eFan performance and noise, using machine learning
Volvo Business Services AB / Maskiningenjörsjobb / Göteborg
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Estimation of installation effects on eFan performance and noise, using machine learning
Introduction
Electric fans, eFans, play an important role in the cooling system of the future battery electric vehicles, BEV, and fuel cell electric vehicles, FCEV. Not only during driving conditions, but also during charging and fast charging, eFans produce cooling air flow for forced convection of hot components in the thermal management systems. In the absence of the noisy combustion engines, the noise from eFans are more audible and have to be reduced to comply with the new restrictive noise legislation.
Who we are
The Thermal Management department within Vehicle Technology is responsible for developing, delivering, and maintaining an optimized Cab Climate and Thermal Supply systems for all types of propulsion installations to all truck brands within the Volvo Group. We are responsible for leading the work with strategies and advanced engineering globally. We are located at Gothenburg and Bangalore, and we have close cooperation with the engineering sites located in Greensboro and Lyon. We understand the final customer needs and apply our knowledge to develop technical concepts and solutions that satisfy customer and business needs. The work is based on innovation, shared technology, common architecture, and brand uniqueness. We want to make a difference by being there for our customers and by providing uptime and reliable products.
As a master thesis student in the Thermal Management Verification and Validation team you will be a valued contributor to our deliveries and continuous learning. You will be surrounded by a global and diverse team of highly skilled and engaged colleagues who will be interested in the progress of your work and eager to help and support along the way.
Description of tasks and expected outcome
In this MSc project the aerodynamic performance and noise of the eFans will be studied by means of measurements in the Volvo GTT fan test rig. The impact of installation and unfavorable inlet conditions will be studied with the main goal to develop a methodology for estimation of radiated noise based on a reduced-order model. The reduced-order model will be developed using machine learning and data from the measurement campaign. The developed method is, in the long term, planned to be used for estimation of noise with flow data obtained from measurements or simulations as input. The method should be able to predict changes in noise signature due to installation effects.
This project is a part of an on-going cluster of eFan-related PhD projects at Chalmers with several senior academic and industrial supervisors involved, financed by the Swedish Energy Agency and industrial partners.
Literature study
Participation in the design and assembly of the fan assembly
Short education in the measurement technique
Conduction of measurements and comparison of the data with simulation data
Developing of the reduced-order model for noise estimation
Documentation methodology
Suitable background
This master thesis project is a complex task that requires analytical and experimental skills, background in Aeroacoustics, Fluid Dynamics and understanding of NVH and CFD tools. To be successful in this master thesis project we believe that it is important that you recognize yourself in the following description.
Final year student in Master program in Eng. Physics, Mechanical Eng. or equivalent
Experience of using CFD and/or NVH simulation tools
Programming skills in Python, Matlab or similar
Profound background in Aeroacoustics and Fluid Dynamics
Analytical mindset and problem-solving skills
Fluent in English
Thesis level: Master, 30 ECTS credits
Number of students: 1 (or 2)
Start date: As soon as possible or upon agreement.
Industrial supervisors:
Ass Prof Sassan Etemad
sassan.etemad@volvo.comDr Mikael Karlsson
kmk@kth.seAcademic supervisor:
Prof Niklas Andersson, Chalmers,
niklas.andersson@chalmers.seLocation: Volvo Lundby site, Gothenburg, Sweden
We look forward to receiving your application!
Kindly note that due to GDPR, we will not accept applications via mail. Please use our career site.
Så ansöker du Sista dag att ansöka är 2023-12-15
Klicka på denna länk för att göra din ansökan Omfattning Detta är ett heltidsjobb.
Arbetsgivare Volvo Business Services AB (org.nr 556029-5197)
405 08 GÖTEBORG
Arbetsplats Volvo Group
Jobbnummer 8292064
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