基于超大断面沉管隧道火灾试验平台进行的火灾排烟试验数据。数据集涵盖了超大断面沉管隧道不同排烟方式、不同火灾规模、不同火源位置、不同纵向风速等试验工况下火灾排烟大比尺试验数据。每个数据样本均以沉管隧道火灾工况下时间轴和空间轴匹配关键位置监测点的火灾温度场、烟雾场等数据信息。\n
饱和交通下超大断面沉管隧道火灾排烟技术数据集主要包括超大断面沉管隧道火灾排烟数值模拟计算数据，1:15火灾模型试验温度采集数据、1:1.2大比尺实体火灾试验数据。数据包括不同工况下烟雾场及温度场分布规律及相关数据分析。\n
火灾排烟数值模拟计算数据：1）时间范围，每个工况从火灾发展开始计算为1000s，记录整个计算过程的试验数据；2）时间精度，每2S记录一个数据；3）空间精度，记录隧道内不同位置处的温度、CO浓度、可见度等数据；4）计算方式，采用设置好的模块实现自动采集，计算过程展现实时数据。\n
1：15火灾试验试验数据：1）时间范围，记录每个试验工况从火灾发展开始至火源熄灭全过程的试验数据；2）时间精度，每10s记录一个数据；3）空间精度，记录隧道内不同位置处的温度数据、烟雾场数据、风速分布；4）计算方式，采用设置好的模块实现自动采集，试验过程展现实时数据。\n
1：1.2大比尺火灾试验数据：1）时间范围，记录每个试验工况从火灾发展开始至火源熄灭全过程的试验数据；2）时间精度，每10s记录一个数据；3）空间精度，记录隧道内不同位置处的温度数据、烟雾场数据、风速分布；4）计算方式，采用设置好的模块实现自动采集，试验过程展现实时数据。", "ds_source": "
沉管隧道火灾排烟数值模拟数据和沉管隧道火灾排烟模型试验数据。", "ds_process_way": "
模拟和模型试验。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": "2017-11-10 00:00:00", "ds_acq_end_time": "2019-12-27 00:00:00", "ds_acq_place": "", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 69922180, "ds_files_count": 2, "ds_format": "excel，csv", "ds_space_res": null, "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "321c4729-fcb8-4d46-975f-c37229179e79.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "57a5e5a3-6fc5-43b5-a2ba-1f2ca54b7727", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2023-03-31 22:55:01", "last_updated": "2025-06-30 14:52:33", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB2824.2023", "i18n": { "en": { "title": "Dataset of smoke exhaust technology for super large cross-section immersed tube tunnels under saturated traffic conditions (November 2017 December 2019)", "ds_format": "excel，csv", "ds_source": "
Numerical simulation data of smoke exhaust in immersed tube tunnel fire and experimental data of smoke exhaust model in immersed tube tunnel fire.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
Fire smoke exhaust test data based on a large cross-section immersed tube tunnel fire test platform. The dataset covers large-scale fire smoke exhaust test data of super large section immersed tube tunnels under different smoke exhaust methods, fire scales, fire source locations, and longitudinal wind speeds. Each data sample matches the fire temperature field, smoke field, and other data information of key monitoring points in the time and spatial axes under the fire conditions of immersed tube tunnels.\n
The dataset of fire and smoke exhaust technology for super large section immersed tube tunnels under saturated traffic mainly includes numerical simulation calculation data of fire and smoke exhaust for super large section immersed tube tunnels, 1:15 fire model test temperature collection data, and 1:1.2 large-scale physical fire test data. The data includes the distribution patterns of smoke and temperature fields under different operating conditions and related data analysis.\n
Numerical simulation calculation data for fire smoke exhaust: 1) Time range, each operating condition is calculated from the development of the fire for 1000 seconds, and the experimental data of the entire calculation process is recorded; 2) Time accuracy, recording data every 2 seconds; 3) Spatial accuracy, recording temperature, CO concentration, visibility and other data at different locations inside the tunnel; 4) The calculation method adopts pre-set modules to achieve automatic collection, and the calculation process displays real-time data.\n
1: 15. Fire test data: 1) Time range, record the test data of each test condition from the development of the fire to the extinguishing of the fire source; 2) Time accuracy, recording data every 10 seconds; 3) Spatial accuracy, recording temperature data, smoke field data, and wind speed distribution at different locations inside the tunnel; 4) The calculation method adopts pre-set modules to achieve automatic collection, and the experimental process displays real-time data.\n
1: 1.2 Large scale fire test data: 1) Time range, record the test data of each test condition from the development of the fire to the extinguishing of the fire source throughout the entire process; 2) Time accuracy, recording data every 10 seconds; 3) Spatial accuracy, recording temperature data, smoke field data, and wind speed distribution at different locations inside the tunnel; 4) The calculation method adopts pre-set modules to achieve automatic collection, and the experimental process displays real-time data.
", "ds_time_res": "日", "ds_acq_place": "", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Simulation and model testing.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "火灾排烟", "模拟试验", "火灾实验平台" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [ 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "张琦", "email": "zhangqi@cmhk.com", "work_for": "招商局重庆交通科研设计院有限公司", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张琦", "email": "zhangqi@cmhk.com", "work_for": "招商局重庆交通科研设计院有限公司", "country": "中国" } ], "ds_managers": [ { "true_name": "张琦", "email": "zhangqi@cmhk.com", "work_for": "招商局重庆交通科研设计院有限公司", "country": "中国" } ], "category": "其他" }