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Ingrid Cloud is a software and service platform for computational fluid dynamics simulations. More specifically, Ingrid Cloud provides applications that run wind simulations in a fully automated process.
The simulation is based on a proprietary code and applies implicit large eddy simulation (LES) in an automated workflow. By eliminating manual work, Ingrid Cloud allows users with no knowledge in fluid dynamics to run CFD simulations, being part of a democratization movement in the engineering industry.
Ingrid Cloud was founded by the Adaptive Simulations in 2015. The company is a spin-out from the research conducted on automated simulations at Swedish Royal Institute of Technology (KTH), Stockholm.
In 2017, Adaptive Simulations was one of the finalists for Sweden's "Entrepreneur of the Future" award. In the same year, the company was one among the top 50 finalists at the Pioneers500 awards.
In 2018 the company was a finalist in London Construction Awards, category Process Innovation of the Year.
- 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 Finite Element Method. Viscous dissipation is assumed to be dominated by turbulent dissipation proportional to the residual of the equations, and the effect of skin-friction is observed to be 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
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.
Since 2010, Ingrid Cloud's technology has been regularly validated in benchmark workshops organized by the American Institute of Aeronautics and Astronautics (AIAA), by NASA and by other institutes.
In 2011, Ingrid Cloud's algorithms was submitted to a benchmark workshop organized by NASA/AIAA. The case consisted in a direct comparison of simulation results obtained using Ingrid Cloud's intelligent algorithm with wind tunnel measurements on a Gulfstream G550 nose landing gear. Results showed 80% accuracy for turbulence sound, 97% reduction in total simulation time and 84% lower cost compared to the largest competitor in the market.
In 2018 This benchmark was proposed by the Architectural Institute of Japan (AIJ) for validation of CFD codes when used for the computation of flow around buildings and city areas. Results showed 95% accuracy for wind at pedestrian height compared to wind tunnel data provided by AIJ, 97% reduction in total simulation time and 84% lower cost compared to the large competitor in the market.
All public benchmark cases are available in scientific publications and summarized on Ingrid Cloud's webpage.
- Langnau, Leslie (3 October 2018). "'Human-aided engineering': Automated high-fidelity flow simulation from Ingrid Cloud". 3dcadworld.com. https://www.3dcadworld.com/human-aided-engineering-automated-high-fidelity-flow-simulation-from-ingrid-cloud/. Retrieved 3 March 2019.
- "Adaptive Simulations is now Ingrid Cloud". thingstockholm.com. 6 August 2018. http://www.thingstockholm.com/newsdesk/ingridcloud. Retrieved 3 March 2019.
- "Meet Two Mind-Bending Founders From Stockholm". https://www.inc.com/peter-cohan/meet-two-mind-bending-founders-from-stockholm.html. Retrieved 9 January 2019.
- "Stockholm-based Adaptive Simulations secures €1.5 million". EU-startups. https://www.eu-startups.com/2017/05/stockholm-based-adaptive-simulations-secures-e1-5-million-to-democratize-the-virtual-simulations-market/. Retrieved 9 January 2019.
- "The Leading Construction & Design Awards For London". London Construction Awards. https://www.londonconstructionawards.com/. Retrieved 3 March 2019.
- Jansson, N (2015). Towards a parameter-free method for high Reynolds number turbulent flow simulation based on adaptive finite element approximation. http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A709363&dswid=-5607.
- Jansson, N (2015). Computability and Adaptivity in CFD. http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A949761&dswid=4702.
- Hoffman J (2005). Computation of mean drag for bluff body problems using Adaptive DNS/LES. https://www.researchgate.net/publication/220412038_Computation_of_mean_drag_for_bluff_body_problems_using_Adaptive_DNSLES.
- Hoffman J (2008). Efficient computation of mean drag for the subcritical flow past a circular cylinder using general Galerkin G2. https://onlinelibrary.wiley.com/doi/abs/10.1002/fld.1865.
- Desand, Sebastian (27 February 2018). "Ingrid Cloud - Launch of new service for fully automated CFD simulations". cfd-online.com/Forum. https://www.cfd-online.com/Forum/news.cgi/read/103097. Retrieved 3 March 2019.
- Dan H. Neuhart (2009). "Aerodynamics of a Gulfstream G550 Nose Landing Gear Model". 15th AIAA/CEAS Aeroacoustics Conference (30th AIAA Aeroacoustics Conference). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.713.762&rep=rep1&type=pdf.
- Vilela de Abreu (2011). Adaptive Computation of Aeroacoustic Sources for a Rudimentary Landing Gear Using Lighthill's Analogy. https://arc.aiaa.org/doi/10.2514/6.2011-2942.
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