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Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and Deep neural network

International Journal of Minerals,Metallurgy and Materials 2024年第1期31卷 106-117页

作者： Xin Shao Qing Liu Zicheng Xin Jiangshan Zhang Tao Zhou Shaoshuai Li State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China Engineering Research Center of MES Technology for Iron&Steel Production Ministry of EducationBeijing 100083China Laiwu Iron and Steel Group Yinshan Section Steel Co. Ltd.Jinan 271104China

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and Deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

关键词： basic oxygen furnace oxygen consumption oxygen blowing time oxygen balance mechanism Deep neural network hybrid model

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A Deep neural network based surrogate model for damage identification in full-scale structures with incomplete noisy measurements

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 393-410页

作者： Tram BUI-NGOC Duy-Khuong LY Tam T TRUONG Chanachai THONGCHOM T.NGUYEN-THOI Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Faculty of Civil Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 70000Vietnam Department of Computer Science Aarhus UniversityAarhus 8000Denmark Thammasat University research unit in structural and foundation engineering Department of Civil EngineeringThammasat UniversityPathumthani 12120Thailand Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 70000Vietnam Thammasat School of Engineering Thammasat UniversityPathumthani 12120Thailand

The paper introduces a novel approach for detecting structural damage in full-scale structures using surrogate models generated from incomplete modal data and Deep neural networks(DNNs).A significant challenge in this field is the limited availability of measurement data for full-scale structures,which is addressed in this paper by generating data sets using a reduced finite element(FE)model constructed by SAP2000 software and the MATLAB programming loop.The surrogate models are trained using response data obtained from the monitored structure through a limited number of measurement devices.The proposed approach involves training a single surrogate model that can quickly predict the location and severity of damage for all potential scenarios.To achieve the most generalized surrogate model,the study explores different types of layers and hyperparameters of the training algorithm and employs state-of-the-art techniques to avoid overfitting and to accelerate the training process.The approach’s effectiveness,efficiency,and applicability are demonstrated by two numerical examples.The study also verifies the robustness of the proposed approach on data sets with sparse and noisy measured data.Overall,the proposed approach is a promising alternative to traditional approaches that rely on FE model updating and optimization algorithms,which can be computationally intensive.This approach also shows potential for broader applications in structural damage detection.

关键词： vibration-based damage detection Deep neural network full-scale structures finite element model updating noisy incomplete modal data

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Fast solution to the free return orbit's reachable domain of the manned lunar mission by Deep neural network

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Journal of Systems Engineering and Electronics 2024年第2期35卷 495-508页

作者： YANG Luyi LI Haiyang ZHANG Jin ZHU Yuehe College of Aerospace Science and Engineering National University of Defense TechnologyChangsha 410073China China Astronauts Research and Training Center Beijing 100094China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions Changsha 410073China

It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

关键词： manned lunar mission free return orbit reachable domain(RD) Deep neural network computation efficiency

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The Short-Term Prediction ofWind Power Based on the Convolutional Graph Attention Deep neural network

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Energy Engineering 2024年第2期121卷 359-376页

作者： Fan Xiao Xiong Ping Yeyang Li Yusen Xu Yiqun Kang Dan Liu Nianming Zhang State Grid Hubei Electric Power Research Institute Wuhan430077China College of Energy and Electrical Engineering Hohai UniversityNanjing210098China

The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale.Therefore,wind power forecasting plays a key role in improving the safety and economic benefits of the power grid.This paper proposes a wind power predicting method based on a convolutional graph attention Deep neural network with multi-wind farm data.Based on the graph attention network and attention mechanism,the method extracts spatial-temporal characteristics from the data of multiple wind farms.Then,combined with a Deep neural network,a convolutional graph attention Deep neural network model is constructed.Finally,the model is trained with the quantile regression loss function to achieve the wind power deterministic and probabilistic prediction based on multi-wind farm spatial-temporal data.A wind power dataset in the U.S.is taken as an example to demonstrate the efficacy of the proposed model.Compared with the selected baseline methods,the proposed model achieves the best prediction performance.The point prediction errors(i.e.,root mean square error(RMSE)and normalized mean absolute percentage error(NMAPE))are 0.304 MW and 1.177%,respectively.And the comprehensive performance of probabilistic prediction(i.e.,con-tinuously ranked probability score(CRPS))is 0.580.Thus,the significance of multi-wind farm data and spatial-temporal feature extraction module is self-evident.

关键词： Format wind power prediction Deep neural network graph attention network attention mechanism quantile regression

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Nuclear mass predictions based on a Deep neural network and finite-range droplet model(2012)

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Chinese Physics C 2024年第2期48卷 112-123页

作者：姚道驄梁豪兆李曉菁 Department of Physics The University of Hong KongHong Kong 999077China Department of Physics Graduate School of ScienceThe University of TokyoTokyo 113-0033Japan Interdisciplinary Theoretical and Mathematical Sciences Program(iTHEMS) RIKENWako 351-0198Japan

A neural network with two hidden layers is developed for nuclear mass prediction,based on the finiterange droplet model(FRDM12).Different hyperparameters,including the number of hidden units,choice of activation functions,initializers,and learning rates,are adjusted explicitly and systematically.The resulting mass predictions are achieved by averaging the predictions given by several different sets of hyperparameters with different regularizers and seed numbers.This can provide not only the average values of mass predictions but also reliable estimations in the mass prediction uncertainties.The overall root-mean-square deviations of nuclear mass are reduced from 0.603 MeV for the FRDM12 model to 0.200 MeV and 0.232 MeV for the training and validation sets,respectively.

关键词： nuclear mass machine learning Deep neural network

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FlowDNN:a physics-informed Deep neural network for fast and accurate flow prediction

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Frontiers of Information Technology & Electronic Engineering 2022年第2期23卷 207-219页

作者： Donglin CHEN Xiang GAO Chuanfu XU Siqi WANG Shizhao CHEN Jianbin FANG Zheng WANG College of Computer National University of Defense TechnologyChangsha 410073China State Key Laboratory of High Performance Computing National University of Defense TechnologyChangsha 410073China School of Computing University of LeedsLeeds LS29JTUK

For flow-related design optimization problems,e.g.,aircraft and automobile aerodynamic design,computational fluid dynamics(CFD)simulations are commonly used to predict flow fields and analyze performance.While important,CFD simulations are a resource-demanding and time-consuming iterative process.The expensive simulation overhead limits the opportunities for large design space exploration and prevents interactive design.In this paper,we propose Flow DNN,a novel Deep neural network(DNN)to efficiently learn flow representations from CFD results.Flow DNN saves computational time by directly predicting the expected flow fields based on given flow conditions and geometry shapes.Flow DNN is the first DNN that incorporates the underlying physical conservation laws of fluid dynamics with a carefully designed attention mechanism for steady flow prediction.This approach not only improves the prediction accuracy,but also preserves the physical consistency of the predicted flow fields,which is essential for CFD.Various metrics are derived to evaluate Flow DNN with respect to the whole flow fields or regions of interest(RoIs)(e.g.,boundary layers where flow quantities change rapidly).Experiments show that Flow DNN significantly outperforms alternative methods with faster inference and more accurate results.It speeds up a graphics processing unit(GPU)accelerated CFD solver by more than 14000×,while keeping the prediction error under 5%.

关键词： Deep neural network Flow prediction Attention mechanism Physics-informed loss

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Fast prediction of aerodynamic noise induced by the flow around a cylinder based on Deep neural network

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Chinese Physics B 2022年第6期31卷 470-475页

作者：孟海洋徐自翔杨京梁彬程建春 Key Laboratory of Modern Acoustics MOEInstitute of AcousticsDepartment of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China

Accurate and fast prediction of aerodynamic noise has always been a research hotspot in fluid mechanics and aeroacoustics.The conventional prediction methods based on numerical simulation often demand huge computational resources,which are difficult to balance between accuracy and efficiency.Here,we present a data-driven Deep neural network(DNN)method to realize fast aerodynamic noise prediction while maintaining accuracy.The proposed deep learning method can predict the spatial distributions of aerodynamic noise information under different working conditions.Based on the large eddy simulation turbulence model and the Ffowcs Williams-Hawkings acoustic analogy theory,a dataset composed of 1216samples is established.With reference to the deep learning method,a DNN framework is proposed to map the relationship between spatial coordinates,inlet velocity and overall sound pressure level.The root-mean-square-errors of prediction are below 0.82 dB in the test dataset,and the directivity of aerodynamic noise predicted by the DNN framework are basically consistent with the numerical simulation.This work paves a novel way for fast prediction of aerodynamic noise with high accuracy and has application potential in acoustic field prediction.

关键词： aerodynamic noise prediction Deep neural network aeroacoustics

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Evaluation of hydraulic fracturing of horizontal wells in tight reservoirs based on the Deep neural network with physical constraints

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Petroleum Science 2023年第2期20卷 1129-1141页

作者： Hong-Yan Qu Jian-Long Zhang Fu-Jian Zhou Yan Peng Zhe-Jun Pan Xin-Yao Wu State Key Laboratory of Petroleum Resources and Prospecting China University of PetroleumBeijing102249China School of Artificial Intelligence China University of PetroleumBeijing102249China Unconventional Oil and Gas Institute China University of PetroleumBeijing102249China School of Petroleum Engineering China University of PetroleumBeijing102249China Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development Ministry of EducationNortheast Petroleum UniversityDaqingHeilongjiang163318China Engineering Technology Research Institute PetroChina Xinjiang Oilfield CompanyKaramayXinjiang834000China

Accurate diagnosis of fracture geometry and conductivity is of great challenge due to the complex morphology of volumetric fracture network. In this study, a DNN (Deep neural network) model was proposed to predict fracture parameters for the evaluation of the fracturing effects. Field experience and the law of fracture volume conservation were incorporated as physical constraints to improve the prediction accuracy due to small amount of data. A combined neural network was adopted to input both static geological and dynamic fracturing data. The structure of the DNN was optimized and the model was validated through k-fold cross-validation. Results indicate that this DNN model is capable of predicting the fracture parameters accurately with a low relative error of under 10% and good generalization ability. The adoptions of the combined neural network, physical constraints, and k-fold cross-validation improve the model performance. Specifically, the root-mean-square error (RMSE) of the model decreases by 71.9% and 56% respectively with the combined neural network as the input model and the consideration of physical constraints. The mean square error (MRE) of fracture parameters reduces by 75% because the k-fold cross-validation improves the rationality of data set dividing. The model based on the DNN with physical constraints proposed in this study provides foundations for the optimization of fracturing design and improves the efficiency of fracture diagnosis in tight oil and gas reservoirs.

关键词： Evaluation of fracturing effects Tight reservoirs Physical constraints Deep neural network Horizontal wells Combined neural network

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A point cloud Deep neural network metamodel method for aerodynamic prediction

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Chinese Journal of Aeronautics 2023年第4期36卷 92-103页

作者： Fenfen XIONG Li ZHANG Xiao HU Chengkun REN School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Department of Mechanical Engineering Imperial College LondonLondon SW72AZUK

Aiming to reduce the high expense of 3-Dimensional(3D)aerodynamics numerical sim-ulations and overcome the limitations of the traditional parametric learning methods,a point cloud deep learning non-parametric metamodel method is proposed in this paper.The 3D geometric data,corresponding to the object boundaries,are chosen as point clouds and a deep learning neural net-work metamodel fed by the point clouds is further established based on the PointNet architecture.This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities.With the proposed aerodynamic metamodel approach,the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain,which can maintain the boundary smoothness and allow the network to detect small changes between geometries.Moreover,the point clouds are easily accessible from 3D sensors.The point cloud deep learning neural network,which employs re-sampling technique,the spatial transformer network and the fully connected layer,is developed to predict the aerodynamic char-acteristics of 3D geometry.The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing.The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based Deep neural network.

关键词： Aerodynamic prediction Deep neural network Metamodel Point clouds Robust shape optimization

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Improve Robustness and Accuracy of Deep neural network with L_(2,∞) Normalization

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Journal of Systems Science & Complexity 2023年第1期36卷 3-28页

作者： YU Lijia GAO Xiao-Shan Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

In this paper,the L_(2,∞)normalization of the weight matrices is used to enhance the robustness and accuracy of the Deep neural network(DNN)with Relu as activation functions.It is shown that the L_(2,∞)normalization leads to large dihedral angles between two adjacent faces of the DNN function graph and hence smoother DNN functions,which reduces over-fitting of the DNN.A global measure is proposed for the robustness of a classification DNN,which is the average radius of the maximal robust spheres with the training samples as centers.A lower bound for the robustness measure in terms of the L_(2,∞)norm is given.Finally,an upper bound for the Rademacher complexity of DNNs with L_(2,∞)normalization is given.An algorithm is given to train DNNs with the L_(2,∞)normalization and numerical experimental results are used to show that the L_(2,∞)normalization is effective in terms of improving the robustness and accuracy.

关键词： Deep neural network global robustness measure L_(2,∞)normalization over-fitting Rademacher complexity smooth DNN

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