Aerodynamic dataset for selected doubly curved membrane canopy structures.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Anoop Kodakkal, Kimberly Adamek, Ann-Kathrin Goldbach, Tibebu Birhane, Rodrigo Castedo-Hernandez, Guillermo Martínez-López, Máté Péntek, Kai-Uwe Bletzinger, Roland Wüchner, Girma Bitsuamlak
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引用次数: 0

Abstract

A dataset of aerodynamic measurements is collected for doubly curved membrane structures as part of a comprehensive experimental testing campaign on wind effects on structural membranes conducted at the WindEEE Dome at Western University, Canada. Common doubly curved membrane geometries - the hypar, ridge valley, arch supported, cone, and umbrella - were tested in isolated instances. The cone geometry was also tested in both a 1 × 3 row and a 3 × 3 group arrangement. All models were tested at a 1:25 scale under atmospheric boundary layer (ABL) flow at angles of attack ranging from 0° to 180° in 10° increments (and 45°, and 135° -depending on the line of symmetry). In addition to ABL, the hypar geometry was subjected to two other distinct flow scenarios: tornado, and downburst. Pressure time series at various tap locations are included in the data. In total, approximately 425 tests were conducted, providing a comprehensive dataset on the aerodynamic behavior of doubly curved structures under wind loads. This experimental data set offers valuable insights for the design and analysis of such structures in architectural and engineering applications and future design guideline developments. Data is available on an open-source dataset Zenodo as part of the ERIES-WENSS project.

所选双弯曲膜冠结构的气动数据集。
本文收集了双弯曲膜结构的空气动力学测量数据集,作为在加拿大西部大学的WindEEE Dome进行的关于风对结构膜影响的综合实验测试活动的一部分。常见的双弯曲膜几何形状- hypar,脊谷,拱形支撑,锥形和伞形-在孤立的情况下进行了测试。在1 × 3排和3 × 3组排列中也测试了锥体的几何形状。所有模型都在大气边界层(ABL)气流的1:25比例尺下进行了测试,迎角范围为0°至180°,增量为10°(以及45°和135°-取决于对称线)。除了ABL外,超高架几何结构还受到另外两种不同的流动情景的影响:龙卷风和降爆。在不同的水龙头位置的压力时间序列包括在数据中。总共进行了大约425次测试,提供了双弯曲结构在风荷载下的空气动力学行为的综合数据集。该实验数据集为建筑和工程应用中此类结构的设计和分析以及未来设计指南的发展提供了有价值的见解。作为erie - wenss项目的一部分,数据可在开源数据集Zenodo上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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