Enhanced Cooling of Laptop Computer for Improvement of Processing Performance

A major problems in the operation of laptop computers is overheating since it can affect the performance and stability, sometimes leading to system crash and hardware fatality. The objective of this work was to study the thermal behavior inside a laptop computer and to test the effectiveness of aproposed cooling method to overcome overheating problem. The proposed cooling system contained a thermoelectric device that reduced the intake air temperature into the laptop internal cooling system. An external exhaust blower, located at the exhaust air outlet of the laptop, was mounted to ensure sufficient air flow rate delivered by the cooling system. To assess the effectiveness of the system, temperatures of critical components in the computer were measured. It was found from the study that, under extreme utilization situation, the temperature of the graphic processing unit could increase to 99B0;C. The proposed cooling system could bring down the temperature by up to 6B0;C

Hamilton-Pontryagin Integrators on Lie Groups

In this thesis structure-preserving time integrators for mechanical systems whose configuration space is a Lie group are derived from a Hamilton-Pontryagin (HP) variational principle. In addition to its attractive properties for degenerate mechanical systems, the HP viewpoint also affords a practical way to design discrete Lagrangians, which are the cornerstone of variational integration theory. The HP principle states that a mechanical system traverses a path that extremizes an HP action integral. The integrand of the HP action integral consists of two terms: the Lagrangian and a kinematic constraint paired with a Lagrange multiplier (the momentum). The kinematic constraint relates the velocity of the mechanical system to a curve on the tangent bundle. This form of the action integral makes it amenable to discretization. In particular, our strategy is to implement an s-stage Runge-Kutta-Munthe-Kaas (RKMK) discretization of the kinematic constraint. We are motivated by the fact that the theory, order conditions, and implementation of such methods are mature. In analogy with the continuous system, the discrete HP action sum consists of two parts: a weighted sum of the Lagrangian using the weights from the Butcher tableau of the RKMK scheme, and a pairing between a discrete Lagrange multiplier (the discrete momentum) and the discretized kinematic constraint. In the vector space context, it is shown that this strategy yields a well-known class of symplectic partitioned Runge-Kutta methods including the Lobatto IIIA-IIIB pair which generalize to higher-order accuracy. In the Lie group context, the strategy yields an interesting and novel family of variational partitioned Runge-Kutta methods. Specifically, for mechanical systems on Lie groups we analyze the ideal context of EP systems. For such systems the HP principle can be transformed from the Pontryagin bundle to a reduced space. To set up the discrete theory, a continuous reduced HP principle is also analyzed. It is this reduced HP principle that we apply our discretization strategy to. The resulting integrator describes an update scheme on the reduced space. As in RKMK we parametrize the Lie group using coordinate charts whose model space is the Lie algebra and that approximate the exponential map. Since the Lie group is non abelian, the structure of these integrators is not the same as in the vector space context. We carry out an in-depth study of the simplest integrators within this family that we call variational Euler integrators; specifically we analyze the integrator's efficiency, global error, and geometric properties. Because of their variational character, the variational Euler integrators preserve a discrete momentum map and symplectic form. Moreover, since the update on the configuration space is explicit, the configuration updates exhibit no drift from the Lie group. We also prove that the global error of these methods is second order. Numerical experiments on the free rigid body and the chaotic dynamics of an underwater vehicle reveal that these reduced variational integrators possess structure-preserving properties that methods designed to preserve momentum (using the coadjoint action of the Lie group) and energy (for example, by projection) lack. In addition we discuss how the HP integrators extend to a wider class of mechanical systems with, e.g., configuration dependent potentials and non trivial shape-space dynamics.</p

BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones

Recent research on solar irradiance forecasting has attracted considerable attention, as governments worldwide are displaying a keenness to harness green energy. The goal of this study is to build forecasting methods using deep learning (DL) approach to estimate daily solar irradiance in three sites in Kuwait over 12 years (2008&ndash;2020). Solar irradiance data are used to extract and understand the symmetrical hidden data pattern and correlations, which are then used to predict future solar irradiance. A DL model based on the attention mechanism applied to bidirectional long short-term memory (BiLSTM) is developed for accurate solar irradiation forecasting. The proposed model is designed for two different conditions (sunny and cloudy days) to ensure greater accuracy in different weather scenarios. Simulation results are presented which depict that the attention based BiLSTM model outperforms the other deep learning networks in the prediction analysis of solar irradiance. The attention based BiLSTM model was able to predict variations in solar irradiance over short intervals in continental climate zones (Kuwait) more efficiently with an RMSE of 4.24 and 20.95 for sunny and cloudy days, respectively

Symmetries and Patterns in Sandpiles

The Abelian sandpile is a deterministic diffusion process on graphs which produces striking, kaleidoscopic patterns. Why do these patterns appear? Are the patterns robust to noise? Do different graphs generate distinct patterns? This thesis presents some answers to these questions

Statistical Optimization of Pyrolysis Process for Thermal Destruction of Plastic Waste Based on Temperature-Dependent Activation Energies and Pre-Exponential Factors

The massive increase in disposable plastic globally can be addressed through effective recovery methods, and one of these methods is pyrolysis. R software may be used to statistically model the composition and yield of pyrolysis products, such as oil, gas, and waxes to deduce an effective pyrolysis mechanism. To date, no research reports have been documented employing the Arrhenius equation in R software to statistically forecast the kinetic rate constants for the pyrolysis of high-density plastics. We used the Arrhenius equation in R software to assume two series of activation energies (Ea) and pre-exponential factors (Ao) to statistically predict the rate constants at different temperatures to explore their impact on the final pyrolysis products. In line with this, MATLAB (R2020a) was used to predict the pyrolysis products of plastic in the temperature range of 370–410 °C. The value of the rate constant increased with the temperature by expediting the pyrolysis reaction due to the reduced frequency factor. In both assumed series of Ea and Ao, a significantly larger quantity of oil (99%) was predicted; however, the number of byproducts increased in the first series analysis compared to the second series analysis. It was revealed that an appropriate combination of Ea, Ao, and the predicted rate constants could significantly enhance the efficiency of the pyrolysis process. The major oil recovery in the first assumed series occurred at 390 °C to 400 °C, whereas the second assumed series of Ea and Ao occurred at 380 °C to 390 °C. In the second series at 390 °C to 400 °C, the predicted kinetic rate constants behaved aggressively after 120 min of the pyrolysis process. The second assumed series and anticipated rate constants at 380 °C to 390 °C can be applied commercially to improve oil production while saving energy and heat