[Objective] Intelligent ventilation on demand is crucial for ensuring environmental safety in underground caving groups and for the high-quality construction and development of hydropower projects. Ventilation systems for large underground caving groups during construction frequently exhibit complex three-dimensional layouts, different air loads across regions, and dynamic demand under varying regulation conditions. [Methods] To achieve spatial node extraction, branch correlation decoupling, and stable joint adjustment of complex flow fields, this paper examines the development characteristics of fluids under construction ventilation in extensive spatial structures. It demonstrates the necessity of constructing a graph structure based on the ventilation flow characteristics for analyzing and adjusting ventilation system parameters. The regional modeling theory is discussed, detailing the principles and methods of node extraction for one-dimensional tube bundle fluids (network) and three-dimensional spatial flow field elements (field). Among these, the area where fluid parameter information changes along the main airflow direction employs network node extraction, while the regions with multi-directional complex flow paths utilize the three-dimensional field node extraction method. Virtual branches address the network-field coupling problem, utilizing the nodal pressure approach. This method treats the nodal pressure as the unknown variable and airflow deviation as the assessment criterion. Nodes with known pressure values serve as reference nodes for solving the pressure at all network nodes, and are further assigned to field simulation boundaries. By numerically simulating the three-dimensional spatial flow field, the virtual branch air flow rates are iteratively fed back into the air network calculation for a coupled solution. This paper also introduces the node-property-edge triplet, which effectively reflects the structure, performance, and behavioral characteristics of nodes. Furthermore, to optimize the ventilation coordination efficiency, a hypergraph structure for joint adjustment, with edges as the analysis object, displays the coupling interactions between the ventilation branches and loops. Considering the joint adjustment sensitivity, an optimal resistance control method is proposed, which involves constructing target and response node sets, setting response efficiency constraints, and optimizing to form a ventilation adjustment plan. An intelligent ventilation coordination platform integrates the resistance control model of coupling interactions, including modules for network design, ventilation design, field-network integration, loop generation, and optimization analysis. Within this framework, the network design module is dedicated to reconstructing the physical model of the ventilation system, while the ventilation design and field-network integration modules are used to assign basic fluid characteristic parameters of ventilation to the established model. The loop generation and optimization analysis modules are employed for solving the overall wind network parameters, including air volume, air pressure, and wind resistance. [Results] The field-network coupling method using nodal pressure eliminated the need for loop identification and effectively addressed the interdependent coupling between network nodes and flow field boundaries. The intelligent ventilation coordination platform was integrated with online environmental monitoring devices to automatically gather critical ventilation environment parameters, thereby enabling real-time calculations of the ventilation system based on environmental monitoring data and providing 3D visualization and early warning capabilities. [Conclusions] The ventilation design parameters of an engineering project are used to implement targeted air volume control deployment. The integrated control system exhibits high responsiveness. On the premise that the air volume of each unit meets the threshold requirements, the air volume adjustment efficiency of the target unit and the overall stability of the air distribution network can always fulfill the specified requirements. The results indicate a timely and stable system response and can provide a reference for similar projects.
[1] AN R N, LIN P, LI Z C, et al. Intelligent ventilation-on-demand control system for the construction of underground tunnel complex [J]. Journal of Intelligent Construction, 2024, 2(2): 9180032.
[2] XIANG Y F, LIN P, AN R N, et al. Full participation flat closed-loop safety management method for offshore wind power construction sites [J]. Journal of Intelligent Construction, 2023, 1(1): 9180006.
[3] SEMIN M A, LEVIN L Y. Stability of air flows in mine ventilation networks [J]. Process Safety and Environmental Protection, 2019, 124: 167-171.
[4] CROSS H. Analysis of flow in networks of conduits or conductors [J]. University of Illinois Bulletin, 1936, 286(1): 42-47.
[5] 刘剑. 矿井智能通风关键科学技术问题综述[J]. 煤矿安全, 2020, 51(10): 108-111. LIU J. Overview on key scientific and technical issues of mine intelligent ventilation [J]. Safety in Coal Mines, 2020, 51(10): 108-111. (in Chinese)
[6] 魏连江, 周福宝, 朱华新. 通风网络拓扑理论及通路算法研究[J]. 煤炭学报, 2008, 33(8): 926-930. WEI L J, ZHOU F B, ZHU H X. Topology theory of ventilation network and path algorithm [J]. Journal of China Coal Society, 2008, 33(8): 926-930. (in Chinese)
[7] 谢贤平, 冯长根, 赵梓成. 矿井通风网络模糊优化数学模型及其数值解法[J]. 中国安全科学学报, 1999, 9(6): 23-28. XIE X P, FENG C G, ZHAO Z C. Mathematical model of fuzzy optimization for mine ventilation networks and its numerical solutions [J]. China Safety Science Journal, 1999, 9(6): 23-28. (in Chinese)
[8] 马恒, 尹彬, 刘剑. 矿井风流温度预测分析研究[J]. 中国安全科学学报, 2010, 20(11): 91-95. MA H, YIN B, LIU J. Research on prediction of airflow temperature in mine [J]. China Safety Science Journal, 2010, 20(11): 91-95. (in Chinese)
[9] AN R N, LIN P, LIU C, et al. Smoke diffusion mechanism and mitigation design for fire accidents in a tunnel-groove structure of a large hydropower station [J]. Tunnelling and Underground Space Technology, 2024, 149: 105785.
[10] LONG Z, ZHONG M H. Experimental study on the vertical temperature and thermal stratification for subway station fire [J]. Journal of Intelligent Construction, 2023, 1(4): 9180030.
[11] 夏勇, 安瑞楠, 张伟锋, 等. 高海拔长大水工隧洞群挡烟垂壁控烟机制[J]. 中国安全科学学报, 2024, 34(4): 58-66. XIA Y, AN R N, ZHANG W F, et al. Fire control mechanism of smoke barrier for long and large hydraulic tunnel group in high altitude areas [J]. China Safety Science Journal, 2024, 34(4): 58-66. (in Chinese)
[12] 安瑞楠, 林鹏, 夏勇, 等. 网络型地下隧洞群控排烟策略优化仿真研究[J/OL]. 煤炭学报.(2024-03-21) [2024-09-04]. DOI: 10.13225/j.cnki.jccs.2023.1317. AN R N, LIN P, XIA Y, et al. Simulation study on optimization of smoke control and exhaust strategies for networked underground tunnel groups [J/OL]. Journal of China Coal Society. (2024-03-21) [2024-09-04]. DOI: 10.13225/j.cnki.jccs.2023.1317. (in Chinese)
[13] CHENG H H, WU L, CHEN J F, et al. Experimental and numerical study on fire smoke propagation and ventilation modes in powerhouse of hydropower station during construction stage [J]. Journal of Intelligent Construction. 2024, 2(2): 9180035.
[14] 李秉芮, 王伟, 陈凤梅, 等. 基于矿井通风网路解算的优化计算方法[J]. 安全与环境学报, 2022, 22(3): 1240-1246. LI B R, WANG W, CHEN F M, et al. An optimization algorithm based on mine ventilation network analysis [J]. Journal of Safety and Environment, 2022, 22(3): 1240-1246. (in Chinese)
[15] NEL A J H, ARNDT D C, VOSLOO J C, et al. Achieving energy efficiency with medium voltage variable speed drives for ventilation-on-demand in south African mines [J]. Journal of Cleaner Production, 2019, 232: 379-390.
[16] 马恒, 苗倩斐, 韩宝华, 等. 矿井通风系统稳定性SD预测仿真分析[J]. 中国安全生产科学技术, 2019, 15(12): 108-114. MA H, MIAO Q F, HAN B H, et al. SD prediction and simulation analysis on stability of mine ventilation system [J]. Journal of Safety Science and Technology, 2019, 15(12): 108-114. (in Chinese)
[17] HUANG Y R, CHENG W J, TANG C L, et al. Study of multi-agent-based coal mine environmental monitoring system [J]. Ecological Indicators, 2015, 51: 79-86.
[18] REN C, CAO S J. Development and application of linear ventilation and temperature models for indoor environmental prediction and HVAC systems control [J]. Sustainable Cities and Society, 2019, 51: 101673.
[19] 魏连江, 周福宝, 夏同强, 等. 矿井智能通风与灾变应急决策平台[J]. 中国安全科学学报, 2022, 32(9): 158-167. WEI L J, ZHOU F B, XIA T Q, et al. Mine intelligent ventilation and disaster emergency decision platform [J]. China Safety Science Journal, 2022, 32(9): 158-167. (in Chinese)
[20] 张伟峰, 李春光, 肖昌昊, 等. 基于图论的两相渗流排序求解并行算法研究[J]. 力学季刊, 2024, 45(1): 88-98. ZHANG W F, LI C G, XIAO C H, et al. Research on parallel strategy in the topological sorting algorithm of solving two phase seepage flow based on graph theory [J]. Chinese Quarterly of Mechanics, 2024, 45(1): 88-98. (in Chinese)
[21] 涂贵宇, 潘文林, 张天军. 基于信息熵的超网络重要节点识别方法[J/OL]. 复杂系统与复杂性科学. (2023-12-04) [2024-09-04]. https://link.cnki.net/urlid/37.1402.N.20231204.0952.002. TU G Y, PAN W L, ZHANG T J. Identification methods of important nodes based on information entropy in hypernetwork [J/OL]. Complex Systems and Complexity Science. (2023-12-04) [2024-09-04]. https://link.cnki.net/urlid/37.1402.N.20231204.0952.002. (in Chinese)
[22] 钟吴君, 李培强, 涂春鸣. 基于尾流关联的动态超图风电功率超短期预测方法[J/OL]. 中国电机工程学报. (2024-05-28) [2024-09-04]. https://link.cnki.net/urlid/11.2107.tm.20240528.0953.003. ZHONG W J, LI P Q, TU C M. Dynamic hypergraph wind power ultra short term prediction method based on wake correlation [J/OL]. Proceedings of the CSEE. (2024-05-28) [2024-09-04]. https://link.cnki.net/urlid/11.2107.tm.20240528.0953.003. (in Chinese)
[23] 陆齐力, 官燕玲, 王巧宁. 房间自然通风运用多区域网络模型的修正[J]. 土木建筑与环境工程, 2017, 39(6): 105-110. LU Q L, GUAN Y L, WANG Q N. Correction method of multizone airflow model in matural ventilation room [J]. Journal of Civil, Architectural & Environmental Engineering, 2017, 39(6): 105-110. (in Chinese)
[24] LI X X, LIU C H, LEUNG D Y C, et al. Recent progress in CFD modelling of wind field and pollutant transport in street canyons [J]. Atmospheric Environment, 2006, 40(29): 5640-5658.
