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Volume 107
Pages 1-376 (December 2025)
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Volume 106
Pages 1-336 (November 2025)
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Volume 105
Pages 1-356 (October 2025)
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Volume 104
Pages 1-332 (September 2025)
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Volume 103
Pages 1-314 (August 2025)
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Volume 102
Pages 1-276 (July 2025)
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Volume 101
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Volume 100
Pages 1-256 (May 2025)
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Volume 99
Pages 1-242 (April 2025)
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Pages 1-256 (February 2025)
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Volume 96
Pages 1-340 (January 2025)
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Volume 107
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Volumes 84-95 (2024)
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Volume 95
Pages 1-392 (December 2024)
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Volume 94
Pages 1-400 (November 2024)
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Volume 93
Pages 1-376 (October 2024)
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Volume 92
Pages 1-316 (September 2024)
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Volume 91
Pages 1-378 (August 2024)
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Volume 90
Pages 1-580 (July 2024)
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Volume 89
Pages 1-278 (June 2024)
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Volume 88
Pages 1-350 (May 2024)
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Volume 87
Pages 1-338 (April 2024)
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Volume 86
Pages 1-312 (March 2024)
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Volume 85
Pages 1-334 (February 2024)
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Volume 84
Pages 1-308 (January 2024)
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Volume 95
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Volumes 72-83 (2023)
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Volume 83
Pages 1-258 (December 2023)
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Volume 82
Pages 1-204 (November 2023)
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Volume 81
Pages 1-188 (October 2023)
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Volume 80
Pages 1-202 (September 2023)
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Volume 79
Pages 1-172 (August 2023)
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Volume 78
Pages 1-146 (July 2023)
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Volume 77
Pages 1-152 (June 2023)
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Volume 76
Pages 1-176 (May 2023)
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Volume 75
Pages 1-228 (April 2023)
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Volume 74
Pages 1-200 (March 2023)
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Volume 73
Pages 1-138 (February 2023)
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Volume 72
Pages 1-144 (January 2023)
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Volume 83
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Volumes 60-71 (2022)
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Volume 71
Pages 1-108 (December 2022)
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Volume 70
Pages 1-106 (November 2022)
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Volume 69
Pages 1-122 (October 2022)
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Volume 68
Pages 1-124 (September 2022)
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Volume 67
Pages 1-102 (August 2022)
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Volume 66
Pages 1-112 (July 2022)
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Volume 65
Pages 1-138 (June 2022)
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Volume 64
Pages 1-186 (May 2022)
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Volume 63
Pages 1-124 (April 2022)
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Volume 62
Pages 1-104 (March 2022)
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Volume 61
Pages 1-120 (February 2022)
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Volume 60
Pages 1-124 (January 2022)
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Volume 71
- Volumes 54-59 (2021)
- Volumes 48-53 (2020)
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- Volumes 36-41 (2018)
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- Volumes 12-17 (2014)
- Volume 11 (2013)
- Volume 10 (2012)
- Volume 9 (2011)
- Volume 8 (2010)
- Volume 7 (2009)
- Volume 6 (2008)
- Volume 5 (2007)
- Volume 4 (2006)
- Volume 3 (2005)
- Volume 2 (2004)
- Volume 1 (2003)
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中国石油大学（北京）：MAP-Net：融合多层注意力与物理约束的神经网络用于旋风分离效率预测本文提出一种融合物理先验与注意力机制的神经网络学习模型——多层注意力物理网络（MAP-Net），用于准确预测旋风分离器的分离效率。2026-07-02 -
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北京科技大学李诗媛教授团队：基于深度学习模型的氢基流化床中气泡识别分割方法研究本研究系统评估了不同主干网络配置下深度学习DeepLabV3+模型在多工况条件下对床层流态的分割性能。2026-06-16 -
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荷兰埃因霍芬理工大学：基于 CFD-DEM 解析烧结对氧化铁流化床还原过程的影响本文通过多模型耦合的CFD-DEM数值方法，为流化床氢直接还原工艺避免粘结失流、优选最佳操作温度区间提供理论支撑。2026-06-05 -
