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August 2020


Aremi MacDonald

NASA envisioned advances that CFD should reach by 2030. Ingrid Cloud is there already.

10 years ahead of its forecast, Ingrid Cloud has achieved many of NASA's 2030 visions for CFD, today.

Computational Fluid Dynamics is one of the most complex branches of science. Over the past several decades, advances in high-performance computing and CFD have positively disrupted industries from aerospace, to HVAC, automotive, architecture and construction – the list goes on.

In 2015, a NASA-funded study outlined a vision for what CFD should look like in the year 2030 - identifying key areas where development is needed. Although these goals aren’t sought out to be achieved until at least 10 years from now, Ingrid Cloud has already reached many of them, today.

The ongoing development of CFD brings critical value to multidisciplinary industries beyond aerospace that will continue to propagate highly innovative and efficient solutions, which is why it’s important to recognise that these 2030 visions for CFD are advancing today in highly-performative and practical frameworks. Now, in 2020, these developments are tangible and are not far-fetched at all.

1. Predictive Modelling

2030 Vision: Emphasis on physics-based, predictive modelling.

Accuracy is a critical consideration among industries such as aerospace and many others, which depend on reliable solvers and data that dramatically influence design decisions, project spend and safety protocols (to name a few).

While NASA acknowledges that RANS-based models continue to be used as a standard approach in certain cases for predicting fluid flow, a shift towards LES-based solvers is recognised:


"More effective discretizations and solvers designed specifically for LES type problems must be sought."

- CFD Vision 2030 Study/NASA


Continuous advancements in HPC and more abundant access to computational resources than ever before are eliminating the once-conceived barriers to LES-based modelling. Emerging software-as-a-service platforms like Ingrid Cloud aim to further democratise CFD by enabling anyone to access accurate simulations in an easy-to-use, highly accurate and affordable way.

2. Error Management & Estimation

2030 Vision: Management of errors and uncertainties resulting from all possible sources: (i) physical modelling errors and uncertainties related to item no. 1, (ii) numerical errors arising from mesh and discretization inadequacies, and (iii) aleatory uncertainties derived from natural variability, as well as epistemic uncertainties due to the lack of knowledge in the parameters of a particular fluid flow problem.

Ingrid Cloud uses a parameter-free method for simulating turbulent flow at high Reynolds numbers, thereby eliminating the errors and uncertainties of parameterised turbulence models. The numerical errors from the finite element discretisation are controlled by quantitative a posteriori error estimation techniques with adjoint sensitivity analysis, together with adaptive algorithms that optimise the local resolution of the mesh. Hence, the parameter space to sample for a particular fluid flow problem is reduced to model the aleatory uncertainties.

3. Automation & Mesh Refinement

2030 Vision: A much higher degree of automation in all steps of the analysis process including geometry creation, mesh generation and adaptation (…). Inherent to all these improvements is the requirement that every step of the solution chain executes at high levels of reliability/robustness to minimise user intervention.

Full automation for predictive fluid flow analysis is a novel focal point of Ingrid Cloud’s core technology. An initial mesh is automatically generated based on geometrical heuristics, after which adaptive algorithms optimise the mesh based on a posteriori error estimation of the error in the output of interest in the simulation.


"Manual design of the mesh does not only take time from the more urgent tasks of an engineer, but is also prone to errors and inefficiencies. To manually design a mesh, one needs to estimate the main features of the flow before the simulation. Turbulent flow is simply too complex to predict, even by the highest skilled engineer, that is why we rely on CFD in the first place."

- Professor Johan Hoffman, Head of Research & Development, Ingrid Cloud.


Automating mesh generation and refinement significantly increases accuracy and efficiency of a fluid flow analysis by reducing the need for human intervention, and the likelihood of human error.

4. HPC

2030 Vision: Ability to effectively use massively parallel, heterogeneous and fault-tolerant HPC architectures that will be available in the 2030 timeframe. For complex physical models with non-local interactions, the challenges of mapping the underlying algorithms onto computers with multiple memory hierarchies, latencies and bandwidths must be overcome.

CFD is one of the most computationally demanding areas of computational science, therefore, high scalability of HPC systems is paramount. As a cloud-based software, Ingrid Cloud’s computational resources are consistently evolving through the highest-performing systems available on the market.  


"Ingrid Cloud uses these advances in high performance computing towards innovation, instead of getting stuck in old legacy schemes of unreliable modelling or GPU shortcuts."

- Sebastian Desand, CEO


Screenshot 2020-08-04 at 15.46.38

A consistent transition not only safeguards peak performance, but also ensures that systems remain highly-scalable and consistent as HPC systems advance at a fleeting rate.


Ingrid Cloud’s adaptive algorithms and mesh refinement techniques are developed in a way that are scalable to both current and emerging HPC architectures.

- Niclas Jansson, Head of HPC


Ingrid Cloud’s computational platform is based on an open-source framework, developed and maintained by Ingrid Cloud, currently contributing and strengthening the European HPC Strategy for future high-performance computing hierarchies.

5. Project-based Capability & Capacity

2030 Vision: Flexibility to tackle capability- and capacity-computing tasks in both industrial and research environments so that both very large ensembles of reasonably sized solutions (such as those required to populate full-flight envelopes, operating maps or for parameter studies and design optimization) and small numbers of very large-scale solutions (such as those needed for experiments of discovery and understanding of flow physics) can be readily accomplished.

Originally founded as a spin-off company from research at the KTH Royal Institute of Technology in Stockholm, Ingrid Cloud today offers an industrial service for analysis of fluid flow for the AEC segment. At the same time, numerical methods and open-source software are developed in collaboration with researchers at KTH and Uppsala University. The same open-source framework upon which Ingrid Cloud has built its fully automated industrial service, is also used in science research projects that push the limit both with respect to complexity in flow physics and scale of simulations.

Hence, with access to thousands of compute cores, our automated technology and adaptive algorithms are optimised to produce high-quality solutions to projects, small and large, standard and complex, without compromising the traditional trade-off between speed and ease-of-use on one hand, and accuracy and reliability on the other.

In parallel to this, an emphasis on optimised visualisation will be a key component to the advancement of CFD. Our platform with in situ data analysis capabilities allows users to visualise large-scale unsteady flow simulations through intuitive interactive 3D visualisations to enhance the value of the simulations.

6. Multidisciplinary Analysis

2030 Vision: Seamless integration with multidisciplinary analyses without sacrificing accuracy or numerical stability of the resulting coupled simulation, and without requiring a large amount of effort such that only a handful of coupled simulations are possible.

It’s important to recognise that Ingrid Cloud’s computational platform is not developed as an exclusive solution for CFD. The use of a fully-automated finite element solver based on discretisation of general partial differential equations, enables our service to seamlessly integrate models from different disciplines. Instead of complex and unstable code coupling, a coupled multiphysics problem, such as fluid-structure interaction or heat transfer, is approached as a coupled partial differential equation to be solved on the computational platform.

Conclusion: What’s after 2030?

Discussing thoroughly each topic listed in this article requires an immense number of extra words. Even so, we’d like to conclude by sharing our future vision for CFD. One that goes beyond 2030. We believe that the current developmental work will in the long-term make Ingrid Cloud more accurate than physical wind tunnel experiments in some specific use cases such as wind simulations in urban areas and wind farms. It will be a disruptive milestone for the CFD industry and will make the democratisation of Computational Fluid Dynamics a reality.


These visions are positioned to focus primarily on the development of CFD for the purpose of aerospace and clean aviation, but the goals apply across many other disciplines and industries where CFD plays a critical role in fluid flow assessment. For the purpose of brevity in this post, the 2030 visions have been edited down to clarify the main conceptual findings of the study. The full excerpts can be found here