检索结果-内蒙古大学图书馆

rapid prediction of flow and concentration fields in solid-liquid suspensions of slurry electrolysis tanks

International Journal of Minerals,Metallurgy and Materials 2024年第9期31卷 2006-2016页

作者： Tingting Lu Kang Li Hongliang Zhao Wei Wang Zhenhao Zhou Xiaoyi Cai Fengqin Liu State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China State Key Laboratory of Intelligent Optimized Manufacturing in Mining&Metallurgy Process Beijing 102628China School of Materials Science and Engineering Lanzhou University of TechnologyLanzhou 730050China School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing 100083China

Slurry electrolysis(SE),as a hydrometallurgical process,has the characteristic of a multitank series connection,which leads to various stirring conditions and a complex solid suspension *** computational fluid dynamics(CFD),which requires high computing resources,and a combination with machine learning was proposed to construct a rapid prediction model for the liquid flow and solid concentration fields in a SE *** scientific selection of calculation samples via orthogonal experiments,a comprehensive dataset covering a wide range of conditions was established while effectively reducing the number of simulations and providing reasonable weights for each ***,a prediction model of the SE tank was constructed using the K-nearest neighbor *** results show that with the increase in levels of orthogonal experiments,the prediction accuracy of the model improved *** model established with four factors and nine levels can accurately predict the flow and concentration fields,and the regression coefficients of average velocity and solid concentration were 0.926 and 0.937,*** with traditional CFD,the response time of field information prediction in this model was reduced from 75 h to 20 s,which solves the problem of serious lag in CFD applied alone to actual production and meets real-time production control requirements.

关键词： slurry electrolysis solid-liquid suspension computational fluid dynamics K-nearest neighbor algorithm rapid prediction

来源：评论

学校读者我要写书评

暂无评论

rapid prediction model of terahertz transmission in hypersonic plasma sheath under different flight speeds for different vehicle types

引用

JOURNAL OF PHYSICS D-APPLIED PHYSICS 2025年第8期58卷 085208-085208页

作者： Guo, Shiping Cen, Haicheng Ouyang, Wenchong Liu, Donglin Wu, Zhengwei Xi An Jiao Tong Univ Sch Phys MOE Key Lab Nonequilibrium Synth & Modulat Condens Xian 710049 Peoples R China Univ Sci & Technol China Sch Earth & Space Sci Deep Space Explorat Lab Hefei 230026 Peoples R China Univ Sci & Technol China Sch Nucl Sci & Technol Hefei 230026 Peoples R China XiDian Univ Key Lab Informat & Struct Efficiency Extreme Envir Minist Educ China Xian 710071 Peoples R China

This work clarifies the systematic relationship and difference mechanism between flight speed, plasma sheath and terahertz transmission under different vehicle types by joint simulation model of hypersonic plasma flow and improved scattering matrix method. Significant differences in plasma sheath and terahertz transmission characteristics are observed in different vehicle types. Radio Attenuation Measurement C (RAM C) vehicle has a larger collision frequency and sheath thickness, resulting in higher terahertz attenuation than Hypersonic International Flight Research Experimentation (HIFiRE) vehicle. With the increase of flight speed, electron density, collision frequency and terahertz attenuation of the two different types of vehicles all show a significant increase, and the sheath thickness shows an opposite trend. However, the impact of flight speed on HIFiRE vehicle is much smaller than that on RAM C vehicle, which means that flight parameters have higher control accuracy for RAM C vehicle. On this basis, the systematic relationship between plasma sheath distribution and flight speed is further determined, and a rapid prediction model for terahertz transmission attenuation of different types of vehicle is developed based on a large amount of plasma sheath data. The rapid prediction model greatly reduces the calculation time and resources compared with traditional numerical methods, and its related prediction coefficients show significant differences on different vehicle types.

关键词： plasma sheath terahertz wave flight speed different vehicle types rapid prediction

来源：评论

学校读者我要写书评

暂无评论

rapid prediction method of ZIF-8 immobilized Candida rugosa lipase activity by near-infrared spectroscopy

引用

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023年第1期302卷 123072页

作者： Chen, Shiyi Ma, Mengli Peng, Juan He, Xiaogang Wang, Qian Chu, Ganghui Kashi Univ Coll Chem & Environm Sci Xinjiang Lab Native Med & Edible Plant Resources C Kashi 844000 Peoples R China

Candida rugosa lipase (CRL, EC3.1.1.3) is one of the main enzymes synthesizing esters, and ZIF-8 was chosen as an immobilization carrier for lipase. Enzyme activity testing often requires expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on nearinfrared spectroscopy (NIRs) was developed for predicting CRL/ZIF-8 enzyme activity. The absorbance of the immobilized enzyme catalytic system was evaluated using UV-Vis spectroscopy to investigate the amount of CRL/ZIF-8 enzyme activity. The powdered samples' near-infrared spectra were obtained. The sample's enzyme activity data were linked with each sample's original NIR spectra to establish the NIR model. A partial least squares (PLS) model of immobilized enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The experiments were completed within 48 h to eliminate inaccuracies between the reduction in enzyme activity with increasing laying-aside time throughout the test and the NIRs modeling. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The nearinfrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's root-mean-square error of cross-validation (RMSECV) was 0.368 U/g, the correlation coefficient of calibration set (R_cv) value was 0.943, the root-mean-square error of prediction (RMSEP) set was 0.414 U/g, the correlation coefficient of validation set (R) value was 0.952, and the ratio of prediction to deviation (RPD) was 3.0. The model demonstrates that the fitting relationship between the predicted and the reference enzyme activity value of the NIRs is satisfactory. The findings revealed a strong

关键词： Candida rugosa lipase Metal-Organic Frameworks Immobilized enzyme activity Near-infrared spectroscopy CARS variable screening rapid prediction

来源：评论

学校读者我要写书评

暂无评论

rapid prediction of the In Situ Pyrolysis Performance of Tar-Rich Coal Using the POD Method

引用

PROCESSES 2023年第10期11卷 2994页

作者： Wang, Zhendong Ye, Qianhao Li, Mingjie Cheng, Xiangqiang Wei, Jinjia Yang, Fu Duan, Zhonghui Shaanxi Prov Coal Geol Grp Co Ltd Xian 710021 Peoples R China Minist Nat Resources Key Lab Coal Resources Explorat & Comprehens Utili Xian 710054 Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Peoples R China

In this paper, a POD reduced-order interpolation model for solving the in situ pyrolysis process of tar-rich coal is employed to predict the flow and heat transfer performance in the porous media region so as to save computational resources and realize fast calculations. Numerical simulation using the finite volume method (FVM) is firstly used to obtain sample data, based on the samples through the primary function and spectral coefficients of the solutions. The physical field information and parameter distribution under different conditions of inlet temperature, inlet velocity and permeability are predicted. The results are compared with those of FVM to verify the accuracy of the calculated results. The relative mean deviation (RME) of the results of the POD prediction of each parameter for each working condition was synthesized to be no more than 5%. The performance of in situ pyrolysis of tar-rich coal is then investigated, and the oil and gas production are predicted. As the inlet velocity increases from 0.3 m/s to 0.9 m/s, the fraction of high-quality oil and gas production reaches 0.47 and then decreases to 0.38. Increasing the inlet temperature and permeability has a negative effect on the fraction of high-quality hydrocarbon production, after which the quality fraction of high-quality oil and gas dropped sharply to about 0.22. Porosity has a positive impact on the oil and gas production. When the porosity reaches 0.3, the quality fraction of high-quality oil and gas can reach 0.27.

关键词： POD rapid prediction tar-rich coal in situ pyrolysis

来源：评论

学校读者我要写书评

暂无评论

A machine learning and CFD based approach for fouling rapid prediction in shell-and-tube heat exchanger

引用

NUCLEAR ENGINEERING AND DESIGN 2025年 432卷

作者： He, Shaopeng Ye, Yibo Wang, Mingjun Zhang, Jing Tian, Wenxi Qiu, Suizheng Su, G. H. Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China

rapid prediction of fouling and heat transfer performance over the full life cycle of an industrial heat exchanger is critical. Establishing a rapid-prediction model of the heat exchanger to realize full life cycle monitoring is an effective approach to assistant its operation, decontamination, and maintenance. With the advancement of machine learning, rapid prediction of heat exchanger fouling level used deep learning methods has become a research hotspot. Based on machine learning and CFD methods, a rapid fouling prediction surrogate model for the industrial heat exchanger was proposed on the example of steam generators in nuclear power plants. The mathematical model of fouling layer thermal resistance was developed. The 3D numerical simulation under different fouling levels was carried out. The high fidelity CFD simulation database was built, and four deep learning models (BPNN, PSO-BPNN, CNN, RBFNN) were adopted. The fouling thermal resistance of SG could be predicted rapidly according to operating parameters. The root-mean-square error of the four neural networks is less than 10(-7) K/W. BPNN with PSO algorithm achieves the best balance between calculation time and prediction accuracy. The anti-noise performance of the prediction surrogate model was evaluated at different noise level of actual operating parameters. When the noise level is 5 %, predicted R-2 remains at 0.7615 and the mean relative error is still less than 15 %. The low-cost and fast prediction surrogate model developed in this paper can provide an effective reference for the maintenance and decontamination of industrial heat exchanger.

关键词： Heat exchanger Fouling deposition rapid prediction Machine learning

来源：评论

学校读者我要写书评

暂无评论

A Fourier neural operator-based method for rapid prediction of 3D indoor airflow dynamics

引用

BUILDING SIMULATION 2025年第6期18卷 1435-1451页

作者： Ding, Xiaoxiao Zhang, Haotian Zhang, Weirong Zhang, Weijia Xuan, Yingli Beijing Univ Technol Key Lab Green Built Environm & Energy Efficient Te Beijing 100124 Peoples R China Tokyo Polytech Univ Dept Architecture Tokyo Japan

Indoor airflow distribution significantly influences temperature regulation, humidity control, and pollutant dispersion, directly impacting thermal comfort and occupant health. Accurate and efficient prediction of airflow fields is essential for optimizing ventilation systems and enabling real-time control. However, existing computational approaches for dynamic ventilation are computationally intensive and have limited generalization capabilities. This study leverages the Fourier neural operator (FNO), a method rooted in operator learning and Fourier transform principles, to develop a three-dimensional (3D) airflow simulation model capable of predicting velocity and its components. The model was trained using 200 s of sinusoidal ventilation data (amplitude: 0.4) and evaluated under diverse air supply patterns, including sinusoidal (amplitude: 0.8), intermittent, and stepwise periodic ventilation. Additionally, the model's performance was assessed with low-resolution training data and further tested for recursive prediction accuracy. Results reveal that the FNO method achieves high accuracy, with a mean square error of 9.906 x 10-5 for sinusoidal amplitude 0.8 and 4.004 x 10-5 over 400 time steps for sinusoidal, intermittent, and stepwise conditions. Further evaluations, including tests on low-resolution training data and recursive prediction, were conducted to examine the model's resolution invariance and assess its performance in iterative forecasting. These findings demonstrate the FNO method's potential for robust, efficient prediction of 3D unsteady airflow fields, providing a pathway for real-time ventilation system optimization.

关键词： velocity field Fourier neural operator rapid prediction computational fluid dynamics

来源：评论

学校读者我要写书评

暂无评论

rapid prediction method of α-Glycosidase inhibitory activity of Coreopsis tinctoria extract from different habitats by near infrared spectroscopy

引用

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022年 268卷 120601-120601页

作者： He, Xiaogang Han, Xiang Yu, Jiaping Feng, Yulong Chu, Ganghui Kashi Univ Coll Chem & Environm Sci Xinjiang Lab Nat Med & Edible Plant Resources Che Kashi 844000 Peoples R China

alpha-Glucosidase is one of the main enzymes causing elevated blood glucose, and Coreopsis tinctoria extract can be used as a natural inhibitor of alpha-Glucosidase. Therefore, a new method was proposed for predicting the inhibitory activity on alpha-Glucosidase of Coreopsis tinctoria extract based on near infrared spectroscopy. The absorbance of the inhibitory system was measured by ultraviolet spectroscopy, which was used to study the inhibitory activity on alpha-glucosidase of Coreopsis tinctoria extract. The near infrared spectra of the solid samples were collected. By selecting spectral preprocessing and optimizing spectral bands, a rapid prediction model of the inhibitory activity was established by partial least squares regression. The root mean square error of cross-validation (RMSECV), correlation coefficient (R) value and the ratio of prediction to deviation (RPD) value were used as indicators of the evaluation model. The near infrared spectrum model was established by combining the best spectral preprocessing of the continuous wavelet transform (CWT) and the best spectral band. The root mean square error of cross-validation (RMSECV) of this model was 0.815%, the correlation coefficient (R) value was 0.942, and the ratio of prediction to deviation (RPD) was 3.0. The root mean square error of prediction (RMSEP) of the model by prediction set was 0.819%, the correlation coefficient (R) value was 0.950, and the RPD was 3.2. The model shows that the fitting relationship between the predicted inhibition value and the reference inhibition value of the near infrared spectral model is good. The results showed that there was a good correlation between near infrared spectroscopy and the inhibitory activity of Coreopsis tinctoria extract. Thus, the established model was robust and effective and could be used for rapid quantification of alpha-Glucosidase inhibitory activity. The prediction method is simple and rapid, and can be extended to study the inhibition of othe

关键词： alpha-Glucosidase Coreopsis tinctoria extract Near infrared spectroscopy Inhibitory activity rapid prediction

来源：评论

学校读者我要写书评

暂无评论

BIM and ANN-based rapid prediction approach for natural daylighting inside library spaces

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 3285-3297页

作者： Ni, Ting Wang, Bo Jiang, Jiaxin Wang, Meng Qing Lei Deng, Xinman Feng, Cuiying Chengdu Univ Technol Coll Environm & Civil Engn Chengdu Sichuan Peoples R China Sichuan Univ Business Sch Chengdu Sichuan Peoples R China Southwest Jiaotong Univ Sch Architecture Chengdu Sichuan Peoples R China Chengdu Univ Technol Coll Geophys Chengdu Sichuan Peoples R China Zhejiang Univ Technol Sch Management Hangzhou Zhejiang Peoples R China

The issue of how to fully utilize natural daylighting of public buildings is one of the greatest practical objectives for lighting savings. The rapid and accurate prediction of the daylighting coefficient at the early design stage can provide a quantitative basis for energy-saving optimization. However, it is not comprehensive to determine the design parameters according to experience. The key problem that is still facing designers is the interoperability between building modeling and energy simulation tools. In this paper, an integrated approach using a dataset created by building information modeling and artificial neural network technology is developed for the fast optimal daylight factor prediction of large public spaces at the early design stage. According to this approach, the value of daylight factors is calculated for different windowsill heights, window heights and widths by Autodesk (R) Revit and Ecotect Analysis to form a dataset. With this dataset, an artificial neural network model is established using the backpropagation algorithm to predict the relevant design parameters. With their large interior spaces, the reading areas of the aboveground five floors in Chengdu University of Technology Library are selected to carry out the daylight factor experiment and rapid prediction. A total of 495 groups of experimental data are randomly divided into training and testing sets. The root mean squared errors are below 0.1, which indicates a high regression model fitting. A total of 225,369 groups of prepared data are used in the prediction model to obtain the optimal windowsill height (1.0 m), window height (2.4 m) and window width (2.1 m) for five floors in the case of the maximum daylighting coefficient. Finally, a smartphone app is designed to facilitate daylight factor prediction without any experience in modeling and simulation tools, which is simple and available to realize prediction visualization and historical result analysis.

关键词： Daylight factor rapid prediction building information modelling artificial neural network library app

来源：评论

学校读者我要写书评

暂无评论

rapid prediction of indoor airflow field using operator neural network with small dataset

引用

BUILDING AND ENVIRONMENT 2024年 251卷

作者： Gao, Hu Qian, Weixin Dong, Jiankai Liu, Jing Harbin Inst Technol Sch Architecture Harbin 150090 Peoples R China Minist Ind & Informat Technol Key Lab Cold Reg Urban & Rural Human Settlement En Harbin 150090 Peoples R China

Indoor airflow is one of the most critical factors affecting room comfort. The accurate prediction of indoor airflow fields is essential for efficient environmental control. However, representative methods for calculating indoor airflow organization, such as computational fluid dynamics (CFD), are complex and time consuming. Therefore, this study proposes a machine learning approach that can rapidly reconstruct indoor airflow fields. This method employs an improved deep operator network (DeepONet) and a Fourier neural operator (FNO) neural network to learn a set of solutions to a problem resembling a flow field and then realizes an accuracypreserving prediction of an unseen domain. First, we decompose the computational domain into subdomains and then reconstruct the flow field for the subdomains. Subsequently, the subdomains are stitched into the original computational domain. Using data enhancement techniques along with a small dataset, this method achieves satisfactory training results. The results indicate that for a two-dimensional isothermal flow field problem, both the improved DeepONet and FNO models accurately reconstruct the flow field. The mean squared errors are 0.28% and 1.89% for the improved DeepONet and FNO models, respectively, with both requiring less than 0.01s. This method does not require repeated training under different operating conditions, which significantly reduces the computation time. Compared with conventional CFD technology, the proposed method improves the speed by up to three to six orders of magnitude.

关键词： Deep learning DeepONet FNO Operator neural network Data -driven rapid prediction

来源：评论

学校读者我要写书评

暂无评论

rapid prediction of pollutants behaviours under complicated gate control for the middle route of South-to-North water transfer project

引用

ENVIRONMENTAL TECHNOLOGY 2021年第26期42卷 4208-4220页

作者： Long, Yan Yang, Yilin Li, Youming Zhang, Yunxin Hebei Univ Engn Sch Water Conservancy & Hydroelect Power Handan 056002 Hebei Peoples R China Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin Peoples R China Bgi Engn Consultants LTD Beijing Peoples R China

Many crossing hydraulic structures have been constructed in the Middle Route of the South-to-North Water Transfer Project (MR-SNWTP), which increases the likelihood of sudden water pollution accidents. After an accident, managers need to assess the extent of pollution under conditions of gate control, and it's necessary to make suitable emergency control decision under this assessment. Therefore, we researched the rapid prediction of pollutants behaviours under conditions of complicated gate control in this paper, by presenting three characteristic parameters of pollutant migration and diffusion. According to the simulation results, the influencing reasons and rapid prediction formulas for the characteristic parameters (peak transport distance, pollutant longitudinal length and peak concentration) after a sudden water-soluble pollution accident are proposed. Also, the approval results show that the formulas can accurately predict the location and range of the pollutant after the emergency accident. Finally, the rapid prediction formulas for the characteristic parameters played a fundamental role in the decisions involved in the Emergency Environmental Decision Support System is proved by two application examples.

关键词： Characteristic parameters gate control rapid prediction sudden water-soluble pollution accident South-to-North water transfer Project

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：