ABSTRACT: This paper explores the use of cyber-physical systems (CPS) for optimal design in wind engineering. The approach combines the accuracy of physical wind tunnel testing with the ability to efficiently explore a solution space using numerical optimization algorithms. The approach is fully automated, with experiments executed in a boundary layer wind tunnel (BLWT), sensor feedback monitored by a high-performance computer, and actuators used to bring about physical changes in the BLWT. Because the model is undergoing physical change as it approaches the optimal solution, this approach is given the name “loop-in-the-model” testing.
The building selected for this study is a low-rise structure with a parapet wall of variable height. Parapet walls alter the location of the roof corner vortices, alleviating large suction loads on the windward facing roof corner and edges and setting up an interesting optimal design problem. In the BLWT, the model parapet height is adjusted using servo-motors to achieve a particular design. The model surface is instrumented with pressure taps to measure the envelope pressure loading. The taps are densely spaced on the roof to provide sufficient resolution to capture the change in roof corner vortex formation. Experiments are conducted using a boundary BLWT located at the University of Florida Natural Hazard Engineering Research Infrastructure (NHERI) Experimental Facility.
The proposed CPS approach enables the optimal solution to be found quicker than brute force methods, in particular for complex structures with many design variables. The parapet wall provides a proof-of-concept study with a single design variable that has a non-monotonic influence on a structure’s wind load. This study focuses on envelope load effects, seeking the parapet height that minimizes roof and parapet wall suction loading. Implications are significant for more complex structures where the optimal solution may not be obvious and cannot be reasonably determined with traditional experimental or computational methods.
KEYWORDS: Cyber-physical systems, optimization, boundary-layer wind tunnel, parapet wall, NHERI
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Security engineering with machine learning for adversarial resiliency in cyber physical systems
Recent technological advances provide the opportunities to bridge the physical world with cyber-space that leads to complex and multi-domain cyber physical systems (CPS) where physical systems are monitored and controlled using numerous smart sensors and cyber space to respond in real-time based on their operating environment. However, the rapid adoption of smart, adaptive and remotely accessible connected devices in CPS makes the cyberspace more complex and diverse as well as more vulnerable to multitude of cyber-attacks and adversaries. In this paper, we aim to design, develop and evaluate a distributed machine learning algorithm for adversarial resiliency where developed algorithm is expected to provide security in adversarial environment for critical mobile CPS.
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- Award ID(s):
- 1828811
- NSF-PAR ID:
- 10094357
- Date Published:
- Journal Name:
- SPIE Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
- Page Range / eLocation ID:
- 59
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2519372
