The effect of dilution oil in the torque performance of magnetorheological grease

Magnetorheological (MR) brake is a device that uses MR material to produce braking torque according to induced current. Even though MR Fluid (MRF) is widely used in MR brake as it has high response and is easy to fabricate, the sedimentation issue has degraded the performance of M R brake. Therefore, MR grease (MRG) is introduced to overcome the drawbacks of MRF. Another important benefit of MRG is its self-sealing property which can solve the leaking problem in MRF. Despite the advantageous of no sedimentation of MRG, high viscosity of MRG lower the MR response to the current induced. Thus, the high viscosity of MRG can be reduced by adding dilution oil. Furthermore, the effect of the oil in diluted MRG in MR brake has not been investigated. Several samples of MRGs with different types of dilution oil were prepared by mixing grease and spherical carbonyl iron particles (CIP) using mechanical stirrers. The rheological properties in rotational mode were tested by using rheometer meanwhile the torque performance of MRGs in MR brake were evaluated by changing the current of 0A, 0.4A, 0.8A and 1.2A and fixed the angular speed. The result shows that MRG 3 has the lowest viscosity which is almost 93% reduction while the reduction of viscosity of MRG 2 was 25%. Yet, the torque performances generated by MRG 3 was the highest, 1.44 Nm, followed by MRG 2 and MRG 1. This phenomenon indicated that the improvement of torque performance was dependent on the viscosity of MRGs without the occurrence of sedimentation. Thus, the use of MRG with dilution oil as a substitution of MRF could reduce the sedimentation in MR device and improve the torque performance of MRGs in MR brake

Bicyclist Longitudinal Motion Modeling

69A43551747123Bike is a promising, human-powered, and emission-free transportation mode that is being increasingly advocated as a sustainable mode of transportation due to its significant positive impacts on congestion and the environment. Cities in the United States have experienced a rapid increase in bicycle ridership over the past decade. However, despite the growing popularity of bicycles for short-distance commuting and even for mid-distance recreational trips, researchers have generally ignored the investigation of bicycle traffic flow dynamics. Due to the shared space and frequent interactions among heterogeneous road users, bicycle flow dynamics should be evaluated to determine the tendency of lateral dispersion and its effects on traffic efficiency and safety. Therefore, this research effort proposes to model bicyclist longitudinal motion while accounting for bicycle interactions using vehicular traffic flow techniques. From the comparison of different states of motion for these two transport modes, the authors assumed there is no major difference between vehicular and bicyclist traffic characteristics. The study revamps the Fadhloun-Rakha car-following model previously developed by the research team to make it representative of bicycle traffic flow dynamics. The possibility of capturing cyclists\u2019 behaviors through revamping certain aspects of existing car-following models is investigated. Accordingly, 33 participants were recruited to ride the bike simulator and drive the car simulator simultaneously. The participants were recruited to operate a bike-simulator in order to test the proposed model under realistic traffic conditions and verify the output of the proposed model formulation remains valid when bicyclists are operating under realistic traffic conditions. Both simulators were integrated together, and each participant could inform about the location of another participant in the simulation interval. Six scenarios based on the initial position of the bike and car were developed. Based on the collected data, the Fadhloun-Rakha model was validated to ensure the development of a good descriptor for speed and acceleration and deceleration behaviors. A reliable sample including 100 model parameters values was selected. Root Mean Square Error (RMSE) for the mentioned sample was obtained, and the smallest RMSE in each scenario was identified. Using the obtained RMSEs, the speed and acceleration trajectories for the smallest RMSE in each scenario were drawn. Eventually, the optimal values of the model parameters (a,b,d) in each scenario were specified