Keeganmoore: /* Current Members */

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Current Members

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	\| PhD student working on physics-based, reduced-order modeling of bolted joint loosening.		\| PhD student working on physics-based, reduced-order modeling of bolted joint loosening.
	\|-		\|-
	!~~Mohammad Nasr~~		!Sobhan Mohammadi
	\|MS student working ~~on smart, predictive automatic modal impact hammers~~.		\|PhD student working wave-induced vibrations in ships in extreme environments.
			\|-
			!Sayantan Ghosh
			\|PhD student studying data-driven nonlinear system identification using energy-based techniques.
	\|}		\|}

Keeganmoore at 15:32, 10 December 2024

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← Older revision		Revision as of 11:32, 10 December 2024
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	sd		== Research Area ==
			=== Vision ===
			The [https://modal.ae.gatech.edu Moore Dynamics and Analytics Laboratory (MoDAL)] synergistically combines theory, mathematical and computational modeling, and experimentation to understand and exploit strongly nonlinear dynamical phenomena. Our vision is to place nonlinear dynamics in the toolbox of every vibration engineer. Our approach is to leverage data, machine learning, and autonomy to remove barriers for utilizing and understanding nonlinearity.

			[[File:2023 MoDALVision.jpg \|frameless\|upright=2.5\|center\|alt=MoDAL vision]]
			=== Active Research Directions===
			Active areas of research include:
			# Physics-based, data-driven modeling and discovery of governing equations directly from measurements<ref>K.J. Moore, “Characteristic Nonlinear System Identification: A Data-driven Approach for Local Nonlinear Attachments,” ''Mechanical Systems and Signal Processing'', 131:335347, 2019. [https://dx.doi.org/10.1016/j.ymssp.2019.05.066]</ref><ref>A. Singh, K.J. Moore, “Characteristic Nonlinear System Identification of Clearance Nonlinearities in Local Attachments,” ''Nonlinear Dynamics'', 102:1667-1684, 2020. [https://dx.doi.org/10.1007/s11071-020-06004-8] </ref><ref>A. Singh, K.J. Moore, “Identification of Multiple Local Nonlinear Attachments Using a Single Measurement,” ''Journal of Sound and Vibration'', 513:116410, 2021. [https://dx.doi.org/10.1016/j.jsv.2021.116410]</ref>.
			# AI-based automated testing of mechanical structures<ref>A. Singh, K.J. Moore, “An Open-source, Scalable, Low-cost Automatic Modal Hammer for Studying Nonlinear Dynamical Systems,” ''Experimental Techniques'', 46:775-792, 2022. [https://dx.doi.org/10.1007/s40799-021-00516-7]</ref>.
			# Reduced-order modeling of nonlinear structures <ref>K.J. Moore, “A Reduced-order Model for Loosening Mechanics of Axial Joints,” ''ASME Journal of Applied Mechanics'', 86(12):121007, 2019. [https://dx.doi.org/10.1115/1.4044813]
			</ref><ref>S. Aldana, K.J. Moore, “Dynamic Interactions Between Two Axially Aligned Threaded Joints Undergoing Loosening,” ''Journal of Sound and Vibration'', 520:116625, 2022. [https://dx.doi.org/10.1016/j.jsv.2021.116625]</ref>.
			# Nonlinear energy flows in mechanical structures<ref>C. Wang, G. Yãnez González, C. Wittich, K.J. Moore, “Energy Isolation in a Multi-floor Nonlinear Structure Under Harmonic Excitation,” ''Nonlinear Dynamics'', 110:20492077, 2022. [https://dx.doi.org/10.1007/s11071-022-07744-5]</ref><ref>
			C. Wang 3, K.J. Moore, “On Nonlinear Energy Flows in Nonlinearly Coupled Oscillators with Equal Mass,” ''Nonlinear Dynamics'', 103:343-366, 2021. [https://dx.doi.org/10.1007/s11071-020-06120-5]</ref><ref>C. Wang, E. Krings, A.T. Allen, E.J. Markvicka, K.J. Moore, “Low-to-High Frequency Targeted Energy Transfer Using a Nonlinear Energy Sink with Softening-hardening Nonlinearity,” ''International Journal of Non-linear Mechanics'', 147:104194, 2022. [https://dx.doi.org/10.1016/j.ijnonlinmec.2022.104194]</ref>.
			# Novel vibration mitigation strategies employing nonlinearities<ref>C. Wang, J.D. Brown, A. Singh, K.J. Moore, “A Two-dimensional Nonlinear Vibration Absorber Using Elliptical Impacts and Sliding,” ''Mechanical Systems and Signal Processing'', 189:110068, 2023. [https://dx.doi.org/10.1016/j.ymssp.2022.110068]</ref>.
			# Advanced signal processing for nonlinear time series data<ref>C. López, D. Wang, Á. Naranjo, K.J. Moore, “Box-Cox-Sparse-Measures-Based Blind Filtering: Understanding the Difference between the Maximum Kurtosis Deconvolution and the Minimum Entropy Deconvolution,” ''Mechanical Systems and Signal Processing'', 165:108376, 2022. [https://dx.doi.org/10.1016/j.ymssp.2021.108376]</ref><ref>C. López, Á. Naranjo, K.J. Moore, “Hidden Markov Model based Stochastic Resonance and Its Application to Bearing Fault Diagnosis,” ''Journal of Sound and Vibration'', 528:116890, 2022. [https://dx.doi.org/10.1016/j.jsv.2022.116890]</ref>.
			# Applications of nonlinear dynamics and vibrations to novel fields (e.g., wave-induced vibrations of ships).

			== Equipment ==

			=== Digital Image Correlation (DIC) ===
			[[File:2023_DICCameras.png\|thumb\|right\|alt=High-speed digital cameras\|The pair of high-speed digital cameras in MoDAL.]]
			We have a high-speed 3D DIC system for non-contact measurements for systems where discrete sensors affect the dynamics, can't be used due to geometry, or where full-field data is needed. The cameras can record up to 500,000 frames per second (FPS) at reduced resolution and at 4,000 FPS at a 1-megapixel resolution. We typically film vibrations and dynamic responses around 4k to 10k FPS. We have previously performed measurements on strongly nonlinear vibrating structures and high-speed catastrophic failure (e.g., 3D-printed pressure vessel exploding).

			====Equipment List ====
			VIC-3D Digital Image Correlation System (Correlated Solutions):
			* Two Photron AX100 540K-M-32GB (1024 x 1024 @ 4,000 fps) high-speed digital cameras
			* Lenses: two Nikon 24mm wide angle manual focus, two Nikon 50mm, and two Tokina 100mm 1:1 macro lenses
			* One 6000 Lumen High-Speed LED lighting System with Flood Controller
			* Workstation with rackmount Quad-core PC, 64GB RAM, Win 10 64 bit, 1TB SSD, 8TB HD, dual 24" LCD monitors
			* One 8-channel USB analog data acquisition system for high-speed measurements with a maximum sampling rate of 1 MS/s (National Instruments NI 6361)
			* Accompanying accessories (cases, speckle paint, etc.)
			==== Videos ====
			{{#widget:Youtube
			\|id = bmO4yS2d7rY
			\| height = 444
			\| width = 790
			}}
			{{#widget:Youtube
			\|id = EVvM8j_iUys
			\| height = 444
			\| width = 790
			}}

			=== Standard Vibration Measurement Equipment ===
			* Thirty-six channel data acquisition and digital signal processing hardware (24 input and 12 programmable channels), 80kHZ bandwidth, 24-bit ADC (Data Physics Abacus 906)
			** Full SignalCalc DP900 software suite for Data Physics DAQ and modal shaker
			** One modal test shaker, 18 lbf (80 N) maximum force, 1 in (25.4 mm) maximum displacement, 60 in/s (1.5 m/s) max velocity, with accompanying amplifier (Data Physics SignalForce GW-M20/PA100EC)
			* Thirty-five uniaxial and ten triaxial accelerometers (PCB 353B15 and 356A03)
			* Eight-channel, ICP sensor signal conditioner (PCB 483C05)
			* Four modally tuned impact hammers of various load ratings (PCB 086E80, 086C03, 086D05, and 086D20)

			== Current Members ==
			{\| class="wikitable"
			\|-
			! scope="col"\| Researcher
			! scope="col"\| Research
			\|-
			! scope="row"\| Keegan Moore
			\| MoDAL director. Focus on nonlinear dynamics and vibrations, data analytics, data-driven modeling, machine learning, reduced-order modeling, passive redirection of mechanical energy, and others.
			\|-
			! scope="row"\| Cristian López
			\| PhD student studying data-driven nonlinear system identification.
			\|-
			! scope="row"\| Manal Mustafa
			\| PhD student investigating the effect of mass on energy exchanges in nonlinear oscillators and the passive manipulation of energy flows in nonlinear structures.
			\|-
			! scope="row"\| Qirui He
			\| PhD student working on physics-based, reduced-order modeling of bolted joint loosening.
			\|-
			!Mohammad Nasr
			\|MS student working on smart, predictive automatic modal impact hammers.
			\|}

			[[File:ROM Joint Loosening Summary .jpg\|800px\|center\|Summary of our recent work on reduced-order modeling of joint loosening mechanics.]]

			<div style="text-align: center;">Summary of our recent work on reduced-order modeling of joint loosening mechanics. Sponsored by an
			[https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237715 NSF CAREER award].</div>

			== Data-driven Nonlinear Dynamics and Vibrations Course ==
			The lectures from our course titled "Data-driven Nonlinear Dynamics and Vibrations" can be accessed through [https://www.youtube.com/playlist?list=PLW7h3DNOAgFy88IaF5on76YDUoPq2Alaw YouTube].

			== References ==
			[[Category:Research Groups]]

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