Data-driven seismic response prediction of structural components

2021 ◽  
pp. 875529302110533
Author(s):  
Huan Luo ◽  
Stephanie German Paal
Keyword(s):  
Experimental Data ◽  
Seismic Response ◽  
Computational Cost ◽  
Data Driven ◽  
Computational Time ◽  
Lateral Stiffness ◽  
Hysteretic Model ◽  
Structural Components ◽  
Rc Columns ◽  
Detailed Model

Lateral stiffness of structural components, such as reinforced concrete (RC) columns, plays an important role in resisting the lateral earthquake loads. The lateral stiffness relates the lateral force to the lateral deformation, having a critical effect on the accuracy of the lateral seismic response predictions. The classical methods (e.g. fiber beam–column model) to estimate the lateral stiffness require calculations from section, element, and structural levels, which is time-consuming. Moreover, the shear deformation and bond-slip effect may also need to be included to more accurately calculate the lateral stiffness, which further increases the modeling difficulties and the computational cost. To reduce the computational time and enhance the accuracy of the predictions, this article proposes a novel data-driven method to predict the laterally seismic response based on the estimated lateral stiffness. The proposed method integrates the machine learning (ML) approach with the hysteretic model, where ML is used to compute the parameters that govern the nonlinear properties of the lateral response of target structural components directly from a training set composed of experimental data (i.e. data-driven procedure) and the hysteretic model is used to directly output the lateral stiffness based on the computed parameters and then to perform the seismic analysis. We apply the proposed method to predict the lateral seismic response of various types of RC columns subjected to cyclic loading and ground motions. We present the detailed model formulation for the application, including the developments of a modified hysteretic model, a hybrid optimization algorithm, and two data-driven seismic response solvers. The results predicted by the proposed method are compared with those obtained by classical methods with the experimental data serving as the ground truth, showing that the proposed method significantly outperforms the classical methods in both generalized prediction capabilities and computational efficiency.

Application of Machine Learning in Turbulent Combustion for Aviation Gas Turbine Combustor Design

2021 ◽  
Author(s):  
Vishwas Verma ◽  
Kiran Manoharan ◽  
Jaydeep Basani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Flow Field ◽  
Gas Turbine ◽  
Computational Cost ◽  
Data Driven ◽  
Navier Stokes ◽  
Computational Time ◽  
Reacting Flow ◽  
Data Driven Approach

Abstract Numerical simulation of gas turbine combustors requires resolving a broad spectrum of length and time scales for accurate flow field and emission predictions. Reynold’s Averaged Navier Stokes (RANS) approach can generate solutions in few hours; however, it fails to produce accurate predictions for turbulent reacting flow field seen in general combustors. On the other hand, the Large Eddy Simulation (LES) approach can overcome this challenge, but it requires orders of magnitude higher computational cost. This limits designers to use the LES approach in combustor development cycles and prohibits them from using the same in numerical optimization. The current work tries to build an alternate approach using a data-driven method to generate fast and consistent results. In this work, deep learning (DL) dense neural network framework is used to improve the RANS solution accuracy using LES data as truth data. A supervised regression learning multilayer perceptron (MLP) neural network engine is developed. The machine learning (ML) engine developed in the present study can compute data with LES accuracy in 95% lesser computational time than performing LES simulations. The output of the ML engine shows good agreement with the trend of LES, which is entirely different from RANS, and to a reasonable extent, captures magnitudes of actual flow variables. However, it is recommended that the ML engine be trained using broad design space and physical laws along with a purely data-driven approach for better generalization.

scholarly journals Fast and effective pseudo transfer entropy for bivariate data-driven causal inference

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Riccardo Silini ◽  
Cristina Masoller
Keyword(s):  
Time Series ◽  
Causal Inference ◽  
Gaussian Approximation ◽  
Computational Cost ◽  
Transfer Entropy ◽  
Data Driven ◽  
Computational Time ◽  
Real World Data ◽  
Short Time Series ◽  
Short Time

AbstractIdentifying, from time series analysis, reliable indicators of causal relationships is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years many methods for data-driven causal inference have been proposed; however, their success largely depends on the characteristics of the system under investigation. Often, their data requirements, computational cost or number of parameters limit their applicability. Here we propose a computationally efficient measure for causality testing, which we refer to as pseudo transfer entropy (pTE), that we derive from the standard definition of transfer entropy (TE) by using a Gaussian approximation. We demonstrate the power of the pTE measure on simulated and on real-world data. In all cases we find that pTE returns results that are very similar to those returned by Granger causality (GC). Importantly, for short time series, pTE combined with time-shifted (T-S) surrogates for significance testing strongly reduces the computational cost with respect to the widely used iterative amplitude adjusted Fourier transform (IAAFT) surrogate testing. For example, for time series of 100 data points, pTE and T-S reduce the computational time by $$82\%$$ 82 % with respect to GC and IAAFT. We also show that pTE is robust against observational noise. Therefore, we argue that the causal inference approach proposed here will be extremely valuable when causality networks need to be inferred from the analysis of a large number of short time series.

scholarly journals The effect of mesh parameters on computational cost and results in simulation of milling in Inconel 718

2021 ◽  
Vol 43 ◽  
pp. e52363
Author(s):  
Felipe dos Anjos Rodrigues Campos ◽  
Felipe Chagas Rodrigues de Souza ◽  
Pedro Henrique Pires França ◽  
Leonardo Rosa Ribeiro da Silva
Keyword(s):  
Experimental Data ◽  
Finite Element ◽  
Inconel 718 ◽  
Computational Cost ◽  
Cutting Speed ◽  
Cutting Conditions ◽  
Computational Time ◽  
Machining Processes ◽  
Element Mesh ◽  
Minimum Element

The Finite Element Method analysis of machining processes has become a ubiquitous feature to the area, however, there sometimes occur considerable deviations between experimental and simulated results due to the inherent complexity of the process. The basis for such may conceivably be related to imprecisions in the material and friction modelling, besides improper setup of mesh parameters. Elements should be small enough to allow for the proper representation of the chip formation, but taking into account that the computational time increases accordingly with mesh downsizing. Simulations of the milling process of Inconel 718 were conducted using the software Thirdwave AdvantEdge under different cutting conditions for three different meshes. Power and temperature output were compared to experimental results, most of which were measured via Hall-effect sensors and thermographic camera, respectively. The tool cutting edge radius was found to be an important factor and was estimated using Scanning Electron Microscope images. The influence of the finite element mesh size was higher for harsher cutting conditions, with effects felt on machining power only. In this case, finer mesh produced results that showed a higher agreement with experimental data, but at higher computational cost as shown by analysis of elapsed processing time. Although errors higher than 40% were observed, power and temperature trends from simulations were always in accordance with that found in experimental tests. Comparisons with experimental data from other studies showed the errors tend to grow for higher feed and cutting speed, which indicates the constitutive model of the material is more adequate for softer machining conditions. Simulation time seemed to be exponentially proportional to the inverse of minimum element size, and measured values might serve as a reference for other users.

Evaluation of Parametric Models Dedicated to a Magnetorheological Actuator Including Uncertainty and Sensitivity Analyses

2019 ◽  
Vol 5 (4) ◽  
Author(s):  
Philippe C. Fernandes Teixeira ◽  
Núbia dos Santos Saad ◽  
Fabian Andres Lara-Molina ◽  
Aldemir Ap Cavalini ◽  
Valder Steffen
Keyword(s):  
Experimental Data ◽  
Computational Cost ◽  
Parametric Models ◽  
Sensitivity Analyses ◽  
Engineering Systems ◽  
Hysteretic Model ◽  
Mr Dampers ◽  
Interval Approach ◽  
The Difference ◽  
Parametric And Nonparametric Approaches

Abstract Semi-active actuators have been used in engineering systems for vibration control purposes. For instance, magnetorheological (MR) dampers are applied in support of vehicle seats and smart suspensions of bridges and buildings. Parametric and nonparametric approaches were developed to model MR actuators, in which the former presents well-established and representative models. In this context, this work aims at comparing the so-called Bingham, modified Bouc-Wen (BW), and hysteretic models dedicated to MR actuators. Typical inverse problems were solved to minimize the difference between the forces determined by using these models and experimental data. The obtained results demonstrated that the hysteretic model is better adapted to represent the considered MR actuator, presenting lower computational cost and easy implementation. Additionally, uncertainty and sensitivity analyses based on the interval approach were applied on the updated MR models aiming to determine the working envelopes associated with the most important parameters of the models.

Simulation of Film Cooling Heat Transfer and Simulation Improvement With a Modified DES Turbulence Model

2018 ◽  
Author(s):  
Feiyan Yu ◽  
Savas Yavuzkurt
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Film Cooling ◽  
Cooling System ◽  
Near Field ◽  
Density Ratio ◽  
Computational Cost ◽  
Computational Time ◽  
Spanwise Direction ◽  
Des Model

Modeling the heat transfer characteristics of the highly turbulent flow in gas turbine film cooling is important for better engineering solutions to the film cooling system design. URANS, LES, DES and modified DES models capability in simulating film cooling with a density ratio of 2.0 and blowing ratio of 1.0 are studied in this work. Detailed comparisons of simulation results with experimental data regarding the near-field and far-fields are made. For near field predictions, DES gives decent prediction with a 21.4 % deviation of centerline effectiveness, while LES and URANS have deviation of 33.6% and 51.2% compared to the experimental data. Despite good predictions for near field, DES under predicts the spanwise spreading of counter rotating vortex pair and temperature field, therefore it over predicts the centerline effectiveness in the far field. To compensate for this shortcoming of DES, the eddy viscosity in the spanwise direction is increased to enhance spanwise-diffusion of the cooling jets. The modified DES prediction of overall centerline effectiveness deviates 12.4% from experimental data, while LES, unmodified DES and URANS predictions deviate 10.8%, 31.9% and 46.9%. The modified DES model has adequate predictions of vortices evolutions which URANS modeling lacks and consumes significant less computational time than LES. It can be said that the modified DES model results in satisfactory film cooling modeling with a moderate computational cost and time.

scholarly journals Low-Computational-Cost Technique for Modeling Macro Fiber Composite Piezoelectric Actuators Using Finite Element Method

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4316
Author(s):  
Diaa Emad ◽  
Mohamed A. Fanni ◽  
Abdelfatah M. Mohamed ◽  
Shigeo Yoshida
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Fiber Composite ◽  
Computational Cost ◽  
Computational Time ◽  
Detailed Model ◽  
Computational Costs ◽  
Macro Fiber Composite ◽  
Low Computational Cost ◽  
Element Method

The large number of interdigitated electrodes (IDEs) in a macro fiber composite (MFC) piezoelectric actuator dictates using a very fine finite element (FE) mesh that requires extremely large computational costs, especially with a large number of actuators. The situation becomes infeasible if repeated finite element simulations are required, as in control tasks. In this paper, an efficient technique is proposed for modeling MFC using a finite element method. The proposed technique replaces the MFC actuator with an equivalent simple monolithic piezoceramic actuator using two electrodes only, which dramatically reduces the computational costs. The proposed technique was proven theoretically since it generates the same electric field, strain, and displacement as the physical MFC. Then, it was validated with the detailed FE model using the actual number of IDEs, as well as with experimental tests using triaxial rosette strain gauges. The computational costs for the simplified model compared with the detailed model were dramatically reduced by about 74% for memory usage, 99% for result file size, and 98.6% for computational time. Furthermore, the experimental results successfully verified the proposed technique with good consistency. To show the effectiveness of the proposed technique, it was used to simulate a morphing wing covered almost entirely by MFCs with low computational cost.

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.

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.

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