Microprocessor designers use the design of multicore systems on a chip to increase their computing power. Adding the number of cores leads to an increase in the power density, followed by enhancement of temperature. Reactive and proactive approaches are two sets of the methods for managing the temperature. Unlike the reactive methods that act based on threshold temperature, proactive approaches utilize a thermal prediction model in thermal management. In this paper, two multilayer perceptron neural networks has been used for thermal prediction and temperature control. An appropriate dataset is provided for training each model. This dataset consists of some features that are read by sensors and measurement tools and new features that are produced by proposed processes. In this regard, historical features are suggested for thermal model. Proper features of thermal model are selected by using feature selection based on mutual information. The temperature is controlled by setting the processor frequency and fan speed. The features of control model are selected by non-dominated sorting genetic algorithm. The error of thermal model for different time distances is about 0. 5 ° C. The thermal control model has respectively 2% and 0. 6% errors in determining the processor frequency and fan speed.

Increasing the number of cores in order to the demand of more computing power has led to increasing the processor temperature of a multi-core system. One of the main approaches for reducing temperature is the Dynamic thermal management techniques. These methods divided into two classes, reactive and proactive. Proactive methods manage the processor temperature, by forecasting the temperature before reaching the threshold temperature. In this paper, the effects of using proper features for processor thermal management have been considered. In this regard, three models have been proposed for temperature prediction, control response estimation, and thermal management, respectively. A multi-layered perceptron neural network is used to predict the temperature and to control the response. Also, an adaptive neuro-fuzzy inference system is utilized for controlling temperature. An appropriate data set, which includes a variety of processor temperature variations, has been created to train each model. Some features of the dataset are collected by monitoring the thermal sensors and performance counters. In addition, a number of features are created by proposing processes to increase the accuracy of each model. Then, the features of each model are selected by the proposed method. The evaluation of the proposed model for predicting and controlling the processor temperature for different time distances is below 0. 6 ° C.

Increase in chip temperature causes more power consumption in multi core processors and decreases the CPU lifetime. The optimization of online task assignment to processing cores is an efficient approach to control chip-wide temperature distribution. However, task assignment faces some uncertainties in the system (including: stochastic task arrival, random task pairings, and time-varying thermal profile variations). In this paper, an online task to core assignment approach is presented which uses Semi-Markov Decision Process to prevent performance reduction and considers randomness and uncertainties in system. As the transitional properties are not accessible and due to high dimension of system state components, the proposed approach uses function approximation to approximate action values in any system state. The simulation results show 6 centigrade decrease in system average peak temperature and 66 milliseconds decreases in task service time.

In this paper, an offline energy management system (EMS) is proposed for parallel hybrid electric vehicles (HEVs). The proper energy management system is necessary for dividing torque between electrical motor and Internal Combustion Engine (ICE). The battery is a crucial component of hybrid electric vehicles and affects significantly the cost and the performance of the whole vehicle. The primary factors accelerating battery aging are high temperatures and high states of charge (SOC) of the battery. SOC is the most important state variable in EMS, and usually considered as the only Dynamic variable in past researches, but the battery temperature is often considered to be constant for simplicity and the effects of EMS on the temperature variations are neglected. In this paper, first, Dynamic programming is applied to a parallel HEV without considering variation of the temperature of the battery. Then, the model of battery is improved by modelling the cooling system to take into account temperature variations and show how neglecting thermal Dynamics of the battery in EMS is impractical. Finally, by integrating the battery temperature as a state variable in the optimization problem, a new energy management strategy controlling variations of the battery temperature and SOC is proposed. The simulation results on tested vehicle show that in the proposed method charge and temperature of the battery is controlled so that the proposed EMS method prevents uncontrolled variations of the battery temperature and reduces the degradation rate of it.

Isothermal crystallization kinetics, melting, structure and Dynamic mechanical properties of a polypropylene (PP) random copolymer were studied. In order to study the effect of cooling rate on melting, two different cooling rates were applied to the samples. While water cooled samples revealed a broad single melting endotherm with  a-crystalline structure, the slow cooled samples showed a shoulder at lower temperature followed by a sharp melting endotherm and a mixture of a and g-crystalline phases. The effect of annealing on log E of the b-relaxation for the PP copolymer showed that the b-transition increased in intensity with increasing annealing temperature. No considerable difference was found in the activation enthalpy of relaxation, ΔH, values for the annealed compared to that of the water cooled samples using the Arrhenius equation. The isothermal crystallization kinetics was also studied for PP and the values of nucleation constant, Kg, and end-surface free energy of the crystal growth, se, were calculated using Lauritzen-Hofffman theory.

Due to the lack of measurements in many regions, wave characteristics are estimated using different methods. Wave climate hindcasting/forecasting is mostly conducted by numerical models or empirical methods. Until now, different empirical methods have been developed for wave hindcasting. However, with the development of high speed processors, several sophisticated numerical models have been developed for wave prediction. These models are mostly phase-averaged spectral wave models developed in three generations. In the last two decades, third generation wave models have been used widely in academic and practical projects. In this regard, Port and Maritime Organization has produced his own model, PMO Dynamic. This model has been developed as a part of first three phases of Monitoring and Modeling of Study of Iranian Coasts project. PMO Dynamic package is a software available for engineering purposes. It has several modules that have been developed for different objectives. Wave model is the module which is used for the generation and transformation of wind waves in coastal areas. In this paper, in order to test the PMO Dynamic model capabilities, it has been applied for the prediction of wave parameters in Bushehr Bay and the results have been compared with MIKE21 SW model and measured data.