The Research on the Methods of the Aero Engine Blades Evaluation

2013 ◽  
Vol 333-335 ◽  
pp. 322-326
Author(s):  
Wen Qi Ma ◽  
Shu Gui Liu
Keyword(s):  
Engine Performance ◽  
Least Square Method ◽  
Chord Length ◽  
Least Square ◽  
Trailing Edge ◽  
Inspection Method ◽  
Maximum Thickness ◽  
Spline Fitting ◽  
Aero Engine ◽  
The Mean

As an important part of the aero engine, the quality of the blade seriously affects the engine performance, so the inspection method of the blade is significant. Based on the research of the HB 5647-98<<to mark dimensions, tolerances of the vane type and the surface roughness of the blade body>> , this paper concludes different evaluation methods of the blade. The main evaluated parameters include the leading and trailing edge radius, the chord length, the maximum thickness of blade profile, the mean camber line, and the surface twist and so on. Here we mainly discuss the previous three terms. The least square method is adopted to fit the arc of the leading and trailing edge; introduce the projection method to calculate the chord length; at last, we employ the spline fitting method to get the mean line and then obtain the maximum thickness of the blade section.

Improved Multiple Point Non-Linear Genetic Algorithm Based Performance Adaptation Using Least Square Method

2011 ◽  
Author(s):  
Y. G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Engine Performance ◽  
Least Square Method ◽  
Scaling Factor ◽  
Least Square ◽  
Multiple Point ◽  
Search Range ◽  
Non Linear ◽  
Performance Adaptation ◽  
Adaptation Method

At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A non-linear multiple point Genetic Algorithm based performance adaptation developed earlier by the authors using a set of non-linear scaling factor functions has been proven capable of making accurate performance prediction over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of trial and error process. In this paper, an improvement on the present adaptation method is presented using a Least Square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the Least Square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.

Application of Photoelectric Sensor in Vehicle Power Control System

2020 ◽  
Vol 15 (6) ◽  
pp. 700-706
Author(s):  
Yifan Zhao ◽  
Mengyu Wang ◽  
Kai Wang
Keyword(s):  
Power Control ◽  
Electric Energy ◽  
Least Square Method ◽  
Least Square ◽  
Motor Speed ◽  
Time Calculation ◽  
The Mean ◽  
The Difference ◽  
Artificial Neural Network Ann ◽  
Mean Square Errors

Due to its characteristics of using clean electric energy and bringing no damage to the environment, electric vehicles (EVs) have become a new developmental direction for the automotive industry. Its reliability issues have also attracted the attention of experts and professionals. In the field of automotive power control, from the perspective of motor control, this study uses the photoelectric sensors (PSs) as the research objects and elaborates on the measurement principles of motor speed with PSs. Meanwhile, a diagnosis scheme is proposed for various faults in the measurement. Among them, the measurement speed is converted by the photoelectric signal, and the measured waveform is amplified. In the fault detection process, the Radial Basis Function (RBF) artificial neural network (ANN) is analyzed. By using this method, the difference in the motor speed detected by the sensor is calculated to determine the cause of the failure. The test uses the least-square method to compare the tested motor speed with the actual motor speed. The results show that PSs can measure the motor speed of EVs. As for the motor failures, the mean square errors (MSEs) of motor speeds generated by different faults are compared to determine the fault points according to the speed changes. In addition, the cause of motor failure can be determined by the real-time calculation of the speed differences. The above tests fully prove the effectiveness of measuring the speed of electric motors by PSs; therefore, PSs have broad application prospects in vehicle power control systems.

scholarly journals The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression

2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
Chunxiao Zhang ◽  
Junjie Yue
Keyword(s):  
Kernel Function ◽  
Forecast Error ◽  
Engine Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Algorithm ◽  
Performance Parameters ◽  
Exhaust Temperature ◽  
Function Transformation ◽  
Aero Engine

The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT) time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS) based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS) method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.

Improved Multiple Point Nonlinear Genetic Algorithm Based Performance Adaptation Using Least Square Method

2011 ◽  
Vol 134 (3) ◽  
Author(s):  
Y.G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Engine Performance ◽  
Least Square Method ◽  
Scaling Factor ◽  
Least Square ◽  
Multiple Point ◽  
Search Range ◽  
Design Point ◽  
Performance Adaptation ◽  
Adaptation Method

At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A nonlinear multiple point genetic algorithm based performance adaptation developed earlier by the authors using a set of nonlinear scaling factor functions has been proven capable of making accurate performance predictions over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain the optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of the trial and error process. In this paper, an improvement on the present adaptation method is presented using a least square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the least square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.

A Camera Calibration Method Based on Free Points

2011 ◽  
Vol 130-134 ◽  
pp. 1885-1888
Author(s):  
Jing Lei Zhang ◽  
Kai Bo Fan ◽  
Yan Jiao Wang
Keyword(s):  
Standard Deviation ◽  
Ccd Camera ◽  
Least Square Method ◽  
Calibration Method ◽  
Initial Guess ◽  
Least Square ◽  
Camera Model ◽  
Extrinsic Parameters ◽  
The Mean ◽  
Constraint Method

A new accurate calibrating technique for intrinsic parameters and extrinsic parameters of CCD camera is described. The camera model is derived by the pinhole projection theory. Then other parameters of the model are resolved under the radial alignment constraints and orthogonal constraints. In order to get a fine initial guess for the nonlinear searching solution, the least square method is introduced, and finally uses radial alignment constraint method to get the results. The experimental results show that the mean absolute differences in x direction and y direction are 0.0070 and 0.1430 separately while the standard deviation are 0.5006 and 1.2046 separately.

scholarly journals A regression approach for prediction of Youtube views

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Lau Tian Rui ◽  
Zehan Afizah Afif ◽  
R. D. Rohmat Saedudin ◽  
Aida Mustapha ◽  
Nazim Razali
Keyword(s):  
Least Square Method ◽  
Absolute Error ◽  
Descent Method ◽  
Least Square ◽  
Gradient Descent Method ◽  
Ordinary Least Square ◽  
The Public ◽  
Dependent Variables ◽  
Regression Approach ◽  
The Mean

YouTube has grown to be the number one video streaming platform on Internet and home to millions of content creator around the globe. Predicting the potential amount of YouTube views has proven to be extremely important for helping content creator to understand what type of videos the audience prefers to watch. In this paper, we will be introducing two types of regression models for predicting the total number of views a YouTube video can get based on the statistic that are available to our disposal. The dataset we will be using are released by YouTube to the public. The accuracy of both models are then compared by evaluating the mean absolute error and relative absolute error taken from the result of our experiment. The results showed that Ordinary Least Square method is more capable as compared to the Online Gradient Descent Method in providing a more accurate output because the algorithm allows us to find a gradient that is close as possible to the dependent variables despite having an only above average prediction.

Prediction of Log-Transmissivity

1995 ◽  
Vol 26 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Steen Christensen
Keyword(s):  
Prediction Error ◽  
Regression Line ◽  
Specific Capacity ◽  
Covariance Structure ◽  
Prediction Method ◽  
Least Square Method ◽  
Least Square ◽  
Practical Applications ◽  
Well Efficiency ◽  
The Mean

The log-transmissivity may in many cases be predicted from the log-transformed specific capacity of wells by applying a linear statistical model. The coefficients of the linear equation can be estimated by the least-square method. It is shown that if the estimated slope of the line differs from unity then it may indicate that the specific capacity is correlated with the well efficiency. The above prediction method is applied to case studies of aquifers in three different formations: the prediction should not be used for a homogeneous fluvioglacial formation because the variance of the well efficiency dominates the variance of the transmissivity; the prediction is fair for a heterogeneous fluvioglacial formation; and the prediction is poor for a homogeneous limnic formation. In the study of the first formation a correlation between specific capacity and well efficiency can be identified directly from the slope of the regression line. If the predictor values are taken from the drillers log then the standard error of prediction in all the cases is 0.35. This seems to be unacceptable in most practical applications. However, if one needs to predict the mean of a domain and the domain contains a number of observations of the predictor, then the averaging will reduce the prediction error. The averaging procedure ought to take the covariance structure of the variables into consideration.

scholarly journals Forming a Hierarchical Choquet Integral with a GA-Based Heuristic Least Square Method

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1155
Author(s):  
Chen ◽  
Huang
Keyword(s):  
Choquet Integral ◽  
Least Square Method ◽  
Least Square ◽  
Integral Model ◽  
Fuzzy Measures ◽  
Proposed Model ◽  
Choquet Integrals ◽  
The Mean ◽  
Input Variables ◽  
Least Mean Squares Algorithm

: Identifying the fuzzy measures of the Choquet integral model is an important component in resolving complicated multi-criteria decision-making (MCDM) problems. Previous papers solved the above problem by using various mathematical programming models and regression-based methods. However, when considering complicated MCDM problems (e.g., 10 criteria), the presence of too many parameters might result in unavailable or inconsistent solutions. While k-additive or p-symmetric measures are provided to reduce the number of fuzzy measures, they cannot prevent the problem of identifying the fuzzy measures in a high-dimension situation. Therefore, Sugeno and his colleagues proposed a hierarchical Choquet integral model to overcome the problem, but it required the partition information of the criteria, which usually cannot be obtained in practice. In this paper, we proposed a GA-based heuristic least mean-squares algorithm (HLMS) to construct the hierarchical Choquet integral and overcame the above problems. The genetic algorithm (GA) was used here to determine the input variables of the sub-Choquet integrals automatically, according to the objective of the mean square error (MSE), and calculated the fuzzy measures with the HLMS. Then, we summed these sub-Choquet integrals into the final Choquet integral for the purpose of regression or classification. In addition, we tested our method with four datasets and compared these results with the conventional Choquet integral, logit model, and neural network. On the basis of the results, the proposed model was competitive with respect to other models.

Parameter estimation via homogenization for stochastic dynamical systems with oscillating coefficients

2018 ◽  
Vol 18 (03) ◽  
pp. 1850025
Author(s):  
Xinyong Zhang ◽  
Hui Wang ◽  
Yanjie Zhang ◽  
Haokun Lin
Keyword(s):  
Parameter Estimation ◽  
Dynamical Systems ◽  
Exit Time ◽  
Least Square Method ◽  
Least Square ◽  
Stochastic Dynamical Systems ◽  
Escape Probability ◽  
Mean Exit Time ◽  
Oscillating Coefficients ◽  
The Mean

This paper is devoted to studying parameter estimation for a class of stochastic dynamical systems with oscillating coefficients. We show that the homogenized systems faithfully capture the dynamical quantities such as mean exit time and escape probability. Exacting data from observations on the mean exit time (or escape probability) of the original systems, we try to fit the mean exit time (or escape probability) of the homogenized systems by least square method. In this way, we can accurately estimate the unknown parameter in the drift under appropriate assumptions. Furthermore, we conduct some numerical experiments to illustrate our method.

A correction to van Wyk’s model of the compressibility of fibrous materials

2021 ◽  
pp. 004051752110174
Author(s):  
Amit Rawal
Keyword(s):  
Least Square Method ◽  
Least Square ◽  
Fibrous Materials ◽  
Compression Direction ◽  
Volume Relationship ◽  
Pressure Volume Relationship ◽  
The Mean ◽  
Relationship Of ◽  
Analytical Approaches ◽  
The Continuum

van Wyk put forward a compression model of fibrous materials utilizing a library of analytical approaches, including the continuum mechanics, stereological, geometrical probability, least square method, and excluded area concept. In this letter, we wish to point out a key error noted in van Wyk’s work with the objective of correcting misconceptions that are held by the majority of us. Through this contribution, we question the “inverse cube” pressure-volume relationship of random fibrous materials. The pressure-volume relationship has been revisited by modifying the formulation of the mean length of a fiber element between consecutive contacts projected on the compression direction.

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