Our technology
Ingrid Cloud

Our software is a cloud-based computational tool that automates the entire process of flow simulation and design optimization by building the computational model without human intervention.

Self-learning algorithms

Proprietary, self-learning algorithms, which form the core of Ingrid Cloud, enable the automation of the entire simulation process, increasing accuracy and eliminating costly process repetition due to human error. The algorithms also reduce computing costs, since they optimize the usage of computational resources.

The foundation

The foundation of Ingrid Cloud is Adaptive Simulations’ ground-breaking Computational Fluid Dynamics (CFD) framework, which uses the Finite Element Method (FEM) together with adaptive mesh refinement based on adjoint techniques and a posteriori error estimation.

Since 2010, Adaptive Simulations’ technology has been regularly validated in benchmark workshops organized by the American Institute of Aeronautics and Astronautics (AIAA) and by NASA. The team behind Adaptive Simulations has dedicated over 30 person-years of research to:

  • Develop a methodology that allows the automatic creation of highly-complex simulation models from CAD-geometries,
  • Implement this method in a scientific code, and
  • Optimize and test the code on the fastest supercomputers in the world (K computer in Japan, Beskow in Stockholm, and Hazel Hehn in Stuttgart, among others).

Two unique innovations

The technology is based on two unique innovations that provide superior capabilities. The method resulting from these innovations has no adjustable parameters, and no ad hoc design of the mesh is needed, enabling full automation of the simulation workflow. The capabilities are:

  • A parameter-free method for simulation of turbulent flow at high Reynolds numbers, in the form of weak solutions of the Navier–Stokes equations approximated by adaptive FEM. Here, viscous dissipation is assumed to be dominated by turbulent dissipation proportional to the residual of the equations, and skin-friction at solid walls is considered small compared to inertial effects.
  • Algorithms for adaptive optimization of the mesh based on adjoint techniques and a posteriori error estimation, so that the output quantities of interest, in the form of functionals of the solution, converge to become independent of mesh resolution.

Unique characteristics

  • High fidelity turbulent flow simulations.
  • Setup simulation with simple wizard and intuitive user interface.
  • Automated, smart mesh generation based on adjoint techniques and error estimation.
  • No explicit turbulence model.
  • Very few input parameters when compared to traditional CFD.

References

  • Hoffman, J. (2005), Computation of Mean Drag for Bluff Body Problems Using Adaptive DNS/LES, SIAM Journal on Scientific Computing, 27:1, 184-207.
  • Hoffman, J. (2006), Adaptive simulation of the subcritical flow past a sphere. Journal of Fluid Mechanics, 568, 77-88. doi:10.1017/S0022112006002679
  • Hoffman, J. (2009), Efficient computation of mean drag for the subcritical flow past a circular cylinder using general Galerkin G2, Int. J. Numer. Meth. Fluids, 59: 1241–1258. doi:10.1002/fld.1865.
  • Jansson, N., Hoffman, J, Jansson, J. (2012), Framework for Massively Parallel Adaptive Finite Element Computational Fluid Dynamics On Tetrahedral Meshes, SIAM Journal on Scientific Computing, vol. 34, no. 1, s. C24-C42.
  • Vilela de Abreu, R., Jansson, N., Hoffman, J (2016), Computation of aeroacoustic sources for a Gulfstream G550 nose landing gear model using adaptive FEM, Computers & Fluids, 124: 136-146, ISSN 0045-7930, https://doi.org/10.1016/j.compfluid.2015.10.017.