Industrial and engineering applications
Many industries such as aircraft, oil & gas, transportation, use digital models to quickly evaluate and improve their products or processes. It is critical to save costs, improve quality, reach Time To Market to remain competitive.Virtual prototyping and large-scale data modelling resource are the typical industrial and engineering applications that require high performance computing.
For some domains, Exaflop capacities are needed to solve industrial problems:
- Aeronautics: Improved predictions of complex flow phenomena around full aircraft configurations with advanced physical modeling and increased resolution, multi-disciplinary analysis and design, real time simulation of maneuvering aircraft, aerodynamic and aero elastic data prediction, Exaflop not the ultimate goal, need at least Zetaflop for LES of complete aircraft. Many ―farming‖ applications (almost independent simulations)
- Seismic, Oil &Gas: Largely embarrassingly parallel, major problems are programming model, memory access and data management, need Zetaflop for full inverse problem
- Engineering: Optimization, Monte Carlo type simulations, LES/RANS/DNS … (main problem: equations themselves, physics, coupling, …)
For these domains, production problems will be solved by “farming” applications.
EESI1 studies have identified impacts of exascale in several sectors.
As examples, in the energy domain, the energy process needs to continuously improve its environmental impact (thermal discharge of nuclear/thermal power plants, long term geomorphology in rivers due to hydropower, water or air quality, …). This can be achieved by using multi-physics, multi-scale, complex three- dimensional time-dependent models
Improving the efficiency of the search for new oil reservoirs, including non-traditional ressources, can only be done through very advanced wave- propagation models and image processing methods.
In transportation, future systems will have to meet the constantly increasing needs of European citizens for travel and transport, as well as the strong requirements to preserve the environment and quality of life. By 2050, building green aircrafts and gain certification in short timeframe, will require to reduce key indicators:
- CO2 by 75%
- a NOx by 90%
- perceived aircraft noise by 65%
- accident rate by 80%
The main challenge facing nuclear industry is, today more than ever, to design safe nuclear power plants. HPC and in particular Exaflop computing will contribute to improve plant design, involving the use of multi-physics, multi-scale, complex three- dimensional time-dependent models.
Nevertheless access to HPC resources is a challenge. While some large companies have their own systems, eg Total, many companies do not have access to such resource, either because of the complexity, price, or lack of information.
Path: In order to raise awareness and foster innovation and competitiveness of SMEs through HPC, some national initiatives have been launched, such as SHAPE project of PRACE or Fortissimo EC funded project.
For aerospace, green objectives require to flight-test a virtual aircraft with all its multi-disciplinary interactions, with guaranteed accuracy. This means to couple LES simulations at all the different stages of turbines, to develop both simulation codes as well as flexible coupler and optimised coupling techniques.
Roadmap: investments into large petascale HPC systems have started in Europe by companies like Total or Airbus, becoming HPC bigger users in their domain.
After success in combustion, with a first jet noise simulation on one million core in collaboration with Aachen University and PRACE, aerospace companies set ambitious roadmaps for developing next generation of gas turbines by 2020.
Automotive has a strong use acceleration of advance HPC for crash analysis or steel replacement by composite.
The European industrial roadmaps have been challenged against US competitors and the results are strongly coherent.