1 / 26

Modeling Tire Wear and Driver Behaviour in Open Pit Haulage Operations

Modeling Tire Wear and Driver Behaviour in Open Pit Haulage Operations. ExtendSIM Software . Dynamic modeling of real-world processes Uses building blocks to explore processing steps Benefits Easy to use Inexpensive MS-Windows environment Handles both Discrete and Deterministic Models.

payton
Télécharger la présentation

Modeling Tire Wear and Driver Behaviour in Open Pit Haulage Operations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Modeling Tire Wear and Driver Behaviour in Open Pit Haulage Operations

  2. ExtendSIM Software • Dynamic modeling of real-world processes • Uses building blocks to explore processing steps • Benefits • Easy to use • Inexpensive • MS-Windows environment • Handles both Discrete and Deterministic Models

  3. Discrete and Deterministic • Discrete Events • Probabilistic method • Maintenance, Loading, Dumping • Deterministic • First Principles • Truck movement • Fuel consumption • Tire temperature • Fuzzy Models (A.I.) • Road conditions (rolling resistance and traction) • Tire wear • Driver behaviour (velocity, acceleration, reaction time)

  4. Fuzzy Road Conditions • Rolling Resistance varies from 2.5% to 3.5% • Traction varies from 0.44 to 0.55 • Value depends on schedule for grader and water truck and rain/snow intensity/duration

  5. Rolling Resistance Fuzzy Model

  6. Conventional Approach to Tire Wear • All tire suppliers use the TKPS (TMPS) method • Tonnes-Kilometers per Hour • Actually, this is simply an Alarm System • If TKPH is exceeded on a real-time basis, the truck is prevented from operating in 5th gear to restrict velocity • A better method would be to monitor tire temperature and pressure in real time

  7. Real-time Measurement of Tire Temperature • External chassis-mounted IR temperature sensor • Temperature sensor embedded in tire tread • External sensor subject to ambient conditions (shade/sun) • Embedded sensor can wirelessly send data to on-board computer

  8. Tire Temperature Decline (until Ttire = Tatm) • Dynamic calculation every 100 msec • Tire load and speed determine temperature change • Temperature drop by ambient heat loss: ΔTd = (Tatm – Ttire)·e-kdt where ΔTd = temperature decline (°C) Tatm = ambient temperature (°C) Ttire= current tire temperature(°C) kd = heat transfer coefficient (1.6 x 10-4) t = time step (seconds)

  9. Tire Temperature (continued) • Temperature increase due to load and velocity: ΔTi = KT(1 – e-kit) – ΔTd where ΔTd = temperature increase (°C) KT = 8.344 x 10-3(P + GMW)V ki = 6.836 x 10-7(P + GMW)V2 t = time step (seconds) ΔTd = temperature decline (°C) P = payload (tonnes) GMW = gross machine weight + fuel (tonnes)

  10. Tire Temperature Change

  11. Tire temperature cycles (14.7% idle time) Velocities = 16 kph loaded / 32 kph empty

  12. Tire temperature cycles (9.3% idle time) Velocities = 16 kph loaded / 32 kph empty

  13. Tire temperature cycles (9.3% idle time) Velocities = 19 kph loaded / 38 kph empty

  14. Wear rate as a function of tire temperature Tire wear rate reported by Miller Rubber Co. in 1928 Popular Mechanics, (1928). Burning 'em Up, June, 49(6), p.938-942. (Miller Rubber Co. graph, p.940)

  15. Wear rate as a function of tire temperature Tire wear rate reported by Miller Rubber Co. in 1928 Popular Mechanics, (1928). Burning 'em Up, June, 49(6), p.938-942. (Miller Rubber Co. graph, p.940)

  16. Wear rate as a function of tire temperature Wear Rate = 21.699V2e-7,106/RT + 11,931Ve-8,621/RT There are two terms in the equation: First term relates to Energy flow through the tire Second term relates to force (momentum of tire)

  17. Scale-up to a Haulage Truck tire Miller Tire Calculated wear rate = 0.274 mm / 10,000 km @ 15 kph and 45 °C Calculated wear rate = 0.528 mm / 10,000 km @ 25 kph and 45 °C Estimated Load (Miller tire) = 2.44 kg/cm2 Load (CAT793) - full = 4.44 kg/cm2 Load ratio = 1.82 Load (CAT793) - empty = 2.00 kg/cm2 Load ratio = 0.82 Tire surface element contact ratio = 1.22 Road surface condition ratio = 12.5 CAT 793D Travelling fully-loaded = 0.274 x 1.82 x 1.22 x 12.5 = 7.61 mm / 10,000 km Travelling empty = 0.528 x 0.82 x 1.22 x 12.5 = 6.69 mm / 10,000 km

  18. Validation from Real Tire Wear Data CAT 793D Travelling fully-loaded = 7.61 mm / 10,000 km Travelling empty = 6.69 mm / 10,000 km Average = 7.15 mm / 10,000 km Calculated Tread Depth Change = 7.15 x 11 = 78.7 mm Mine Data Typical Tread Depth Change at scrap = 75 mm for ~ 110,000 km (5,500 hrs) Error = 4.9% Assumed Maximum Wear Rate = 10 mm / 10,000 km

  19. Fuzzy Tire Wear Model (mm/10,000 km) Three main factors: payload, speed, tire temperature Additional factors: tire pressure, road conditions, tire rotation

  20. Tire Wear Model based on Fuzzy Logic Calibration factors: maximum tire wear rate = 10 mm / 10,000 km maximum velocity = 35 kph maximum payload = 440 tonnes (average = 219 tonnes)

  21. Driver Behaviour Sub-Model

  22. Behaviour Criteria • Driving Speed • Acceleration • Braking • Reaction Time • Lateral Position Control • Many factors – gender, energy level, age, health, family and personal issues, tiredness, skill level, time since training, personality, time in shift, time in work period • Too many variables and far too complex to validate

  23. Driver Behaviour – Aggressiveness Factor

  24. Driver Behaviour – Set Points (average) Driver Velocity (kph) Acceleration Reaction Time Type Loaded Empty (m/s2) (msec) Passive 12 22 0.31 400 ± 100 Normal 15 26 0.42 300 ± 100 Aggressive 18 30 0.70 250 ± 50 Autonomous 13 23 0.42 100 ± 0

  25. Driver Behaviour – Aggressiveness Factor

  26. Driver Behaviour - 1 km modeled test drive

More Related