22,156 research outputs found

    SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

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    The lack of reliable methods for identifying descriptors - the sets of parameters capturing the underlying mechanisms of a materials property - is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials properties, within the framework of compressed-sensing based dimensionality reduction. SISSO (sure independence screening and sparsifying operator) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability are tested beyond the training data: It rediscovers the available pressure-induced insulator->metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.Comment: 11 pages, 5 figures, in press in Phys. Rev. Material

    Knowledge-based energy functions for computational studies of proteins

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    This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

    Porous titanium manufactured by a novel powder tapping method using spherical salt bead space holders: characterisation and mechanical properties

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    Porous Ti with open porosity in the range of 70–80% has been made using Ti powder and a particulate leaching technique using porous, spherical, NaCl beads. By incorporating the Ti powder into a pre-existing network of salt beads, by tapping followed by compaction, salt dissolution and “sintering”, porous structures with uniform density, pore and strut sizes and a predictable level of connectivity have been produced, showing a significant improvement on the structures made by conventional powder mixing processes. Parts made using beads with sizes in the range of 0.5-1.0 mm show excellent promise as porous metals for medical devices, showing structures and porosities similar to those of commercial porous metals used in this sector, with inter-pore connections that are similar to trabecular bone. The elastic modulus (0.86GPa) is lower than those for commercial porous metals and more closely matches that of trabecular bone and good compressive yield strength is retained (21MPa). The ability to further tailor the structure, in terms of the density and the size of the pores and interconnections has also been demonstrated by immersion of the porous components in acid

    Silsesquioxane polymer as a potential scaffold for laryngeal reconstruction

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    Cancer, disease and trauma to the larynx and their treatment can lead to permanent loss of structures critical to voice, breathing and swallowing. Engineered partial or total laryngeal replacements would need to match the ambitious specifications of replicating functionality, outer biocompatibility, and permissiveness for an inner mucosal lining. Here we present porous polyhedral oligomeric silsesquioxane-poly(carbonate urea) urethane (POSS-PCUU) as a potential scaffold for engineering laryngeal tissue. Specifically, we employ a precipitation and porogen leaching technique for manufacturing the polymer. The polymer is chemically consistent across all sample types and produces a foam-like scaffold with two distinct topographies and an internal structure composed of nano- and micro-pores. Whilst the highly porous internal structure of the scaffold contributes to the complex tensile behaviour of the polymer, the surface of the scaffold remains largely non-porous. The low number of pores minimise access for cells, although primary fibroblasts and epithelial cells do attach and proliferate on the polymer surface. Our data show that with a change in manufacturing protocol to produce porous polymer surfaces, POSS-PCUU may be a potential candidate for overcoming some of the limitations associated with laryngeal reconstruction and regeneration

    Development of an ontology supporting failure analysis of surface safety valves used in Oil & Gas applications

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    Treball desenvolupat dins el marc del programa 'European Project Semester'.The project describes how to apply Root Cause Analysis (RCA) in the form of a Failure Mode Effect and Criticality Analysis (FMECA) on hydraulically actuated Surface Safety Valves (SSVs) of Xmas trees in oil and gas applications, in order to be able to predict the occurrence of failures and implement preventive measures such as Condition and Performance Monitoring (CPM) to improve the life-span of a valve and decrease maintenance downtime. In the oil and gas industry, valves account for 52% of failures in the system. If these failures happen unexpectedly it can cause a lot of problems. Downtime of the oil well quickly becomes an expensive problem, unscheduled maintenance takes a lot of extra time and the lead-time for replacement parts can be up to 6 months. This is why being able to predict these failures beforehand is something that can bring a lot of benefits to a company. To determine the best course of action to take in order to be able to predict failures, a FMECA report is created. This is an analysis where all possible failures of all components are catalogued and given a Risk Priority Number (RPN), which has three variables: severity, detectability and occurrence. Each of these is given a rating between 0 and 10 and then the variables are multiplied with each other, resulting in the RPN. The components with an RPN above an acceptable risk level are then further investigated to see how to be able to detect them beforehand and how to mitigate the risk that they pose. Applying FMECA to the SSV mean breaking the system down into its components and determining the function, dependency and possible failures. To this end, the SSV is broken up into three sub-systems: the valve, the actuator and the hydraulic system. The hydraulic system is the sub-system of the SSV responsible for containing, transporting and pressurizing of the hydraulic fluid and in turn, the actuator. It also contains all the safety features, such as pressure pilots, and a trip system in case a problem is detected in the oil line. The actuator is, as the name implies, the sub-system which opens and closes the valve. It is made up of a number of parts such as a cylinder, a piston and a spring. These parts are interconnected in a number of ways to allow the actuator to successfully perform its function. The valve is the actual part of the system which interacts with the oil line by opening and closing. Like the actuator, this sub-system is broken down into a number of parts which work together to perform its function. After breaking down and defining each subsystem on a functional level, a model was created using a functional block diagram. Each component also allows for the defining of dependencies and interactions between the different components and a failure diagram for each component. This model integrates the three sub-systems back into one, creating a complete picture of the entire system which can then be used to determine the effects of different failures in components to the rest of the system. With this model completed we created a comprehensive FMECA report and test the different possible CPM solutions to mitigate the largest risks

    Guidelines for the EERI Seismic Design Competition

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    The Earthquake Engineering Research Institute (EERI) has been dedicated to exposing undergraduate students to the wide range of interdisciplinary subjects related to earthquake engineering through the Seismic Design Competition. In 2022, California Polytechnic State University, San Luis Obispo (Cal Poly) sent 14 students to Salt Lake City, Utah for the competition – to compete with 31 other teams who all tackled the competition problem statement uniquely. The 2022 team was tasked with the research, design, analysis, and construction of a new structure to be built in downtown Salt Lake City, with the goal of replicating the design sequence of real-world engineering. The following report outlines the preparation, organization, and timeline taken by the Cal Poly team in advance of the seismic design competition, with the intent of guiding future EERI teams. It should be noted that this report is not only intended to serve as a guide for future students, but also will explain the demand of interdisciplinary subjects in the competition in hopes of attracting undergraduate students who are interested in broadening their vision of engineering to participate in the competition. For public access, this document, including additional supplementary materials, will be available in the Cal Poly Digital Commons
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