Smart Tire: a pattern based approach using FEM Prof. Dr.-Ing. Ch. Oertel – Brandenburg University of Applied Sciences Dipl.-Ing. Jan Hempel – Brandenburg University of Applied Sciences Overview basics of the smart tires – sensors and data processing related questions – where, how many, which properties pattern bases approach – generating patterns system design tools – FEM and added features static and steady state analysis – preparing the pattern data base applications and – wheel load identification, contact forces summary - conclusions

Smart Tires actions targets educated tire adding sensors adding knowledge adding algorithms targets inflation pressure angular velocity normal force tangential force friction estimate camber temperature ? educated tire signals for control systems pressure monitoring system estimate road conditions

single sensor approach Smart Tires – state of the art: measurements FN = 9000 [N] FN = 4500 [N] single sensor approach hall sensor accelerometer strain gauge location sidewall location belt displacement, stress and strain for one revolution from FE analysis (RMOD-K FEM)

Smart Tires – state of the art: data processing processing procedure data acquisition at specific rate averaging multiple revolutions identify characteristic properties generating related results: R - d uniqueness ? patent examples: DE 3937966 A1 DE 4329591 C2 DE 10329700 A1 US 20050097949 A US 20040112128 A1 WO 0108908 A1 US 20050057346 A1 US 20040158441 A1 US 20050075825 A1 US 5749984 A

Smart Tires – patterns based approach processing procedure data acquisition at specific rate multiple sensors, different locations individual pattern at current time measured by ten sensors pattern data base at different wheel loads measurement or FE pattern comparing matching value mv

simulation based design using FE model Smart Tires – related questions sensor type distance acceleration strain point strain integrated others sensor location inner tire surface sidewall area inner tire surface belt area integrated in sidewall integrated in belt area other number of sensors cost and energy consumption uniqueness fail safeness real-time pattern search memory used in pattern base simulation based design using FE model sensor type strain integrated sensor location design variable (integrated in sidewall) design variable (integrated in belt area) number of sensors design variable

Smart Tires – development tool sensors description number of location orientation FE tire model based on cross section mesh post processing data collection pattern matching DOE: variables and parameters, parameter ranges, number of steps

assembly view (example) Smart Tires – embedded measurement fiber in FE analysis element view embedded in rebar layer or measurement layer in isoparametric HEX 8 element given measurement fiber orientation fiber reference points assembly view (example) curved (polygonal) path of fiber segments building one measurement fiber fiber parameters: location, length, orientation preprocessing path thru elements element 1 element 7 layer fiber

Smart Tires – measurement fibers: some details initial configuration (without inflation) calculate length of fibers segment in each element calculate the total fiber length calculate fiber electric resistance deformed configuration (inflated and loaded) calculate length of fibers segment in each element calculate the total fiber length calculate fiber electric resistance changes arbitrary element deformation by nodal displacements for matrix and embedded rebar element

design parameters support of development tool in FE preprocessor Smart Tires – measurement fibers: design variation design parameters support of development tool in FE preprocessor fiber length in belt in initial configuration fiber length in carcasses in initial configuration circumferential distance (number of elements) between two sensor fibers in belt circumferential distance (number of elements) between two sensor fibers in carcasses offset measured from belt edge offset measured from bead long fibers integrating strain over the fiber length measuring the average strain in each fiber fibers in belt and sidewall as alternatives or integrated system short fibers measuring the local strain “at a point” similar to conventional strain gauges fibers in belt and sidewall as alternatives or integrated system

FE model with 48240 degrees of freedom (RMOD-K FEM) Smart Tires – getting an initial design guess tire under inflation and vertical load strain distribution in the carcass layer from FE model strain at gaussian points for each layer identify regions with positive strain in different load cases (camber, longitudinal and lateral slip, different loads, different inflations) optimize the sensors fiber locations and length wrt. durability 12 sensors used in the examples shown verify the fiber strain measurement algorithm FE model with 48240 degrees of freedom (RMOD-K FEM)

Smart Tires – building patterns define the pattern data base variables to be taken into account number of steps for each variable range for each variable define jobs of all required sets (63 in this case) define incremental variable (yaw angle) run jobs automatically (DOE-feature) collect results in a pattern data base small example data base inflation and wheel load taken into account nonlinear model, incremental solver three jobs for 33 patterns each point represents one pattern entry in data base amount of data depends on the number of sensors

Smart Tires – find a matching pattern pi = 2.0 [bar] pi = 2.5 [bar] pi = 3.0[bar] test case outside the data base no correct tire state predictable significant difference between both inflations, estimate of wheel load may be taken from minimum matching value possible matching procedures compute all matching values find most close point or interpolate between most close points or fit surface and find minimum or search in prior used area matching procedure based on interpolation possible, very good match of test case parameters inflation and wheel load

Smart Tires – example 1 static deformation variations of sensor properties reduce the number of sensors total of 48 sensors total of 40 sensors total of 24 sensors FE static deformation analysis variation of inflation pressure (DOE) variation of wheel load by load increments number of steps inflation: number of jobs number of steps wheel load: number of increments per job post processing collecting data from each job (defined in batch file from DOE) build the data base comparing with current tire state (load step j of single analysis) compare different pattern matching algorithms

Smart Tires – example 1 matching: all sensors total of 48 sensors nearest pi=2.20 [bar], =17.60 [mm] test case pi=2.30 [bar], =18.20 [mm] resolution pi=0.40 [bar], = 6.00 [mm] total of 24 sensors nearest pi=2.20 [bar], =17.00 [mm] test case pi=2.30 [bar], =18.20 [mm] resolution pi=0.40 [bar], = 6.00 [mm]

Smart Tires – example 1 matching: selected sensors total of 6 sensors in left belt area nearest pi=2.20 [bar], =17.60 [mm] test case pi=2.30 [bar], =18.20 [mm] resolution pi=0.40 [bar], = 6.00 [mm] total of 6 sensors in left sidewall nearest pi=2.20 [bar], =17.60 [mm] test case pi=2.30 [bar], =18.20 [mm] resolution pi=0.40 [bar], = 6.00 [mm]

combined slip situation at a certain wheel load Smart Tires – example 2 steady state rolling: combined slip combined slip situation at a certain wheel load DOE variable 1 wheel load (vertical displacement) DOE variable 2 angular rim velocity incremental variable yaw angle pattern base DOE variable 1 7 steps: resolution = 3.33 [mm] DOE variable 2 7 steps: resolution = 0.035 [rad/s] incremental variable 50 increments = 0.3 [deg] 49 steady state jobs 2450 pattern base states 4 alternative sensor locations with 6 sensors per location DOE variable 1 DOE variable 2 incremental variable

ALE – approach for steady state analysis Smart Tires – example 2 building pattern base ALE – approach for steady state analysis rigid body motion and elastic deformation velocity field in the contact area, normal load steady state tangential contact forces iterative method to solve coupled PDE-system mesh remains non-rolling steady state analysis given wheel load given longitudinal slip given camber angle yaw angle as incremental variation

Smart Tires – example 2 matching: all sensors nearest = 25.00 [mm] = 7.50 [deg] = 27.45 [rad/s] test case = 25.50 [mm] = 7.50 [deg] = 27.40 [rad/s] resolution = 3.33 [mm] = 0.30 [deg] = 0.035 [rad/s] test results

Smart Tires – example 2 matching: selected sensors right carcass 6 sensors = 25.00 [mm] = 7.50 [deg] = 27.40 [rad/s] point number 1176 left carcass 6 sensors = 25.00 [mm] = 7.50 [deg] = 27.45 [rad/s] point number 1175 left belt 6 sensors = 25.00 [mm] = 7.50 [deg] = 27.45 [rad/s] point number 1176 test case = 25.50 [mm] = 7.50 [deg] = 27.40 [rad/s] resolution = 3.33 [mm] = 0.30 [deg] = 0.035 [rad/s]

Smart Tires – further development subject topics rolling tire rotating the measured pattern for pattern matching reducing the data base from double to integer reducing the data base optimal number of patterns compare different matching methods optimal balance between data base size and real time matching algorithm road noise disturbance filters and averaging procedures (depending on system sample rate) data transfer use conventional pressure monitoring radio transmission building a prototype testing strain measurement fibers (smart textiles) courtesy of SFE

contact: christian.oertel@fh-brandenburg.de Smart Tires - summary pattern base identification of tire state proven for static and steady state cases information about inflation, normal and tangential force, angular velocity, friction … at real time available simulation based design of the sensor system supported by FEM tool RMOD-K FEM test pattern data base by FEM tool RMOD-K FEM further development concentrate on rolling tires first application could be race tires contact: christian.oertel@fh-brandenburg.de