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Otakaari 1 grandhall. Photo: Esa KapilaPhoto by Esa Kapila

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Recent Submissions

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    Mapping the Impact of Pitch and Ride Height on the Aerodynamic Performance of a Race Car
    (2026-07-01) Grönroos, Joakim
    School of Engineering | Bachelor's thesis
    In modern prototype racing, vehicle aerodynamics serves as one of the primary differentiating factors for on-track performance. Currently, under increasingly strict aerodynamic design regulations, the primary focus has shifted from maximiz- ing peak aerodynamic forces toward optimizing performance across the vehicle’s full range of dynamic attitudes. These varying attitudes occur as the vehicle operates as a dynamic platform relative to the ground. During braking, accelera- tion, and cornering, the weight transfer causes movement in the cars suspension, resulting in continuous variations in pitch, roll, and ride height. These factors strongly influence aerodynamic performance, as prototype aerodynamics rely heavily aerodynamics affected by ground proximity. This bachelor’s thesis investigates how variations in pitch and ride height affect a Formula Student vehicle by mapping its performance under realistic changes in these parameters. Computational Fluid Dynamics (CFD) simulations in Ansys Fluent were utilized as the primary methodology. Static Reynolds-Averaged Navier- Stokes (RANS) simulations were employed to approximate the vehicle’s dynamic behavior, evaluating a parameter range of ±1◦ in pitch and 35–45 mm in ride height. The results demonstrate significant variations in the vehicle’s primary aero- dynamic characteristics: the lift coefficient (Cl), the drag coefficient (Cd), the lift-to-drag ratio (L/D), and shifts in the aerodynamic balance between the front and rear axles. Distinct trends emerge in relation to pitch angle and ride height, and these aerodynamic changes correlate constructively with vehicle performance. Further, the shifts in aerodynamic balance assist vehicle rotation during cornering, while the coefficients increase when advantageous and decrease when detrimental to performance. Flow visualizations indicate that the front wing is the primary component causing these variations, interacting downstream with the rest of the vehicle as the airflow propagates rearward.
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    Maximal effect of aggregation on the KL-divergences of short-time Markov chains
    (2026-06-24) Heino, Viktor
    School of Science | Bachelor's thesis
    Aggregation is a fundamental tool for reducing the complexity and storage require- ments of large datasets. It is commonly applied to stochastic models such as Markov chains. One common aggregation method for Markov chains is occupancy time, which records the total time a chain spends in a given state, thus disregarding the chronological order of the visited states. This thesis studies the maximal loss of information caused by occupancy time aggregation in discrete-time two-state Markov chains, measured by Kullback-Leibler (KL) divergence. The information loss from this aggregation is measured by calcu- lating the ratio between the KL-divergences of two stationary Markov chains and the KL-divergence of their aggregated occupancy time distributions. The research question addresses how much the distinguishability between two Markov chains P and Q can suffer under such aggregation. The goal is to determine whether the ratio of the divergences is bounded from above, or whether it can diverge to infinity. The analysis focuses on sequences of two and three time steps taken in the Markov chain. For two time steps it was proven that aggregation does not lose any information, and the KL-divergence remains identical for any two stationary Markov chains and their aggregated counterparts. This guarantees that the aggregated distribution preserves all of the information from the original path distribution of the Markov chain. For three time steps, specific Markov chains were constructed via an ansatz, for which the ratio of the divergences grows without bound as ϵ goes to zero, behaving asymptotically like 1/ϵ. This proves that the ratio of the KL-divergences can be made arbitrarily large, and thus the distinguishability of two Markov chains P and Q can be almost completely lost through aggregation. The results are consistent with previous literature such as the data processing inequality. They show that although computationally convenient, occupancy time can lead to significant loss of information, and should therefore not be applied to all situations.
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    Näytös/Näyttely 26
    (2026) Valle Noronha, Julia; Salolainen, Maarit; Hyötyläinen, Ilona; Agostini, Vanessa
    School of Arts, Design and Architecture | J Muu elektroninen julkaisu
    Näytös/Näyttely is the Aalto University BA and MA Fashion and Textile graduates’ show and exhibition, taking place in May 2026, at Lasipalatsi square and Bio Rex, Helsinki. In this publication, we are proud to present the works of 30 students, the result of their graduation project, exploring new takes in materials, forms and concepts within fashion and textiles. The works highlight well Aalto’s strengths — an ability to think responsible futures beyond material development, while attuning to the power of materiality in clothing and textiles. The works investigate a multiplicity of matters, such as body-diversity, craft, friendship, technology, ecology, social phenomena, through multidisciplinary research. In addition, we present and celebrate the work of our collaborating students, professionals and alumni, who come together in a big collective effort. This year, we have collaborated with diverse fields at Aalto ARTS, including Visual Communication Design, Film and Sound Design. This year a recurring point of discussion comes as an invitation to stop and listen — to the quality of materials, to the feeling of being in and with clothes, to the (extensive and complex) process that is often hidden behind garments and textiles construction. An invitation we would like to extend to you, readers, as you explore the pages in this publication.
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    Hyperparameter and Model Structure Selection in Lattice Structure Applications
    (2024-08-16) Mueller, Stefan
    School of Engineering | Bachelor's thesis
    The intersection of data science and materials engineering presents significant opportunities for advancing the state of lattice material research using machine learning (ML). However, a gap remains in the understanding of hyperparameter tuning and model structure selection for material scientists applying graph neural networks (GNNs) to lattice materials. This thesis aims to bridge that gap by providing a guide to the processes behind model design. The goal is to ultimately improve the ability of materials engineers to better utilize GNNs in their research of both lattice materials and broader materials topics. The study begins with an overview of lattice materials and their unique properties, followed by a detailed breakdown of the core parameters that govern the performance of neural networks, particularly GNNs. A review of recent work from several materials science research groups highlights current applications of ML techniques in the study of lattice materials. To illustrate the parameter selection process, a dummy task is introduced, showcasing how hyperparameter tuning and structure selection impacts model performance. Results from this task demonstrate the critical importance of appropriate parameter choices for accurate and efficient modeling. My research aims to be readable by both materials engineers and data scientists, providing both a practical framework for materials engineers to apply GNNs and a good overview of the research space for data scientists who may wish to collaborate on related projects.
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    Water Systems for Agriculture in the Taita Hills: A System Mapping Study
    (2026) Charlu, Sushmita; Chazalmartin, Saima; Jokinen, Elli; Kangas, Saara-Kaisla; Rissanen, Essi
    School of Engineering | S harjoitus- ja seminaarityöt