Chalmers Open Digital Repository

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Student theses and papers
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Research publications, reports and dissertations can be found in research.chalmers.se

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Recent Submissions

An Evaluation of the Applicability of Social Life Cycle Assessment in the Aerospace Industry
(2026) Angel, Johannes; Bisseberg, Oscar
This master’s thesis evaluates the applicability and effectiveness of Social Life Assessment (S-LCA) as a method for assessing social sustainability within the aerospace industry. Given the increasing emphasis in recent years on corporate sustainability, the study investigates whether S-LCA can provide meaningful insights into the complex social impacts associated with aerospace products and their global value chains. The research combines a methodological review of S-LCA and its applicability with a case study on a selected aerospace product. The study assesses how S-LCA can be implemented across different life cycle stages, from raw material extraction to usage, a so-called “cradle-to-gate” approach, and identifies social hotspots on organizational as well as on industry level. The findings indicate that S-LCA is, in theory, a valuable tool for identifying social hotspots and, by extension, feasible to use for addressing social sustainability challenges. However, the methodology presents limitations, including data availability and quality challenges, the inherent subjectivity of the assessment, and sensitivity to selected system boundaries. The case study demonstrates that results vary to a great degree depending on made assumptions and what data sources are used, which highlights the need for careful methodological choices. To summarize, the thesis concludes that while S-LCA is a promising approach, its use and effectiveness in the aerospace industry is dependent on methodological development and increased data transparency
Making Climate Data Actionable in Energy Investment Projects
(2026) Lilliedahl, Alfred; Åkerlund, Daniel
Göteborg Energi has established ambitious targets to reduce the climate footprint of its procurement by 90% by 2030. While Life Cycle Assessment (LCA) is an established methodology for quantifying environmental impact, its practical integration into investment decisions is often constrained by organizational and data quality barriers rather than technical limitations. This thesis investigates how Göteborg Energi’s working approach for climate data can be developed to support both early investment decisions, LCA-based follow-up and declarations in larger investment projects. Using a mixed-methods approach structured around the DMAIC framework, a retrospective pilot LCA was conducted on the biomass-fired combined heat and power plant Rya BKV, delimited to the main supplier Valmet’s scope of delivery. This were combined with semi-structured interviews, questionnaires, process observations, and supplier dialogue. The analysis reveals three categories of barriers. Process-related barriers include late and unclear requirements specification in procurement. Data quality barriers including a strong reliance on generic emission factors due to limited availability of product-specific Environmental Product Declarations (EPDs). Organizational barriers arise from unclear allocation of responsibilities between project management, procurement, and the environmental function. In response, an improved working method is proposed that separates climate data into two distinct flows: a limited decision-support flow for use in tender evaluation and a comprehensive follow-up flow post-award, utilizing a standardized supplier data template. The method is reinforced by a shared terminology structure for climate data types, explicit allocation of responsibilities, and a four-level fallback process for missing emission factors. The result is a scalable and structured approach that bridges the gap between early climate screening and rigorous LCAbased follow-up, without assuming complete product-specific data availability at all project stages.
Evaluation of Ergonomic Risks in Labour-Intensive Remanufacturing Tasks
(2026) Aravindakshan, Harikrishna Menon; Vettiyattil Sasikumar, Karthik
Remanufacturing is an important circular economy strategy that prolongs product life and lowers the consumption of virgin raw materials, thereby offering both economic and environmental benefits for companies. However, the process remains labour-intensive because cores arrive in varying states of wear and damage, which creates challenges for operators during their tasks.This study aims to analyse and evaluate the ergonomic risks associated with a labour-intensive remanufacturing workstation by applying ergonomic assessment methods. A mixed-methods approach was adopted, combining theoretical insights from the literature, semi-structured interviews with industry and academic experts, and a simulation-based analysis with ergonomic evaluations using the Rapid Entire Body Assessment (REBA) method and a separate Key Indicator Method (KIM) assessment. Based on the findings from the semi-structured interviews, the disassembly workstation was selected for the simulation-based ergonomics analysis. Three scenarios were modelled and evaluated with REBA in the simulation, while KIM was applied independently to determine the physical workload.The analysis suggests that the disassembly process presents significant ergonomic challenges. Identified risks include awkward body postures, high force exertions, repetitive movements, limited accessibility, and the handling of heavy components. The REBA assessment indicated a moderate ergonomic risk for all three postures studied, whereas the KIM results showed higher physical workload for the bolt removal and trolley transportation tasks. The results indicate that digital simulation combined with ergonomic evaluation tools such as REBA and KIM can effectively identify high-risk activities within remanufacturing workstations.
Equivariant Inductive Biases for Weather Prediction with PEAR - Investigating the exploitation of rotational symmetries for accurate transformer-based weather forecasting over the HEALPix discretisation
(2026) Rosso, Pietro
Weather forecasting is a complex challenge due to its intrinsically complex physical dynamics that define the evolution of the system. In recent years, deep learning weather prediction has emerged as a promising alternative to classical numerical weather prediction, matching or outperforming it on several benchmarks at a fraction of the inference time. This thesis contributes to this direction by analysing the symmetries of this system in relation to the group SO(2): the rotation of the Earth around its own axis. The study builds on Pangu Equal ARea (PEAR), a transformer-based model operating on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) discretisation of the sphere, and examines whether the symmetry awareness of this architecture can be increased from two complementary perspectives: the data on which the model is trained, and the architecture itself. In the first part, starting from ERA5, the reanalysis dataset that provides global estimates of the surface and atmospheric variables, we introduce a new 2-hourly sampling, which allows a comparison of PEAR’s equivariance behaviour across three configurations of dataset and forecast horizon. The analysis shows that the equivariance error is dominated by the architecture and the forecast horizon rather than by the sampling. The second part introduces two architectural modifications, an iso-latitude interspersed windowing scheme and a set of HEALPix-aware convolutions, designed to better align the model with the rotational symmetry of the sphere. These modifications successfully reduce the equivariance error at the surface level, but fail to improve it at the upper atmospheric levels, and do not translate into a forecasting advantage over the baseline. This outcome highlights the difficulty of embedding inductive biases in the case of domains that involve using high-dimensional samples, specifically in relation to window-based attention mechanisms.
Testing the Semigroup Property of Generative Models for Dynamical Systems - Developing a test based on the Chapman–Kolmogorov equation
(2026) Green, Max; Wennberg, Hedvig
Surrogate models for molecular dynamics, particularly those based on generative artificial intelligence, offer an efficient way to model molecular systems across timescales that may be difficult to access through simulation. However, such models should remain consistent with the underlying physics. For Markovian dynamics, the Chapman Kolmogorov equation is a cornerstone of this consistency, describing how transition dynamics across different timescales should relate to each other. One such surrogate model, the Implicit Transfer Operator (ITO) framework, learns transition dynamics across multiple timescales, making it natural to question whether the learned dynamics remain consistent. Existing methods to assess this quantitatively use comparisons of distributions in the molecular space, while the test proposed in this work instead evaluates distributions in latent space, enabling metrics that were previously unavailable. In this thesis, we develop and evaluate a Chapman–Kolmogorov test for ITO models operating in the latent space of the model. The test is evaluated on both a one-dimensional model trained on the dynamics from a potential well and a three-dimensional transferable model trained on molecular dynamics data. The one dimensional model passes the test consistently, while the three-dimensional model gives more uncertain results, leading to a discussion about both the model and the multivariate version of the test. We further show that the CK-test’s performance improves alongside the learning of correct dynamics during training, suggesting that the semigroup property is learned rather than being inherent to the model architecture. However, passing the test does not guarantee that the model has learned the correct dynamics, as models with poor dynamical accuracy can still satisfy the CK-test.