Particle filter-based delamination shape prediction in composites subjected to fatigue loading

Modeling generic size features of delamination, such as area or length, has long been considered in the literature for damage prognosis in composites through specific models describing damage state evolution with load cycles or time. However, the delamination shape has never been considered, despite that it holds important information for damage diagnosis and prognosis, including the delamination area, its center, and perimeter, useful for structural safety evaluation. In this context, this paper develops a novel particle filter (PF)-based framework for delamination shape prediction. To this end, the delamination image is discretized by a mesh, where control points are defined as intersections between the grid lines and the perimeter of the delamination. A parametric data-driven function maps each point position as a function of the load cycles and is initially fitted on a sample test. Then, a PF is independently implemented for each node whereby to predict their future positions along the mesh lines, thus allowing delamination shape progression estimates. The new framework is demonstrated with reference to experimental tests of fatigue delamination growth in composite panels with ultrasonics C-scan monitoring

Particle filter-based damage prognosis using online feature fusion and selection

Damage prognosis generally resorts to damage quantification functions and evolution models to quantify the current damage state and to predict the future states and the remaining useful life (RUL). The former typically consists of a function describing the relationship between the damage state and a statistical feature extracted from the measured signals, thus the prognostic performance will strongly depend on the selection of a proper feature. Given the best feature may vary for different specimens or even at each time instant for the same specimen during damage progression, such selection is a challenging task but has received little investigation so far. In this context, this paper proposes a particle filter-based damage prognosis framework, which involves an online feature fusion and selection scheme. A prognostic model is considered for each feature, with a multivariate process equation, formulated using both a damage degradation function and a bias parameter, and a measurement equation linking the damage state and that feature considering a data-driven model and the bias. One PF is used to estimate the damage state, its evolution parameters, and the bias for each model. Then, at each step, the feature with the smallest estimated bias is selected as the best feature providing the most likely state vectors and is used to select the most likely samples of the damage state and growth parameters for predicting the RUL and for calculating the prior at the next step. The proposed prognostic framework is demonstrated by an experimental study, where an aluminum lug structure subject to fatigue crack growth is monitored by a Lamb wave measurement system

Experimental investigation on the mechanical behavior of an innovative parabolic trough collector

In the present work an experimental program aimed at assessing the mechanical behavior of an innovative parabolic solar trough is presented. More specifically, a lightweight and low-cost collector making large use of adhesive joints, which can be easily assembled on-site, still performing at a high efficiency, was designed. Static and fatigue tests were performed on a full-scale prototype of the collector in the pre-production stage. The tests included differential torsion, concentrated and distributed bending, and distributed load (wind effect). During the tests, a network of strain gauges was placed in the most critical locations to measure the strain field, while laser sensors and cable transducers were placed in strategic positions to measure the displacements. The results demonstrate the strengths of the innovative parabolic trough collector and support the assessment of its structural integrity

Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil

[EN] Stochastic upscaling of flow and reactive solute transport in a tropical soil is performed using real data collected in the laboratory. Upscaling of hydraulic conductivity, longitudinal hydrodynamic dispersion, and retardation factor were done using three different approaches of varying complexity. How uncertainty propagates after upscaling was also studied. The results show that upscaling must be taken into account if a good reproduction of the flow and transport behavior of a given soil is to be attained when modeled at larger than laboratory scales. The results also show that arrival time uncertainty was well reproduced after solute transport upscaling. This work represents a first demonstration of flow and reactive transport upscaling in a soil based on laboratory data. It also shows how simple upscaling methods can be incorporated into daily modeling practice using commercial flow and transport codes.The authors thank the financial support by the Brazilian National Council for Scientific and Technological Development (CNPq) (Project 401441/2014-8). The doctoral fellowship award to the first author by the Coordination of Improvement of Higher Level Personnel (CAPES) is acknowledged. The first author also thanks the international mobility grant awarded by CNPq, through the Sciences Without Borders program (Grant Number: 200597/2015-9). The international mobility grant awarded by Santander Mobility in cooperation with the University of Sao Paulo is also acknowledged. DHI-WASI is gratefully thanked for providing a FEFLOW license.Almeida De-Godoy, V.; Zuquette, L.; Gómez-Hernández, JJ. (2019). Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil. 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State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters

The aim of this study is that of presenting a new diagnostic and prognostic method aimed at automatically detecting deviations from the expected degradation dynamics of the batteries due to changes in the operating conditions, or, possibly, anomalous behaviors, and predicting their remaining useful life (RUL) in terms of their state-of-life (SOL), without needing to derive any complex physics-based models and/or gather huge amounts of experimental data to cover all possible operative/fault conditions. The proposed method in fact exploits the real time framework offered by particle filtering and resorts to neural networks in order to build a suitable parametric measurement equation, which provides the algorithm with the capability of automatically adjusting to different battery's dynamic behaviors. The results of this study demonstrate the satisfactory performances of the algorithm in terms of adaptability and diagnostic sensibility, with reference to suitably identified case studies based on actual Lithium-Ion battery capacity data taken from the prognostics data repository of the NASA Ames Research Center database and of the CALCE Battery Group

Particle filter-based hybrid damage prognosis considering bias

Hybrid prognosis combining both the physical knowledge and data-driven techniques has shown great potential for damage prognosis in structural health monitoring (SHM). Current practices consider the physics-based process and data-driven measurement equations to describe the damage evolution and the mapping between the damage state and the SHM signal (or the feature extracted from SHM signal), respectively. However, the bias between the measurements predicted by data-driven equation and the actual SHM measurements, arising from uncertainties like damage geometries and sensor placement or noise, can lead to inaccurate prognosis results. To account for this problem, this paper adopts a methodology typically applied for sensor fault diagnosis, and develops a new hybrid state space model with a bias parameter included into the state vector and the measurement equation. Particle filter (PF) serves as the estimation technique to identify the state and parameters relating to the damage as well as the bias parameter, and RUL can be predicted by the PF estimates and physics-based process equation. The numerical study about the fatigue crack growth shows the new model together with PF can provide satisfactory estimation and prediction results in case of bias in the measurement model

Vulnerability assessment to projectiles: Approach definition and application to helicopter platforms

Survivability is defined as the capability of a platform to avoid or withstand a man-made hostile environment. Military aircraft in particular, but also other kinds of platforms subjected to external, impacting threats, are commonly designed according to increasing survivability requirements. The concept of survivability was first formalized by R. Ball in 1985 in its seminal work on combat aircraft survivability. On the basis of the theory presented in his work, many computer programs have been developed which implement the modelling techniques and computations required by vulnerability assessments. However, a clear and general view of the operative computational procedures is still lacking. Moreover, to date only a limited number of applications to helicopter platforms have been investigated in the survivability field, even though these platforms experience numerous flight conditions exposing the system to different types of threats. In this context, this work aims at establishing a multi-purpose general framework for the vulnerability assessment of different types of platforms subjected to external threats, with a focus on helicopters. The in-house software specifically developed for this application is here described in detail and employed to present a case study on a representative military helicopter

Application of Particle Filtering for Estimating the Dynamics of Nuclear Systems

System dynamics estimation is a crucial issue for the safe operation and control of nuclear power plants. Typically, the estimation is based on a model of the plant dynamics and related measurements. In practice, the non-linearity of the dynamics and non-Gaussianity of the noise associated to the process and measurements lead to inaccurate results even with advanced approaches, such as the Kalman, Gaussian-sum and grid-based filters. On the contrary, accurate results may be obtained with Monte Carlo-based estimation methods, also called particle filters. The present paper illustrates the developments of a previous work by the same authors with regards to the comparison of the so called Sampling Importance Resampling filter method with the standard and extended Kalman filtering techniques. Two case studies are analyzed to separately highlight the effect of non-linearity and non-Gaussianity in the process noise