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An experimental study was conducted to compare the performance of an in-house built novel double semi-active damper against a conventional semi-active single damper. Different performance metrics were analyzed, and the performance of the two dampers was evaluated based on these metrics. A Hybrid Skyhook–Groundhook control algorithm was developed and implemented on the variable orifice double damper. The semi-active single damper is governed via two separate control strategies, namely—Skyhook and Groundhook control, respectively. The effectiveness of each algorithm is better understood by adding a normal load on top of the Shock Dyno, thus modifying it to act as a quarter car test rig. The sprung and unsprung acceleration data are collected via the accelerometers mounted on the Shock Dyno through a Data Acquisition System. The results obtained from this experiment provide a strong basis that the semi-active double damper performs better in terms of the comfort cost than that of the commercial semi-active single dampers. 

Abstract Hydroplaning is a phenomenon that occurs when a layer of water between the tire and pavement pushes the tire upward. The tire detaches from the pavement, preventing it from providing sufficient forces and moments for the vehicle to respond to driver control inputs such as breaking, accelerating, and steering. This work is mainly focused on the tire and its interaction with the pavement to address hydroplaning. Using a tire model that is validated based on results found in the literature, fluid–structure interaction (FSI) between the tire-water-road surfaces is investigated through two approaches. In the first approach, the coupled Eulerian–Lagrangian (CEL) formulation was used. The drawback associated with the CEL method is the laminar assumption and that the behavior of the fluid at length scales smaller than the smallest element size is not captured. To improve the simulation results, in the second approach, an FSI model incorporating finite element methods (FEMs) and the Navier–Stokes equations for a two-phase flow of water and air, and the shear stress transport k–ω turbulence model, was developed and validated, improving the prediction of real hydroplaning scenarios. With large computational and processing requirements, a grid dependence study was conducted for the tire simulations to minimize the mesh size yet retain numerical accuracy. The improved FSI model was applied to hydroplaning speed and cornering force scenarios. 

ABSTRACT Being able to estimate tire/rubber friction is very important to tire engineers, materials developers, and pavement engineers. This is because of the need for estimating forces generated at the contact, optimizing tire and vehicle performance, and estimating tire wear. Efficient models for contact area and interfacial separation are key for accurate prediction of friction coefficient. Based on the contact mechanics and surface roughness, various models were developed that can predict real area of contact and penetration depth/interfacial separation. In the present work, we intend to compare the analytical contact mechanics models using experimental results and numerical analysis. Nano-indentation experiments are performed on the rubber compound to obtain penetration depth data. A finite element model of a rubber block in contact with a rough surface was developed and validated using the nano-indentation experimental data. Results for different operating conditions obtained from the developed finite element model are compared with analytical model results, and further model improvements are discussed. 

The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability.