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Machine Learning for Air Flow Characterization: An application of Theory-Guided Data Science for Air Fow characterization in an Industrial Foundry
Karlstad University.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Maskininlärning för Luftflödeskarakterisering : En applikation för en Teorivägledd Datavetenskapsmodell för Luftflödeskarakterisering i en Industrimiljö (Swedish)
Abstract [en]

In industrial environments, operators are exposed to polluted air which after constant exposure can cause irreversible lethal diseases such as lung cancer. The current air monitoring techniques are carried out sparely in either a single day annually or at few measurement positions for a few days.In this thesis a theory-guided data science (TGDS) model is presented. This hybrid model combines a steady state Computational Fluid Dynamics (CFD) model with a machine learning model. Both the CFD model and the machine learning algorithm was developed in Matlab. The CFD model serves as a basis for the airflow whereas the machine learning model addresses dynamical features in the foundry. Measurements have previously been made at a foundry where five stationary sensors and one mobile robot were used for data acquisition. An Echo State Network was used as a supervised learning technique for airflow predictions at each robot measurement position and Gaussian Processes (GP) were used as a regression technique to form an Echo State Map (ESM). The stationary sensor data were used as input for the echo state network and the difference between the CFD and robot measurements were used as teacher signal which formed a dynamic correction map that was added to the steady state CFD. The proposed model utilizes the high spatio-temporal resolution of the echo state map whilst making use of the physical consistency of the CFD. The initial applications of the novel hybrid model proves that the best qualities of these two models could come together in symbiosis to give enhanced characterizations.The proposed model could have an important role for future characterization of airflow and more research on this and similar topics are encouraged to make sure we properly understand the potential of this novel model.

Abstract [sv]

Industriarbetare utsätts för skadliga luftburna ämnen vilket över tid leder till högre prevalens för lungsjukdomar så som kronisk obstruktiv lungsjukdom, stendammslunga och lungcancer. De nuvarande luftmätningsmetoderna genomförs årligen under korta sessioner och ofta vid få selekterade platser i industrilokalen. I denna masteruppsats presenteras en teorivägledd datavetenskapsmodell (TGDS) som kombinerar en stationär beräkningsströmningsdynamik (CFD) modell med en dynamisk maskininlärningsmodell. Både CFD-modellen och maskininlärningsalgoritmen utvecklades i Matlab. Echo State Network (ESN) användes för att träna maskininlärningsmodellen och Gaussiska Processer (GP) används som regressionsteknik för att kartlägga luftflödet över hela industrilokalen. Att kombinera ESN med GP för att uppskatta luftflöden i stålverk genomfördes första gången 2016 och denna modell benämns Echo State Map (ESM). Nätverket använder data från fem stationära sensorer och tränades på differensen mellan CFD-modellen och mätningar genomfördes med en mobil robot på olika platser i industriområdet. Maskininlärningsmodellen modellerar således de dynamiska effekterna i industrilokalen som den stationära CFD-modellen inte tar hänsyn till. Den presenterade modellen uppvisar lika hög temporal och rumslig upplösning som echo state map medan den också återger fysikalisk konsistens som CFD-modellen. De initiala applikationerna för denna model påvisar att de främsta egenskaperna hos echo state map och CFD används i symbios för att ge förbättrad karakteriseringsförmåga. Den presenterade modellen kan spela en viktig roll för framtida karakterisering av luftflöden i industrilokaler och fler studier är nödvändiga innan full förståelse av denna model uppnås.

Place, publisher, year, edition, pages
2019. , p. 60
Keywords [en]
Machine learning, ML, Echo State Map, ESM, Echo State Network, ESN, Gaussian Process, GP, Computational Fluid Dynamics, CFD, Theory-Guided Data Science, TGDS, Physics-Guided Data Science, Data science, Cross-discipline, Hybrid model, MatLab
Keywords [sv]
Maskininlärning, ML, Echo State Map, ESM, Echo State Network, ESN, Gaussiska Processer, GP, Beräkningsströmningsdynamik, CFD, MatLab
National Category
Other Physics Topics Computer Sciences
Identifiers
URN: urn:nbn:se:kau:diva-72782OAI: oai:DiVA.org:kau-72782DiVA, id: diva2:1327817
External cooperation
Örebro Universitet
Educational program
Engineering: Engineering Physics (300 ECTS credits)
Presentation
2019-06-03, Karlstad, 08:49 (English)
Supervisors
Examiners
Available from: 2019-06-20 Created: 2019-06-20 Last updated: 2019-06-20Bibliographically approved

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Thesis Robin Lundström(15088 kB)66 downloads
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