A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis

This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model

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

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

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

The novel adenosine A_2A antagonist prodrug MSX-4 is effective in animal models related to motivational and motor functions

Adenosine A2A and dopamine D2 receptors interact to regulate diverse aspects of ventral and dorsal striatal functions related to motivational and motor processes, and it has been suggested that adenosine A2A antagonists could be useful for the treatment of depression, parkinsonism and other disorders. The present experiments were performed to characterize the effects of MSX-4, which is an amino acid ester prodrug of the potent and selective adenosine A2A receptor antagonist MSX-2, by assessing its ability to reverse pharmacologically induced motivational and motor impairments. In the first group of studies, MSX-4 reversed the effects of the D2 antagonist eticlopride on a concurrent lever pressing/chow feeding task that is used as a measure of effort-related choice behavior. MSX-4 was less potent after intraperitoneal administration than the comparison compound, MSX-3, though both were equally efficacious. With this task, MSX-4 was orally active in the same dose range as MSX-3. MSX-4 also reversed the locomotor suppression induced by eticlopride in the open field, but did not induce anxiogenic effects as measured by the relative amount of interior activity. Behaviorally active doses of MSX-4 also attenuated the increase in c-Fos and pDARPP-32(Thr34) expression in nucleus accumbens core that was induced by injections of eticlopride. In addition, MSX-4 suppressed the oral tremor induced by the anticholinesterase galantamine, which is consistent with an antiparkinsonian profile. These actions of MSX-4 indicate that this compound could have potential utility as a treatment for parkinsonism, as well as some of the motivational symptoms of depression and other disorders

Trans locus inhibitors limit concomitant polysaccharide synthesis in the human gut symbiont Bacteroides fragilis

Bacteroides is an abundant genus of bacteria of the human intestinal microbiota. Bacteroides species synthesize a large number of capsular polysaccharides (PS), a biological property not shared with closely related oral species, suggesting importance for intestinal survival. Bacteroides fragilis, for example, synthesizes eight capsular polysaccharides per strain, each of which phase varies via inversion of the promoters located upstream of seven of the eight polysaccharide biosynthesis operons. In a single cell, many of these polysaccharide loci promoters can be simultaneously oriented on for transcription of the downstream biosynthesis operons. Here, we demonstrate that despite the promoter orientations, concomitant transcription of multiple polysaccharide loci within a cell is inhibited. The proteins encoded by the second gene of each of these eight loci, collectively designated the UpxZ proteins, inhibit the synthesis of heterologous polysaccharides. These unique proteins interfere with the ability of UpxY proteins encoded by other polysaccharide loci to function in transcriptional antitermination of their respective operon. The eight UpxZs have different inhibitory spectra, thus establishing a hierarchical regulatory network for polysaccharide synthesis. Limitation of concurrent polysaccharide synthesis strongly suggests that these bacteria evolved this property as an evasion-type mechanism to avoid killing by polysaccharide-targeting factors in the ecosystem