scholarly journals Study of the Cone-Shaped Drogue for a Deep-Towed Multi-Channel Seismic Survey System Based on Data-Driven Simulations

2021 ◽  
Vol 9 (12) ◽  
pp. 1367
Author(s):  
Xiangqian Zhu ◽  
Mingqi Sun ◽  
Tianhao He ◽  
Kaiben Yu ◽  
Le Zong ◽  
...  
Keyword(s):  
Neural Network ◽  
Surrogate Model ◽  
Polynomial Regression ◽  
Angular Acceleration ◽  
Data Driven ◽  
Seismic Survey ◽  
Hydrodynamic Characteristics ◽  
Rotational Angle ◽  
Sample Data ◽  
Survey System

A drogue is used to stabilise and straighten seismic arrays so that seismic waves can be well-received. To embed the effect of a cone-shaped drogue into the numerical modelling of the deep-towed seismic survey system, one surrogate model that maps the relationship between the hydrodynamic characteristics of the drogue and towing conditions was obtained based on data-driven simulations. The sample data were obtained by co-simulation of the commercial software RecurDyn and Particleworks, and the modelling parameters were verified by physical experiments. According to the Morison formula, the rotational angle, angular velocity, angular acceleration, towing speed, and towing acceleration of the drogue were selected as the design variables and drag forces and aligning torque were selected as the research objectives. The sample data of more than 8500 sets were obtained from virtual manoeuvres. Subsequently, both polynomial and neural network regression algorithms were used to study these data. Finally, analysis results show that the surrogate model obtained by machine learning has good performance in predicting research objectives. The results also reveal that the neural network regression algorithm is superior to the polynomial regression algorithm, its largest error of mean square is less than 0.8 (N2/N2 mm2), and its R-squared is close to 1.

scholarly journals A Stochastic FE2 Data-Driven Method for Nonlinear Multiscale Modeling

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2875
Author(s):  
Xiaoxin Lu ◽  
Julien Yvonnet ◽  
Leonidas Papadopoulos ◽  
Ioannis Kalogeris ◽  
Vissarion Papadopoulos
Keyword(s):  
Neural Network ◽  
Surrogate Model ◽  
Probabilistic Models ◽  
Constitutive Law ◽  
Data Driven ◽  
Computational Time ◽  
Hybrid Neural Network ◽  
The Neural Network ◽  
Heterogeneous Structures ◽  
Stochastic Data

A stochastic data-driven multilevel finite-element (FE2) method is introduced for random nonlinear multiscale calculations. A hybrid neural-network–interpolation (NN–I) scheme is proposed to construct a surrogate model of the macroscopic nonlinear constitutive law from representative-volume-element calculations, whose results are used as input data. Then, a FE2 method replacing the nonlinear multiscale calculations by the NN–I is developed. The NN–I scheme improved the accuracy of the neural-network surrogate model when insufficient data were available. Due to the achieved reduction in computational time, which was several orders of magnitude less than that to direct FE2, the use of such a machine-learning method is demonstrated for performing Monte Carlo simulations in nonlinear heterogeneous structures and propagating uncertainties in this context, and the identification of probabilistic models at the macroscale on some quantities of interest. Applications to nonlinear electric conduction in graphene–polymer composites are presented.

scholarly journals Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model

2020 ◽  
Vol 9 (1) ◽  
pp. 18
Author(s):  
Jing-Wei Jiang ◽  
Yang Yang ◽  
Tong-Wei Ren ◽  
Fei Wang ◽  
Wei-Xi Huang
Keyword(s):  
Neural Network ◽  
Discrete Spectrum ◽  
Surrogate Model ◽  
Large Scale ◽  
Low Frequency ◽  
Data Driven ◽  
Prediction Errors ◽  
Nsga Ii ◽  
Thrust Coefficient ◽  
Evolutionary Optimisation

For practical problems with non-convex, large-scale and highly constrained characteristics, evolutionary optimisation algorithms are widely used. However, advanced data-driven methods have yet to be comprehensively applied in related fields. In this study, a surrogate model combined with the Non-dominated Sorting Genetic Algorithm II-Differential Evolution (NSGA-II-DE) is applied to reduce the low-frequency Discrete-Spectrum (DS) force of propeller noise. Reduction of this force has drawn a lot of attention as it is the primary signal used in the sonar-based detection and identification of ships. In the present study, a surrogate model is proposed based on a trained Back-Propagation (BP) fully connected neural network, which improves the optimisation efficiency. The neural network is designed by analysing the depth and width of the hidden layers. The results indicate that a four-layer neural network with 64, 128, 256 and 64 nodes in each layer, respectively, exhibits the highest prediction accuracy. The prediction errors for the first order of DST, second order of DST and the thrust coefficient are only 0.21%, 5.71% and 0.01%, respectively. Data-Driven Evolutionary Optimisation (DDEO) is applied to a standard high-skew propeller to reduce DST. DDEO and a Traditional Evolutionary Optimisation Method (TEOM) obtain the same optimisation results, while the time cost of DDEO is only 0.68% that of the TEOM. Thus, the proposed DDEO is applicable to complex engineering problems in various fields.

Reconstructing the conductivity profile of a graphite block using inductance spectroscopy with data-driven techniques

2021 ◽  
Vol 63 (2) ◽  
pp. 82-87
Author(s):  
J Hampton ◽  
H Tesfalem ◽  
A Fletcher ◽  
A Peyton ◽  
M Brown
Keyword(s):  
Neural Network ◽  
Electrical Conductivity ◽  
Convolutional Neural Network ◽  
Depth Profile ◽  
Test Data ◽  
Polynomial Regression ◽  
Data Driven ◽  
Radial Depth ◽  
Ill Posed ◽  
Reactor Type

The radial depth profile of the electrical conductivity of the graphite channels in the UK's advanced gas-cooled reactors (AGRs) can be reconstructed and estimated by solving a non-linear optimisation problem using the mutual inductance spectra of a set of coils. This process is slow, as it requires many iterations of a forward solver. Alternatively, a data-driven approach can be used to provide an initial estimate for the optimisation algorithm, reducing the amount of time it takes to solve the ill-posed inverse problem. Two data-driven approaches are compared: multi-variable polynomial regression (MVPR) and a convolutional neural network (CNN). The training data are generated using a finite element (FE) model and superimposed on a noise floor in the interval [20, 60] dB of the weakest amplitude point in the corresponding spectrum. A total of 5000 simulated datasets are generated for training. The results on smoothed test data show that the two models have a comparable mean percentage error norm of 17.8% for the convolutional neural network and 17.3% for multivariable polynomial regression. A further 500 unsmoothed profiles are tested in order to assess the performance of each algorithm on conductivity distributions where the conductivity of each layer is independent of another. The performance of both algorithms is then assessed on reactor-type test data. The results show that the two data-driven algorithms have a comparable performance when estimating the electrical conductivity depth profile of a typical reactor-type distribution, as well as vast deviations. More generally, it is thought that data-driven approaches for depth profiling of some electromagnetic quantity have the potential to be applied to other ill-posed inverse problems where speed is a priority.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals Optimising Performance for NB-IoT UE Devices through Data Driven Models

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Omar Nassef ◽  
Toktam Mahmoodi ◽  
Foivos Michelinakis ◽  
Kashif Mahmood ◽  
Ahmed Elmokashfi
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Narrow Band ◽  
Learning Algorithm ◽  
Base Station ◽  
User Equipment ◽  
Data Driven ◽  
Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

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