Research on Modeling of Propeller in a Turboprop Engine

2015 ◽  
Vol 32 (2) ◽  
Author(s):  
Jiaqin Huang ◽  
Xianghua Huang ◽  
Tianhong Zhang
Keyword(s):  
Approximate Method ◽  
Real Time ◽  
Integrated Control ◽  
Computing Time ◽  
High Accuracy ◽  
Surface Theory ◽  
Model Based ◽  
Lifting Surface ◽  
Strip Theory ◽  
Lift And Drag

AbstractIn the simulation of engine-propeller integrated control system for a turboprop aircraft, a real-time propeller model with high-accuracy is required. A study is conducted to compare the real-time and precision performance of propeller models based on strip theory and lifting surface theory. The emphasis in modeling by strip theory is focused on three points as follows: First, FLUENT is adopted to calculate the lift and drag coefficients of the propeller. Next, a method to calculate the induced velocity which occurs in the ground rig test is presented. Finally, an approximate method is proposed to obtain the downwash angle of the propeller when the conventional algorithm has no solution. An advanced approximation of the velocities induced by helical horseshoe vortices is applied in the model based on lifting surface theory. This approximate method will reduce computing time and remain good accuracy. Comparison between the two modeling techniques shows that the model based on strip theory which owns more advantage on both real-time and high-accuracy can meet the requirement.

A simplified model for predicting the propeller-wing interaction

2018 ◽  
Vol 90 (1) ◽  
pp. 196-201
Author(s):  
Xianghua Huang ◽  
Xiaochun Zhao ◽  
Jiaqin Huang
Keyword(s):  
Real Time ◽  
High Accuracy ◽  
Simplified Model ◽  
Content Type ◽  
Aircraft Model ◽  
Model Based ◽  
Strip Theory ◽  
Real Time Computing ◽  
Lifting Line ◽  
Wing Interaction

Purpose The traditional numerical methods to predict the interaction between the wing and propeller are too complex and time-consuming for computation to a certain extent. Therefore, they are not applicable for a real-time integrated turboprop aircraft model. This paper aims to present a simplified model capable of high-precision and real-time computing. Design/methodology/approach A wing model based on the lifting line theory coupled with a propeller model based on the strip theory is used to predict the propeller-wing interaction. To meet the requirement of real-time computing, a novel decoupling parameter is presented to replace lifting line model (LLM) applied for wings with a simplified fitting model (FM). Findings The comparison between the LLM and the simplified FM demonstrates that the results of the FM have a good agreement with the results of the LLM, which means that the simplified FM has the advantages of both high-accuracy and real-time computation. Practical implications After simplification, the propeller-wing interaction model is suitable for a real-time integrated turboprop aircraft model. Originality/value A novel decoupling parameter is presented to replace LLM applied for wings with a simplified FM, which has the advantages of both high-accuracy and real-time computation.

Three-Dimensional Lifting-Surface Theory for an Annular Blade Row

1979 ◽  
Author(s):  
G. F. Homicz ◽  
J. A. Lordi
Keyword(s):  
Integral Equation ◽  
Three Dimensional ◽  
Computer Time ◽  
Two Dimensional ◽  
Blade Loading ◽  
Surface Theory ◽  
Lifting Surface ◽  
Strip Theory ◽  
Blade Row ◽  
Flow Through

A lifting-surface analysis is presented for the steady, three-dimensional, compressible flow through an annular blade row. A kernel-function procedure is used to solve the linearized integral equation which relates the unknown blade loading to a specified camber line. The unknown loading is expanded in a finite series of prescribed loading functions which allows the required integrations to be performed analytically, leading to a great savings in computer time. Numerical results are reported for a range of solidities and hub-to-tip ratios; comparisons are made with both two-dimensional strip theory and other three-dimensional results.

An improved compact propulsion system model based on batch normalize deep neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Juan Fang ◽  
Qiangang Zheng ◽  
Haibo Zhang ◽  
Chongwen Jin
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Real Time ◽  
Data Storage ◽  
High Accuracy ◽  
Propulsion System ◽  
System Model ◽  
Support Vector ◽  
Baseline Model ◽  
Model Based

Abstract Aero-engine on-board steady state model is an important part of many advanced engine control algorithms. In order to build a high accuracy and real-time steady-state onboard model in a large envelope, an ICPSM (improved compact propulsion system model) based on batch normalize neural network is proposed in this paper. Compared with piecewise linearization model and support vector machine model, conventional CPSM which is mainly composed of baseline model and nonlinear sub model has the advantages of high real-time performance and small data storage. However, as the similarity conversion error increases with the distance from the design point, the cumulative error of the conventional baseline model also increases, which makes the model unable to maintain high accuracy in the full envelope. Thus, a high accuracy baseline model in full envelope based on batch normalize neural network is proposed in this paper. The simulation result shows that compared with the conventional compact propulsion system model, the percentage error of parameters of the improved compact propulsion system model based on the batch neural network is reduced by two times, the single step operation time is reduced by 18%, and the data storage of the onboard model is reduced as well.

A terrain segmentation method based on pyramid scene parsing-mobile network for outdoor robots

2021 ◽  
Vol 18 (5) ◽  
pp. 172988142110486
Author(s):  
Botao Zhang ◽  
Tao Hong ◽  
Rong Xiong ◽  
Sergey A Chepinskiy
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Robot Navigation ◽  
Computing Time ◽  
Mobile Network ◽  
High Accuracy ◽  
Segmentation Method ◽  
Data Set ◽  
Time Performance ◽  
Scene Parsing

Terrain segmentation is of great significance to robot navigation, cognition, and map building. However, the existing vision-based methods are challenging to meet the high-accuracy and real-time performance. A terrain segmentation method with a novel lightweight pyramid scene parsing mobile network is proposed for terrain segmentation in robot navigation. It combines the feature extraction structure of MobileNet and the encoding path of pyramid scene parsing network. The depthwise separable convolution, the spatial pyramid pooling, and the feature fusion are employed to reduce the onboard computing time of pyramid scene parsing mobile network. A unique data set called Hangzhou Dianzi University Terrain Dataset is constructed for terrain segmentation, which contains more than 4000 images from 10 different scenes. The data set was collected from a robot’s perspective to make it more suitable for robotic applications. Experimental results show that the proposed method has high-accuracy and real-time performance on the onboard computer. Moreover, its real-time performance is better than most state-of-the-art methods for terrain segmentation.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

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