Speed In Situations Research Articles

Brushless DC Motor Modeling and Simulation in the MATLAB/SIMULINK Software Environment

The brushless DC motors (BLDCM) are capable of maintaining a constant speed in situations where speed and power are controlled at the same time. This motor, compared to DC motors, is able to generate far more power and simultaneously operate. An open-loop BLDCM output with hardware support and a three-segment method on closed-loop BLDCM were both examined in this study. The research provides a brushless DC motor model that considers the motor's commutation behavior. To make sure that the drive system for BLDC motors works properly, it is important to know the exact torque value, which is based on the back-EMF. The BLDC motor is simulated in MATLAB/Simulink after a basic mathematical model is developed. In open-loop circuits, the intensity may be adjusted by varying the pulse width (or duty), and the motor speed can be increased or decreased by altering the input voltage. Pulse widths and speeds are measured and compared to real-world hardware in this study. This paper presents a comparison of the outcomes of the BLDC motors based on the examination of time responses.

Quantifying accident risk and severity due to speed from the reaction point to the critical conflict in fatal motorcycle accidents

In fatal road vehicle accidents motorcycles are overrepresented per vehicle kilometre travelled. Fatal accidents involving motorcycles contain mode specific characteristics, and in common with fatal accidents involving all road users, speed typically presents as a significant contributory factor. The aim of the present study is to provide quantitative estimates for the contribution of speed in situations commencing from the reaction location to the safety critical event involving a motorcyclist and resulting in a fatal accident. The contribution of speed to the resulting accident risk and accident severity is considered from this reaction point. A speed-squared versus stopping distance domain, termed the severity-risk space, is examined to determine the accident measures. The defined accident measures, namely, accident risk, accident severity and accident severity risk are calculated for sixteen fatal accidents from a police dataset of recent UK motorcycle accidents. The estimates of the defined measures are provided in terms relative to values estimated for the vehicle travelling at the speed limit at the safety critical event. The relative accident risk in response to a safety critical situation shows a partial speed dependent reaction phase and a speed-squared dependent braking phase and ranges from 1.3 to 2.8. The speed-squared dependent accident severity measure ranges from 1.4 to 7.3 at pre-impact speeds. The relative accident severity risk shows speed squared to speed cubed dependency components during the reaction phase and a speed to the power of four dependent braking phase and ranges from 2.3 to 22.8. In eight cases the collision would have been avoided had the motorcyclist been travelling at the speed limit at the critical point and in the other eight cases the relative accident severity at impact ranged from 1.4 to 17.2. The speed-squared versus stopping distance domain provides an informative parameter space for considering the accident risk and accident severity dimensions of road user accidents.

BADCO: Behavioral Application-Dependent Superscalar Core Models

Microarchitecture research and development rely heavily on simulators. The ideal simulator should be simple and easy to develop, it should be precise, accurate and very fast. But the ideal simulator does not exist, and microarchitects use different sorts of simulators at different stages of the development of a processor, depending on which is most important, accuracy or simulation speed. Approximate microarchitecture models, which trade accuracy for simulation speed, are very useful for research and design space exploration, provided the loss of accuracy remains acceptable. Behavioral superscalar core modeling is a possible way to trade accuracy for simulation speed in situations where the focus of the study is not the core itself. In this approach, a superscalar core is viewed as a black box emitting requests to the uncore at certain times. A behavioral core model can be connected to a cycle-accurate uncore model. Behavioral core models are built from cycle-accurate simulations. Once the time to build the model is amortized, important simulation speedups can be obtained. We describe and study a new method for defining behavioral models for modern superscalar cores. The proposed Behavioral Application-Dependent Superscalar Core model, BADCO, predicts the execution time of a thread running on a superscalar core with an error less than 10% in most cases. We show that BADCO is qualitatively accurate, being able to predict how performance changes when we change the uncore. The simulation speedups we obtained are typically between one and two orders of magnitude.