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Title: Open-jet boundary-layer processes for aerodynamic testing of low-rise buildings
Investigations on simulated near-surface atmospheric boundary layer (ABL) in an open-jet facility are carried out by conducting experimental tests on small-scale models of low-rise buildings. The objectives of the current study are: (1) to determine the optimal location of test buildings from the exit of the open-jet facility, and (2) to investigate the scale effect on the aerodynamic pressure characteristics. Based on the results, the newly built open-jet facility is well capable of producing mean wind speed and turbulence profiles representing open-terrain conditions. The results show that the proximity of the test model to the open-jet governs the length of the separation bubble as well as the peak roof pressures. However, test models placed at a horizontal distance of 2.5H (H is height of the wind field) from the exit of the open-jet, with a width that is half the width of the wind field and a length of 1H, have consistent mean and peak pressure coefficients when compared with available results from wind tunnel testing. In addition, testing models with as large as 16% blockage ratio is feasible within the open-jet facility. This reveals the importance of open-jet facilities as a robust tool to alleviate the scale restrictions involved more » in physical investigations of flow pattern around civil engineering structures. The results and findings of this study are useful for putting forward recommendations and guidelines for testing protocols at open-jet facilities, eventually helping the progress of enhanced standard provisions on the design of low-rise buildings for wind. « less
Authors:
;
Award ID(s):
1361908
Publication Date:
NSF-PAR ID:
10041267
Journal Name:
Wind and Structures
Volume:
25
Issue:
3
Page Range or eLocation-ID:
233-259
ISSN:
1226-6116
Sponsoring Org:
National Science Foundation
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The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases. ACKNOWLEDGMENTS This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. REFERENCES [1] N. Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432. [2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/. [3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015. [4] C.-J. 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