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  • 11th Asia-Pacific Regional Conference of the ISTVS
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  • Papers of the 11th Asia-Pacific Regional Conference of the ISTVS
    • 0303 / Composite Beam Tests with Closed Cell Polyurethane and Aluminum Foam
    • 0356 / Design and Simulation Analysis of Intelligent Suspension for Manned Lunar Rover
    • 0861 / Review of the Reconfigurable Wheel-Tracked System
    • 0963 / A Wheel and Vehicle Mobility Index Based on Traction and Velocity...
    • 1128 / Semi-Active Reinforcement Learning Suspension Control for the Off-Road Vehicles
    • 1491 / Design and Verification of a Creeping Mars Rover
    • 1534 / Foothold Selection Considering Constraint and Slippage Evaluation for Legged Robots
    • 1561 / Prominent Problems and Thoughts of “Paddy Soil-Terrain Machine System”...
    • 1655 / Modeling of Lunar Rover Vehicle Wheel-Soil Interaction Using Fem-Dem Method
    • 2034 / A Comprehensive Lumped Parameter Approach for the Dynamic Simulation...
    • 2149 / Investigation of the Shear Stress Dynamics on Silty Loam Soil and Measurement...
    • 2190 / Tyre Parameterization Tests: Dynamic vs. Static
    • 2539 / Model Predictive Control of a Robot Driven Vehicle for Testing of Advanced Driver...
    • 2632 / Energy Consumption Analysis of Door Opening with a Mobile Manipulator...
    • 2643 / An Improved Simultaneous Localization and Mapping Method Base on LeGO-LOAM and Motion Compens
    • 3351 / Benchmarking of Compression Testing Devices in Snow
    • 4054 / Field Validation of Egress Process for Planetary Rover
    • 4243 / Soil Compaction Monitoring Technique Using Deep Learning
    • 4260 / The Running Gear Construction Impact on Obstacles Overcoming by Light High-Mobility UGV
    • 4409 / Design of Self-Driving Bulldozer System
    • 4744 / Terrain Classification Using Mars Raw Images Based on Deep Learning Algorithms...
    • 4774 / Steadily Learn to Drive with Virtual Memory
    • 4782 / Experimental Study of Track-Soil Interactions of the Steering Performance of Tracked...
    • 4812 / Multi-Fidelity Machine Learning Modeling for Wheeled Locomotion on Soft Soil
    • 4827 / Introducing Polibot: A High Mobility Tracked Robot with Innovative Passive Suspensions
    • 5060 / Bionic Quadruped Robot for Mars Surface Exploration
    • 5408 / Ride Comfort Comparison Between Suspension Modes: Input Towards Designing Difference...
    • 5800 / Interaction Modeling and Dynamic Control Strategy for C-Shaped Leg with Sandy Terrain...
    • 5979 / Research on Drag Reduction Performance of Sliding Plate of Rice Direct Seeding Machine...
    • 6174 / Factors Affecting Bevameter Soil Characterization
    • 6316 / Perceptive Locomotion of Legged Robot Coupling Model Predictive Control and Terrain Mapping
    • 6718 / Research on Vehicle Running Performance on Paved Roads Covered with Falling Volcanic Ash
    • 6796 / Nonparametric Terrain Estimation Based on the Interaction Simulation Between Planetary...
    • 7018 / A Review of Modeling and Validation Techniques for Tire-Deformable Soil Interactions
    • 7092 / A Time Domain Passivity Controller for Teleoperation of Four Wheeled Differential...
    • 7199 / Vehicle Dynamic Factor Characterized by Actual Velocity and Combined Influence...
    • 7233 / Study of Passive Steering Mechanism for Mars Surface Exploration Rovers
    • 7399 / Tire-Soil Tangential Force Reinforcement Learning Modeling
    • 7878 / A Method for Fast Obtaining of Soil Shear Strength Index Based on Dem Free-Fall Cone...
    • 8131 / Parameters Calibration of Red Clay Soil in Hilly Area of Southwest China for Discrete...
    • 8349 / The Effect of Integrating a Bio-Inspired Convex Structure with a Low-Surface Energy...
    • 8654 / Construction of a Soil Clods Recognition Bench-Scale Experiment for Discrete Element...
    • 8658 / Investigation of the Relationship between the Cone Index and the Physical and...
    • 9352 / 3D-DEM Simulation and Post-Process Method of Wheel-Terrain Interaction for Planetary Rovers
    • 9768 / Design and Traction Performance Test of Bionic Paddy Wheel Based on Cattle Hoof
    • 9913 / Acquisition of Flipper Motion in Step-Climbing of Tracked Robot Using Reinforcement Learning
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  1. Papers of the 11th Asia-Pacific Regional Conference of the ISTVS

3351 / Benchmarking of Compression Testing Devices in Snow

Paper presented at the 11th Asia-Pacific Regional Conference of the ISTVS

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Last updated 2 years ago

Title: Benchmarking of Compression Testing Devices in Snow

Authors: Mohit Shenvi, Corina Sandu, and Costin Untaroiu

Abstract: Traction testing of tires for 'severe snow use' certification is performed according to ASTM F1805 in the Unites States and Canada. The ASTM standard provides guidelines for the preparation and compaction of the snow required for the testing and its evaluation using the CTI (Compliance Testing Incorporated) penetrometer. However, the CTI penetrometer provides a measure of the degree of compactness of the snow. From a computational perspective, this information in itself is not sufficient for modeling and/or validation of a snow model for virtual snow traction testing, which is important in the design stage of tires. This work attempts to compare the relative compression testing performance of three devices, namely a CTI penetrometer (rounded cone tip impactor), a standard Clegg Hammer (flat surface impactor), and an in-house developed device inspired by the Russian Snow Penetrometer (cone tip impactor). The approach used here includes a comparison of the available direct and indirect outputs of all the devices and a statistical analysis of these. The observed differences may be due to differences in the mass and drop height between the devices. The findings of this work provide a baseline for further research into the optimal mass and drop height for the evaluation of compacted snow properties. From a modeling perspective, the values generated using the Clegg impact hammer could be useful. However, it tends to underestimate the elastic modulus, when its results are compared to the laboratory technique, and to overestimate the ram resistance when compared to the in-house device. Further improvements to the in-house device may improve its ability to measure the snow properties most relevant to the modeling of snow.

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https://doi.org/10.56884/OTSP3025
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