Aircraft landing gear thermo-tribomechanical model development

A transient numerical model for studying the thermo-tribomechanical behavior of an aircraft landing gear is presented. The study reveals the major heat sources and heat sinks that impact the characteristic thermal behavior of the landing gear shock absorber. The severe in-service performance degradation and reported structural damage can be explained as a consequence of the heat generated by the high drag loads induced by rough runways on the bearings, and by the high sliding velocities of the piston. A conclusive model may lead to improved landing gear performance once the transient process of heat generation in a phase-changing grease-lubricated lower bearing is fundamentally understood. A novel tribotopological lubrication theory is derived in order to take into account all distinct physical phases of the non-Newtonian Bingham lubricant. The governing equations are solved using a hybrid numerical solver that is optimized for numerical efficiency and fast convergence. The proposed framework is validated against existing theories and results, and it demonstrates accurate predictions of the thermal performance of the landing gear. Strategies to passively optimize the lower bearing lubrication mechanism are further suggested in order to achieve optimal thermal performance of future aircraft landing gear

Lubrication mechanism design for aircraft landing gear bearings

A shock absorber with lubricated bearings for an aircraft landing gear includes a piston that is received in a cylinder, and an upper bearing fixed to the piston that slidably engages an inner surface of the cylinder. A lower bearing extends inwardly from a lower portion of the cylinder and engages an outer surface of the piston. The lower bearing has a center axis and defines an annular bearing surface configured to slidably engage the piston outer surface. The annular bearing surface has a first portion that extends circumferentially more than one hundred eighty degrees about the center axis at a constant radius, defining a circular annular segment. A second portion closes the circular annular segment and defines a shallow channel or pocket in the annular bearing surface. In some embodiments the lower bearing further comprises oppositely disposed frustoconical thrust portions

DistributedFBA.jl: High-level, high-performance flux balance analysis in Julia.

MOTIVATION: Flux balance analysis, and its variants, are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations. RESULTS: DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes. AVAILABILITY: The code is freely available on github.com/opencobra/COBRA.jl. The documentation can be found at opencobra.github.io/COBRA.jl

The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. To address this gap, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the COBRA Toolbox. The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox

Model-based assessment of mammalian cell metabolic functionalities using omics data.

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie)

The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

Aircraft landing gear greased slider bearing steady-state thermo-elastohydrodynamic concept model

This paper presents a steady-state concept model for studying the thermal behavior of a greased aircraft landing gear lower slider bearing. Structural damage has been reported as a consequence of excessive heat generated by the high loads induced by rough runways on the bearings, and by the high sliding velocities of the piston. The goal of the model is to enable fundamental understanding of the frictional heat generation. The governing equations are adapted for grease flow and special attention is given to the underlying algorithm of the developed numerical framework used to efficiently solve the governing equations. The developed numerical code is validated against existing results. Numerical results indicate fundamental differences in fluid flow behavior between greased and oil-lubricated bearings