Contributions Towards the NASA 2030 Vision for Simulation

Dawes, W. N.; Kellar, Will; Verdicchio, John; Racz, Tamas; Meah, Nabil; Kudryavtsev, Andrey; Vasco, Carlos Eduardo; Yi, Lü; Li, Kai

doi:10.2514/6.2018-1499

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…This potentially solves the scalability challenge, but not the ability to respond to data-driven feedback. We have been developing an alternate approach (Dawes et al [2018]) based on Digital Geometry which overcomes both of the drawbacks of standard BREP CAD; this will be described in the following Section.…”

Section: Geometry Is the Digital Threadmentioning

confidence: 99%

“…This is essentially the core idea in digital photography -a picture -in 3D this becomes geometry and pixels, voxels. Our Boxer software (see Dawes et al [2005Dawes et al [ -2018) is built on Digital Geometry using generalised 3D versions of the fundamental Bresenham algorithm; Figure 8 illustrates this. This consists of an integer representation of geometry down to a chosen length scale -voxels which determine "spatial occupancy": either occupied, vacant or cut.…”

Section: Digital Geometrymentioning

confidence: 99%

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Digital Geometry to Support a Gas Turbine Digital Twin

Dawes

Meah²,

Kudryavtsev³

et al. 2019

AIAA Scitech 2019 Forum

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Digitalisation", Industry 4.0, the Digital Twin, Data-driven design & manufacturing are set to revolutionise the way we do business. This paper considers this in the engineering world of power generation, gas turbines & power plant-and asks the question: What will it take to make a Digital Twin real. A Digital Twin must have physics-based simulation at its heart and confront three major challenges: Scale of simulation; Scaling the simulation; Responding to data-driven feedback. This paper will discuss these issues in turn and make the case that the ability to represent & manage geometry is the Digital Thread which supports a Digital Twin. We discuss the use of classical BREP CAD and the new Digital Geometry solid modelling kernel we have been developing. We illustrate with examples of recent work we have been performing aimed at addressing these challenges.

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Section: Geometry Is the Digital Threadmentioning

confidence: 99%

Section: Digital Geometrymentioning

confidence: 99%

Digital Geometry to Support a Gas Turbine Digital Twin

Dawes

Meah²,

Kudryavtsev³

et al. 2019

AIAA Scitech 2019 Forum

Self Cite

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“…But as science fiction writer William Gibson says, "The future is already here-it's just not very evenly distributed." [10] For example, consider the inviscid flow simulation capability of Cart3D, which has automated, discretization-errorbased grid adaptation [11]; the mostly-automated, mild-compressibility viscous capability of Lattice-Boltzmann flow solvers [12][13][14]; or the highly-automated viscous simulation capability of Dawes et al [15,16]. Each addresses one or more elements of the CFD Vision 2030 toward making the CFD grid "invisible;" but to date, none have all elements to achieve fully-automated viscous CFD, which is essential for crossing the chasm from an artisan product to a commodity solution.…”

Section: Introductionmentioning

confidence: 99%

Sketch-to-Solution: An Exploration of Viscous CFD with Automatic Grids

Kleb

Park

Wood

et al. 2019

AIAA Aviation 2019 Forum

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Numerical simulation of the Reynolds-averaged Navier-Stokes (RANS) equations has become a critical tool for the design of aerospace vehicles. However, the issues that affect the grid convergence of three dimensional RANS solutions are not completely understood, as documented in the AIAA Drag Prediction Workshop series. Grid adaption methods have the potential for increasing the automation and discretization error control of RANS solutions to impact the aerospace design and certification process. The realization of the CFD Vision 2030 Study includes automated management of errors and uncertainties of physics-based, predictive modeling that can set the stage for ensuring a vehicle is in compliance with a regulation or specification by using analysis without demonstration in flight test (i.e., certification or qualification by analysis). For example, the Cart3D inviscid analysis package has automated Cartesian cut-cell gridding with output-based error control. Fueled by recent advances in the fields of anisotropic grid adaptation, error estimation, and geometry modeling, a similar work flow is explored for viscous CFD simulations; where a CFD application engineer provides geometry, boundary conditions, and flow parameters, and the sketch-to-solution process yields a CFD simulation through automatic, error-based, grid adaptation.

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Geometry Modelling: Underlying Concepts and Requirements for Computational Simulation (Invited)

Taylor

Haimes

2018

2018 Fluid Dynamics Conference

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Contributions Towards the NASA 2030 Vision for Simulation

Cited by 6 publications

References 11 publications

Digital Geometry to Support a Gas Turbine Digital Twin

Digital Geometry to Support a Gas Turbine Digital Twin

Sketch-to-Solution: An Exploration of Viscous CFD with Automatic Grids

Geometry Modelling: Underlying Concepts and Requirements for Computational Simulation (Invited)

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