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基于DeepFeatureNet的侧向受限垂向浮力射流模拟研究

闫晓惠 张文俊 周聪 刘思笛 曹华德

大连理工大学学报2026,Vol.66Issue(3):291-301,11.
大连理工大学学报2026,Vol.66Issue(3):291-301,11.DOI:10.7511/dllgxb202603009

基于DeepFeatureNet的侧向受限垂向浮力射流模拟研究

Study of laterally constrained vertical buoyancy jet simulation based on DeepFeatureNet

闫晓惠 1张文俊 2周聪 3刘思笛 4曹华德5

作者信息

  • 1. 东华理工大学江西省防震减灾与工程地质灾害探测工程研究中心,江西南昌 330013||大连理工大学建设工程学院,辽宁大连 116024
  • 2. 清华大学水利水电工程系,北京 100084
  • 3. 东华理工大学江西省防震减灾与工程地质灾害探测工程研究中心,江西南昌 330013
  • 4. 大连理工大学建设工程学院,辽宁大连 116024
  • 5. 中国地质大学(北京)海洋学院,北京 100083
  • 折叠

摘要

Abstract

Buoyancy jets are widely present in environmental and industrial fields,playing a key role in applications such as wastewater discharge,ocean diffusion,and cooling water release.Due to the dynamics of buoyancy jets involving density differences,turbulent mixing,and interactions with the surrounding environment,predicting their behavior under different conditions becomes extremely complex.Therefore,a jet simulation method based on DeepFeatureNet is introduced and evaluated using a laterally constrained vertical buoyancy jet as an example.The method is also systematically compared with traditional machine learning methods,such as Support Vector Regression,Decision Trees,and Lasso Regression.Through comparing the predictive performance of different models,the applicability,strengths and weaknesses of each approach on complex datasets are assessed.The results show that DeepFeatureNet achieves excellent predictive accuracy on both the training and testing datasets,significantly outperforming traditional methods.Although Support Vector Regression and Decision Tree models also perform well,their predictive capabilities are slightly inferior to that of DeepFeatureNet.In contrast,Lasso Regression demonstrates relatively weaker performance in capturing data features.The stability of the models is further analyzed through confidence distribution plots,revealing that the confidence scores of DeepFeatureNet are more concentrated,indicating more stable predictive performance and a reduced likelihood of extreme biases.The findings highlight the significant potential of DeepFeatureNet in simulating fluid dynamics,pollutant transport and diffusion in water.The research findings provide valuable reference for future studies of water pollution simulations.

关键词

射流模拟/侧向约束/浮力射流/DeepFeatureNet架构/非线性预测

Key words

jet simulation/lateral constraint/buoyancy jet/DeepFeatureNet architecture/nonlinear prediction

分类

建筑与水利

引用本文复制引用

闫晓惠,张文俊,周聪,刘思笛,曹华德..基于DeepFeatureNet的侧向受限垂向浮力射流模拟研究[J].大连理工大学学报,2026,66(3):291-301,11.

基金项目

江西省防震减灾与工程地质灾害探测工程研究中心开放基金资助项目(SDGD202202) (SDGD202202)

国家自然科学基金资助项目(52309079) (52309079)

国家重点研发计划资助项目(2022YFC3702300). (2022YFC3702300)

大连理工大学学报

1000-8608

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