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Senast inlagda
Performance comparison of simulation models and parameters for engine CFD applications.
(2026) Oinonen, Ilmari
In this thesis, the performance of computational fluid dynamics (CFD) simulation models and parameters are systematically compared for an engine model based on the Imperial college experimental engine. A set of simulation models and parameters
were chosen to be studied. For each model and parameter, a few different options were chosen and simulation cases created for each possible different combination of these options. A custom code was written for creating the cases out of all the combinations of model and parameter options.
The simulations for this thesis were performed with OpenFOAM, which is an opensource CFD software. The engine model has been created at Wärtsilä and used in-house meshing tools to create a moving mesh.
After the simulations had ran, the results were post-processed using a custom code written for this thesis. The post-processed results were directly compared to results from a high-fidelity direct numerical simulation (DNS) study based on the Imperial college case, which has great agreement with the experimental results. For each simulation, two metrics were calculated that measured the difference between simulation and DNS results. These metrics were used to assess the performance differences
between cases with different model and parameter combinations, and thus the performance of the models and parameters themselves.
The models and parameters studied in this thesis were the turbulence model, velocity advection scheme, maximum Courant number, mesh resolution and the number and thickness of surface inflation layers of the mesh. These were divided into three
individual studies.
A few of the turbulence models had clearly better performance than the others. Change in the velocity advection scheme, maximum Courant number and surface inflation parameters had only slight effects on performance. An increased mesh
resolution generally lead to better performance.
Dynamic load of timber truck
(2025) Munavalli, Ramkumar Huleppa; Larsson Rosén, Viktor; Vasudevan, Rohan Kumaar; Wu, Boxuan
This project investigates the dynamic load generated by heavy timber truck combination
as part of a collaborative research effort between Chalmers university of Technology,
NTNU and volvo trucks. The overarching goal of the research is to improve the understanding
of how trucks and their loads influence bridge structure, with the long term
objective of enabling safe reclassification of exiting swedish bridge fie higher load limits
(BK4).
In this project,the focus on developing a simplified yet representative dynamics model
of a timber truck combination that can later be used for studying Dynamic Amplification
factor(DFA) and truck-bridge interaction. The part involved deriving the equation
of motion for the truck and trailer, identifying and estimating key model parameters,
and implementing the model in a suitable simulation environment. Parametric studies
were carried out to analyze how factors such as road surface irregularities, vehicle speed,
suspension characteristic and axle configuration affect the resulting dynamic loads.
Furthermore, the project included the planning and preparation of experimental test
using an instrumented timber truck for future model validation. The developed model and
proposed testing methodology together provide a foundation for accurately simulating the
dynamic behaviour of heavy vehicle and for supporting future studies on the interaction
between vehicle and bridges.
Self-Supervised Fixed-Scene Adaptation for Object-Detection in Real-Time Surveillance: A Comparative Study of YOLO11 and RF-DETR
(2026) Justad, Jacob
This thesis investigates self-supervised fixed-scene adaptation for real-time objectdetectors
in an edge-computing surveillance context. While modern object-detectors
achieve strong results on general-purpose benchmarks, deployment in static camera
scenes introduces distinct challenges: domain shift to a specific viewpoint, limited
availability of scene-specific labels, and stringent compute and memory budgets ondevice.
At the same time, the stationary background of surveillance footage provides
exploitable structures, as do their temporal dependencies of video-frames. This study
conducts a comparative analysis of two state-of-the-art object detection architectures:
the Transformer-dominant RF-DETR and the convolutional neural network (CNN)-
dominant YOLO11. The thesis employs the 100Scenes dataset to represent a broad
range of surveillance environments. Experimental results demonstrate that RF-DETR
consistently achieves higher accuracy, smoother convergence, and greater robustness
than YOLO11, albeit with higher hardware demands. In contrast, YOLO11 variants
(with a frozen backbone) leverage the larger trainable capacity of the neck and head
to enable high scene-specific adaptability. While this yields significant gains under
quality labeling, it tends to increase sensitivity to imperfect pseudo-labels and the
risk of overfitting. Furthermore, by systematically varying model scales, adaptation
strategies and environmental conditions the experimental design yields more than 3400
distinct runs. First the work examines the extent to which smaller, specialized models
can match the approach of substantially larger models. The experimental results show
that a small specialised model can compete with larger general models. Secondly, the
study evaluated a proposed on-device self-supervised labeling strategy that integrates
SAHI with a bidirectional implementation of ByteTrack. The proposed self-supervised
labeling strategy provided reliable performance gains across all architectures and
configurations, by recovering hard negatives, more specifically small, occluded and
low confidence instances. Thirdly, the study investigated background-context fusion
(BF). It proved to be consistently improving the performance in general for RFDETR,
while it proved inconsistent for YOLO11 and failed to increase robustness
against seasonality, suggesting it induced background-dependent overfitting. Finally,
the study shows that all models being trained on a summer scene exhibit a decrease
in relative performance compared with the non-adapted models during a seasonal
domain shift to a winter scene.
Budget efficient 3D-mesh of engravings using photometric stereo
(2025) Heinmetz, Edvard
This paper describes a process for calculating approximate light positions in a photo
set captured of an inscribed wall, with the purpose of applying photometric stereo.
The light sources across the photo set are of constant intensity and their positions
are inferred using one reflective indicator sphere and the lighting of the wall. The
light positions are then used to create a depth map of the wall in an optimization
scheme that alternates between depth and albedo values. Various strategies for using
the depth map to highlight and isolate inscriptions are then applied.
Designing a Collaboration and Communication Process for a Garment Renewal Service
(2026) Gunnelius, Lovisa
The textile industry has a significant environmental impact and is one of the largest contributors
to land and water use, greenhouse gas emissions, and raw material consumption. Transitioning
the industry toward a circular economy is therefore essential, and one way to support this shift
is by extending the lifespan of garments through repair and refresh. RecoMended is a company
that provides industrial scale garment renewal services for other companies, enabling the resale
or continued use of existing products. However, as industrial scale garment renewal services
are new to the market, established collaboration and communication processes for developing
service specifications are limited. As a result, the collaboration and communication process
currently used by RecoMended is time-consuming, resource-intensive, and highly customerspecific,
making it difficult to scale.
This study aims to investigate how the collaboration and communication process between
RecoMended and its customers can be improved to support more efficient development of
service specifications for garment renewal and allow RecoMended to scale its production. The
research was conducted through interviews, workshops, and contextual inquiries with
RecoMended and several of its customer companies. The collected data were analyzed and
synthesized, resulting in the design of a proposed future collaboration and communication
process together with a service toolkit.
The proposed design outlines a structured, step-based collaboration and communication process
that is intended to support RecoMended and its customers in developing service specifications
in a more informed and efficient way. The process is envisioned to be supported by a service
toolkit consisting of standardized service packages intended to enable scalability in the
production workshop, a reference library with visual and material examples of available
renewal procedures, and a set of guiding questions designed to ensure that key decisions are
addressed. While further development and testing would be required, the proposed process and
service toolkit are intended to provide a foundation for clearer communication, more informed
decision-making, and improved collaboration, thereby supporting RecoMended’s ability to
scale its garment renewal services.