Engineering topics

Leser, Patrick E.

Publications and source records attributed to Leser, Patrick E..

6 records

NASA NTRS2017

Baseline-Subtraction-Free (BSF) Damage-Scattered Wave Extraction for Stiffened Isotropic Plates

Lamb waves enable long distance inspection of structures for health monitoring purposes. However, this capability is diminished when applied to complex structures where damage-scattered waves are often buried by scattering from various structural components or boundaries in the time-space domain. Here, a baseline-subtraction-free (BSF) inspection concept based on the Radon transform (RT) is proposed to identify and separate these scattered waves from those scattered by damage. The received time-space domain signals can be converted into the Radon domain, in which the scattered signals from structural components are suppressed into relatively small regions such that damage-scattered signals can be identified and extracted. In this study, a piezoelectric wafer and a linear scan via laser Doppler vibrometer (LDV) were used to excite and acquire the Lamb-wave signals in an aluminum plate with multiple stiffeners. Linear and inverse linear Radon transform algorithms were applied to the direct measurements. The results demonstrate the effectiveness of the Radon transform as a reliable extraction tool for damage-scattered waves in a stiffened aluminum plate and also suggest the possibility of generalizing this technique for application to a wide variety of complex, large-area structures.

He, Jiaze↗

NASA NTRS2017

A Diagnosis-Prognosis Feedback Loop for Improved Performance Under Uncertainties

The feed-forward relationship between diagnosis and prognosis is the foundation of both aircraft structural health management and the digital twin concept. Measurements of structural response are obtained either in-situ with mounted sensor networks or offline using more traditional techniques (e.g., nondestructive evaluation). Diagnosis algorithms process this information to detect and quantify damage and then feed this data forward to a prognostic framework. A prognosis of the structure's future operational readiness (e.g., remaining useful life or residual strength) is then made and is used to inform mission- critical decision-making. Years of research have been devoted to improving the elements of this process, but the process itself has not changed significantly. Here, a new approach is proposed in which prognosis information is not only fed forward for decision-making, but it is also fed back to the forthcoming diagnosis. In this way, diagnosis algorithms can take advantage of a priori information about the expected state of health, rather than operating in an uninformed condition. As a feasibility test, a diagnosis-prognosis feedback loop of this manner is demonstrated. The approach is applied to a numerical example in which fatigue crack growth is simulated in a simple aluminum alloy test specimen. A prognosis was derived from a set of diagnoses which provided feedback to a subsequent set of diagnoses. Improvements in accuracy and a reduction in uncertainty in the prognosis- informed diagnoses were observed when compared with an uninformed diagnostic approach.

Leser, Patrick E.↗

NASA NTRS2016

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results.

Leser, Patrick E.↗

NASA NTRS2016

A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

Warner, James E.↗

NASA NTRS2015

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

Leser, Patrick E.↗

NASA NTRS2014

Mitigation of Crack Damage in Metallic Materials

A system designed to mitigate or heal crack damage in metallic materials has been developed where the protected material or component is coated with a low-melting temperature film. After a crack is formed, the material is heated, melting the film which then infiltrates the crack opening through capillary action. Upon solidification, the healing material inhibits further crack damage in two ways. While the crack healing material is intact, it acts like an adhesive that bonds or bridges the crack faces together. After fatigue loading damages, the healing material in the crack mouth inhibits further crack growth by creating artificially-high crack closure levels. Mechanical test data show that this method sucessfully arrests or retards crack growth in laboratory specimens.

Leser, Patrick E.↗