Innovating the Future of Flight. HPCXXL Conference April 2019

Transcription

1 Innovating the Future of Flight HPCXXL Conference April 2019

2 Content Introduction to Aircraft Business Driver for HPC in Aircraft Industry Current HPC /Grid Infrastructure Current Limitations Upcoming Requirements Future Challenges 2

3 Aviation drives our global economy. Air traffic doubles every 15 years 3.6 billion Passengers 62.7million Jobs supported 51.2 million Tonnes of freight $2.7trillion Global GDB annually 3

4 Passion for aviation. Commercial Aircraft s global workforce is united by a passion for aviation and restless desire to create better ways to fly. 7,390 Backlog 400+ Operators 56,000 Employees 53.8 billion Annual revenue, restated IFRS15 4

5 An Airbus takes off or lands every 1.4 seconds. 19,241 Aircraft sold 60 Produced monthly 25,000+ Daily flights 11,851 Delivered End February

6 New Commercial Aircraft Delivery Record Deliveries 800 Orders 747net Backlog 7,577 aircraft Deliveries Orders % * 747* 6,874 7,265 7,577* *including A220: 20 deliveries, 135 orders, Backlog 480 aircraft 6

7 Global Company UK 11,304 (9%) Germany 44,214 (34%) France 47,865 (37%) 133,700 employees from 135 nationalities Located across 35 countries USA 2,707 (2%) Other countries 10,178 (8%) Spain 13,177 (10%) China 1,150 on more than 180 sites International Sales 31% Europe 69% Non-Europe 7

8 Content Introduction to Aircraft Business Driver for HPC in Aircraft Industry Current HPC /Grid Infrastructure Current Limitations Upcoming Requirements Future Challenges 8

9 Capability Flight Envelope Coverage We have achieved a tremendous increase in Flight Physics simulation capability over the last 20 years 3D simulation of installed engine CFD*-based Aero Data Optimisation on Engine integration Complete physical modelling Laminar-Turbulent Transition Multidisciplinary Optimisation Support to Flight-Tests Unsteady Aerodynamics High fidelity Aerodynamics Simulation Reducing standard wind tunnel testing Flexible Aircraft Representation High speed CFD*-based scaling Simplified geometry Physical Complexity * CFD = Computational Fluid Dynamics 9

10 Pushing the boundaries with the A350 Heavy use of simulation and HPC for aerodynamics design around 5000 data sets Cryogenic Wind Tunnel Testing replicating flight conditions Advanced CFD + Optimisation Flight Re Testing 40 % less test than in A380 Optimised aircraft design, finest aerodynamics and lowest fuel burn 10

11 HPC enables the virtualization of aircraft development HPC at Airbus is a key enabler for Engineering to achieve the main objectives of new Aircraft programmes: Optimize the level of performance Increase maturity at entry into service Deal with the complexity Answer to certification requirements Decrease lead time and costs Engineering domains using HPC: Flight Physics - Aerodynamics design and data Structure - Optimization, composite, virtual testing, global Finite Element Method Power plant - Noise of turbo machinery, CROR, airframe, cabin & cockpit noise Systems - Lightning, fuel Multi Disciplinary Optimization 11

12 Aircraft Industry 12

13 Content Introduction to Aircraft Business Driver for HPC in Aircraft Industry Current HPC /Grid Infrastructure Current Limitations Upcoming Requirements Future Challenges 13

14 Airbus Scientific Computing Architecture AIRBUS GRID SERVICE Bremen HPC Service Hamburg Airbus Defence &Space 29 location in Europe Germany France Spain UK Italy Filton Donauwörth Ottobrunn Getafe Toulouse HPC Service Marignane Airbus Helicopters GRID SERVICE AEC I Bangalore HPC Service 14

15 HPC4/5 Functional Architecture View Toulouse Hamburg 10 Gb Ethernet 56 Gb Infiniband 8 x 10 Gb 4 x 10 Gb Admin Nodes Grid Nodes Transfer Nodes Login Nodes Admin Nodes Grid Nodes Transfer Nodes Login Nodes SCRATCH MIDTERM Storage SCRATCH Infiniband Infiniband MIDTERM Storage Type1 Nodes Type2 Nodes Type1 Nodes Type2 Nodes 15

16 Hardware-Details Compute nodes (Skylake 16 core /socket, 2 sockets by node) Type1 96 GB Mem/node (3 GB/core) 1TB HD local storage Type2 384GB Mem/node (12 GB/core) 7,8 TB SSD local storage Login nodes enable user interactive logon on Cluster Transfer nodes to Transfer Data to/from the Cluster Scratch (DDN Lustre) to store temporary data during execution Midterm Storage (PANASAS) to store data for multidisciplinary optimization Grid Nodes to run LSF resource management Administration nodes to support DCX cluster Administration 16

17 POD Performance Optimized Datacenter Key characteristics: - Super computer in a container - Outsourced service Secured Power Supply Key advantages: - High flexibility for growth and technology refresh - High density - Energy Efficiency (PUE 1.25) Key Performance Factors: - Expected ~ 3,3 PFLOP by cores with 235 TB Memory - 2,8 PB Scratch and 750 TB Mid-Term Storage Chillers Power Generator PODs TLS POD area 17

18 Content Introduction to Aircraft Business Driver for HPC in Aircraft Industry Current HPC /Grid Infrastructure Current Limitations Upcoming Requirements Future Challenges 18

19 Current Technology Need to Scale Number of core will increase Energy by socket will be stable CPU clock frequency will decrease Single core performance will decrease

20 Top 6 November different processor types /accelerator IBM Power/NVIDIA, Sunway FPGA, Intel Xeon / THE-2, Intel Xeon/NVIDIA, Intel Xeon / Intel Phi

21 Software Scalability Software is the #1 roadblock 21

22 Outlook New high scalable CFD Code will be available AMD Rome will be used. The AMD Rome chip has ¼ more memory capacity /bandwidth compared with Intel Skylake General the CPU become data mover, Compute Power are from Accelerators (GPUs or Vector Units) ARM become a serious Player on CPU market (not at Skylake level, but not far away) 22

23 Content Introduction to Aircraft Business Driver for HPC in Aircraft Industry Current HPC /Grid Infrastructure Current Limitations Upcoming Requirements Future Challenges 23

24 Emerging Technologies in Scope 24

25 25

26 HPC User Forum Forecast AI, ML, DL 26

27 Additional Methods for Engineering Design and Science Model Physics + Maths +HPC Prediction Artificial Intelligence Machine Learning Deep Learning Top Down deductive Bottom Up inductive Learn from past models & Simulations Prediction Model Big Data & Machine Learning Future Simulations will take benefit from HPC and BigData/ML 27

28 Airbus; Gerd Büttner Manufacturing Capabilities explained Augmented Reality through geo-located product-dmu, enriched with process & product info Fastener geo-located & mapped items NC NC NC NC Conc. To do what? Example use cases Sys. Installation Inspect aircraft brackets, ESN fasteners Manage Non- Conformities Bracket check overlay mode Verify build status Capture & report Assess & map damages Trouble shoot Identify parts & error root causes Initiate corrective actions 28

29 Content Introduction to Aircraft Business Driver for HPC in Aircraft Industry Current HPC /Grid Infrastructure Current Limitations Upcoming Requirements Future Challenges 29

30 We are a leader in designing, manufacturing, and delivering aerospace products, services and solutions to customers on a global scale. Airbus is an international pioneer in the aerospace industry. A commercial aircraft manufacturer with two other Divisions - Defence and Space and Helicopters. 30

31 Future Architecture view Cloud AI HPC Hybrid HPC HPDA 31

32 Data Centric Architecture PLM MES 0 Data Lakes Data 32

33 Factory of the future - Implementation strategy Planning 33

34 Factory of the future - Implementation requirements to Information Management Integration between HPC, BigData and AI IoT as one driver for Factory of the Future BigData Analytics on lifestream data from the factory HPC requirements to o Analyse data o Run simulation optimization models (Digital Twin) AI decision processes based on Data Analytics input 34 Page 34

35 Quantum Computing 35

36 Five Problem Statements Aircraft Climb Optimisation Computational Fluid Dynamics 5 distinct flight physics problems with varying degrees of complexity Quantum Neural Networks for Solving Partial Differential Equations Wingbox Design Optimisation Aircraft Loading Optimisation 36

37 Innovation creates value. Industry firsts 1 st twin-engine widebody aircraft, A300 1 st full fly-by-wire commercial airliner 1 st double decker airliner, A380 1 st manufacturer to make extensive use of advanced materials A business driven innovation. Accelerate company s transformation take full advantage of new technologies. Speed up innovation Co-innovate with external partners. Airbus Ventures funds start-ups around the globe. 37

38 Thank you