Joints Committee - User contributions [en]

Georgia Institute of Technology

2024-12-10T15:37:06Z

Keeganmoore: /* Current Members */

== 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.
|-
!Sobhan Mohammadi
|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.
|}

[[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]]

Georgia Institute of Technology

2024-12-10T15:32:49Z

Keeganmoore:

== 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]]

Category:Research Groups

2024-12-10T15:17:53Z

Keeganmoore:

Here is a list of current or former researchers in the joint mechanics group:

{| class="wikitable sortable"
|-
! scope="col"| Name
! scope="col"| Affiliation
! scope="col"| Email
|-
! scope="row"| Matt Brake
| [[Rice University]]
| brake@rice.edu
|-
! scope="row"| [[Matt Allen]]
| [[Brigham Young University]]
| matt.allen@byu.edu
|-
! scope="row"| Rob Kuether
| [[Sandia National Labs]]
|
|-
! scope="row"| Keegan Moore
| [[Georgia Institute of Technology]]
| kmoore@gatech.edu
|-
!Dan Roettgen
|
|
|-
!Dan Segalman
|
|
|-
!Malte Krack
| [[University of Stuttgart]]
|krack@ila.uni-stuttgart.de
|-
!Scott Smith
| [[Norwich University]]
|ssmith18@norwich.edu
|-
![[Trevor Jerome]]
|[[NSWCCD]]
|trevor.w.jerome.civ@us.navy.mil
|-
!
|Imperial
|
|-
!
|University of Stuttgart
|
|-
!
|University of Cambridge
|
|-}
|}

Georgia Institute of Technology

2024-12-10T15:17:29Z

Keeganmoore: Created page with "sd"

University of Nebraska-Lincoln

2023-09-18T17:40:12Z

Keeganmoore:

== Research Area ==
=== Vision ===
The [https://engineering.unl.edu/MoDAL/ 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"| Javier Arroyo
| PhD student studying the physics of bolted joint loosening.
|-
! scope="row"| Felipe Kobayashi
| PhD student working on physics-based, reduced-order modeling of bolted joint loosening.
|-
!Geoffrey Soneson
|PhD student working on solitary wave control using engineered defects in metamaterials.
|-
!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]]

University of Nebraska-Lincoln

2023-09-18T17:01:09Z

Keeganmoore:

== Research Area ==
=== Vision ===
The [https://engineering.unl.edu/MoDAL/ 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"| Javier Arroyo
| PhD student studying the physics of bolted joint loosening.
|-
! scope="row"| Felipe Kobayashi
| PhD student working on physics-based, reduced-order modeling of bolted joint loosening.
|-
!Geoffrey Soneson
|PhD student working on solitary wave control using engineered defects in metamaterials.
|-
!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]]

2023-09-14T16:56:10Z

Keeganmoore:

University of Nebraska-Lincoln

2023-09-14T16:52:17Z

Keeganmoore:

== Research Area ==
The [https://engineering.unl.edu/MoDAL/ 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.
# AI-based automated testing of mechanical structures.
# Reduced-order modeling of nonlinear structures (e.g., reduced-order modeling of bolt loosening).
# Nonlinear energy flows in mechanical structures.
# Novel vibration mitigation strategies employing nonlinearities.
# Applications of nonlinear dynamics and vibrations to novel fields (e.g., wave-induced vibrations of ships).

== Equipment ==

=== Digital Image Correlation (DIC) ===
[[File:Cameras&Tripod.jpeg|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.)

=== 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
| 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"| Javier Arroyo
| PhD student studying the physics of bolted joint loosening.
|-
! scope="row"| Felipe Kobayashi
| PhD student working on physics-based, reduced-order modeling of bolted joint loosening.
|-
!Geoffrey Soneson
|PhD student working on solitary wave control using engineered defects in metamaterials.
|-
!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>

[[Category:Research Groups]]

File:2023 MoDALVision.jpg

2023-09-14T16:28:55Z

Keeganmoore:

University of Nebraska-Lincoln

2023-09-14T16:10:31Z

Keeganmoore:

== Research Area ==

== Equipment ==

=== Digital Image Correlation (DIC) ===
[[File:Cameras&Tripod.jpeg|frame|right|alt=High-speed digital cameras|The pair of high-speed digital cameras in MoDAL.]]
We have a high-speed 3D DIC system that we use 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 are capable of recording up to 500,000 frames per second (FPS) at reduced resolution and at 4,000 FPS at 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 maximum sampling rate of 1 MS/s (National Instruments NI 6361)
** Accompanying accessories (cases, speckle paint, etc.)

=== 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)

https://engineering.unl.edu/MoDAL/

== Current Members ==
{| class="wikitable"
|-
! scope="col"| Researcher
! scope="col"| Research
|-
! scope="row"| Keegan
| 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"| Javier Arroyo
| PhD student studying the physics of bolted joint loosening.
|-
! scope="row"| Felipe Kobayashi
| PhD student working on physics-based, reduced-order modeling of bolted joint loosening.
|-
!Geoffrey Soneson
|PhD student working on solitary wave control using engineered defects in metamaterials.
|-
!Mohammad Nasr
|MS student working on smart, predictive automatic modal impact hammers.
|}

[[File:ROM Joint Loosening Summary .jpg|700px|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>

[[Category:Research Groups]]

2021-10-08T14:07:18Z

Keeganmoore:

Category:Research Groups

2021-10-08T14:01:49Z

Keeganmoore:

University of Nebraska-Lincoln

2021-10-08T14:00:42Z

Keeganmoore:

https://engineering.unl.edu/MoDAL/

University of Nebraska-Lincoln

2021-10-08T14:00:26Z

Keeganmoore: https://engineering.unl.edu/MoDAL/

MoDAL

2021-10-08T13:57:27Z

Keeganmoore: Created page with "https://engineering.unl.edu/MoDAL/"

https://engineering.unl.edu/MoDAL/

Category:Research Groups

2021-10-08T13:55:36Z

Keeganmoore: