This content will become publicly available on April 18, 2023
High-speed data acquisition and computing for real-time active control of civil structures subject to seismic base excitation
Active structural control of civil infrastructure in response to large external loads, such as earthquake or wind, requires the rapid integration of information between sensing nodes, computational nodes, and actuating nodes. Because of this, it is still not widely employed due to several key issues, such as latency in the system and challenges with information exchange. In this study, the Martlet, a high-speed data acquisition and computing node that was designed based on a Texas Instruments Piccolo microcontroller and capable of peer-to-peer wireless communication, is used for all three steps in the active control process. For rapid sensing, the Martlet is equipped with an interface board that interfaces with a displacement transducer and has an on-board differentiating circuit to derive velocity. The sensing Martlet transmits its data (i.e., displacement and velocity) to the actuating Martlet. The actuating Martlet calculates the necessary control force using an optimal control law, the full-state linear quadratic regulator. The resulting control force is then conveyed to the actuator via a controller interface board. This complete process is experimentally validated on a partial-scale, four-story shear structure and it is demonstrated that due to the fast processing speeds of the Martlet, real-time control of the structure can more »
- Editors:
- Zonta, Daniele; Su, Zhongqing; Glisic, Branko
- Award ID(s):
- 1662655
- Publication Date:
- NSF-PAR ID:
- 10334778
- Journal Name:
- SPIE Smart Structures + Nondestructive Evaluation
- Volume:
- 12046
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract
<p>This data set for the manuscript entitled "Design of Peptides that Fold and Self-Assemble on Graphite" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software NAMD, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text and supporting information. They include molecular model structure files (NAMD psf or Amber prmtop format), force field parameter files (in CHARMM format), initial atomic coordinates (pdb format), NAMD configuration files, Colvars configuration files, NAMD log files, and NAMD output including restart files (in binary NAMD format) and trajectories in dcd format (downsampled to 10 ns per frame). Analysis is controlled by shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.</p> <p>Version: 2.0</p> <p>Changes versus version 1.0 are the addition of the free energy of folding, adsorption, and pairing calculations (Sim_Figure-7) and shifting of the figure numbers to accommodate this addition.</p> <p><br /> Conventions Used in These Files<br /> ===============================</p> <p>Structure Files<br /> ----------------<br /> - graph_*.psf or sol_*.psf (original NAMD (XPLOR?) format psf file including atom details (type, charge, mass), -
Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data servedmore »
-
The monitoring of data streams with a network structure have drawn increasing attention due to its wide applications in modern process control. In these applications, high-dimensional sensor nodes are interconnected with an underlying network topology. In such a case, abnormalities occurring to any node may propagate dynamically across the network and cause changes of other nodes over time. Furthermore, high dimensionality of such data significantly increased the cost of resources for data transmission and computation, such that only partial observations can be transmitted or processed in practice. Overall, how to quickly detect abnormalities in such large networks with resource constraints remains a challenge, especially due to the sampling uncertainty under the dynamic anomaly occurrences and network-based patterns. In this paper, we incorporate network structure information into the monitoring and adaptive sampling methodologies for quick anomaly detection in large networks where only partial observations are available. We develop a general monitoring and adaptive sampling method and further extend it to the case with memory constraints, both of which exploit network distance and centrality information for better process monitoring and identification of abnormalities. Theoretical investigations of the proposed methods demonstrate their sampling efficiency on balancing between exploration and exploitation, as well asmore »
-
All-solid-state batteries (ASSBs) have garnered increasing attention due to the enhanced safety, featuring nonflammable solid electrolytes as well as the potential to achieve high energy density. 1 The advancement of the ASSBs is expected to provide, arguably, the most straightforward path towards practical, high-energy, and rechargeable batteries based on metallic anodes. 1 However, the sluggish ion transmission at the cathode-electrolyte (solid/solid) interface would result in the high resistant at the contact and limit the practical implementation of these all solid-state materials in real world batteries. 2 Several methods were suggested to enhance the kinetic condition of the ion migration between the cathode and the solid electrolyte (SE). 3 A composite strategy that mixes active materials and SEs for the cathode is a general way to decrease the ion transmission barrier at the cathode-electrolyte interface. 3 The active material concentration in the cathode is reduced as much as the SE portion increases by which the energy density of the ASSB is restricted. In addition, the mixing approach generally accompanies lattice mismatches between the cathode active materials and the SE, thus providing only limited improvements, which is imputed by random contacts between the cathode active materials and the SE during the mixingmore »
