solar impulse, first round-the-world solar flight · introduction to organization and mission. 4 es...
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Solar Impulse, First Round-The-World Solar Flight
Ralph Paul
Head of Flight Test and Dynamics
Solar Impulse
June 22, 2017
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Key Takeaways
1. Why Solar Energy ?
– Renewable, no fossil fuel or polluting emissions
– Demonstrates that clean technologies can achieve impossible goals
2. Simulation made it possible, “model-as-you-go”
– Simulations and analysis accelerated the mission by 10x over the last 3 years
– Design iterations completed in hours, not days
– Golden reference established enterprise-wide, low hanging fruit!
3. Time-consuming testing tasks eliminated
4. Confidence in “production” code quality maintained
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Introduction to Organization and Mission
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es
BERTRAND PICCARDPSYCHIATRIST-EXPLORER
HANG-GLIDING CHAMPION
GOODWILL AMBASSADOR
1ST ROUND WORLD BALLOON FLIGHT
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es
ANDRÉ BORSCHBERGENGINEER-ENTREPRENEUR
GRADUATE OF MIT
SWISS AIRFORCE PILOT
WORLD’S LONGEST SOLO FLIGHT
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Challenges
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Design Mission Flight as a Golden Reference
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Design Drivers
▪ 3.8 m3 Space for 6 Days
– Fly
– Work
– Live
– Drink and Eat
– Sleep
▪ Critical Systems
– Oxygen
– Electric
– Avionics / Navigation
– Autopilot
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Innovation Challenges and Achievements
▪ Completing the historic round-the-world trip!
▪ Transitioning a vision into reality within tight
schedules and limited budget
▪ No references, first of its kind!
▪ Top down mission to aircraft and cockpit design
▪ CAD drawings to high fidelity simulations
▪ Establishment of training activities using the
simulations
▪ Lack of reusable Commercial off-the-shelf systemsBertrand’s Model in 2007
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Innovation Challenges and Achievements
▪ Create trustworthy baseline with
simulation for Federal Office of Civil
Aviation (FOCA) approval
– Aircraft design
– Operational aspects with emphasis on
multiday flying
▪ Redesign and certification impact
of software and hardware
Maximize Power Efficiency
Reduce Weight
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Innovation Challenges and Achievements
▪ Redundancy management per
ARP4754A and ARP4761
– ARP4754A:
Guidelines For Development Of Civil
Aircraft and Systems
– ARP4761:
Guidelines and Methods for
Conducting the Safety Assessment
Process on Civil Airborne Systems and
Equipment
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Tail Sizing,
Fuselage Shape
Engine
Position
Wing Dihedral,
Ailerons
Autopilot, Avionics, Inertial Platform
(Automatically Generated Code)
Model-Based Design of the Aircraft
Battery
Performance
Assessment
Tuning of Battery
Thermal Models, in
mission, for the Deriving
of Flight Plans
Modelling of
Solar Panel Study to Decide
One Aileron Servo vs. Two Rudder Servos
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Formal Analysis of Avionic Software to DO-178B, Multiple PlatformsMathWorks Code Verification Technologies for Various Design Assurance Levels
▪ > 350k Lines of Code from the Power Management Computer (PMC) alone
▪ Power Management / Mission Information Computer→ QNX on COTS Board (x86, 32 Bit, 500 MHz, UNIX RTOS)
▪ Throttle Box, Air Data Computer, Independent Display→ ATMEL on SI Boards (ATCAN90, 8 Bit, 8 MHz, No OS)
▪ Monitoring and Alert System→ ARM on ALTRAN Board (Cortex-M4F, 32 Bit, 168 MHz, No OS)
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Autopilot
Verified and Validated with
Model-Based Design
Avionics
Verified and Validated
with Polyspace
Flight Testing
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15André is flying at low altitude
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16Bertrand is Resting
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17Flight Plan over Time, Created and Animated with MATLAB
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Two Critical Issues During the Mission, Japan to HawaiiSimulation, Analysis, Prediction and Verification Helps Resolve the Issues in a Timely Manner
1. False alarm in the monitoring and alert system
• MathWorks code verification technologies were applied to solve both software and
hardware specific issues
2. Overheating of all four batteries
• Thermal behaviour of the battery compartment was modelled to predict and prevent
overheating issues
• Models were injected back into the telemetry system and used to guide the pilot to
enable manual timely vent control
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19Significantly Improved Thermal Monitoring System
MATLAB
Embedded
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First Mission Flight of 2016Used > 1TB of flight data for data analytics, improved predictions and fixed issues
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Final Landing, Last Leg Flight 17, Cairo to Abu Dhabi
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Autopilot Use: Prediction versus Reality
Flight Autopilot ON [%] Airborne [h]
RTW01 63% 12:59:11
RTW02 64% 15:20:16
RTW03 88% 13:15:02
RTW04 57% 13:35:01
RTW05 30% 20:29:07
RTW06 49% 17:22:35
RTW07 88% 44:10:13
RTW08 86% 117:49:16
RTW09 86% 62:29:10
RTW10 77% 15:52:24
RTW11 86% 18:09:35
RTW12 76% 16:33:54
RTW13 61% 16:46:47
RTW14 56% 4:40:59
RTW15 84% 71:08:37
RTW16 84% 48:50:19
RTW17 80% 48:36:56
Total 79% 558:09:22
*RTW Round The World
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Concluding Remarks
Model-Based Design with MATLAB and Simulink helped us
▪ Complete the historic round-the-world trip!
▪ Prepare emergency scenarios, for example weather and system failures
▪ Reuse, build, test, tune and fly whilst exploring new ideas and concepts
▪ Make key design decisions early, saving time and avoiding manual coding errors
▪ Focus on design and development instead of low-level coding
▪ Survive in-flight emergencies and provide critical data to the pilot
Saved 2+ Man-years using Polyspace Code Verifiers
▪ Identified and fixed run-time errors and unsafe code
▪ Formally verified codebase, statically analysed “without test cases”
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An idea born in Switzerland