Domain modeling and grid generation for multi-block structured grids with application to aerodynamic and hydrodynamic configurations

About five years ago, a joint development was started of a flow simulation system for engine-airframe integration studies on propeller as well as jet aircraft. The initial system was based on the Euler equations and made operational for industrial aerodynamic design work. The system consists of three major components: a domain modeller, for the graphical interactive subdivision of flow domains into an unstructured collection of blocks; a grid generator, for the graphical interactive computation of structured grids in blocks; and a flow solver, for the computation of flows on multi-block grids. The industrial partners of the collaboration and NLR have demonstrated that the domain modeller, grid generator and flow solver can be applied to simulate Euler flows around complete aircraft, including propulsion system simulation. Extension to Navier-Stokes flows is in progress. Delft Hydraulics has shown that both the domain modeller and grid generator can also be applied successfully for hydrodynamic configurations. An overview is given about the main aspects of both domain modelling and grid generation

Elliptic surface grid generation on minimal and parmetrized surfaces

An elliptic grid generation method is presented which generates excellent boundary conforming grids in domains in 2D physical space. The method is based on the composition of an algebraic and elliptic transformation. The composite mapping obeys the familiar Poisson grid generation system with control functions specified by the algebraic transformation. New expressions are given for the control functions. Grid orthogonality at the boundary is achieved by modification of the algebraic transformation. It is shown that grid generation on a minimal surface in 3D physical space is in fact equivalent to grid generation in a domain in 2D physical space. A second elliptic grid generation method is presented which generates excellent boundary conforming grids on smooth surfaces. It is assumed that the surfaces are parametrized and that the grid only depends on the shape of the surface and is independent of the parametrization. Concerning surface modeling, it is shown that bicubic Hermite interpolation is an excellent method to generate a smooth surface which is passing through a given discrete set of control points. In contrast to bicubic spline interpolation, there is extra freedom to model the tangent and twist vectors such that spurious oscillations are prevented

Modelling the Innate Immune Response against Avian Influenza Virus in Chicken

At present there is limited understanding of the host immune response to (low pathogenic) avian influenza virus infections in poultry. Here we develop a mathematical model for the innate immune response to avian influenza virus in chicken lung, describing the dynamics of viral load, interferon-α, -β and -γ, lung (i.e. pulmonary) cells and Natural Killer cells. We use recent results from experimentally infected chickens to validate some of the model predictions. The model includes an initial exponential increase of the viral load, which we show to be consistent with experimental data. Using this exponential growth model we show that the duration until a given viral load is reached in experiments with different inoculation doses is consistent with a model assuming a linear relationship between initial viral load and inoculation dose. Subsequent to the exponential-growth phase, the model results show a decline in viral load caused by both target-cell limitation as well as the innate immune response. The model results suggest that the temporal viral load pattern in the lungs displayed in experimental data cannot be explained by target-cell limitation alone. For biologically plausible parameter values the model is able to qualitatively match to data on viral load in chicken lungs up until approximately 4 days post infection. Comparison of model predictions with data on CD107-mediated degranulation of Natural Killer cells yields some discrepancy also for earlier days post infection

Modeling convergent ON and OFF pathways in the early visual system

For understanding the computation and function of single neurons in sensory systems, one needs to investigate how sensory stimuli are related to a neuron’s response and which biological mechanisms underlie this relationship. Mathematical models of the stimulus–response relationship have proved very useful in approaching these issues in a systematic, quantitative way. A starting point for many such analyses has been provided by phenomenological “linear–nonlinear” (LN) models, which comprise a linear filter followed by a static nonlinear transformation. The linear filter is often associated with the neuron’s receptive field. However, the structure of the receptive field is generally a result of inputs from many presynaptic neurons, which may form parallel signal processing pathways. In the retina, for example, certain ganglion cells receive excitatory inputs from ON-type as well as OFF-type bipolar cells. Recent experiments have shown that the convergence of these pathways leads to intriguing response characteristics that cannot be captured by a single linear filter. One approach to adjust the LN model to the biological circuit structure is to use multiple parallel filters that capture ON and OFF bipolar inputs. Here, we review these new developments in modeling neuronal responses in the early visual system and provide details about one particular technique for obtaining the required sets of parallel filters from experimental data

Uncertainty quantification of the flow predictions around the NATO STO AVT-251 unmanned combat aerial vehicle

Turbulence models based on Reynolds-averaged Navier-Stokes (RANS) equations remain the workhorse in the computation of high Reynolds-number wall-bounded flows. While these methods have been deployed to design the configuration developed within the NATO STO AVT-251 Task Group, their deficiencies in modelling complex flows are well-documented. However, an understanding of the sources of errors and uncertainties in RANS solvers, arising for example from different numerical schemes and flow modelling techniques, is missing to date. The aim of this work is to establish and quantify the impact that epistemic uncertainties within RANS solvers have on the flow predictions (shock wave locations, vortex breakdown, etc.). This will produce a range of all possible values of interest due to the inherent uncertainty of RANS solvers, which is expected to be highly dependent on the flow conditions and geometry configuration. This information, in turn, will be used to establish the robustness of the AVT-251 design and its performance metrics considering uncertain predictions of the dominant flow features. The benefits of this work will also extend to the structural design, whereby appropriate factors of safety can be integrated in the process.</p

Temporal constraints on the grouping of contour segments into spatially extended objects

The speed of contour integration was investigated in a task that can be solved by grouping contour segments into elongated curves. Subjects had to detect a continuous curve, which could be intersected by one or two other curves. At locations where these curves came in close proximity, the assignment of contour segments to the different curves could be based on collinearity. Reaction times exhibited a strong dependence on (1) the presence of intersections among curves; and (2) the context provided by the stimulus set from which individual stimuli were selected. Reaction times were shortest when grouping of contour segments depended on information at a single location in the visual field. In this condition, responses to stimuli containing an intersection were faster than responses to stimuli that did not. When responses were determined by information at spatially separate locations, responses were delayed, and every intersection increased the reaction time considerably. This result contrasts with earlier investigations which have suggested that contour integration on the basis of collinearity is performed pre-attentively but is in accordance with studies on curve tracing. We propose that the assignment of contour segments to equally coherent curves, a process which may be called figure-figure segregation, is a function of object-based attention. Moreover, the protracted reaction times for some of the stimuli indicate that spread of attention within an object costs time. This implies that object recognition is not always as fast as is sometimes assume

The effects of pair-wise and higher order correlations on the firing rate of a post-synaptic neuron

Coincident firing of neurons projecting to a common target cell is likely to raise the probability of firing of this postsynaptic cell. Therefore, synchronized firing constitutes a significant event for postsynaptic neurons and is likely to play a role in neuronal information processing. Physiological data on synchronized firing in cortical networks are based primarily on paired recordings and cross-correlation analysis. However, pair-wise correlations among all inputs onto a postsynaptic neuron do not uniquely determine the distribution of simultaneous postsynaptic events. We develop a framework in order to calculate the amount of synchronous firing that, based on maximum entropy, should exist in a homogeneous neural network in which the neurons have known pair-wise correlations and higher-order structure is absent. According to the distribution of maximal entropy, synchronous events in which a large proportion of the neurons participates should exist even in the case of weak pair-wise correlations. Network simulations also exhibit these highly synchronous events in the case of weak pair-wise correlations. If such a group of neurons provides input to a common postsynaptic target, these network bursts may enhance the impact of this input, especially in the case of a high postsynaptic threshold. The proportion of neurons participating in synchronous bursts can be approximated by our method under restricted conditions. When these conditions are not fulfilled, the spike trains have less than maximal entropy, which is indicative of the presence of higher-order structure. In this situation, the degree of synchronicity cannot be derived from the pair-wise correlation

The spatial profile of visual attention in mental curve tracing

In a curve-tracing task, subjects have to judge whether items are located on a single, continuous curve. Spatially separate segments of such a curve are related to each other through grouping criteria, like collinearity and connectedness. These grouping cues need to be exploited during curve tracing, but it is still an open issue how grouping of contour segments is achieved by the visual system. Many contemporary theories of visual perception assume that grouping operations are carried out pre-attentively, with unlimited capacity. The present study examines this assumption by investigating the involvement of attention in curve tracing. The results show that attention is directed to contour segments that need to be grouped together. The distribution of attention is guided by grouping criteria, such as connectedness. Apparently, attention is required to group spatially separate contour segments into a coherent representation of a curv