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Senast inlagda
Dynamic Control of HVAC Attributes. Improving Energy Efficiency of HVAC System Using Machine Learning and Computational Fluid Dynamics
(2025) Subramani Venkatachalam, Raj Gopalakrishna
Heating, Ventilation and Air-Conditioning (HVAC) accounts for a major share of building energy use. This thesis develops a data-driven HVAC control framework that couples high-fidelity Computational Fluid Dynamics (CFD) with machine learning. Three-dimensional CFD simulation is performed to model the flow field in the room using Star-CCM+. 973 steady state CFD simulations representing realistic boundary conditions were performed to form a comprehensive dataset of temperature and velocity fields. Fourier Neural Operator (FNO) was trained as a surrogate model to CFD. This model reproduces the temperature and velocity flow
fields with adequate resolution, with over 90% of predicted temperature and velocity fields across unseen samples deviating within a 5% error, making it suitable for closed-loop use. The error distribution analysis shows that the median temperature error is at 0.0169 °C and median velocity error is 0.0034 m/s, indicating that the surrogate model is reliable to predict temperature and velocity fields.
The surrogate model coupled with a Soft Actor-Critic (SAC) controller, which was designed to regulate inlet air temperature, inlet mass-flow rate, and radiator surface temperature to maximize the reward, that provides an optimal control solution to reduce the energy cost.
The controller was evaluated for a year of weather data with a 20-minute control step and benchmarked against a PID controller. Results show that the SAC consumed 4,836 kWh compared to 7,460 kWh for PID, which corresponds to approximately 35% in energy cost reduction. SAC occasionally produces larger deviations than PID, leading to a higher median temperature error (0.487 °C vs 0.273 °C), but fluctuations beyond ±2.5 °C occurred only 2.3% of the time, indicating that comfort violations remained rare. Seasonal analysis shows SAC controller’s energy savings persist across the year and strengthen in the late-year window (36% vs 34% earlier), reflecting adaptive use of outdoor conditions and smoother control.
Overall, the work demonstrates that combining CFD data trained surrogate model with entropy-regularized reinforcement learning can deliver substantial energy savings with acceptable comfort tracking, and provides a practical route to incorporate detailed physics (e.g. radiative gains, occupancy, humidity/CO2) and more advanced control designs in future studies.
Route Planning for Mobile Care Teams using Digital Twin Technology
(2025) Andersson, Wilma; Johansson, Alexandra; Thedin Olsson, Tove; You, Sophie
The ageing global population that we are facing is significantly increasing the strain on health-
care systems worldwide. Mobile care teams, a key component of the Hospitals at Home initiative
in Sweden, Västra Götalandsregionen, offer a promising solution by delivering hospital-level care
directly in patients’ homes. This report explores the potential of integrating Digital Twin (DT)
technology to optimize the routing and coordination of these teams. By creating a real-time
virtual replica of the mobile care workflow, DT technology can dynamically adjust routes based
on patient status, team locations, and potential emergency calls, improving resource efficiency
and healthcare in general. The proposed solution utilizes existing healthcare data systems,
algorithms like TOA*, and real-time data integration to enhance care delivery. The solution
also addresses challenges like scalability and workflow efficiency of today’s operations of the
mobile care team unit.
Sahlgrenska at Home: Improving Communication and Collaboration for Effective Program Development
(2025) Al-Bazi, Sogeta; Hübner, Klara Johanna; Nielsen, Cornelia; Sterne, Sara
This study investigates how various areas at Sahlgrenska University Hospital collaborate and
communicate in relation to the Sahlgrenska at Home model. The aim is to provide an overview
of how these factors operate both within individual departments and across departments. Addi-
tionally, the study evaluates how Sahlgrenska can learn from the practices of other international
hospitals that offer similar forms of care. Through interviews with people from the different areas
of Sahlgrenska, the main problems related to collaboration and communication around the service
were identified. Furthermore, inspiration and guidelines could be obtained through interviews
with external people from the Northern Ireland Hospital and Medtronic. The findings and rec-
ommendations that address the problems focus mainly on strategy, structure, processes, rewards,
and people. In order to achieve improved cooperation between different departments, motivation,
trust, and commitment are required from the employees. In addition, a clear structure is needed
where roles and responsibilities are defined. Improved communication can possibly be achieved
through a centralized communication platform. But also through regular meetings, where ongoing
feedback between teams and employees will help continuously refine the service. The study also
provides guidelines for continued work with Sahlgrenska at Home.
En analys av närsjukvårdens arbete i Göteborg
(2025) Kalentun, Ida; Oliv, Hannah; Wiksfors, Emma
The healthcare system faces significant challenges, with a pressing shortage of hospital beds
and a growing need for healthcare services. One potential solution to face this challenge is
home care, which can reduce the burden on existing healthcare facilities and improve
resource efficiency. By treating patients pre-hospitally and giving those possible treatments at
home, the risk of them requiring hospital admission is reduced, thereby also reducing
pressure on healthcare staff. In this context, mobile healthcare teams play a crucial role in
delivering this care at home.
This project aims to analyze the work of mobile healthcare teams at Östra Hospital, Mölndal
Hospital, and Sahlgrenska Hospital within the Sahlgrenska University Hospital network in
Gothenburg. With a particular focus on understanding the shared practices and collaboration
between the hospitals, as well as identifying barriers and opportunities for improvement. The
project was carried out through a combination of qualitative research methods including
semi-structured interviews and observations with staff working within the mobile teams and a
literature review.
The findings indicate that while each hospital has its own strengths and practices, differences
in resources and prior experience have led to distinct working methods. The differences in
resources means staff, equipment and technologies. While all teams share the overarching
goal of providing equitable and high-quality care to all patients in Gothenburg, varying
approaches have led to friction between them, particularly in how they prioritize urgent
versus planned care and handle communication. For example, Östra Hospital is more focused
on managing urgent visits and has greater resources, while Mölndal emphasizes long-term
care, particularly for elderly patients. Sahlgrenska strikes a balance between urgent and
planned care. Despite these differences, all teams are highly competent and dedicated to their
work, which ultimately contributes to the overall success of the home care initiative.
The study underscores the need for a shared vision and improved coordination among the
teams. Collaboration, common guidelines, and shared goals are essential to overcoming
friction and ensuring long-term success and organizational learning. Recommendations
include regular workshops, feedback systems, and performance metrics to foster learning and
knowledge-sharing. By leveraging each team’s strengths and optimizing resource allocation,
the hospitals can establish a cohesive approach to mobile home care, which will play an
increasingly vital role in healthcare’s future.
A Chalmers University of Technology Conceptional Design Method for Propellers. An implementation of Drela method for minimum induced loss and Garrick and Watkins method for sound pressure level
(2025) Helaleh, George
The ongoing development of electric aircraft aims to make the aviation industry more sustainable, with a focus on reducing energy consumption and noise emissions. Propellers, as a key component of electric airplanes, play a critical role in achieving these goals. This study focuses on designing optimized propellers for electric aircraft that maximize efficiency while minimizing energy loss and sound pressure levels (SPL). By employing Drela’s propeller design methodology for aerodynamic optimization and Garrick and Watkins models for noise analysis.
The primary tool used in this study is OptoProp, a numerical simulation program developed in Python to optimize propeller efficiency. The methodology involves several key steps. First, the program is verified using a documented case to ensure its accuracy in predicting propeller performance. Following verification, a parameter sensitivity study is conducted to understand how critical design elements, such as blade count, diameter, and angular speed, influence efficiency and noise levels. The final step involves conducting noise simulations using the calculated aerodynamic data to analyze the relationship between propeller parameters and SPL, providing deeper insight into the effects of each parameter.
The results of the parameter study reveal several important trends. The propeller’s diameter and blade count are pivotal in determining its overall efficiency. A larger propeller size enhances efficiency by reducing the power required and lowering the generated torque, both of which are critical for achieving sustainable performance.
However, increasing the diameter also leads to a positive effect on SPL, as larger propellers tend to produce less noise due to their inverse relationship with the sound pressure level. While this trend offers a valuable approach to designing propellers with better performance and reduced noise levels, there is a threshold for both the diameter and blade count. Beyond this threshold, further increases in these parameters have detrimental effects on performance and can lead to suboptimal propeller designs. This suggests that simply increasing the size or number of blades does not always result in improved performance, and a balanced design is essential.
On the other hand, the effect of angular speed on propeller performance is also significant. Increasing the angular speed improves efficiency up to a certain optimal value, after which further increases yield diminishing returns. However, angular speed has a negative impact on SPL, meaning that higher speeds generate more noise. This introduces a critical trade-off between performance and noise, as designers must be cautious not to increase the angular speed beyond the optimal level if low noise levels are a priority. The sensitivity of noise to angular speed makes it an important factor in designing propellers for electric aircraft, where minimizing noise
pollution is a key concern.
In conclusion, the study provides valuable guidelines for designing electric aircraft propellers that balance high aerodynamic performance with low noise emissions. The findings suggest that an optimal propeller design must consider not only aerodynamic
efficiency but also the impact of various parameters on SPL. The balance between propeller size, blade count, and angular speed is crucial for achieving sustainable performance, with thresholds for each parameter that must be respected to avoid diminishing returns in both efficiency and noise reduction. These insights contribute to the development of electric aircraft technology and offer a pathway
toward more sustainable aviation solutions, where both environmental impact and noise pollution are minimized without compromising on performance.