Y. Lemmens, T. Benoit, J. de Boer, T. Olbrechts LMS, A Siemens Business Real-time Mechanism and System Simulation To Support Flight Simulators Smarter decisions, better products.
Contents Introduction Solver advancements Demo 1: Multi-body model of a Business Jet Demo 2: Electrical network model of a UAV Summary & Outlook Page 2
Introduction Page 3
Introduction Unified modelling approach Mechatronic System Testing: OEM is responsible to specify and integrate increasingly complex supplier delivered subsystems Page 4
Introduction DAIMLER - Integrated Full Vehicle Simulation approach C-code Page 5 Real Time Vehicle Integration Controls Tires Chassis DUR - Virtual Test Rigs
Introduction Why is real-time simulation needed? Requirements Design Validation Operation Specification System Life Cycle Concept Subsystem Testing Design Component Refinement Detailed Design Engineering Page 6
Introduction Real-time simulation Proposition Use existing offline high fidelity Vehicle models for MIL in SIL, HIL and PIL applications Motivation Reduce variation between high-fidelity and real-time models No loss of parameters available in the real-time models: physical and vehicle Reduced effort to maintain multiple levels of models Increase the frequency range of for Realtime 2x modeling effort 88 DoF CPU 1 88 DoF 15 DoF Parameter loss 0-15+Hz 0-10Hz CPU 2 Page 7 LMS Confidential
Introduction Aeronautical Applications of Real-time simulation 1. Pilot-in-the-loop Human interaction with virtual model Interactive simulation User input Virtual.Lab Motion Real-time solver Visualisation e.g. Flight simulator demonstrator 2. Hardware-in-the-loop Hardware interaction with virtual model e.g. testing of hardware components as part of a virtual system model e.g. testing of hardware controllers, by connecting them to a virtual plant model Virtual.Lab Motion Real-time solver 3. State estimation Redundancy of instrumentation Estimation of mass distribution Incident analysis Gust Load Alleviation Virtual sensing Hardware controller Page 8
Demo 1: Multi-body model of a Business Jet Page 16
Demo 1: Multi-body model of a Business Jet VL Motion Flight dynamics AMESim - Aerodynamics AMESim Brakes Dashboard Flight Gear Page 17
Demo 1: Multi-body model of a Business Jet LMS Virtual.Lab Motion multi-body model Airframe Business jet MOTW: 21000 kg Rigid body Landing Gear Simple tire tire model Steerable nose landing gear Brakes on main landing gear wheel axis Vertical struts: non-linear stiffness & damping Engines Force vector Aerodynamics Force/moment vector @ CoG Fz [N] Stiffness Fz [N] Damping d [m] v [m/s] Page 18
Demo 1: Multi-body model of a Business Jet LMS Virtual.Lab Motion multi-body model Submechanisms Nose landing gear (1x) Main landing gear (2x) Vertical strut Wheel assembly Assembly 29 rigid bodies 204 generalized coordinates 16 degrees of freedom Page 19
Demo 1: Multi-body model of a Business Jet Aerodynamics Coefficient look-up method (database interpolation): C i = C i0 α, M, β + C i,q α, M, q static term dynamic derivative in pitch cq bp + C 2U i,p α, M, p + C 2U i,δ α, M, δ δ + dynamic derivative in roll control derivative α: angle of attack β: sideslip angle M: mach number δ: control surface deflection p: roll rate q: pitch rate r: yaw rate Database can be created using CFD, wind tunnel tests, flight tests L Forces & moments calculated as F i = 1 2 ρv2 SC i for i = L, D, Y M i = 1 2 ρv2 StC i for i = m, l, n Y D Page 20
Demo 1: Multi-body model of a Business Jet Aerodynamics Page 21
Demo 1: Multi-body model of a Business Jet Brake model Brake model Brake disks Low pass filter Input Brake signal (%) Output Braking torque Page 22
Demo 1: Multi-body model of a Business Jet Interaction with Virtual.Lab Motion model Model inputs Aileron / elevator / rudder Steering angle Thrust Left / right Braking torque Left / right Flaps Aerodynamic force / moment Model outputs Position / orientation Velocity Compression struts Page 23
Demo 1: Multi-body model of a Business Jet Connecting to external programs / modules User input Virtual.Lab Motion C-code solver Visualisation Generic Cosimulation DLL For communication with programs / modules outside Virtual.Lab User DLL coded in C DLL is called each time step Aerodynamics Page 24
Demo 1: Multi-body model of a Business Jet Connecting to external programs / modules Page 25
Demo 1: Multi-body model of a Business Jet Connecting to external programs / modules Dashboard Page 26
Demo 1: Multi-body model of a Business Jet Running on multiple platforms Demonstrator setup Network Network User Input Virtual.Lab Motion C-code solver Visualisation (FlightGear) Base platform Target platform Base platform Page 27
Demo 1: Multi-body model of a Business Jet Post-processing & Performance Virtual.Lab Motion Post-processing Import results into Virtual.Lab Motion All Virtual.Lab post-processing tools can now be used Animation Plots Bodies 29 Performance No effort was done to optimize the model for speed All on one core Generalized coordinates 204 DOF 16 Step size 1.00 ms Turnaround time 0.65 ms Page 28 Margin 35%
Hardware setup of the PLM Flight Simulator Side screens to show additional info Steering stick Engine Throttle Main screen with cockpit view Simulator seat and support frame Pedals Simulation computer with dashboard Page 29
Demo 2: Electrical network model of a UAV Page 30
Demo 2: Electrical network model of a UAV VIVES Litus UAV Project Objective To develop an UAV platform for scientific monitoring of the Flemish coast and North Sea Main Partners Vives Aerospace Department Kulab, Department of Mechanical engineering LMS, A Siemens Business, Aerospace Competence Centre Duration Aircraft development 2010 2013 GVT tests performed in May 2014 Test flights planned at the summer of 2014 Page 31
Demo 2: Electrical network model of a UAV Specifications and design Canard configuration MTOW: 60 kg Payload: 5kg Stall speed: 50km/h Cruise speed: 80km/h Cruise altitude: 1000ft Endurance: 2h Range: 160km Wing span: 5,8m Fuselage width: 0,5m Page 32
Demo 2: Electrical network model of a UAV Design Large open and flexible payload bay Flat bottom for easy payload implementation (e.g. camera s, radars,...) Page 33
Demo 2: Electrical network model of a UAV Systems Flight Test configuration Attitude control Recovery parachute RRS Radio controller transmitter Receiver Receiver Servomotors for control Electromotor propeller Wired control Wireless control Battery Battery supply required Page 34
Demo 2: Electrical network model of a UAV Overview electrical network architecture Mission Profile & Environment RSS Transmitter Receiver Receiver Power box Servo L (Canard, Aileron, Rudder) Servo R (Canard, Aileron, Rudder) ESC L ESC R Batteries Propeller L Motor L Page 35 Motor R Propeller R
Demo 2: Electrical network model of a UAV Electro-thermal LMS Imagine.Lab AMESim model Energetic behaviour of all main electrical components: 2 receivers RRS Powerbox 13 servos 2 motors 2 ESCs 2 propellers 4 battery sets Page 36
Demo 2: Electrical network model of a UAV Real-time model Change to make model real-time: Different models for batteries and electric cables were used to eliminate implicit-states. Servo models were adapted to capture their dynamic behaviour Thermal models were removed Page 37
Demo 2: Electrical network model of a UAV Pilot-in-the-Loop setup architecture Page 38
Summary & Outlook Unified modelling approach leads to interest in reuse of detailed engineering models for real-time application Advancements in LMS/Siemens solvers for multi-body and multi-physics simulation have been included to make the implicit solver deterministic (but not necessary faster than normal solver!) 2 Demo cases presented Real-time multi-body model of a business jet with landing gear Real-time model of electrical system of a UAV Outlook: - Combine the electrical model with the multi-body model of the Litus as a flight dynamics - Include support for flexible bodies in real-time multi-body simulations - Application of technology to industrial aeronautical models Page 39
Questions? Contact info: Dr Yves Lemmens LMS, A Siemens Business Aerospace Competence Center Yves.lemmens@lmsintl.com Smarter decisions, better products.