Evaluation and Optimization of Trimaran Configurations Using Deep Neural Network

2019 ◽  
Author(s):  
Dongchi Yu ◽  
Lu Wang ◽  
Qian Zhong ◽  
Ronald W. Yeung
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Transportation Cost ◽  
Computational Method ◽  
Design Parameters ◽  
Minimal Amount ◽  
Computational Results ◽  
Typical Situation ◽  
Water Transportation ◽  
Calm Water

Abstract To determine the optimal trimaran configuration for best calm-water transportation efficiency, a Deep Neural Network (DNN) is trained with sufficient computational results provided by, as an example, an in-house developed potential-flow code called Multi-hull Simple-source Panel Method (MSPM). Even though the computational method is extremely efficient in accurately establishing the mapping relation between the key design parameters governing the trimaran configuration problem and the resulting calm-water transportation cost, the modeling efforts are non-trivial since the number of geometric and configuration parameters in a typical situation is large. In this work, we demonstrate how the “Big Data” of computational results can be effectively utilized in training a DNN. An optimal trimaran configuration solution within a specified design space, subject to realistic range constraints, can be quickly determined in a minimal amount of time with the DNN. A demonstrative case study is provided for illustration.

scholarly journals DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 354
Author(s):  
Lu Zhang ◽  
Xinyi Qin ◽  
Min Liu ◽  
Ziwei Xu ◽  
Guangzhong Liu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Area Under The Curve ◽  
Nucleotide Composition ◽  
Computational Method ◽  
Feature Subset ◽  
Accurate Identification ◽  
Genome Wide ◽  
Dinucleotide Composition ◽  
Optimal Feature Subset

As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurate identification of m6A sites in genome-wide is vital. As the traditional experimental methods are time-consuming and cost-prohibitive, it is necessary to design a more efficient computational method to detect the m6A sites. In this study, we propose a novel cross-species computational method DNN-m6A based on the deep neural network (DNN) to identify m6A sites in multiple tissues of human, mouse and rat. Firstly, binary encoding (BE), tri-nucleotide composition (TNC), enhanced nucleic acid composition (ENAC), K-spaced nucleotide pair frequencies (KSNPFs), nucleotide chemical property (NCP), pseudo dinucleotide composition (PseDNC), position-specific nucleotide propensity (PSNP) and position-specific dinucleotide propensity (PSDP) are employed to extract RNA sequence features which are subsequently fused to construct the initial feature vector set. Secondly, we use elastic net to eliminate redundant features while building the optimal feature subset. Finally, the hyper-parameters of DNN are tuned with Bayesian hyper-parameter optimization based on the selected feature subset. The five-fold cross-validation test on training datasets show that the proposed DNN-m6A method outperformed the state-of-the-art method for predicting m6A sites, with an accuracy (ACC) of 73.58%–83.38% and an area under the curve (AUC) of 81.39%–91.04%. Furthermore, the independent datasets achieved an ACC of 72.95%–83.04% and an AUC of 80.79%–91.09%, which shows an excellent generalization ability of our proposed method.

Prediction of Non-Linear Mechanical Behavior With Deep Neural Network: Application on Low Pressure Turbine Disc

2020 ◽  
Author(s):  
Yuan Jin ◽  
Weichen Li ◽  
Zheyi Yang ◽  
Olivier Jung
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Displacement Field ◽  
Deep Neural Network ◽  
Low Pressure ◽  
Constitutive Law ◽  
Design Parameters ◽  
Low Pressure Turbine ◽  
Turbine Disc ◽  
Nonlinear Constitutive Law

Abstract Thanks to the increase of computational capacity and the diversification of computational means, deep learning techniques have shown great successes in learning representations from data in the past decade. Following this trend, efforts have been made in the literature to apply Deep Neural Network (DNN) as surrogate model. Common practice consists in utilizing a single DNN to predict a certain physical property given input design parameters, and the DNN is trained by corresponding simulation results. However, most of the complex high-fidelity simulations involve nonlinear physical laws, e.g. elasto-plasticity, which cannot be explicitly depicted by the applied single DNN model. In the present work, static mechanical simulation with nonlinear constitutive law is addressed with a novel approach in a deep learning framework. We approximate the displacement and the nonlinear constitutive law by two deep neural networks. The first DNN acts as a prior on the unknown displacement field, while the second network aims at describing the nonlinear strain-stress relationship. The dependence of the strainstress relationship on the strain level is taken into consideration by taking the first order derivative with respect to spatial coordinates of the first DNN as an input of the second network. A new loss model combining the error in displacement field prediction and constitutive law description is proposed to train the two DNNs together. We demonstrate the effectiveness of the proposed framework on a low pressure turbine disc design problem.

An Analysis of Rigid Sidewall Surface Effect Craft for High-Speed Personnel Transportation

1970 ◽  
Vol 7 (01) ◽  
pp. 55-68
Author(s):  
Eugene R. Miller
Keyword(s):  
High Speed ◽  
Surface Effect ◽  
Transportation Cost ◽  
Total Power ◽  
Design Parameters ◽  
Economic Criterion ◽  
Calm Water ◽  
And Performance ◽  
Commercial Applications ◽  
Sidewall Surface

A number of commercial applications have been proposed for rigid sidewall surface effect craft. The transport of crews to offshore operations is an application which is well-suited to the immediate use of moderately sized craft of this type. Because the crews are paid while they are in transit, high speeds are required to minimize the total transportation costs. The characteristics and performance of rigid sidewall surface effect craft suitable for crew transport operations are developed. The major design parameters studied include pay-load, total power, and machinery type. Performance estimates are made for operations in both calm water and waves. An economic model is developed to simulate crewboat operations. Cost estimates are based on current technology and price levels. The total unit transportation cost is used as the economic criterion in the determination of the relative merit of various craft. For the purpose of comparison the characteristics and costs of planing hull crewboats for the same mission are developed. It is concluded that rigid sidewall surface effect craft have the potential of being economically superior to planing boats for crew transport operations.

scholarly journals Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7807
Author(s):  
Muhammad Saeed ◽  
Abdallah S. Berrouk ◽  
Burhani M. Burhani ◽  
Ahmed M. Alatyar ◽  
Yasser F. Al Wahedi
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Design Parameters ◽  
Speed Ratio ◽  
Inlet Flow ◽  
Flow Angle ◽  
Multifaceted Approach ◽  
Design And Optimization ◽  
Radial Turbine ◽  
Turbine Design

Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO2-BC). At the same time, the turbine design and optimization process for the sCO2-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO2 power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO2 are implemented by coupling the code with NIST’s Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio (rs4r3), hub ratio (rs4r3), speed ratio (νs) and inlet flow angle (α3) and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine’s rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO2-turbine performance parameters are most sensitive to the design parameter speed ratio (νs), followed by inlet flow angle (α3), and are least receptive to shroud ratio (rs4r3). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design and optimization phase and can be beneficial to achieve realistic and efficient design to the turbine for sCO2-BC.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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