Examensarbeten för masterexamen
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- PostAdaptive Path Following Driver Model(2025) Sathiya Venkata Narayanan, Balaji; Manickam, Muralikrishna; Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Bruzelius, Fredrik; Lindström, HolgerThe evolution of advanced driver assistance systems (ADAS) and autonomous driving technologies has heightened the need for robust and adaptive driver models. This thesis focuses on developing an adaptive driver model within a Software-in-the-Loop (SIL) framework, designed to handle dynamic environments, complex scenarios, and disturbances with high precision. A state-space model is formulated to capture vehicle dynamics and error dynamics, essential for precise trajectory tracking. The error dynamics state-space model updates in real time, accounting for deviations in lateral position, yaw angle, and other key variables. This real-time updation enables the model to compute optimal control inputs using both a Linear Quadratic Regulator (LQR)-based controller and a Model Predictive Control (MPC)-based approach. MPC, with its ability to anticipate future states and optimize control inputs over a finite horizon, complements LQR by providing enhanced performance in managing constraints and nonlinearities, especially in dynamic environments. The SIL framework integrates real-time data exchange between components, leveraging middleware to maintain simulation fidelity and responsiveness. By iteratively refining error dynamics, adapting to changes in each simulation setup, and leveraging both LQR and MPC for trajectory tracking, the proposed driver model enhances precision and adaptability. This research contributes to advancing SIL frameworks, supporting safer and more reliable autonomous driving technologies while meeting industry standards.
- PostDeep learning methods for naturalness evaluation of forests based on canopy height model(2024) Bauner, Andreas; Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Della Vedova, Marco L; Della Vedova, Marco LForest evaluation has historically been done through field surveys by experts from national forest agencies or from forestry companies. This is costly manual labor that consumes a lot of time. A solution could be to use remote sensing ecological data and automate the naturalness evaluation of forests with the use of computers. The aim of this thesis is to develop a machine learning model that could help automate naturalness evaluation of forests. The remote sensing data is in the form of a Canopy Height Model (CHM), that is height of trees obtained from airborne laser-scanning. Ground truth data for forest naturalness is given in the form of annotated, georeferenced polygons. The study area is limited to a 50×50 km2 area north-east of the city of Jönköping in Sweden. After applying different processing steps on the data, it is then used for training a convolutional neural network, based on U-Nets, on this semantic segmentation task. The evaluation of the model shows good results, achieving an accuracy of 94.1% on the test set. This performance is competitive with currently used models for related tasks and shows the feasibility of using machine learning in the relatively new field of automated naturalness evaluation of forests.
- PostOptimization of Server Room HVAC Systems for Energy Efficiency. Leveraging CFD and AI-Driven Techniques(2024) Carlsson, John; Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Nilsson, Håkan; Löseth, OlaThe increasing demand for energy efficiency in server rooms and Heating, Ventilation & Air Conditioning (HVAC) systems necessitates advanced optimization techniques to reduce energy consumption while maintaining thermal stability. This thesis focuses on holistic energy optimization of a server room’s HVAC system by optimizing the room’s geometry and HVAC parameters using computational methods such as CFD and machine learning models, including an Artificial Neural Network (ANN). The study employs a range of meta heuristic optimization algorithms, including Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), to identify the best configuration for reducing energy usage. The goal of the optimization process is to maximize the inflow air temperature and minimize the mass flow while ensuring that the average room temperature does not exceed 28°C. The project was conducted at a worst case scenario, when the outside air temperature is 30 degrees Celsius and the server room is running on maximum capacity, generating waste heat of 72kW. A Latin Hypercube Sampling (LHS) method was employed to capture the underlying differential equations behavior throughout the high-dimensional parameter space, and a neural network was trained on this sample data to predict room temperature. Particle Swarm Optimization was used in order to find the optimal parameters for minimal energy consumption. The results demonstrate that the proposed optimization techniques can significantly enhance the energy efficiency of HVAC systems in server rooms at a worse case scenario. By adjusting the inflow temperature and air mass flow, a reduction in cooling energy consumption of up to 22.69% was achieved. Future work could include hybrid optimization approaches to further improve system performance and the application of multi-objective optimization to accommodate varying operational phases.
- PostAerodynamics investigations and optimization of a simplified pick-up truck with wind tunnel and CFD testing(2024) Yathiraj, Karthik; Chaithanya, Pavan; Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Vdovin, Alexey; Vdovin, AlexeyThis master’s thesis presents a comprehensive study on the aerodynamics and optimization of a simplified generic pick-up truck model through combined wind tunnel testing and computational fluid dynamics (CFD) simulations in StarCCM+. The primary objective is to understand and investigate the aerodynamic behavior of the generic pickup truck and design new attachments for the pickup truck on the trailer. Based on existing knowledge, design optimization techniques were used to find design changes that could reduce aerodynamic drag. A flat underbody, a closed grill gap, and various rear attachments were among the changes made to the truck’s shape. A comprehensive study and cross-validation of the suggested aerodynamic improvements were made possible by the combination of wind tunnel testing and CFD. The ANSA software was used to optimize the CAD model. 3D printing was later used to create a 1/10 scaled-down model of the generic pickup truck, along with three distinct attachments called Flat back, Hatch back, and Fastback. Later tested the 3D printed model in Chalmers University of Technology’s wind tunnel in Sweden. Drag forces were captured with the aid of wind tunnel experiments. These experimental findings served as a standard by which to validate the CFD models. The airflow surrounding the vehicle was then simulated using extensive CFD analyses using the StarCCM+ software. The findings presented in this paper are the outcome of research and comprehension of the vehicle’s aerodynamic behavior. They also show enhancements in the pickup truck’s aerodynamic performance, with the quick back attachment lowering the drag coefficient. In addition to highlighting the potential for significant fuel and pollution reductions in pick-up trucks through aerodynamic optimization, this work shows how well experimental and computational methodologies may be used for aerodynamic investigations.
- PostAssessing the influence of show, don’t tell principle on external human-machine interfaces across cultures(2024) Saha, Shouvanik; Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Benderius, Ola; Benderius, OlaTogether with the shift towards green renewable energy sources, the automotive industry is currently witnessing a rapid advancement in the context of autonomous vehicle technology. In this setting, the way in which the autonomous vehicle interacts with the vulnerable road users will be indispensable both in terms of safety and acceptance. Therefore, understanding the societal perceptions and cultural influences on the external human-machine interfaces (eHMIs) has become significant. This research investigates the intersection of social constructivism and the show, don’t tell principle within the context of eHMIs for autonomous vehicles. Grounded in the hypothesis of social constructivism and technological insights from the show, don’t tell principle, the study aims to analyse the alignment between the current theoretical frameworks and the practical design solutions. Specifically, it explores how cultural factors impact the acceptance and effectiveness of eHMIs among pedestrians. In order to achieve autonomous driving with minimal or zero human intervention, seamless integration of these vehicles into complex urban traffic is required. This research suggests that the development of culturally sensitive design solutions may facilitate the harmonious co-existence of autonomous vehicles and vulnerable road users in urban landscapes.