Multiscale modelling of dendrite growth during vacuum arc remelting

Vacuum Arc Remelting (VAR) is the accepted method for producing homogeneous, fine microstructures that are free of inclusions required for rotating grade applications. However, as ingot sizes are increasing INCONEL 718 becomes increasingly susceptible to defects such as freckles, tree rings, and white spots increases for large diameter billets. Therefore, predictive models of these defects are required to allow optimization of process parameters. In this paper, a multiscale and multi-physics model is presented to predict the development of microstructures in the VAR ingot during solidification. At the microscale, a combined stochastic nucleation approach and finite difference solution of the solute diffusion is applied in the semi-solid zone of the VAR ingot. The micromodel is coupled with a solution of the macroscale heat transfer, fluid flow and electromagnetism in the VAR process through the temperature, pressure and fluid flow fields. The main objective of this study is to achieve a better understanding of the formation of the defects in VAR by quantifying the influence of VAR processing parameters on grain nucleation and dendrite growth. In particular, the effect of different ingot growth velocities on the microstructure formation was investigated. It was found that reducing the velocity produces significantly more coarse grains.

Predictive reliability, prognostics and risk assessment for power modules

This paper describes a framework that is being developed for the prediction and analysis of electronics power module reliability both for qualification testing and in-service lifetime prediction. Physics of failure (PoF) reliability methodology using multi-physics high-fidelity and reduced order computer modelling, as well as numerical optimization techniques, are integrated in a dedicated computer modelling environment to meet the needs of the power module designers and manufacturers as well as end-users for both design and maintenance purposes. An example of lifetime prediction for a power module solder interconnect structure is described. Another example is the lifetime prediction of a power module for a railway traction control application. Also in the paper a combined physics of failure and data trending prognostic methodology for the health monitoring of power modules is discussed.

An Experimental and Predictive Evaluation of Unsteady Gas Flow through Automotive Catalyst Elements

The incorporation of one-dimensional simulation codes within engine modelling applications has proved to be a useful tool in evaluating unsteady gas flow through elements in the exhaust system. This paper reports on an experimental and theoretical investigation into the behaviour of unsteady gas flow through catalyst substrate elements. A one-dimensional (1-D) catalyst model has been incorporated into a 1-D simulation code to predict this behaviour.

Experimental data was acquired using a ‘single pulse’ test rig. Substrate samples were tested under ambient conditions in order to investigate a range of regimes experienced by the catalyst during operation. This allowed reflection and transmission characteristics to be quantified in relation to both geometric and physical properties of substrate elements. Correlation between measured and predicted results is demonstrably good and the model provides an effective analysis tool for evaluating unsteady gas flow through different catalytic converter designs.

Multibody modelling of a TB31 and a TB32 crash test with vertical portable concrete barriers: model verification and sensitivity analysis.

In this article the multibody simulation software package MADYMO for analysing and optimizing occupant safety design was used to model crash tests for Normal Containment barriers in accordance with EN 1317. The verification process was carried out by simulating a TB31 and a TB32 crash test performed on vertical portable concrete barriers and by comparing the numerical results to those obtained experimentally. The same modelling approach was applied to both tests to evaluate the predictive capacity of the modelling at two different impact speeds. A sensitivity analysis of the vehicle stiffness was also carried out. The capacity to predict all of the principal EN1317 criteria was assessed for the first time: the acceleration severity index, the theoretical head impact velocity, the barrier working width and the vehicle exit box. Results showed a maximum error of 6% for the acceleration severity index and 21% for theoretical head impact velocity for the numerical simulation in comparison to the recorded data. The exit box position was predicted with a maximum error of 4°. For the working width, a large percentage difference was observed for test TB31 due to the small absolute value of the barrier deflection but the results were well within the limit value from the standard for both tests. The sensitivity analysis showed the robustness of the modelling with respect to contact stiffness increase of ±20% and ±40%. This is the first multibody model of portable concrete barriers that can reproduce not only the acceleration severity index but all the test criteria of EN 1317 and is therefore a valuable tool for new product development and for injury biomechanics research.

Modelling and characterization of ultrasonic consolidation process of Aluminium alloys

Ultrasonic consolidation process is a rapid manufacturing process used to join thin layers of metal at low temperatures and low energy consumption. In this work, finite element method has been used to simulate the ultrasonic consolidation of Aluminium alloys 6061 (AA-6061) and 3003 (AA-3003). A thermomechanical material model has been developed in the framework of continuum cyclic plasticity theory which takes into account both volume (acoustic softening) and surface (thermal softening due to friction) effects. A friction model based on experimental studies has been developed, which takes into account the dependence of coefficient of friction upon contact pressure, amount of slip, temperature and number of cycles. Using the developed material and friction model ultrasonic consolidation (UC) process has been simulated for various combinations of process parameters involved. Experimental observations are explained on the basis of the results obtained in the present study. The current research provides the opportunity to explain the differences of the behaviour of AA-6061 and AA-3003 during the ultrasonic consolidation process. Finally, trends of the experimentally measured fracture energies of the bonded specimen are compared to the predicted friction work at the weld interface resulted from the simulation at similar process condition. Similarity of the trends indicates the validity of the developed model in its predictive capability of the process. © 2008 Materials Research Society.

Predicting the crushing behaviour of composite material using high-fidelity finite element modelling

The capability to numerically model the crushing behaviour of composite structures will enable the efficient design of structures with high specific energy absorption capacity. This is particularly relevant to the aerospace and automotive industries where cabin structures need to be shown to be crashworthy. In this paper, a three-dimensional damage model is presented, which accurately represents the behaviour of composite laminates under crush loading. Both intralaminar and interlaminar failure mechanisms are taken into account. The crush damage model was implemented in ABAQUS/Explicit as a VUMAT subroutine. Numerical predictions are shown to agree well with experimental results, accurately capturing the intralaminar and interlaminar damage for a range of stacking sequences, triggers and composite materials. The use of measured material parameters required by the numerical models, without the need to ‘calibrate’ this input data, demonstrates this computational tool's predictive capabilities

Modelling Damage in Thin-Walled Structures [Keynote speaker]

A high-fidelity composite damage model is presented and applied to predict low-velocity impact damage, compression after impact (CAI) strength and crushing of thin-walled composite structures. The simulated results correlated well with experimental testing in terms of overall force-displacement response, damage morphologies and energy dissipation. The predictive power of this model makes it suitable for use as part of a virtual testing methodology, reducing the reliance on physical testing.  

Improving composite damage modelling through automatic placement of cohesive elements

Numerous experimental studies of damage in composite laminates have shown that intralaminar (in-plane) matrix cracks lead to interlaminar delamination (out-of-plane) at ply interfaces. The smearing of in-plane cracks over a volume, as a consequence of the use of continuum damage mechanics, does not always effectively capture the full extent of the interaction between the two failure mechanisms. A more accurate representation is obtained by adopting a discrete crack approach via the use of cohesive elements, for both in-plane and out-of-plane damage. The difficulty with cohesive elements is that their location must be determined a priori in order to generate the model; while ideally the position of the crack migration, and more generally the propagation path, should be obtained as part of the problem’s solution. With the aim of enhancing current modelling capabilities with truly predictive capabilities, a concept of automatic insertion of interface elements is utilized. The consideration of a simple traction criterion in relation to material strength, evaluated at each node of the model (or of the regions of the model where it is estimated cracks might form), allows for the determination of initial crack location and subsequent propagation by the insertion of cohesive elements during the course of the analysis. Several experimental results are modelled using the commercial package ABAQUS/Standard with an automatic insertion subroutine developed in this work, and the results are presented to demonstrate the capabilities of this technique.

Numerical modelling of radionuclide migration in near-surface systems

Em todo o mundo são usados, hoje em dia, modelos numéricos hidrogeoquímicos para simular fenómenos naturais e fenómenos decorrentes de actividades antrópicas. Estes modelos ajudam-nos a compreender o ambiente envolvente, a sua variabilidade espacial e evolução temporal. No presente trabalho apresenta-se o desenvolvimento de modelos numéricos hidrogeoquímicos aplicados no contexto do repositório geológico profundo para resíduos nucleares de elevada actividade. A avaliação da performance de um repositório geológico profundo inclui o estudo da evolução geoquímica do repositório, bem como a análise dos cenários de mau funcionamento do repositório, e respectivas consequências ambientais. Se se escaparem acidentalmente radionuclídeos de um repositório, estes poderão atravessar as barreiras de engenharia e barreiras naturais que constituem o repositório, atingindo eventualmente, os ecosistemas superficiais. Neste caso, os sedimentos subsuperficiais constituem a última barreira natural antes dos ecosistemas superficiais. No presente trabalho foram desenvolvidos modelos numéricos que integram processos biogeoquímicos, geoquímicos, hidrodinâmicos e de transporte de solutos, para entender e quantificar a influência destes processos na mobilidade de radionuclídeos em sistemas subsuperficiais. Os resultados alcançados reflectem a robustez dos instrumentos numéricos utilizados para desenvolver simulações descritivas e predictivas de processos hidrogeoquímicos que influenciam a mobilidade de radionuclídeos. A simulação (descritiva) de uma experiência laboratorial revela que a actividade microbiana induz a diminuição do potencial redox da água subterrânea que, por sua vez, favorece a retenção de radionuclídeos sensíveis ao potencial redox, como o urânio. As simulações predictivas indicam que processos de co-precipitação com minerais de elementos maioritários, precipitação de fases puras, intercâmbio catiónico e adsorção à superfície de minerais favorecem a retenção de U, Cs, Sr e Ra na fase sólida de uma argila glaciar e uma moreia rica em calcite. A etiquetagem dos radionuclídeos nas simulações numéricas permitiu concluir que a diluição isotópica joga um papel importante no potencial impacte dos radionuclídeos nos sistemas subsuperficiais. A partir dos resultados das simulações numéricas é possivel calcular coeficientes de distribuição efectivos. Esta metodologia proporciona a simulação de ensaios de traçadores de longa duração que não seriam exequíveis à escala da vida humana. A partir destas simulações podem ser obtidos coeficientes de retardamento que são úteis no contexto da avaliação da performance de repositórios geológicos profundos.

Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudoabsence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study showsthat ifwe do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.

Wildfire susceptibility modelling in mainland Portugal

Tese de doutoramento, Geografia (Geografia Física), Universidade de Lisboa, Instituto de Geografia e Ordenamento do Território, 2015

Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data

The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year. The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

A spatial modelling framework for assessing climate change impacts on freshwater ecosystems: Response of brown trout (Salmo trutta L.) biomass to warming water temperature

Mountain regions worldwide are particularly sensitive to on-going climate change. Specifically in the Alps in Switzerland, the temperature has increased twice as fast than in the rest of the Northern hemisphere. Water temperature closely follows the annual air temperature cycle, severely impacting streams and freshwater ecosystems. In the last 20 years, brown trout (Salmo trutta L) catch has declined by approximately 40-50% in many rivers in Switzerland. Increasing water temperature has been suggested as one of the most likely cause of this decline. Temperature has a direct effect on trout population dynamics through developmental and disease control but can also indirectly impact dynamics via food-web interactions such as resource availability. We developed a spatially explicit modelling framework that allows spatial and temporal projections of trout biomass using the Aare river catchment as a model system, in order to assess the spatial and seasonal patterns of trout biomass variation. Given that biomass has a seasonal variation depending on trout life history stage, we developed seasonal biomass variation models for three periods of the year (Autumn-Winter, Spring and Summer). Because stream water temperature is a critical parameter for brown trout development, we first calibrated a model to predict water temperature as a function of air temperature to be able to further apply climate change scenarios. We then built a model of trout biomass variation by linking water temperature to trout biomass measurements collected by electro-fishing in 21 stations from 2009 to 2011. The different modelling components of our framework had overall a good predictive ability and we could show a seasonal effect of water temperature affecting trout biomass variation. Our statistical framework uses a minimum set of input variables that make it easily transferable to other study areas or fish species but could be improved by including effects of the biotic environment and the evolution of demographical parameters over time. However, our framework still remains informative to spatially highlight where potential changes of water temperature could affect trout biomass. (C) 2015 Elsevier B.V. All rights reserved.-

Predicting overall service quality: a structural equation modelling approach

In this article, the results of a modified SERVQUAL questionnaire (Parasuraman et al., 1991) are reported. The modifications consisted in substituting questionnaire items particularly suited to a specific service (banking) and context (county of Girona, Spain) for the original rather general and abstract items. These modifications led to more interpretable factors which accounted for a higher percentage of item variance. The data were submitted to various structural equation models which made it possible to conclude that the questionnaire contains items with a high measurement quality with respect to five identified dimensions of service quality which differ from those specified by Parasuraman et al. And are specific to the banking service. The two dimensions relating to the behaviour of employees have the greatest predictive power on overall quality and satisfaction ratings, which enables managers to use a low-cost reduced version of the questionnaire to monitor quality on a regular basis. It was also found that satisfaction and overall quality were perfectly correlated thus showing that customers do not perceive these concepts as being distinct