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Calibri 83ffff̙̙3f3fff3f3f33333f33333.8TU Delft Repositoryg ;uuidrepository linktitleauthorcontributorpublication yearabstract
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departmentresearch group programmeprojectcoordinates)uuid:8e9a3b1e-d4f3-4b5e-9a6c-5c177dba91b2Dhttp://resolver.tudelft.nl/uuid:8e9a3b1e-d4f3-4b5e-9a6c-5c177dba91b2RAkka Decision Engine: An actor based decision engine on the DMN 1.1 specificationsAcda, Mark (TU Delft Electrical Engineering, Mathematics and Computer Science); de Boer, Toon (TU Delft Electrical Engineering, Mathematics and Computer Science); Bos, Thomas (TU Delft Electrical Engineering, Mathematics and Computer Science)VPoulsen, Casper (mentor); Delft University of Technology (degree granting institution)_Decision engines can decide from a certain input what the output should be. This is done in a table with columns for inputs and outputs and rows for a combination of inputs together with its corresponding output. A row is also called a rule. A simple program to decide such a decision table can easily be made, like Camunda. However, when the output of one table is also the input of another table and so on and the amount of rules get enormously big, the problem gets more complicated and Camunda takes a very long time to solve such structures.<br/>We created a decision engine in Scala that can decide the output when there are thousands of tables linked together in less than a minute with the help of Akka. Akka is an actor model, which means that it can create multiple actors, which each can perform a certain task. Actors can run in parallel, which speeds up the decision engine. Actors send messages to each other and an actor will only start working when they receive a message. The decision engine reads DMN files and parses it to tables. For better performance the decision tables get parsed into a tree structure with for every table the input tables are its children. In this way the decision engine is very quick in solving tables, however the parsing into trees still takes some time. This is not a big problem, since the parsing is only done once and the tree can be saved and the solving can be done very often. Also the deciding of a single table is improved, because we created our own FEEL-expressions that can decide the rules very fast. The result is that after a very large table with 50,000 rules is parsed, the solving that took Camunda 400 milliseconds only takes 9 milliseconds for the new decision engine and when the parsing is left out, the new engine is faster in computing 500,000 rules than Camunda with 1 rule. Also when the parsing is included in the time, the difference gets only bigger. For 50,000 rules, Camunda takes 20 seconds to parse the file and solve the table, while the new decision engine takes only a little more than 1 second to do this all. When the files get larger, so does the difference.Decision Engine; Akka; Camundaenbachelor thesis)uuid:3485b873-142c-4987-b6d8-f53390cf7919Dhttp://resolver.tudelft.nl/uuid:3485b873-142c-4987-b6d8-f53390cf7919ENumerical Simulation of Radial Non-Newtonian Foam Flow in a Reservoir9Bos, Martijn (TU Delft Civil Engineering and Geosciences)wRossen, Bill (mentor); Salazar Castillo, Rodrigo (mentor); Delft University of Technology (degree granting institution)Non-Newtonian foam flow in a reservoir can be modeled numerically by discretization of the corresponding analytical formulas. The injection of foam is compared to the injection of water by comparing the injection pressures, which is represented as a dimensionless pressure rise at the injection well. The model first applies the forward-difference method to compute the changes in water saturation over space and time as the foam is injected. These changes in water saturation are related, via Darcy s Law, to changes in dimensionless pressure. The non-Newtonian foam behavior is implemented in the model by making the gas relative permeability a function of position in radial flow, based on the exponent defined for a power law fluid.<br/>T<he validity of the model is assessed by a comparison with an analytical model using the method of characteristics to simulate Newtonian foam flow. This model was created by A.H. Al Ayesh. From this comparison, it follows that the numerical model converges to a correct solution for sufficient fine discretizations. Finer discretizations do however introduce drawbacks, such as long computation times and high computer-memory requirements. Another drawback of the numerical model is the inevitable error that is introduced by a numerical artifact in the computation of the total relative mobility in each grid block at the front as foam advances. This error can only be reduced by even-finer discretizations. <br/>The validity of the model for non-Newtonian foam flow simulations is not assessed directly in this thesis. But the model is expected to have similar or coarser grid-refinement criteria for shear-thinning foam flow, and finer or similar grid refinement criteria for shear-thickening foam flow.]Petroleum Engineering; Enhanced Oil Recovery; Foam; Numerical Simulation; Numerical ModellingApplied Earth Sciences)uuid:bbfabc0b-77a2-4bd9-8224-264d27a3b143Dhttp://resolver.tudelft.nl/uuid:bbfabc0b-77a2-4bd9-8224-264d27a3b143DelftVMBos, M.0Dulman, S.O. (mentor); Langendoen, K.G. (mentor)Final report about DelftVM.+virtual machine; delftvm; spatial computing8Electrical Engineering, Mathematics and Computer ScienceDistrubuted Systems)uuid:b4019f6f-35ab-4db8-a65b-14493d26d448Dhttp://resolver.tudelft.nl/uuid:b4019f6f-35ab-4db8-a65b-14493d26d448Formation Flying~Bos, D.A.; Dijkers, H.P.A.; Gutleb, T.L.M.; Herinckx, L.E.; Van Nunen, R.; Radfar, H.; Van Rompuy, E.; Sahin, S.E.; De Wit, J.kBeelaerts van Blokland, W.W.A. (mentor); Roling, P. (mentor); Giorgi, G. (mentor); Verhgen, W.J.C. (mentor)A first outline for the concept of formation flying is investigated. The objective is to create a sustainable air transportation system for the next generation of aircraft from a formation flying perspective. When flying in formation over long distances the trailing aircraft can benefit from the wake vortices created by the preceding aircraft. Aircraft flying in formation can reduce their fuel consumption substantially by benefitting from wake vortices. Consequently, the emission of carbon dioxide is significantly reduced. The four main areas of investigation are aerodynamics, air traffic operations, aircraft design and navigation. Additionally, the technical design development and requirements are described.-Formation; Innovative; future; fuel reductionAerospace EngineeringAir Transport & Operations
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