Examensarbeten för masterexamen

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https://odr.chalmers.se/handle/20.500.12380/47

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Virtual Verification Framework for Vehicle Motion Systems
(2024) Blakqori, Albijon; Kotur, Mille; Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Bruzelius, Fredrik; Gröndahl, Albin; Mark, Hans
The automotive industry continually strives to enhance vehicle development processes to be faster, more cost-effective, and of higher quality. This thesis presents the development of a Virtual Verification Framework (VVF) to improve the Computer Aided Engineering (CAE) verification process for vehicle motion systems. The focus is on the initial stages of vehicle development, specifically replacing traditional Vehicle-in-the-loop (ViL) testing with more efficient Software-in-the-loop (SiL) methods. The framework is developed using IPG CarMaker, a widely adopted simulation software, and Simulink, allowing detailed subsystem simulations such as braking systems. The objective is to create a correlated CAE environment that can perform high-fidelity simulations and provide reliable data for system verification. This involves implementing accurate simulation models, selecting relevant verification scenarios, and analyzing both simulations’ and real-world data’s performance and accuracy. Key research questions addressed include the analysis of output data reliability for correlation studies between SiL and ViL and the potential expansion of the SiL stage to replace some aspects of ViL in system verification. The thesis demonstrates that while a complete VVF is not yet realized, significant progress has been made, particularly in implementing system-specific models and functional testing within CarMaker for Simulink (CM4SL). Challenges identified include simulated and real-world data discrepancies, particularly with tire modeling and sensor frequency differences. Despite these, the framework shows promise for future scalability and application, aiming to reduce reliance on physical prototypes, enhance safety in early-stage testing, and streamline the vehicle development process. The work concludes that a more robust and trustworthy virtual verification environment can be established, significantly benefiting the automotive industry’s development cycles.
Lattice-Boltzmann for Aeronautical Flows: An introduction to and evaluation of the Lattice-Boltzmann Method
(2024) Ellénius, Emil; Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Yao, Huadong; Carlsson, Magnus
In aircraft design, there is a need for accurate, efficient and robust computational fluid dynamics (CFD) simulations. Industry dominated methods are based on the non-linear Navier-Stokes equations which are rather computationally expensive to solve. The Lattice-Boltzmann method (LBM) is an alternative CFD method that has risen in popularity lately due to the promised performance gain resulting from its linear equations. The method describes the evolution of a particle distribution function (PDF) at the meso-scale through the Boltzmann equation. The PDF is a statistical function describing the probability of finding a particle with a certain velocity at a certain location in time and space and is connected to the macro-scale through integrals over velocity space. In the standard LBM, the discretisation of the Boltzmann equation involves expressing the PDF at equilibrium through a truncated polynomial expansion. This allows for exact computation of the macroscopic density and fluid velocity through finite sums and a limited set of particle velocities. However, the truncation introduces an error scaling with the Mach number, limiting the method to Ma ≲ 0.3. There is also a correlation between the viscosity, grid spacing and time step. To simulate high Reynolds number (Re) flows the grid must therefore be very fine, which adds computational cost. In this master’s thesis, the standard LBM has been evaluated for aeronautical applications. It was implemented in Python, where part of the work focused on increasing the performance resulting in 30 times faster code. The Euler equations were used as a baseline, but since the standard LBM is always viscous there were difficulties reaching good correspondence. Partly, this was due to using simple boundary conditions (BCs), but a great improvement could be shown through a proposed modification. The limitation in Re was still an issue, however, and the conclusion is that more advanced BCs should be used for arbitrary geometries. Through a minor modification to the equilibrium PDF, an Euler equation test case for isentropic vortex convection was successfully simulated, although with some viscous dissipation present. The stability of the method was also explored, finding that the Ma limit was stricter at low viscosities since the method operates closer to its stability limit there. Lastly, the initialisation proved another challenge due to the interplay between the macro- and meso-scales, often leading to polluting the solution with numerical acoustic noise. It is possible to create non-reflecting BCs, but stability problems where the solution diverges were encountered when using established methods, leading to the development of new boundary treatments.
Streamlining the Aircraft Conceptual Design Process. From Concept to Flight: Streamlining the Aircraft Conceptual Design Process through Advanced Mass and Balance Techniques
(2024) Crona, Michael; Dinger, Simon; Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Xisto, Carlos; Lindegger, Anderson
This master thesis investigates mass and balance techniques in the conceptual design phase of aircraft, with a focus on the ES-30 electric aircraft developed by Heart Aerospace. The thesis centers on developing and validating a computational tool for precise calculation of mass properties and center of gravity (CG) envelopes. This tool facilitates adjustments in aircraft design and has been applied to a case study on a conceptual liquid hydrogen (LH2) aircraft. The process aims to reduce the iterations needed in design modifications, speeding up the pre-flight testing phase and ensuring adherence to safety and performance standards. The findings demonstrate the tool’s applicability in future aircraft design projects, particularly for integrating new propulsion technologies such as electric and LH2 systems.
Adaptive Payload Estimation: A Universal Payload Estimation System for Wheel Loaders Using Recurrent Neural Networks and Transfer Learning
(2024) Carlsson, Marcus; Hiljemark, Elin; Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Forsberg, Peter; Liljeqvist, Simon
A wheel loader is a versatile construction machine designed for various tasks related to the loading and transport of materials. In many wheel loader applications, accurately knowing the mass of the load being lifted is crucial. For example, when loading materials onto trucks, it is essential to ensure that the load of the truck does not exceed legal weight limits. Therefore, various solutions have been developed to help operators determine the payload mass. However, many existing systems use mechanical equations to estimate the payload, consequently these models are heavily dependent on the geometry of the machine it is implemented for. Using technologies emerging from the field of deep learning, this thesis aimed to investigate the possibilities of a universal payload estimation model. By leveraging real operational data from wheel loaders and formatting it into time sequences, a neural network based on long short-term memory units was constructed. In addition, a classification network was developed based on both convolutional neural network and autoencoder architectures to assess the reliability of data sequences. To generalize the model across different wheel loader models, transfer learning was employed. The results showed that transfer learning enabled the model to accurately estimate payload mass for different wheel loader models. However, the classification network for sensor data reliability was found to be redundant, as it reduced the performance of the payload estimation model. Furthermore, the result also showed a trade-off between the amount of data used for fine-tuning the general model and the accuracy of the model. To further investigate the adaptability and usage of the adaptive payload estimation model, more operational scenarios needs to be tested as well as further examination of the adaptability to different sizes of wheel loaders.
Autonomous Docking: Trajectory planning and dynamic route adaptation for autonomously docking a marine vessel using Model Predictive Control
(2024) Laitala, Erik; Strömdahl, Evelina; Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper; Chalmers University of Technology / Department of Mechanics and Maritime Sciences; Forsberg, Peter; Måneskiöld, Axel; Söderberg, Daniel
This thesis presents a comprehensive study on the application of Model Predictive Control (MPC) for the autonomous docking of marine vessels. The research focuses on developing and implementing trajectory planning and dynamic route adaptation algorithms that enable a vessel to autonomously dock in various maritime environments. The research on autonomous docking is important for its potential to enhance safety, efficiency, and reliability in maritime operations, particularly in crowded or challenging docking scenarios. This technology aims to minimize human error and simplify the docking procedure in busy marine environments. The key challenges addressed include path planning, collision avoidance, trajectory tracking, and the integration of real-time dynamic adjustments to account for moving obstacles and environmental changes. Our methodology utilizes MPC to continuously predict and optimize the vessel’s path, thereby ensuring safe and efficient docking maneuvers. A simulation environment created in Python and real-world simulations created in a Unity-based environment were utilized to validate the effectiveness of the proposed algorithm. Simulation results demonstrated a functional trajectory planner capable of successfully following a reference path, avoiding obstacles, and docking a marine vessel in narrow spaces, indicating the potential for using MPC to autonomously dock a boat. Initial tests in a real-world environment were performed to further confirm the potential of the proposed solution. Comparative performance analysis highlights the strengths and limitations of the system during different conditions, demonstrating its potential for real-world application. Future works aim to enhance the complexity of the maritime scenarios and vessel dynamics handled by the algorithms, as well as additional testing in real-world environments to further validate and refine the system. The presented analysis also demonstrates the potential for additional features to improve the stability and reliability of the MPC-based system. This includes the integration of various sensor data for extensive environmental mapping providing real-time updates about the surroundings to ensure a safer docking procedure.

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