A method is disclosed for analyzing the rolling of a rotating body that will make it possible to simultaneously achieve reduction in computational cost and attainment of adequate analytic precision. The method includes: a step (S100, S101) in which a structural model is acquired; a step (S103) in, which a region Ar1 on a rotating body T at which finely divided computational mesh cells are established is made to come in contact with the ground, and rolling analysis processing is performed in which rolling is made to occur in an amount that is an angle corresponding to N minimum units (where N is a natural number not less than 1); and a step (S105) in which mapping processing is performed in which physical quantities at computational mesh cells as they exist following the rolling analysis processing are copied to corresponding computational mesh cells at a rolling start time.

A method includes a step in which time series data pertaining to displacements of node groups including all nodes that constitute an exterior surface of a tire including grooves thereon, and at least a portion of all nodes that constitute groove space elements, is acquired from results of arithmetic operations for deformation in accordance with the model being such that having the groove space elements; and a step in which a fluid analytic model in which space around the tire is expressed as a plurality of computational mesh cells is used to perform arithmetic operations for fluid analysis in which arithmetic operations are carried out with respect to a physical quantity of fluid for each of the computational mesh cells as locations of the computational mesh cells are varied in such fashion that the node groups in the time series data are made to serve as control points.

A computer-program product, system and method for designing a tread pattern for a target tire. A device obtains a footprint of the tire. A predictive equation is created for tire tread spacing from values of scalable parameters related to footprints of a collection of tires. A target tread pattern is evaluated by applying the predictive equation to the target tread pattern. The target tread pattern for the target tire is selected based on the evaluation. The creation of the predictive equation and its use in evaluating the target tread pattern can be performed on a processor.

The prediction model includes the steps of calculating a surface area S.sub.1 of a control mold, measuring the force F.sub.1 for demolding from the control mold, determining first and second test specimens with respective surface areas S.sub.0, S.sub.0, measuring the force F.sub.0 for demolding from the first test specimen, measuring the force F.sub.0 for demolding from the second test specimen, calculating the ratio of S.sub.0 and S.sub.0 so as to define a test specimen surface area ratio R.sub.se, calculating the ratio of the force F.sub.0 for demolding from the first test specimen and F.sub.0 for demolding from the second test specimen so as to define a force ratio R.sub.fe, measuring the molding surface area S.sub.m of a mold to be measured and calculating the force F.sub.m for demolding from the mold to be measured such that F.sub.m=F.sub.1S.sub.m/S.sub.1R.sub.fe/R.sub.se.

A non-pneumatic tire includes a generally annular inner ring that attaches to a wheel, a generally annular outer ring, and an interconnected web between the generally annular inner ring and the generally annular outer ring. The interconnected web defines a plurality of openings circumferentially spaced around the tire and radially spaced at varying distances from an axis of rotation, so as to support a load by working in tension. The interconnected web includes a plurality of web elements having a varying thickness, including a first plurality of web elements above the axis of rotation and a second plurality of web elements below the axis of rotation. The varying thickness is configured to facilitate buckling of the interconnected web. When a load is applied, the first plurality of web elements are subjected to a tensile force while the second plurality of web elements buckle.

Systems and methods for analyzing tread surface data to assess tire tread parameters, such as irregular wear characteristics of a tire tread, are provided. For example, tread surface data, such as a tread surface map, can be processed to generate a convex hull for the tire. The convex hull can approximate the convex outer surface of the tire. The convex hull can be used as a reference for analyzing the tread surface data. In particular, irregular wear zones in the tire tread can be mathematically concave relative to the convex hull. Comparing the tread surface data to the convex hull can reveal information indicative of irregular wear characteristics of the tire. For instance, the local depth of the measured tread surface data relative to the convex hull can provide a quantitative measure of irregular wear characteristics of the tire.

There is disclosed a method, for determining a suitable tire for use on a vehicle, wherein the vehicle comprises an electronic device capable of collecting driving data relating to the driving of the vehicle. The method comprises obtaining driving data (18), from the electronic device, relating to the driving of the vehicle, and determining, from the driving data, an amount of driving performed on at least one of a plurality of different road types (20). The method further comprises forming a road type driving profile (22) comprising one or more road types and the associated determined amount of driving performed thereon and selecting, from a plurality of different tires, a suitable tire (24) for the vehicle based on at least the road type driving profile.

A tire damage detection method includes a preliminary stage comprising: performing tests involving test tire impacts against/on different obstacles at different motor vehicle speeds; measuring/acquiring test-related wheel speeds during the performed tests; computing test-related normalized wheel speeds based on the test-related wheel speeds; and determining a predefined tire damage model based on the test-related normalized wheel speeds corresponding to the test tire impacts and of associated test-related average wheel speeds. A tire damage detection stage comprises: acquiring signals indicative of a motor vehicle wheel speed; computing, based on quantities indicative of the wheel speed, a normalized wheel speed indicative of a ratio of the wheel speed to an average wheel speed indicative of motor vehicle speed; and detecting potential damage to a tire of the wheel based on the predefined tire damage model and on the normalized wheel speed.

A system and method are provided for estimating and applying vehicle tire traction. Vehicle data (e.g., movement and location-based data) and tire sensor data are collected at a vehicle and transmitted to a remote computing system (e.g., cloud server). A wear status is determined, and traction characteristics determined for at least one tire, based at least on the vehicle data and the determined tire wear status. The predicted tire traction characteristics are transmitted from the remote computing system to an active safety unit associated with the vehicle, or a fleet management system, wherein the recipient is configured to modify vehicle operation settings based on at least the predicted tire traction characteristics. A maximum speed for the vehicle may be defined by the recipient, or a minimum following distance where, e.g., the vehicle is one of multiple vehicles in a defined platoon.

Non-Pneumatic Tire (NPT) has been widely used due to their various advantages. Existing solutions/approaches provide limited flexibility on the overall tire performance and have less stiffness and poor damage performance. Embodiments of the present disclosure provides systems and methods that generate optimized spoke design for non-pneumatic tires. More specifically, the system of the present disclosure can generate optimized topology with customized property/performance outcomes for NPTs. The variety of spoke designs have been created using a computer generative method. The performance of these generative spoke designs has been investigated using a finite element method (FEM) technique, wherein output of the FEM technique is used for training machine learning model(s) that enable selection of optimal spoke design for tire manufacturing.