About

Vision

The 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.

MoDAL Vision
Active Research Directions
  1. Physics-based, data-driven modeling and discovery of governing equations directly from measurements1 2 3
  2. AI-based automated testing of mechanical structures4.
  3. Reduced-order modeling of nonlinear structures5 6.
  4. Nonlinear energy flows in mechanical structures7 8 9.
  5. Novel vibration mitigation strategies employing nonlinearities10.
  6. Advanced signal processing for nonlinear time series data11 12.
  7. Applications of nonlinear dynamics and vibrations to novel fields (e.g., wave-induced vibrations of ships).
Equipment
Digital Image Correlation
The pair of high-speed digital cameras in MoDAL.
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.)
References

  1. K.J. Moore, “Characteristic Nonlinear System Identification: A Data-driven Approach for Local Nonlinear Attachments,” Mechanical Systems and Signal Processing, 131:335347, 2019. ↩
  2. A. Singh, K.J. Moore, “Characteristic Nonlinear System Identification of Clearance Nonlinearities in Local Attachments,” Nonlinear Dynamics, 102:1667-1684, 2020. ↩
  3. A. Singh, K.J. Moore, “Identification of Multiple Local Nonlinear Attachments Using a Single Measurement,” Journal of Sound and Vibration, 513:116410, 2021. ↩
  4. 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. ↩
  5. K.J. Moore, “A Reduced-order Model for Loosening Mechanics of Axial Joints,” ASME Journal of Applied Mechanics, 86(12):121007, 2019. ↩
  6. S. Aldana, K.J. Moore, “Dynamic Interactions Between Two Axially Aligned Threaded Joints Undergoing Loosening,” Journal of Sound and Vibration, 520:116625, 2022. ↩
  7. 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. ↩
  8. C. Wang 3, K.J. Moore, “On Nonlinear Energy Flows in Nonlinearly Coupled Oscillators with Equal Mass,” Nonlinear Dynamics, 103:343-366, 2021. ↩
  9. 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. ↩
  10. 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. ↩
  11. 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. ↩
  12. 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. ↩