Development of a Common Research Model for Applied CFD Validation Studies

The development of a wing/body/nacelle/pylon/horizontal-tail configuration for a common research model is presented, with focus on the aerodynamic design of the wing. Here, a contemporary transonic supercritical wing design is developed with aerodynamic characteristics that are well behaved and of high performance for configurations with and without the nacelle/pylon group. The horizontal tail is robustly designed for dive Mach number conditions and is suitably sized for typical stability and control requirements. The fuselage is representative of a wide/body commercial transport aircraft; it includes a wing-body fairing, as well as a scrubbing seal for the horizontal tail. The nacelle is a single-cowl, high by-pass-ratio, flow-through design with an exit area sized to achieve a natural unforced mass-flow-ratio typical of commercial aircraft engines at cruise. The simplicity of this un-bifurcated nacelle geometry will facilitate grid generation efforts of subsequent CFD validation exercises. Detailed aerodynamic performance data has been generated for this model; however, this information is presented in such a manner as to not bias CFD predictions planned for the fourth AIAA CFD Drag Prediction Workshop, which incorporates this common research model into its blind test cases. The CFD results presented include wing pressure distributions with and without the nacelle/pylon, ML/D trend lines, and drag-divergence curves; the design point for the wing/body configuration is within 1% of its max-ML/D. Plans to test the common research model in the National Transonic Facility and the Ames 11-ft wind tunnels are also discussed

Drag Prediction for the NASA CRM Wing-Body-Tail Using CFL3D and OVERFLOW on an Overset Mesh

In response to the fourth AIAA CFD Drag Prediction Workshop (DPW-IV), the NASA Common Research Model (CRM) wing-body and wing-body-tail configurations are analyzed using the Reynolds-averaged Navier-Stokes (RANS) flow solvers CFL3D and OVERFLOW. Two families of structured, overset grids are built for DPW-IV. Grid Family 1 (GF1) consists of a coarse (7.2 million), medium (16.9 million), fine (56.5 million), and extra-fine (189.4 million) mesh. Grid Family 2 (GF2) is an extension of the first and includes a superfine (714.2 million) and an ultra-fine (2.4 billion) mesh. The medium grid anchors both families with an established build process for accurate cruise drag prediction studies. This base mesh is coarsened and enhanced to form a set of parametrically equivalent grids that increase in size by a factor of roughly 3.4 from one level to the next denser level. Both CFL3D and OVERFLOW are run on GF1 using a consistent numerical approach. Additional OVERFLOW runs are made to study effects of differencing scheme and turbulence model on GF1 and to obtain results for GF2. All CFD results are post-processed using Richardson extrapolation, and approximate grid-converged values of drag are compared. The medium grid is also used to compute a trimmed drag polar for both codes

Drag Prediction for the DLR-F6 Wing/Body and DPW Wing using CFL3D and OVERFLOW Overset Mesh

A series of overset grids was generated in response to the 3rd AIAA CFD Drag Prediction Workshop (DPW-III) which preceded the 25th Applied Aerodynamics Conference in June 2006. DPW-III focused on accurate drag prediction for wing/body and wing-alone configurations. The grid series built for each configuration consists of a coarse, medium, fine, and extra-fine mesh. The medium mesh is first constructed using the current state of best practices for overset grid generation. The medium mesh is then coarsened and enhanced by applying a factor of 1.5 to each (I,J,K) dimension. The resulting set of parametrically equivalent grids increase in size by a factor of roughly 3.5 from one level to the next denser level. CFD simulations were performed on the overset grids using two different RANS flow solvers: CFL3D and OVERFLOW. The results were post-processed using Richardson extrapolation to approximate grid converged values of lift, drag, pitching moment, and angle-of-attack at the design condition. This technique appears to work well if the solution does not contain large regions of separated flow (similar to that seen n the DLR-F6 results) and appropriate grid densities are selected. The extra-fine grid data helped to establish asymptotic grid convergence for both the OVERFLOW FX2B wing/body results and the OVERFLOW DPW-W1/W2 wing-alone results. More CFL3D data is needed to establish grid convergence trends. The medium grid was utilized beyond the grid convergence study by running each configuration at several angles-of-attack so drag polars and lift/pitching moment curves could be evaluated. The alpha sweep results are used to compare data across configurations as well as across flow solvers. With the exception of the wing/body drag polar, the two codes compare well qualitatively showing consistent incremental trends and similar wing pressure comparisons

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery