Unsteady aerodynamic simulation of multiple bodies in relative motion: A prototype method

A prototype method for time-accurate simulation of multiple aerodynamic bodies in relative motion is presented. The method is general and features unsteady chimera domain decomposition techniques and an implicit approximately factored finite-difference procedure to solve the time-dependent thin-layer Navier-Stokes equations. The method is applied to a set of two- and three- dimensional test problems to establish spatial and temporal accuracy, quantify computational efficiency, and begin to test overall code robustness

Unsteady Simulation of the Viscous Flow About a V-22 Rotor and Wing in Hover

Results of an unsteady thin-layer Navier-Stokes simulation of a 0.658-scale V-22 rotor and wing configuration in hover are presented. All geometric components of the flapped-wing and rotor test rig, including rotor blades, are accurately modeled. Rotor motion and rotor/airframe interference effects are simulated directly using moving body overset grid methods. Tiltrotor hover aerodynamics are visualized via unsteady particle trace images. Wing download predictive ability is demonstrated. Simulation results are compared with experimental data

Grid related issues for static and dynamic geometry problems using systems of overset structured grids

Grid related issues of the Chimera overset grid method are discussed in the context of a method of solution and analysis of unsteady three-dimensional viscous flows. The state of maturity of the various pieces of support software required to use the approach is considered. Current limitations of the approach are identified

On the Spatial and Temporal Accuracy of Overset Grid Methods for Moving Body Problems

A study of numerical attributes peculiar to an overset grid approach to unsteady aerodynamics prediction is presented. Attention is focused on the effect of spatial error associated with interpolation of intergrid boundary conditions and temporal error associated with explicit update of intergrid boundary points on overall solution accuracy. A set of numerical experiments are used to verify whether or not the use of simple interpolation for intergrid boundary conditions degrades the formal accuracy of a conventional second-order flow solver, and to quantify the error associated with explicit updating of intergrid boundary points. Test conditions correspond to the transonic regime. The validity of the numerical results presented here are established by comparison with existing numerical results of documented accuracy, and by direct comparison with experimental results

Global Flowfield About the V-22 Tiltrotor Aircraft

This final report includes five publications that resulted from the studies of the global flowfield about the V-22 Tiltrotor Aircraft. The first of the five is 'The Chimera Method of Simulation for Unsteady Three-Dimensional Viscous Flow', as presented in 'Computational Fluid Dynamics Review 1995.' The remaining papers, all presented at AIAA conferences, are 'Unsteady Simulation of the Viscous Flow About a V-22 Rotor and Wing in Hover', 'An Efficient Means of Adaptive Refinement Within Systems of Overset Grids', 'On the Spatial and Temporal Accuracy of Overset Grid Methods for MOving Body Problems', and 'Moving Body Overset Grid Methods for Complete Aircraft Tiltrotor Simulations.

Domain connectivity among systems of overset grids

This progress report describes research performed to achieve algorithmic improvements in the use of overset grids for the computation of unsteady flowfields about geometrically complex and moving component configurations. A hole-map computational method is detailed that reduces computational demand and corresponding demand on human resources. Results indicate that the method is both efficient and flexible for use in complex simulation cases. Tests were performed on shuttle orbiter/external tank, wing/store, and V-22 tilt-rotor configurations. The number of simulated points and the corresponding time on a SGI 4D-210 workstation are presented

An Efficient Means of Adaptive Refinement Within Systems of Overset Grids

An efficient means of adaptive refinement within systems of overset grids is presented. Problem domains are segregated into near-body and off-body fields. Near-body fields are discretized via overlapping body-fitted grids that extend only a short distance from body surfaces. Off-body fields are discretized via systems of overlapping uniform Cartesian grids of varying levels of refinement. a novel off-body grid generation and management scheme provides the mechanism for carrying out adaptive refinement of off-body flow dynamics and solid body motion. The scheme allows for very efficient use of memory resources, and flow solvers and domain connectivity routines that can exploit the structure inherent to uniform Cartesian grids

Placental glucocorticoid receptor isoforms in a sheep model of maternal allergic asthma

Maternal asthma increases the risk of adverse pregnancy outcomes and may affect fetal growth and placental function by differential effects on the expression of glucocorticoid receptor (GR) isoforms, leading to altered glucocorticoid signalling. Our aim was to examine the effect of maternal asthma on placental GR profiles using a pregnant sheep model of asthma. Nine known GR isoforms were detected. There was a significant increase in the expression of placental GR isoforms that are known to have low trans-activational activity in other species including GR A, GR P and GRγ which may result in a pro-inflammatory environment in the presence of allergic asthma

Creating Physical 3D Stereolithograph Models of Brain and Skull

The human brain and skull are three dimensional (3D) anatomical structures with complex surfaces. However, medical images are often two dimensional (2D) and provide incomplete visualization of structural morphology. To overcome this loss in dimension, we developed and validated a freely available, semi-automated pathway to build 3D virtual reality (VR) and hand-held, stereolithograph models. To evaluate whether surface visualization in 3D was more informative than in 2D, undergraduate students (n = 50) used the Gillespie scale to rate 3D VR and physical models of both a living patient-volunteer's brain and the skull of Phineas Gage, a historically famous railroad worker whose misfortune with a projectile tamping iron provided the first evidence of a structure-function relationship in brain. Using our processing pathway, we successfully fabricated human brain and skull replicas and validated that the stereolithograph model preserved the scale of the VR model. Based on the Gillespie ratings, students indicated that the biological utility and quality of visual information at the surface of VR and stereolithograph models were greater than the 2D images from which they were derived. The method we developed is useful to create VR and stereolithograph 3D models from medical images and can be used to model hard or soft tissue in living or preserved specimens. Compared to 2D images, VR and stereolithograph models provide an extra dimension that enhances both the quality of visual information and utility of surface visualization in neuroscience and medicine

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks