Computational Analysis of Heat Transfer through Fins with Different Types of Notches

The Engine is one of the important component in an automobile which is subjected to high temperature and thermal stresses. In order to cool the engine the fins are another component which are used to dissipate the heat from the Engine. Fins are generally used to increase the heat transfer rate from the system to the surroundings. By doing computational flow analysis on the engine cooling fins, it is helpful to know about the heat dissipation rate and the Principle implemented in this project is to increase the heat transfer rate, so in this analysis, the fins are modified by putting different types of notches and are of same material. The knowledge of efficiency and effectiveness of the fins are necessary for proper designing of fins. The main objective of our analysis is to determine the flow of heat at various notches available and the analysis is done by using ANSYS – CFD Fluent software

Computational analysis of the LRRK2 interactome.

LRRK2 was identified in 2004 as the causative protein product of the Parkinson's disease locus designated PARK8. In the decade since then, genetic studies have revealed at least 6 dominant mutations in LRRK2 linked to Parkinson's disease, alongside one associated with cancer. It is now well established that coding changes in LRRK2 are one of the most common causes of Parkinson's. Genome-wide association studies (GWAs) have, more recently, reported single nucleotide polymorphisms (SNPs) around the LRRK2 locus to be associated with risk of developing sporadic Parkinson's disease and inflammatory bowel disorder. The functional research that has followed these genetic breakthroughs has generated an extensive literature regarding LRRK2 pathophysiology; however, there is still no consensus as to the biological function of LRRK2. To provide insight into the aspects of cell biology that are consistently related to LRRK2 activity, we analysed the plethora of candidate LRRK2 interactors available through the BioGRID and IntAct data repositories. We then performed GO terms enrichment for the LRRK2 interactome. We found that, in two different enrichment portals, the LRRK2 interactome was associated with terms referring to transport, cellular organization, vesicles and the cytoskeleton. We also verified that 21 of the LRRK2 interactors are genetically linked to risk for Parkinson's disease or inflammatory bowel disorder. The implications of these findings are discussed, with particular regard to potential novel areas of investigation

Computational analysis of a plant receptor interaction network

Trabajo fin de máster en Bioinformática y Biología ComputacionalIn all organisms, complex protein-protein interactions (PPI) networks control major biological functions yet studying their structural features presents a major analytical challenge. In plants, leucine-rich-repeat receptor kinases (LRR-RKs) are key in sensing and transmitting non-self as well as self-signals from the cell surface. As such, LRR-RKs have both developmental and immune functions that allow plants to make the most of their environments. In the model organism in plant molecular biology, Arabidopsis thaliana, most LRR-RKs are still represented by biochemically and genetically uncharacterized receptors. To fix this an LRR-based Cell Surface Interaction (CSI LRR ) network was obtained in 2018, a protein-protein interaction network of the extracellular domain of 170 LRR-RKs that contains 567 bidirectional interactions. Several network analyses have been performed with CSI LRR . However, these analyses have so far not considered the spatial and temporal expression of its proteins. Neither has it been characterized in detail the role of the extracellular domain (ECD) size in the network structure. Because of that, the objective of the present work is to continue with more in depth analyses with the CSI LRR network. This would provide important insights that will facilitate LRR-RKs function characterization. The first aim of this work is to test out the fit of the CSI LRR network to a scale-free topology. To accomplish that, the degree distribution of the CSI LRR network was compared with the degree distribution of the known network models of scale-free and random. Additionally, three network attack algorithms were implemented and applied to these two network models and the CSI LRR network to compare their behavior. However, since the CSI LRR interaction data comes from an in vitro screening, there is no direct evidence whether its protein-protein interactions occur inside the plant cells. To gain insight on how the network composition changes depending on the transcriptional regulation, the interaction data of the CSI LRR was integrated with 4 different RNA-Seq datasets related with the network biological functions. To automatize this task a Python script was written. Furthermore, it was evaluated the role of the LRR-RKs in the network structure depending on the size of their extracellular domain (large or small). For that, centrality parameters were measured, and size-targeted attacks performed. Finally, gene regulatory information was integrated into the CSI LRR to classify the different network proteins according to the function of the transcription factors that regulate its expression. The results were that CSI LRR fits a power law degree distribution and approximates a scale- free topology. Moreover, CSI LRR displays high resistance to random attacks and reduced resistance to hub/bottleneck-directed attacks, similarly to scale-free network model. Also, the integration of CSI LRR interaction data and RNA-Seq data suggests that the transcriptional regulation of the network is more relevant for developmental programs than for defense responses. Another result was that the LRR-RKs with a small ECD size have a major role in the maintenance of the CSI LRR integrity. Lastly, it was hypothesized that the integration of CSI LRR interaction data with predicted gene regulatory networks could shed light upon the functioning of growth-immunity signaling crosstalk

Computational Analysis of APEC Trade Liberalization

In this study, we use the Michigan Model of World Production and Trade to analyze the economic welfare effects of APEC free trade, unilateral free trade for individual APEC members, and global free trade for all countries/regions covered in the Michigan Model. The Michigan Model is a multi-country, multi-sectoral computational general equilibrium (CGE) model of the global trading system. The version of the model used includes 31 countries/regions plus the rest-of-world and 27 sectors in each country/region. Nineteen APEC members are covered. The computational results suggest that APEC free trade would result in sizable increases in the economic welfare of the individual APEC members in both absolute terms and as a percentage of GDP. There would be trade diversion effects for non-APEC countries, except for the Rest of Middle East. Unilateral free trade for the APEC members would result in larger welfare gains as compared to APEC free trade for 7 of the 19 APEC members. The welfare benefits of APEC free trade are thus larger for more APEC members than unilateral free trade. Finally, global (multilateral) free trade by all of the countries/regions covered in the Michigan Model suggests much larger benefits for all APEC members compared to APEC free trade and APEC unilateral free trade. While global free trade is a limiting case, the computational results presented are testimony to the significant welfare benefits that could be realized from successful pursuit of future multilateral trade liberalization.APEC, trade

Computational analysis of a car chassis frame under a frontal collision.

This paper aims at studying the frontal collision of a car frame using non-linear FEA (Finite Element Analysis). Three frontal crash situations are evaluated: a full frontal impact against a rigid barrier and two frontal impacts with 40% overlap against an ODB (Offset Deformable Barrier). These three simulations are intended to mimic the FMVSS no.208, the 96/79/EC and the EURONCAP tests. The model of the chassis used in the simulations – a Ford F150 - is based on one that has previously been published in another paper. However, in that paper, the simulation only considers a static load on the bumper (a pressure) and the conclusions do not reflect what would happen during a real impact with dynamic loads. Several results are presented and discussed: the dissipated energy during the impact, the acceleration time history and the HIC (Head Injury Criterion) are evaluated from the set of results so obtained. Furthermore, different test situations and initial conditions have been applied, aiming at better understanding the frame’s response in a real impact situation

Computational analysis of scramjet dual mode operation

One critical element in the design of a Scramjet is the detailed understanding of the complex flow field in the engine during various phases of operation. One area of interest is the computation of chemically reacting flows in the vicinity of flame holders. The characteristics of a method for solving the Navier-Stokes equations with chemical reactions are proposed. Also of interest are the flame holding characteristics of simple ramp and rearward facing steps. Both of these configurations are considered candidates for Scramjet flame holders

Computational analysis of single rising bubbles influenced by soluble surfactant

This paper presents novel insights about the influence of soluble surfactants on bubble flows obtained by Direct Numerical Simulation (DNS). Surfactants are amphiphilic compounds which accumulate at fluid interfaces and significantly modify the respective interfacial properties, influencing also the overall dynamics of the flow. With the aid of DNS local quantities like the surfactant distribution on the bubble surface can be accessed for a better understanding of the physical phenomena occurring close to the interface. The core part of the physical model consists in the description of the surfactant transport in the bulk and on the deformable interface. The solution procedure is based on an Arbitrary Lagrangian-Eulerian (ALE) Interface-Tracking method. The existing methodology was enhanced to describe a wider range of physical phenomena. A subgrid-scale (SGS) model is employed in the cases where a fully resolved DNS for the species transport is not feasible due to high mesh resolution requirements and, therefore, high computational costs. After an exhaustive validation of the latest numerical developments, the DNS of single rising bubbles in contaminated solutions is compared to experimental results. The full velocity transients of the rising bubbles, especially the contaminated ones, are correctly reproduced by the DNS. The simulation results are then studied to gain a better understanding of the local bubble dynamics under the effect of soluble surfactant. One of the main insights is that the quasi-steady state of the rise velocity is reached without ad- and desorption being necessarily in local equilibrium