Predicting the influence of strain on crack length measurements performed using the potential drop method

The potential drop (PD) crack growth measurement technique is sensitive to strain accumulation which is often erroneously interpreted as crack extension. When testing ductile materials these errors can be significant, but in many cases the optimum method of minimising or supressing them remains unknown because it is extremely difficult to measure them experimentally in isolation from other sources of error, such non-ideal crack morphology. In this work a novel method of assessing the influence of strain on PD, using a sequentially coupled structural electrical finite element (FE) model, has been developed. By comparing the FE predictions with experimental data it has been demonstrated that the proposed FE technique is extremely effective at predicting trends in PD due to strain. It has been used to identify optimum PD configurations for compact tension, C(T), and single edge notched tension, SEN(T), fracture mechanics specimens and it has been demonstrated that the PD configuration often recommended for C(T) specimens can be subject to large errors due to strain accumulation. In addition, the FE technique has been employed to assess the significance of strain after the initiation of stable tearing for a monotonically loaded C(T) specimen. The proposed FE technique provides a powerful tool for optimising the measurement of crack initiation and growth in applications where large strains are present, e.g. J-R curve and creep crack growth testing

Microstructure modelling of hot deformation of Al–1%Mg alloy

This study presents the application of the finite elementmethod and intelligent systems techniques to the prediction of microstructural mapping for aluminium alloys. Here, the material within each finite element is defined using a hybrid model. The hybrid model is based on neuro-fuzzy and physically based components and it has been combined with the finite element technique. The model simulates the evolution of the internal state variables (i.e. dislocation density, subgrain size and subgrain boundary misorientation) and their effect on the recrystallisation behaviour of the stock. This paper presents the theory behind the model development, the integration between the numerical techniques, and the application of the technique to a hot rolling operation using aluminium, 1 wt% magnesium alloy. Furthermore, experimental data from plane strain compression (PSC) tests and rolling are used to validate the modelling outcome. The results show that the recrystallisation kinetics agree well with the experimental results for different annealing times. This hybrid approach has proved to be more accurate than conventional methods using empirical equations

Improvements in the Measurement of Creep Crack Initiation and Growth Using Potential Drop

To predict the residual life of components operating in the creep regime, it is vital to accurately identify crack initiation, and measure subsequent crack growth, in laboratory tests. Potential drop (PD) measurements, used for this purpose, are susceptible to errors caused by the accumulation of creep strain. For creep ductile materials, this can result in highly conservative crack initiation models and the implementation of unnecessary inspection and maintenance programmes that can cost millions of pounds in lost revenue. Conversely, the crack growth models can be non-conservative. Using a novel combination of interrupted creep crack growth (CCG) tests and sequentially coupled structural-electrical finite element analyses a new method of interpreting PD data has been developed and validated. It uses an increase in gradient on a plot of PD vs. load-line displacement to accurately identify crack initiation. This has been compared to the current method in ASTM E1457-15 by reanalysing data from CCG tests performed on a range of materials at various temperatures and loads. The initiation times, measured using the current ASTM method, were underestimated by factors of up to 23 and the subsequent crack growth rates were underestimated by factors of up to 1.5

Dental Caries, Fluorosis, and Fluoride Exposure in Michigan Schoolchildren

This study relates the prevalence of caries and fluorosis among Michigan children, residing in four different areas, to the various concentrations of F in the communities' water supplies. Demographic information, details of F history, and dental attendance data were collected by a questionnaire form filled out by parents. Children ages six to 12 were screened for caries by means of the NIDR criteria and for fluorosis by means of the TSIF index. Results pertain only to continuous residents and the permanent dentition. The prevalence of both caries and fluorosis was significantly associated with the F concentration in the community water supply. Approximately 65% of all children were caries-free, ranging from 55.1 % in fluoride-deficient Cadillac to 73.7% in Redford (1. 0 ppm F). About 36% of all children had dental fluorosis, ranging from 12.2 in Cadillac to 51.2 in Richmond (1.2 ppm). All of the fluorosis was very mild. From logistic regression, the prevalence of caries was significantly associated with age, dental attendance, and the use of a water supply fluoridated at 1.0 ppm. The odds of experiencing fluorosis increased at every F level above the baseline (Cadillac), with the use of topical F rinses, and with age. Results suggest that children in the four communities may be ingesting a similar level of F from sources such as dentifrices, dietary supplements, and professional applications, but the factor that differentiates them with respect to the prevalence of caries and fluorosis is the F concentration in the community water supply.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66926/2/10.1177_00220345880670050101.pd

Exact Asymptotic Results for Persistence in the Sinai Model with Arbitrary Drift

We obtain exact asymptotic results for the disorder averaged persistence of a Brownian particle moving in a biased Sinai landscape. We employ a new method that maps the problem of computing the persistence to the problem of finding the energy spectrum of a single particle quantum Hamiltonian, which can be subsequently found. Our method allows us analytical access to arbitrary values of the drift (bias), thus going beyond the previous methods which provide results only in the limit of vanishing drift. We show that on varying the drift, the persistence displays a variety of rich asymptotic behaviors including, in particular, interesting qualitative changes at some special values of the drift.Comment: 17 pages, two eps figures (included

Aspirin: A review of its neurobiological properties and therapeutic potential for mental illness

There is compelling evidence to support an aetiological role for inflammation, oxidative and nitrosative stress (O&NS), and mitochondrial dysfunction in the pathophysiology of major neuropsychiatric disorders, including depression, schizophrenia, bipolar disorder, and Alzheimer's disease (AD). These may represent new pathways for therapy. Aspirin is a non-steroidal anti-inflammatory drug that is an irreversible inhibitor of both cyclooxygenase (COX)-1 and COX-2, It stimulates endogenous production of anti-inflammatory regulatory 'braking signals', including lipoxins, which dampen the inflammatory response and reduce levels of inflammatory biomarkers, including C-reactive protein, tumor necrosis factor-α and interleukin (IL)--6, but not negative immunoregulatory cytokines, such as IL-4 and IL-10. Aspirin can reduce oxidative stress and protect against oxidative damage. Early evidence suggests there are beneficial effects of aspirin in preclinical and clinical studies in mood disorders and schizophrenia, and epidemiological data suggests that high-dose aspirin is associated with a reduced risk of AD. Aspirin, one of the oldest agents in medicine, is a potential new therapy for a range of neuropsychiatric disorders, and may provide proof-of-principle support for the role of inflammation and O&NS in the pathophysiology of this diverse group of disorders

Distributed Response Time Analysis of GSPN Models with MapReduce

widely used in the performance analysis of computer and communications systems. Response time densities and quantiles are often key outputs of such analysis. These can be extracted from a GSPN’s underlying semi-Markov process using a method based on numerical Laplace transform inversion. This method typically requires the solution of thousands of systems of complex linear equations, each of rank n, where n is the number of states in the model. For large models substantial processing power is needed and the computation must therefore be distributed. This paper describes the implementation of a Response Time Analysis module for the Platform Independent Petri net Editor (PIPE2) which interfaces with Hadoop, an open source implementation of Google’s MapReduce distributed programming environment, to provide distributed calculation of response time densities in GSPN models. The software is validated with analytically calculated results as well as simulated ones for larger models. Excellent scalability is shown. I