The effects of carbon nanotubes on cpu cooling

Computers today have evolved from being big bulky machines that took up rooms of space into small simple machines for net browsing and into small but complicated multi-core servers and supercomputing architectures. This has been possible due to the evolution of the processors. Today processors have reached 45nm specifications with millions of transistors. Transistors produce heat when they run. Today more than ever we have a growing need for managing this heat efficiently. It is indicated that increasing power density can cause a difficulty in managing temperatures on a chip. It is also mentioned that we need to move to a more temperature aware architecture. In this research we try and address the issue of handling the heat produced by processors in an efficient manner. We have tried to see if the use of carbon nanotubes will prove useful in dissipating the heat produced by the processor in a more efficient way. In the process we have also tried to come up with a repeatable experimental setup as there is not work that we have been able to find describing this exact procedure. The use of carbon nanotubes seemed natural as they have a very high thermal conductivity value. Also one of the uncertain aspects of the experiment is the use of carbon nanotubes as they are still under study and their properties have not been completely understood and there has been some inconsistency in the theoretical values of their properties and the experimental results obtained so far. The results that we got were not exactly what we expected but were close, and were in the right direction indicating that more work in future would show better and consistent results

Understanding and Modeling Residential Electricity Demand in India

Residential electricity demand arises from the need for households to meet various end-use energy services. This demand has seen consistent growth over the last decade currently accounting for close to a quarter of the total electricity consumption in the country. In developing economies like India this sector will also be a key contributor to future greenhouse emissions given that we are starting from a comparatively low base. But our understanding of this sector is still limited. To gain a better understanding of this space, we approach this problem in two parts. In the first part, we outline a methodology to design and conduct a representative survey by presenting the case study of a primary survey we conducted of Bengaluru. Using the survey, we model appliance ownership and usage patterns identifying key contributing end-use categories and variations in patterns of electricity consumption across households. Next, we develop a bottom-up, end-use model, disaggregated by regions, to project growth in end-use energy service categories. We identify growth in ownership of key appliances and changes in consumption driven by this growth. We model changes in consumption patterns at different regional disaggregation identifying key demand drivers. Based on model insights from the primary survey and national model, we identify key policy amendments and suggest some new policy directions to manage the growth of demand from the residential sector

Deep learning reconstruction of sunspot vector magnetic fields for forecasting solar storms

Solar magnetic activity produces extreme solar flares and coronal mass ejections, which pose grave threats to electronic infrastructure and can significantly disrupt economic activity. It is therefore important to appreciate the triggers of explosive solar activity and develop reliable space-weather forecasting. Photospheric vector-magnetic-field data capture sunspot magnetic-field complexity and can therefore improve the quality of space-weather prediction. However, state-of-the-art vector-field observations are consistently only available from Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) since 2010, with most other current and past missions and observational facilities such as Global Oscillations Network Group (GONG) only recording line-of-sight (LOS) fields. Here, using an inception-based convolutional neural network, we reconstruct HMI sunspot vector-field features from LOS magnetograms of HMI as well as GONG with high fidelity (~ 90% correlation) and sustained flare-forecasting accuracy. We rebuild vector-field features during the 2003 Halloween storms, for which only LOS-field observations are available, and the CNN-estimated electric-current-helicity accurately captures the observed rotation of the associated sunspot prior to the extreme flares, showing a striking increase. Our study thus paves the way for reconstructing three solar cycles worth of vector-field data from past LOS measurements, which are of great utility in improving space-weather forecasting models and gaining new insights about solar activity.Comment: 19 Pages, 11 Figures, Accepted for publication in Ap

Policy-driven approach to demand management from space cooling and water heating appliances: insights from a primary survey of urban Bengaluru, India

Appliances that provide thermal comfort services like space cooling and water heating have high energy demands and significant seasonal variation in usage. Ownership and usage of these appliances increase rapidly with income. Given the significant impact of these appliances on electricity demand, it is key to analyse their ownership and usage. A well-designed policy and standards framework can help transition households as well as manufacturers towards a higher efficiency ecosystem, and significantly lower electricity demand growth rates. In this study, we analyse ownership and usage patterns of these appliances using data from a primary survey of Bengaluru, India. We suggest some passive demand-side management frameworks based on current policies implemented for these appliance categories

Développement d'un outil numérique pour l’optimisation de la structure interne de pièce imprimée avec le FDM

L'objectif de cette thèse est de développer un outil numérique pour optimiser la structure interne des pièces imprimées en 3D produites par le procédé dépôt de fil fondu (DFF). En impression 3D, le terme remplissage fait référence à la structure interne de la pièce. Pour créer la conception de remplissage, un logiciel de tranchage est utilisé, qui crée généralement le remplissage uniformément dans toute la pièce. Lorsqu'une telle pièce est soumise à une charge externe, toutes les régions de remplissage ne subiront pas la même quantité de contrainte. Par conséquent, l'utilisation d'un remplissage uniforme dans toute la pièce n'est pas la solution la plus optimisée en termes d'utilisation des matériaux. Nous visons à développer un outil numérique pour faire évoluer la conception du remplissage par rapport aux contraintes mécaniques générées par les charges externes. Pour y parvenir, nous proposons deux méthodologies différentes basées sur un processus itératif utilisant des techniques de raffinement et de remaillage couplées à la simulation par éléments finis (simulation EF) pour contrôler la structure interne de la pièce sans modifier le contour. Ces méthodologies visent à renforcer le remplissage de la pièce sans modifier le contour, dans la zone où la résistance mécanique doit être améliorée pour renforcer la structure, mais aussi à diminuer la quantité de matière pour réduire le temps d'impression.The objective of this thesis is to develop a numerical tool to optimise the internal structure of 3D printed parts produced by the Fused Deposition Modelling (FDM) process. In 3D printing, the term infill refers to the internal structure of the part. To create the infill design, slicing software is used, which generally creates the infill uniformly throughout the part. When such a part is subjected to external loading, not all the infill regions will experience the same amount of stress. Therefore, using uniform infill throughout the part is not the most optimised solution in terms of material usage. We aim to develop a numerical tool to evolve the infill design with respect to the mechanical stresses generated by the external loads. To achieve this, we propose two different methodologies based on an iterative process using refinement technique and remeshing techniques coupled to Finite Element simulation (FE simulation) to control the internal structure of the part without changing the contour. These methodologies aim to reinforce the infill of the part without changing the contour, in the area where the mechanical strength must be improved to strengthen the structure, but also to decrease the amount of material to reduce the printing time

Infill Design Reinforcement of 3D Printed Parts Using Refinement Technique Adapted to Continuous Extrusion

In this paper, we introduce an advanced numerical tool aimed to optimise the infill design of 3D printed parts by reducing printing time. In 3D printing, the term infill refers to the internal structure of a part. To create the infill design, slicing software is used, which generally creates the infill uniformly throughout the part. When such a part is subjected to external loading, all the infill regions will not experience the same amount of stress. Therefore, using uniform infill throughout the part is not the most optimised solution in terms of material usage. We do propose to evolve the infill design with respect to the mechanical stresses generated by the external loads. To achieve this, an advanced numerical tool has been developed, based on refinement techniques, to control the infill design. This tool is coupled with Finite Element Simulation (FE Simulation) software, which helps to identify the zones where the material is required as an infill to reinforce a part, whereas the refinement technique makes it possible to place the material as an infill in such a way that the airtime during the printing of the part is zero. Zero airtime printing is defined as the ability to deposit each layer of a part, without stopping the material extrusion during the displacement of the nozzle. Therefore, the proposed numerical tool guides us to generate the infill design of a part, in such a way that it will consume zero airtime while manufacturing. Simultaneously, it will increase the stiffness of the part. The proposed approach is here applied to a rectangular structure subjected to four-point bending, made up of PLA material (Poly-Lactic Acid)

Refractory thrombotic thrombocytopenic purpura treated successfully with monoclonal antibody (rituximab)

Thrombotic thrombocytopenic purpura (TTP) is a nonimmune, microangiopathic hemolytic anemia, associated with thrombocytopenia, fever, neurologic, or renal dysfunction. Plasma exchange (PEX) with or without steroids constitutes first-line therapy in TTP. However, a subset of the patients may be refractory to PEX. Rituximab appears to be an alternative effective therapy for refractory or relapsing TTP. Here, we report a case of TTP in a 43-year-old female presented with fever, generalized weakness, headache, vomiting, and ecchymotic patches over forearms and upper chest for 7 days along with one episode of seizure. The laboratory evaluation revealed severe thrombocytopenia, anemia, and indirect hyperbilirubinemia with peripheral blood smear showing schistocytes (fragmented red blood cells). Initial therapy with multiple PEXs along with parenteral corticosteroids resulted in only minimal improvement of platelet count. Subsequently, rituximab was administered which helped in normalization of platelet count and overall clinical improvement. This case highlights the importance of timely utilization of second-line drugs such as rituximab in refractory TTP

Solution NMR Structure and Backbone Dynamics of Partially Disordered <i>Arabidopsis thaliana</i> Phloem Protein 16-1, a Putative mRNA Transporter

Although RNA-binding proteins in plant phloem are believed to perform long-distance systemic transport of RNA in the phloem conduit, the structure of none of them is known. <i>Arabidopsis thaliana</i> phloem protein 16-1 (<i>At</i>PP16-1) is such a putative mRNA transporter whose structure and backbone dynamics have been studied at pH 4.1 and 25 °C by high-resolution nuclear magnetic resonance spectroscopy. Results obtained using basic optical spectroscopic tools show that the protein is unstable with little secondary structure near the physiological pH of the phloem sap. Fluorescence-monitored titrations reveal that <i>At</i>PP16-1 binds not only <i>A</i>. <i>thaliana</i> RNA (<i>K</i>_diss ∼ 67 nM) but also sheared DNA and model dodecamer DNA, though the affinity for DNA is ∼15-fold lower. In the solution structure of the protein, secondary structural elements are formed by residues 3–9 (β1), 56–62 (β2), 133–135 (β3), and 96–110 (α-helix). Most of the rest of the chain segments are disordered. The N-terminally disordered regions (residues 10–55) form a small lobe, which conjoins the rest of the molecule via a deep and large irregular cleft that could have functional implications. The average order parameter extracted by model-free analysis of ¹⁵N relaxation and {¹H}–¹⁵N heteronuclear NOE data is 0.66, suggesting less restricted backbone motion. The average conformational entropy of the backbone NH vectors is −0.31 cal mol^–1 K^–1. These results also suggest structural disorder in <i>At</i>PP16-1