Efficient dynamic analysis of a whole aeroengine using identified nonlinear bearing models

2011 ◽  
Vol 226 (1) ◽  
pp. 66-81 ◽  
Author(s):  
K H Groves ◽  
P Bonello ◽  
P M Hai
Keyword(s):  
Dynamic Performance ◽  
Computational Cost ◽  
Parametric Identification ◽  
Frequency Domain Analysis ◽  
Reduced Form ◽  
Squeeze Film ◽  
Computational Time ◽  
Parameter Study ◽  
Accurate Identification ◽  
Non Linear

Essential to effective aeroengine design is the rapid simulation of the dynamic performance of a variety of engine and non-linear squeeze-film damper (SFD) bearing configurations. Using recently introduced non-linear solvers combined with non-parametric identification of high-accuracy bearing models it is possible to run full-engine rotordynamic simulations, in both the time and frequency domains, at a fraction of the previous computational cost. Using a novel reduced form of Chebyshev polynomial fits, efficient and accurate identification of the numerical solution to the two-dimensional Reynolds equation (RE) is achieved. The engine analysed is a twin-spool five-SFD engine model provided by a leading manufacturer. Whole-engine simulations obtained using Chebyshev-identified bearing models of the finite difference (FD) solution to the RE are compared with those obtained from the original FD bearing models. For both time and frequency domain analysis, the Chebyshev-identified bearing models are shown to mimic accurately and consistently the simulations obtained from the FD models in under 10 per cent of the computational time. An illustrative parameter study is performed to demonstrate the unparalleled capabilities of the combination of recently developed and novel techniques utilised in this paper.

Identification of Numerical Squeeze-Film Damper Models for Efficient Aeroengine Vibration Analysis

2010 ◽  
Author(s):  
Keir Groves ◽  
Philip Bonello
Keyword(s):  
Reynolds Equation ◽  
Numerical Solutions ◽  
Computational Cost ◽  
Industrial Applications ◽  
Dynamical Analysis ◽  
Squeeze Film ◽  
Accurate Identification ◽  
General Applicability ◽  
Squeeze Film Damper ◽  
Input Variables

The accuracy of the dynamic analysis of rotor systems incorporating squeeze film damper (SFD) bearings is typically limited by a trade-off between the capabilities and the computational cost of the bearing model used. Simplified solutions to the Reynolds equation such as short and long bearing models, and their variants, while providing rapid solution, significantly restrict the general applicability of the solutions. Numerical solutions to the Reynolds equation allow its solution in full form, with a variety of boundary conditions. Solving the Reynolds equation numerically imposes a significant computational cost on the dynamical analysis, rendering it computationally prohibitive for industrial applications. To surmount this problem, the present paper develops the use of Chebyshev polynomial fits to mimic the hydrodynamic relationship obtained through the finite difference (FD) solution of the incompressible Reynolds equation. In order to overcome limitations of previous interpolation approaches, the proposed method has three features: (i) a reduced number of input variables with a clearly defined finite range; (ii) interpolation of the pressure rather than the bearing force; (iii) division of the pressure function into its static and dynamic parts. These manipulations allow for efficient and accurate identification in the presence of cavitation and the presence of the groove, feed-ports and end-plate seals. The ability of Chebyshev polynomials to rapidly reproduce results obtained using FD routines is demonstrated. The advanced bearing models developed are proven to give more accurate results than alternative analytical bearing models when compared to experimental results.

Reduced Order Modelling of Non-Linear Squeeze Film Dynamics

1992 ◽  
Vol 206 (4) ◽  
pp. 225-238 ◽  
Author(s):  
H Esmonde ◽  
J A Fitzpatrick ◽  
H J Rice ◽  
F Axisa
Keyword(s):  
Experimental Data ◽  
Numerical Simulation ◽  
Film Thickness ◽  
Dynamic Performance ◽  
Random Excitation ◽  
Squeeze Film ◽  
Linear Component ◽  
Fluid Viscosity ◽  
Reduced Order ◽  
Non Linear

The contribution of non-linear forces to the dynamic performance of liquid squeeze films is examined as a function of film thickness and fluid viscosity. The linear and non-linear component forces are identified from experimental data for two thicknesses at viscosities of 1.0, 13.19 and 26.87 cS using procedures based on random excitation. A procedure by which terms that contribute little to the overall dynamics of the model can be eliminated is developed and applied to the experimental data. This leads to a reduction in the complexity of the model for particular systems by removing those terms that have little significance. The technique is then validated by examining the relative contributions of the non-linear forces using a numerical simulation.

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

The prediction of vehicle vibration transmitted to the occupant using a modular transfer matrix

2021 ◽  
pp. 107754632199759
Author(s):  
Jianchun Yao ◽  
Mohammad Fard ◽  
John L Davy ◽  
Kazuhito Kato
Keyword(s):  
Finite Element ◽  
Transfer Matrix ◽  
Dynamic Performance ◽  
Complex Structure ◽  
Vehicle Body ◽  
Computational Time ◽  
Accurate Estimation ◽  
Finite Element Models ◽  
Transfer Matrices ◽  
Body Vibration

Industry is moving towards more data-oriented design and analyses to solve complex analytical problems. Solving complex and large finite element models is still challenging and requires high computational time and resources. Here, a modular method is presented to predict the transmission of vehicle body vibration to the occupants’ body by combining the numerical transfer matrices of the subsystems. The transfer matrices of the subsystems are presented in the form of data which is sourced from either physical tests or finite element models. The structural dynamics of the vehicle body is represented using a transfer matrix at each of the seat mounting points in three triaxial (X–Y–Z) orientations. The proposed method provides an accurate estimation of the transmission of the vehicle body vibration to the seat frame and the seated occupant. This method allows the combination of conventional finite element analytical model data and the experimental data of subsystems to accurately predict the dynamic performance of the complex structure. The numerical transfer matrices can also be the subject of machine learning for various applications such as for the prediction of the vibration discomfort of the occupant with different seat and foam designs and with different physical characteristics of the occupant body.

scholarly journals A divide-and-conquer algorithm for quantum state preparation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Israel F. Araujo ◽  
Daniel K. Park ◽  
Francesco Petruccione ◽  
Adenilton J. da Silva
Keyword(s):  
Computational Cost ◽  
Quantum Circuit ◽  
Divide And Conquer ◽  
Computational Time ◽  
Quantum Computers ◽  
Dimensional Vector ◽  
Quantum Devices ◽  
Divide And Conquer Algorithm ◽  
Quantum State Preparation ◽  
Quantum Device

AbstractAdvantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

scholarly journals A Fast Estimator for Binary Choice Models with Spatial, Temporal, and Spatio-Temporal Interdependence

2021 ◽  
pp. 1-7
Author(s):  
Julian Wucherpfennig ◽  
Aya Kachi ◽  
Nils-Christian Bormann ◽  
Philipp Hunziker
Keyword(s):  
Computational Cost ◽  
Choice Models ◽  
Reduced Form ◽  
Binary Choice ◽  
Monte Carlo Experiments ◽  
Binary Choice Models ◽  
Spatio Temporal ◽  
Computationally Intensive ◽  
Pseudo Maximum Likelihood ◽  
Parameter Values

Abstract Binary outcome models are frequently used in the social sciences and economics. However, such models are difficult to estimate with interdependent data structures, including spatial, temporal, and spatio-temporal autocorrelation because jointly determined error terms in the reduced-form specification are generally analytically intractable. To deal with this problem, simulation-based approaches have been proposed. However, these approaches (i) are computationally intensive and impractical for sizable datasets commonly used in contemporary research, and (ii) rarely address temporal interdependence. As a way forward, we demonstrate how to reduce the computational burden significantly by (i) introducing analytically-tractable pseudo maximum likelihood estimators for latent binary choice models that exhibit interdependence across space and time and by (ii) proposing an implementation strategy that increases computational efficiency considerably. Monte Carlo experiments show that our estimators recover the parameter values as good as commonly used estimation alternatives and require only a fraction of the computational cost.

scholarly journals Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

2021 ◽  
Vol 11 (2) ◽  
pp. 813
Author(s):  
Shuai Teng ◽  
Zongchao Liu ◽  
Gongfa Chen ◽  
Li Cheng
Keyword(s):  
Feature Extraction ◽  
Crack Detection ◽  
Computational Cost ◽  
Concrete Structures ◽  
Detection Algorithm ◽  
Computational Time ◽  
Image Size ◽  
Important Indicator ◽  
Feature Extractor ◽  
Model Training

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.

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