scholarly journals A Generalized Stress Inversion Approach With Application to Residual Stress Estimation

2020 ◽  
Vol 87 (11) ◽  
Author(s):  
Mark J. Chen ◽  
Wilkins Aquino ◽  
Timothy F. Walsh ◽  
Phillip L. Reu ◽  
Kyle L. Johnson ◽  
...  
Keyword(s):  
Residual Stress ◽  
Large Scale ◽  
Optimization Methods ◽  
Image Correlation ◽  
Free Form ◽  
Inversion Method ◽  
Stress Inversion ◽  
Generalized Inversion ◽  
Stress Estimation ◽  
Matrix Free

Abstract We develop a generalized stress inversion technique (or the generalized inversion method) capable of recovering stresses in linear elastic bodies subjected to arbitrary cuts. Specifically, given a set of displacement measurements found experimentally from digital image correlation (DIC), we formulate a stress estimation inverse problem as a partial differential equation-constrained optimization problem. We use gradient-based optimization methods, and we accordingly derive the necessary gradient and Hessian information in a matrix-free form to allow for parallel, large-scale operations. By using a combination of finite elements, DIC, and a matrix-free optimization framework, the generalized inversion method can be used on any arbitrary geometry, provided that the DIC camera can view a sufficient part of the surface. We present numerical simulations and experiments, and we demonstrate that the generalized inversion method can be applied to estimate residual stress.

Global optimization with deep-learning-based acceleration surrogate for large-scale seismic acoustic-impedance inversion

Geophysics ◽  
2021 ◽  
pp. 1-74
Author(s):  
Zhaoqi Gao ◽  
Wei Yang ◽  
Yajun Tian ◽  
Chuang Li ◽  
Xiudi Jiang ◽  
...  
Keyword(s):  
Global Optimization ◽  
Deep Learning ◽  
Acoustic Impedance ◽  
Large Scale ◽  
Optimization Methods ◽  
Inversion Method ◽  
Local Minima ◽  
Practical Applications ◽  
Global Optimization Methods ◽  
Low Efficiency

Seismic acoustic-impedance (AI) inversion, which estimates the AI of the reservoir from seismic and other geophysical data, is a type of nonlinear inverse problem that faces the local minima issue during optimization. Without requiring an accurate initial model, global optimization methods have the ability to jump out of local minima and search for the optimal global solution. However, the low-efficiency nature of global optimization methods hinders their practical applications, especially in large-scale AI inversion problems (AI inversion with a large number of traces). We propose a new intelligent seismic AI inversion method based on global optimization and deep learning. In this method, global optimization is used to generate datasets for training a deep learning network and it is used to first accelerate and then surrogate global optimization. In other words, for large-scale seismic AI inversion, global optimization only inverts the AI model for a few traces, and the AI models of most traces are obtained by deep learning. The deep learning architecture that we used to map from seismic trace to its corresponding AI model is established based on U-Net. Because the time-consuming global optimization inversion procedure can be avoided for most traces, this method has a significant advantage over conventional global optimization methods in efficiency. To verify the effectiveness of the proposed method, we compare its performance with the conventional global optimization method on 3D synthetic and field data examples. Compared with the conventional method, the proposed method only needs about one-tenth of the computation time to build AI models with better accuracy.

scholarly journals Application of 3D DIC Displacement Field Measurement in Residual Stress Calculations

2017 ◽  
Vol 17 (3) ◽  
pp. 46-53 ◽  
Author(s):  
T. Brynk
Keyword(s):  
Residual Stress ◽  
Displacement Field ◽  
Inverse Method ◽  
Imaging System ◽  
Image Correlation ◽  
Stress Determination ◽  
Strain Measurements ◽  
Experimental Stand ◽  
Data Points ◽  
Stress Estimation

Abstract The knowledge of residual stress distribution is of great importance from the viewpoint of both, industrial and basal research. The most commonly utilized method of residual stress determination is based on strain measurements near the drilled holes of known geometry made by means of tensometric rosettes. An alternative to tensometers way of strain measurement is Digital Image Correlation (DIC). This optical method utilizes digital images registered during observed object deformation and delivers results in the form of displacement field maps consisting of hundreds or thousands of data points. Therefore, it is possible to deliver much more data in comparison to rosettes (only 3 or 6 tensometers, usually) and use them in the inverse method numeric procedure for residual stress calculations. In the paper the experimental stand consisting of micro driller and stereo imaging system for 3D DIC measurement and its application to residual stress estimation in prestrained steel samples are presented followed by obtained results discussion.

Data Registration Method Based on Three Dimensional Target

2011 ◽  
Vol 103 ◽  
pp. 87-91
Author(s):  
Xiao Ping Lou ◽  
Nai Guang Lv ◽  
Peng Sun ◽  
Yi Min Lin
Keyword(s):  
Large Scale ◽  
Vision System ◽  
Three Dimensional ◽  
Optimization Methods ◽  
Free Form ◽  
Registration Method ◽  
Scale Free ◽  
Optical Scanning ◽  
Data Registration ◽  
Processing Steps

Data registration method using special three dimensional target to track the structured light measurement system is discussed. Optical scanning device, tracking target and stereo vision system are integrated together to fulfill profile inspection of large-scale free-form surface objects without extra mark points. System architecture and processing steps are introduced and layout optimization methods of three dimensional target are illustrated. Experimental results are showed to evaluate the validity of the registration method and suggests are given to improve the accuracy of the system.

scholarly journals Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

2021 ◽  
Vol 11 (10) ◽  
pp. 4438
Author(s):  
Satyendra Singh ◽  
Manoj Fozdar ◽  
Hasmat Malik ◽  
Maria del Valle Fernández Moreno ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Methods ◽  
Bidding Strategy ◽  
Wind Speed Profile ◽  
Gravitational Search ◽  
Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

scholarly journals A New Hybrid Inversion Method for 2D Nuclear Magnetic Resonance Combining TSVD and Tikhonov Regularization

2021 ◽  
Vol 7 (2) ◽  
pp. 18
Author(s):  
Germana Landi ◽  
Fabiana Zama ◽  
Villiam Bortolotti
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Tikhonov Regularization ◽  
Large Scale ◽  
Regularization Parameter ◽  
High Sensitivity ◽  
Inversion Method ◽  
Nmr Relaxometry ◽  
Value Decomposition ◽  
Nuclear Magnetic

This paper is concerned with the reconstruction of relaxation time distributions in Nuclear Magnetic Resonance (NMR) relaxometry. This is a large-scale and ill-posed inverse problem with many potential applications in biology, medicine, chemistry, and other disciplines. However, the large amount of data and the consequently long inversion times, together with the high sensitivity of the solution to the value of the regularization parameter, still represent a major issue in the applicability of the NMR relaxometry. We present a method for two-dimensional data inversion (2DNMR) which combines Truncated Singular Value Decomposition and Tikhonov regularization in order to accelerate the inversion time and to reduce the sensitivity to the value of the regularization parameter. The Discrete Picard condition is used to jointly select the SVD truncation and Tikhonov regularization parameters. We evaluate the performance of the proposed method on both simulated and real NMR measurements.

scholarly journals Structured Two-Stream Attention Network for Video Question Answering

2019 ◽  
Vol 33 ◽  
pp. 6391-6398 ◽  
Author(s):  
Lianli Gao ◽  
Pengpeng Zeng ◽  
Jingkuan Song ◽  
Yuan-Fang Li ◽  
Wu Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Free Form ◽  
Image Region ◽  
Attention Network ◽  
Natural Language Question ◽  
Temporal Structures ◽  
Text Features ◽  
Video Input ◽  
Language Question

To date, visual question answering (VQA) (i.e., image QA and video QA) is still a holy grail in vision and language understanding, especially for video QA. Compared with image QA that focuses primarily on understanding the associations between image region-level details and corresponding questions, video QA requires a model to jointly reason across both spatial and long-range temporal structures of a video as well as text to provide an accurate answer. In this paper, we specifically tackle the problem of video QA by proposing a Structured Two-stream Attention network, namely STA, to answer a free-form or open-ended natural language question about the content of a given video. First, we infer rich longrange temporal structures in videos using our structured segment component and encode text features. Then, our structured two-stream attention component simultaneously localizes important visual instance, reduces the influence of background video and focuses on the relevant text. Finally, the structured two-stream fusion component incorporates different segments of query and video aware context representation and infers the answers. Experiments on the large-scale video QA dataset TGIF-QA show that our proposed method significantly surpasses the best counterpart (i.e., with one representation for the video input) by 13.0%, 13.5%, 11.0% and 0.3 for Action, Trans., TrameQA and Count tasks. It also outperforms the best competitor (i.e., with two representations) on the Action, Trans., TrameQA tasks by 4.1%, 4.7%, and 5.1%.

Residual stress estimation of a silicon carbide-Kovar joint

1991 ◽  
Vol 26 (11) ◽  
pp. 2887-2892 ◽  
Author(s):  
Toshihiro Yamada ◽  
Motohiro Satoh ◽  
Akiomi Kohno ◽  
Kazuaki Yokoi
Keyword(s):  
Residual Stress ◽  
Silicon Carbide ◽  
Stress Estimation
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