A continuum mechanics model for fatigue life prediction with pre- corrosion and sequential corrosion fatigue

We present a continuum model to predict pre-corrosion fatigue which is a prevalent damage mechanism in aerospace structures under operational conditions. It is assumed that the process of corrosion and fatigue sometimes exist separately to a large extent. In this scenario, it is assumed that when an aircraft is in fight at high altitude, cyclic loading due to engine vibration and flutter is at its maximum, whereas the corrosive processes due to moisture or temperature are minimal. And when the aircraft is on the ground the corrosive process is at its maximum, whereas vibration loading is non-existent. For demonstration purposes, we study the effect of prior corrosion on fatigue life of aluminum alloy 7075-T6. In this work, we employ Continuum Damage Mechanics (CDM) as the modeling platform to study the fatigue crack initiation and growth from a pre-existing corrosion pit. In the CDM approach, a crack is assumed to initiate when damage variable, D, attains a critical value Dc. We use the corrosion-free fatigue data to calibrate Dc0 for 7075-T6. This value for critical damage signifies the failure of a representative value element (RVE) when corrosion is non-existent, see Fig. 1. In other words, the corrosion exposure time is zero, t = 0. The corrosion RVE starts to corrode as time elapses. The effect of corrosion is shown by increased in surface roughness. At initial times of exposure, damage occurs as corrosion pits and increased surface roughness. As time passes, pits grow in size and spread over the entire surface of RVE. After long time of exposure, the RVE will corrode in a self-similar manner, meaning that we assume that surface roughness reaches a limit value while uniform surface recession continues. We refer to this model as the concept of corroded RVE as shown in Fig. 1. We used this model to predict the fatigue life of 7075-T6 exposed for 0, 96, 768 and 1536 hrs in the prehesion spray. The predicted results are in a reasonable agreement with experimental data. We further tested the model for life prediction of sequential corrosion-fatigue scenarios where corrosion and fatigue occur in sequence. For maximum stress of σmax = 340 MPa, load ratio of R = 0.1 and exposure time of texp = 100 hrs, the model predicts 17% increase in fatigue life for sequential corrosion-fatigue than the pre-corrosion fatigue. This result is interesting since it shows the interaction between corrosion and fatigue cycles. The result infers that if the corrosion time is spread over the fatigue cycles the life may increase. Please click Additional Files below to see the full abstract

Discourse-Level Language Understanding with Deep Learning

Designing computational models that can understand language at a human level is a foundational goal in the field of natural language processing (NLP). Given a sentence, machines are capable of translating it into many different languages, generating a corresponding syntactic parse tree, marking words that refer to people or places, and much more. These tasks are solved by statistical machine learning algorithms, which leverage patterns in large datasets to build predictive models. Many recent advances in NLP are due to deep learning models (parameterized as neural networks), which bypass user-specified features in favor of building representations of language directly from the text. Despite many deep learning-fueled advances at the word and sentence level, however, computers still struggle to understand high-level discourse structure in language, or the way in which authors combine and order different units of text (e.g., sentences, paragraphs, chapters) to express a coherent message or narrative. Part of the reason is data-related, as there are no existing datasets for many contextual language-based problems, and some tasks are too complex to be framed as supervised learning problems; for the latter type, we must either resort to unsupervised learning or devise training objectives that simulate the supervised setting. Another reason is architectural: neural networks designed for sentence-level tasks require additional functionality, interpretability, and efficiency to operate at the discourse level. In this thesis, I design deep learning architectures for three NLP tasks that require integrating information across high-level linguistic context: question answering, fictional relationship understanding, and comic book narrative modeling. While these tasks are very different from each other on the surface, I show that similar neural network modules can be used in each case to form contextual representations

Electrochemical stress intensity approach to modeling galvanic coupling and localized damage initiation in Navy Structures

Traditionally, airframe structures are designed for immediate mechanical performance and loads-only structural response; the lifetime of aircraft structures is predicted on these analyses and environmental degradation of properties over the life cycle and during operations is often an afterthought. Although the maintenance of aircraft structures is primarily determined by material degradation, galvanic management of airframe designs and corrosion resistant material selection has never been done systematically. From end of life tear-down inspections, we know that, predominantly, structural failures are initiated from corrosion features, especially those accelerated by dissimilar material coupling. In its most simplistic form, this environmental exposure, “loading”, creates corrosion features, such as pitting, that produce crack initiation morphologies, cracks nucleate from these features and then grow under the combined influence of mechanical stress and corrosion, eventually leading to structural failure. There is clearly a strong correlation between corrosion and structural damage, which we think of as corrosion fatigue and stress corrosion cracking. We propose that it is possible to treat “electrochemical stress” mathematically in a similar way to mechanical stress, with numerically equivalent approaches. Using such a model, the combined influence of electrochemistry and stress can, in principle, be treated as the sum of these two stresses, allowing us to develop models to predict the risk of environmentally assisted fatigue and stress corrosion cracking damage. ONR’s Sea-Based Aviation program is developing computational approaches to corrosion activity prediction, crack initiation, and crack growth, with the ultimate aim of predicting service life in terms of the combination of mechanical and chemical stress. This approach is intended to be the basis for design of durable aircraft structures, using design principles that will take into account both stress and corrosion in the design phase, rather than designing for stress and then maintaining for corrosion

Building environmental history for naval aircraft

Operating environments of Navy aircraft vary to a good degree depending upon the squadron location, flight requirements, and other related field and ground activities. All these conditions promote both mechanical and environmental damage of various types. Uniform stress free corrosion arises from the geographical location ambient weather and the average ground time of the aircraft. Even a small scratch during operation can lead to a corrosion cell arising under a surface moisture film. Use of de-icing salts in cold environments will also accelerate the corrosion process. Crevice corrosion, meanwhile, results from the accumulation of dirt and debris in confined spaces such as access door flanges and wheel wells; as well as capillary action that keeps faying surfaces wetted even in low humidity exterior conditions. Due to the presence of wet condensates and appreciable concentrations of chloride (and other active) ions, many areas of the aircraft are prone for pitting, intergranular attack or exfoliation. The aircraft operations will also have influence on type and morphology of corrosion. Thus, building an environmental history of the aircraft is crucial to correctly identify different corrosion and mechanical damage processes to monitor and track the development of attack in many areas of the aircraft structure. We outline a method for building the environmental history of Naval aircraft using three available resources: maintenance and materials management (3M) system data, daily weather history data of the squadron location, and field activity data as recorded in logbooks. This includes development of a part-specific microclimate builder which tracks a local climate history specific to a part or component. The climate builder takes the flight information such as flight duration, altitude, geographical location, etc., as well as daily weather history data from the NOAA Database. It will translate the asset service history (such as: stored indoors vs. outdoors) into a series of scenarios and assigns weighting factors to these scenarios based on geographical location. The underlying approach that is currently used in the climate scenario builder is based on both models/simulations and sensor data. Our current suite of models combined with the climate builder will provide an integrated framework within which life prediction for a combinatorial situation of mechanical and environmental history can be performed

Electrochemical-mechanical phase field model for electroplating process

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Fatigue crack growth behavior under multiaxial variable amplitude loading

This study compares both uniaxial and multiaxial variable amplitude experimental crack growth data for naturally initiated fatigue cracks in tubular specimens of 2024-T3 aluminum alloy to predictions based on two state-of-the-art analysis codes: UniGrow and FASTRAN. For variable amplitude fatigue tests performed under pure axial nominal loading conditions, both UniGrow and FASTRAN analyses were found to produce mostly conservative growth life predictions, despite good agreement with constant amplitude crack growth data. For variable amplitude torsion and combined axial-torsion crack growth analyses, however, the conservatism in growth life predictions was found to reduce. This was attributed to multiaxial nominal stress state effects, such as T-stress and mixed-mode crack growth, which are not accounted for in either UniGrow or FASTRAN, but were found in constant amplitude fatigue tests to increase experimental crack growth rates. Since cracks in this study were initiated naturally, different initial crack geometry assumptions were also investigated in the analyse

UnioGrow & UnioCorr Life Prediction Models

In this brief, UniGrow-UniCorr framework for fatigue crack initiation, growth, and corrosion are discussed along with perspectives on modeling and sensor development for detecting on-set of corrosion damage. The UniGrow crack growth program is first described with brief discussion of its formulation, implementation, and validation results. Various features that are currently present in UniGrow software and planned extensions are also discussed. The current status and development plans for UniCorr to address stress-free and stress-assisted environment assisted cracking are then described. Salient aspects of corrosion modeling are briefly described along with a description of a continuum damage mechanics model to study pit-to-crack transition life in aluminum alloys. Current development efforts that are underway to advance model prediction based on multi-physics study of corrosion fatigue damage mechanism by taking into account the microstructural features are described. In this context, coupled modeling of micro-plasticity and micro galvanic process is discussed to address the synergistic effect between stress and corrosion. The effects of intermetallic particles on pit initiation and growth mechanisms as well as stress effect on corrosion kinetics are described. Requirements and challenges in appropriate sensor development especially for those intended as leave-in-place devices and for detecting on-set of corrosion crack initiation are outlined

Multiaxial Fatigue Spectrum Editing by Using Combined Wavelet Analysis and Stress Invariant Approach

The practicalities of structural fatigue testing limit the fidelity of the cyclic load history that can be applied to a test structure. Testing is, therefore, a compromise between fatigue damage fidelity and test economy. A new methodology is proposed for multiaxial loading spectrum editing to extract cycles that contribute negligible damage during fatigue crack initiation. The method is based on projection by projection (PbP) technique and wavelet transform analysis (WTA) procedure. In this approach, the cycles with negligible contribution to damage in every decoupled projected loading path (i.e. obtained from PbP approach) are extracted using the WTA procedure. Each extracted segment is then replaced with an equivalent cycle that produces the same amount of damage. The effectiveness of the edited spectrums is evaluated by the degree of fatigue damage retention as the original damage and preservation of statistical parameter values. As a case study, the proposed approach has been applied to the numerically produced random bending-torsion fatigue spectrum in plane-stress condition. The result shows an average of 75% reduction of the original spectrums with retention of 90% of the original spectrums’ damage values