Fuel performance codes are used to demonstrate with confidencethat nuclear fuel rods will sustain normal operation and transientevents without being damaged. However, the execution time of a typ-ical fuel rod simulation ranges from tens of seconds to minutes which can be impractical in certain applications. In the scope of this work,at least two such applications are identified; code-calibration and fuelcore evaluations. In both of these cases, possible improvements can be obtainedby creating neural network surrogate models. For code calibration,a Deep Neural Network is enough since calibration is performed onmodel constants. But for full-core evaluations, a surrogate model mustbe able to predict a time-dependent target as a function of a time-dependent input. In this work, Temporal Convolutional Networks are investigated for the second application. In both applications, targetdata are generated with a Cladding Oxidation Model. The result of the study shows that both models succeeded in their respective tasks with good performance metrics. However, furtherwork is needed to increase the number of input and target variablesthat the Deep Neural Network can handle, verify the flexibility ofinput data files for the TCN, try out the TCN on a real code, and combine the two models and achieve a broader set of use-cases. / <p>Kursnamn: Fördjupande projektarbete i energisystem</p><p>Kurskod: 1FA394</p>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-455904 |
| Date | January 2021 |
| Creators | Nerlander, Viktor |
| Publisher | Uppsala universitet, Tillämpad kärnfysik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | FYSAST ; FYSENER1006 |
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