dc.contributor.advisor | Masoud, Naseri | |
dc.contributor.author | Dhar, Sushmit | |
dc.date.accessioned | 2025-03-06T15:12:33Z | |
dc.date.available | 2025-03-06T15:12:33Z | |
dc.date.embargoEndDate | 2030-03-20 | |
dc.date.issued | 2025-03-20 | |
dc.description.abstract | Sea-spray icing poses a significant risk to maritime operations in Arctic and sub-Arctic regions, threatening the stability, safety, and efficiency of vessels and offshore structures. Current icing estimation models are constrained by limited data and are often tailored to specific vessels or structures. A critical challenge lies in accurately estimating spray flux, which is the amount of water available for freezing. While the freezing fraction is well-researched, spray flux estimation relies on field data that is challenging to collect due to extreme weather and the absence of standardized equipment.
This research addresses these limitations by focusing on the development of specialized equipment, collection of field data, and enhancement of estimation models. Two novel devices, the "SPRICE sea-spray collector" (a catching-type collector inspired by the cyclone separator principle) and the "SPRICE sea-spray sensor" (a high-resolution capacitive sensor), were developed to measure spray flux, frequency, and duration. These devices were tested at a fish farm in Northern Norway under real-world conditions, yielding valuable field data. Classical statistical and machine learning models were developed using this data to accurately estimate spray parameters, including spray frequency and duration, forming a more reliable foundation for spray flux estimation.
Additionally, to tackle the challenges of limited field data availability, synthetic data generation was explored using generative machine learning models, resulting in enhanced model robustness. This research demonstrates that the innovative equipment and data-driven approaches hold significant potential to improve the reliability of spray flux estimation, effectively addressing critical gaps in marine icing models. These advancements can contribute to improved safety and operational efficiency of maritime operations in cold regions. | en_US |
dc.description.abstract | Sjøsprøyt-islegging utgjør en betydelig risiko for maritime operasjoner i Arktis og subarktiske områder, og truer stabiliteten, sikkerheten og effektiviteten til fartøy og offshore-strukturer. Eksisterende isleggingsmodeller er begrenset av utilstrekkelig data og er ofte tilpasset spesifikke fartøyer eller strukturer. En kritisk utfordring er nøyaktig estimering av sprøytfluks, som er mengden vann tilgjengelig for frysing. Selv om frysefraksjonen er godt utforsket, er estimering av sprøytfluks avhengig av feltdata som er utfordrende å samle inn på grunn av ekstreme værforhold og mangel på standardisert utstyr.
Denne forskningen adresserer disse begrensningene ved å fokusere på utvikling av spesialisert utstyr, innsamling av feltdata og forbedring av estimeringsmodeller. To nye enheter, "SPRICE sjøsprøytsamler" (en fangsttype samler inspirert av syklonseparatorprinsippet) og "SPRICE sjøsprøytsensor" (en høyoppløselig kapasitiv sensor), ble utviklet for å måle sprøytfluks, frekvens og varighet. Disse enhetene ble testet på et oppdrettsanlegg i Nord-Norge under reelle forhold, og ga verdifulle feltdata. Klassiske statistiske og maskinlæringsmodeller ble utviklet ved bruk av disse dataene for nøyaktig å estimere sprøytparametere, inkludert sprøytfrekvens og varighet, noe som dannet et mer pålitelig grunnlag for sprøytfluksestimering.
I tillegg, for å håndtere utfordringene knyttet til begrenset tilgjengelighet av feltdata, ble syntetisk datagenerering utforsket ved hjelp av generative maskinlæringsmodeller, noe som resulterte i forbedret modellrobusthet. Denne forskningen viser at det innovative utstyret og de data-drevne tilnærmingene har betydelig potensial til å forbedre påliteligheten av sprøytfluksestimering og effektivt adressere kritiske mangler i modeller for isleggingsprediksjon. Disse fremskrittene kan bidra til økt sikkerhet og operasjonell effektivitet i maritime operasjoner i kalde regioner. | en_US |
dc.description.doctoraltype | ph.d. | en_US |
dc.description.popularabstract | Accurately predicting sea-spray icing events is crucial, as this phenomenon poses significant risks to the safety of marine operations in cold regions. Spray flux, which determines the water available for freezing, is a key parameter for creating accurate icing prediction models. However, current models lack reliability due to the limited spray flux data they rely on, as data collection remains challenging in harsh Arctic conditions. To address this, two novel devices are introduced: the SPRICE Sea-Spray Collector and the SPRICE Sea-Spray Sensor. Tested in Northern Norway above the Arctic Circle, these devices provided valuable field data. This data is used to develop machine learning and classical statistical models of spray parameters to enhance spray flux estimation. Additionally, to address data limitations, generative machine-learning techniques are explored to create realistic synthetic datasets. This research contributes to improving icing prediction models and enables safer Arctic maritime operations. | en_US |
dc.description.sponsorship | This thesis is the result of a three-year PhD program funded by the Research Council of Norway, provided through the “Multidisciplinary approach for spray icing modelling and decision support in the Norwegian maritime sector” (SPRICE) project, supported by the MAROFF-2 Program [Grant Number: 320843]. | en_US |
dc.identifier.isbn | 978-82-8236-617-5 - pdf | |
dc.identifier.isbn | 978-82-8236-616-8 - print | |
dc.identifier.uri | https://hdl.handle.net/10037/36637 | |
dc.language.iso | eng | en_US |
dc.publisher | UiT Norges arktiske universitet | en_US |
dc.publisher | UiT The Arctic University of Norway | en_US |
dc.relation.haspart | <p>Paper 1: Dhar, S., Samuelsen, E.M., Naseri, M., Aarsæther, K.G. & Edvardsen, K. (2022). Spray Icing on ONEGA Vessel - A Comparison of Liquid Water Content Expressions. <i>Proceedings of the ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. Volume 5A: Ocean Engineering</i>. Hamburg, Germany. June 5–10, 2022. V05AT06A033. Published version not available in Munin due to publisher’s restrictions. Published version available at <a href=https://doi.org/10.1115/OMAE2022-79919>https://doi.org/10.1115/OMAE2022-79919</a>. Accepted manuscript version available in Munin at <a href=https://hdl.handle.net/10037/28865>https://hdl.handle.net/10037/28865</a>.
<p>Paper 2: Dhar, S., Naseri, M., Khawaja, H.A., Samuelsen, E.M., Edvardsen, K. & Barabady, J. (2024). Sea-spray measurement tools and technique employed in marine icing field expeditions: A critical literature review and assessment using CFD simulations. <i>Cold Regions Science and Technology, 217</i>, 104029. Also available in Munin at <a href=https://hdl.handle.net/10037/32297>https://hdl.handle.net/10037/32297</a>.
<p>Paper 3: Dhar, S., Naseri, M., Khawaja, H., Edvardsen, K. & Zhu, T. (2023). Design, Development and Deployment of a Novel Sea Spray Collector for Sea-Spray Flux Measurements. <i>Cold Regions Science and Technology, 218</i>, 104096. Also available in Munin at <a href=https://hdl.handle.net/10037/32500>https://hdl.handle.net/10037/32500</a>.
<p>Paper 4: Dhar, S., Naseri, M., Zhu, T. & Edvardsen, K. (2024). A Novel Device for Accurate Measurement of Spray Frequency and Duration using Capacitive Liquid Sensors in Marine Icing Estimation. <i>34th International Ocean and Polar Engineering Conference, Rhodes, Greece, June 2024</i>. Published version not available in Munin due to publisher’s restrictions. Published version available at <a href=https://onepetro.org/ISOPEIOPEC/proceedings/ISOPE24/All-ISOPE24/ISOPE-I-24-305/546731>https://onepetro.org/ISOPEIOPEC/proceedings/ISOPE24/All-ISOPE24/ISOPE-I-24-305/546731</a>. Accepted manuscript version available in Munin at <a href=https://hdl.handle.net/10037/36627>https://hdl.handle.net/10037/36627</a>.
<p>Paper 5: Dhar, S., Naseri, M., Ceolto, R., Zhu, T., Khawaja, H.A. & Edvardsen, K. (2024). Measurement and Modelling of Sea-Spray Frequency Using a Probabilistic Approach, Multivariate Regression, and Tree-Based ML Techniques. (Submitted manuscript).
<p>Paper 6: Dhar, S., Naseri, M., Zhu, T. & Edvardsen, K. (2024). Measurement and Modelling of Spray Frequency and Duration Using Capacitive Sensors for Marine Icing Prediction. (Accepted manuscript). | en_US |
dc.rights.accessRights | embargoedAccess | en_US |
dc.rights.holder | Copyright 2025 The Author(s) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | en_US |
dc.rights | Attribution 4.0 International (CC BY 4.0) | en_US |
dc.subject | sea-spray icing | en_US |
dc.subject | sea-spray flux | en_US |
dc.subject | marine-icing-estimation model | en_US |
dc.subject | SPRICE sea-spray collector | en_US |
dc.subject | SPRICE sea-spray sensor | en_US |
dc.subject | field data collection | en_US |
dc.subject | synthetic data | en_US |
dc.subject | machine learning | en_US |
dc.title | Empirical Study and Modelling of Sea-Spray Parameters for Robust Marine Icing Estimation | en_US |
dc.type | Doctoral thesis | en_US |
dc.type | Doktorgradsavhandling | en_US |