this first article presents a computational tool designed to simulate aircraft fuel sensor data readings as a function of fuel level, fuel tank geometry, sensor location, and aircraft attitude. Developed in an open-source environment, the tool aims to support the statistical inference required for accurate modeling in which synthetic data generation becomes a crucial component. A discretization procedure accurately maps fuel tank geometries and their mass properties. The tool, then, intersects these geometries with fuel-level planes and calculates each new volume. It integrates descriptive geometry to intersect these fuel planes with representative capacitive level-sensing probes and computes the sensor readings for the simulated flight conditions. The method is validated against geometries with analytical solutions. This process yields detailed fuel measurement responses for each sensor inside the tank, and for different analyzed fuel levels, providing insights into the sensors’ signals’ non-linear behavior at each analyzed aircraft attitude. The non-linear behavior is also influenced by the sensor saturation readings at 0 when above the fuel level and at 1 when submerged. The synthetic fuel sensor readings lay the baseline for a better understanding on how to compute the true fuel level from multiple sensor readings, and ultimately optimizing the amount of used sensors and their placement. The tool’s design offers significant improvements in aircraft fuel gauging accuracy, directly impacting aerostructures and instrumentation, and it is a key aspect of flight safety, fuel management, and navigation in aerospace technology.

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“…Furthermore, feature learning and RUL estimation are mutually enhanced by supervised feedback. In order to accurately calculate fuel savings after aeroengine washing, convolutional neural network is used in Cui et al's research[9]. The results demonstrate that prediction accuracy gets improved by replacing integral operation with convolution operation.…”

mentioning

confidence: 99%

Analytical Redundancy of Variable Cycle Engine Based on Proper Net considering Multiple Input Variables and the Whole Engine’s Degradation

Zhang

Huang

Zhang

2021

International Journal of Aerospace Engineering

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In this paper, Proper net is proposed to construct variable cycle engine’s analytical redundancy, when all control variables and environmental variables change simultaneously, also accompanied with the whole engine’s degradation. In another word, Proper net is proposed to solve a multivariable, strongly nonlinear, dynamic, and time-varying problem. In order to make the topological structure of Proper net physically explainable, Proper net’s topological structure is designed according to physical relationship between variables, by which means analytical redundancy based on Proper net achieves higher accuracy with less calculation time. Experiments were compared with performance of analytical redundancy based on Proper net, seven convolutional neural network topological structures, and five shallow learning methods. Results demonstrate that under condition of average relative error less than 1.5%, Proper net is the most accurate and the least time-consuming one, which proves not only the effectiveness of Proper net but also the feasibility of topological structures’ design method based on physical relationship.

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Fuel savings model after aero-engine washing based on convolutional neural network prediction

Cited by 9 publications

References 15 publications

The Optimization Research of Diesel Cylinder Gasket Parameters Based on Hybrid Neutral Network and Improved Grey Wolf Algorithm

The Optimization Research of Diesel Cylinder Gasket Parameters Based on Hybrid Neutral Network and Improved Grey Wolf Algorithm

Computational Tool for Aircraft Fuel System Analysis

Analytical Redundancy of Variable Cycle Engine Based on Proper Net considering Multiple Input Variables and the Whole Engine’s Degradation

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