Multi-scale modelling of Suzuki segregation in gamma' precipitates in Ni and Co-base superalloys

The high temperature strength of alloys with (gamma +gamma') microstructure is primarily due to the resistance of the ordered precipitate to cutting by matrix dislocations. Such shearing requires higher stresses since it involves the creation of a planar fault. Planar fault energy is known to be dependent on composition. This implies that the composition on the fault may be different from that in the bulk for energetic reasons. Such segregation (or desegregation) of specific alloying elements to the fault may result in Suzuki strengthening which has not been explored extensively in these systems. In this work, segregation (or desegregation) of alloying elements to planar faults was studied computationally in Ni-3(Al, Ti) and Co-3(W, Al) type gamma' precipitates. The composition dependence of APB energy and heat of mixing were evaluated from first principle electronic structure calculations. A phase field model incorporating the first principles results, was used to simulate the motion of an extended superdislocation under stress concurrently with composition evolution. Results reveal that in both systems, significant (de) segregation occurs on equilibration. On application of stress, solutes were dragged along with the APB in some cases. Additionally, it was also noted the velocity of the superdislocation under an applied stress is strongly dependent on atomic mobility (i. e. diffusivity).

Modelling ternary effects on antiphase boundary energy of Ni3Al

The shearing of ordered gamma' precipitates by matrix dislocations results in the formation of antiphase boundaries (APB) in Ni-base superalloys. The APB energy is an important source of order-strengthening in disk and blade alloys where Ti and Ta substitute for Al in gamma'. While the importance of APB energy is well-acknowledged, the effect of alloying on APB energy is not fully understood. In the present study, the effect of Ti and Ta additions on the {111} and {010} APB energies was probed via electronic structure calculations. Results suggest that at low levels of Ti/Ta, APB energies on either plane increases with alloying. However, at higher Ti/Ta levels, the APB energies decrease with alloying. These trends understood by accounting for nearest neighbour violations about the APB and additionally, invoking the effect of precipitate composition on the energy penalty of the violations. We propose an Environment Dependent Nearest Neighbour Bond (EDNNB) model that predicts APB energies that are in close agreement to calculated values.

Modelling the influence of land-use changes on biophysical and biochemical interactions at regional and global scales

Land-use changes since the start of the industrial era account for nearly one-third of the cumulative anthropogenic CO2 emissions. In addition to the greenhouse effect of CO2 emissions, changes in land use also affect climate via changes in surface physical properties such as albedo, evapotranspiration and roughness length. Recent modelling studies suggest that these biophysical components may be comparable with biochemical effects. In regard to climate change, the effects of these two distinct processes may counterbalance one another both regionally and, possibly, globally. In this article, through hypothetical large-scale deforestation simulations using a global climate model, we contrast the implications of afforestation on ameliorating or enhancing anthropogenic contributions from previously converted (agricultural) land surfaces. Based on our review of past studies on this subject, we conclude that the sum of both biophysical and biochemical effects should be assessed when large-scale afforestation is used for countering global warming, and the net effect on global mean temperature change depends on the location of deforestation/afforestation. Further, although biochemical effects trigger global climate change, biophysical effects often cause strong local and regional climate change. The implication of the biophysical effects for adaptation and mitigation of climate change in agriculture and agroforestry sectors is discussed. center dot Land-use changes affect global and regional climates through both biochemical and biophysical process. center dot Climate effect from biophysical process depends on the location of land-use change. center dot Climate mitigation strategies such as afforestation/reforestation should consider the net effect of biochemical and biophysical processes for effective mitigation. center dot Climate-smart agriculture could use bio-geoengineering techniques that consider plant biophysical characteristics such as reflectivity and water use efficiency.

Modelling Proximity Effects in Transverse Magnetic Mode for Lightning Studies

Lightning strike to instrumented and communication towers can be a source of electromagnetic disturbance to the system connected. Long cables running on these towers can get significant induction to their sheath/core, which would then couple to the connected equipments. For a quantitative analysis of the situation, suitable theoretical analysis is necessary. Due to the dominance of the transverse magnetic mode during the fast rising portion of the stroke current, which is the period of significant induction, a full wave solution based on Maxwell's equations is necessary. Owing to the large geometric aspect ratio of tower lattice elements and for feasibility of a numerical solution, the thin-wire formulation for the electric field integral equation is generally adopted. However, the classical thin-wire formulation is not set for handling non-cylindrical conductors like tower lattice elements and the proximity of other conductors. The present work investigates further into a recently proposed method for handling such a situation and optimizes the numerical solution approach.

Modelling staphylococcal pneumonia in a human 3D lung tissue model system delineates toxin-mediated pathology

Staphylococcus aureus necrotizing pneumonia is recognized as a toxin-mediated disease, yet the tissue-destructive events remain elusive, partly as a result of lack of mechanistic studies in human lung tissue. In this study, a three-dimensional (3D) tissue model composed of human lung epithelial cells and fibroblasts was used to delineate the role of specific staphylococcal exotoxins in tissue pathology associated with severe pneumonia. To this end, the models were exposed to the mixture of exotoxins produced by S. aureus strains isolated from patients with varying severity of lung infection, namely necrotizing pneumonia or lung empyema, or to purified toxins. The necrotizing pneumonia strains secreted high levels of alpha-toxin and Panton-Valentine leukocidin (PVL), and triggered high cytotoxicity, inflammation, necrosis and loss of E-cadherin from the lung epithelium. In contrast, the lung empyema strain produced moderate levels of PVL, but negligible amounts of alpha-toxin, and triggered limited tissue damage. alpha-toxin had a direct damaging effect on the epithelium, as verified using toxin-deficient mutants and pure alpha-toxin. Moreover, PVL contributed to pathology through the lysis of neutrophils. A combination of alpha-toxin and PVL resulted in the most severe epithelial injury. In addition, toxin-induced release of pro-inflammatory mediators from lung tissue models resulted in enhanced neutrophil migration. Using a collection of 31 strains from patients with staphylococcal pneumonia revealed that strains producing high levels of alpha-toxin and PVL were cytotoxic and associated with fatal outcome. Also, the strains that produced the highest toxin levels induced significantly greater epithelial disruption. Of importance, toxin-mediated lung epithelium destruction could be inhibited by polyspecific intravenous immunoglobulin containing antibodies against alpha-toxin and PVL. This study introduces a novel model system for study of staphylococcal pneumonia in a human setting. The results reveal that the combination and levels of alpha-toxin and PVL correlate with tissue pathology and clinical outcome associated with pneumonia.

Modelling and analysis of an open-loop induction motor drive incorporating the effect of inverter dead-time

The objective of this paper is to study the influence of inverter dead-time on steady as well as dynamic operation of an open-loop induction motor drive fed from a voltage source inverter (VSI). Towards this goal, this paper presents a systematic derivation of a dynamic model for an inverter-fed induction motor, incorporating the effect of inverter dead-time, in the synchronously revolving dq reference frame. Simulation results based on this dynamic model bring out the impact of inverter dead-time on both the transient response and steady-state operation of the motor drive. For the purpose of steady-state analysis, the dynamic model of the motor drive is used to derive a steady-state model, which is found to be non-linear. The steady-state model shows that the impact of dead-time can be seen as an additional resistance in the stator circuit, whose value depends on the stator current. Towards precise evaluation of this dead-time equivalent resistance, an analytical expression is proposed for the same in terms of inverter dead-time, switching frequency, modulation index and load impedance. The notion of dead-time equivalent resistance is shown to simplify the solution of the non-linear steady-state model. The analytically evaluated steady-state solutions are validated through numerical simulations and experiments.

Modelling within-host spatiotemporal dynamics of invasive bacterial disease

Mechanistic determinants of bacterial growth, death, and spread within mammalian hosts cannot be fully resolved studying a single bacterial population. They are also currently poorly understood. Here, we report on the application of sophisticated experimental approaches to map spatiotemporal population dynamics of bacteria during an infection. We analyzed heterogeneous traits of simultaneous infections with tagged Salmonella enterica populations (wild-type isogenic tagged strains [WITS]) in wild-type and gene-targeted mice. WITS are phenotypically identical but can be distinguished and enumerated by quantitative PCR, making it possible, using probabilistic models, to estimate bacterial death rate based on the disappearance of strains through time. This multidisciplinary approach allowed us to establish the timing, relative occurrence, and immune control of key infection parameters in a true host-pathogen combination. Our analyses support a model in which shortly after infection, concomitant death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs, a process controlled by host antimicrobial mechanisms. Later, decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally, with subsequent mixing of bacteria between organs via bacteraemia and further stochastic selection. This approach provides us with an unprecedented outlook on the pathogenesis of S. enterica infections, illustrating the complex spatial and stochastic effects that drive an infectious disease. The application of the novel method that we present in appropriate and diverse host-pathogen combinations, together with modelling of the data that result, will facilitate a comprehensive view of the spatial and stochastic nature of within-host dynamics. © 2008 Grant et al.