Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
A Physics-Informed, Data-Driven Framework for Spatially Resolved Motion Sickness Prediction on The SA Agulhas II
Thesis (MEng)--Stellenbosch University, 2026.
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Stellenbosch : Stellenbosch University
2026
|
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867614019073867776 |
|---|---|
| access_status_str | Open Access |
| author | Melim, Micaela Do Amaral |
| author2 | Bekker, Anriette |
| author_browse | Bekker, Anriette Melim, Micaela Do Amaral |
| author_facet | Bekker, Anriette Melim, Micaela Do Amaral |
| author_sort | Melim, Micaela Do Amaral |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2026. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/136242 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:45:22.846Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/136242 A Physics-Informed, Data-Driven Framework for Spatially Resolved Motion Sickness Prediction on The SA Agulhas II Melim, Micaela Do Amaral Bekker, Anriette Taylor, Nicole Catherine Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering. Thesis (MEng)--Stellenbosch University, 2026. Melim, M. D. 2026. A Physics-Informed, Data-Driven Framework for Spatially Resolved Motion Sickness Prediction on The SA Agulhas II. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/b54817b8-a7b6-4c93-bd52-7eb18856e170 Marine engineering has long excelled in translating environmental inputs, such as wave spectra, into predictable ship dynamics, necessary for ship design and operational planning. However, a notable gap persists in fully integrating these predicted motions with their direct implications for human occupants, particularly motion sickness. To bridge this divide, a physics-informed, data-driven framework was developed linking environmental forcing, vessel response, and human factors. This systematic framework predicts position-dependent Motion Sickness Dose Values along a vessel's longitudinal centre. This framework's human- centric predictive capability aligns with Maritime 4.0 digitalization goals, illustrating how physics-based models and integrated data analytics offer insight to potentially improve seafarer well-being and voyage resilience in the future, offering a transferable template for MSDVs on vessels in extreme seas. For a case study on the SA Agulhas 11, the framework was systematically implemented, leveraging data acquired during its Southern Ocean Seasonal SCALE winter cruise voyage in July 2022. ERAS reanalysis data provided sea-state inputs parameterized through JONSWAP spectra, representing hourly wave energy distributions along the vessel's operational route. Response Amplitude Operators for heave and pitch were then applied to these spectra to compute frequency domain motion responses, with roll deemed negligible due to the focus on the longitudinal centreline. The methodology incorporated both amplitude and phase information, allowing heave pitch coupling to be resolved across longitudinal positions. From these responses, vertical accelerations were derived and frequency weighted in accordance with 1SO 2631-1 {1997} to compute the corresponding estimated MSDV values. The framework's estimated MSDVs underwent a comparative evaluation, benchmarked against encountered ship motion and MSDV metrics derived from full-scale accelerometer measurements during the voyage. This comparison revealed strong agreement across temporal and spatial dimensions, with the framework successfully capturing both the magnitude and timing of high motion periods. Coefficients of determination {R2) consistently ranged from 0.82 to 0.92, with the highest correlation observed at midship. Furthermore, Root Mean Square Error, which were as low as 3.24 m.s-1.5 at midship, confirmed accurate trend reproduction, although these values increased to 12.7 m.s-1.5 at the bow, reflecting amplified pitch response. Spatially, the analysis confirmed expected patterns of vertical motion exposure, which was lowest at midship and progressively higher toward the bow and stern, consistent with the increasing pitch-induced vertical motion contribution at larger longitudinal offsets, while exhibiting symmetry about midship with comparable values at equivalent distances fore and aft. A prototype dashboard was developed to demonstrate the framework's practical potential, visually presenting wave spectra, ship motion analyses, and MSDVs. With dual research and bridge interfaces, it supports both scientific interrogation and decision-making. Deployed onboard, such a tool could enable crew and researchers to anticipate motion sickness conditions, reassign personnel, or adjust operating profiles to mitigate motion sickness and maintain performance. Future work involves extending this approach to real-time assimilation and adaptive forecasting, and expanding the framework to calculate Motion Sickness Incidence, thus positioning vessels like the SA Agulhas II at the forefront of data-driven, human-centred maritime operations. Masters 2026-04-29T09:34:51Z 2026-04-29T09:34:51Z 2026-03 Thesis https://scholar.sun.ac.za/handle/10019.1/136242 en Stellenbosch University 137 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Melim, Micaela Do Amaral A Physics-Informed, Data-Driven Framework for Spatially Resolved Motion Sickness Prediction on The SA Agulhas II |
| title | A Physics-Informed, Data-Driven Framework for Spatially Resolved Motion Sickness Prediction on The SA Agulhas II |
| title_full | A Physics-Informed, Data-Driven Framework for Spatially Resolved Motion Sickness Prediction on The SA Agulhas II |
| title_fullStr | A Physics-Informed, Data-Driven Framework for Spatially Resolved Motion Sickness Prediction on The SA Agulhas II |
| title_full_unstemmed | A Physics-Informed, Data-Driven Framework for Spatially Resolved Motion Sickness Prediction on The SA Agulhas II |
| title_short | A Physics-Informed, Data-Driven Framework for Spatially Resolved Motion Sickness Prediction on The SA Agulhas II |
| title_sort | physics informed data driven framework for spatially resolved motion sickness prediction on the sa agulhas ii |
| url | https://scholar.sun.ac.za/handle/10019.1/136242 |
| work_keys_str_mv | AT melimmicaeladoamaral aphysicsinformeddatadrivenframeworkforspatiallyresolvedmotionsicknesspredictiononthesaagulhasii AT melimmicaeladoamaral physicsinformeddatadrivenframeworkforspatiallyresolvedmotionsicknesspredictiononthesaagulhasii |