Funded by the National Science Foundation through the Faculty Early Career Development Program (CAREER) program.
This research clarifies how loss of integrity of bolted joints affects the resilience and progression to failure of safety- and reliability-critical mechanical structures, with application to vehicles, industrial equipment, biomedical implants, space telescopes, and playground equipment, thereby promoting the progress of science, and advancing the national prosperity and welfare. Loose bolts and screws are a common problem in US infrastructure. Structural failures often have catastrophic consequences, for example, resulting in vehicle crashes or train derailments that lead to casualties, economic loss, and environmental damage. Little is known about how a structure’s dynamics influences the loosening of bolted joints over long periods of operation. Models calibrated using data from the time of assembly fail to enable optimized preventative maintenance over a structure’s lifetime. This project overcomes these challenges by developing predictive modeling approaches that capture the coupling between structural dynamics and the integrity of bolted joints over timespans consistent with the structure’s service life, leading to improved health monitoring of aging infrastructure, toughened designs against the impacts of extreme weather, and more reliable energy generation from renewable sources. The project advances STEM learning and enhances diversity through a “Teach for Discovery” approach that engages students’ natural curiosity through game-based learning and virtual reality experiences. Advise from, and outreach to, industry and national labs ensures that the research activities are informed by industry needs and that results are accessible and effectively assessed.
This research aims to develop the foundations of a modeling and validation framework for predicting the long-term nonlinear dynamics of assembled structures with loosening bolted joints. It achieves this aim partly by combining new experimental and modeling techniques to map the strains around a tightened bolt head to the underlying contact conditions inside the interface as the joint evolves dynamically, for example, to test the hypothesis that the rate of loosening is independent of the relative displacement across the interface. Models relating bolt tension to joint stiffness and damping and differential equations governing the evolution of the tensions are then derived from experimental measurements. Finally, the theory of nonlinear normal modes is expanded to incorporate bolt tension as a dependent variable and used to characterize the influence of internal resonances on the progression of local damage.
Nonlinear Dynamics of Wave-induced Vibrations in Ships and Aircraft
Fluid-induced vibrations significantly influence the structural integrity and reliability of many systems by dramatically increasing both fatigue and damage. These vibrations undergo changes in their intensity and characteristics depending on the motion of the fluid, such that their underlying nature is inherently nonlinear. The overall objective of this proposal is to leverage structural nonlinearity to reproduce strongly nonlinear fluid-structure interactions in ground vibration testing. The research places a central emphasis on using tools from nonlinear dynamics to explore and comprehend the mechanisms that give rise to extreme motions, such as slamming in ships and gust excitation in aircraft, and their effects on the structure. The overall objective will be achieved through three research objectives: 1) produce physics-based reduced-order models (ROMs) for fluid-structure interactions; 2) determine the nonlinear mechanisms governing extreme motions; and 3) translate the modeling methodology into reduced-order testing methods.
This research will produce new methods for reproducing fluid-structure interactions during ground vibration testing including extreme motions. These methods will enable new agile approaches to design and operation that exploit strong nonlinearity to increase resilience, durability, and sustainability. The first objective will produce physics-based ROMs capable of accurately predicting structural loads and stress distributions throughout the system. With significantly reduced simulation times, these ROMs will enable the prediction of long-term fatigue and structural damage evolution. The second objective will decipher the underlying bifurcations, nonlinear resonances, and nonlinear energy transfers governing the complex nonlinear responses caused by fluid-structure interactions. This will produce a new understanding of how dynamic transitions and instabilities give rise to extreme motions, such as slamming in ships or gust responses in aircraft. The third objective will generate new simulation-driven testing approaches capable of reproducing fluid-structure interactions in experimental systems through ground-vibration testing. These methods will empower engineers to swiftly prototype designs using 3D printing and evaluate their structural responses within a laboratory setting, eliminating the requirement for specialized facilities (e.g., wind tunnels) during early design phases.
Digital Engineering the Test and Modeling Process: Autonomous Methods for Reconciling Test and Model ResultsFunded by AFOSR Young Investigator Award.
This research focuses on data-driven and deep learning approaches for autonomizing the validation and updating of digital models using Test and Evaluation (T&E) data. The first part of this research will create novel overlapping neural networks that leverage the principle of time reversibility to autonomously repair T&E data with missing data segments. The second portion will produce advanced mathematical techniques for infusing physics into autoencoder neural networks for extracting corresponding universal representations from both test and model results, facilitating the comparison of similar but disparate datasets. The third part will introduce and deploy new generator-discriminator-translator networks by leveraging the power of generative adversarial networks to autonomously update digital model parameters using T&E data. The new deep learning frameworks will be employed on data taken from computer-generated signals, numerical simulations, and experimental measurements.
Multi-directional Vibration Mitigation in Aircraft with High-aspect Ratio Wings
The overall efficiency and performance of commercial airliners must increase to meet growing public demands for reduced environmental impact. One approach targeted by NASA is to enhance the aerodynamic performance of current aircraft designs using high-aspect-ratio (HAR) wings. Specifically, HAR wings exhibit low-frequency, high-amplitude vibrations in both vertical (yaw axis) and longitudinal (roll axis) directions. To combat these unwanted vibrations, this research investigates novel vibration mitigation strategies that employ two-dimensional nonlinear vibration absorbers (2D-NVAs) to rapidly dissipate unwanted vibrations in HAR-wing aircraft. Current efforts focus on a 2D-NVA composed of two low-mass, rigid bodies namely the housing and the impactor. The housing consists of a ring-like rigid body in the shape of an ellipse, while the impactor is a solid cylindrical mass that resides inside the cavity of the housing. The 2D-NVA is designed such that the impactor can contact the inner surface of the housing under both impacts and constrained sliding motion. The research so far has demonstrated that the 2D-NVA can mitigate multiple modes of vibration excited by impulsive loading of a model airplane with HAR wings in both vertical and longitudinal directions. Ongoing efforts focus on the optimization of the housing shape to enhance the performance of the 2D-NVA and the use of other types of nonlinearities.
Detecting Gait Phases with Raised Metabolic Cost using Robotic Perturbations and System Identification for Enabling Targeted Rehabilitation TherapyThis project is jointly funded by the Disability and Rehabilitation Engineering Program (DARE) and the Established Program to Stimulate Competitive Research (EPSCoR).
Being able to walk easily is strongly associated with independence and quality of life. Aging is accompanied by a significant reduction in mobility. Existing treatments and therapies rely on respiratory measurements of walking effort. These respiratory measurements can only quantify the average effort of walking. As a result of this limitation, existing treatments and therapies sometimes fail to target the phases of the walking motion that need the most assistance. This project will use data-driven approaches and models to overcome limitations in the ability to measure the effort of walking. Access to this new information will enable evaluating how therapies affect different stages of motion. The data-driven methods will be initially developed using a dataset generated by a computer walking models with physically induced changes to specific stages of motion. For example, forward-pulling forces will be applied to the waist of the model to induce changes that can be leveraged to detect the fluctuations in walking effort. This computer walking model provides access to a complete measure of the effort required for walking, which will be used to validate the data-driven methods. Next, the new data-driven methods will be validated using measurements from real human walking experiments. In these human experiments, pulling forces will be applied by a robotic tether connected to the waist of the participant to induce changes that will be used to detect the effort of the different motion stages. In the final studies, the methods will be used to determine how the effort required for walking differs in younger and older adults. The differences in the effort will be characterized in each stage of motion using human experiments with both younger and older adults. The outcomes of this project will help lead to the creation of enhanced treatments and assistive devices that improve all stages of motion. Throughout this project, the investigators will provide courses for older adults on the mechanics and health aspects of walking and data science and digital engineering through the Osher Lifelong Learning Institute.
The goal of this project is to leverage new data-driven approaches to characterize differences in metabolic cost of phases of the gait cycle in old versus young adults. The project will combine novel, data-driven approaches based on system identification and robotic perturbations to characterize the time profile of signals that cannot be measured directly, such as metabolic cost. The first objective will produce the time profile of metabolic cost within simulated gait data. Novel data-driven approaches will be developed based on weighted regression, neural networks, and autoencoders to identify the metabolic cost time profile from biomechanical signals. Initially, these methods will be created in a predictive walking simulation from which the metabolic time profile is fully known, such that the new methods can be evaluated during their development. The second objective will evaluate different time profile estimation approaches in human experiments. The methods created in the first objective will be tested using human experiments with robotic perturbations. The capacity of using the data-driven methods to detect changes in swing and push-off will also be investigated using human experiments where elastic ankle tethers or added mass are used to introduce direct changes to the gait cycle. The third objective will characterize the differences in cost contributions of the phases of the gait cycle between older and younger adults. The first subtask will characterize the phase-specific differences in metabolic cost by applying the data-driven methods to compute the instantaneous costs using measured data from younger and older adults. The second subtask will determine the generalizability of the data-driven time-profile estimation approaches across different populations. This research will transform gait analysis by providing access to dynamic metabolic cost time profiles, which cannot be measured using existing techniques. Access to this new information will lead to improvements across multiple biomechanics applications, including (1) diagnosis of motion impairments, (2) prescription of targeted assistive devices, and (3) targeted rehabilitation exercises.
This research introduces the characteristic nonlinear system identification (CNSI) procedure, which is a novel, data-driven approach for modeling the dynamics of local, nonlinear attachments. The CNSI method is unique in that it requires no prior knowledge of the linear or nonlinear dynamics of the primary structure or the attachment. Instead, the procedure relies entirely on the measured response of the attachment and its connection points (such that the relative motion can be computed), the mass of the attachment, and a model for its dynamics. The method is applied in two phases: first, the measured response is post-processed to obtain characteristic displacements and velocities (comparable to instantaneous amplitudes), and instantaneous frequency and damping curves based on the relative motion. Second, the analyst proposes a model for the dynamics of the attachment and performs a systematic identification of the unknown parameters in the model using the post-processed data from the previous phase. The result is a reduced-order model for the nonlinear physics governing the response of the attachment that incorporates both nonlinear stiffness and damping models.
Publications:
- K.J. Moore, “Characteristic Nonlinear System Identification: A Data-driven Approach for Local Nonlinear Attachments,” Mechanical Systems and Signal Processing, 131:335-347, 2019. http://dx.doi.org/10.1016/j.ymssp.2019.05.066
- A. Singh, K.J. Moore, “Characteristic Nonlinear System Identification of Clearance Nonlinearities in Local Attachments,” Nonlinear Dynamics, 102:1667-1684, 2020. http://dx.doi.org/10.1007/s11071-020-06004-8
- A. Singh, K.J. Moore, “Identification of Multiple Local Nonlinear Attachments Using a Single Measurement,” Journal of Sound and Vibration, 513:116410, 2021. http://dx.doi.org/10.1016/j.jsv.2021.116410
Funded by AFOSR Summer Faculty Fellowship Program.
During sled-based warhead penetration testing, concrete slabs are often stacked together to form thicker targets under the assumption that the stacked slabs will behave the same as a comparable solid target. However, recent testing has shown that, when the slabs are stacked, they exhibit substantially different damage patterns than comparable single slabs. Specifically, the stacked slabs exhibit significant cracking at the lateral surfaces and minimal damage at the rear surface even when the warhead perforates the rear surface. This damage pattern is drastically different than that of the single slabs where no cracking occurs in the lateral surfaces, but substantial damage is observed in the rear surface. Furthermore, the deceleration of the warhead during the penetration of the stacked slabs is significantly less than that during the penetration of non-touching slabs with comparable total thickness. This research investigates the physical mechanisms that govern the behavior of the slabs under stacked and non-stacked conditions and proposes a reduced-order model for the deceleration of the warhead during penetration. The reduced-order model treats the warhead as a particle and the slab as a buckling spring with a depth- and impact-velocity-dependent stiffness coefficient. The reduced-order model is applied to test cases of a single four-foot-thick slab, two slabs spaced twenty inches apart (non-touching case), and four slabs stacked together spaced 1.67 inches apart. The results substantiate the proposed interpretation of the physical mechanisms and interactions that differentiate the behavior of stacked slabs from the single slabs.