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A new friction device using band brake technology, termed the Banded Rotary Friction Damper (BRFD), has been fabricated at the NHERI Lehigh Experimental Facility. The damping mechanism is based on band brake technology and leverages a self-energizing mechanism to produce large damping forces with low input energy. The device is a second-generation BRFD, where the friction mechanism is achieved using two electric actuators. The BRFD generates a damping force as a function of the input force provided by the electric actuators, where the ratio of BRFD force output-to-electric actuator force input is equal to about 112. The paper presents the results of a study using real-time hybrid simulations (RTHS) to investigate the performance of the BRFD’s in mitigating seismic hazards of a two-story reinforced concrete building. The building has two and three special moment resisting frames (SMRFs) in the east-west and north-south directions, respectively. In order to perform the RTHS, the north south SMRF is considered and the BRFD along with a parallel elastic member is used as a base isolation system to mitigate the effects of earthquake hazards by reducing story drift and floor accelerations of the structure. For the RTHS the building and the elastic component of the isolator are part of the analytical substructure while the experimental substructure is comprised of the BRFD. The response of the structure is investigated involving six Maximum Considered Earthquake (MCE) hazard level events that includes three near-field and three far-field ground motions. The explicit, unconditionally stable dissipative Modified KR-α integration algorithm is used to accurately integrate the equations of motion during the RTHS. The model for the reinforced concrete building is created using explicit non-linear force-based fiber elements to discretely model each member of the structure. First, the details of the prototype of the BRFD are presented. Second, the details of the isolator system consisting of a linear spring element and the BRFD are discussed. Finally, the details of the RTHS study and the results are presented. The building’s inter-story peak and residual story drift from base-isolated and fixed-based conditions are compared. Results show that the proposed isolator system produces a significant reduction in both maximum inter-story drift and residual drift, and reduces the damage developed in the structure during the MCE.
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Physics-informed machine learning for dry friction and backlash modeling in structural control systems
Modeling dry friction is a challenging task. Accurate models must incorporate hysteretic rise of force across displacement and non-linearity from the Stribeck effect. Though sufficiently accurate models have been proposed for simple friction systems where these two effects dominate, certain rotational friction systems introduce self-energizing and accompanying backlash effects. These systems are termed self-energizing systems. In these systems, the friction force is amplified by a mechanical advantage which is charged through motion and released during reversing the direction of travel. This produces energized and backlash regimes within which the friction device follows different dynamic behaviors. This paper examines self-energizing rotational friction, and proposes a combined physics and machine learning approach to produce a unified model for energized and backlash regimes. In this multi-process information fusion methodology, a classical LuGre friction model is augmented to allow state-dependent parameterization provided by a machine learning model. The method for training the model from experimental data is given, and demonstrated with a 20 kN banded rotary friction device used for structural control. Source code replicating the methodology is provided. Results demonstrate that the combined model is capable of reproducing the backlash effect and reduces error compared to the standard LuGre model by a cumulative 32.8%; in terms modeling the tested banded rotary friction device. In these experimental tests, realistic pre-defined displacements inputs are used to validate the damper. The output of the machine learning model is analyzed and found to align with the physical understanding of the banded rotary friction device.
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- PAR ID:
- 10527775
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Mechanical Systems and Signal Processing
- Volume:
- 218
- Issue:
- C
- ISSN:
- 0888-3270
- Page Range / eLocation ID:
- 111522
- Subject(s) / Keyword(s):
- Friction Backlash Machine learning Physics-informed machine learning Structural control
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ABSTRACT Real‐time hybrid simulation (RTHS) is an experimental testing methodology that divides a structural system into an analytical and an experimental substructure. The analytical substructure is modeled numerically, and the experimental substructure is modeled physically in the laboratory. The two substructures are kinematically linked together at their interface degrees of freedom, and the coupled equations of motion are solved in real‐time to obtain the response of the complete system. A key challenge in applying RTHS to large or complex structures is the limited availability of physical devices, which makes it difficult to represent all required experimental components simultaneously. The present study addresses this challenge by introducing Online Cyber‐Physical Neural Network (OCP‐NN) models–neural network‐based models of physical devices that are integrated in real‐time with the experimental substructure during an RTHS. The OCP‐NN framework leverages real‐time data from a single physical device (i.e., the experimental substructure) to replicate its behavior at other locations in the system, thereby significantly reducing the need for multiple physical devices. The proposed method is demonstrated through RTHS of a two‐story reinforced concrete frame subjected to seismic excitation and equipped with Banded Rotary Friction Dampers (BRFDs) in each story. BRFDs are challenging to model numerically due to their complex behavior which includes backlash, stick‐slip phenomena, and inherent device dynamics. Consequently, BRFDs were selected to demonstrate the proposed framework. In the RTHS, one BRFD is modeled physically by the experimental substructure, while the other is represented by the OCP‐NN model. The results indicate that the OCP‐NN model can accurately capture the behavior of the device in real‐time. This approach offers a practical solution for improving RTHS of complex structural systems with limited experimental resources.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.ymssp.2024.111522
